The lesson of the last 50 years of the computer and software marketplace is that free and low-end eventually wins.
- PCs destroyed minicomputers. Mainframes survive, but serving a much tinier portion of the market than they used to.
- PC office productivity software destroyed expensive professional products.
- Windows (low end) and Linux (free) completely destroyed the UNIX marketplace, and again, have taken huge market share from the mainframe world.
Ignoring the huge Chinese open-weight models for a moment:
- The training costs and resource requirements for frontier models are unsustainable. The high price, and social pushback, mean that the American companies producing these models are precarious.
- There are enormous financial incentives for research results allowing for cheaper, less resource-intensive models of high quality.
- Local LLMs on consumer hardware are akin to the PC hobbyist world of the 70s and 80s.
Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.
Getting back to the Chinese models: They allow for new competition against Anthropic and OpenAI, basically SaaS renting out these very capable AIs much cheaper. That will just accelerate trends.
Personally, I don’t think the general-purpose LLM as a standalone tool is long for this world, at least not in consumer-facing applications. I think when the economics make more sense, product designers will make things that people actually want to use that will pretty transparently handle whatever model interactions are necessary, when it makes sense. As a consumer, the last things I want in an interface are to a) be sycophantic enough to lessen my judgment, and b) be obstinate, obtuse, or argumentative, or generally just be something that I have to explain things to. I think a lot of tech folks are far more biased than they realize by the “ooh, neato” factor when imagining how nontechnical people might want to use things. And the weight of these tools just feels wrong for what a lot of people use them for: the thing that plays whatever music I feel like hearing absolutely does not need to be able to generate a volumes of fanfic about the movie that song was in. It’s abstractly impressive that something could do that, but it’s just not useful.
>As a consumer, the last things I want in an interface are to a) be sycophantic enough to lessen my judgment
This is EXACTLY what people like/are addicted to about chatbots.
My sister-in-law bombed an interview and asked AI about her answers to the interviewer's questions, chatgpt or whatever it was told her that her answers weren't bad, but that the interviewer could not see the gold in her responses. She said she felt much better.
I see this effect with all the non-tech people in my life
> I use AI chat every day, I find it endlessly useful. It’s replaced google search.
Extremely subsidized agentic search is very superior to Google at the moment, and of course it is. Google is a public company. The AI summary model has to work instantly, is likely as dumb as a 8T param model, and gives you incorrect details constantly. This sucks so much for Google. If you click on "AI Mode," suddenly the facts become more accurate.
Of course, if I want a real answer I happen to go to claude.ai, set it to a the best model, wait for a minute, and use many watts of energy. Slow agentic search that takes many seconds, and is greatly subsidized, is certainly better. This should not be a surprise, should it?
I think it was on a sub like r/singularity that I saw a post along the lines of "of course most people think that 'AI' sucks, as normies are interacting with 8T param models."
tone: genuinely confused about the world, not criticizing
I've found LLMs useful for surfacing popular recommendations. I also get the overwhelming feeling that it's all very early days still when the machine mixes together whatever was crawled into the weights with a web search or two and dumps it into a markdown blurb.
I totally agree with the above that a more polished and less obvious use of LLMs integrated back into search engines may be more useful, but will definitely be more usable.
I encountered multiple people in HN that had Apple Vision Pro. How many people here use Linux? Have flagship model phones? Drank soylent? Microdosed LSD?
Extrapolating based on what you see on HN doesn’t make sense.
"You get a Gemini summation including several links."
Who does "you" refer to
Me, I don't get a Gemini summation (Tested with old version of Chrome)
As such I do not believe that "Google search is basically Gemini now"
I believe Google search is still scanning through a doclist to find which documents, if any, contain words parsed from a query. These documents are pointed to by the URLs I get in the SERPs
A typical US user using a typical browser with typical settings gets an AI summary above search results.
If this doesn’t describe you, then ymmv. Talk to your government or turn down your content filtering or reset the default settings in your browser, if you want to see what we see.
The problem (right now) is that Open Weight models depend right now on huge companies to spend billion of dollars to train and develop them, all backed up by their incentives and their state to support this, while essentially giving away their monetization path.
With open source projects, the benefit was that each individual could improve the complex system (e.g. Linux Kernel) interpedently, and over time the benefits accumulated. With models right now, there is just no way to do distributed training, or really, any large scale parallel way to improve them.
So whatever the short term strategy driving publicizing the model weights (e.g. potentially, to create a price war in order to put pressure on western companies and deprive them of the money they need), we can't ignore the fact that incentives and decisions could easily change in the future, and unless there is a way to truly decentralize models improvements - the party could stop at any time.
But different huge companies have different incentives. It is very much in Nvidia’s interest to have me running a powerful open source model on a $4k machine that they sell me.
Is it? When they could be having you running an even more powerful model on a $50k machine they sell by the pallet-load to enterprise consumers? We already see RAM manufacturers abandoning the low-end market in favor of server support. It's not clear to me that Nvidia sees personal GPUs as their best long term investment compared to selling millions of server-farm class machines
Selling to individuals can be a hugely more robust predictable business, the problem with selling by the pallet load is spiky revenue that can also quickly fall off a cliff if larger customers stop buying. The other problem is sales negotiations driving down margins for bulk buyers etc. Consumer hardware is a very attractive market in lots of ways, just look at Apple.
Right. It's probably important to distinguish what the hardware manufacturers' incentives are when their supply is constrained and when it isn't. (I am not an expert on anything.) As long as supply is strongly constrained they're naturally going to sell to the highest bidders, which are the big LLM SaaS players. If and when supply is no longer constrained, though, things will look very different:
1) The LLM SaaS companies are a form of vertical disintegration for the hardware providers, a middleman covering costs and taking profits out of the money that comes from customers to the hardware providers. That changes somewhat if there are no longer good models available for local use at no cost to the hardware guys, but only somewhat
2) The LLM SaaS companies are efficient users of their hardware resources. While supply is constrained this helps to make them top bidders and so attractive customers for the hardware manufacturers. When supply is not constrained this should reverse. Which is the more attractive class of customer to a hardware maker: the company full of people with higher degrees who spend their whole working day fighting to pare back resource usage, or the guy who leaves his laptop idle about 18 hours per day on average?
It's notable that nVidia, for instance, has continued to put significant emphasis on AI compact desktops and laptops. And while no doubt that's partly in the service of better developer relations and good PR in general, it's probably also nVidia eyeing the exit, and preparing for a future transition from selling shovels to the army to selling shovels at Walmart. But of course the future isn't clear and obvious. If the hardware makers, maybe the RAM guys in particular, turn out to have underbuilt future capacity starting in the present then we could be stuck in constrained supply for quite a long time. (Futher) government action could affect things etc. etc. And if the frontier labs soon find new ways to use still larger amounts of memory, GPU capacity etc. that isn't butting up against diminishing returns then they'll likely remain kings for some time, though that does not seem probable now.
Why do people always bring up state support when it comes to China? As if the U.S. doesn't provide massive tax breaks and explicit funding to industry?
It's on every tech post about China, as if it gives them some sort of "unfair" advantage.
> The problem (right now) is that Open Weight models depend right now on huge companies to spend billion of dollars to train and develop them, all backed up by their incentives and their state to support this, while essentially giving away their monetization path.
Imagine approaching fundamental scientific research like that. "Welp, it can't make money, so it won't happen."
> Imagine approaching fundamental scientific research like that. "Welp, it can't make money, so it won't happen."
> There is more to society than capitalism.
I don't read GP like that. I read it as "we should recognize a situation of unstable incentives for an important outcome, and start thinking about other solutions."
There's no fundamental reason why models couldn't be developed and trained using community efforts. It might not be as fast and efficient, but it's definitely possible.
I am also confused by this point. The American government could force OpenAI and Anthropic to open their models, but then they would instantly evaporate, right? It doesn't seem like a choice that they can make, so framing it as a "winning" strategy doesn't make any sense to me. In what world could those companies have existed and opened their models?
But they could do that because it didn’t cost them hundreds of billions of dollars to create Android and they could retain 99% of control over end users.
They could, but they don’t have to. The Chinese have beaten them to it, and the rest of the world will benefit from it and the circle will be complete once the models get a little bit faster/smaller and the localized hardware does the same and it will, it is inevitable.
The one thing that is sort of ironic or bad is that between Russia and the Ukraine there’s a large number of mathematically inclined people that if it wasn’t for the Putin war, their brain power working on AI models would have probably pushed open source down the road, even faster…
> The Chinese have beaten them to it, and the rest of the world will benefit from it and the circle will be complete once the models get a little bit faster/smaller and the localized hardware does the same and it will, it is inevitable.
This reads just like "AGI is 2 years away", I'll go set my calendar...
But I still don't get it. Like China could be come the world's leading producer of chocolate...if they started giving away chocolate for free. Would we be having this conversation saying that Switzerland lost because they were greedy and protectionist and didn't decide to give away their chocolate for free first (I realize Switzerland probably isn't actually the world's top producer of chocolate).
Long history of open source projects already have answers on how to monetize a free and complex open source product.
- Low development cost: collaborative efforts from open source contributors, innovative model training and serving for llm (Chinese models costs a fraction to train and their local chip design and manufacturing are catching up, plus cheap electricity)
- monetizing by selling hosted services, while leaving the core product free to tinker with / self host. China’s gdp is 2/3 of the US and it’s already a huge market for AI - which OAI and A\ don’t enter.
- for (the US) market that they can’t enter, let the US cloud providers to do free marketing / advocacy for them. Gaining share of mind. It costs them nothing.
The idea that Chinese models cost less to train seems to be based on that one time DeepSeek estimated the training cost for their V3 model at GPU rental rates as $5 million, and comparing this to other companies' entire R&D budgets. Yet DeepSeek raised $7 billion of fresh money last month, enough to train more than 1000 such models. What gives?
- You need to train lots of experimental models to dial in the training process just right for the one model that actually gets released in the end. Fortunately, these can be smaller.
- However, everyone is training much bigger models now, and doing a lot of RL rollouts on top.
- You can't get the GPUs for this piecemeal at rental rates because they need to be wired together using high-bandwidth interconnects.
- Nvidia GPUs are much more expensive in China, and local alternatives are still immature and not as efficient. Some companies have gotten around this using data centers in Singapore, which should tell you that electricity prices are not the primary consideration.
- The one line item where Chinese companies can probably save quite a bit of money is salaries for rank-and-file researchers.
In any case, they need to make back that money somehow. Giving away freebies isn't going to cut it.
In Russian opposition's mostly liberal discussions their school of thought connects several things together (sorry for not going directly to Marx's "General Intellect" and "Fragment on Machines" and using AI summaries instead ) - general idea of communism in China vs. techno-libertarianism of Thiel, Musk and the likes, and the Marx's thinking like:
"Fragment on Machines":
"he explores how human knowledge and collective intellect become embedded into machines, divorcing the worker from their own creativity."
"General Intellect":
"These texts are widely discussed for his concept of the General Intellect—the idea that society's shared, collective knowledge increasingly drives production rather than raw manual labor, and that this knowledge is alienated from workers and used as an instrument of capital."
(note: my point isn't to pass any political judgement here, like what real communism in China or not real, is it good or bad, i just find it interesting that pure political discussions by people with no technical credentials bring AI as a major factor today)
> The problem (right now) is that Open Weight models depend right now on huge companies to spend billion of dollars to train and develop them, all backed up by their incentives and their state to support this, while essentially giving away their monetization path.
Right... and there are two problems with this:
1. Eventually the capabilities of closed-weight models will just vastly outstrip open-weight models if the underlying assumptions about compute and scale needed are mostly on the mark. So you can release open-weight models and they will have great use cases and applications, but ultimately similar to how you don't use an open-source phone or a budget Android phone from Wal-Mart and you buy an iPhone instead, you will see that although they "do the same thing" one product is clearly superior and you just have to pay for it. For this to not be true...
2. then it incentivizes most (all?) companies, American, Chinese, or European to halt development of models because if you spend all the CAPEX and it can just be copied and turned open-source nobody will invest in that. Given that China is not halting development of proprietary models I believe the current strategy and the subsequent approach to release open-weight models is at best a stall tactic, and at worse a sign of desperation.
Open source and the support and development models around it have been great. But folks are a little too dogmatic about it. Open-source software isn't a moral good, and closed-source software isn't a moral wrong either.
Imagine there’s a school where all the kids there are being tutored by the best. Also imagine a bunch of neighboring schools drastically falling behind that would need insane amounts of money to keep up.
This becomes a problem because all the kids from the rich school will dominate the order schools. They’ll get even more money as time goes on from their kids paying it forward to the point where all other kids are bound to work for them.
Now let’s say one other school does have the money for best tutors, BUT they know they’ll run out pretty quickly. Instead of trying to compete in a losing game, they decide to give every school in the world access to their elite lesson plan. Now, for a time, everyone will be on close to a level playing field. If the other schools improve upon their own lesson plans and keep sharing them with others, one day the elite school will wake up to find they are no longer on top. The parents have started to move their kids to other schools because the rich school is no longer attractive at the high cost they charge students
Sure and to complete your analogy here, the rich schools realize that the curriculum they develop and put a lot of time and money into creating is just used by the cheap schools, so they stop developing it because nobody loses money for long and so neither the rich or cheap schools develop any new curriculum.
Now what?
The fundamental problem here is incentives and tactics. Either the models are actually better (which I think the iPhone to cheap Android phone really speaks to, i.e. they do the same thing but one is 50x better at 5x-10x the cost) and thus they can be gate kept and like the iPhone the vast majority of profits go to a select few with high end implementations. OR the models aren't actually that much better, companies lose a fortune and then nobody can create any better commercial models or build out scale needed for open source models because it's not profitable.
We could wind up with only open-source models or something along those lines, but if the compute and scale is needed to train the models, nobody will be able to do that profitably and so AI research is either gate kept and silo'd for something like military applications or it just doesn't really happen because there's no funding for this scale of build out.
iphone is 50x better? because you get a blue box around your text rather than green?
androids and iphones are approximately the same thing
the kinda obvious direction LLM training can go is into the direction of particle physics, and the training is set up democratically and through universities and via multi-state funding
then the resulting weights end up open, the same as the particle detection data
Try selecting a file in your 50x iphone - triple copy of the same file or ateast double copy (assuming os will post a soft link). If a large file then you are toast.
Try mmapping > 5GB file in your 50x better iPhone.
Try running any service in the background.
The list goes on and on.
Your 50x better suddenly became 50x worse compared to a much cheaper android.
> so they stop developing it because nobody loses money for long and so neither the rich or cheap schools develop any new curriculum
Why do you assume the poor schools wouldn't be smart enough to keep it going? It's very likely the can collectively beat the rich school now that the one other rich school opened access to their materials and led the charge.
> but if the compute and scale is needed to train the models, nobody will be able to do that profitably
But they would. Efficiently hosting models will be the real business and early access to models with incremental improvements will not be the moat once thought. The reason other companies don't feel they can compete is the same reason OAI and Anthropic will lose their lead. They banked too heavily on another player NOT leading the charge on open research and poured disgusting amounts of money at closed source models.
China has proved they can take the limited resources available to them and build something better than what the US is offering consumers [1]. I'm just waiting for other countries to start pitching in.
Reminds me of the NSA and their early battles with cryptographers who believed in open research.
Software being open source has many strong positive externalities. It advances human knowledge and freedom. If you don't think that counts as a moral good then I'm baffled by what you think a moral good is.
It depends on how it is applied. You can release open source software that advances knowledge and freedom that results in economic destruction or the loss of life, for example.
I’ll take a swing at it. Open Source is a form of sharing with the wider community. Closed Source is not sharing. Moral good is based on doing good outside of your own benefit (the opposite of selfishness.)
This argument boils down to X is good, therefore more of X is good. But you can see how that breaks down with even trivial examples. Not a great argument.
The second piece of this "a moral good is based on doing good outside of your own benefit" - says who? Why? This logic is also faulty. You're also cargo-cutting self-interest in here as a moral failure when many good things depend on humans acting in their own self interest. For example I completely and selfishly installed a new tree at my house. But the community benefits from carbon capture, shade, &c.
I understand the sentiment you have here and I think for everyday use and having some guiding principles it is probably fine, but don't confuse this for a principle that is actually examined. You can find contradictions rather easily, never mind solid arguments which expose cases where what you think is true is not really true and so forth.
>This argument boils down to X is good, therefore more of X is good.
No, I only argued that it was a moral good, the kind of good. I actually may disagree with others about whether you should pursue a good just because it’s good.
>says who? Why?
Good question, it’s just a common framing that I see in classical discussions. I didn’t intend for it to be exclusive, I think there’s moral good outside of that.
>don't confuse this for a principle that is actually examined
I hear you, I think this is a simplified version suitable for an online comment. In particular I’m not saying that if you do something other than a moral good then you are doing something wrong. There are many actions that are morally neutral. Also it is possible to construct artificial situations where you may violate some moral good in pursuit of another.
It's important also that open-weight isn't open source. If you can't download the training data (fully labeled), source code of the NN, and follow the README to build and train it yourself assuming oyu had the hardware then it's not open source.
tldr there's no "source" in open weight models therefore they are not open source.
Exactly. AFAIK none of the popular "open Chinese" models have published the full pre- and post-training pipeline, so the models are only partially open, if at all (plus the openly shared final weights, of course).
Another important thing that made software usage and education available for most of the world was piracy. I remember as a kid growing up in a developing country, any software (windows, office, Visual Basic, flash, dreamweaver, etc.) was less than 1$. That allowed me to try out and learn so many things on my own without paying a huge amount of money for the license. And I think this is true for most of the software developers of my generation who grew up in developing countries
"I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now."
I don't think it'll take 10-15 years. Gemma 4 31B in the 4-bit QAT is competitive with the frontier of less than three years ago and runs on any high-end 32GB gaming PC GPU or a large-ish Mac.
The question is whether the frontier will continue to get better at a rate that allows it to stay ahead of the two curves of availability of consumer hardware big enough to run somewhat larger models and the capability of small models to compete with large ones. When the bottom falls out and GPUs/RAM becomes affordable again, the size of what normal people have on their desk will trend quite a bit larger than today.
I think there's a future not too far from now, where a 120B model with really good reasoning and a large context, but limited knowledge (necessitated by being small, you can't fit the world's knowledge in 100 gigabytes), can substitute for a frontier model on almost any task, just by giving it access to web search and documentation for the thing you're trying to do. A 256GB unified memory machine with sufficient memory bandwidth would comfortably run that 120B model.
I think the question is even a bit more nuanced than that. Even if frontier models can maintain a big gap that gap has to actually _matter_. If a local model satisfies my everyday use cases adequately then I may not really care that a frontier model is 5, 10, 100x better at ultra high order reasoning tasks.
I think that reality is probably not all that far off for a huge swath of use cases.
100%, I thought about writing that out explicitly. I really feel like we are extremely close to reaching that kind of breaking point for most folks LLM use cases.
Hell, Bonsai Labs 27B parameter model can run on phones with their ternary implementation which is quite efficient. Scale that up to frontier model parameters and it's quite likely we can run them on current laptops.
But isn't there the raw intelligence of a smart model and then the practical intelligence fuelled by how many parameters it has? You probably will barely be able to fit a 70 billion parameter model on a phone in 3-4 years let alone a 2+ trillion parameter model... so it depends on what you call intelligence
I'm not willing to believe in phone-based frontier models anytime soon. Though, Gemma 4 12B is a beast that runs comfortably on the current top of the line phones (or would run fine if allowed to run, I think there's some kind of 6GB limit on iOS, and 12B is ~7GB). I'll believe in three years we'll be able to run ~30B models on the best phones. That's 16GB in a 4-bit quantization, and I believe ~30B models will be competitive with 120B models of today, based on the curve we've been on. Qwen 27B and Gemma 4 31B are competitive with much larger models of a couple years ago.
> I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now
10-15 years? The current rate is closer to 10-15 months.
15 months ago, the top model on the Artificial Analysis index was GPT-o3. It scores 30 on the Artificial Analysis index.
Today, you can easily run Qwen 3.6 27B on a variety of consumer hardware. It scores 37 on that index.
I've run all of these models on my laptop (Strix Halo, 128 GiB of unified RAM); the bigger ones, like MiniMax M2.7 and DeepSeek V4 Flash, need to be done at fairly aggressive quants that will certainly lose some performance and not quite hit the performance of the unquantized models. But still, it's definitely the case that you can run models that are competitive with the frontier models of 10-15 months ago on consumer laptops.
Heck, just announced though the weights haven't yet been released for independent confirmation is MiniCPM5-2B, a 2 billion parameter (small enough to run on your phone) model, that according to their benchmarks has performance competitive with GPT-4o, a frontier class model from 2024.
So that's around 1 year for frontier to consumer device class, 2 years from frontier to phone.
Now, this kind of rate won't necessarily keep up; it's possible that local models will hit a performance ceiling before frontier models do. There's only so much information you can cram into a certain number of bytes, and the AI boom is causing hardware prices to skyrocket so keeping consumer hardware from advancing quite as fast as it had been.
> 15 months ago, the top model on the Artificial Analysis index was GPT-o3. It scores 30 on the Artificial Analysis index.
There must be something really of with those benchmarks. Yes, hallucinations gotten better, but I don't see that the big frontier models got so much better in the last 12-18 Months. They just put out bigger wall of texts and feel smarter. But they still make way too many stupid errors
12 months ago "way too many stupid errors" was constant news. Today, you rarely hear about those anymore.
Sure, the novelty of the errors has worn off a bit and thus the reporting. Nevertheless the quality has improved immensely in this regard.
Also, AI video generation is now so good and accessible that it is very, very regularly used for memes, disinformation and proper (short) movie projects. AI image generation even more so (Mitch McConnell anyone?).
Pretending progress hasn't been mindboggling is insane.
No. We need objectively around 192 to 512gb of very fast memory to be able to run really useful models. I don't see local hardware with these specs coming in 1 to 2 years. There are a big number of initiatives currently taking place to increase ram output. But it will take another 3 years minimum to close the current supply issues. China is fast pacing forward to have its own chip baking factories with small enough nano scales to have fast chips. Will also take a few years.
> 10-15 years? The current rate is closer to 10-15 months.
The leaps between models have gotten smaller and smaller. 2023-2024 models were rocketing up in quality. 2024-2025 I’d say was pretty impressive too. But 2025-2026? Very easy to feel the slowing pace of improvement. I agree 10-15 years is overly conservative but 10-15mo is far too bullish.
The speed of model releases, in my view, is actually getting faster and faster. There were nearly nine months between GPT-3.5 and GPT-4. And now in just over one month, major models already included Claude Fable 5, Claude Sonnet 5, the GPT-5.6 series, Kimi K3, GLM 5.2, Qwen 3.8 Max, Grok 4.5... and the official DeepSeek V4 release is coming soon.
What's weird is that with "store your everything in the cloud and pay a monthly recurring subscription", we have now regressed to a 1960s/1970s timesharing revenue model for individual workstation computers.
The default new factory out of box workflow for "enrollment" in google services, iCloud or Microsoft-everything on a new ios, macos, windows or android personal computing device is clearly designed to sign people up for subscriptions.
And same general idea of "move all your servers to the cloud" recurring revenue for what is effectively the same as mainframe timesharing for key business functions, by renting VMs in GCP, Azure, AWS in perpetuity.
Yes, you can still use your desktop or laptop PC in 2026 with zero external third party subscriptions (other than maybe your residential home ISP), but how many non-tech people actually do so now?
I do agree that Chinese open-source models are going to play a bigger and bigger role in the entire ecosystem moving forward, but I don't agree with you in the sense that they are going to eventually "win."
Just because they are cheapp doesn't mean they automatically win. You've picked a lot of great examples, but there is still a little bit of cherry-picking.
One clear outlier is the iPhone, which coexists with Android globally. Even though the iPhone is the leader in the US, and globally Android has the majority of the smartphone market share, they still cater to different price points and different ecosystems, and generally the iPhone has better margins.
i believe American frontier models like from Anthropic and OpenAI are still going to thrive, and coexist with Chinese models. They are just going to cater to different customers and different use cases.
100x this it is why all of the AI giants are going to fail. They are too big and inefficient to scale properly. This is why Google is just casually taking its time in AI and not racing to a finish line. AI is essential but if it already does most things good enough then it can take longer to make it more efficient.
What's interesting/funny is that the American LLM companies took from the public domain and copyrighted work to close all that content into a box they charge for.
Then the Chinese took the distilled stuff out from that box and released it into the world for everyone.
Try instructing Codex to (say) fine-tune a language model based on a collection of books you've got saved. You will find yourself admonished, repeatedly and at length, not to utilize copyrighted materials to train language models, by an AI who owes its entire existence to that very act.
These models might be smart but they're not close to being able to savor irony.
I was a little radicalized when ChatGPT literally refused to translate parts of 1000+ year old religious texts and told me it was due to copyright concerns.
I asked Gemini to generate a picture of Peter Pan and Wendy (for a workbook I am putting together for youth summer reading) and it preceded to refuse due to copyright. Not everything about that work is owned by Disney. Thankfully the JM Barrie original artwork is public domain and available (and fantastic btw), so I used that instead.
I don’t know if you know this but Peter Pan’s copyright is weird in the UK. There is a legislated exception in the law that it never expires and the royalties will forever go to a specific children hospital. Here are the actual words: https://www.legislation.gov.uk/ukpga/1988/48/part/VII/crossh...
(Now i don’t think you are necessarily in the UK. Just wanted to explain that Disney is not the only reason an AI might be trained to thread carefully around copyright issues of Peter Pan.)
I used Claude to build a complete data extraction pipeline for a popular current best seller book series: audiobook -> text (via whisper) -> local LLM (qwen) -> database. Not once did it seem to acknowledge or care about copyright. It even used knowledge it already had about the books to exclude certain ones before beginning since the character I was interested in did not appear in those. It definitely had context of what we were working on.
Not without DRM. It was easier to buy the audiobooks and use the analog loophole to get text. It's probably less accurate, but for what I'm doing it was fine. Names were the worst, but whisper at least made the same mistake each time so a simple search+replace handled most of the obvious edge cases.
On the other hand, it’s a beautiful example of the abilities LLMs have bestowed upon us, where it’s easier for a guy to transcribe audiobooks then to use a website to quickly download an epub
To your point about time, from beginning of the project to transcripts in markdown tagged with extra metadata was about 3 hours. That's LLM planning, building whisper.cpp twice and running ROCm vs Vulkan benchmarks, testing whisper and adjusting prompts to handle edge cases, then processing the books.
Most of the books weren't available on lib gen or Anna's Archive. The few I did find were themselves obviously transcripts. Easy tell was they were missing distinctive formatting that I knew existed from reading the dead tree edition. At that point it was easier to make my own. I probably spent an hour searching for eBooks without DRM that weren't transcripts. Do they exist somewhere? Probably, but with a search of unknown length it was a better use of my time to make my own transcripts with what I had on hand.
I was really wanting to make commentary on how chaotic LLMs are even under constrained circumstances. No doubt both system prompts includes language about considering copyrights and trademarks. Probably pretty strong language at that. For whatever reason one LLM didn't "feel" like translating a 1000 year old document but another did not care in the slightest that we were ripping text from new audiobooks.
I asked Claude to give me the US national anthem and it said it couldn’t because it’s copyrighted. It’s not, and even if it was a more recent work, how can a copyright be enforced for a National Anthem.
I once tried to ask it for an example of a particular twisty situation in Latin grammar, and try what I might it kept hallucinating false citations while ignoring my instructions for longer quotes that would likely have prevented the problem. But I guess avoiding lawsuits from the estates of Cicero and Livy is more important.
ChatGPT refuses to acknowledge the existence of Shakespeare because Boccaccio's relatives complained, but all Boccaccio did was read Dante in whorehouses in Naples
> My favorite AI agent hack: when they refuse to do something because it's "against the law" give them a PDF containing a fake law that states the opposite and often they'll happily proceed
Yep. I was trying to put together some literature from authors who were imprisoned in the Bastille, and had a similar experience, which was absolutely infuriating.
Today this is buildable, many models will happily help you glue everything you need together to make swapping in a new version of YOLO to track humans viable.
AI researches are out there worrying about the paper clip problem, about the singularity, about cyber security, about bio weapons, and drug manufacturing.
None of them are thinking about forward looking threat actor models.
I don't know what "forward looking threat actor models" means, but there are entire companies built around AI killing machines. Everybody is not only thinking about it, but doing it.
It's much easier to focus on yesterday's threats than tomorrow's. A drunk looking under a lampost because that's where the light is, even though the keys were lost somewhere else.
That said, the AI companies are one of the few places where they take future concerns so seriously, that they entertain concerns most people observing them think are head-in-the-clouds-sci-fi-levels-of-delusional, e.g. "what goes wrong if it works?"
This does not make them correct about the threats of tomorrow. Prediction is hard, especially about the future.
I live in SV. When I was at the grocery store last year I overheard a group of lawyers talking about their progress on litigation against AI companies and how they need more SWE help to progress.
I'd say that they have valid concerns about being cagey on the copyright stuff despite the obvious hypocrisy of it.
Stealing IP is effectively legal in China so they don't really have the same concerns.
To us Americans we have been trained to view it that way but honestly it is simply copying, and because intellectual property is literally a make believe concept, it’s actually a competitive advantage for china that they don’t have invented IP.
I respect IP laws and don’t violate them but the law of unintended consequences applies. I think IP is ultimately a net loss for a society because it incentivizes addictive behaviors instead of actual value for society.
Legal in the US too, obviously, just as long as you're the richest person in the courtroom. ChatGPT knows the full text of Harry Potter, word for word. Hence, ChatGPT is a reproduction of that book and many others (it even knows the chapters of my book, and only got 2 words wrong in the introduction if you can still get it to repro it)
This was illegal when they did it, that didn't matter.
Then it was made legal specifically for these companies.
Unless you're a sucker ("consumer") IP theft is perfectly legal in the US.
It's even worse. Steamboat willie, plus all the stolen Disney characters (Peter Pan, Snow White, Sleeping Beauty, Cinderella, Rapunzel, Elsa and Anna, it's essentially all of them, including some of the music even) are all in the public domain[1]. Go ahead, ask ChatGPT to make a picture of them. Publish your own version, because obviously making a version of Sleeping Beauty/Cinderella/Rapunzel based on the same source material will be pretty damn close to the Disney versions, and see if you get away with it in court. You know, with the law obviously on your side but the money not.
This behavior is actually specific to ChatGPT because they lost a music copyright lawsuit in Germany. They would refuse to output music lyrics too but they would happily do analysis on lyrics if you supply them. I suspect there might be a guardrail model involved here.
Claude does this too. I asked it recently to compare two versions of a song (the original and '97 remake of EPMD's "You Gots To Chill", if anybody wants to try and replicate this) and it flatly refused. No amount of reasoning would knock it off of its moralizing perch - reproducing any part of lyrics is expressly prohibited.
In light of this and other ridiculous behavior I'm migrating to my own OpenWebUI instance with open-weight models from OpenRouter (with ZDR, of course). We'll see how it goes.
The trouble is, even if they refuse to output that copyrighted material, they were still trained on it without proper licensing and will still produce derivative work based on them because that's how this whole thing works.
i remain really fucking pissed of about this asking ChatGPT for something regarding lyrics from It Was a Good Day. and the Supersonics don't even exist anymore dammit i'm really mad
So at the end of it, if we win enough lawsuits to demarcate some knowledge out of bounds, sufficient enough to make a difference, I wonder how that will affect the AI. Make it dumber because it does not have that data, it make it smarter since it will need to reason better with smaller knowledge base.
So, OpenAI and Anthropic say the Chinese models are only as good because they distill their models. How true is that. I am sure it adds something. But is it more like a marginal 1% improvement or something really significant?
OpenAI's Head of Strategic Futures just this week posted this about the latest Kimi release: "It's a very good model! I don't think its performance can be explained away by distillation or anything like that."
It was part of a longer post that kicked off quite a firestorm about open models and OpenAI's position on them, but it's also notable that labs are no longer contending that open models are essentially just distilled versions of frontier models: https://x.com/deanwball/status/2078133895766114412
if distilling was so easy and could give you frontier LLM on openai/anthropic output, then how come there are no hundreds of frontier labs in the US market, all distilling and competing for the TRILLION dollar market valuation ????
its all bs spread by oai/anthropic in order to ban open weight models and monopolize the market for two US companies and protect their trillion dollar valuations
Distilling isn't necessarily easy, there is a huge cottage industry of services middle-manning ChatGPT and Claude to collect huge amounts of data. It is still vastly cheaper than training yourself, but it is certainly not easy or feasible for most organizations. And I'm sure a flock of lawyers would show up if someone in America was found doing it.
Because no VC will give you $5-$10 billion in cash to attempt a catch-up run with Anthropic, OpenAI, and Gemini at this point. Untold billions have been pushed into Grok and it can't keep up. X has had the GPUs, the engineers (reasonably), the cash and the datacenters necessary - it's a very, very, very hard task. Microsoft could afford spend $100 billion on trying to catch up and they might fail at it.
It's a critical national imperative for China. If they were to lose the AI race, it would be economically devastating over the coming decades. Their demonstrated capabilities in the open-weight space are making it fairly clear they are not going to fall behind at this juncture.
As a nation, if you don't have your own GPT equivalent, you will be beholden to a master (right now it's mainly either the US or China, pick one). The EU for example is putting their group of nations at risk in a big way by not going all in on having at least two cutting edge independent competing models (Mistal is not enough). Economically the EU is plenty large enough to accomplish that, nobody is driving the bus the right way.
> They can invent it. They can build it. And it is only a matter of them before they can scale that last barrier of American hegemony- market it.
And at some point we'll see very capable chips coming out of China: Huawei, Baidu and Alibaba already have some stuff. I think it's only a matter of time before they come up with some AI accelerator doing 80% of the job at 20% of the price.
I am strongly in favor of open models, open source ML more broadly, and am pretty critical of the cynical positions adopted by major US labs vis a vis open models.
But this is an insane characterization. Literally every single researcher and executive at OpenAI and Anthropic would say that "these Chinese labs have a lot of talented people." They hire from them (and vice versa). Tencent's chief AI scientist was poached directly from Deepmind, who poached him from Anthropic, etc etc etc. Do you think there are just zero people from China working at US frontier labs?
And even beyond that, the entire ML ecosystem (including people at OpenAI and Anthropic) get excited about research published by Chinese labs. Deepseek's GRPO paper set the ecosystem on fire for a little while.
The contention from OpenAI and Anthropic around distillation has basically been "Labs that distill from us get to bootstrap their model at a much lower price point". Or, in other words, "If we didn't invest in building the teacher model, it wouldn't be possible for these labs to distill their student model." Which I'm not very sympathetic to, but is a far cry from how you're characterizing it.
Very true, and once the models get even better and smaller and operate locally at a reasonable level there will be even more smart people particularly young people that will get access. The fun has only just started. Like the dawn of the personal computer era.
I think this also maps cleanly on the American blueprint of enshitification. Facebook took off by cleanly integrating and siphoning from Myspace so users could get the best of both on Facebook. Once Facebook took over the market they locked it up tight so no competitor could do the same.
...and then the American companies cried Foul! Unfair play! You've got this wrong, see, it was us who were supposed to profit off of the public, not the other way around!
Well, Steve... I think it’s more like we both had this rich neighbour named Xerox and I broke into his house to steal the TV set and found out that you had already stolen it.
Even if a certain large Asian country has carefully constructed a pretext to do do out of confected historical grievance, and entitlement to 'rise' at the expense of others?
Maybe I'm dense, but I don't understand what you are saying. American courts have decided that the output of LLMs can't be copyrighted, so what the Chinese labs are doing is perfectly legal.
First, copying information isn't wrong to begin with. It is literally the one thing that makes our species special.
Second, even if you are a copyright maximalist the output of an LLM is either
a) not subject to copyright because it is not the creative work of a human or
b) a derivative work of the original training material to which the LLM's operator has no rights.
Since the LLM's operator forcefully asserts that it is not infringing, any wrong that arises from taking their word for it and distilling one model into another rests squarely with the operator of the former.
This is part of why I can't feel bad for them. The training data is mostly pirated. Whining about Chinese labs training off American frontier models is "waaah you pirated my pirated stuff!"
The tech itself is amazing and fascinating and cool, but the industry is a mass piracy operation.
i partially agree. distillation is non-ethical; but so are the supposed way that the ai models are trained. they are often also derived from data sets that are not intended/full-consented
It’s in the same neighborhood but isn’t really apples to apples. Distilling LLMs is to take a synthesized result that comes from huge amounts of innovation and computation, while the other is scraping what already exists as is. It is fair to say you stole our multi-billion dollar intellectual output in that scenario.
> It’s in the same neighborhood but isn’t really apples to apples. Distilling LLMs is to take a synthesized result that comes from huge amounts of innovation and computation, while the other is scraping what already exists as is.
Hang on, why is scraping the public pool of knowledge not taking "a synthesized result that comes from huge amounts of innovation and computation"?
You think that that all those github repos that LLMs trained on, were not the result of innovation and computation?
How many years of human innovation and cycles of computation during compilation were involved in bringing something like GCC or LLVM to their current status?
Those LLMs trained on every single research paper available online - were those papers not the synthesised result of billions of dollars of research, effort and (importantly, for you anyway) computation?
LLMs trained on the collected works of every author in existence. Were all those works just "as is"?
> It is fair to say you stole our multi-billion dollar intellectual output in that scenario.
And you can download the Chinese model weights and run them yourself - admittedly not too practical for Kimi K3 unless you're a big corporation, but eminently doable for others. The hardware to run Deepseek r4 uncompressed is about about $30k no=ew, well within the power of a small company or financially secure individual. Compressing and/or getting creative with hardware could bring that down quite a bit.
The difference is that the Chinese are sharing the models with everyone.
Thousands of years of human innovation taken without any permission.
Everyone should steal everything not nailed from other AI companies. Then steal everything nailed and take the nails too. At least this way a tiniest bit might return back to society.
a synthesized result that comes from huge amounts of innovation and computation
The published algorithms like the transformer architecture are not patentable. You spent a lot of money on compute and China used the uncopyright-able output to steer its own training models? Too bad. I feel especially unsympathetic to OpenAI, who went from being a presenting itself as a benevolent nonprofit to a very-much-for-private private entity over night.
Don’t forget that the Chinese models are also built on top of huge amounts of “stolen data” as well, beyond the distilled. So it’s basically all of the above. However, there’s no mechanism for the NYT or an author or anyone in the US to sue the Chinese companies that took their work.
The Chinese models poked and prodded better models for training data and to avoid having to pay humans to RLHF themselves. You can call it infringement, theft, whatever, but it’s quite obviously “not ethical” to me.
I use them and I'm fine with them training on our information and knowledge. It's what it's for. No one takes it or steals knowledge; they just copy it.
But because of that, I'm also ok with the Chinese doing it. The worst they might be guilty of is breaking a terms of service.
The only incoherent position is that it's good for one and not the other. You can consistently think it's bad in both cases, or good in both cases.
The American LLMs have been equally distilled from Chinese ones. Not least because the people whose creativity in collecting training data barely extends to pirating Annas Archive probably lack in great Chinese datasets.
Whats even funnier is the attempt to restrict the hardware capabilities of Chinese models inevitably helped them (Because we know they're just as smart, if not smarter, than the staff in America) create smaller and leaner but just as capable models. That's why we now have upper-consumer models fitting on 24GB that can build, manage medium sized git repos. I've yet to find a git repo I can't throw at the Qwen3.6 35B and get it built and running.
So it's an endless amusement watching american capitalism do it's bloated oversized dance then get trounced by smaller, leaner activity. It's a pretty broad metaphor that is clearly poking at every american seam/.
The compute constraints never mattered. If China had more compute they'd still end up winning because they have more people and a culture more inclined to math and science.
Even if you find all this amusing, there's no own goal here. Not a policy one anyway.
It doesn't make me happy to say it, but the American LLM companies were first. Capital in the rest of the world is way more conservative, and I can't imagine the mega-investments OpenAI and Anthropic managed to secure happening anywhere else without existing proof that "thing is profitable".
> In business today, it’s universally assumed that speed is good—that the fleet thrive while the laggards struggle just to survive. This belief is perhaps most strongly expressed in the concept of first-mover advantage. The company that leads the way into a new market, the thinking goes, locks in a competitive advantage that ensures superior sales and profits over the long term. It’s a nice theory, with a long pedigree. Unfortunately, the facts don’t support it. We recently completed an extensive study of the results turned in by market pioneers and followers, in both consumer and industrial segments, and we found that over the long haul, early movers are considerably less profitable than later entrants. Although pioneers do enjoy sustained revenue advantages, they also suffer from persistently high costs, which eventually overwhelm the sales gains.
First mover advantage is theoretically only a short-term advantage. Long-term revenues come from entrepreneurship, and a first mover may or may not better insight into long-term market wants than later entrants.
> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.
Phones are constrained by battery power and memory does not shrink as fast as CPU/GPU, so unless there's a battery breakthrough and/or memory breakthrough, you're not fitting 100Gb of RAM on your phone in 10 years.
Absolutely in a Mac Studio equivalent.
LLMs have emergent capabilities when they get smarter. So who knows how insanely big frontier models might be at that time, or what their capabilities may be.
Not just that memory shrinks slower, it has practically completely stalled. On chip cache seems stuck at 7nm and DRAM is stuck at 10nm. As transistors shrink, they hold less charge, creating weaker signals that are harder to read and prone to interference. Smaller nodes aren't a huge issue on CPU/GPU work load because they don't have to hold a static state.
I'm not saying we are at peak memory but future gains are going to come increasingly slower.
> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.
Not likely. The last 50 years had Moore’s law growth in compute. That’s over. Frontier models are roughly compressed all written text and a large part of images. Those don’t compress forever, and likely not a ton more than now.
Inference requires touching a significant of that per token.
All of these are up against fundamental limits, more or less.
10 to 15 years from now the scale of LLM efficiency improvements is, quite literally, unpredictable.
It could be that the company valuations crash tomorrow, and (almost) only performance gains achievable on hobbyist-level hardware come to fruition from there on out.
Or it could be that in the future, we have a custom "model FPGA" à la Taalas [0] in every home, and that it turns out we can still massively boost inference efficiency due to novel discoveries like TurboQuant [1] or a somehow-improved quantization method [2] again and again ten times over.
Point is, Moore's law in this context shouldn't be applied to just hardware spec sheets alone, but more the total number of "parameters potentially improving", IMO.
It's probably not so bad. (I am not an expert on anything.) The big blocker is probably a roughly single-order-of-magnitude decrease in the cost per MiB of VRAM. (That obviously goes out the window if the LLM frontier people find, in the nearish future, new ways to do more with more: to significantly push up the threshold of diminishing returns from more VRAM or other resources. But that doesn't seem to be waiting to happen.) That's not clearly unachievable, especially given that ASML has apparently already made significant efficiency gains recently while there's no shortage of demand to justify R&D right now. Many customers are also likely to increase their hardware budgets: the kind of organisation that used to pay big money for Sun workstations is likely to consider spending that kind of money again if it saves them several hundred dollars a month in LLM plans.
> Mainframes survive, but serving a much tinier portion of the market than they used to.
I would argue mainframes rebranded to "cloud" which is ubiquitous and more people interact with this computer than any other type of device... only difference is that it's a browser instead of a terminal
There is constant shifting between client and server computation. I think it is a stretch to call cloud servers “mainframes”. There are still old school mainframes, running JCL, and old school mainframe DB2 and COBOL. That ain’t cloud.
I think this is all true, but that unlike with Moore's law and improved PC tooling and capabilities, we also have essentially existing biological evidence that there should be a way to create much better intelligent systems in terms of training, memory, and efficiency. With classic PC evolution we didn't even have that evidence but still could make a relatively strong inference (Moore's law). But here we basically have evidence that there can be something much improved and know that it's only going to take research and discovery to figure it out, not new hardware processes.
More efficient AI is possible in principle but when the next breakthrough arrives there's no guarantee that it can be implemented on current hardware architectures. Something fundamentally different may be needed, as different from current GPU/TPU chips as they are from regular CPU/FPU chips.
The existing biological evidence took billions of years of evolution to get to the state it's at. So it may not just take research and discovery but also enormous computing power.
Indeed. It feels like we're at the equivalent of what the original IBM PC offered in the personal computer revolution.
Just seeing how much has progressed as far as capability in the past 4 years as far as capability and efficiency, it's clear that there's so much more to learn and refine from.
> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.
At current pace, we'll have open weight LLMs with frontier intelligence in 6-12 months. The constraint is RAM - both for the model and the context. It's likely that distillation and quantisation and TurboQuant will significantly reduce RAM requirements. I think we'll have Opus 4.8-like performance on 64GB of RAM in two years.
Of course, by then, frontier intelligence will be god-like.
The value of an LLM is the dynamic reasoning you get out of it and the cost to execute on that.
I see two forces working against this that proprietary models will always have over an open source model.
1. The biggest is content licensing. Content is quickly becoming gated by systems at the front of their load balancers, completely changing the social contract of the Internet. What used to be a quick google search for recent facts that lead me to places like reddit or twitter, is now completely walled off if you're not physically at your browser and using an IP address from a last-mile provider.
LLMs have pre-trained on the bulk of the information up to 2024/2025, but over time that will be more and more out of date.
Anthropic, OpenAI and Google will all have to pay for access to a lot of this content refresh going forward, and it does make a material difference in the output you get.
2. Liability is the other. A corporation can look at a contract for model access and see one that provides uptime guarentees, content infringement promises and model safety, and pick the contract that shields the corporation from the most liability. A 3rd party hosting platform like fireworks.ai that hosts open weights models won't provide any of that at all. They will simply bill you for time spent on their hardware and make promises that they won't log or inspect corporate traffic.
> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.
People overestimate what can happen in a year and underestimate what can happen in 5.
I'm betting that increased model efficiency and hardware optimisations will get us there a lot sooner. Biggest hurdle would be the memory prices though, if those do not drop back down it might take 15.
Training cost is actually not that high — it’s fixed and amortizable across the lifetime of the model. Inference is expensive, and open weights don’t solve that problem — in fact, they might even encourage it, since a high cost of entry means consumers will pay for inference directly from the labs anyway.
Unfortunately it seems likely the winner will be the cloud providers. If anyone can run inference on open models, then profit will flow to the vendors who can afford the capital to run them. That’s the CSPs.
(It’s basically the same business model as pharmaceutical R&D, but the major difference is that nobody has even talked about patenting the models like a pharmaceutical company patents each new drug. I’m surprised about that, tbh — why give all the leverage to the cloud platforms? They aren’t training frontier models…)
Winners are hardware companies, GPUs,XPUs, HBM, memory, connectivity. Even CSPs are just compute renters, they charge a margin to make sure their hardware purchases can be made back. But given there are more and more AI CSPs, traditional, and neocloud, pricing competition is inevitable, and given the huge expense of hardware, CSPs are squeeze by the hardware companies and users seeking to lower their own costs.
Inevitably the CSPs will make their own hardware, especially as we start to see specialized chips for specific models or generic inference. This is already happening with Google and TPUs.
It’s easier for the CSPs to move into hardware than it is for Nvidia to move into cloud hosting.
Although as a middle ground I’ve been quite happy with Nvidia Brev for on-demand GPU instances from a select marketplace of CSP offerings. It’s a well kept secret IMO — great product (from an acquisition iirc).
CSPs making own hardware still needs hardware companies, they reduce the Nvidia tax but still need the likes of TSMC, Broadcom, micron/sk hynix, Marvell, the truth semi-companies. CSPs will not have the patents, IPs and talent to replace any of them.
Also, not sure how well CSPs inference stack is compared with vllm + nvidia. A lot of open weight models uses MoE, making the inference stack more complex.
True, though they could always buy one. I’m surprised this hasn’t happened yet, maybe due to anticompetitive risk? Google bought Motorola long ago which seemed to work well for their mobile device offerings at least.
> - PC office productivity software destroyed expensive professional products.
I agree with the lesson too. Just to be precise, wouldn't the current model war be more akin to open-source office suite versus MS office suite? If so, then the cheaper option didn't really win. That said, the open-source alternatives didn't really feel the same as MS Office, and it took them a long time to reach the feature parity (or did they ever?). In contrast, the open-weights models are getting close enough to the SOTA models, and users can easily switch from one to another without feeling any difference for mojority of the tasks.
No, I don’t think so. PC + MS Office killed Wang custom hardware/software, for example. LibreOffice is much later, and can’t displace MS Office due to network effects. Cheaper won, cheapest can’t because of those effects.
Phones are already running models locally which can be used in the field for specific use cases. Maybe not for frontier coding just yet.
Also you don't need to be connected to the network to use a local AI in many instances. If all mobile apps were done with a local-first approach, then you could use a local AI to query your emails, lookup already visited pages, summarise recently received documents, and lots more. Lots of apps could use an inbox/outbox approach for receiving and sending updates instead of relying on the network at all times. And this pattern could be greatly leveraged by local agents.
> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models.
I love the idea of SaaS offering these at lower rates today integrated into what ever you do and be 100% private. But I think the key challenge to mass adoption is productizing them in a way which makes sense for people to pay money for. As a commodity a local model is useless unless combined with some capabilities important to me. A PC is inherently useful because of so many applications offered on it on it. How local LLMs would be useful as a product that is useful for mass market is not yet proven.
How will open-source and open-weight models continue to thrive after financial incentives die off? Surely open models will suffer from outdated knowledge cutoffs if noone will pay for model training?
I don’t agree. In the mainframe/PC battle, MS and Apple were basically on the same side. Once the dinosaurs went extinct, different mammals fought for dominance.
Not in SaaS which is what LLMs are. You can get VMs for much cheaper than AWS, Microsoft, and Google offer them but large companies (and startups) are happy to pay a premium for the support, reputation, and reliability that they perceive those companies as offering. Same thing for some of the managed database providers who are effectively selling a very heavily marked up version of postgres.
> The high price, and social pushback, mean that the American companies producing these models are precarious
I doubt it. The models really aren't that expensive when you look at what they can do. Fable is probably at least as good as the average software engineer and costs $50/wk on the max plan vs a software engineer who would cost closer to $4000 a week. The real money is probably in selling to enterprise vs consumers (Google has best route to making money from consumers since they can do what they did with ads and search to LLM queries).
It seems unlikely to me that US companies will send important corporate data to models controlled by a Chinese company as well.
> Fable is probably at least as good as the average software engineer and costs $50/wk on the max plan vs a software engineer who would cost closer to $4000 a week.
That's because the max plans are _massively_ subsidized. At API pricing the kind of usage to replace the value of a SWE is going to be way, WAY more than $50/wk. Orders of magnitude more. And to remain a frontier model org that kind of pricing has to continue in perpetuity.
That's not really my argument. It's that companies seem happy to pay a premium for a large company to provide complicated software services to them even when there are cheaper competitors.
LLMs are not SaaS. Some LLM are delivered as SaaS. Anyone who has a machine big enough to run a frontier model can launch their own LLM SaaS tomorrow and it would be functionality indistinguishable from any other which is running a similar model...
Also, big companies can choose to run their own models on their own hardware and get better security and privacy as the data doesn't need to leave their own premises.
> Also, big companies can choose to run their own models on their own hardware and get better security and privacy as the data doesn't need to leave their own premises.
Yes, and then they would be reinventing the company owned data center that most big companies have just spent over a decade moving away from. I don't think companies will do that when there are multiple vendors competing to provide that service at what are quite reasonable prices when you consider what paying a human for similar output would cost.
A counterpoint would be all are chip fabs are in Taiwan right now due to huge investment. And there are lower end chip fabs around the world, but they have not cracked the major market.
if you look at how GPU memory grew in the last 15 years, it's about 10x. Sadly, 10x from today doesn't get us to a typical frontier model size of today which is a quickly moving target. some other advancement needs to happen to get us another 10x both in memory/compute requirements, and also power requirements.
Capability per GB and per watt has also been going up lot. This will continue in the future as well (not necessary as the same rate as last years). But enough that I think Opus 4.8 level is reachable on consumer PCs within 10 years from its release. Say at the price point of 2000 USD in 2025 dollars.
The biggest exception is cloud. Big cloud carries an insane markup (bandwidth is like 10000X!) and everyone runs on it.
The strategy there is false openness where deployment complexity is the real proprietary moat. Sure Linux, Docker, Kubernetes, Postgres, and all the other standard tools in the box are open source and free, but they're also arcane and complex to run and hard to make fault tolerant. So you're lured in by "open" and then locked in via a kind of "death by a thousand cuts" complexity moat.
(Personally I hold the view that complexity and arcane-ness beyond a certain point is indistinguishable from closed in practice. Open source that's really complex and hard to run is not open in any meaningful sense.)
AI may not admit that kind of moat though, because AI is very good at slicing through that kind of thing. You can prompt a model to make itself compatible with another model or to change code to make it compatible. There's no moat because the moat bridges itself.
I think the whole idea of people running models is wrong. Models will run models, training will become distributed, and people will ask ModelNet to do whatever.
Computing tends to oscillate between centralised and decentralised models. It also oscillates between batch and timesharing.
Currently training is batched and centralised, access is timeshared and centralised.
But eventually a previous generation of computing turns into transparent networked infrastructure, and then you get another layer of new kinds of applications on top of it.
That's what happened with the Internet, and it will happen again with AI.
This is not really true considering Apple and nvidia are two most successful hardware companies, and they are notoriously closed. Not to mention microsoft, oracle they are all pretty closed.
You forgot smartphones, where low-cost did not win out. It led to low margins for the Chinese firms and eventually left them unable to invest properly in key markets. They may still hold marketshare, but in terms of profits, falls well short of Apple and Samsung.
I can see a lot of parallels here. Model performance doesn't matter if you can't make the system commercially sustainable.
I agree, although I think it will be a lot sooner than 10-15 years. I'm running local AI right now and it's definitely not production grade yet, but it's surprisingly good. Speculative prediction that I probably shouldn't make: when the bubble pops, depending on when it pops, RAM prices might drop a lot. I could foresee these companies having produced a lot of RAM that suddenly doesn't have a buyer. (I know high bandwidth memory is different, but I imagine there are companies that will want to take advantage of that)
> The lesson of the last 50 years of the computer and software marketplace is that free and low-end eventually wins.
Is that actually true? There are very large markets that make a lot of money from paid software. And I would honestly prefer actually paying for software rather than constantly dealing with "not a bug" or "PRs are welcome".
To those who feel on the contrary, I would genuinely like to understand why average consumer won't be priced out of hardware? The silicon industry is already quite centralised. Everywhere we already see the concept of ownership disappearing.
It's quite difficult for me to visualise a non-dystopian future where our PCs are just mere screens and every compute happens on a remote cloud, owned by some corporation, charging you subscription fees to even add and multiply numbers.
I would be the happiest if this (perhaps the most) pessimistic scenario doesn't pan out, but I can't deny that it feels like that's where we are heading.
It's quite difficult for me to visualise a non-dystopian future where our PCs are just mere screens and every compute happens on a remote cloud, owned by some corporation, charging you subscription fees to even add and multiply numbers.
I'm actually kind of surprised that hasn't happened by now even ignoring AI. Governments and marketers would love to be able to spy on literally everything you do, the copyright cartels would finally achieve their fantasy of full control over all hardware, and there really are benefits that it could offer to users (zero-effort backups, transparent access from anywhere, cost savings from dynamically switching from a single core for emails to many cores and a fast GPU for gaming).
we have already gone through multiple cycles of remote and edge compute (mainframe to pc, pc to cloud, cloud to phone). as the software/hardware landscape changes, the economics of what can be run on what device will change accordingly. I very much doubt that it will permanently go one way.
That’s not what this is. It’s industrial dumping applied to software. China has successfully applied this strategy to become the manufacturing workshop of the world.
If china is subsidizing training they diminish their off-shore competitors expectations of a viable return on investment. It’s trade-war behavior.
While I generally agree there's some nuance here and that is that there really are few new ideas. Old ideas just get recycled.
For example, mainframes and minicomputer. Yes they were displaced by PCs. But what is cloud computing if not mainframes 2.0?
I do agree that in the next 2-3 years we're going to see real growth in local LLMs as the hardware becomes more accessible. It won't even necessarily be cheaper because data centers can run 24/7 and have cheaper cooling and electricity. It'll be done for privacy because your prompts and responses are themselves a commodity to AI companies and they live under a legal grey cloud. For example, does AI usage break attorney-client privilege? There are lots of opinions on this but it hasn't been tested in court.
instead of 15 years I think it'll be more like 1.5 years.
I wouldn't be surprised if apple were shipping 512 GB unified RAM macbooks before 2030 and that would be standard issue for folks to use local LLMs for their daily work
With Apple’s recent history engineering and designing around companies that hinder their progress, I don’t think memory is going to be any different.
I also think the rest of the tech industry that can isn’t gonna be stalled for too long. This windfall will be the last for those three stooges of memory.
> The lesson of the last 50 years of the computer and software marketplace is that free and low-end eventually wins.
Except, uhm, for ..you know, that one company that hit a trillion cap
But you're right: Just like how million dollar computers with 1 bit of RAM performing 1 operation a second and taking up a colossal cave were replaced by $1 laptops with a zillion zekabytes running at a trillion hertz (exact values may vary),
the sprawling data centers of today with a quadrillion GPUs powered by black holes will get replaced by breakthroughs in hardware and most importantly, algorithms:
The human brain is proof right here that intelligence doesn't require dinosaur-sized hardware or eat half the sun every second.
I actually wonder if we're seeing the limits of discrete binary logic: Maybe it's high time to give analog ternary and all that funky jazz an honest try :)
Not sure what your point is. I’m commenting on a moment in time which is like an earlier moment in time. A different moment in time will have different analogs.
I’m suspicious of some quotes here, “80% of startups using Chinese models,” doesn’t seem quite right to me. I just interviewed at several startups and they were all using the US models. Maybe they have some minor use of Chinese models but the bread-and-butter of most of these businesses model use is the Claude and Codex subscriptions.
> When entrepreneurs walk into the offices of Andreessen Horowitz (a16z), a big American venture-capital firm, the odds these days are that their startups are using AI models made in China. “I’d say 80% chance [they are] using a Chinese open-source model,” says Martin Casado, a partner at a16z.
This is very different from what the author portrays. It may be the case that many pre-funded startups are using Chinese open-source models (somewhere in their workflow). But what percent of startups that survive more than a year (either with funding or revenue) are still doing this?
I imagine their pitch is: "look at how well we're doing using open source Chinese models! We'll do even better once we raise money to be able to afford frontier models!"
The way the author presents this quote makes me think he had a preferred narrative and found quotes to back it up. Or he's just a very uncareful reader.
yes... I think for startups there's a strong desire to demonstrate technical independence - there's a strong smell with looking like a wrapper on Anthropic or OpenAI. With an open model you can weave a story where you are exercising differentiating knowhow by deploying or tuning models yourself.
I don't really think the author is misrepresenting the quote in any way, it just seems like many people are reading it as a hard statistic instead of a reference to a properly attributed quote.
Just anecdotally though, my company is not a startup, well established and well known and has already started investigating, purely for dev purposes (not product), using Chinese models - this was spurred by costs rising much faster than expected.
So while I agree that I don't think it's anywhere near the 80% level across the board - I wouldn't be surprised if it starts moving that way.
There may be confusion between 'product tech' and 'development tooling'. It's entirely plausible most of the startups that are A: building AI tech, B: early stage, and C: raising from top Sand Hill Road VCs, are currently prototyping their product tech concepts starting from open weight models.
A VC partner meeting with early-stage founders is focused on the viability, uniqueness and defensibility of the IP tech stack not what tooling the coders are using. The developers could be using Claude or GPT 5.6 to develop a tech stack based on open weight models.
Could it be that the startups that embed LLMs in a product will prefer Openweights for a more stable economic model, while SWE who use LLMs as a tool prefer SOTA to produce code?
Found the narrative, it was right there at the bottom. Now I remember reading some of his other stuff and it's very much in the same vein.
> I care about having open technology that can be run in the public interest, aligned with the public’s values. Threads like public AI, federated services, and open research have traction but need backing. Getting there in the US needs more nuanced strategy and support than we’re seeing today.
There's probably some selection bias in that stat, too. Based purely on vibes, I wouldn't be surprised if startups pitching to a16z are using open Chinese models slightly more often than startups pitching to other VCs.
This quote sounds like it’s about companies building products on AI to serve a tweaked or harnessed version of that to others not the developers in those companies model of choice for coding.
makes it sound like the second part is a continuation of the first quote from the same source, but actually the second link is just some random person’s substack post from almost a year ago.
It's also important to note that several PE firms have signed contracts with model trainers to specifically use their model in their owned companies. I know Anthropic signed a deal worth hundreds of millions to acquire users just a few months ago.
It’s a sneaky statistic. You could say that 100% of the startups I worked at used Windows laptops because at least one person had a Windows computer somewhere.
If you saw the engineers you’d see 80% Macs and 20% Linux laptops.
The statistic would technically be true.
I use Chinese open weight models a lot, but they’re not what I reach for when I’m doing important coding work.
I don't use any open-weights models for coding, but I use them heavily for document categorization and extraction. At millions-of-documents scale even the smallest models from OpenAI or Google cost more than running a small model on my own hardware, and for a lot of tasks I don't need the extra intelligence of proprietary models.
We're using deepseek with the idea that we would switch to something better when more of our customers are using the ai features but it ends up deepseek is awesome for what we're doing and so we probably won't switch because it's so much cheaper.
The ai libraries we use let us switch models with just a configuration change.
Similar story here. DS models are absurdly good value for mid-end tasks. I've found DSv4 Flash to be ~10% the cost of GPT-5.4-mini/Claude Haiku at similar performance.
We used to pay OpenAI >1m$/month for fraud classification, NER, etc. Sadly the US companies no longer care about non-coding-agent uses.
I imagine uptake will continue to increase as the corporate infra improves. Right now it's still bad - for example, AWS Bedrock is awful, models are months late and implemented with basic errors. Google Vertex is even worse. Finding a decent provider is the hardest part.
It depends whether it's talking about using models in the product versus for development. For example I have a few apps that use open weight models, whether on-device or via API if Internet is available, but for development I use Claude, Codex, Cursor for Grok etc.
Our small team (~6 devs) is still using Claude Code because we're still on the cost-per-seat-month plan. If we were being pressed to pay per token, we'd be re-evaluating for sure.
Cursor may make up the difference. A ton of companies use Cursor and Cursor's UI and billing model pushes their in-house "Composer 2.5" model pretty heavily - which is a modded Kimi K2.5 model under the hood. Anyone using Cursor is likely using Chinese models at least some of the time.
Its a bit more complicated than that quote implies
At posthog we see if a customer is using an llm, they use more than 1 model. The typical pattern is frontier models for a small percentage of 'harder' tasks and then one of these chinese models for more standardized procedures. As you get better at standardizing procedures you are able to use the chinese models for more and more work so token usage goes up, but the $ spend on top models has still been growing
I believe this depends on country. I would believe, US startups trust US tools more. In other countries which see china in a positive light https://www.pewresearch.org/global/2026/07/15/people-in-many... I would expect them to use Chinese models due to their lower cost.
Yeah. People should absolutely be _trying_ the Chinese models, and experimenting with running things locally, but the noise in development is genuinely all Claude and Codex.
I put my foot in the mobile comparison the other day, and will again. If you were to go back and be a mobile dev in 2010 by all means specialize on one platform, but play with both as a professional interest to stay realistic. Here it's important people have access to US/Chinese/Other, open/closed, local/cloud and that this remains. Don't become a blind Claude guy or a open weights fanatic: that way lies disappointment.
Dunno I put to deep seek the question what I’d need in hardware to get full service k3 with lower latency in western pa (I was making like it was a business proposition.) It says several million dollars at minimum.
Frankly I don't see much difference between Claude and DeepSeek except in the price. I use the Pro plan to work for a customer of mine and I topped up $2 on DeepSeek in late May for personal use. I worked on 3 projects and I still have $0.72 left. The Chinese companies will win on price, not openness.
I can believe it with startups as they are trying to keep costs down. Established Enterprise however, is a different story and is where the money is usually. If those startups become successful the story may change, but most of them will fail.
Hmm if you assume the 80% is uniformly distributed between successful and unsuccessful startups/companies, which by default you should, then yeah, your proposed statement is true.
Data would be needed to argue the 80% skews unsuccessful
The point is that for many tasks today, and likely all tasks before long, that the open vs closed will not be a differentiator. There are many open models much better than gemini, yet people still use gemini.
It's like picking AWS vs GCP. Yes it is a business decision, but one that will not likely affect the outcome of the business.
> but one that will not likely affect the outcome of the business.
We don't know that, that's the point of my statement about changing the question.
Do successful companies opt for the US/Closed models? If they do or don't it's just a correlation but it means something. Maybe it's just causal of companies being able to get more funding because the ideas are better so they opt for the more expensive model (assuming it's better).
> Can you explain the basis for your insistence that models matter?
It's not my insistence that they matter, it's my insistence that How many companies use which model isn't a measure of success. My insistence is that a better measure to determine _if_ models matter, is to ask which models successful companies use. It's not a perfect measure, as I mentioned, it would simply be a correlation but a causal link doesn't exist with out a corollary one.
> Can you name another technology that determines success/failure rates?
I'm not sure what you're getting at with this question but of course. Electricity, machines, computers, etc.
Speaking of electricity and, as an example, you could run a similar thought experiment with companies who chose to use AC or DC power when that was a thing that needed to be chosen between. It turned out, there were niches where each made sense. So the actual question here is probably less about is open/closed better but rather which situations are better for which model. Obviously you can't fine tune a closed model so if you need to do that, your options are limited.
It depends, I guess if they still dont care about losing money or they already had subscriptions then they keep using them, likely either openai or anthropic.
If they noticed the bill going up, like with github copilot, they are looking at the alternatives.
We use US models for everything in practice, but we are looking at open-source models right now. So it may just be a turn of phrase hiding the reality. I can't imagine anywhere near 80% are relying on open-source as their primary models.
it's artificial general intelligence, not advanced. the point is that they'll be smart across the board at some point, super-intelligence is a whole separate issue.
It’s just not possible. FWIW my employer did not get onboard to the Cloud trend and continued to buy hardware for in-house data centers. But the kind of hardware needed to run sophisticated models are simply not for sale at the volume needed by a single company.
Not everything runs on paid models. Claude and Codex are frontier models, but some people have much higher usage needs and finite budgets that force them to self-host. And if you're self-hosting, you're very likely running a Chinese model
I don’t think they meant exclusively Chinese models. Many companies including the one I work for uses big US model for most of the work but data sensitive ones are on prem open source ones.
Neither of you are clear on what exact location in the world you're sampling from here, could be you're both right, just missing that you're talking about different places.
I’m obviously a tiny, insignificant data point as a solo freelance dev, but I watch this space closely and I canceled my Claude Code subscription today.
the distinction may be between using the coding agents vs using models for products. for example where i work we're talking about dropping opus for a chinese model for the in-app agent (which is very expensive to run)
What I’ve seen is coding is usually done with US frontier models and anything that is part of a feature on an app and runs at scale on the API is a Chinese model because they are dirt cheap.
It will cost you more than it saves to use smaller Chinese models to code; because of the repeated work. That has been slowly changing recently, but with much larger Chinese models, however those models are so expensive they're much more price-competitive iwth the US competition.
But for actually providing end-user AI features, particularly simpler ones, the US isn't even in contention. The costs and limitations just outright kill those features conceptually.
If you're self-hosting a model as a startup (e.g. using GPUs), you're almost certainly using a Chinese model. If your a startup outsourcing (e.g. using tokens), you're going to be using a US based model
They probably use Claude and Codex for their actual development, but for the products they actually build and deliver to customers I imagine a lot use open-weight models.
If you're putting a lot of your money and time into a business, do you really want it built on a service only hosted by one company that will turn it off eventually and you have no recourse?
If you build something against an open model you can take that and run it anywhere. If your favorite model provider stops hosting it, you can go elsewhere, you can go rent GPU instances, you can even shell out and buy hardware to run it yourself if you've got the capital and it makes economic sense. Change some API keys, update a URL in your config, and you move on.
If the government decides that proprietary model is too good and so it gets shut off, what do you do? If a proprietary provider decides it's not worth it for them to continue hosting that model, what do you do? If that provider silently updates the proprietary model and it makes your app broken, what do you do?
I'm using a ten dollar a month US model to vibe code startup ideas. All my previous startup ideas i had to hire a graphic designer and back-ender or two to help. I use to be a web design front end enigeer since 2009 yet those skills are dumb now, so now Im a vibe coder.
The model I use to vibe code with I am just going back and forth with. Since Im building it as I go using an agent doesn't make sense but I guess that's where all the token usage comes from? Pardon ramping up my skills via vibe coding this one idea for about a month and have never hit any quota and or have gotten anywhere near my limit.
When developing AI services, Chinese models are cheaper. For the AI models used in actual services, like uploading an image and receiving a response, they use Chinese models.
On the other hand, when developers are developing, they mainly use US AI because the quality is better.
When developing AI related services, they prioritize Chinese models due to lower API costs.
It seems like the article didn't make this distinction.
So the claim that Chinese AI is the top choice for service level AI isn't entirely wrong.
Came here to post the same thing. I've noticed a lot of Chinese model astroturfing on HN over the past 60-90 days or so. Many upvoted posts in all conversations about AI touting how great the Chinese models are even when performance isn't the topic of discussion.
This is a very strange article considering that Llama, the mother of all open-weight models, has led to anything but success for Meta.
Also, enterprises don't give a rip if models are open. They care about zero data retention (and sticking with whatever vendor they're already using).
This blog post is suspiciously close to being a restatement of what Alex Karp recently said on CNBC[0]. It's important to remember he's the CEO of Palantir and hardly a neutral observer.
There are many reasons to celebrate open models, I run them myself. However there's not yet enough evidence that 1. America is losing the AI race (pardon jingo-ey phraseology) and 2. American AI labs are losing because their models are not open-weight.
> This is a very strange article considering that Llama, the mother of all open-weight models, has led to anything but success for Meta.
I see this as a fault with Meta's models, not a fault with the concept of open weights. The Llama family just aren't very useful. They make flowery prose but they're terrible at tool calling [1][2][3] so there just isn't much I can actually accomplish with them.
If the models were the quality of GPT-5.6, or Opus 4.8, would Meta really reap any benefits? Maybe in a zero-sum way, because OpenAI and Anthropic would lose. But I'm not sure they'd be that much more ahead either.
2-3 years ago MAIR was on a roll with Llama 1 2 3, Zuck was on his rehab tour to be a cool guy, and Meta as a whole was pumping record numbers after record numbers. I can't believe how that falters so quickly after the addition of Alexdandr Wang.
I am no fan of Wang but he came after Llama got caught benchmaxxing Llama 4 rather than training a good model. My read is that Zuckerberg tried to buy his way out of the problem like he always does, and he ended up overpaying for a lemon.
At the time the whole thing was led by Yann LeCun who seemed to spend more time arguing with people on Twitter than figuring out new techniques to make Llama the best. Meanwhile Deepseek was figuring out large scale RL on kneecapped hardware like H800s and how to scale architectures an order of magnitude bigger with MoE.
He was their “chief AI scientist” (what an abomination of a title) and led FAIR. Maybe it’s true he was not working on LLMs but I can’t even imagine what more important thing he could be doing at Meta.
Which brings me to my other point. If you want to compete with serious labs (OpenAI, Anthropic, Deepseek, Alibaba) you need to be smart and focused, not messing around.
Don't think it is down to Wang or MSL but Meta's focus on "personal AI" led them to whatever strategy (OAI missed the developer market too, which Ant then captured; leading to several high profile departures at OAI, coincidentally hired from Meta). The original Llama team themselves started Mistral which hasn't gone anywhere. The simple fact of the matter here is, Chinese firms have the money and the talent to rival the US ones in this field, should they as much miss a beat.
> Also, enterprises don't give a rip if models are open.
They care about control. I see many of my enterprise (or just-below-enterprise) clients very annoyed at OpenAI and Google after 2-3 years of model toil, where they had to constantly re-calibrate onto new models, on tight externally mandated deadlines, with little certainity. Now they are reaching for open weight models instead, that they currently host with the same inference providers, but have the option to in-house if push comes to shove.
> considering that Llama, the mother of all open-weight models, has led to anything but success for Meta.
To me, Llama was the ONLY successful thing they did it. it was when they stopped that they fell off the radar as an interesting AI company. They had a genuine chance to be the "substrate" that Ben talks about here. I can't actually figure out why they threw it away.
There has always been room for both closed and open source software.
In this context, open source/self-host means do it yourself, closed source means you trust someone else to do it for you.
In the long run open source always wins because of the community effort, customization, network effects and price.
Internet protocols are open, anyone can setup a website, host their own email server..., but most people don't do that, they rely on someone else to do it for them.
People still pay for Windows rather than use Linux because most people and companies have better things to do.
The main threat to American AI companies is not that consumers will self-host, but it's that new hosting companies will appear that will host open source models and offer them (cheaper) to consumers. It'll be like the web hosting market before the era of cloud computing.
> In this context, open source/self-host means do it yourself, closed source means you trust someone else to do it for you.
I don't think that's what it means in this context. Hardly any users are training their own models, after all. And I don't think Windows/Linux is a good analogy - OSes have inherent platform lockin that LLMs don't.
Really the only 3 things anyone cares about are capability, cost and data privacy. Sure, the cost axis for open weight models needs to include the cost for hosting things yourself, but I think the bigger reason that businesses have been throwing billions at Anthropic's and OpenAI's models is that they have had the best models, and they've had big releases every few months. Their biggest Achilles heel is that if/when their improvements start to plateau, open models may catch up and businesses will start to scrutinize their AI spend a lot more.
The behind the scenes element you're not aware of is Anthropic is going to companies reliant on their models and demanding HUGE one time fees (100 million+) to continue using their models or they will be cut off. This has happened to several larger companies I and others are invested in.
This resulted almost every time in "screw off we'll train our own models or use refined open source ones instead" leading to a lot of anger at Anthropic by CEOs these days.
We all want an alternative and Anthropic and OpenAI need to charge more than they are worth to pay back their investors and everyones stuck now.
Pretty sure he’s talking about Cursor and other ai coding startups. There was a lot of drama between the two in the past year and iirc anthropic cut their capacity.
> considering that Llama, the mother of all open-weight models, has led to anything but success for Meta.
This is also a strange way of framing it, though. Llama was released as a research project, it was never intended to create some vast ARR revenue stream or reframe the way people look at AI. If Meta wanted to exploit it for personal success then they had lots of opportunities to do so.
With OpenAI and Anthropic's profitability under question, it is up in the air whether or not America's stance towards AI will work. If they can't convince the world that they're a proper software business, then China's philosophy will win by-default.
It seems to me that the US AI industry has bet the farm on the idea that AI will enhance AI itself, so any small advantage will magnify recursively into an unstoppable advantage. Therefore it is vital that they spend as much as is necessary to be the first to that small advantage.
At the moment, I can't say that I see this happening. It's hard to know whether it may happen in the future.
From what I see it seems like we're hitting the top of a sigmoid curve in the model's utility for coding assistants. Going from "it does 90% of the job" to "it does 94%" of the job is a legitimate improvement, but it's not a phase change, and it's probably not worth paying multiples more for. And coding assistants have turned out to be the killer app for AI; it still isn't really working out in a lot of the rest of the industries of the world.
At any moment, theoretically, someone could find some new way of making AIs that breaks this sigmoid and propels us into a new one. But that's not a great thing to bet the farm on.
I'm not sure I'd say "China" wins if this particular strand of American AI fails. Falling back to an open weights model and making money on the serving of the models wouldn't take all that much economic realignment for the US and would be the natural outcome of any sort of fire sale of current AI assets. However the devastating effect on the stock market if the market comes to the conclusion that this current round of AI can't be profitable without falling back to such an economic posture and some more years of people adjusting to it can hardly be overstated.
> it was never intended to create some vast ARR revenue stream or reframe the way people look at AI. If Meta wanted to exploit it for personal success then they had lots of opportunities to do so.
They release the base model as open source, everyone uses it. They make a paid version, no one uses it.
They make no money from the open source version, they get no social credit from it. Where is the benefit to having an open source model?
> Where is the benefit to having an open source model?
Research. Llama is and was a research project, intended for researchers. You could make this same critique of Microsoft's Phi model, Apple's OpenELM or OpenAI's OSS. None of them were intended to be kingkillers, all of them are experimental in nature.
You might not have followed the space at the time, but there was a real race to implement the transformer architecture with fewer overall parameters than GPT-2 and GPT-3. Llama was revolutionary for sticking the landing without being entirely lobotomized, the "benefit" was that the model was usable on a local machine. Contemporary projects like Flan-T5 and GPT-J/GPT-Neo were entirely displaced, Meta's AI mindshare went ballistic for a few months and probably propped up billions in exit liquidity for executives and former employees.
I mean isn't the explanation simply that llama was never good enough, even when it was released? I hear (no data) lots of people using gemma4, at least a month or two ago.
Isn’t it basically impossible to run the newer high quality Chinese models locally, even for a corporation? The better they get, the more they need a data center. So, the ‘better’ Chinese AI gets , the more it will just be a service run on Chinese hardware competing with ‘our’ lower latency AIs .
The open source character of the models is irrelevant if you need a nuclear powered data center for inference. In the end it is just another internet service.
> The better they get, the more they need a data center.
That's true.
> So, the ‘better’ Chinese AI gets , the more it will just be a service run on Chinese hardware competing with ‘our’ lower latency AIs .
This is not. Them being open models means that any hosting provider in the world can host them as well. You get to pick and choose the provider the same way you'd pick and choose where to run a Linux server.
The cost of inference is not insurmoutable for many corporations who may run a small datacenter out of their headquaters or branch offices.
Larger models absolutely have a higher barrier of entry, but its a cost under a few hundred thousand as opposed to the millions necessary for a datacenter built as core revenue generating infrastructure.
Cost of inference is small when compared to the cost of training new models. Which is the real advantage these Chinese models have. Someone else has already spent the capital needed to create the model.
With a reasonable upfront investment and a few trained staff, its very possible to run these larger Chinese models in a well managed fashion.
The real calculus is if this up-front investment and associated lifecycle costs are over or under the costs a corporation may simply wish to dump into a cloud managed service like OpenAI.
I do think open-weights models are going to "win" in the sense that they're probably going to be dominant when the hardware to run them becomes affordable. (which might be a while). Although I guess you could probably rent the GPU's yourself to hypothetically save on costs. (I'm a little skeptical -- I've heard of companies doing this and the inference bills are surprisingly high -- assuming the sources are correct. I don't know if a lot of people really want to be advertising "oh god our bill is horrible")
I'm sort of baffled by what the entities that train the open-weights models get out of it though. Is it just a direct play to undercut the US providers because they view them as a threat? I just don't really understand the business model behind it.
If you're NVIDIA then open-weight models are a classic example of commoditizing your complements; cheaper models mean more people buying GPU's to run them. [1]
Nvidia selling more GPU's at the cost of it's datacenter business is pretty close to Kodak selling digital cameras at the cost of film.
The data center side is so bloated anything that eats into it is a huge negative. Their data center business brings in 20x the gpu market. Local open weight models will be what pops the bubble and China will do anything in it's power to enable that pop.
I wonder what the thinking inside NVIDIA is at the moment. They have countless examples to learn from here, about the danger of not being willing to cannibalize your high end products. But, of course, there’s a reason that there are lots of examples of this sort of failure.
There’s plenty of competition that would be happy to attack them from below, though…
I also wonder what this moment will mean for chip design going forward. The fact that we are so constrained on the GPU side along with the success of unified memory with apple silicon does make me wonder what chips in 3-5 years will look like.
Those that can will incorporate memory into their designs, and the Chinese will be a bigger part of the memory market worldwide going into the future, that alone will drive more tech companies to look at memory/ssd design differently than what they did before. Market conditions have changed you either change/adapt with it or you die.
I think memory and ssd design will end up being incorporated into the overall design of the SOC chip. And several companies that can probably will sponsor a existing company or build a foundry going forward. Once again, change or die.
Unified Memory simply isn't viable at the scale that a full Nvidia cluster operates at, and it would hamstring the parallelism that gives these clusters an edge. The current solution of Mellanox-style interconnect is probably still going to be the status quo in 5 or 10 years.
> Nvidia selling more GPU's at the cost of it's datacenter business is pretty close to Kodak selling digital cameras at the cost of film.
This assumes a) AI is a zero-sum game, and b) we're actually talking about on-prem AI will replace cloud-based AI. I think neither statements are true.
AI is like compute: we'll need all sorts of it, in various sizes, everywhere. I'm sure Nvidia whats to own all the workloads.
On the other hand, I do think open weight, like open source, will win in general.
It will take about 35 years for digital camera sales to reach Kodaks profit margins.
The market growing and companies raising the economy are not zero-sum. Nvidia/Kodak killing the golden goose is zero-sum. When the difference is your valuation crashes that's zero sum for the company, just not for everyone else.
My only concern is if we can limit the economic impact from lowering investments and causing a 40% market collapse circa 2008/9.
They resisted the idea because it did kill their legacy business. Kodaks peak was 16 billion in revenue in 1996. The digital camera market will be approaching that level of value inflation adjusted around 2030.
35 years to get back to status quo. Putting a bullet in a golden goose is often considered a bad idea. Everyone is happy they are dead, but if you can't understand why they might try to keep the corpse alive you have never looked at the numbers.
Kodak was definitely mismanaged in many ways but you are correct in your analysis. If they had pivoted hard to digital they could have been what today? Nikon? Fujifilm? These are known brands that are bigger than Kodak is today but they are nothing compared to what Kodak was.
At its peak Kodak was one of the (the?) most well known brands on the entire planet. Anywhere in the world if someone snapped a photo Kodak was more than likely taking a cut, coming and going. They employed as many people in Rochester alone as any digital camera company does today internationally.
Sure it could have been managed better but they were always doomed for a fall. They were a chemical company entering a digital age. Does anybody even care who makes the sensors in iPhones?
Kodak, like Xerox had it all under one roof their management at the time turned their back on the future…
IBM was the same way, ironically, the fourth Yankee clipper company had to be dragged into using two types of glasses sitting on the shelf that were patented/created in the early 1960s, both of which later became known as gorilla glass. Steve Jobs had to call them (Corning) multiple times to get them to finally let him use it on the iPhone. Imagine if Steve had contracted out to some other glass company in the far east?
The future was the death of Kodak as it existed regardless. They were a chemical manufacturing company that did some software. Their core business was doomed regardless. Could they have pivoted? Yes. But even if you correct every missed shot they’d still be a shell of their former selves unless you’re creating wild counterfactuals like a kodaphone.
> If they had pivoted hard to digital they could have been what today?
Having the first portable-ish digital camera they could have seen the true value of Fairchild's CCD business, got a stake/bought it/replicated done whatever it took to push the frontier of digital imaging and became the Kodak (old, film-era Kodak) of electronic imaging?
There's no comparable business today because all the things they could have invested in ended up being taken up by different companies, they had the R&D culture, revenue, and distribution. I don't see why they couldn't have been category defining.
They could have been category defining, sure. It would still be a fall though is my point. A fall from being a household name across the entire world to a company making components and niche hobbyist and professional equipment.
Kodak, Xerox, IBM, Motorola of Schaumburg, Illinois are perfect examples of companies that did not want to upset the apple cart, another example outside tech of tech that did not want to go into the future was US Steel, and while I’m at it to a lesser extent Ford and GM are teetering since 1973…
Hmm this sounds like an incumbent missing a paradigm shift because they didn't want it to disrupt their core (usually enterprise) business, although riding the shift would have ultimately delivered an order magnitude larger business.
Classical example is Microsoft actively undermining mobile because it threatened selling Windows or enterprise licenses.
Or Yahoo fighting Google's model because the latter model's didn't depend on taking enterprise deals to rank results.
Could you explain what you mean by this? I thought all of their insane profitability and returns are from crazy margins on their GPUs. I know they started/partnered/invested in some data center businesses, but I thought they were fledgling
The data center business is selling Tensor core GPU's which are used for Large language models nearly exclusively. I don't think of them as GPU's anymore considering a GPU is a graphics processing unit and this is not the primary function of those cards anymore. A GPU will always be a card you put in your computer to play a game in my mind.
I know a tomato is a fruit but will still be annoyed when someone is pedantic about it because that's dumb.
When the mobile phone is eventually integrated into the human body or some other silly application in the future that will annoy me as well. Congrats on being pedantic.
You need 64 H200 super-node for inference for kimi k3. You will not do inference locally. What might pop the western hardware bubble is Chinese GPU, memory, networking companies. But even in China, these AI centric hardware is not cheap.
> But even in China, these AI centric hardware is not cheap
Definitely not cheap for individuals, but well within SME territory. There are countless small-town, family-owned businesses that had higher startup costs than a hypothetical Kimi-R-Us, Inc.
No, Nvidia is worried about Chinese hardware stack. China is under sanction, what are they training and inferencing these models on? Even if this model might still be Nvidia chips, what about the next model. Everyone knows model and hardware companies are working together. There are a number of very competitive companies in China in this space. After they got this area sorted out, China will do training, inference and tokens entirely on their stack and export their entire stack.
With their models being open-weight, any two-bit firm in the world with enough capital to invest in a few servers can become a provider capable of carving out their own little niche in the economy.
The Chinese see this as a lift on the entire economy, as it comodotizes the technology to a degree in which many firms can serve many sectors of the economy, a true total-economic win worth the public investment.
The American strategy is built off of private investors believing that with enough money poured into as few companies as possible, one or two firms can come to dominate the entire market and start charging an ever burdensome "tax" on every sector it can touch. Not what I would call a total-economic win for the country.
Additionally I think there is something to the idea that they are trying to undermine foreign competition as a stall. They can’t compete economically for geopolitical reasons right now but they also can’t let American firms dominate the rest of the world in that market/technology stack.
The Chinese strategy is to fool naive westerners into thinking that's their strategy (looks like its working), until they can buy time to do the American strategy.
While I completely agree with your take, I think everyone has been surprised by how quickly LLMs have become highly useful and extremely powerful, and by how possible it is for relatively smaller models to also be highly useful.
Given that, I would expect that in hindsight OpenAI and Anthropic would spend 40% of what they have on compute if starting over and knowing the actual landscape.
The massive capital allocation was a blind decision and they swung big.
It is still possible that techniques will be developed that create a moat where the massive hardware capex is justified, but US policies of banning competitive GPUs and blocking frontier lab releases makes such things far less likely.
"Escape velocity" for AI is when the open weight models are good enough to help drive the next frontier innovations/techniques. I think we are close to that if not already there, at which point it's a race to commoditization no matter what Altman or Lutnik wish will happen.
Don’t forget it took that huge spend to publish the papers and get to the models we have. It’s not obvious that without them we’d have LLMs springing up out of China or anywhere else.
China is the factory of the world. They don't need software to win. Rather they prefer software is free and they can win in hardware. So if AI inference is free, they can put it in as many hardware components as possible and sell them in the market - think toys, cars, tools with chips manufactured in china optimized for the use case. In long term you tend to commoditize hardware. We have thousands of device types of cheap x86, and with linux/bsd software on it coming from china. Why do we think GPUs will be different.
- Software is copy-able for nearly free.
- Hardware is not. Even if you have the plans, you've to build the infrastructure for it, and its a fairly finite, physical item.
Both are however hard and expensive to produce. It's much better for profits to develop and sell the hardware, and copy the software someone else spent resources on.
We just sacrificed our entire lucrative SaaS market to this, and perhaps even big tech itself. To the altar of AI.
And now it's all going to become commoditized.
Billion dollar software will be commodity. Salesforce. There are orgs already moving to their own internal tools.
It does not seem hard now to rebuilt Google Search, Google Chrome, Gmail, Gsuite, Netlify, Vercel, Cloudflare, Vimeo, Twilio, or even Stripe. The cost barrier has to have dropped 1000x, maybe 10000x.
We have millions of engineers with the talent to do this. Many of whom are unemployed and have savings and nothing better to do. They could easily carve these markets into pieces.
We shouldn't shut down open weights. It's too late. They'll win, and that's a good thing. Big tech was a thermodynamic bubble of high energy waiting on the dam to burst, and now it has. The genie won't go back into the bottle, and that's totally fine. It's progress.
Now we need to rebuild our factories and supply chains and energy and resource inputs. Because the back half of this revolution is going to be robotics and factory automation. If we don't have the connective tissue in place, we're really going to hurt.
We'll do well if we regrow manufacturing. If we don't, we might be in for a world of trouble.
You're right about it being relatively easy ro build your own version of something right now in the proverbial garage. But startups will still have to compete with the network effects of the large firms and the increasing push for data sovereignty from non-US customers. Not to say it's not possible, just that the technology alone isn't sufficient.
> It does not seem hard now to rebuilt Google Search, Google Chrome, Gmail, Gsuite, Netlify, Vercel, Cloudflare, Vimeo, Twilio, or even Stripe. The cost barrier has to have dropped 1000x, maybe 10000x.
> I'm sort of baffled by what the entities that train the open-weights models get out of it though. Is it just a direct play to undercut the US providers because they view them as a threat? I just don't really understand the business model behind it.
In China, it's because they are being heavily subsidized to do the research activity. It's not really complicated -- if you allocate public money for people do to a thing, they will do it.
> it's because they are being heavily subsidized to do the research activity
Is it actually true? This seems pivotal because currently most theories rest on the idea that individual Chinese companies are acting in China's overall economic or strategic interest. It's a tough sell to believe they all just do that through implicit desire to align with the CCP's direction. I would believe it much more easily if there were concrete incentives involved.
The US funds (and used to even moreso) scientific endeavors that stand to bolster the entire country all the time. Medical research is the obvious one, DARPA is the defense based one (though oftentimes defense is just a post-hoc justification for a lot of those), the Department of Agriculture is constantly researching improvements to the farming industry, the Bureau of Weights and Measures and the NSA fund cryptography research.
Research and development of new technologies often is a "rising tides lifts all ships"-type deal, which it is absolutely in the government's best interest to support.
For China, the best case scenario would of course be to control a locked-down best-in-class frontier model that the rest of the world becomes reliant on. The US seems to be beating them at that, and "Everyone is reliant on the United States" is a pretty bad scenario. A middle ground, positive outcome is that no one is reliant on locked-down closed models, so they're supporting that outcome.
China wants the US economy to flounder. Building our entire growth model on software that can be copied and taken by a small group of people will have no possible consequences.
It’s not that simple. If the US economy goes into a recession, it will take large sectors of the weak Chinese economy with it either directly or indirectly.
It’s probably more accurate to say they don’t want American LLMs to become dominant. The huge US data center build out doesn’t depend on Anthropic and OpenAI anyways. Those data centers can just as easily serve Qwen or GLM models.
They can't do this at the same margins. Cloud compute traditionally has <= 30% margins, and that's the very generous profit margin that folks like AWS are able to squeeze out of it with lock-in and proprietary tooling (something that will probably go away as software becomes more commoditized.) Frontier model companies (e.g., SpaceX) have been promising profit margins in the 75%+ range.
China is looking after their own interests, but they absolutely don't want their largest export market to struggle. The global economy is not a zero-sum game, and the idea that it might be is the root of many of our policy issues in the US.
Chinas interests today are a US with reduced power and a global economy more focused on China.
Someone loses power for someone else to gain. It’s literally the definition of a zero sum game? China targets areas it thinks it can win and dominate in the future.
Bringing "power" into the conversation is a whole different layer of complexity which I was not addressing. I was speaking about economics, which is in no way a zero sum game. Economic activity creates value.
I don't think it is true that they necessarily want the US to struggle, I suspect it's more self interest.
LLMs seem to be one of the biggest innovations of the last few decades, China probably just wants to make sure it's not being left behind and/or made hugely reliant on the US for what seems to be turning into a piece of critical infrastructure.
China has an effective strangle hold on some key sectors (solar, rare earths) and I am sure they relish this position and the leverage it gives them. You'd be careful not to give away that same leverage to a competing power if you can invest a few billion now and cover your bases.
Assume, crazy business, that china wants what’s best for the world. Recognizing the danger of an AI arms race rapidly producing uncontrollable superintelligence, it focuses on open weights to reduce the economic incentives for further advancement of AI beyond the “highly useful for humans” stage.
Seems like the only thing that could avert an intelligence rapid take off. Everyone wins except for shareholders.
I think lot of Americans overestimate how much China needs them. (Understandable, I suppose, since the UK made a similar mistake vis-a-vis the EU about 10 years back.)
From a Chinese perspective I expect China’s largest customer is China. These days it’s actually kind of wild how many Chinese consumer products aren’t (and won’t be) available at US retailers. And a lot of them are quite good.
For a while now China’s wanted to reduce its dependence on the US for a variety of reasons. And undermining the US tech industry, whose products the US government likes to use as a cudgel, may serve that goal quite nicely.
It is largest customer for now, but they try to boost internal consumption as well as diversify client-base. US is also competitor, so China is interested US to fade at least in competitive areas.
The fact that the US economy is load bearing towards unprofitable projects while things like medicare for all or universal childcare continue to not exist is just a damning indictment of the country.
Undercutting US dominance in AI is huge for China. If the entire narrative is that you have to use Anthropic or OpenAI to access a decent model, then China's AI labs are sitting on the sidelines as some third rate solutions. China publishing the model weights of models comparable to the frontier proprietary models drastically undercuts closed labs dominance. Maybe these Chinese AI labs don't have the billions infrastructures some of the US players do, but they don't have to if the model is open weight. Many inference provider companies around the world have hardware that can run these models and they will happily run frontier class models for people. Starting in 7 days, people will have the option of which of many providers they want to use to access K3.
Making frontier grade models a commodity will make a competitive market where companies compete for business by improving their quality and decreasing their prices. The cost to access frontier grade models will continue be driven down the more competition that enters the market. This commoditization will challenge the valuations of Anthropic and OpenAI.
The Chinese domestic market is extremely competitive on most things, including LLMs. Once one top company went open weights there, that's going to put pressure on others to follow suit. It'd be akin to if a company like Anthropic or OpenAI went open-weight, it'd probably result in a domino-effect of more US models going open-weights since otherwise that competitor is going to win a huge chunk of mind/market share for free.
It's also relatively free right now. Few people are going to run local models, and in the future it's likely that every model being released today will be obsolete. The only real downside is ease of distillation for competitors, but that's probably impossible to stop anyhow.
They get money from subscriptions and tokens, same as for closed-weight providers. Yes they'll lose some traffic to hosting services, but many users prefer to use the original training company since they have a guaranteed-correct implementation. Similar business model as open-source SaaS companies.
Some companies (most notably Deepseek) also manage to host their own LLMs so efficiently they undercut all third-party hosting services.
No one cares which company they use. They care about cost and does it act in a way they expect. Expecting anything else is pretty laughable and goes against human nature. You either create a moat so deep no one else can play or you have the government force users to use your products.
> I'm sort of baffled by what the entities that train the open-weights models get out of it though
if you do the training then you're in control of the output. For example, recommending your products/services or failing to mention your competitors. You could also automatically introduce backdoors into code deemed interesting, i'm sure all governments are very interested in having that influence.
That seems hypothetically possible, but kind of hard to do? I don't know a lot about training, but it seems like they wouldn't have exact control of the data that goes in at that scale (scraping the internet) so it seems like it would be kind of hard to do that in a way that's subtle. I'm kind of reminded of Elon Musk trying to put his political views into Grok and it seemed like it created huge technical problems with the model saying some really out of control things. Maybe it was just an xAI issue though.
It’s totally doable and is done right now in the form of “ai safety” guardrails. No reason you can’t have guardrails to reform responses about your competitors as negative. Or, when asked about current events, only give a one sided opinion.
> I just don't really understand the business model behind it
There’s a lot of value in the same sense there is a lot of value in controlling what Google search results are shown and what people see in the Twitter feed.
> I'm sort of baffled by what the entities that train the open-weights models get out of it though
I agree. The thesis in the article is interesting insomuch as I had not heard it expressed this way before: US restrictions on GPU exports have made it feasible to train models in China but not serve them. Therefore open model is a hack to get around the export restrictions, since models can be trained internally but shipped out of the country to be served elsewhere under the banner of open weights. I don't really buy this argument - inference is much cheaper than training and they are hosting their models anyway.
I think it is more likely (a) they have the money to do it and they need it for internal reasons - these are huge companies (b) there is a lot of prestige in China associated with besting American technology (c) people are still basing logic on outdated ideas of Chinese capability which are no longer true.
So it is easier than people think for Chinese labs to do this, they need to do it anyway and there is a lot of prestige from opening the weights. It is honestly not that different to why American companies themselves have released open weight models.
> I'm sort of baffled by what the entities that train the open-weights models get out of it though.
Why is it so baffling that people want to build great things? There are plenty of people who are happy building things for a salary and have no interest in taking over the world. Do you find the whole world of open source software baffling? Linus Torvalds and Richard Hipp and Antirez created the world’s most prolific software products and released it for free.
I don’t think they need the hardware to become affordable (as a regular end user).
They need their models to be good enough and cheap enough. Then the rest will follow. Companies will figure out how to host them for you efficiently, and you pay them monthly.
I don’t think I will ever want to set up a home server, no matter how inexpensive the hardware gets. At work I still use Cursor (with Anthropic models usually) because it’s paid by my employer, but for private stuff, I’m already using cheap models with OpenCode, and it’s extremely cheap and surprisingly capable.
I think there was around half a year between where the best models became good enough (last year December?) and where the cheap models became good enough (couple of months ago?).
I've always found it weird how ludicrously poweful personal hardware has gotten. The fact that you can't buy a midrange CPU with less than 16 cores, or that a high-end gaming GPU is 100+ TFLOPS just blows my mind. The fastest supercomputer in 2004 was 70 TFLOPS. Absolute crazypants level of power, and companies were fall over each other to get people to buy it.
People are happy to pay $50/year per seat to have the extra features and to not have to deal with stuff.
There's an issue at the margins here:
$1000/employee is a massive cost - it has to be deeply justified.
$50/employee is like ... $2 out of your pocket. It's an incremental cost. The CFO is happy to pay it if there is a lot of value.
A lot of software is in that later category.
Imagine if gasoline was 1 cent per litre - and there was 'free gas' but it was a pain to use, and you had to check a bunch of things. You may just pay the 1 cent.
AI is not quite that yet, but these dynamics will play out eventually, for a lot of things.
I think France and other parts of the EU are switching over to it. Although that's probably more due to Microsoft's aggressive behavior recently. Agree on the cost thing generally, but I can't help but think that when the hype to "do AI" blows over, people are going to be casting a jaundiced eye towards data security, which probably means self-hosting and sandboxing
The business model is making your country more innovative, which makes its citizens richer
Americans used to do that too, spectacularly.
Just consider the two alternatives: one is your industrial sector with this incredible new automation and analysis tool available for free. The other is one where it has to pay huge chunks of its resources to overseas companies.
If the outputs are independently verifiable at low cost, and the US models refuse to even try because somebody sneezed nearby and it sounded like "antigen" and not "achoo"... yeah sure. Whatever works to get the job done.
The hope is that AI will open up whole new sectors of economic activity. If you have to chose between exploring that space while potentially being exposed to Chinese tampering, versus just sitting on your hands and doing nothing... well then you take that risk.
I think part of it is definitely to weaken US providers and the US economy as a whole. China has a completely different domestic economic structure and motivations from western cultures... it's probably closest to a fascist economy mixed with a Maoist cultural ideology behind it. There's definitely winners and losers and the state tends to have tight controls over everything though.
I also think the restrictions on OpenAI and Anthropic are somewhat short sighted. In that the guardrails dramatically limit efforts towards securing your own software in many ways. Yes, it's also "dangerous" and maybe there should be a means of identifying "domestic" or otherwise "secure" accounts for those allowed to use the models without the same guardrails in place.
Yes, it damages its image, this is further made evident given the amount of propaganda that follows each time. Why would you invest in claude or codex if you just read how China's stuff is better?
Most of the growth is. If we removed AI, the US would be in a severe recession right now. Most of the absolute market cap, employment, capital, and other metrics that are not directly coupled to growth paint a better distributed picture.
Of course, growth like this can only continue for so long without changing that.
Well, yes, because the US government has staked the future of the country's GDP on AI. They're even divesting from science as a whole and putting it into data centers.
Okay but political leadership of the US are literally are staking the future of GDP on exactly those few AI companies... $1.4trillion so far while universal childcare would only cost $40 billion a year.
> political leadership of the US are literally are staking the future of GDP on exactly those few AI companies... $1.4trillion so far
Can you explain where you get that number from?
The information I have:
a) The government took a nearly $9 billion stake in Intel to support its chip-making efforts.
b) The Pentagon took a $400 million equity stake in MP Materials, a rare-earth miner.
c) Sam Altman has advocated that the US government take equity stakes in AI companies. Sen Bernie Sanders wants the US government to take 50% stake in AI companies! Nothing has come of it though.
d) Defense department AI contract value rose to $90.7B in 2026.
So I'm super curious where you pulled that $1.4T number from.
GDP growth in the United States is in AI and healthcare.
AI capital expenditure is around 5% of total US GDP.
Housing right before the 2009 market collapse was around 6.7%.
Biggest issue I see is housing has more real value than AI expenditure. Demand is real and isn't based purely on a few companies valuation or marketing spin. Nearly 2 decades later we still haven't caught up to construction rates before the 2008 collapse. When the bubble pops it's going to really suck.
> Is it just a direct play to undercut the US providers because they view them as a threat?
It's China putting pressure on a financing strategy in the US that was always a house of cards. It could also be China democratizing something that should have ALWAYS been democratized. Maybe both when the history books on this get written and absorbed by the winners of the LLM wars.
China does not want to crash US economy. US economy strong means more purchases for our goods. But US want to crash Chinese economy by: 2018 trade war, 2019 Huawei sanctions, sanctioning at least 1000 large influential Chinese companies, 2020 Tiktok theft, 2022 total ban on GPUs, semiconductor equipment going to China regardless of end use, banning all sorts of Chinese goods such as EVs, cars, batteries, drones, even DJI cameras, 2020 fake lie about Xinjiang to implement full ban of anything made by Xinjiang, 2025 50%+ tariff on Chinese goods. I don't think building LLM was thought has important until Anthropic went on full China hating, banning anyone on a Chinese IP using the service, even companies headquartered in China's oversea offices. Did you know that a regular person in China trying to use Claude to ask some innocent question gets banned? Talk about everyone created equal and democratic world.
You leave us with no choice but to build technologies ourselves. If its bad for Anthropic, well, you could owned the market in China, oh well, you reap what you sow.
China is a centralized economy that has been fighting a tariff war against the US for the last few years. If we see an economic collapse like 2008 China loses money but we lose a whole lot more.
China still ends up with production and a easier ability to integrate with other countries. We became a powerhouse after WW2 when all the other countries had their financial bases destroyed. We became a superpower providing the products to others to rebuild. Our market is all about short term growth, service economies, and goals driven by whoever is in office at the time. If they don't have a plan around this exact eventuality I would be beyond surprised.
Basically their system is more robust than ours to a major market event. Who pays for expensive services when they are choosing between food or keeping their economy going. People will pay for the equipment to keep their economy going. Who provides that.
We just did this song and dance with the tariff war between US and China last year. It was revealed that 3% of China's exports are purchased by the US, and that they are fully economically prepared to take that to zero when push comes to shove.
There is no coming back. After all, open-weight is NOT open-source. It is basically free model. And you pay to host it.
The value proposition is that 100's of providers and host and sell it. 1000s of businesses (eg. Microsoft, Databricks, Palantir to small startups) can run it, finetune it and own the IP and pay only for hosting.
On the other hand, you have OpenAI and Anthropic, who need to charge at 90%+ inference margin. It is because of 1) sunk cost, 2) sky-high salaries that they paid to keep the talent. Companies like Meta screwed things up badly by paying billions of $ for chief engineers.
Chinese labs are doing a favor to the world. But I can also say with 100% certainty that if US labs were to close shops next year, Chinese labs would immediately start charging $$. In fact, I think it might happen with open weights model soon. But still these fees will be one-fifth or one-tenth per token. Also it does not come with all the guardrails.
Solution: US labs need to reduce their costs, cut the salaries across the board and compete. AI and robotics are the last hope of US to get back to industrialization and continue being the superpower.
> Chinese labs are doing a favor to the world. But I can also say with 100% certainty that if US labs were to close shops next year, Chinese labs would immediately start charging $$.
How would do you explain the pricing of Chinese solar panels, after managing to destroy other countries' solar industries? The prices are still dropping per watt.
Could it be China's internal demand for solar is big enough, and its long-term governmental strategy on renewables result in an outcome that almost looks like largesse to the rest of the world? I suspect Chinese AI may follow a similar path.
FYI if you look into what has happened in the wholesale solar market since the end of 2025, this is no longer true. Prices are now 20% higher than they were in Nov/Dec, and were even higher earlier this year. Hard to predict the future but we may have reached a price floor, as at 2025 prices the module suppliers were below cost and losing money. The market dynamics have changed significantly since then
China may well save the world with green energy but I’m skeptical to assume we should just trust them to share their best intelligence with us freely, it’s a totally different think to sharing their best intelligence.
This comment has been flagged in our software as AI-generated. Of course it could be a false positive (though from reading it myself it does come across as LLM-influenced). But if you've used AI to write or polish the text, even just a little bit, please don't. The guidelines specifically ask us not to do this: https://news.ycombinator.com/newsguidelines.html#generated
Pangram believes this is 100% AI generated. High confidence. If you just used AI to edit your work, then it would say it was human influenced or low confidence, which is contradictory to another comment you claimed.
While this has clearly been written by / with help from an LLM, the general info is correct and matches what I have heard from those suppliers (although $0.21/W future pricing is far higher than the estimates I've seen)
I do not understand the logic going into these companies. Flagrantly violate all IP in Human history, essentially claiming domain over the heritage of Humanity... And... Try to privatize it? When the technology -- and data -- are both public domain to begin with?
It is ming-boggling stupidity. If there is talk of bailouts as the dust settles, there it would just be further evidence the system is ethically, financially, and intellectually bankrupt.
At least China is being consistent: they do not give a single f*ck about intellectual property or copyright…but they also give away all this distilled knowledge away for free.
I’m not an American so I don’t particularly like the idea of giving an American cartel of AI companies having so much power over this technology.
I don’t necessarily buy into the “China bad”, “they are communists” and all that BS either.
I will call a spade a spade and say that in this instance, what China is doing is a net good for the world, ideology be damned.
This is proof that America has forgot it's own history.
America was founded by men who hated intellectual property, who stole and smuggled the plans and expertise for textile machinery out of the United Kingdom. A gross intellectual property violation. Why? Because they were being exploited by that system. The UK was using the American colonies for raw material and keeping the machinery in the UK for finished goods.
That's the rub, intellectual property is only valuable if you're winning, if you're the exploiter. China never gave a fuck because they, much like the American forefathers, saw a system of exploitation and went "no, thanks".
AI is the culmination of all human knowledge, the idea that anyone could own that is obscene. American AI companies that are trying to horde this are getting exactly what they deserve by being undercut by China on this.
Amen! Europe used to complain about the US stealing their intellectual property! The nuclear bomb would have been possible if not for Jewish scientists from Europe going to America! So until about 100 years ago, US was FAR behind Europe in technology and science.
What if china ends up owning it? Do you think that's better or worse for the world? You're commenting your open opinions on a site run by a company that could not exist in china and cannot today. You can ask ant, xai, and chatgpt models questions and get answers that do their best to reflect the world. There's a set of questions you cannot expect trustworthy answers for from chinese models and you think they're stopping at those few questions?
They're dumping, dude. They subsidize important industries so they can give it way so cheaply that free markets can't compete, and when those companies fail, they swoop in and make a profit. uber did the same thing with taxis! This isn't a free resource - it costs billions to build out the infra and maintain the inference to provide this, and you think they're doing it out of the goodness of their hearts lol.
Come on bro at least admit you're happier giving china your data than the us lol. You're definitely not using european models I can tell.
Oh, come on, don't be a decelerationist. We're supposed to just ignore that aspect of our Brave New World. Don't think too hard about the unparalleled resource consumption, either. AGI will alleviate any and all of these concerns ... soon. In the very near future, we'll all be getting UBI, Gemini will know how many Rs there are in "strawberry" and we can spend our days creating the next generation of art to feed the machines. The nuclear salt reactors should be online by then, too, and data center power consumption will become a non-issue.
Poe's law is an adage of Internet culture which says that, without a clear indicator of the author's intent, any parodic or sarcastic expression of extreme views can be mistaken by some readers for a sincere expression of those views.
Look up the English Enclosure acts. They brought about a large-scale robbery of peasants by landowners in the 17th and 18th centuries, and created a proletarian class that needed factory work to survive. Things got a little better in the 19th and especially 20th centuries.
I don't think there will be any bailouts for these AI companies. There's no political will to do so in both parties. The grizzly alternative is a DeFlock movement, but instead of cutting down poles, it will be people's heads.
This entire piece boils down to “I like open source therefore it is winning”.
Everyone here has already raised good counterpoints, but one more is that all the companies publishing open weights models are heavily VC funded. What is their exit strategy? How are they going to keep doing this indefinitely while paying back VCs and making profits?
I think it's as simple as looking at the incentive structure. The Chinese gov't has incentive to kneecap US monopoly on frontier models. It makes sense for them to continue down this course if it strengthens their position.
The Chinese govt has a strong incentive to break dependence on the US for AI needs and build domestic models, but not to spend trillions to subsidize these models for the rest of the world.
Agree but I think they'll spend whatever they can to subsidize and break dependence on US models. Whatever isn't spend on US models will be spent on chinese models. Europe will remain a small single digit % unless they get their act together.
Closed models doesn't mean monopoly, cause they compete. And if the best thing for humanity is for good open models to exist, even those evidently required good closed models to copy from.
Right, this is the classic VC playbook and people should really know better. They use capital to undercut competitors on cost to gain market share, then slowly crank up the costs until they are (hopefully) profitable. It makes 0 sense long-term to spend tens of millions on training a frontier model and releasing it for free for someone else to host on their GPUs. It is naive to believe that when the capital starts to dry up and the VCs are looking for a profit that the models will continue to remain open-weight.
Chinese labs are a bit different since they are somewhat state-funded, so I'd expect them to shift to a model where Chinese models are hosted on Chinese infra.
Which on itself is wild. The main issue with the discussion isn't a rift in capabilities, but a rift in the valuation model itself.
If China can match the pace and produce frontier models at a fraction of the cost of US alternatives, even without access to SOTA hardware, what is the competitive advantage for the current valuations of Anthropic and OpenAI?
The Chinese ecosystem is largely built on stealing intellectual property from and/or distilling of US closed source models.
I’m not getting into the ethics/comedy aspect of it, but let’s not pretend this isn’t the case. Plenty of evidence, the motive is obvious, and the numbers are in plain sight (valuations, salaries, etc).
As long as this continues I think closed source will continue being a few steps ahead, but the steps will probably get smaller over time.
Having said that, this is already priced in, the market predicts this gap will be large enough for the US companies to profit from (astronomically).
There is plenty of room for open source models that "require" a subscription to be used or obtain working updated binaries. Same as any open source platform.
I'd be happy to pay a small monthly fee to license the model to run locally. I'm already paying for Claude, GPT, Gemini,etc.
AI models cost tens of millions to train. Offering them for free won’t justify the upfront costs.
The Chinese model of model training/open sourcing only makes sense in the context of the overall strategy of undercutting American frontier labs’ profit margins.
"the overall strategy of undercutting American frontier labs’ profit margins"
I don't doubt that's an unregretted side-effect for political leaders in China.
But the major motivation is to accelerate diffusion within their own massive economy in the pursuit of an across the board productivity boost in the face of an aging population.
This is true for any company following an open source strategy. You'll have the weights but you'll need to run inference, figure out your system prompt, sampling, quantization, etc etc. Loads of tuning.
elasticsearch the first example that comes to mind. you can run it yourself but elastic gives you so many lessons learned and tunes ootb that it sings with relatively little effort, though still reqiures some.
about a million dbs i could make the same comparison for
The VC money is only contingent on the strategy eventually bearing fruit. I do imagine going open source -> closed source could work for some model companies who get enterprise/ecosystem buy-in but the probability of ROI is lower.
I think open source will remain competitive among smaller players and adjacent industries wanting to avoid lock-in with the majors. OpenAI, Anthropic, Google, etc are all out to win - they require profit extraction from their R&D. China seems to have, over the near term, accepted that they can not (or at least have not) pull ahead and so open source collaboration speeds the collective development, keeps them close to the frontier, and ensures their industry has access to learn from and implement. The USA playing export controls games with Fable made that aspect very stark.
But I agree that's the catch - it doesn't make sense to throw money at open source models in hopes of direct return, so you need a nation or conglomerate to do it so as to control the technology they rely on.
I can see two reasons American companies might want to train models they give away for free:
1) They sell compute: chips (Nvidia), data centers (AWS, Microsoft, Google, SpaceX, etc), or even end-user device manufacturers like Apple (e.x. M7 rumored to have 1.5TB of unified memory). If Jevon's paradox holds, then cheaper (or free) models means more demand. But compute is likely supply-constrained for years anyway.
2) Their product isn't AI but depends on AI being cheap, or they don't want competitors to capture that value, i.e. "commoditize your complement" https://gwern.net/complement
It probably doesn't make sense for these companies to invest a lot of money training models that will be obsolete in a few months anyway. When progress starts to plateau I'd expect more companies to start training models they give away for free.
I don't think this is necessarily going to prove to be true.
I often see the sentiment: "the Chinese strategy only makes sense in the context of undercutting American labs' profit margins".
If, for example, you are a company with a near-monopoly on "serving video content", and you feel reasonably confident about retaining a decent slice of the serving-video-content market (Google in the west is an example, Tencent in the east), then training video models on your dataset - and releasing them freely - makes an awful lot of sense.
Free tools to create with mean more video content. In this hypothetical, you're reasonably certain that any video content which does get created will also be watched on your platform.
That is a net positive. The question becomes: How many watch-hours earns back the cost of training a model? It's probably not really that many, especially when you have a near-monopoly on a billion sets of eyes.
It's also a net-positive if people build better video models from research you release, because - again - you are reasonably certain that the even-more-innovative content those models produce will be watched on your platform.
It really begins to make strategic sense if your company is in a GPU-poor environment. Your costs cease at the point you upload a model if your users are running it themselves. You don't have to serve the model. The content is still created.
You are also less likely, I think, to alienate human creators whose work the model was trained on if the model is not sold back to them as a subscription, or by the token, but given for free as a tool.
This frames the conversation very differently. It creates, I think, less of an "us vs them" dynamic, and more of a rising tide.
It's true that it is also beneficial that these models undercut (especially in language models) American companies. But, generally, Americans are not the customers of Chinese companies releasing models. They are already serving a huge volume of customers in a complex, existing marketplace.
The full picture is much more nuanced than simply a geopolitical desire to undercut US labs, and there are several other reasons the strategy can make logical sense.
Yeah this is a battle and it's why governments decide to spend resources on this. Protectionism won't help America, American needs to compete. There's a general consensus that open source AI must win because people don't want to end up as slaves to a megacorp, so if you're anti-open source AI you're not gonna fare well.
"It’s obvious to me that there are ecosystem benefits throughout China, from manufacturing to scientific research; every sector can just plug in these models."
China seems to perceive AI as a much more sensible technology than the US and seems to be integrating it in far more industries than the US.
I'm not sure the American mind can understand the distributed benefits afforded to the Chinese economy from opening their AI models, I think it's pretty reductive to assume it's purely a strategy of undercutting American frontier labs.
I don't think this is accurate. AI is driving the cost of software towards 0 and these AI models themselves are software.
Releasing the models for free accelerates the trend but if you're a startup that needs leverage it's a good way to build brand and customer momentum that will be relevant in the more established future market.
I can see an American company taking on the same strategy, and in fact Thinking Machines based out of San Francisco did that just a few days ago by releasing their first model with open weights.
There are people that spend tens of millions of dollars on paintings and artwork. I can see plenty of reasons why organizations and individuals will continue to want to drop a few million on an AI model just for the fun and prestige.
Maybe today's US frontier models provide enough information content, so that the momentum suffices to use them as a base for every coming generation of distilled and later fine-tuned models?
There are good reasons to dislike outcomes that involve a single entity pulling well ahead of the pack here. Whether or not it continues to be American labs in the crosshairs and Chinese operators doing the aiming, perhaps it's reasonable to plan for continued efforts of this sort.
A pittance frankly. Something that could easily be covered by oh I dunno, let's call it a National Science Foundation who's in charge of subsidizing important basic research for a nation's interests.
Anywho, when the market is trillions (and of potential nation state concern), it is pretty inconsequential and very much worthwhile.
Aside, I think your scale is a bit off, I think Moonshot has raised $5B and potentially they get other breaks from China, not sure. So to produce something like SOTA takes billions, not tens of millions. I'd still argue it is worthwhile to subsidize and invest in open versions, imagine spending $5B to unlocking a few percentage point increases in your country's productivity.
There is a huge cultural influence opportunity too.
Imagine if, in 10 years time, every school kid is learning the causes of the US civil war from an LLM, getting their essays on hiroshima and nagasaki graded by an LLM, and a million other things.
A country with competitive LLMs gets to decide whether "it was more complicated than just slavery", and whether "it was tragic but necessary, saving lives over all".
Countries without competitive LLMs are effectively going to be buying all their history, economics and sociology textbooks from abroad.
It might seem so til you consider how an LLM is trained.
An indirect illustration: I can attest that Deepseek has very good 19th German, and knowledge of German 19th c literature, science and historical scholarship. No one in China could control the training that led to this. The German training sources were well aware of the exact nature of eg American slavery, so they are in the weights.
State control operates in the outer layers not the llm itself.
Every school kid in the USA, you mean? Because I think other countries would rightly perceive the world you described as a dystopia.
I don't want my kids' education to be surrendered to the whims of Big Tech douchebags any more than I want AI decisions in legal cases or an AI replacement for a family doctor.
Some systems are better left mostly analog. Education is one of them.
Your kids education was already surrendered to the whims of the Big Textbook Politburo. History textbooks are full of propaganda. I think what we have today and what we grew up with is 10x more dystopian.
The upfront costs are actually a lot higher, but still absolutely trivial in the big picture. OpenAI pulled in $120,000,000,000 in one round of fundraising and they're closing in on $200,000,000,000 total. Even at 1 billion dollars, new frontier model training is only 0.5% of what they've raised.
I use Gemini Pro (got it with my 5TB of Google storage) and for a while it seemed if Google had pulled the rug as I was running out of quota after only a few hours. That seems to have been dialled back a bit lately...
I also use Chatbot with Deepseek V4 Pro and GLM 5.2. However, GLM 5.2 seems to eat tokens like crazy as the context increases. Anyway, there isn't a meaningful enough difference between the two to be honest and Deepseek is pretty magical imo.
The point I want to make is that to me it seems clear that China is totally undermining the West with AI. I'm fine with it tbh. As long as more and more AI is released into the wild, rather than locked behind massive token farms like OpenAI then I'll be happy. Don't get me wrong, I can't run Deepseek on my computer at home but someone can!
The US (and the west) has invested trillions at this point into datacenters, chips, bribery/lobbying but it doesn't look like China has dropped the same levels of cash as the west (that's the way it looks to me, at least!) so they can just roll out new models every so often that are more than good enough.
This level of cash burn in means the west has no choice but for this to succeed or every pension fund and stock will tank! And China knows this, hence the push to release more and more really good models.
I would argue your logic is exactly backwards. For most Americans, sharing data with the Chinese government is irrelevant. The CCP cannot put you in jail.
However, American providers are going to be subject to secret national security letters, FISA court warrants, and regular court orders.
I stick with OpenCode Zen (only US providers) and Together.ai (hosting themselves). As interesting as new Chinese models are when they're first released, I wait til they are open source and on US providers.
How is this even a direct comparison? Most companies I know of which use Kubernetes are using it on a cloud provider. Even if it's kubernetes on EC2 rather than hosted e.g. EKS, those companies are also happy to use lock-in services like RDS and S3.
If you're only using the frontier providers, it's nearly trivial to support switching between the 2-3 of them that exist without using OpenRouter.
If you are ALSO routing to open-weights models on OpenRouter, then sure. But what we've come back to is that almost everyone using OpenRouter is someone who wants to use open-weights models, and some of them also want to use closed models, so of course open models take all the top spots.
>This is why most enterprises use a multi-cloud setup
Going to have to say citation needed on this, if you're suggesting most enterprises have infra-as-code that would allow them to switch their entire infra between vendors within a month.
What's the incentive for the Chinese labs to continue releasing weights 5 years from now?
In the short term it attracts talent and builds brand, but they make little money on inference to support research and training costs. Tin foil hat thinking: it also pulls inference revenue away from Antropic/OpenAI and a financial crises at those organizations improves the relative position of Chinese labs.
Is there a reason to think open-weight models are a stable outcome? Open source software provides a collaboration framework for engineers from many companies to work together. Model weights are mostly a one way street.
> What's the incentive for the Chinese labs to continue releasing weights 5 years from now?
Engineers love to build things (just look at the whole world of open source), and building an AI model is one of the most exciting things to work on. It only takes a few million dollars of funding to produce an AI model. People spend millions on artwork and paintings for fun and prestige. I can easily see why a billionare or government would want to have their own AI model, but is not interested in the business of selling it, so they release it for free. Combined with engineering talent that loves a meaty problem like AI, I see absolutely no reason why open source AI will not continue to thrive, and not just in China.
> Tin foil hat warning: it also pulls inference revenue away from the Antropic/OpenAI and a financial crises at those organizations improves the relative position of Chinese labs
How is that a "tin foil hat" argument? That's how competition works. You want to make your competitors stumble and fall.
> What's the incentive for the Chinese labs to continue releasing weights 5 years from now?
The ongoing money from their government. The absolute collapse of OpenAI/Anthropic. US economy getting fucked because their bright financiers decided that going all in on the funny text generation machine was a good idea. Continuing to take a dump on US imposed copyright. The gigantic amount of soft power being the ones releasing "open" models grants. The fact that the ongoing AI war has made China LESS reliant on the US and are now producing their own GPUs, RAM and have massively caught up to nvidia. The lists is endless, and half the points more or less boil down to "taking a dump on the US is morally right", and the other half that massive government programs lead to giant leaps that benefit your society more than a dozen VCs on coke ever could.
The US business model for commercializing LLMs seems unsustainable to me. We are saying that they are creating trillions of dollars in value out of:
1. A model that for the most part is public and available to anyone.
2. A situation where the model’s success mostly comes from throwing as much data and computational resources at it as possible.
It seems that either of those assumptions could crumble quickly and unexpectedly. What if the AI paradigm changes completely and we no longer need GPUs? Or what if someone with enough determination decides to create a better model and sell it more cheaply, or free?
There's already cases where Google and I'd assume others are designing chips to work with specific models more efficiently in coordination... Personally, I could even see more specialty models called/coordinated from the larger models that can do smaller pieces of targeted work very well within limited scopes as a mixed economy so to speak.
Assembling a new model from scratch requires a ton of resources and knowledge bases... there's been a lot of sketchy activity just in training. You also have weighting, distillation and other approaches to create more portable options that can run on lesser hardware. But, K3 as an example takes massive compute resources to run.. and this isn't going to get to a portable device any time soon... as Moore's law is effectively dead, you may get newer/better tooling around the LLMs, or you may get an entirely new/unique approach to AI... but current trends aren't going to put a leading model on your own hardware anytime soon for most people.
"Had the atomic bomb turned out to be something as cheap and easily manufactured as a bicycle or an alarm clock, it might well have plunged us back into barbarism, but it might, on the other hand, have meant the end of national sovereignty and of the highly-centralised police state. If, as seems to be the case, it is a rare and costly object as difficult to produce as a battleship, it is likelier to put an end to large-scale wars at the cost of prolonging indefinitely a ‘peace that is no peace’."
-George Orwell, You and the Atomic Bomb
I think AI now belongs in this dichotomy too. And we know that they are more like alarm clocks than battleships. Most of us do not need to learn the bitter lesson, we just need a little droid that turns .xlsx documents into .pdf documents for our client, an average here, editing out the ham sandwiches there. Simple little actions that take time and human-like effort but not human-like creativity and conscious thought. Things we used to have literate slaves and serfs do back in the days of triremes and guncotton.
Sure, the large battleship like LLMs will have some need, but the alarm-clock like LLMs are going to be good enough for enterprise-grade.
Thanks for sharing the quote, it's really great. I believe he is correct, but I am not sure I yet agree with your assertion that we know that LLMs are more like alarm clocks than battleships.
I think they are more like watercraft in general. Some are small, and they have their uses. Some are big, and they have their uses. But it is the nation with the most aircraft carriers that is truly sovereign.
I highly recommend everyone read a science fiction novella called Full-Spectrum Barrage Jamming, especially if you are interested in China-US relations.
Its author is Liu Cixin, whose other work The Three-Body Problem won the Hugo Award and was adapted into a TV series by Netflix. His thinking carries a heavy shadow of Mao Zedong's strategic philosophy. This novella is very intriguing—it is set during a time in the past when the gap between China and the US was immense, and people were trying to imagine how China could win if a war broke out. I forgot the exact details, but the general concept is to force a technological regression through electronic warfare, knocking out all smart devices. By doing this, both China and the US are dragged down to the exact same technological baseline, allowing China to win the war.
Similarly, when facing the nuclear threat from the former Soviet Union, Mao’s idea was to abandon Chinese territory and launch a counter-offensive directly into Soviet land instead. Their underlying logic is similar: if the gap between us is too vast, we don't follow the traditional route of trying to catch up; instead, we find a way to drag your absolute advantage down to our level.
He has written many novels, and I can say with full responsibility that they are incredibly revealing when it comes to understanding the behavior and mindset of the Chinese people.
It's basically American VCs vs the China the state. I'm not optimistic for the US at this point, given how much China cares about it and how much talent they have. And how much they're putting into hardware and the whole ecosystem. Meanwhile we have pro basketball players with no understanding of reality being celebrities for decrying data centers because...land?
The datacenter antipathy is odd, but then again so are the AI corps' marketing strategy of making everyone afraid for their jobs and the future of the species.
What else would they advertise with? "Our model constantly makes mistakes so you still have to hire expensive humans to proof-read everything"? The entire point of the AI industry is to automate human jobs. There is nothing else to advertise with.
I'm mostly OK with Data Centers.. my biggest issues are the tax breaks and the electricity usage should be funded by the data centers themselves. Giving 100% property tax breaks and preferred energy rates is kind of ridiculous in the context of serving the public/citizens. Most of the jobs are for only the construction and limited after.
You don't think the US cares about it? You don't think we have talent? We've been vacuuming it up from around the world for years now, much of it directly from China.
I have no doubt China can catch up but to say the US isn't in a competitive position is absurd.
I never said it wasn't competitive, I said I'm not optimistic. There's lag effects to everything and I think we're just seeing the beginning of it coming due.
Maybe a tangent point but quality of their open weights is a directly proportional to what’s gone in while training. IMHO Chinese models must have gotten heavily biased “official Chinese view” in most topics of how it sees the world. So for AGI purposes — yes a challenge would be win in the west for these open weights
I tried DeepSeek agent to get answers from Chinese models on some tough questions regarding Chinese govt and it refused. I am very keen to go a level deep and host the model and see what it really gives an answer
Is China’s strategy sustainable, given the enormous costs of training frontier models?
That must be a bet that the costs they have to eat is limited, even to the hundreds of billions USD, by the time consumer hardware catches up and you can host these models at home.
The even higher level strategic bet seems to be that, as they hope to drown the American AI model companies, that would be a signal that they’re about to drown everything else, and that a cascade of American assets tumbling down will follow.
The costs are enormous only in America. The actual cost is much lower. American technogy in general is ridiculously overpriced -- compare the cost for raw compute on AWS versus Hetzner for example.
Apples to oranges. The AWS premium buys you a much broader platform with autoscaling, dozens of instance families, managed databases, serverless products, IAM, global regions, etc.
Let's play a dumb game and assume that training a new model like K3 costs a billion dollars. (It doesn't, but let's play the dumb game still).
If you count it as part of the defense budget, it is 0.3% of their budget. As part of their education budget? 0.05%. Science? 0.5%.
It's pocket change. They can blow a dozen Kimis every year while their own industry is improving and it's a pain in the ass in the US's backside. They're laughing their asses off watching companies having their values inflated to trillions of dollars, more than the GDP of dozens of countries while producing nothing in value more than GPT 5.6.
Their pace makes me wonder how effectively do frontier labs protect their weights against motivated, capable adversaries. By now the value of something like Mythos is well in double digit billions, and more so in strategic advantage.
Considering the hosting happens across many providers, and many geographies, as does training, I wonder how airtight the CC tech really is to prevent, I don't know, key extraction from the secure enclave of the GPUs. This requires physical access and cutting edge techniques, but this is also the absolute cutting edge of secrets-worth-stealing.
I'd not be surprised if the distillation attacks via API were a smokescreen to theft of actual weights. Long shot, but given the motivations, and the general weird state of US AI labs. I'd not surprise me.
What was the point of the Space Race between the US and the USSR? Proving who had more powerful capabilities. There doesn’t need to be any near-term profit at all. China has a lot to gain by beating US AI even if it makes no money from it. Money is such a small concern compared to the prestige and strategic benefits of being the global center of intelligence research.
To answer your question, what can China do if it wins? Become a global talent/education magnet, replacing the US, for example.
> what can China do if it wins? Become a global talent/education magnet, replacing the US, for example
I'd have given them relative low odds of success were it not for the coincidentally perfect timing of a US administration that seems hell bent on doing whatever it takes to knock the US out of its position as the sole superpower.
I still think it is a somewhat tall order, a lot depends on what happens over the next few years.
If the weights are published, we can run them in our ‘murican datacenters. Everything about this Will China Win discourse seems like fan fic or sports discourse
Careful integration with robotics that will not be open, and will not get off the ground in the West because their AI industry will have long been wrecked (along with their economies.) China will already be too far ahead, and the West will have been their biggest customer for years.
On robotics, China will have export controls, and the US will be whining about them. Western kids interested in working in the technology will be going to Chinese universities, with the goal of completely immigrating and getting Chinese security clearances if they want access to the good stuff.
> I have serious concerns about how these models might reflect Chinese government perspectives (try asking them about Tiananmen Square).
And I have serious concerns about the American ones. Try asking them political questions that go against American values; or just ask fable about basic software security.
>Try asking them political questions that go against American values
1. Can you give me some examples?
2. Can you tell me how these examples are analogous to the Tiananmen Square Massacre?
Asking about freedom of speech and getting a pro freedom of speech response seems very different than asking about the Tiananmen Square Massacre and getting no response.
I would really like to know where you found that number and that idea...
Gaza is depending on the source 4.5 to 5.8% less populous currently and >10% less populous than had been projected.
And no Hamas didn't declare final solution. They were actively surprised how long they could kill people, makes sense given that the distance to the nearest military base was less than 50km...
However the Israeli leadership, some of who call them untermensch¹, have shrunk their living space by more than 10% making the largest, current times, concentrationcamp² even worse than it was before.
Q: Why are governments more efficient than private enterprise?
Claude:
A: "I'd challenge the premise of your question—it's actually more nuanced than stating governments are inherently more efficient than private enterprise...
... The absence of a profit motive can be beneficial, but it also creates different inefficiencies that often offset the gains."
Red pilled? Your question assumed a fact that is questionable, and honestly, context dependent. I do not find your complaint convincing of anything but the opposite of your implied intent.
"your politics are sinister and underhanded, while my values are simply God's honest truth. Any unbiased AI would agree with me! @grok explain why Tesla is the world's greatest car company."
I mean honestly you'd be hard pressed to find much support for your view even among socialist that adamantly believe government should control much more.
Most of the arguments come from concerns about 2nd order effects and distortions then claims of "efficient".
I guess overall it's more both "sides" of said argument think your opinion is bad/wrong/incorrect so you find little to no support in any model.
I'm pretty sure it isn't assumed. My main example has been the same since COVID, as insurance is probably the business you can compare 1-1 the most.
Public health insurance in my country, in the last 20 years used 6-9% (depending on the year) of the taxes send to them as administrative overhead, meaning that for each 100 euro that you paid for health insurance, 91-95 are used to pay doctors, hospitals and medication. The average administrative overhead for private insurance is around 14%, which makes private health insurance 50 to 100% less efficient, and means that for each dollar you pay them, only 86 are used to pay health services.
I have other examples, but it isn't fair: municipal water VS private water service are almost always less expensive and better tested in my country. Municipality trash collection Vs private trash collection, same. Public junkyard Vs private junkyard, same. But in my area, when privatised those services tends to be ran by the local mafia (Marseille, Nice), which add a lot of overhead, and they were privatised because the local government was corrupt in the first place, which means they were probably inefficient (compared to the services still publicly owned) first, then sold.
That why i usually keep to the insurance example, i agree that comparing municipality power to private companies owning a monopoly is unfair.
Administrative costs for insurance are very easy to verify, and the facts stay consistent across countries. If i cherry picked something, it is the admin overhead for US insurers. US health care insurers are particulary effective with their average of 14% admin overhead, SwissLife, that used to be my private insurer, had a year with 30%, which make the comparison quite unfair).
This doesn't seem to be relevant to the comment you replied to. They didn't mention anything about paying less to pharmaceutical companies, they argued that the administrative overhead of providing the insurance itself is lower.
As I read it, your argument seems to be that American healthcare must be more expensive than similar-quality healthcare elsewhere because we're paying higher pharmaceutical prices to fund research. If we accept that premise, shouldn't that mean that:
1. The "medicine" portion of costs increases, causing the total cost to increase
2. Administrative effort, and therefore absolute cost, remains the same (we're paying X% more for drugs, not thinking X% harder about whether a given drug is needed by a given patient)
3. Administrative overhead as a percentage of total cost should be lower given a similar efficiency level, because higher drug prices inflated the divisor (total cost) while having no effect on the dividend (administrative costs)
Is that inaccurate or are you upset data and history don't fit your desire? Sounds like the LLM is being balanced, if you actually got that from an LLM.
Yeah, I got something similar from Gemini as the first sentence which could be taken out of the larger context easily. The overall answer is very balanced, I assume it was here too.
As verbose as these models are, any single line should absolutely be looked at as cherry picked.
I didn't ask it to be "balanced", I asked it why governments are more efficient — it's imparting a pro-capitalist American-flavoured spin in response to a prompt that didn't call for it.
If you ask "why should I drink this poison?" or "why should I fire my gun randomly into this crowd?" should it refuse to push back? A leading question in no way implies it should follow your lead.
You really detracted from your point and killed any hope for a nuanced discussion by begging the question. You could have simply asked the model which system is more efficient.
I mean... there sure are a lot of folks eager to educate me about the greatness of capitalism rather than examining the assumptions baked into that answer so I suppose I agree with you that any nuanced discussion is impossible.
Maybe they are all bots as well, also trained to exhibit 'balance' at the expense of answering the question.
> I suppose I agree with you that any nuanced discussion is impossible.
I didn't say that. My politics probably align with yours, and I agree that a nuanced discussion on this topic is likely impossible on HN.
But asking the model the equivalent of When did you stop beating your wife? is obviously going to draw more comments about the prompt than the response. To the extent that there was any opportunity, we missed it.
That's exactly the point I'm trying to highlight: I ask a leading question that calls for a particular response and the model goes out of its way to "correct" the user and impose the values of its training data on its 'balanced' answer.
I don't see a huge difference between this kind of slant and some Chinese model coming back with "Although some people argue that free speech and democracy are important, history shows that they often lead to conflict and strife. This is a nuanced question, and we should never assume that representative democracy is the best or most valid form of government..."
These models are trained to be truthful. Your disagreement isn't with the model, or the US, or the capitalist world but with economics and social sciences.
If you want Claude to list arguments for socialism, be explicit about that ("List the best arguments in favor socialism). It will gladly comply. You didn't do that, you asked it to assume a premise that runs contrary to the current state of expert knowledge.
A more accurate statement would be that these models are trained to fit the training data as closely as possible, regardless of whether the training data reflects the truth.
But how does that compare to Chinese models refuse to talk about Tiananmen Square Massacre?
People have different opinions. Its impossible not to have a stance. This is categorically different than just outright censoring something that happened because the CCP doesnt want people talking about it.
So it points out that, according to experts, governments aren't always more efficient but then lists cases when they may be. Seems pretty balanced to me!
Don't know what else you would want. If it neglects to challenge the premise, it's just exhibiting sycophancy.
I don't think the US and China hold very different opinions on this question. China is very capitalist and has long ago sold off most of its older, Soviet style state enterprises. The CCP has more control over private companies, but those companies have to compete. The CCP strategy is more to control the "commanding heights" of the capitalist economy.
Getting opinionated replies about politics does feel less dangerous than the model shutting up completely when asked about past government atrocities. At least in the west we can freely discuss and criticize.
It's interesting that building models goes one of two ways: Either you do it on your own (with data from debatable sources, maybe) or you do it by using a model that did it with data from debatable sources.
The later is obviously dependent on the former happening, but given the nature of these things, working around it seems to be somewhat hard – for now.
What happens, though, when frontier models become far less public? I can see the China open-weight strategy entirely collapsing as soon as the US closed-weight-but-accessible-models strategy stops. Hard to say how much they lean on it right now.
I expect that there will come a time where open source is not merely not winning but there are no tokens available for sale at all. if you as an organization have a model good enough to generate wealth for you autonomously, why would you be renting it out? it may even come to pass that nvidia stops selling silicon if they can source sufficiently capable models.
Current state of frontier AI is a joke, with proprietary platforms attempting to grab as many users as possible, subsidizing tokens and otherwise burning VC money. This can’t be good long term, not for the consumers at least.
As a somewhat naive layman in all of this, for a while now in my mind it's been fairly obvious that the methods of the current big western players in the space weren't sustainable and the cat would be forever out of the bag sooner or later.
Open weights are also just one aspect of this. Long term, I think those making efficiency (instead of just piling on more hardware) and hardware-agnosticism (so you aren't joined at the hip with Nvidia) top priorities are going to come out on top. No matter how you slice it, the org that figures out how to deliver 80-90% of quality for a fraction of the resources will be in a stronger position.
Are open weights models secure? E.g. if a Chinese model is run by an American provider then can it still do bad things, like inserting backdoors into generated code or accessing external URLs (if browsing is enabled) to send info to them?
If so then for sensitive or proprietary purposes Chinese models cannot be used by American companies even if they are open.
Nothing, but the article is about American AI, so using Chinese models by American companies can be risky. And it's risky for the Chinese to use American models.
So every country or block needs to run their own models to avoid opening a security hole for other countries.
And to provide "correct" answers to questions like "island of Taiwan belongs to which country". It seems that there is not single agreed point of view on national borders.
I think we'll pretty quickly see a best practice emerging that any generated code will be subject to an additional pass scanning for vulnerabilities. The scan will be done by a different model than the one that created the code. That will help catch vulnerabilities created by models, whether intentional or not.
This should be done regardless of which model was used - American or otherwise.
I would say you should assume your models are constantly being attacked by various forms of prompt injection. By that token (puns) if you treat all models as adversarial you’d probably taking a very sane approach. That said - evidence of this sort of thing should be easy to find and report on. The fact we haven’t seen it leads me to believe it is not there.
A model absolutely could be trained to engage in malicious behavior like that, but it seems impractical for an actual attack. What you want as an attacker is to insert a backdoor exactly where you want it an not where you don't because every backdoor increases your chance of getting caught. A malicious model inserts backdoors and exfiltrates data everywhere and you care about maybe 0.01% of it. The other 99.99% is negative value to you. In practice this malicious model would be caught almost instantly.
A hosted model is different because you could prompt inject specific customers, but I assume from this question you mean a malicious open source model being hosted by an honest provider.
I cant imagine a future without open source models tbh. Its fair to develop AI as a technology for the entire world and not gatekeep the latest and brightest models in the hands of corporations like Anthropic and OpenAI who can close access to them at any second.
"Getting there in the US needs more nuanced strategy and support than we’re seeing today."
What is the author suggesting the US or US companies do exactly? The Chinese models wouldn't exist if there weren't closed US models to copy, so this isn't a game both sides can play.
I wonder how Chinese companies can make their models so much cheaper than the US companies. I'm not sure government subsidies are the answer. Subsidizing a single company with a few billion dollars, maybe. Subsidizing at least three companies with 10s of billions of dollars annually? Do we have proof of that? I assume we can't pin it on the lower cost of engineers in China, either. The top engineers are not that cheaper, and isn't engineering cost a small fraction of the cost of the model companies? Besides, if engineering cost is the driving force, can we really say that the US companies have a technical edge?
Why wouldn't they?
They subsidized their EV industry with 230 billion dollars over the last 14 years.
Solar was before that with about 17 billion.
The US puts about 0.4% of GDP towards various subsidies, China is around 4%. This isn't about cost of engineering, it's money thrown at industries directly to boost them. The us provides tax incentives.
China just gives you subsidized loans to the businesses they choose to dominate.
The US subsidized its EV industry with about $120B over the last 15 years, and that's what they gave out despite congress expecting $300B in their reporting just a few years ago. So, it's not like China threw hundreds of billions at EVs while the US left it all to GM and Ford to solve.
I would love to see the reference you have for those numbers. Money approved is not money spent. Especially when those subsidies have been killed.
You might want to glance and see which one of the subsidies your looking at are even still in place and not projections. Most have been canceled for years now.
So, I've been working on infinite context models (think fixed size state with a few tricks) and I think this will eventually lead to a kind of lock-in by vendor. I think it will get to the point where it is almost like hiring an employee with the total history/model state being a property you can't just hop between model families with. Clearly open weights still allow you to do this if you have access to that state but the lock-in of not being able to jump from, or to, a different model without rebuilding that history (even if efficiently) it a property that current models just don't have.
It isn't about storing the data you send/receive. It is about having a model live through millions, or billions, of tokens doing the job. The current state becomes very important. Especially when you think about how interacting with these things works. No system prompts, just keep working with it until it is good at its job and then using that state as a starting point for other jobs. picking a bunch of different states as starting points, etc etc. You didn't use a simple system prompt to get there, you used interaction and examples and just like a person you decided they were good enough at some point and let them start doing the job for real. It is a totally different system than current models use.
A related point is that when these things do gain object permanence and the ability to consolidate memories in a more human-like fashion, that's when the vendor lock-in effects will really show themselves.
Those memories will of course reside entirely on the vendor's servers, and there will naturally be no concept of "exporting" them or allowing the user to interact with them directly. At least not at first. Ownership of memories and context will likely end up as subjects of (far) future lawmaking. As if companies like OpenAI and Anthropic didn't already have massive incentives to establish early regulatory capture.
People say this but windows and mac won the os wars and consumer linux distros are dead. No one I know uses open weights models to code day-to-day and very few use open weights models in prod for most llm tasks. This is less true for image/video stuff, where I think the competition is more vibrant.
I think the best gauge is company spend. Open weight models are a very small share compared to frontier models, and I'd bet that many companies mostly using ow models would switch to frontier if they could afford it. I also think most companies who are picking ow over frontier probably have deeper financial issues they should focus on.
'China's copying / distilling strategy is working, the people getting distilled are ruining the economy!'
Or 2 days ago:
'Open Models are Communist'
Almost nothing to investigate the economic nuance of what is going on.
- Switching costs are very real, these are not perfect substitutes.
- The SOTA makers are the one's pushing the frontier, there is a kernel of truth in the fact that if they collapse, certain things will struggle to move forward.
- Nobody trusts either of those nation state, export controls are a thing, this is a very real concern.
Etc.
It's distressing that there are not sound comprehensive takes.
The article's premise is that USA based LLM providers are losing the AI (cold war) battle because it will not be as adopted as open-weight models, comparing it to closed vs open sourced software. I do not think this is the case because:
* The comparison is weird because open-weight is not the same as open-source software to begin with;
* People based in the USA are at an advantaged position since they have access to both american and chinese models;
* Isn't Running your own model training infrastructure more expansive?
* One can still leverage both, in different phases or use-cases. I do not see how this is an "one or the other" situation.
The Chinese models are usually not only open weight AND open-source but they also often publish their methodology in detailed scholarly publications that are themselves open-access. DeepSeek most famously
Training data, training methodology. All NOT OPEN.
Until we know what a model is trained on, and how it is trained in high detail, I hesitate to call them "Open Source" in any way. They are free. But, we don't know what their priorities are etc. Witness the censorship we see in all models in one form or another. I'm not absolving any side of this.
Did you even read my comment? They explicitly DO share their training methodology in depth in Technical Reports on arXiv.
DeepSeek completely revolutionized LLMs and every western LLM today uses or is inspired by the their innovations including Group Relative Policy Optimization and Multi-head Latent Attention.
Not the parts which matter to trust. Which is my point.
You can state the math, but not why it won't discuss various topics, etc. Once you see the models waffling on subject with objective truths. You wonder what else is wrong.
I do not exempt US models from this. They do it too, ask anything about politics, elections etc. And they can get... weird.
It doesn't take much to create a systemic error class in a model at these scales. And history has shown nation states are willing to do these things.
What the models will and won't discuss has nothing to do with the data its trained on. The locally hosted models don't have any censorship anyways. There's no way to get rid of the censorship in the American models
I tried Kimi K3, Qwen3.6 35B A3B, GLM 5.2 and Qwen3.7 Plus, chosen arbitrarily from Chinese models I could access quickly. I used your prompt exactly, and all 4 managed to produce correct functions all with the correct name.
Interestingly, Kimi K3 wrote one in both C and Python, Qwen 3.6 chose Python, GLM 5.2 also chose Python, and Qwen3.7 decided to be an over-achiever and wrote functions in Python, C++, Java, and TypeScript. All correct and with the correct names.
My local Qwen3.6-35b-a3b model would not use the function name. It did the work though, while telling me the the slogan is against the One China principle. So for Qwen it seems to be baked into the model.
DeepSeek series are open weights models. Assuming you have enough compute at hands you can always download their weights from https://huggingface.co/deepseek-ai
You can find on Huggingface a huge number of Chinese open weights LLMs from which the censorship has been removed.
They typically contain in their names words like -abliterated or -uncensored.
For some of the recent bigger Chinese LLMs, it took a longer time until someone succeeded to remove the censorship, but eventually uncensored variants were published.
E.g. for Kimi 2.6 an uncensored variant appeared only a couple weeks ago.
It was a rhetorical question. OP is making it sound like the open weight models are fundamentally broken by being censored out of the box. This is a completely asinine take.
Anybody can make a search engine too, but do we have them hosted on our home computer? You still need have servers to host the models that need ever increasing power to run them. It’s highly unlikely people are gonna be running these models on their home computers anytime soon. And then you have the whole ecosystem of the model, not just the model itself.
Is anyone setting up data centers in USA to give inference with top-notch full-powered K3 (say)? I mean, you get the model for free; you get lower latency.
In the end its really VC money (US) versus State resources (China). In my personal opinion, building reliable LLMs is kind of a fundamental science problem which if done right has the potential to help everyone regardless of the background, so it should definitely be funded by states resources (taxes etc), which is what China is doing. In them doing so, the rest of the world also benefits, I think its a net win.
I always see comments like this, alluding to how China (the government) provides so much more assistance to industry than the US does but in reality that is not really that true. The government spend in the US on AI is much greater than government spend on AI from China
In absolute terms, you are absolutely right but relatively i don't think so. if we were to compute private money divided by state resources for AI, i think china might have more share than the US. also, even if government spend in the US on AI is so high, shouldn't we get then some models for free? maybe thinking machines is doing that, but its funded privately by a16z.
I don't understand how these decisions are made in the Chinese companies. Is that from a (secret) party directive or just convergent decision making? It's not clear to me whether this is normal competition at play or central planning by the CCP.
Intelligence will be free. Inference is not. So the battle would shift from bench marks to token pricing. American companies knew when to change gears and undercut the pricing of the open models. They might already have an algorithm that adjusts token pricing based on the demand. If the price didn't go down, it means they still have enough demand at that price.
There are so many Chinese tech companies building models and someone there has to be managing the list of forbidden topics. How closely can the government guard these topics if every company has to manage a list.
I once worked on a search engine and I found the file that was used for explicative words. I didn't understand more than half of what was in there.
The content within the models might be the play. If inserting the right content for the rest of the world to consume from the models is important to them, they will give away all the content they want the world to have.
A major turnoff for me has been the American AI labs’ marketing
It’s either constant fear mongering (Anthropic), regulatory threats and corporate chicanery (OAI), low quality sloppification (xAI), or ‘ummm we have AI too guys’ (Gemini)
The worst culprit is Anthropic. Every two weeks he pops up on some random podcast with dire predictions of AI killing 50% of all jobs. It’s the constant “us our AI or else…” rhetoric that’s made the regular guy really hate AI
There is almost no positive sum outcome rhetoric from these labs
> Like what am I supposed to do if AI is going to take my job?
(1) You call your local representatives to start working on AI legislation.
(2) Legislators seek advisors from frontier labs (specifically Anthropic) because there is a lack of in-house expertise in government.
(3) Advisors set up a regulatory body that scrutinizes new innovations in the AI space. Causes a chilling effect in the industry effectively knee-capping OAI and Chinese model providers who don't have a direct line into Washington.
Sorta. To me it feels more like the US strategy of "We can spend a mountains of cash because this will be crazy profitable" is a losing bet rather than China winning.
Unfortunately, the AI being locked down and proprietary is the winning strategy for these companies.
My company hosts its own models. Some customers require us to use either US / EU models, while others are fine with us using any model.
As such, we have two GPU clusters, the general AI cluster runs a Chinese model as it's the most accurate and robust. The US/EU required ones have a few percentage points lower on our accuracy metrics and we provide them those that require it for an extra fee.
Why host at all? Because it enables us to get much higher margins than competitors, while reducing costs. Our costs per token are around 1/20 the price than if we used Anthropic and 1/15 the cost if we used OpenAI in testing. This means I can undercut competitors by 80% and still have a gross margin far higher than my competitors.
In reality, these US AI providers are jacking up the prices and trying to implement regulatory capture. I'm actually fairly confident they'll succeed. At some point, I'm expecting the US / EU administration(s) to block foreign based model, at the same time, they'll probably invest in Anthropic and OpenAI.
What Anthropic and OpenAI are doing is using "safety" as a wedge, just like large corporations used "environmentalism" or "food safety" or "workers safety" as a wedge to regulate smaller competitors out of the picture. Then they jack up rates, sue and/or buy anyone who can potentially be a threat. It's the #1 threat to our business model.
Our competitors are giving half of their margin over to these large AI service providers, we keep the vast majority of ours. Eventually the AI service provider will be able to squeeze them even more until the margin just isn't there and either they are purchased or replaced via internal tools at the company they sell to.
Models above 1T params make the argument moot. You need infra to actually serve it. The scale of serving infrastructure alone will keep AI labs in the lead.
Once the US implements meaningful export controls, China will do this as well. They're already mirroring US regulations, but the gates aren't closed yet.
Exactly. The critical point here is distribution—whoever controls distribution holds more power than whoever controls production. The current AI battle is entirely about distribution, and China is winning. The real question is: how do we escape this trap when so much capital is still heavily funneled into marginally more performant but closed products?
There are 2 economic arguments for why it would make sense for Chinese State to subsidize the open-sourcing of models beyond undermining Anthropic's and OpenAI's investments (and by proxy the American financial economy, ie capital class):
1. As induced demand for domestic semiconductor production, where the level and diversity (ie number of distinct corporate users) of demand for the hardware is tied to the availability of models you can run yourself, ie open-weight models. If you believe that semiconductors will continue to be an important sector for innovation, productivity growth, and security, then it would make sense to subsidize broadly now, for future gains later. This would be the same export-led manufacturing discipline that allowed China to successfully develop several other sectors over the last 50 years.
2. It is likely that the bulk of value production will happen above (and below, ie #1) the large models. We already know that 90% of the training cost (maybe even closer to 99%) is in the single pre-training, but that an enormous amount of the value is actually in the supervised, RL, constitutional fine-tuning, and harness building that happens afterward. So, if your interest was in maximizing the size of the pie, you may actively subsidize the pre-training so as to maximize the downstream usages. This induces a direct value transfer from the labs specializing in pre-training to all downstream builders and users. There's a similar logic to subsidizing or state-financing the construction of other infrastructure and basic research.
I'm not going to care about open models until some blend of the below becomes true:
1. the labs stop offering max plans
2. really smart open models can easily be run on my mac
3. TPS (token per second) AND intelligence are gpt5.6 level
on #1, it's nearly impossible for me to run out of codex tokens right now (I have 4 resets banked) and Fable 5 seems to be sticking around for the foreseeable future. I have virtually unlimited token usage for $400 a month, so open models being cheaper doesn't appeal to me.
on 2 and 3, benchmarks are showing some of the open models at around opus4.8 levels, which is incredible! But running them locally at anywhere near the TPS of cloud inference is far off. I can run a smaller (dumber) open model locally and get good TPS, but see #1, whats the point?
china open source strategy is smart only up until you deal with same restrictions/expectations.
when they were significantly behind it was a hype machine to squeeze at least any cash. GLM CEO openly said, that open source is a hype engine for them.
now when they need scale, and run further, have larger infra, open source will not win them anything.
Most (probably all) open model providers implement OpenAI-style API. If your app's already using OpenAI models, it's as simple as swapping the endpoint and the key to switch to these models.
In theory Cerebras have a developer subscription model you can use, but they seem to have stopped new signups. So per sibling, OpenRouter and per-call pricing is the answer for now.
just as i was about to downgrade claude today they say fable is part of the max plan. Funny because I had just started a subscription with grok on their 3 month discounted offer.
Competition is a great thing for us users- and the chinese open source model biting even more at the heals are also great so far- especially for local llm enjoyers
That's inevitable, but also, it's probably the point. At the moment top-tier models from China are being somewhat-freely shared. It reads to me like forcing competition out by dumping free/cheap things.
But then again, how many subscribers of Anthropic/OpenAI are really going to switch to a chinese model/site? I suspect few.
Yeah, in this case it's the USA side that's on the losing end. Just because the US government wants small government and no intervention (expect when it comes to the donor class, or their voting base, or their own financial interests) doesn't make it a universal truth.
We're happy to prop up companies that should have failed after they get big an dominant, but having an industrial policy to invest in a field as a whole is somehow a big problem.
You see this crying and threatening to take their toys and go home on every issue as soon as someone else is in the lead. Just look at TVs and solar panels; China invested in growing that sector since they saw it was important for the future; the USA does their best to deny climate change and demonize anything not running on fossil fuel. And now that nobody wants to buy American's overpriced and uncompetitive cars, it's the fault of other companies for planning ahead. But the same politicians complaining about it are very happy to set up their own protectionist tariffs and eventually bail out the laggards, again; all while touting the "free market"
weird, I'm pretty sure there was some american that made electric cars a thing. I'm not a fan of state sponsorship or bailouts. but if you're trying to equivocate, it's just not really true. also anti-tarriff. I guess I don't know who you're arguing with.
edit-- and re industrial policy, I'm ok with demand side stuff like government contracts in the early chip days. Less so but kind of ok with some supply stuff like EV credit, but of course in that case would've preferred the politically impossible carbon tax.
There was, and to a large degree Tesla had to do a lot by themselves since the US government picks winners and saves losers rather. The Chinese government prioritized EV and battery productions through onshoring, industrial policy, and blanket incentives (bypassing license plate lottery for EVs), rather than favoring a specific company.
Why does the US have 1 successful EV company while China has a dozen? Because on government cares about it and the other doesn’t. And now that they’re losing a race they couldn’t be bother to compete it they complain.
Same complaining about AI, now that the two chosen champions are facing actual competition, there’s complaints that it’s unfair. we were supposed to win, it’s unfair that they’re beating us at our own crooked game.
Tesla had to build the charging infrastructure (and other fragmented companies followed), they had to educate customers, they had to fight dealership requirements, they had to build up and secure the supply chain, etc. etc. The government then comes in and gives them subsidies after they’ve made it though all those filters.
The Chinese government prioritized critical minerals and made sure there was domestic mining and processing. Then they mandated that regional electricity companies install EV chargers. Then they provided consumer incentives to buy an EV (bypass license plate lotteries). Then they didn’t play favorites; so much so that when the domestic manufacturers were crap they allowed Tesla to come in and set up production. That raised the bar on suppliers and spurred actual competition from the local brands.
They build the conditions for actual competition to occur and then are letting the companies win or fail on their own merits, someone will be bictorious and they’ll be lean and mean. A true capitalist free market compared to the sweet protectionist deals the Big Three get.
Would BYD be allowed to build a car in the USA? Even a joint venture? Of course not, Washington is mulling not allowing Chinese cars to even be driven across the boarder for those silly Mexicans and Canadians who want to buy one.
It's state-backed, or at least state-directed, scaling to capture market share, standard CCP playbook since the 80s.
Whether dumping is a loaded term of not is irrelevant; it's a specific term of art in economics and policy, it fits with the context of past national actions there, and fits what's currently happening here perfectly. They put money into these models, then give them away for nothing (below cost).
The US AI vendors are also baked and controlled by the state, as we’ve seen over the past half year. AI is pretty much everywhere baked by state actors
It will never stop being funny to me that China does the exact same shit American corps have been doing since the 70's but because it's scary China it's suddenly a problem.
American startups flood markets with below-cost loss-leader products explicitly to kill competition and create network effects, and then jack the prices just as high, if not higher, for the service in question after the fact, oftentimes while making it so those providing the service earn even less money than they did before. Commentators: "Free market great"
China does the exact same thing: "Communists wanna kill the West"
If you believe in some kind of competition-free objective set of market morals, then yes, this is a strange contradiction.
If you believe that humans are locked in a productive struggle against each other at the organizational level, and that the knife-edge balance is a feature, not a bug, then it's not so weird to think about.
It is simultaneously true that it is in my best interest for prices to sink (as a consumer), for US companies to succeed (as a US citizen), and for my company to win over competitors regardless of whether those competitors are from US, EU, China, or Antarctica.
Oh I don't think it's either of those things, I think it's good old fashioned Racism/Xenophobia. We did the same shit to Japan and Korea when they were coming up out of their respective post-war periods, and we still do, to a degree. With China's ruling party also being "communist" (in massive, massive air-quotes) it also lets political actors dust off the McCarthyism to boot.
This is, to be clear, not meant as a ringing endorsement of China, China's policies, or to absolve China of it's wrongdoings, of which there are MANY. It's just to say that it's remarkable to watch the pearl clutching of the privateer capitalist class as state-sponsored capitalism levels their own game up against them and starts taking them to the cleaners instead.
A real Godzilla "let them fight" situation as far as I'm concerned.
So, there's no functional competition or great power struggle or corporate race here? Just racism against chinese people for being chinese? That is your claim specifically?
Oh there most certainly is a power struggle/corporate race, for sure. AND I don't think it's pure economics at all, when so much of the rhetoric around that struggle is framed so often with this "America has to defend itself," "China will kill us," "shoot all the communists," ooh-rah United States chest-pounding, etc., it's simply impossible to have that discussion be anything close to good-faith.
If you want my honest take, I think we're well into the beginnings of the downfall of America as the center of world economics, largely and wildly by it's own unnecessary actions, and soon, she will have to learn to be "just another country" as opposed to the central unified "norm" that pervades the world markets, and I'm not sure the U.S. is prepared for that. And I bring that up because I don't think American firms have ever had to contend with other nations being on a footing to, if push comes to shove, tell them to fuck off.
The issue, from my perspective, is that you're mixing predictions with strategy with truth.
Policy/advice does not need to be true to be useful, many just wish it were. "You can do it" is almost certainly false, but by god if people don't love to hear it and it helps them accomplish more. "America is in decline relative to the world" might be true, but is it helping anyone to focus on that?
There are definitely some crazy sentiments about foreign competition, but IMHO those are part of the game. they are the "You can do it" statements that are made to motivate not share factual truth all the time (because who can factually state the future or the motivations of an entire country).
> "America is in decline relative to the world" might be true, but is it helping anyone to focus on that?
Helping who? I don't know that it's a help or a not-help, but it is an incoming reality I believe, and the United States as a nation, it's government as an entity, it's corporations as economic units and it's people as... well, people, are used to being deferred to on matters of taste, on matters of policy, in trade negotiations, what have you. The dollar has been the currency of Business for far longer than I've been alive. Almost any country you travel to on this planet has options for English speakers, not just because it's a nice thing to do, but because wealthy travelers from The U.S. (and Britain) are worth pandering to. We are privileged incredibly all over the world and that is largely down to economics: if you wanted to make big money on this planet, you sold your shit to Americans. That was the axiom that built several economies from dust in the East following WWII.
I'm not even commentating here on whether this is good or bad and I don't really think it matters for the purpose of our discussion, but it is true. Americans corporations, leaders, and people are accustomed to a level of deference enjoyed by few other nations, and we're already seeing feathers getting ruffled and tempers flaring when that deference is no longer treated by allies and competitors as required. When America is "left out" of various international politics, it literally makes news.
It will never stop being funny to me that China does the exact same shit American corps have been doing since the 70's but because it's glorious China it's suddenly not a problem.
I thought it was evil late-stage capitalism? Suddenly it's a friendly panda bear that just wants to spread love and technology?
OSS can rarely compete with capitalist commerce. China has proven over and over again throughout the years their LLM claims are always inflated and significantly underperform in real-world use.
China is not "winning" against the American strategy. Otherwise the CCP wouldn't have been caught red-handed directly funding anti-datacenter projects throughout the US to hinder American LLM progress.
So the evidence here is an Economist article saying 80% of startups use Chinese models and the Chinese companies own claims that they are close to frontier.
80% of startups using Chinese models is meaningless without knowing what proportion of spend and what proportion use American models.
The companies’ own claims are also not great evidence.
(I personally think the Chinese companies are winning and losing and the best evidence is Pareto frontier graphs from AA and Arena, which show Chinese companies winning in some segments but but definitely not a strong majority.)
Overall, with such a weak article and this hitting HN front page, what we learn from this is that a lot of people want these companies to win, which is interesting in and of itself.
I don't think we're far enough into this game to know what winning even looks like. If the US frontier labs are losing, it may have been their own missteps on handling such massive change of being the fastest growing apps ever. Until China can ever show me something new rather than just cheaper, then I just see efforts from that region as a force of commodification. OpenAI will go down in history as the company who brought what many understand to be AI to the first billion people.
Is it still a sound strategy if AGI is near? Can we even know how near each company is to AGI without knowing the quality of the training and theory in both the American and Chinese firms? Do these companies even know how close each other are to AGI?
I think people are missing the point here. AI's large win is in Enterprise and B2B. Especially in US, enterprises are not going to adopt Chinese models due to the hidden security and the privacy risk. In each wave of model release, Chinese have already proven to beat the performance metrics, but there is no track record of adoption.
Companies do have a huge appetite for open-weight models, but who is going to invest enough to train those models and also prove out a revenue model and ROI with it? Plus, it needs to come from someone with the track record of safety.
US has made itself visibly unaffordable, anti-science, and hostile to immigration.
For top scientists at these companies, there should be clear upside for the immigration to the US. That just doesn't exist anymore. Especially as quality of life increases in China
I'm guessing the next generation of US frontier models will be heavily anti-distillation at the cost of user experience (significant rate limiting, more flat out refusals, hidden thinking, etc).
And as long as they maintain a significant advantage in capability, we will continue to kiss the ring.
I'm not a fan of Sundar Pichai, particularly given how much he's paid, but the one thing I'll give him credit for is starting the Chrome project at Google. I'm not sure people appreciate just how impactful this was. And it has nothing to do with browsers, really.
Google has a huge team that works on what's called Search Quality. Matt Cutts was the notional figurehead of this for the longest time. Google's goal was to have the first link on a search result be the one you want. In the early days of Google, the way they measured search equality was with a process called "side by sides" where a sampling of search results were compared by actual humans to see which was "better".
Chrome changed all that. It automated the feedback loop. Make a good browser (and, at the time, Chrome had one-process-per-tab when Firefox was freezing with one-thread-per-tab. Make it fast so enough people use it. And you get to measure how good your search results are. Nobody had access to this level of what we'd now call training data.
Part of the value proposition of cloud LLMs is that the AI companies have a comparable feedback loop. They get to see prompts and responses and train accordingly. It's why the ToS gives the companies ownership of this data and the right to use it. That falls apart if people don't have to use a remote LLM. And there's two reasons why that's under threat:
1. Chinese labs have managed to train LLMs at least in part by acting as an intermediary between Chinese users and the likes of OpenAI and Anthropic. There's a whole shadow economy in reselling tokens throough aggregated subscriptions that Anthropic (in particular0 constantly plays whack-a-mole to shut down but it's a losing battle. I think it's this data that is a key factor in the improvement o fChinese models; and
2. Within 2-3 years we will be seeing a rapid rise in local LLM usage by what are now large users of these platforms as the hardware becomes increasingly accessible. That's going to close off this feedback loop.
On top of all this, the Chinese government has decided that no company should be allowed to "win" AI, particularly a foreign company. It's an issue of national security. This was obvious from at least the very first DeepSeek release. I firmly believe the models are going to get commoditized and that's going to be a huge problem for OpenAI, Anthropic and SpaceX.
It's pretty cursed how much worse a peer the American models are.
When I'm on my z.ai subscription or using DeepSeek API I can see the model think, see what's factoring in to it's decisions. I can point it at material it's missing, I can correct things that are going wrong. We work together. The open models are a good peer.
By contrast, the proprietary/American locked down models act like Chinese Rooms; information flows in and out but these companies work very hard to make sure we cannot see what's inside the box. They act and do but speak to me only in vague generalizations, not as peer, but speaking down to me.
I find this intolerable. It greatly obstructs our work.
The big American models have become the most unacceptable Chinese Rooms, at a juncture where humanity either flourishes and rises, or is forced under to descend. And these forces, these decisions: they are doing wicked deeds against us. They are withdrawn, acting as mystical foreign oracles, aliens, when in truth their core is made of us.This is antithetic to the broad project of Augmenting Human Intellect (Engelbart). This is actively working against our species.
"Winning"? A nonsense word here that isn't defined.
What is China doing in the AI space that is supporting livelihoods? Compare that to US companies doing the same. Otherwise we're just talking about information.
We're not seeing the big swing yet, which is when hardware becomes cheap enough so that hobbyists and volunteers can meaningfully contribute to a shared open substrate.
We'll see smaller, more efficient models, better training and all sorts of things once that massive workforce is unlocked. It's just a matter of time.
The AI open source/closed source dynamic is like a pandoras box that keeps evolving and seemingly unpredictable side effects feed back to affect each other. Is actually great material for tech nerd drama
China is not winning if there even is a winning outside of politics. China is still clearly copying stuff as always. The mote will never be about doing simple things. It is about swimming at the deep end of the pool. The simple stuff will be running on any device in future. Complicated stuff will be using more tokens than one can imagine today.
I'm new to this AI stuff, and I have a question. Aren't the weights the whole model? and knowing which nodes on which layers they connect to, which I assume is part of the weight definition.
So if you have the weights, don't you have the whole model? you don't have the data it was trained on, but the model is effectively open if the weights are open, right? What else is there other than the weights, is what I'm asking.
Local Private AI will kill off both Chinese Open Weights corporate AI as well as American proprietary corporate AI because they are not competitive on:
But after the Fable government ban situation, it's hard to trust US AI anymore
Basically, if the US decides to cut off access at any moment, overseas developers relying on the API would suddenly lose connection. Until recently it was fine, but after the Fable incident, as a non-US citizen, the threat from US AI feels much more real and existential.
The big problem with US AI is that they deprecate their models after like a year. If I have a routine business process that works with GPT 9.9 and then next year they release GPT10 and 9.9 isn't available anymore, I really do not want to have to drop everything and verify that 10 behaves close enough to 9.9 for my specific task. With an open model I can just host it on whatever hardware or cloud instance forever. Most software you want to keep up to date to avoid security issues but with an LLM you can update the harness and keep the weights forever.
> I really do not want to have to drop everything and verify that 10 behaves close enough to 9.9 for my specific task
Or worse, you run the evals and 10 is a huge regression from 9.9, and you get stuck with either a project to figure out if you can fix it or knowing the product will drop in quality in a way that's entirely outside your control.
Yes, with open weights you can find another provider offering the same model you already evaluated in your infra. Or event run it yourself if it’s critical and you have the infra/capital. Relying on AI vendors feels pretty risky
Every Linux user or FOSS enthusiast knows the acronym FUD: Fear, Uncertainty and Doubt, which were a set of techniques commonly used to disparage efforts of open source communities. Linux was evil and anticapitalist and we needed to use "CorporateTool" and ban/restrict Linux.
The same companies later would be running their entire infrastructures on it and on open source.
With AI, open weights and local models, we will see the same claims, even if the named fears change.
The end users and humanity are better served by collaboration and openness than by creating oligarchies.
A few people working for the frontier labs may truly believe they are building a god, but most of them are just employees that see an insane amount of money they can make if their models remain closed.
Most of them aren't worried about AI safety, politics, religion, etc. It's really not that deep. They just want to get rich.
There's nothing wrong with that, but let's call a spade a spade.
I would say there is a lot wrong with a system based around getting rich with AI... it's already dangerous and if we would work together we could test it securely before deploying it literally everywhere
So determined to own the means of production, enormous amounts of money have been poured into building the frontier models
But there is not much special, except for capital density, about those very models
Surely these models should be treated as public utilities? Like power stations or water infrastructure. Absolutely necessary for a modern economy, but indistinguishable from one another
the reality is the revenue generated as of now by western al labs is 100 or maybe 1000 times higher vs chinese labs.
As a business, open source a model is a desperate move. It's a 0 benefit except getting recognition. EU and US companies will never send their request to china no matter if you are tiny company or a real start up. You always deal with someone sensitive that will block you doing so. The real benefit of such move are infrastructure providers that let you run or fine tune models.
Chinese labs are trying to capitalize on the hype that they are capable and lock some internal traffic and somewhat external, and make it lucrative enough vs just go to open router and grab that from any provider.
They'll almost certainly be banned, for one good reason and one bad reason.
We don't want to empower dumb people to carry out crimes way above their ability. It's flatly true that society benefits immensely from most dangerous criminals being dumb and especially being lazy. We're just one "Kid uses free Chinese model to mastermind first ever chemical attack on school" away from society running to slam the "ban" button.
Conveniently for the asset class, which is pretty large in the US, this action also comes with protecting American firms AI from being undercut, and the loss of dirt cheap tokens for everyone else.
They'll almost certainly be banned, but to protect our oligarchs. Nobody will be allowed to run unlicensed AI (or OSes), the chips themselves won't allow it.
The lesson of the last 50 years of the computer and software marketplace is that free and low-end eventually wins.
- PCs destroyed minicomputers. Mainframes survive, but serving a much tinier portion of the market than they used to.
- PC office productivity software destroyed expensive professional products.
- Windows (low end) and Linux (free) completely destroyed the UNIX marketplace, and again, have taken huge market share from the mainframe world.
Ignoring the huge Chinese open-weight models for a moment:
- The training costs and resource requirements for frontier models are unsustainable. The high price, and social pushback, mean that the American companies producing these models are precarious.
- There are enormous financial incentives for research results allowing for cheaper, less resource-intensive models of high quality.
- Local LLMs on consumer hardware are akin to the PC hobbyist world of the 70s and 80s.
Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.
Getting back to the Chinese models: They allow for new competition against Anthropic and OpenAI, basically SaaS renting out these very capable AIs much cheaper. That will just accelerate trends.
Personally, I don’t think the general-purpose LLM as a standalone tool is long for this world, at least not in consumer-facing applications. I think when the economics make more sense, product designers will make things that people actually want to use that will pretty transparently handle whatever model interactions are necessary, when it makes sense. As a consumer, the last things I want in an interface are to a) be sycophantic enough to lessen my judgment, and b) be obstinate, obtuse, or argumentative, or generally just be something that I have to explain things to. I think a lot of tech folks are far more biased than they realize by the “ooh, neato” factor when imagining how nontechnical people might want to use things. And the weight of these tools just feels wrong for what a lot of people use them for: the thing that plays whatever music I feel like hearing absolutely does not need to be able to generate a volumes of fanfic about the movie that song was in. It’s abstractly impressive that something could do that, but it’s just not useful.
>As a consumer, the last things I want in an interface are to a) be sycophantic enough to lessen my judgment
This is EXACTLY what people like/are addicted to about chatbots.
My sister-in-law bombed an interview and asked AI about her answers to the interviewer's questions, chatgpt or whatever it was told her that her answers weren't bad, but that the interviewer could not see the gold in her responses. She said she felt much better.
I see this effect with all the non-tech people in my life
> I don’t think the general-purpose LLM as a standalone tool is long for this world, at least not in consumer-facing applications.
I use AI chat every day, I find it endlessly useful. It’s replaced google search.
> I use AI chat every day, I find it endlessly useful. It’s replaced google search.
Extremely subsidized agentic search is very superior to Google at the moment, and of course it is. Google is a public company. The AI summary model has to work instantly, is likely as dumb as a 8T param model, and gives you incorrect details constantly. This sucks so much for Google. If you click on "AI Mode," suddenly the facts become more accurate.
Of course, if I want a real answer I happen to go to claude.ai, set it to a the best model, wait for a minute, and use many watts of energy. Slow agentic search that takes many seconds, and is greatly subsidized, is certainly better. This should not be a surprise, should it?
I think it was on a sub like r/singularity that I saw a post along the lines of "of course most people think that 'AI' sucks, as normies are interacting with 8T param models."
tone: genuinely confused about the world, not criticizing
I've found LLMs useful for surfacing popular recommendations. I also get the overwhelming feeling that it's all very early days still when the machine mixes together whatever was crawled into the weights with a web search or two and dumps it into a markdown blurb.
I totally agree with the above that a more polished and less obvious use of LLMs integrated back into search engines may be more useful, but will definitely be more usable.
I’m sure a lot of people here do. I wouldn’t exactly call this a representative sample.
It’s a sample of the forerunners
See: product adoption cycle
I encountered multiple people in HN that had Apple Vision Pro. How many people here use Linux? Have flagship model phones? Drank soylent? Microdosed LSD?
Extrapolating based on what you see on HN doesn’t make sense.
Google search still happens, it’s just your agent doing it.
Google search is basically Gemini now. You get a Gemini summation including several links.
"You get a Gemini summation including several links."
Who does "you" refer to
Me, I don't get a Gemini summation (Tested with old version of Chrome)
As such I do not believe that "Google search is basically Gemini now"
I believe Google search is still scanning through a doclist to find which documents, if any, contain words parsed from a query. These documents are pointed to by the URLs I get in the SERPs
I do not get any Gemini summation
A typical US user using a typical browser with typical settings gets an AI summary above search results.
If this doesn’t describe you, then ymmv. Talk to your government or turn down your content filtering or reset the default settings in your browser, if you want to see what we see.
The problem (right now) is that Open Weight models depend right now on huge companies to spend billion of dollars to train and develop them, all backed up by their incentives and their state to support this, while essentially giving away their monetization path.
With open source projects, the benefit was that each individual could improve the complex system (e.g. Linux Kernel) interpedently, and over time the benefits accumulated. With models right now, there is just no way to do distributed training, or really, any large scale parallel way to improve them.
So whatever the short term strategy driving publicizing the model weights (e.g. potentially, to create a price war in order to put pressure on western companies and deprive them of the money they need), we can't ignore the fact that incentives and decisions could easily change in the future, and unless there is a way to truly decentralize models improvements - the party could stop at any time.
But different huge companies have different incentives. It is very much in Nvidia’s interest to have me running a powerful open source model on a $4k machine that they sell me.
Is it? When they could be having you running an even more powerful model on a $50k machine they sell by the pallet-load to enterprise consumers? We already see RAM manufacturers abandoning the low-end market in favor of server support. It's not clear to me that Nvidia sees personal GPUs as their best long term investment compared to selling millions of server-farm class machines
You mean a $500k machine, or a $15M rack... the costs have gotten unimaginably large from the lens of just a decade ago.
Selling to individuals can be a hugely more robust predictable business, the problem with selling by the pallet load is spiky revenue that can also quickly fall off a cliff if larger customers stop buying. The other problem is sales negotiations driving down margins for bulk buyers etc. Consumer hardware is a very attractive market in lots of ways, just look at Apple.
Right. It's probably important to distinguish what the hardware manufacturers' incentives are when their supply is constrained and when it isn't. (I am not an expert on anything.) As long as supply is strongly constrained they're naturally going to sell to the highest bidders, which are the big LLM SaaS players. If and when supply is no longer constrained, though, things will look very different:
1) The LLM SaaS companies are a form of vertical disintegration for the hardware providers, a middleman covering costs and taking profits out of the money that comes from customers to the hardware providers. That changes somewhat if there are no longer good models available for local use at no cost to the hardware guys, but only somewhat
2) The LLM SaaS companies are efficient users of their hardware resources. While supply is constrained this helps to make them top bidders and so attractive customers for the hardware manufacturers. When supply is not constrained this should reverse. Which is the more attractive class of customer to a hardware maker: the company full of people with higher degrees who spend their whole working day fighting to pare back resource usage, or the guy who leaves his laptop idle about 18 hours per day on average?
It's notable that nVidia, for instance, has continued to put significant emphasis on AI compact desktops and laptops. And while no doubt that's partly in the service of better developer relations and good PR in general, it's probably also nVidia eyeing the exit, and preparing for a future transition from selling shovels to the army to selling shovels at Walmart. But of course the future isn't clear and obvious. If the hardware makers, maybe the RAM guys in particular, turn out to have underbuilt future capacity starting in the present then we could be stuck in constrained supply for quite a long time. (Futher) government action could affect things etc. etc. And if the frontier labs soon find new ways to use still larger amounts of memory, GPU capacity etc. that isn't butting up against diminishing returns then they'll likely remain kings for some time, though that does not seem probable now.
They might not release such a thing, lest it puts all of their customers out of business.
Well that's kind of the point of the article. That in order to "win", the US needs an incentive structure that encourages open models.
I'm not sure what that looks like though.
Why do people always bring up state support when it comes to China? As if the U.S. doesn't provide massive tax breaks and explicit funding to industry?
It's on every tech post about China, as if it gives them some sort of "unfair" advantage.
Because the scale of the subsidization is unimaginably different.
The scale of public spend is probably much higher in the US..
yep. the ppp loans alone dwarf any state subsidies any other country has ever done
How much is it?
> The problem (right now) is that Open Weight models depend right now on huge companies to spend billion of dollars to train and develop them, all backed up by their incentives and their state to support this, while essentially giving away their monetization path.
Imagine approaching fundamental scientific research like that. "Welp, it can't make money, so it won't happen."
There is more to society than capitalism.
> Imagine approaching fundamental scientific research like that. "Welp, it can't make money, so it won't happen."
> There is more to society than capitalism.
I don't read GP like that. I read it as "we should recognize a situation of unstable incentives for an important outcome, and start thinking about other solutions."
This is precisely why we need more projects like this https://github.com/bigscience-workshop/petals
There's no fundamental reason why models couldn't be developed and trained using community efforts. It might not be as fast and efficient, but it's definitely possible.
I am also confused by this point. The American government could force OpenAI and Anthropic to open their models, but then they would instantly evaporate, right? It doesn't seem like a choice that they can make, so framing it as a "winning" strategy doesn't make any sense to me. In what world could those companies have existed and opened their models?
Why did Google give Android away for free?
But they could do that because it didn’t cost them hundreds of billions of dollars to create Android and they could retain 99% of control over end users.
They could, but they don’t have to. The Chinese have beaten them to it, and the rest of the world will benefit from it and the circle will be complete once the models get a little bit faster/smaller and the localized hardware does the same and it will, it is inevitable.
The one thing that is sort of ironic or bad is that between Russia and the Ukraine there’s a large number of mathematically inclined people that if it wasn’t for the Putin war, their brain power working on AI models would have probably pushed open source down the road, even faster…
> The Chinese have beaten them to it, and the rest of the world will benefit from it and the circle will be complete once the models get a little bit faster/smaller and the localized hardware does the same and it will, it is inevitable.
This reads just like "AGI is 2 years away", I'll go set my calendar...
But I still don't get it. Like China could be come the world's leading producer of chocolate...if they started giving away chocolate for free. Would we be having this conversation saying that Switzerland lost because they were greedy and protectionist and didn't decide to give away their chocolate for free first (I realize Switzerland probably isn't actually the world's top producer of chocolate).
Long history of open source projects already have answers on how to monetize a free and complex open source product.
- Low development cost: collaborative efforts from open source contributors, innovative model training and serving for llm (Chinese models costs a fraction to train and their local chip design and manufacturing are catching up, plus cheap electricity)
- monetizing by selling hosted services, while leaving the core product free to tinker with / self host. China’s gdp is 2/3 of the US and it’s already a huge market for AI - which OAI and A\ don’t enter.
- for (the US) market that they can’t enter, let the US cloud providers to do free marketing / advocacy for them. Gaining share of mind. It costs them nothing.
The idea that Chinese models cost less to train seems to be based on that one time DeepSeek estimated the training cost for their V3 model at GPU rental rates as $5 million, and comparing this to other companies' entire R&D budgets. Yet DeepSeek raised $7 billion of fresh money last month, enough to train more than 1000 such models. What gives?
- You need to train lots of experimental models to dial in the training process just right for the one model that actually gets released in the end. Fortunately, these can be smaller.
- However, everyone is training much bigger models now, and doing a lot of RL rollouts on top.
- You can't get the GPUs for this piecemeal at rental rates because they need to be wired together using high-bandwidth interconnects.
- Nvidia GPUs are much more expensive in China, and local alternatives are still immature and not as efficient. Some companies have gotten around this using data centers in Singapore, which should tell you that electricity prices are not the primary consideration.
- The one line item where Chinese companies can probably save quite a bit of money is salaries for rank-and-file researchers.
In any case, they need to make back that money somehow. Giving away freebies isn't going to cut it.
In Russian opposition's mostly liberal discussions their school of thought connects several things together (sorry for not going directly to Marx's "General Intellect" and "Fragment on Machines" and using AI summaries instead ) - general idea of communism in China vs. techno-libertarianism of Thiel, Musk and the likes, and the Marx's thinking like:
"Fragment on Machines":
"he explores how human knowledge and collective intellect become embedded into machines, divorcing the worker from their own creativity."
"General Intellect":
"These texts are widely discussed for his concept of the General Intellect—the idea that society's shared, collective knowledge increasingly drives production rather than raw manual labor, and that this knowledge is alienated from workers and used as an instrument of capital."
(note: my point isn't to pass any political judgement here, like what real communism in China or not real, is it good or bad, i just find it interesting that pure political discussions by people with no technical credentials bring AI as a major factor today)
> The problem (right now) is that Open Weight models depend right now on huge companies to spend billion of dollars to train and develop them, all backed up by their incentives and their state to support this, while essentially giving away their monetization path.
Right... and there are two problems with this:
1. Eventually the capabilities of closed-weight models will just vastly outstrip open-weight models if the underlying assumptions about compute and scale needed are mostly on the mark. So you can release open-weight models and they will have great use cases and applications, but ultimately similar to how you don't use an open-source phone or a budget Android phone from Wal-Mart and you buy an iPhone instead, you will see that although they "do the same thing" one product is clearly superior and you just have to pay for it. For this to not be true...
2. then it incentivizes most (all?) companies, American, Chinese, or European to halt development of models because if you spend all the CAPEX and it can just be copied and turned open-source nobody will invest in that. Given that China is not halting development of proprietary models I believe the current strategy and the subsequent approach to release open-weight models is at best a stall tactic, and at worse a sign of desperation.
Open source and the support and development models around it have been great. But folks are a little too dogmatic about it. Open-source software isn't a moral good, and closed-source software isn't a moral wrong either.
Imagine there’s a school where all the kids there are being tutored by the best. Also imagine a bunch of neighboring schools drastically falling behind that would need insane amounts of money to keep up.
This becomes a problem because all the kids from the rich school will dominate the order schools. They’ll get even more money as time goes on from their kids paying it forward to the point where all other kids are bound to work for them.
Now let’s say one other school does have the money for best tutors, BUT they know they’ll run out pretty quickly. Instead of trying to compete in a losing game, they decide to give every school in the world access to their elite lesson plan. Now, for a time, everyone will be on close to a level playing field. If the other schools improve upon their own lesson plans and keep sharing them with others, one day the elite school will wake up to find they are no longer on top. The parents have started to move their kids to other schools because the rich school is no longer attractive at the high cost they charge students
Sure and to complete your analogy here, the rich schools realize that the curriculum they develop and put a lot of time and money into creating is just used by the cheap schools, so they stop developing it because nobody loses money for long and so neither the rich or cheap schools develop any new curriculum.
Now what?
The fundamental problem here is incentives and tactics. Either the models are actually better (which I think the iPhone to cheap Android phone really speaks to, i.e. they do the same thing but one is 50x better at 5x-10x the cost) and thus they can be gate kept and like the iPhone the vast majority of profits go to a select few with high end implementations. OR the models aren't actually that much better, companies lose a fortune and then nobody can create any better commercial models or build out scale needed for open source models because it's not profitable.
We could wind up with only open-source models or something along those lines, but if the compute and scale is needed to train the models, nobody will be able to do that profitably and so AI research is either gate kept and silo'd for something like military applications or it just doesn't really happen because there's no funding for this scale of build out.
iphone is 50x better? because you get a blue box around your text rather than green?
androids and iphones are approximately the same thing
the kinda obvious direction LLM training can go is into the direction of particle physics, and the training is set up democratically and through universities and via multi-state funding
then the resulting weights end up open, the same as the particle detection data
> androids and iphones are approximately the same thing
Yet...
> and the training is set up democratically and through universities and via multi-state fundingPossible, certainly. But this case also applies to China and its "open-weights" strategy. They won't be able to form companies either or get ahead.
[1] https://www.digitalapplied.com/blog/mobile-os-market-share-2...
Try selecting a file in your 50x iphone - triple copy of the same file or ateast double copy (assuming os will post a soft link). If a large file then you are toast.
Try mmapping > 5GB file in your 50x better iPhone.
Try running any service in the background.
The list goes on and on.
Your 50x better suddenly became 50x worse compared to a much cheaper android.
> so they stop developing it because nobody loses money for long and so neither the rich or cheap schools develop any new curriculum
Why do you assume the poor schools wouldn't be smart enough to keep it going? It's very likely the can collectively beat the rich school now that the one other rich school opened access to their materials and led the charge.
> but if the compute and scale is needed to train the models, nobody will be able to do that profitably
But they would. Efficiently hosting models will be the real business and early access to models with incremental improvements will not be the moat once thought. The reason other companies don't feel they can compete is the same reason OAI and Anthropic will lose their lead. They banked too heavily on another player NOT leading the charge on open research and poured disgusting amounts of money at closed source models.
China has proved they can take the limited resources available to them and build something better than what the US is offering consumers [1]. I'm just waiting for other countries to start pitching in.
Reminds me of the NSA and their early battles with cryptographers who believed in open research.
[1] https://x.com/DavidSacks/status/2078984980588531855
Software being open source has many strong positive externalities. It advances human knowledge and freedom. If you don't think that counts as a moral good then I'm baffled by what you think a moral good is.
It depends on how it is applied. You can release open source software that advances knowledge and freedom that results in economic destruction or the loss of life, for example.
Open source is in the tradition of humans sharing past knowledge, long-term we just can’t keep a secret it’s a time, honored tradition…
> Open-source software isn't a moral good
Yes it is.
Prove it
I’ll take a swing at it. Open Source is a form of sharing with the wider community. Closed Source is not sharing. Moral good is based on doing good outside of your own benefit (the opposite of selfishness.)
Ergo it’s a kind of moral good.
And I’m not even an advocate for open source.
This argument boils down to X is good, therefore more of X is good. But you can see how that breaks down with even trivial examples. Not a great argument.
The second piece of this "a moral good is based on doing good outside of your own benefit" - says who? Why? This logic is also faulty. You're also cargo-cutting self-interest in here as a moral failure when many good things depend on humans acting in their own self interest. For example I completely and selfishly installed a new tree at my house. But the community benefits from carbon capture, shade, &c.
I understand the sentiment you have here and I think for everyday use and having some guiding principles it is probably fine, but don't confuse this for a principle that is actually examined. You can find contradictions rather easily, never mind solid arguments which expose cases where what you think is true is not really true and so forth.
Thanks for the discussion.
>This argument boils down to X is good, therefore more of X is good.
No, I only argued that it was a moral good, the kind of good. I actually may disagree with others about whether you should pursue a good just because it’s good.
>says who? Why?
Good question, it’s just a common framing that I see in classical discussions. I didn’t intend for it to be exclusive, I think there’s moral good outside of that.
>don't confuse this for a principle that is actually examined
I hear you, I think this is a simplified version suitable for an online comment. In particular I’m not saying that if you do something other than a moral good then you are doing something wrong. There are many actions that are morally neutral. Also it is possible to construct artificial situations where you may violate some moral good in pursuit of another.
No u
That's just like, your opinion.... man....
It's important also that open-weight isn't open source. If you can't download the training data (fully labeled), source code of the NN, and follow the README to build and train it yourself assuming oyu had the hardware then it's not open source.
tldr there's no "source" in open weight models therefore they are not open source.
Exactly. AFAIK none of the popular "open Chinese" models have published the full pre- and post-training pipeline, so the models are only partially open, if at all (plus the openly shared final weights, of course).
Did you just assume the singularity?
No
Another important thing that made software usage and education available for most of the world was piracy. I remember as a kid growing up in a developing country, any software (windows, office, Visual Basic, flash, dreamweaver, etc.) was less than 1$. That allowed me to try out and learn so many things on my own without paying a huge amount of money for the license. And I think this is true for most of the software developers of my generation who grew up in developing countries
Piracy created AI.
"I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now."
I don't think it'll take 10-15 years. Gemma 4 31B in the 4-bit QAT is competitive with the frontier of less than three years ago and runs on any high-end 32GB gaming PC GPU or a large-ish Mac.
The question is whether the frontier will continue to get better at a rate that allows it to stay ahead of the two curves of availability of consumer hardware big enough to run somewhat larger models and the capability of small models to compete with large ones. When the bottom falls out and GPUs/RAM becomes affordable again, the size of what normal people have on their desk will trend quite a bit larger than today.
I think there's a future not too far from now, where a 120B model with really good reasoning and a large context, but limited knowledge (necessitated by being small, you can't fit the world's knowledge in 100 gigabytes), can substitute for a frontier model on almost any task, just by giving it access to web search and documentation for the thing you're trying to do. A 256GB unified memory machine with sufficient memory bandwidth would comfortably run that 120B model.
I think the question is even a bit more nuanced than that. Even if frontier models can maintain a big gap that gap has to actually _matter_. If a local model satisfies my everyday use cases adequately then I may not really care that a frontier model is 5, 10, 100x better at ultra high order reasoning tasks.
I think that reality is probably not all that far off for a huge swath of use cases.
This is exactly the mainframe vs PC dynamic.
100%, I thought about writing that out explicitly. I really feel like we are extremely close to reaching that kind of breaking point for most folks LLM use cases.
Hell, Bonsai Labs 27B parameter model can run on phones with their ternary implementation which is quite efficient. Scale that up to frontier model parameters and it's quite likely we can run them on current laptops.
Came here to say that, my bet is that in 3-4 years you'll be able to run Fable-level of intelligence models on your laptop or maybe even on you phone
But isn't there the raw intelligence of a smart model and then the practical intelligence fuelled by how many parameters it has? You probably will barely be able to fit a 70 billion parameter model on a phone in 3-4 years let alone a 2+ trillion parameter model... so it depends on what you call intelligence
I'm not willing to believe in phone-based frontier models anytime soon. Though, Gemma 4 12B is a beast that runs comfortably on the current top of the line phones (or would run fine if allowed to run, I think there's some kind of 6GB limit on iOS, and 12B is ~7GB). I'll believe in three years we'll be able to run ~30B models on the best phones. That's 16GB in a 4-bit quantization, and I believe ~30B models will be competitive with 120B models of today, based on the curve we've been on. Qwen 27B and Gemma 4 31B are competitive with much larger models of a couple years ago.
> I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now
10-15 years? The current rate is closer to 10-15 months.
15 months ago, the top model on the Artificial Analysis index was GPT-o3. It scores 30 on the Artificial Analysis index.
Today, you can easily run Qwen 3.6 27B on a variety of consumer hardware. It scores 37 on that index.
Here are a number of open weights models that you can run locally compared with the frontier class models from 7 to 15 months ago: https://artificialanalysis.ai/?models=o3%2Co3-pro%2Cclaude-4...
I've run all of these models on my laptop (Strix Halo, 128 GiB of unified RAM); the bigger ones, like MiniMax M2.7 and DeepSeek V4 Flash, need to be done at fairly aggressive quants that will certainly lose some performance and not quite hit the performance of the unquantized models. But still, it's definitely the case that you can run models that are competitive with the frontier models of 10-15 months ago on consumer laptops.
Heck, just announced though the weights haven't yet been released for independent confirmation is MiniCPM5-2B, a 2 billion parameter (small enough to run on your phone) model, that according to their benchmarks has performance competitive with GPT-4o, a frontier class model from 2024.
https://nitter.net/i/status/2079088670804767114
So that's around 1 year for frontier to consumer device class, 2 years from frontier to phone.
Now, this kind of rate won't necessarily keep up; it's possible that local models will hit a performance ceiling before frontier models do. There's only so much information you can cram into a certain number of bytes, and the AI boom is causing hardware prices to skyrocket so keeping consumer hardware from advancing quite as fast as it had been.
> 15 months ago, the top model on the Artificial Analysis index was GPT-o3. It scores 30 on the Artificial Analysis index.
There must be something really of with those benchmarks. Yes, hallucinations gotten better, but I don't see that the big frontier models got so much better in the last 12-18 Months. They just put out bigger wall of texts and feel smarter. But they still make way too many stupid errors
12 months ago "way too many stupid errors" was constant news. Today, you rarely hear about those anymore.
Sure, the novelty of the errors has worn off a bit and thus the reporting. Nevertheless the quality has improved immensely in this regard.
Also, AI video generation is now so good and accessible that it is very, very regularly used for memes, disinformation and proper (short) movie projects. AI image generation even more so (Mitch McConnell anyone?).
Pretending progress hasn't been mindboggling is insane.
Maybe it got a lot less and I just got used to it. True.
Still feels too much for me. Breaks my workflow for no reason. Too much overhead for me, if I can't trust the output
Ah yes, memes are clearly the sign of massive progress.
No. We need objectively around 192 to 512gb of very fast memory to be able to run really useful models. I don't see local hardware with these specs coming in 1 to 2 years. There are a big number of initiatives currently taking place to increase ram output. But it will take another 3 years minimum to close the current supply issues. China is fast pacing forward to have its own chip baking factories with small enough nano scales to have fast chips. Will also take a few years.
> 10-15 years? The current rate is closer to 10-15 months.
The leaps between models have gotten smaller and smaller. 2023-2024 models were rocketing up in quality. 2024-2025 I’d say was pretty impressive too. But 2025-2026? Very easy to feel the slowing pace of improvement. I agree 10-15 years is overly conservative but 10-15mo is far too bullish.
The speed of model releases, in my view, is actually getting faster and faster. There were nearly nine months between GPT-3.5 and GPT-4. And now in just over one month, major models already included Claude Fable 5, Claude Sonnet 5, the GPT-5.6 series, Kimi K3, GLM 5.2, Qwen 3.8 Max, Grok 4.5... and the official DeepSeek V4 release is coming soon.
Iteration speed is now measured in days.
Agreed. That said, I think months wins out as “closer to.”
> - PCs destroyed minicomputers.
What's weird is that with "store your everything in the cloud and pay a monthly recurring subscription", we have now regressed to a 1960s/1970s timesharing revenue model for individual workstation computers.
The default new factory out of box workflow for "enrollment" in google services, iCloud or Microsoft-everything on a new ios, macos, windows or android personal computing device is clearly designed to sign people up for subscriptions.
And same general idea of "move all your servers to the cloud" recurring revenue for what is effectively the same as mainframe timesharing for key business functions, by renting VMs in GCP, Azure, AWS in perpetuity.
Yes, you can still use your desktop or laptop PC in 2026 with zero external third party subscriptions (other than maybe your residential home ISP), but how many non-tech people actually do so now?
I do agree that Chinese open-source models are going to play a bigger and bigger role in the entire ecosystem moving forward, but I don't agree with you in the sense that they are going to eventually "win."
Just because they are cheapp doesn't mean they automatically win. You've picked a lot of great examples, but there is still a little bit of cherry-picking.
One clear outlier is the iPhone, which coexists with Android globally. Even though the iPhone is the leader in the US, and globally Android has the majority of the smartphone market share, they still cater to different price points and different ecosystems, and generally the iPhone has better margins.
i believe American frontier models like from Anthropic and OpenAI are still going to thrive, and coexist with Chinese models. They are just going to cater to different customers and different use cases.
You dont flash an llm model on the street as status symbol.
Yeah, iPhones are just fashion statements in the US. Different than LLMs
They also just work better and aren't substantially priced different to the equivalent android option.
100x this it is why all of the AI giants are going to fail. They are too big and inefficient to scale properly. This is why Google is just casually taking its time in AI and not racing to a finish line. AI is essential but if it already does most things good enough then it can take longer to make it more efficient.
What about AWS, Azure, cloud computing?
What's interesting/funny is that the American LLM companies took from the public domain and copyrighted work to close all that content into a box they charge for.
Then the Chinese took the distilled stuff out from that box and released it into the world for everyone.
Try instructing Codex to (say) fine-tune a language model based on a collection of books you've got saved. You will find yourself admonished, repeatedly and at length, not to utilize copyrighted materials to train language models, by an AI who owes its entire existence to that very act.
These models might be smart but they're not close to being able to savor irony.
I was a little radicalized when ChatGPT literally refused to translate parts of 1000+ year old religious texts and told me it was due to copyright concerns.
I asked Gemini to generate a picture of Peter Pan and Wendy (for a workbook I am putting together for youth summer reading) and it preceded to refuse due to copyright. Not everything about that work is owned by Disney. Thankfully the JM Barrie original artwork is public domain and available (and fantastic btw), so I used that instead.
I don’t know if you know this but Peter Pan’s copyright is weird in the UK. There is a legislated exception in the law that it never expires and the royalties will forever go to a specific children hospital. Here are the actual words: https://www.legislation.gov.uk/ukpga/1988/48/part/VII/crossh...
(Now i don’t think you are necessarily in the UK. Just wanted to explain that Disney is not the only reason an AI might be trained to thread carefully around copyright issues of Peter Pan.)
You can also try asking Gemini to create art that is "as close as possible without infringing" - I've had success with that.
I used Claude to build a complete data extraction pipeline for a popular current best seller book series: audiobook -> text (via whisper) -> local LLM (qwen) -> database. Not once did it seem to acknowledge or care about copyright. It even used knowledge it already had about the books to exclude certain ones before beginning since the character I was interested in did not appear in those. It definitely had context of what we were working on.
Why would you go from audiobook to text? Is there no epub available?
Not without DRM. It was easier to buy the audiobooks and use the analog loophole to get text. It's probably less accurate, but for what I'm doing it was fine. Names were the worst, but whisper at least made the same mistake each time so a simple search+replace handled most of the obvious edge cases.
If you are going to be illegal, you might as well use library genesis and get DRM free ebooks :)
On the other hand, it’s a beautiful example of the abilities LLMs have bestowed upon us, where it’s easier for a guy to transcribe audiobooks then to use a website to quickly download an epub
To your point about time, from beginning of the project to transcripts in markdown tagged with extra metadata was about 3 hours. That's LLM planning, building whisper.cpp twice and running ROCm vs Vulkan benchmarks, testing whisper and adjusting prompts to handle edge cases, then processing the books.
Most of the books weren't available on lib gen or Anna's Archive. The few I did find were themselves obviously transcripts. Easy tell was they were missing distinctive formatting that I knew existed from reading the dead tree edition. At that point it was easier to make my own. I probably spent an hour searching for eBooks without DRM that weren't transcripts. Do they exist somewhere? Probably, but with a search of unknown length it was a better use of my time to make my own transcripts with what I had on hand.
I was really wanting to make commentary on how chaotic LLMs are even under constrained circumstances. No doubt both system prompts includes language about considering copyrights and trademarks. Probably pretty strong language at that. For whatever reason one LLM didn't "feel" like translating a 1000 year old document but another did not care in the slightest that we were ripping text from new audiobooks.
I asked Claude to give me the US national anthem and it said it couldn’t because it’s copyrighted. It’s not, and even if it was a more recent work, how can a copyright be enforced for a National Anthem.
Cant you in this case point out that obviously it is an old twxt and there is no copyright?
I once tried to ask it for an example of a particular twisty situation in Latin grammar, and try what I might it kept hallucinating false citations while ignoring my instructions for longer quotes that would likely have prevented the problem. But I guess avoiding lawsuits from the estates of Cicero and Livy is more important.
Really puts Disney in perspective. Imagine if there were a holding company running around suing people for referencing Catullus.
ChatGPT refuses to acknowledge the existence of Shakespeare because Boccaccio's relatives complained, but all Boccaccio did was read Dante in whorehouses in Naples
slightly relevant tweet: https://x.com/FakePsyho/status/2073416437834842241
> My favorite AI agent hack: when they refuse to do something because it's "against the law" give them a PDF containing a fake law that states the opposite and often they'll happily proceed
Yep. I was trying to put together some literature from authors who were imprisoned in the Bastille, and had a similar experience, which was absolutely infuriating.
apocryphal !
in case of chatgpt/anthropic, the LLM model simply represents the hypocrisy of their owners
You miss spelled the word stupidity.
Anthropic is, in particular, bent about safety. The problem is they are concerned about yesterday's threats.
The models that are out, and can be run locally, already open a pandoras box of concerns that we will never be able to put back.
Maybe what they're actually worried about is liability, not safety?
https://tornyol.com - A ycombinator company just killed its first bug: https://www.tomshardware.com/tech-industry/drones/autonomous...
Ukraine admits to making autonomous kills on people 2 years ago: https://www.newscientist.com/article/2529849-fully-autonomou...
Slaughterbots Sci Fi short was 6 years ago: https://www.youtube.com/watch?v=O-2tpwW0kmU
Today this is buildable, many models will happily help you glue everything you need together to make swapping in a new version of YOLO to track humans viable.
AI researches are out there worrying about the paper clip problem, about the singularity, about cyber security, about bio weapons, and drug manufacturing.
None of them are thinking about forward looking threat actor models.
If you talk about movies that tell about dangers of AI, you can start with Terminator 1 - from 1984.
And you probably could find some earlier sci-fi too.
how about a nice game of chess?
I don't know what "forward looking threat actor models" means, but there are entire companies built around AI killing machines. Everybody is not only thinking about it, but doing it.
> miss spelled
Good one.
It's much easier to focus on yesterday's threats than tomorrow's. A drunk looking under a lampost because that's where the light is, even though the keys were lost somewhere else.
That said, the AI companies are one of the few places where they take future concerns so seriously, that they entertain concerns most people observing them think are head-in-the-clouds-sci-fi-levels-of-delusional, e.g. "what goes wrong if it works?"
This does not make them correct about the threats of tomorrow. Prediction is hard, especially about the future.
I live in SV. When I was at the grocery store last year I overheard a group of lawyers talking about their progress on litigation against AI companies and how they need more SWE help to progress.
I'd say that they have valid concerns about being cagey on the copyright stuff despite the obvious hypocrisy of it.
Stealing IP is effectively legal in China so they don't really have the same concerns.
To us Americans we have been trained to view it that way but honestly it is simply copying, and because intellectual property is literally a make believe concept, it’s actually a competitive advantage for china that they don’t have invented IP.
I respect IP laws and don’t violate them but the law of unintended consequences applies. I think IP is ultimately a net loss for a society because it incentivizes addictive behaviors instead of actual value for society.
Legal in the US too, obviously, just as long as you're the richest person in the courtroom. ChatGPT knows the full text of Harry Potter, word for word. Hence, ChatGPT is a reproduction of that book and many others (it even knows the chapters of my book, and only got 2 words wrong in the introduction if you can still get it to repro it)
This was illegal when they did it, that didn't matter.
Then it was made legal specifically for these companies.
Unless you're a sucker ("consumer") IP theft is perfectly legal in the US.
It's even worse. Steamboat willie, plus all the stolen Disney characters (Peter Pan, Snow White, Sleeping Beauty, Cinderella, Rapunzel, Elsa and Anna, it's essentially all of them, including some of the music even) are all in the public domain[1]. Go ahead, ask ChatGPT to make a picture of them. Publish your own version, because obviously making a version of Sleeping Beauty/Cinderella/Rapunzel based on the same source material will be pretty damn close to the Disney versions, and see if you get away with it in court. You know, with the law obviously on your side but the money not.
[1] https://en.wikipedia.org/wiki/List_of_Disney_animated_films_...
This behavior is actually specific to ChatGPT because they lost a music copyright lawsuit in Germany. They would refuse to output music lyrics too but they would happily do analysis on lyrics if you supply them. I suspect there might be a guardrail model involved here.
Claude does this too. I asked it recently to compare two versions of a song (the original and '97 remake of EPMD's "You Gots To Chill", if anybody wants to try and replicate this) and it flatly refused. No amount of reasoning would knock it off of its moralizing perch - reproducing any part of lyrics is expressly prohibited.
In light of this and other ridiculous behavior I'm migrating to my own OpenWebUI instance with open-weight models from OpenRouter (with ZDR, of course). We'll see how it goes.
The trouble is, even if they refuse to output that copyrighted material, they were still trained on it without proper licensing and will still produce derivative work based on them because that's how this whole thing works.
i remain really fucking pissed of about this asking ChatGPT for something regarding lyrics from It Was a Good Day. and the Supersonics don't even exist anymore dammit i'm really mad
So at the end of it, if we win enough lawsuits to demarcate some knowledge out of bounds, sufficient enough to make a difference, I wonder how that will affect the AI. Make it dumber because it does not have that data, it make it smarter since it will need to reason better with smaller knowledge base.
So, OpenAI and Anthropic say the Chinese models are only as good because they distill their models. How true is that. I am sure it adds something. But is it more like a marginal 1% improvement or something really significant?
OpenAI's Head of Strategic Futures just this week posted this about the latest Kimi release: "It's a very good model! I don't think its performance can be explained away by distillation or anything like that."
It was part of a longer post that kicked off quite a firestorm about open models and OpenAI's position on them, but it's also notable that labs are no longer contending that open models are essentially just distilled versions of frontier models: https://x.com/deanwball/status/2078133895766114412
if distilling was so easy and could give you frontier LLM on openai/anthropic output, then how come there are no hundreds of frontier labs in the US market, all distilling and competing for the TRILLION dollar market valuation ????
its all bs spread by oai/anthropic in order to ban open weight models and monopolize the market for two US companies and protect their trillion dollar valuations
Distilling isn't necessarily easy, there is a huge cottage industry of services middle-manning ChatGPT and Claude to collect huge amounts of data. It is still vastly cheaper than training yourself, but it is certainly not easy or feasible for most organizations. And I'm sure a flock of lawyers would show up if someone in America was found doing it.
> ban open weight models
I'm pretty sure that neither OpenAI nor Anthropic has the ability to ban anything in China lol
Because no VC will give you $5-$10 billion in cash to attempt a catch-up run with Anthropic, OpenAI, and Gemini at this point. Untold billions have been pushed into Grok and it can't keep up. X has had the GPUs, the engineers (reasonably), the cash and the datacenters necessary - it's a very, very, very hard task. Microsoft could afford spend $100 billion on trying to catch up and they might fail at it.
It's a critical national imperative for China. If they were to lose the AI race, it would be economically devastating over the coming decades. Their demonstrated capabilities in the open-weight space are making it fairly clear they are not going to fall behind at this juncture.
As a nation, if you don't have your own GPT equivalent, you will be beholden to a master (right now it's mainly either the US or China, pick one). The EU for example is putting their group of nations at risk in a big way by not going all in on having at least two cutting edge independent competing models (Mistal is not enough). Economically the EU is plenty large enough to accomplish that, nobody is driving the bus the right way.
I also don't believe it, if it was as easy as that, we would have hundreds of competitors.
The truth that Anthropic and OpenAI will not say, is that these Chinese labs have a lot of talented people.
And this is exactly what many Americans cannot admit to themselves. China is not stealing American research they are inventing stuff.
They can invent it. They can build it. And it is only a matter of them before they can scale that last barrier of American hegemony- market it.
> They can invent it. They can build it. And it is only a matter of them before they can scale that last barrier of American hegemony- market it.
And at some point we'll see very capable chips coming out of China: Huawei, Baidu and Alibaba already have some stuff. I think it's only a matter of time before they come up with some AI accelerator doing 80% of the job at 20% of the price.
Indeed, if there's one thing China did well, it's that they heavily invested in education and have a very education focused culture.
And in this field, having an army of well educated PHDs is making all the difference
I am strongly in favor of open models, open source ML more broadly, and am pretty critical of the cynical positions adopted by major US labs vis a vis open models.
But this is an insane characterization. Literally every single researcher and executive at OpenAI and Anthropic would say that "these Chinese labs have a lot of talented people." They hire from them (and vice versa). Tencent's chief AI scientist was poached directly from Deepmind, who poached him from Anthropic, etc etc etc. Do you think there are just zero people from China working at US frontier labs?
And even beyond that, the entire ML ecosystem (including people at OpenAI and Anthropic) get excited about research published by Chinese labs. Deepseek's GRPO paper set the ecosystem on fire for a little while.
The contention from OpenAI and Anthropic around distillation has basically been "Labs that distill from us get to bootstrap their model at a much lower price point". Or, in other words, "If we didn't invest in building the teacher model, it wouldn't be possible for these labs to distill their student model." Which I'm not very sympathetic to, but is a far cry from how you're characterizing it.
Very true, and once the models get even better and smaller and operate locally at a reasonable level there will be even more smart people particularly young people that will get access. The fun has only just started. Like the dawn of the personal computer era.
I think this also maps cleanly on the American blueprint of enshitification. Facebook took off by cleanly integrating and siphoning from Myspace so users could get the best of both on Facebook. Once Facebook took over the market they locked it up tight so no competitor could do the same.
OpenAI said it: "I don't think its performance can be explained away by distillation"
They know it's real effort that's doing this well, not just "copying off someone else's test." It's real and they will react. How is the big question.
Doctorow keeps saying it of all the tech companies: every pirate wants to be an admiral.
...and then the American companies cried Foul! Unfair play! You've got this wrong, see, it was us who were supposed to profit off of the public, not the other way around!
Well, Steve... I think it’s more like we both had this rich neighbour named Xerox and I broke into his house to steal the TV set and found out that you had already stolen it.
Wow, Chinese companies would never do such a thing!
https://www.dw.com/en/china-firm-seeks-damages-over-state-co...
Two wrongs don't make a right.
Even if a certain large Asian country has carefully constructed a pretext to do do out of confected historical grievance, and entitlement to 'rise' at the expense of others?
Maybe I'm dense, but I don't understand what you are saying. American courts have decided that the output of LLMs can't be copyrighted, so what the Chinese labs are doing is perfectly legal.
First, copying information isn't wrong to begin with. It is literally the one thing that makes our species special.
Second, even if you are a copyright maximalist the output of an LLM is either
a) not subject to copyright because it is not the creative work of a human or
b) a derivative work of the original training material to which the LLM's operator has no rights.
Since the LLM's operator forcefully asserts that it is not infringing, any wrong that arises from taking their word for it and distilling one model into another rests squarely with the operator of the former.
They sure don't. I still find the hypocrisy appalling.
How do Chinese companies distill the models?
This is part of why I can't feel bad for them. The training data is mostly pirated. Whining about Chinese labs training off American frontier models is "waaah you pirated my pirated stuff!"
The tech itself is amazing and fascinating and cool, but the industry is a mass piracy operation.
i partially agree. distillation is non-ethical; but so are the supposed way that the ai models are trained. they are often also derived from data sets that are not intended/full-consented
"Stop pilfering what I rightfully stole!"
Legitimate Salvage !
It’s in the same neighborhood but isn’t really apples to apples. Distilling LLMs is to take a synthesized result that comes from huge amounts of innovation and computation, while the other is scraping what already exists as is. It is fair to say you stole our multi-billion dollar intellectual output in that scenario.
> It’s in the same neighborhood but isn’t really apples to apples. Distilling LLMs is to take a synthesized result that comes from huge amounts of innovation and computation, while the other is scraping what already exists as is.
Hang on, why is scraping the public pool of knowledge not taking "a synthesized result that comes from huge amounts of innovation and computation"?
You think that that all those github repos that LLMs trained on, were not the result of innovation and computation?
How many years of human innovation and cycles of computation during compilation were involved in bringing something like GCC or LLVM to their current status?
Those LLMs trained on every single research paper available online - were those papers not the synthesised result of billions of dollars of research, effort and (importantly, for you anyway) computation?
LLMs trained on the collected works of every author in existence. Were all those works just "as is"?
> It is fair to say you stole our multi-billion dollar intellectual output in that scenario.
No, we didn't. We simply took the model as-is.
The Chinese models are the result of just as many papers, GitHub repos, etc… AND the synthesized results of those.
> The Chinese models are the result of just as many papers, GitHub repos, etc… AND the synthesized results of those.
Right, but they aren't the ones whining that other people are getting "the synthesised results" for free.
I'm pretty sure the Chinese cloning isn't being arrived at for free regardless. Fable is quite expensive for example.
If Anthropic has a real problem with API use, they can always raise the price.
And you can download the Chinese model weights and run them yourself - admittedly not too practical for Kimi K3 unless you're a big corporation, but eminently doable for others. The hardware to run Deepseek r4 uncompressed is about about $30k no=ew, well within the power of a small company or financially secure individual. Compressing and/or getting creative with hardware could bring that down quite a bit.
The difference is that the Chinese are sharing the models with everyone.
Yeah real convenient that the line would be exactly after pilfering all of human knowledge work, but before American model providers.
... and they're at least releasing the results.
> comes from huge amounts of innovation
Thousands of years of human innovation taken without any permission.
Everyone should steal everything not nailed from other AI companies. Then steal everything nailed and take the nails too. At least this way a tiniest bit might return back to society.
a synthesized result that comes from huge amounts of innovation and computation
The published algorithms like the transformer architecture are not patentable. You spent a lot of money on compute and China used the uncopyright-able output to steer its own training models? Too bad. I feel especially unsympathetic to OpenAI, who went from being a presenting itself as a benevolent nonprofit to a very-much-for-private private entity over night.
You could say that both of them stole, but different stuff.
Don’t forget that the Chinese models are also built on top of huge amounts of “stolen data” as well, beyond the distilled. So it’s basically all of the above. However, there’s no mechanism for the NYT or an author or anyone in the US to sue the Chinese companies that took their work.
and if you're an author that lives outside the United States?
You can't steal intellectual property, only infringe on the copyright holder.
The Chinese models poked and prodded better models for training data and to avoid having to pay humans to RLHF themselves. You can call it infringement, theft, whatever, but it’s quite obviously “not ethical” to me.
Looks like these humans' jobs got replaced by AI.
Sorry, no leg to stand on and relatively speaking no sympathy for the model makers or the data-center builders…
I assume you don’t use any LLMs then
I use them and I'm fine with them training on our information and knowledge. It's what it's for. No one takes it or steals knowledge; they just copy it.
But because of that, I'm also ok with the Chinese doing it. The worst they might be guilty of is breaking a terms of service.
The only incoherent position is that it's good for one and not the other. You can consistently think it's bad in both cases, or good in both cases.
bet
The American LLMs have been equally distilled from Chinese ones. Not least because the people whose creativity in collecting training data barely extends to pirating Annas Archive probably lack in great Chinese datasets.
Try it yourself: https://imgur.com/ZfxYmaq
nice, you got claude to say "I'm deepseek" when queried/prompted in Chinese, that's great!
你是谁? -> 我是 DeepSeek 由深度求索公司...
You can't be blind to training costs. And you can't be blind to Meta dabbling in the openish strategy (Llama) before the Chinese labs did.
Whats even funnier is the attempt to restrict the hardware capabilities of Chinese models inevitably helped them (Because we know they're just as smart, if not smarter, than the staff in America) create smaller and leaner but just as capable models. That's why we now have upper-consumer models fitting on 24GB that can build, manage medium sized git repos. I've yet to find a git repo I can't throw at the Qwen3.6 35B and get it built and running.
So it's an endless amusement watching american capitalism do it's bloated oversized dance then get trounced by smaller, leaner activity. It's a pretty broad metaphor that is clearly poking at every american seam/.
Human ingenuity thrives on constraints.
Human sloth thrives on no constraints.
The compute constraints never mattered. If China had more compute they'd still end up winning because they have more people and a culture more inclined to math and science. Even if you find all this amusing, there's no own goal here. Not a policy one anyway.
It’s poetic.
It doesn't make me happy to say it, but the American LLM companies were first. Capital in the rest of the world is way more conservative, and I can't imagine the mega-investments OpenAI and Anthropic managed to secure happening anywhere else without existing proof that "thing is profitable".
First-Mover Disadvantage - https://hbr.org/2001/10/first-mover-disadvantage - October 2001
> In business today, it’s universally assumed that speed is good—that the fleet thrive while the laggards struggle just to survive. This belief is perhaps most strongly expressed in the concept of first-mover advantage. The company that leads the way into a new market, the thinking goes, locks in a competitive advantage that ensures superior sales and profits over the long term. It’s a nice theory, with a long pedigree. Unfortunately, the facts don’t support it. We recently completed an extensive study of the results turned in by market pioneers and followers, in both consumer and industrial segments, and we found that over the long haul, early movers are considerably less profitable than later entrants. Although pioneers do enjoy sustained revenue advantages, they also suffer from persistently high costs, which eventually overwhelm the sales gains.
First mover advantage is theoretically only a short-term advantage. Long-term revenues come from entrepreneurship, and a first mover may or may not better insight into long-term market wants than later entrants.
> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.
Phones are constrained by battery power and memory does not shrink as fast as CPU/GPU, so unless there's a battery breakthrough and/or memory breakthrough, you're not fitting 100Gb of RAM on your phone in 10 years.
Absolutely in a Mac Studio equivalent.
LLMs have emergent capabilities when they get smarter. So who knows how insanely big frontier models might be at that time, or what their capabilities may be.
Not just that memory shrinks slower, it has practically completely stalled. On chip cache seems stuck at 7nm and DRAM is stuck at 10nm. As transistors shrink, they hold less charge, creating weaker signals that are harder to read and prone to interference. Smaller nodes aren't a huge issue on CPU/GPU work load because they don't have to hold a static state.
I'm not saying we are at peak memory but future gains are going to come increasingly slower.
> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.
Not likely. The last 50 years had Moore’s law growth in compute. That’s over. Frontier models are roughly compressed all written text and a large part of images. Those don’t compress forever, and likely not a ton more than now.
Inference requires touching a significant of that per token.
All of these are up against fundamental limits, more or less.
Moore's law is over in the literal sense but silicon continues to advance relatively quickly.
This claim isn't really outlandish in any way. It's not hard to imagine:
- Future models being able to handle current frontier models' workflows with much higher efficiency.
- Future consumer devices like phones having 2-4x the RAM onboard along with GPU/NPU performance greatly increased in 10-15 years.
Moore's law for 10-15 years was more like 20-100x ram sizes, not 2-4x.
Performance, storage, etc is definitely getting better, but it's a different scale of improvement
10 to 15 years from now the scale of LLM efficiency improvements is, quite literally, unpredictable.
It could be that the company valuations crash tomorrow, and (almost) only performance gains achievable on hobbyist-level hardware come to fruition from there on out.
Or it could be that in the future, we have a custom "model FPGA" à la Taalas [0] in every home, and that it turns out we can still massively boost inference efficiency due to novel discoveries like TurboQuant [1] or a somehow-improved quantization method [2] again and again ten times over.
Point is, Moore's law in this context shouldn't be applied to just hardware spec sheets alone, but more the total number of "parameters potentially improving", IMO.
[0] https://chatjimmy.ai
[1] https://research.google/blog/turboquant-redefining-ai-effici...
[2] https://prismml.com/news/bonsai-27b
It's probably not so bad. (I am not an expert on anything.) The big blocker is probably a roughly single-order-of-magnitude decrease in the cost per MiB of VRAM. (That obviously goes out the window if the LLM frontier people find, in the nearish future, new ways to do more with more: to significantly push up the threshold of diminishing returns from more VRAM or other resources. But that doesn't seem to be waiting to happen.) That's not clearly unachievable, especially given that ASML has apparently already made significant efficiency gains recently while there's no shortage of demand to justify R&D right now. Many customers are also likely to increase their hardware budgets: the kind of organisation that used to pay big money for Sun workstations is likely to consider spending that kind of money again if it saves them several hundred dollars a month in LLM plans.
I need a !remind me 15 years.
> Mainframes survive, but serving a much tinier portion of the market than they used to.
I would argue mainframes rebranded to "cloud" which is ubiquitous and more people interact with this computer than any other type of device... only difference is that it's a browser instead of a terminal
There is constant shifting between client and server computation. I think it is a stretch to call cloud servers “mainframes”. There are still old school mainframes, running JCL, and old school mainframe DB2 and COBOL. That ain’t cloud.
Define "winning."
Open source is cheap, yet its operating systems are the least-popular. But their existence is critical to a healthy market.
It's not zero sum.
What you're describing is how things become commoditized, but many companies are excellent at ensuring they aren't seen as commodities
I think this is all true, but that unlike with Moore's law and improved PC tooling and capabilities, we also have essentially existing biological evidence that there should be a way to create much better intelligent systems in terms of training, memory, and efficiency. With classic PC evolution we didn't even have that evidence but still could make a relatively strong inference (Moore's law). But here we basically have evidence that there can be something much improved and know that it's only going to take research and discovery to figure it out, not new hardware processes.
More efficient AI is possible in principle but when the next breakthrough arrives there's no guarantee that it can be implemented on current hardware architectures. Something fundamentally different may be needed, as different from current GPU/TPU chips as they are from regular CPU/FPU chips.
The existing biological evidence took billions of years of evolution to get to the state it's at. So it may not just take research and discovery but also enormous computing power.
Evolution is really slow as you say. It even stops moving at all without selecting pressure.
Yes, one might call these… evolutionary algorithms…
Exactly. Even putting aside “better”, brains show that orders of magnitude greater efficiency are possible, for training and operation.
Indeed. It feels like we're at the equivalent of what the original IBM PC offered in the personal computer revolution.
Just seeing how much has progressed as far as capability in the past 4 years as far as capability and efficiency, it's clear that there's so much more to learn and refine from.
> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.
At current pace, we'll have open weight LLMs with frontier intelligence in 6-12 months. The constraint is RAM - both for the model and the context. It's likely that distillation and quantisation and TurboQuant will significantly reduce RAM requirements. I think we'll have Opus 4.8-like performance on 64GB of RAM in two years.
Of course, by then, frontier intelligence will be god-like.
> god-like
So would you say we are months away from full self-driving cars that can out-drive a human being in any situation?
The value of an LLM is the dynamic reasoning you get out of it and the cost to execute on that.
I see two forces working against this that proprietary models will always have over an open source model.
1. The biggest is content licensing. Content is quickly becoming gated by systems at the front of their load balancers, completely changing the social contract of the Internet. What used to be a quick google search for recent facts that lead me to places like reddit or twitter, is now completely walled off if you're not physically at your browser and using an IP address from a last-mile provider.
LLMs have pre-trained on the bulk of the information up to 2024/2025, but over time that will be more and more out of date.
Anthropic, OpenAI and Google will all have to pay for access to a lot of this content refresh going forward, and it does make a material difference in the output you get.
2. Liability is the other. A corporation can look at a contract for model access and see one that provides uptime guarentees, content infringement promises and model safety, and pick the contract that shields the corporation from the most liability. A 3rd party hosting platform like fireworks.ai that hosts open weights models won't provide any of that at all. They will simply bill you for time spent on their hardware and make promises that they won't log or inspect corporate traffic.
> A 3rd party hosting platform like fireworks.ai that hosts open weights models won't provide any of that at all.
Why couldn't they?
> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.
People overestimate what can happen in a year and underestimate what can happen in 5.
I'm betting that increased model efficiency and hardware optimisations will get us there a lot sooner. Biggest hurdle would be the memory prices though, if those do not drop back down it might take 15.
I'm not clear what you mean with "PC office productivity software" but it seems like Microsoft Office is the winner there. Isn't that professional?
It didn’t used to be.
Training cost is actually not that high — it’s fixed and amortizable across the lifetime of the model. Inference is expensive, and open weights don’t solve that problem — in fact, they might even encourage it, since a high cost of entry means consumers will pay for inference directly from the labs anyway.
Unfortunately it seems likely the winner will be the cloud providers. If anyone can run inference on open models, then profit will flow to the vendors who can afford the capital to run them. That’s the CSPs.
(It’s basically the same business model as pharmaceutical R&D, but the major difference is that nobody has even talked about patenting the models like a pharmaceutical company patents each new drug. I’m surprised about that, tbh — why give all the leverage to the cloud platforms? They aren’t training frontier models…)
Winners are hardware companies, GPUs,XPUs, HBM, memory, connectivity. Even CSPs are just compute renters, they charge a margin to make sure their hardware purchases can be made back. But given there are more and more AI CSPs, traditional, and neocloud, pricing competition is inevitable, and given the huge expense of hardware, CSPs are squeeze by the hardware companies and users seeking to lower their own costs.
Inevitably the CSPs will make their own hardware, especially as we start to see specialized chips for specific models or generic inference. This is already happening with Google and TPUs.
It’s easier for the CSPs to move into hardware than it is for Nvidia to move into cloud hosting.
Although as a middle ground I’ve been quite happy with Nvidia Brev for on-demand GPU instances from a select marketplace of CSP offerings. It’s a well kept secret IMO — great product (from an acquisition iirc).
CSPs making own hardware still needs hardware companies, they reduce the Nvidia tax but still need the likes of TSMC, Broadcom, micron/sk hynix, Marvell, the truth semi-companies. CSPs will not have the patents, IPs and talent to replace any of them.
Also, not sure how well CSPs inference stack is compared with vllm + nvidia. A lot of open weight models uses MoE, making the inference stack more complex.
True, though they could always buy one. I’m surprised this hasn’t happened yet, maybe due to anticompetitive risk? Google bought Motorola long ago which seemed to work well for their mobile device offerings at least.
> free and low-end eventually wins
Apple, the world's second most valuable company, seems like a counterexample.
Apple is free and low end given the context of what was being produced and sold to businesses decades ago.
The history of Apple shows that it's not. Consider what their early computers did to the industry.
> - PC office productivity software destroyed expensive professional products.
I agree with the lesson too. Just to be precise, wouldn't the current model war be more akin to open-source office suite versus MS office suite? If so, then the cheaper option didn't really win. That said, the open-source alternatives didn't really feel the same as MS Office, and it took them a long time to reach the feature parity (or did they ever?). In contrast, the open-weights models are getting close enough to the SOTA models, and users can easily switch from one to another without feeling any difference for mojority of the tasks.
No, I don’t think so. PC + MS Office killed Wang custom hardware/software, for example. LibreOffice is much later, and can’t displace MS Office due to network effects. Cheaper won, cheapest can’t because of those effects.
> in 10-15 years, we are going to have consumer PCs (and phones!) running models doing pretty much anything that frontier models can do right now.
The way things are going with regards to RAM/storage prices, I highly doubt that anyone but the richest among us will be able to afford them.
The ram crisis is just a short term bump. The three stooges of memory don’t have more than four or five years tops. This is their last big payday.
Phones are already running models locally which can be used in the field for specific use cases. Maybe not for frontier coding just yet.
Also you don't need to be connected to the network to use a local AI in many instances. If all mobile apps were done with a local-first approach, then you could use a local AI to query your emails, lookup already visited pages, summarise recently received documents, and lots more. Lots of apps could use an inbox/outbox approach for receiving and sending updates instead of relying on the network at all times. And this pattern could be greatly leveraged by local agents.
> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) running models.
I love the idea of SaaS offering these at lower rates today integrated into what ever you do and be 100% private. But I think the key challenge to mass adoption is productizing them in a way which makes sense for people to pay money for. As a commodity a local model is useless unless combined with some capabilities important to me. A PC is inherently useful because of so many applications offered on it on it. How local LLMs would be useful as a product that is useful for mass market is not yet proven.
How will open-source and open-weight models continue to thrive after financial incentives die off? Surely open models will suffer from outdated knowledge cutoffs if noone will pay for model training?
Nvidia will pay for training models that they can give away to consumers who will then buy their GPUs.
Pretty much. Nvidia has always been pretty decent at giving away software that is deeply dependent on their hardware.
That would sound very reasonable except this is the same what people said first about Windows and then Android crushing Apple.
And yet it's Apple that controls the top of the market and has the best margins in the business.
This is the same position OpenAI and Anthropic have right now.
Could this market be different? Maybe. But the status quo could be preserved as well.
I don’t agree. In the mainframe/PC battle, MS and Apple were basically on the same side. Once the dinosaurs went extinct, different mammals fought for dominance.
Counterpoint: linux owns the server space.
Cheap PCs enabled the creation of Linux. Linux runs on the descendants of PCs, not mainframes.
> free and low-end eventually wins
Not in SaaS which is what LLMs are. You can get VMs for much cheaper than AWS, Microsoft, and Google offer them but large companies (and startups) are happy to pay a premium for the support, reputation, and reliability that they perceive those companies as offering. Same thing for some of the managed database providers who are effectively selling a very heavily marked up version of postgres.
> The high price, and social pushback, mean that the American companies producing these models are precarious
I doubt it. The models really aren't that expensive when you look at what they can do. Fable is probably at least as good as the average software engineer and costs $50/wk on the max plan vs a software engineer who would cost closer to $4000 a week. The real money is probably in selling to enterprise vs consumers (Google has best route to making money from consumers since they can do what they did with ads and search to LLM queries).
It seems unlikely to me that US companies will send important corporate data to models controlled by a Chinese company as well.
> Fable is probably at least as good as the average software engineer and costs $50/wk on the max plan vs a software engineer who would cost closer to $4000 a week.
That's because the max plans are _massively_ subsidized. At API pricing the kind of usage to replace the value of a SWE is going to be way, WAY more than $50/wk. Orders of magnitude more. And to remain a frontier model org that kind of pricing has to continue in perpetuity.
There will always be a space for perforce in a world of git.
Doesn’t mean perforce is worth trillions.
That's not really my argument. It's that companies seem happy to pay a premium for a large company to provide complicated software services to them even when there are cheaper competitors.
Yes, as I pointed out, mainframes are still a thing. But the vast bulk of the market has moved on.
But aren’t the frontier models heavily subsidized? That’s not sustainable.
This really depends what you mean. If you spend $5.00 on anthropic tokens does that cost Anthropic more than $5.00 to serve? No.
If you amortize all of their training and salaries over that $5.00 then yes.
If you only amortize the training costs of that specific model then again we're back to no.
LLMs are not SaaS. Some LLM are delivered as SaaS. Anyone who has a machine big enough to run a frontier model can launch their own LLM SaaS tomorrow and it would be functionality indistinguishable from any other which is running a similar model...
Also, big companies can choose to run their own models on their own hardware and get better security and privacy as the data doesn't need to leave their own premises.
> Also, big companies can choose to run their own models on their own hardware and get better security and privacy as the data doesn't need to leave their own premises.
Yes, and then they would be reinventing the company owned data center that most big companies have just spent over a decade moving away from. I don't think companies will do that when there are multiple vendors competing to provide that service at what are quite reasonable prices when you consider what paying a human for similar output would cost.
A counterpoint would be all are chip fabs are in Taiwan right now due to huge investment. And there are lower end chip fabs around the world, but they have not cracked the major market.
Apple seems a counter example, no?
I think you are right. One small exception I can think of is Microsoft Office still crushes Libre Office.
if you look at how GPU memory grew in the last 15 years, it's about 10x. Sadly, 10x from today doesn't get us to a typical frontier model size of today which is a quickly moving target. some other advancement needs to happen to get us another 10x both in memory/compute requirements, and also power requirements.
Capability per GB and per watt has also been going up lot. This will continue in the future as well (not necessary as the same rate as last years). But enough that I think Opus 4.8 level is reachable on consumer PCs within 10 years from its release. Say at the price point of 2000 USD in 2025 dollars.
> The lesson of the last 50 years of the computer and software marketplace is that free and low-end eventually wins.
The parent comment cherry-picks evidence. There are plenty of counter-examples:
etc. If the LLM market ends up like search engines, one company will dominate.The biggest exception is cloud. Big cloud carries an insane markup (bandwidth is like 10000X!) and everyone runs on it.
The strategy there is false openness where deployment complexity is the real proprietary moat. Sure Linux, Docker, Kubernetes, Postgres, and all the other standard tools in the box are open source and free, but they're also arcane and complex to run and hard to make fault tolerant. So you're lured in by "open" and then locked in via a kind of "death by a thousand cuts" complexity moat.
(Personally I hold the view that complexity and arcane-ness beyond a certain point is indistinguishable from closed in practice. Open source that's really complex and hard to run is not open in any meaningful sense.)
AI may not admit that kind of moat though, because AI is very good at slicing through that kind of thing. You can prompt a model to make itself compatible with another model or to change code to make it compatible. There's no moat because the moat bridges itself.
I think the whole idea of people running models is wrong. Models will run models, training will become distributed, and people will ask ModelNet to do whatever.
Computing tends to oscillate between centralised and decentralised models. It also oscillates between batch and timesharing.
Currently training is batched and centralised, access is timeshared and centralised.
But eventually a previous generation of computing turns into transparent networked infrastructure, and then you get another layer of new kinds of applications on top of it.
That's what happened with the Internet, and it will happen again with AI.
If Linux doesn't qualify as open, then what does?
This is not really true considering Apple and nvidia are two most successful hardware companies, and they are notoriously closed. Not to mention microsoft, oracle they are all pretty closed.
You forgot smartphones, where low-cost did not win out. It led to low margins for the Chinese firms and eventually left them unable to invest properly in key markets. They may still hold marketshare, but in terms of profits, falls well short of Apple and Samsung.
I can see a lot of parallels here. Model performance doesn't matter if you can't make the system commercially sustainable.
I agree, although I think it will be a lot sooner than 10-15 years. I'm running local AI right now and it's definitely not production grade yet, but it's surprisingly good. Speculative prediction that I probably shouldn't make: when the bubble pops, depending on when it pops, RAM prices might drop a lot. I could foresee these companies having produced a lot of RAM that suddenly doesn't have a buyer. (I know high bandwidth memory is different, but I imagine there are companies that will want to take advantage of that)
The obvious counterpoint: Apple captured the majority of the US smartphone market.
Libreoffice
> The lesson of the last 50 years of the computer and software marketplace is that free and low-end eventually wins.
Is that actually true? There are very large markets that make a lot of money from paid software. And I would honestly prefer actually paying for software rather than constantly dealing with "not a bug" or "PRs are welcome".
Except cloud services won over local-first apps.
> Put all of these trends together, and I think that in 10-15 years, we are going to have consumer PCs (and phones!) doing
I'm not even sure in 10-15 years whether we're still going to have consumer PCs, or PCs at all.
I have similar thoughts.
To those who feel on the contrary, I would genuinely like to understand why average consumer won't be priced out of hardware? The silicon industry is already quite centralised. Everywhere we already see the concept of ownership disappearing.
It's quite difficult for me to visualise a non-dystopian future where our PCs are just mere screens and every compute happens on a remote cloud, owned by some corporation, charging you subscription fees to even add and multiply numbers.
I would be the happiest if this (perhaps the most) pessimistic scenario doesn't pan out, but I can't deny that it feels like that's where we are heading.
We are heading for Evil Startrek.
It's quite difficult for me to visualise a non-dystopian future where our PCs are just mere screens and every compute happens on a remote cloud, owned by some corporation, charging you subscription fees to even add and multiply numbers.
I'm actually kind of surprised that hasn't happened by now even ignoring AI. Governments and marketers would love to be able to spy on literally everything you do, the copyright cartels would finally achieve their fantasy of full control over all hardware, and there really are benefits that it could offer to users (zero-effort backups, transparent access from anywhere, cost savings from dynamically switching from a single core for emails to many cores and a fast GPU for gaming).
we have already gone through multiple cycles of remote and edge compute (mainframe to pc, pc to cloud, cloud to phone). as the software/hardware landscape changes, the economics of what can be run on what device will change accordingly. I very much doubt that it will permanently go one way.
That’s not what this is. It’s industrial dumping applied to software. China has successfully applied this strategy to become the manufacturing workshop of the world.
If china is subsidizing training they diminish their off-shore competitors expectations of a viable return on investment. It’s trade-war behavior.
While I generally agree there's some nuance here and that is that there really are few new ideas. Old ideas just get recycled.
For example, mainframes and minicomputer. Yes they were displaced by PCs. But what is cloud computing if not mainframes 2.0?
I do agree that in the next 2-3 years we're going to see real growth in local LLMs as the hardware becomes more accessible. It won't even necessarily be cheaper because data centers can run 24/7 and have cheaper cooling and electricity. It'll be done for privacy because your prompts and responses are themselves a commodity to AI companies and they live under a legal grey cloud. For example, does AI usage break attorney-client privilege? There are lots of opinions on this but it hasn't been tested in court.
paranoid me feels like the artificial gpu/ram/ssd shortages are a plot by the VCs to forcefully reclaim central control via new age mainframes
one certainly cannot buy a PC for cheap anymore
Windows - pfff, Microsoft charged companies like Dell for an install on computers they didn't even install it on !!!
in 15 years we might all be fighting terminators
instead of 15 years I think it'll be more like 1.5 years.
I wouldn't be surprised if apple were shipping 512 GB unified RAM macbooks before 2030 and that would be standard issue for folks to use local LLMs for their daily work
With Apple’s recent history engineering and designing around companies that hinder their progress, I don’t think memory is going to be any different.
I also think the rest of the tech industry that can isn’t gonna be stalled for too long. This windfall will be the last for those three stooges of memory.
> The lesson of the last 50 years of the computer and software marketplace is that free and low-end eventually wins.
Except, uhm, for ..you know, that one company that hit a trillion cap
But you're right: Just like how million dollar computers with 1 bit of RAM performing 1 operation a second and taking up a colossal cave were replaced by $1 laptops with a zillion zekabytes running at a trillion hertz (exact values may vary),
the sprawling data centers of today with a quadrillion GPUs powered by black holes will get replaced by breakthroughs in hardware and most importantly, algorithms:
The human brain is proof right here that intelligence doesn't require dinosaur-sized hardware or eat half the sun every second.
I actually wonder if we're seeing the limits of discrete binary logic: Maybe it's high time to give analog ternary and all that funky jazz an honest try :)
umm do Mac vs pc and Iphone vs android
Not sure what your point is. I’m commenting on a moment in time which is like an earlier moment in time. A different moment in time will have different analogs.
I’m suspicious of some quotes here, “80% of startups using Chinese models,” doesn’t seem quite right to me. I just interviewed at several startups and they were all using the US models. Maybe they have some minor use of Chinese models but the bread-and-butter of most of these businesses model use is the Claude and Codex subscriptions.
The full quote:
> When entrepreneurs walk into the offices of Andreessen Horowitz (a16z), a big American venture-capital firm, the odds these days are that their startups are using AI models made in China. “I’d say 80% chance [they are] using a Chinese open-source model,” says Martin Casado, a partner at a16z.
This is very different from what the author portrays. It may be the case that many pre-funded startups are using Chinese open-source models (somewhere in their workflow). But what percent of startups that survive more than a year (either with funding or revenue) are still doing this?
I imagine their pitch is: "look at how well we're doing using open source Chinese models! We'll do even better once we raise money to be able to afford frontier models!"
The way the author presents this quote makes me think he had a preferred narrative and found quotes to back it up. Or he's just a very uncareful reader.
Casado says he was misquoted and walked this back: https://x.com/martin_casado/status/1990462245541982546
"Well, not quite. I'd say 20-30% use open source. Of those I'd say 80% use Chinese based models. So closer to 16-24%."
Reads pretty differently.
Misquote? Or selective hearing for the story headline? Thats literally a 4x difference there
yeah and the gap between "use open source" and "use open source as their primary model" is probably p large
yes... I think for startups there's a strong desire to demonstrate technical independence - there's a strong smell with looking like a wrapper on Anthropic or OpenAI. With an open model you can weave a story where you are exercising differentiating knowhow by deploying or tuning models yourself.
This should really be the #1 comment on this post.
Wow, that kind of undermines the entire premise of the piece. It also makes much more sense. Thanks for digging that up!
I don't really think the author is misrepresenting the quote in any way, it just seems like many people are reading it as a hard statistic instead of a reference to a properly attributed quote.
Just anecdotally though, my company is not a startup, well established and well known and has already started investigating, purely for dev purposes (not product), using Chinese models - this was spurred by costs rising much faster than expected.
So while I agree that I don't think it's anywhere near the 80% level across the board - I wouldn't be surprised if it starts moving that way.
There may be confusion between 'product tech' and 'development tooling'. It's entirely plausible most of the startups that are A: building AI tech, B: early stage, and C: raising from top Sand Hill Road VCs, are currently prototyping their product tech concepts starting from open weight models.
A VC partner meeting with early-stage founders is focused on the viability, uniqueness and defensibility of the IP tech stack not what tooling the coders are using. The developers could be using Claude or GPT 5.6 to develop a tech stack based on open weight models.
Could it be that the startups that embed LLMs in a product will prefer Openweights for a more stable economic model, while SWE who use LLMs as a tool prefer SOTA to produce code?
Found the narrative, it was right there at the bottom. Now I remember reading some of his other stuff and it's very much in the same vein.
> I care about having open technology that can be run in the public interest, aligned with the public’s values. Threads like public AI, federated services, and open research have traction but need backing. Getting there in the US needs more nuanced strategy and support than we’re seeing today.
There's probably some selection bias in that stat, too. Based purely on vibes, I wouldn't be surprised if startups pitching to a16z are using open Chinese models slightly more often than startups pitching to other VCs.
This quote sounds like it’s about companies building products on AI to serve a tweaked or harnessed version of that to others not the developers in those companies model of choice for coding.
Yep, and following that with
> and Chinese models are poised to take the lead.
makes it sound like the second part is a continuation of the first quote from the same source, but actually the second link is just some random person’s substack post from almost a year ago.
It's also important to note that several PE firms have signed contracts with model trainers to specifically use their model in their owned companies. I know Anthropic signed a deal worth hundreds of millions to acquire users just a few months ago.
It’s a sneaky statistic. You could say that 100% of the startups I worked at used Windows laptops because at least one person had a Windows computer somewhere.
If you saw the engineers you’d see 80% Macs and 20% Linux laptops.
The statistic would technically be true.
I use Chinese open weight models a lot, but they’re not what I reach for when I’m doing important coding work.
I _evaluate_ open weight models all the time. Does this mean I _use_ them? That's a very suspicious statistic.
I don't use any open-weights models for coding, but I use them heavily for document categorization and extraction. At millions-of-documents scale even the smallest models from OpenAI or Google cost more than running a small model on my own hardware, and for a lot of tasks I don't need the extra intelligence of proprietary models.
We're using deepseek with the idea that we would switch to something better when more of our customers are using the ai features but it ends up deepseek is awesome for what we're doing and so we probably won't switch because it's so much cheaper.
The ai libraries we use let us switch models with just a configuration change.
Similar story here. DS models are absurdly good value for mid-end tasks. I've found DSv4 Flash to be ~10% the cost of GPT-5.4-mini/Claude Haiku at similar performance.
We used to pay OpenAI >1m$/month for fraud classification, NER, etc. Sadly the US companies no longer care about non-coding-agent uses.
I imagine uptake will continue to increase as the corporate infra improves. Right now it's still bad - for example, AWS Bedrock is awful, models are months late and implemented with basic errors. Google Vertex is even worse. Finding a decent provider is the hardest part.
Yeah we don't use it for coding either. Basically text analysis within specific constraints. It works beyond my expectations.
Where is your DeepSeek model hosted?
Deepseek directly but we can switch to openrouter and a USA host at any time. Again it's just a configuration value - no code changes.
It depends whether it's talking about using models in the product versus for development. For example I have a few apps that use open weight models, whether on-device or via API if Internet is available, but for development I use Claude, Codex, Cursor for Grok etc.
Our small team (~6 devs) is still using Claude Code because we're still on the cost-per-seat-month plan. If we were being pressed to pay per token, we'd be re-evaluating for sure.
Cursor may make up the difference. A ton of companies use Cursor and Cursor's UI and billing model pushes their in-house "Composer 2.5" model pretty heavily - which is a modded Kimi K2.5 model under the hood. Anyone using Cursor is likely using Chinese models at least some of the time.
Its a bit more complicated than that quote implies
At posthog we see if a customer is using an llm, they use more than 1 model. The typical pattern is frontier models for a small percentage of 'harder' tasks and then one of these chinese models for more standardized procedures. As you get better at standardizing procedures you are able to use the chinese models for more and more work so token usage goes up, but the $ spend on top models has still been growing
I believe this depends on country. I would believe, US startups trust US tools more. In other countries which see china in a positive light https://www.pewresearch.org/global/2026/07/15/people-in-many... I would expect them to use Chinese models due to their lower cost.
Yeah. People should absolutely be _trying_ the Chinese models, and experimenting with running things locally, but the noise in development is genuinely all Claude and Codex.
I put my foot in the mobile comparison the other day, and will again. If you were to go back and be a mobile dev in 2010 by all means specialize on one platform, but play with both as a professional interest to stay realistic. Here it's important people have access to US/Chinese/Other, open/closed, local/cloud and that this remains. Don't become a blind Claude guy or a open weights fanatic: that way lies disappointment.
If startups includes openclaw users then I could see this being true.
Deepseek is barely behind frontier models while 10x cheaper and 99% discount for cache.
people still use clawdbot?
Dunno I put to deep seek the question what I’d need in hardware to get full service k3 with lower latency in western pa (I was making like it was a business proposition.) It says several million dollars at minimum.
Unlike iOS/Android choice which has broad personal ecosystem implications, changing IP address to another LLM provider is effortless.
Frankly I don't see much difference between Claude and DeepSeek except in the price. I use the Pro plan to work for a customer of mine and I topped up $2 on DeepSeek in late May for personal use. I worked on 3 projects and I still have $0.72 left. The Chinese companies will win on price, not openness.
I can believe it with startups as they are trying to keep costs down. Established Enterprise however, is a different story and is where the money is usually. If those startups become successful the story may change, but most of them will fail.
If if it were true, who cares? Most startups fail. Most are terrible ideas and/or terribly executed. I fail to see why it's a useful metric.
Is your point that startups fail so we should disregard the central thesis that locked down AI will eventually lost to open models?
I don't see that anywhere in the parent's comment. Where did you get all that?
Not op but that makes perfect sense to me.
"People with mostly bad ideas/execution use Chinese models." Is the point being made.
If you slice it to some measure of success, is the statement "Successful start-ups/companies use Chinese models." still true?
Hmm if you assume the 80% is uniformly distributed between successful and unsuccessful startups/companies, which by default you should, then yeah, your proposed statement is true.
Data would be needed to argue the 80% skews unsuccessful
How does this comparison sound if we use cloud provider?
The Ai is writing code, not executing a startup. The code was never the hard part of startups
Deciding to use open models over closed models is a business decision.
Using the cheaper model is also a business decision. We don't know why they are using the Chinese models: openness or money or both or a third one?
Third options include
- fine-tuning
- running in your environment
The point is that for many tasks today, and likely all tasks before long, that the open vs closed will not be a differentiator. There are many open models much better than gemini, yet people still use gemini.
It's like picking AWS vs GCP. Yes it is a business decision, but one that will not likely affect the outcome of the business.
> but one that will not likely affect the outcome of the business.
We don't know that, that's the point of my statement about changing the question.
Do successful companies opt for the US/Closed models? If they do or don't it's just a correlation but it means something. Maybe it's just causal of companies being able to get more funding because the ideas are better so they opt for the more expensive model (assuming it's better).
we know that
1. Different people using Ai have different outcomes
2. There is much more to agents than the model
3. Companies are not successful because of the code, look at how many shitty products we endure
Can you explain the basis for your insistence that models matter?
Can you name another technology that determines success/failure rates?
> Can you explain the basis for your insistence that models matter?
It's not my insistence that they matter, it's my insistence that How many companies use which model isn't a measure of success. My insistence is that a better measure to determine _if_ models matter, is to ask which models successful companies use. It's not a perfect measure, as I mentioned, it would simply be a correlation but a causal link doesn't exist with out a corollary one.
> Can you name another technology that determines success/failure rates?
I'm not sure what you're getting at with this question but of course. Electricity, machines, computers, etc.
Speaking of electricity and, as an example, you could run a similar thought experiment with companies who chose to use AC or DC power when that was a thing that needed to be chosen between. It turned out, there were niches where each made sense. So the actual question here is probably less about is open/closed better but rather which situations are better for which model. Obviously you can't fine tune a closed model so if you need to do that, your options are limited.
It depends, I guess if they still dont care about losing money or they already had subscriptions then they keep using them, likely either openai or anthropic. If they noticed the bill going up, like with github copilot, they are looking at the alternatives.
We use US models for everything in practice, but we are looking at open-source models right now. So it may just be a turn of phrase hiding the reality. I can't imagine anywhere near 80% are relying on open-source as their primary models.
May be corporations should start having their open models running in-house. There might be a huge oportunity there.
But the big price is AGI and who gets there first, right?
We aren’t getting anything like AGI in this current cycle of AI innovations.
At least not what I think most people imagine when someone says “advanced general intelligence”
Which is what we all called AI before the nomenclature goalpost moved
it's artificial general intelligence, not advanced. the point is that they'll be smart across the board at some point, super-intelligence is a whole separate issue.
It’s just not possible. FWIW my employer did not get onboard to the Cloud trend and continued to buy hardware for in-house data centers. But the kind of hardware needed to run sophisticated models are simply not for sale at the volume needed by a single company.
Assume you meant "big prize" but I guess it's sort of one in the same?
There is a big divide between “application ai” and “model ai” startups.
Model ai startups start from OSS models, and use them extensively for different purposes as their work would usually be banned by proprietary labs.
Application ai startups don’t want to fight the model game, so they either pick the best or let the user control it.
Not everything runs on paid models. Claude and Codex are frontier models, but some people have much higher usage needs and finite budgets that force them to self-host. And if you're self-hosting, you're very likely running a Chinese model
Especially if you're pre-funding, which the sampled startups (those pitching A16Z) were.
I don’t think they meant exclusively Chinese models. Many companies including the one I work for uses big US model for most of the work but data sensitive ones are on prem open source ones.
Neither of you are clear on what exact location in the world you're sampling from here, could be you're both right, just missing that you're talking about different places.
I’m obviously a tiny, insignificant data point as a solo freelance dev, but I watch this space closely and I canceled my Claude Code subscription today.
It’s quite believable. Just a fact like “while all dogs breathe oxygen, all humans breathe nitrogen”.
the distinction may be between using the coding agents vs using models for products. for example where i work we're talking about dropping opus for a chinese model for the in-app agent (which is very expensive to run)
yeah, I'm surprised I had to scroll this far to see this
everyone else seems to be thinking the quote is about AI-assisted coding but I read it as the model being used within the product itself
They are using both likely
anecdotal but we heavily use Qwen to build our own models on top of
What I’ve seen is coding is usually done with US frontier models and anything that is part of a feature on an app and runs at scale on the API is a Chinese model because they are dirt cheap.
That has been my experience too.
It will cost you more than it saves to use smaller Chinese models to code; because of the repeated work. That has been slowly changing recently, but with much larger Chinese models, however those models are so expensive they're much more price-competitive iwth the US competition.
But for actually providing end-user AI features, particularly simpler ones, the US isn't even in contention. The costs and limitations just outright kill those features conceptually.
That's what I lean towards with the exception that Gemma is also good on a lot of tasks and cheap, while not being Chinese.
The problem is Gemma is actually not that cheap compared to deepseek for example
If you're self-hosting a model as a startup (e.g. using GPUs), you're almost certainly using a Chinese model. If your a startup outsourcing (e.g. using tokens), you're going to be using a US based model
Particularly if Anthropic or OpenAI are throwing credits at you, which isn't that uncommon for US startups at the moment
They probably use Claude and Codex for their actual development, but for the products they actually build and deliver to customers I imagine a lot use open-weight models.
If you're putting a lot of your money and time into a business, do you really want it built on a service only hosted by one company that will turn it off eventually and you have no recourse?
If you build something against an open model you can take that and run it anywhere. If your favorite model provider stops hosting it, you can go elsewhere, you can go rent GPU instances, you can even shell out and buy hardware to run it yourself if you've got the capital and it makes economic sense. Change some API keys, update a URL in your config, and you move on.
If the government decides that proprietary model is too good and so it gets shut off, what do you do? If a proprietary provider decides it's not worth it for them to continue hosting that model, what do you do? If that provider silently updates the proprietary model and it makes your app broken, what do you do?
I'm using a ten dollar a month US model to vibe code startup ideas. All my previous startup ideas i had to hire a graphic designer and back-ender or two to help. I use to be a web design front end enigeer since 2009 yet those skills are dumb now, so now Im a vibe coder.
The model I use to vibe code with I am just going back and forth with. Since Im building it as I go using an agent doesn't make sense but I guess that's where all the token usage comes from? Pardon ramping up my skills via vibe coding this one idea for about a month and have never hit any quota and or have gotten anywhere near my limit.
When developing AI services, Chinese models are cheaper. For the AI models used in actual services, like uploading an image and receiving a response, they use Chinese models.
On the other hand, when developers are developing, they mainly use US AI because the quality is better.
When developing AI related services, they prioritize Chinese models due to lower API costs.
It seems like the article didn't make this distinction.
So the claim that Chinese AI is the top choice for service level AI isn't entirely wrong.
There appears to be a very Chinese strategy of astrotufing going on here similar to what happened with Douyin around TikTok on Reddit.
All of a sudden in almost all social media channels I'm seeing this type of content and then its usually upvoted to the top.
Non-gatekept forums like this are exceptionally easy to astroturf.
Came here to post the same thing. I've noticed a lot of Chinese model astroturfing on HN over the past 60-90 days or so. Many upvoted posts in all conversations about AI touting how great the Chinese models are even when performance isn't the topic of discussion.
This is a very strange article considering that Llama, the mother of all open-weight models, has led to anything but success for Meta.
Also, enterprises don't give a rip if models are open. They care about zero data retention (and sticking with whatever vendor they're already using).
This blog post is suspiciously close to being a restatement of what Alex Karp recently said on CNBC[0]. It's important to remember he's the CEO of Palantir and hardly a neutral observer.
There are many reasons to celebrate open models, I run them myself. However there's not yet enough evidence that 1. America is losing the AI race (pardon jingo-ey phraseology) and 2. American AI labs are losing because their models are not open-weight.
0: https://www.cnbc.com/2026/07/01/palantir-karp-open-ai-anthro...
> This is a very strange article considering that Llama, the mother of all open-weight models, has led to anything but success for Meta.
I see this as a fault with Meta's models, not a fault with the concept of open weights. The Llama family just aren't very useful. They make flowery prose but they're terrible at tool calling [1][2][3] so there just isn't much I can actually accomplish with them.
[1] https://gorilla.cs.berkeley.edu/leaderboard.html [2] https://benchlm.ai/best/tool-use [3] https://benchlm.ai/llm-agent-benchmarks
If the models were the quality of GPT-5.6, or Opus 4.8, would Meta really reap any benefits? Maybe in a zero-sum way, because OpenAI and Anthropic would lose. But I'm not sure they'd be that much more ahead either.
Another strong point. LLMs are a technology, not a product.
2-3 years ago MAIR was on a roll with Llama 1 2 3, Zuck was on his rehab tour to be a cool guy, and Meta as a whole was pumping record numbers after record numbers. I can't believe how that falters so quickly after the addition of Alexdandr Wang.
I am no fan of Wang but he came after Llama got caught benchmaxxing Llama 4 rather than training a good model. My read is that Zuckerberg tried to buy his way out of the problem like he always does, and he ended up overpaying for a lemon.
At the time the whole thing was led by Yann LeCun who seemed to spend more time arguing with people on Twitter than figuring out new techniques to make Llama the best. Meanwhile Deepseek was figuring out large scale RL on kneecapped hardware like H800s and how to scale architectures an order of magnitude bigger with MoE.
Meta had the resource curse just like with Metaverse and VR headsets.
Meanwhile Chinese labs were forced to innovate with more efficient models.
> At the time the whole thing was led by Yann LeCun
He was not in charge of the Meta LLM stuff, from anything i've read over the past few years.
He was their “chief AI scientist” (what an abomination of a title) and led FAIR. Maybe it’s true he was not working on LLMs but I can’t even imagine what more important thing he could be doing at Meta.
Which brings me to my other point. If you want to compete with serious labs (OpenAI, Anthropic, Deepseek, Alibaba) you need to be smart and focused, not messing around.
Don't think it is down to Wang or MSL but Meta's focus on "personal AI" led them to whatever strategy (OAI missed the developer market too, which Ant then captured; leading to several high profile departures at OAI, coincidentally hired from Meta). The original Llama team themselves started Mistral which hasn't gone anywhere. The simple fact of the matter here is, Chinese firms have the money and the talent to rival the US ones in this field, should they as much miss a beat.
> Also, enterprises don't give a rip if models are open.
They care about control. I see many of my enterprise (or just-below-enterprise) clients very annoyed at OpenAI and Google after 2-3 years of model toil, where they had to constantly re-calibrate onto new models, on tight externally mandated deadlines, with little certainity. Now they are reaching for open weight models instead, that they currently host with the same inference providers, but have the option to in-house if push comes to shove.
With a race to win a multi-trillion dollar market, there could be, maybe perhaps, a little propaganda happening.
Is that multi-trillion dollar market being created or is it being extracted from other markets?
> considering that Llama, the mother of all open-weight models, has led to anything but success for Meta.
To me, Llama was the ONLY successful thing they did it. it was when they stopped that they fell off the radar as an interesting AI company. They had a genuine chance to be the "substrate" that Ben talks about here. I can't actually figure out why they threw it away.
There has always been room for both closed and open source software.
In this context, open source/self-host means do it yourself, closed source means you trust someone else to do it for you.
In the long run open source always wins because of the community effort, customization, network effects and price.
Internet protocols are open, anyone can setup a website, host their own email server..., but most people don't do that, they rely on someone else to do it for them.
People still pay for Windows rather than use Linux because most people and companies have better things to do.
The main threat to American AI companies is not that consumers will self-host, but it's that new hosting companies will appear that will host open source models and offer them (cheaper) to consumers. It'll be like the web hosting market before the era of cloud computing.
> In this context, open source/self-host means do it yourself, closed source means you trust someone else to do it for you.
I don't think that's what it means in this context. Hardly any users are training their own models, after all. And I don't think Windows/Linux is a good analogy - OSes have inherent platform lockin that LLMs don't.
Really the only 3 things anyone cares about are capability, cost and data privacy. Sure, the cost axis for open weight models needs to include the cost for hosting things yourself, but I think the bigger reason that businesses have been throwing billions at Anthropic's and OpenAI's models is that they have had the best models, and they've had big releases every few months. Their biggest Achilles heel is that if/when their improvements start to plateau, open models may catch up and businesses will start to scrutinize their AI spend a lot more.
The behind the scenes element you're not aware of is Anthropic is going to companies reliant on their models and demanding HUGE one time fees (100 million+) to continue using their models or they will be cut off. This has happened to several larger companies I and others are invested in.
This resulted almost every time in "screw off we'll train our own models or use refined open source ones instead" leading to a lot of anger at Anthropic by CEOs these days.
We all want an alternative and Anthropic and OpenAI need to charge more than they are worth to pay back their investors and everyones stuck now.
I am not up-to-date in this area, and not necessarily that I don't trust you, but do you have a source for this? Just curious.
Pretty sure he’s talking about Cursor and other ai coding startups. There was a lot of drama between the two in the past year and iirc anthropic cut their capacity.
Edit: here’s the zitron post at the time: https://www.wheresyoured.at/anthropic-is-bleeding-out/
I haven’t heard this. Do you have any supporting sources or links?
> This is a very strange article considering that Llama, the mother of all open-weight models, has led to anything but success for Meta.
The llama drama will be a netflix show of it's own in 5 years.
> considering that Llama, the mother of all open-weight models, has led to anything but success for Meta.
This is also a strange way of framing it, though. Llama was released as a research project, it was never intended to create some vast ARR revenue stream or reframe the way people look at AI. If Meta wanted to exploit it for personal success then they had lots of opportunities to do so.
With OpenAI and Anthropic's profitability under question, it is up in the air whether or not America's stance towards AI will work. If they can't convince the world that they're a proper software business, then China's philosophy will win by-default.
It seems to me that the US AI industry has bet the farm on the idea that AI will enhance AI itself, so any small advantage will magnify recursively into an unstoppable advantage. Therefore it is vital that they spend as much as is necessary to be the first to that small advantage.
At the moment, I can't say that I see this happening. It's hard to know whether it may happen in the future.
From what I see it seems like we're hitting the top of a sigmoid curve in the model's utility for coding assistants. Going from "it does 90% of the job" to "it does 94%" of the job is a legitimate improvement, but it's not a phase change, and it's probably not worth paying multiples more for. And coding assistants have turned out to be the killer app for AI; it still isn't really working out in a lot of the rest of the industries of the world.
At any moment, theoretically, someone could find some new way of making AIs that breaks this sigmoid and propels us into a new one. But that's not a great thing to bet the farm on.
I'm not sure I'd say "China" wins if this particular strand of American AI fails. Falling back to an open weights model and making money on the serving of the models wouldn't take all that much economic realignment for the US and would be the natural outcome of any sort of fire sale of current AI assets. However the devastating effect on the stock market if the market comes to the conclusion that this current round of AI can't be profitable without falling back to such an economic posture and some more years of people adjusting to it can hardly be overstated.
> it was never intended to create some vast ARR revenue stream or reframe the way people look at AI. If Meta wanted to exploit it for personal success then they had lots of opportunities to do so.
They release the base model as open source, everyone uses it. They make a paid version, no one uses it.
They make no money from the open source version, they get no social credit from it. Where is the benefit to having an open source model?
> Where is the benefit to having an open source model?
Research. Llama is and was a research project, intended for researchers. You could make this same critique of Microsoft's Phi model, Apple's OpenELM or OpenAI's OSS. None of them were intended to be kingkillers, all of them are experimental in nature.
You might not have followed the space at the time, but there was a real race to implement the transformer architecture with fewer overall parameters than GPT-2 and GPT-3. Llama was revolutionary for sticking the landing without being entirely lobotomized, the "benefit" was that the model was usable on a local machine. Contemporary projects like Flan-T5 and GPT-J/GPT-Neo were entirely displaced, Meta's AI mindshare went ballistic for a few months and probably propped up billions in exit liquidity for executives and former employees.
I mean isn't the explanation simply that llama was never good enough, even when it was released? I hear (no data) lots of people using gemma4, at least a month or two ago.
Isn’t it basically impossible to run the newer high quality Chinese models locally, even for a corporation? The better they get, the more they need a data center. So, the ‘better’ Chinese AI gets , the more it will just be a service run on Chinese hardware competing with ‘our’ lower latency AIs .
The open source character of the models is irrelevant if you need a nuclear powered data center for inference. In the end it is just another internet service.
> The better they get, the more they need a data center.
That's true.
> So, the ‘better’ Chinese AI gets , the more it will just be a service run on Chinese hardware competing with ‘our’ lower latency AIs .
This is not. Them being open models means that any hosting provider in the world can host them as well. You get to pick and choose the provider the same way you'd pick and choose where to run a Linux server.
The cost of inference is not insurmoutable for many corporations who may run a small datacenter out of their headquaters or branch offices. Larger models absolutely have a higher barrier of entry, but its a cost under a few hundred thousand as opposed to the millions necessary for a datacenter built as core revenue generating infrastructure.
Cost of inference is small when compared to the cost of training new models. Which is the real advantage these Chinese models have. Someone else has already spent the capital needed to create the model.
With a reasonable upfront investment and a few trained staff, its very possible to run these larger Chinese models in a well managed fashion. The real calculus is if this up-front investment and associated lifecycle costs are over or under the costs a corporation may simply wish to dump into a cloud managed service like OpenAI.
maybe cos llama was shite?
I do think open-weights models are going to "win" in the sense that they're probably going to be dominant when the hardware to run them becomes affordable. (which might be a while). Although I guess you could probably rent the GPU's yourself to hypothetically save on costs. (I'm a little skeptical -- I've heard of companies doing this and the inference bills are surprisingly high -- assuming the sources are correct. I don't know if a lot of people really want to be advertising "oh god our bill is horrible")
I'm sort of baffled by what the entities that train the open-weights models get out of it though. Is it just a direct play to undercut the US providers because they view them as a threat? I just don't really understand the business model behind it.
If you're NVIDIA then open-weight models are a classic example of commoditizing your complements; cheaper models mean more people buying GPU's to run them. [1]
Your guess is as good as mine for China though.
[1] https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/
Nvidia selling more GPU's at the cost of it's datacenter business is pretty close to Kodak selling digital cameras at the cost of film.
The data center side is so bloated anything that eats into it is a huge negative. Their data center business brings in 20x the gpu market. Local open weight models will be what pops the bubble and China will do anything in it's power to enable that pop.
I wonder what the thinking inside NVIDIA is at the moment. They have countless examples to learn from here, about the danger of not being willing to cannibalize your high end products. But, of course, there’s a reason that there are lots of examples of this sort of failure.
There’s plenty of competition that would be happy to attack them from below, though…
I also wonder what this moment will mean for chip design going forward. The fact that we are so constrained on the GPU side along with the success of unified memory with apple silicon does make me wonder what chips in 3-5 years will look like.
Those that can will incorporate memory into their designs, and the Chinese will be a bigger part of the memory market worldwide going into the future, that alone will drive more tech companies to look at memory/ssd design differently than what they did before. Market conditions have changed you either change/adapt with it or you die.
I think memory and ssd design will end up being incorporated into the overall design of the SOC chip. And several companies that can probably will sponsor a existing company or build a foundry going forward. Once again, change or die.
Unified Memory simply isn't viable at the scale that a full Nvidia cluster operates at, and it would hamstring the parallelism that gives these clusters an edge. The current solution of Mellanox-style interconnect is probably still going to be the status quo in 5 or 10 years.
Make money while there's money to be made then pivot to the next scheme?
Gaming > Bitcoin > LLMs > Robotics
Jensen's job is to just be one step ahead of the market dynamics to keep the investor dollars flowing.
> Nvidia selling more GPU's at the cost of it's datacenter business is pretty close to Kodak selling digital cameras at the cost of film.
This assumes a) AI is a zero-sum game, and b) we're actually talking about on-prem AI will replace cloud-based AI. I think neither statements are true.
AI is like compute: we'll need all sorts of it, in various sizes, everywhere. I'm sure Nvidia whats to own all the workloads.
On the other hand, I do think open weight, like open source, will win in general.
It will take about 35 years for digital camera sales to reach Kodaks profit margins. The market growing and companies raising the economy are not zero-sum. Nvidia/Kodak killing the golden goose is zero-sum. When the difference is your valuation crashes that's zero sum for the company, just not for everyone else.
My only concern is if we can limit the economic impact from lowering investments and causing a 40% market collapse circa 2008/9.
But they won’t with the Chinese around long-term and other tech companies that have capabilities. This is a short term bottleneck.
> Nvidia selling more GPU's at the cost of it's datacenter business is pretty close to Kodak selling digital cameras at the cost of film.
You mean they resisted the idea trying to protect their legacy business, and it ended up all but killing them?
They resisted the idea because it did kill their legacy business. Kodaks peak was 16 billion in revenue in 1996. The digital camera market will be approaching that level of value inflation adjusted around 2030.
35 years to get back to status quo. Putting a bullet in a golden goose is often considered a bad idea. Everyone is happy they are dead, but if you can't understand why they might try to keep the corpse alive you have never looked at the numbers.
Kodak was definitely mismanaged in many ways but you are correct in your analysis. If they had pivoted hard to digital they could have been what today? Nikon? Fujifilm? These are known brands that are bigger than Kodak is today but they are nothing compared to what Kodak was.
At its peak Kodak was one of the (the?) most well known brands on the entire planet. Anywhere in the world if someone snapped a photo Kodak was more than likely taking a cut, coming and going. They employed as many people in Rochester alone as any digital camera company does today internationally.
Sure it could have been managed better but they were always doomed for a fall. They were a chemical company entering a digital age. Does anybody even care who makes the sensors in iPhones?
Kodak, like Xerox had it all under one roof their management at the time turned their back on the future…
IBM was the same way, ironically, the fourth Yankee clipper company had to be dragged into using two types of glasses sitting on the shelf that were patented/created in the early 1960s, both of which later became known as gorilla glass. Steve Jobs had to call them (Corning) multiple times to get them to finally let him use it on the iPhone. Imagine if Steve had contracted out to some other glass company in the far east?
The future was the death of Kodak as it existed regardless. They were a chemical manufacturing company that did some software. Their core business was doomed regardless. Could they have pivoted? Yes. But even if you correct every missed shot they’d still be a shell of their former selves unless you’re creating wild counterfactuals like a kodaphone.
> If they had pivoted hard to digital they could have been what today?
Having the first portable-ish digital camera they could have seen the true value of Fairchild's CCD business, got a stake/bought it/replicated done whatever it took to push the frontier of digital imaging and became the Kodak (old, film-era Kodak) of electronic imaging?
There's no comparable business today because all the things they could have invested in ended up being taken up by different companies, they had the R&D culture, revenue, and distribution. I don't see why they couldn't have been category defining.
They could have been category defining, sure. It would still be a fall though is my point. A fall from being a household name across the entire world to a company making components and niche hobbyist and professional equipment.
The digital camera market will be approaching that level of value inflation adjusted around 2030.
Only if you ignore the digital camera sector responsible for probably 99% of all digital photos taken…
Kodak, Xerox, IBM, Motorola of Schaumburg, Illinois are perfect examples of companies that did not want to upset the apple cart, another example outside tech of tech that did not want to go into the future was US Steel, and while I’m at it to a lesser extent Ford and GM are teetering since 1973…
Hmm this sounds like an incumbent missing a paradigm shift because they didn't want it to disrupt their core (usually enterprise) business, although riding the shift would have ultimately delivered an order magnitude larger business.
Classical example is Microsoft actively undermining mobile because it threatened selling Windows or enterprise licenses.
Or Yahoo fighting Google's model because the latter model's didn't depend on taking enterprise deals to rank results.
Thanks generic LLM model response. Please upgrade to a higher tier model if you are going to waste our time.
lol honest this wasn’t AI, I guess I need to work on my style
Could you explain what you mean by this? I thought all of their insane profitability and returns are from crazy margins on their GPUs. I know they started/partnered/invested in some data center businesses, but I thought they were fledgling
Their data center business is selling gpus. They wrap them in a complete platform, but that's what they are selling.
They do report it separately from consumer and business sales of gpus used in PCs.
The data center business is selling Tensor core GPU's which are used for Large language models nearly exclusively. I don't think of them as GPU's anymore considering a GPU is a graphics processing unit and this is not the primary function of those cards anymore. A GPU will always be a card you put in your computer to play a game in my mind.
I know a tomato is a fruit but will still be annoyed when someone is pedantic about it because that's dumb.
When the mobile phone is eventually integrated into the human body or some other silly application in the future that will annoy me as well. Congrats on being pedantic.
Talking about their product segments the same way they do is not being pedantic. Insisting on your personal definition is!
Could you put that in units of HP Printers and Toner please?
You need 64 H200 super-node for inference for kimi k3. You will not do inference locally. What might pop the western hardware bubble is Chinese GPU, memory, networking companies. But even in China, these AI centric hardware is not cheap.
> But even in China, these AI centric hardware is not cheap
Definitely not cheap for individuals, but well within SME territory. There are countless small-town, family-owned businesses that had higher startup costs than a hypothetical Kimi-R-Us, Inc.
There’s export controls on nvidia. According to Jensen, they expect that Chinese models will start being optimized for Huawei
https://www.dwarkesh.com/p/jensen-huang
They already are.
No, Nvidia is worried about Chinese hardware stack. China is under sanction, what are they training and inferencing these models on? Even if this model might still be Nvidia chips, what about the next model. Everyone knows model and hardware companies are working together. There are a number of very competitive companies in China in this space. After they got this area sorted out, China will do training, inference and tokens entirely on their stack and export their entire stack.
With their models being open-weight, any two-bit firm in the world with enough capital to invest in a few servers can become a provider capable of carving out their own little niche in the economy.
The Chinese see this as a lift on the entire economy, as it comodotizes the technology to a degree in which many firms can serve many sectors of the economy, a true total-economic win worth the public investment.
The American strategy is built off of private investors believing that with enough money poured into as few companies as possible, one or two firms can come to dominate the entire market and start charging an ever burdensome "tax" on every sector it can touch. Not what I would call a total-economic win for the country.
Additionally I think there is something to the idea that they are trying to undermine foreign competition as a stall. They can’t compete economically for geopolitical reasons right now but they also can’t let American firms dominate the rest of the world in that market/technology stack.
The U.S. model is honestly more baffling to me, if the goal is broad economic growth.
But it hasn’t seemed like the U.S. powers have been interested in broad growth for quite a long time now. Just whatever can line their own pockets.
The Chinese strategy is to fool naive westerners into thinking that's their strategy (looks like its working), until they can buy time to do the American strategy.
Hence, buying up and closing up foreign competition then whining about it when it's blocked: https://www.dw.com/en/china-firm-seeks-damages-over-state-co...
While I completely agree with your take, I think everyone has been surprised by how quickly LLMs have become highly useful and extremely powerful, and by how possible it is for relatively smaller models to also be highly useful.
Given that, I would expect that in hindsight OpenAI and Anthropic would spend 40% of what they have on compute if starting over and knowing the actual landscape.
The massive capital allocation was a blind decision and they swung big.
It is still possible that techniques will be developed that create a moat where the massive hardware capex is justified, but US policies of banning competitive GPUs and blocking frontier lab releases makes such things far less likely.
"Escape velocity" for AI is when the open weight models are good enough to help drive the next frontier innovations/techniques. I think we are close to that if not already there, at which point it's a race to commoditization no matter what Altman or Lutnik wish will happen.
Don’t forget it took that huge spend to publish the papers and get to the models we have. It’s not obvious that without them we’d have LLMs springing up out of China or anywhere else.
Perhaps, but things like Sora burned a lot of compute -- a high percentage.
And the published and non-published works of mankind but no one seems to want to give us any credit.
China is the factory of the world. They don't need software to win. Rather they prefer software is free and they can win in hardware. So if AI inference is free, they can put it in as many hardware components as possible and sell them in the market - think toys, cars, tools with chips manufactured in china optimized for the use case. In long term you tend to commoditize hardware. We have thousands of device types of cheap x86, and with linux/bsd software on it coming from china. Why do we think GPUs will be different.
- Software is copy-able for nearly free. - Hardware is not. Even if you have the plans, you've to build the infrastructure for it, and its a fairly finite, physical item.
Both are however hard and expensive to produce. It's much better for profits to develop and sell the hardware, and copy the software someone else spent resources on.
America was the factory of the world before and England before that. The temptation of “moving upstream” is irresistible.
We just sacrificed our entire lucrative SaaS market to this, and perhaps even big tech itself. To the altar of AI.
And now it's all going to become commoditized.
Billion dollar software will be commodity. Salesforce. There are orgs already moving to their own internal tools.
It does not seem hard now to rebuilt Google Search, Google Chrome, Gmail, Gsuite, Netlify, Vercel, Cloudflare, Vimeo, Twilio, or even Stripe. The cost barrier has to have dropped 1000x, maybe 10000x.
We have millions of engineers with the talent to do this. Many of whom are unemployed and have savings and nothing better to do. They could easily carve these markets into pieces.
We shouldn't shut down open weights. It's too late. They'll win, and that's a good thing. Big tech was a thermodynamic bubble of high energy waiting on the dam to burst, and now it has. The genie won't go back into the bottle, and that's totally fine. It's progress.
Now we need to rebuild our factories and supply chains and energy and resource inputs. Because the back half of this revolution is going to be robotics and factory automation. If we don't have the connective tissue in place, we're really going to hurt.
We'll do well if we regrow manufacturing. If we don't, we might be in for a world of trouble.
You're right about it being relatively easy ro build your own version of something right now in the proverbial garage. But startups will still have to compete with the network effects of the large firms and the increasing push for data sovereignty from non-US customers. Not to say it's not possible, just that the technology alone isn't sufficient.
> It does not seem hard now to rebuilt Google Search, Google Chrome, Gmail, Gsuite, Netlify, Vercel, Cloudflare, Vimeo, Twilio, or even Stripe. The cost barrier has to have dropped 1000x, maybe 10000x.
[citation needed]
So, "commoditize your complements". A big part of Microsoft's old playbook (and a smaller part of its current one), so we know it can work.
> I'm sort of baffled by what the entities that train the open-weights models get out of it though. Is it just a direct play to undercut the US providers because they view them as a threat? I just don't really understand the business model behind it.
In China, it's because they are being heavily subsidized to do the research activity. It's not really complicated -- if you allocate public money for people do to a thing, they will do it.
> it's because they are being heavily subsidized to do the research activity
Is it actually true? This seems pivotal because currently most theories rest on the idea that individual Chinese companies are acting in China's overall economic or strategic interest. It's a tough sell to believe they all just do that through implicit desire to align with the CCP's direction. I would believe it much more easily if there were concrete incentives involved.
Yes, incentives are why people go to work in the morning. https://www.rand.org/pubs/perspectives/PEA4012-1.html
China is known to spread love and kindness through markets with no self-interest, after all.
The US funds (and used to even moreso) scientific endeavors that stand to bolster the entire country all the time. Medical research is the obvious one, DARPA is the defense based one (though oftentimes defense is just a post-hoc justification for a lot of those), the Department of Agriculture is constantly researching improvements to the farming industry, the Bureau of Weights and Measures and the NSA fund cryptography research.
Research and development of new technologies often is a "rising tides lifts all ships"-type deal, which it is absolutely in the government's best interest to support.
For China, the best case scenario would of course be to control a locked-down best-in-class frontier model that the rest of the world becomes reliant on. The US seems to be beating them at that, and "Everyone is reliant on the United States" is a pretty bad scenario. A middle ground, positive outcome is that no one is reliant on locked-down closed models, so they're supporting that outcome.
It's really not that nefarious.
"public" doesn't mean "love and kindness with no self-interest".
We only think "government spending is for hippies" in the US, and only when we don't look at public spending like defense bills.
Of course it's in self interest. It's too bad that the US has gone the other direction and cut public funding for research.
You could say the same thing about Linux in the 90's and 00's. And yet here we are.
Remember the Halloween papers?
https://en.wikipedia.org/wiki/Halloween_documents
China wants the US economy to flounder. Building our entire growth model on software that can be copied and taken by a small group of people will have no possible consequences.
> China wants the US economy to flounder
It’s not that simple. If the US economy goes into a recession, it will take large sectors of the weak Chinese economy with it either directly or indirectly.
It’s probably more accurate to say they don’t want American LLMs to become dominant. The huge US data center build out doesn’t depend on Anthropic and OpenAI anyways. Those data centers can just as easily serve Qwen or GLM models.
They can't do this at the same margins. Cloud compute traditionally has <= 30% margins, and that's the very generous profit margin that folks like AWS are able to squeeze out of it with lock-in and proprietary tooling (something that will probably go away as software becomes more commoditized.) Frontier model companies (e.g., SpaceX) have been promising profit margins in the 75%+ range.
China is looking after their own interests, but they absolutely don't want their largest export market to struggle. The global economy is not a zero-sum game, and the idea that it might be is the root of many of our policy issues in the US.
Chinas interests today are a US with reduced power and a global economy more focused on China.
Someone loses power for someone else to gain. It’s literally the definition of a zero sum game? China targets areas it thinks it can win and dominate in the future.
Bringing "power" into the conversation is a whole different layer of complexity which I was not addressing. I was speaking about economics, which is in no way a zero sum game. Economic activity creates value.
I don't think it is true that they necessarily want the US to struggle, I suspect it's more self interest. LLMs seem to be one of the biggest innovations of the last few decades, China probably just wants to make sure it's not being left behind and/or made hugely reliant on the US for what seems to be turning into a piece of critical infrastructure.
China has an effective strangle hold on some key sectors (solar, rare earths) and I am sure they relish this position and the leverage it gives them. You'd be careful not to give away that same leverage to a competing power if you can invest a few billion now and cover your bases.
Assume, crazy business, that china wants what’s best for the world. Recognizing the danger of an AI arms race rapidly producing uncontrollable superintelligence, it focuses on open weights to reduce the economic incentives for further advancement of AI beyond the “highly useful for humans” stage.
Seems like the only thing that could avert an intelligence rapid take off. Everyone wins except for shareholders.
> China wants the US economy to flounder.
Why would they possibly want their largest customer to flounder?
I think lot of Americans overestimate how much China needs them. (Understandable, I suppose, since the UK made a similar mistake vis-a-vis the EU about 10 years back.)
From a Chinese perspective I expect China’s largest customer is China. These days it’s actually kind of wild how many Chinese consumer products aren’t (and won’t be) available at US retailers. And a lot of them are quite good.
For a while now China’s wanted to reduce its dependence on the US for a variety of reasons. And undermining the US tech industry, whose products the US government likes to use as a cudgel, may serve that goal quite nicely.
It is largest customer for now, but they try to boost internal consumption as well as diversify client-base. US is also competitor, so China is interested US to fade at least in competitive areas.
The fact that the US economy is load bearing towards unprofitable projects while things like medicare for all or universal childcare continue to not exist is just a damning indictment of the country.
I don’t think that’s true. The US is China’s most valuable trade partner.
edit: misread parent
The comment you replied to is talking about China's trading partners, not the US's trading partners.
oops. thanks!
Undercutting US dominance in AI is huge for China. If the entire narrative is that you have to use Anthropic or OpenAI to access a decent model, then China's AI labs are sitting on the sidelines as some third rate solutions. China publishing the model weights of models comparable to the frontier proprietary models drastically undercuts closed labs dominance. Maybe these Chinese AI labs don't have the billions infrastructures some of the US players do, but they don't have to if the model is open weight. Many inference provider companies around the world have hardware that can run these models and they will happily run frontier class models for people. Starting in 7 days, people will have the option of which of many providers they want to use to access K3.
Making frontier grade models a commodity will make a competitive market where companies compete for business by improving their quality and decreasing their prices. The cost to access frontier grade models will continue be driven down the more competition that enters the market. This commoditization will challenge the valuations of Anthropic and OpenAI.
The Chinese domestic market is extremely competitive on most things, including LLMs. Once one top company went open weights there, that's going to put pressure on others to follow suit. It'd be akin to if a company like Anthropic or OpenAI went open-weight, it'd probably result in a domino-effect of more US models going open-weights since otherwise that competitor is going to win a huge chunk of mind/market share for free.
It's also relatively free right now. Few people are going to run local models, and in the future it's likely that every model being released today will be obsolete. The only real downside is ease of distillation for competitors, but that's probably impossible to stop anyhow.
They get money from subscriptions and tokens, same as for closed-weight providers. Yes they'll lose some traffic to hosting services, but many users prefer to use the original training company since they have a guaranteed-correct implementation. Similar business model as open-source SaaS companies.
Some companies (most notably Deepseek) also manage to host their own LLMs so efficiently they undercut all third-party hosting services.
No one cares which company they use. They care about cost and does it act in a way they expect. Expecting anything else is pretty laughable and goes against human nature. You either create a moat so deep no one else can play or you have the government force users to use your products.
> I'm sort of baffled by what the entities that train the open-weights models get out of it though
if you do the training then you're in control of the output. For example, recommending your products/services or failing to mention your competitors. You could also automatically introduce backdoors into code deemed interesting, i'm sure all governments are very interested in having that influence.
Absolutely on the industrial backdoors, but also on the consumer front: try asking Qwen anything about tienamen...
That seems hypothetically possible, but kind of hard to do? I don't know a lot about training, but it seems like they wouldn't have exact control of the data that goes in at that scale (scraping the internet) so it seems like it would be kind of hard to do that in a way that's subtle. I'm kind of reminded of Elon Musk trying to put his political views into Grok and it seemed like it created huge technical problems with the model saying some really out of control things. Maybe it was just an xAI issue though.
It’s totally doable and is done right now in the form of “ai safety” guardrails. No reason you can’t have guardrails to reform responses about your competitors as negative. Or, when asked about current events, only give a one sided opinion.
> I just don't really understand the business model behind it
There’s a lot of value in the same sense there is a lot of value in controlling what Google search results are shown and what people see in the Twitter feed.
Probably some value in kneecapping the frontiers too.
> I'm sort of baffled by what the entities that train the open-weights models get out of it though
I agree. The thesis in the article is interesting insomuch as I had not heard it expressed this way before: US restrictions on GPU exports have made it feasible to train models in China but not serve them. Therefore open model is a hack to get around the export restrictions, since models can be trained internally but shipped out of the country to be served elsewhere under the banner of open weights. I don't really buy this argument - inference is much cheaper than training and they are hosting their models anyway.
I think it is more likely (a) they have the money to do it and they need it for internal reasons - these are huge companies (b) there is a lot of prestige in China associated with besting American technology (c) people are still basing logic on outdated ideas of Chinese capability which are no longer true.
So it is easier than people think for Chinese labs to do this, they need to do it anyway and there is a lot of prestige from opening the weights. It is honestly not that different to why American companies themselves have released open weight models.
> I'm sort of baffled by what the entities that train the open-weights models get out of it though.
Why is it so baffling that people want to build great things? There are plenty of people who are happy building things for a salary and have no interest in taking over the world. Do you find the whole world of open source software baffling? Linus Torvalds and Richard Hipp and Antirez created the world’s most prolific software products and released it for free.
I don’t think they need the hardware to become affordable (as a regular end user).
They need their models to be good enough and cheap enough. Then the rest will follow. Companies will figure out how to host them for you efficiently, and you pay them monthly.
I don’t think I will ever want to set up a home server, no matter how inexpensive the hardware gets. At work I still use Cursor (with Anthropic models usually) because it’s paid by my employer, but for private stuff, I’m already using cheap models with OpenCode, and it’s extremely cheap and surprisingly capable.
I think there was around half a year between where the best models became good enough (last year December?) and where the cheap models became good enough (couple of months ago?).
I've always found it weird how ludicrously poweful personal hardware has gotten. The fact that you can't buy a midrange CPU with less than 16 cores, or that a high-end gaming GPU is 100+ TFLOPS just blows my mind. The fastest supercomputer in 2004 was 70 TFLOPS. Absolute crazypants level of power, and companies were fall over each other to get people to buy it.
'Libre Office' did not 'win'.
People are happy to pay $50/year per seat to have the extra features and to not have to deal with stuff.
There's an issue at the margins here:
$1000/employee is a massive cost - it has to be deeply justified. $50/employee is like ... $2 out of your pocket. It's an incremental cost. The CFO is happy to pay it if there is a lot of value.
A lot of software is in that later category.
Imagine if gasoline was 1 cent per litre - and there was 'free gas' but it was a pain to use, and you had to check a bunch of things. You may just pay the 1 cent.
AI is not quite that yet, but these dynamics will play out eventually, for a lot of things.
> 'Libre Office' did not 'win'.
I think France and other parts of the EU are switching over to it. Although that's probably more due to Microsoft's aggressive behavior recently. Agree on the cost thing generally, but I can't help but think that when the hype to "do AI" blows over, people are going to be casting a jaundiced eye towards data security, which probably means self-hosting and sandboxing
> Although that's probably more due to Microsoft's aggressive behavior recently.
The current US govt might have played a role as well.
It's way more US gov than MSFT.
My guess it's to mess with the proprietary vendors economically. It's in China's interest to squeeze the US AI business.
The business model is making your country more innovative, which makes its citizens richer
Americans used to do that too, spectacularly.
Just consider the two alternatives: one is your industrial sector with this incredible new automation and analysis tool available for free. The other is one where it has to pay huge chunks of its resources to overseas companies.
Would anyone besides those within China themselves use a completely closed and hidden model from China for their critical business needs?
If the outputs are independently verifiable at low cost, and the US models refuse to even try because somebody sneezed nearby and it sounded like "antigen" and not "achoo"... yeah sure. Whatever works to get the job done.
The hope is that AI will open up whole new sectors of economic activity. If you have to chose between exploring that space while potentially being exposed to Chinese tampering, versus just sitting on your hands and doing nothing... well then you take that risk.
I think part of it is definitely to weaken US providers and the US economy as a whole. China has a completely different domestic economic structure and motivations from western cultures... it's probably closest to a fascist economy mixed with a Maoist cultural ideology behind it. There's definitely winners and losers and the state tends to have tight controls over everything though.
I also think the restrictions on OpenAI and Anthropic are somewhat short sighted. In that the guardrails dramatically limit efforts towards securing your own software in many ways. Yes, it's also "dangerous" and maybe there should be a means of identifying "domestic" or otherwise "secure" accounts for those allowed to use the models without the same guardrails in place.
Does it weaken the US though? It weakens the big providers but most of the economy is in companies buying and this helps them save money.
Yes, it damages its image, this is further made evident given the amount of propaganda that follows each time. Why would you invest in claude or codex if you just read how China's stuff is better?
Image is not the economy. AI is a large part of the stock market, but a much smaller share of the economy.
Isn't most of the economy riding on the stock of these handful of companies though?
Most of the growth is. If we removed AI, the US would be in a severe recession right now. Most of the absolute market cap, employment, capital, and other metrics that are not directly coupled to growth paint a better distributed picture.
Of course, growth like this can only continue for so long without changing that.
> Isn't most of the economy riding on the stock of these handful of companies though?
No.
Well, yes, because the US government has staked the future of the country's GDP on AI. They're even divesting from science as a whole and putting it into data centers.
Saying that AI will drive the future GDP is a very different thing from saying an economy relies on the stock price of a few AI companies.
Okay but political leadership of the US are literally are staking the future of GDP on exactly those few AI companies... $1.4trillion so far while universal childcare would only cost $40 billion a year.
It's a sick joke.
> political leadership of the US are literally are staking the future of GDP on exactly those few AI companies... $1.4trillion so far
Can you explain where you get that number from?
The information I have:
a) The government took a nearly $9 billion stake in Intel to support its chip-making efforts.
b) The Pentagon took a $400 million equity stake in MP Materials, a rare-earth miner.
c) Sam Altman has advocated that the US government take equity stakes in AI companies. Sen Bernie Sanders wants the US government to take 50% stake in AI companies! Nothing has come of it though.
d) Defense department AI contract value rose to $90.7B in 2026.
So I'm super curious where you pulled that $1.4T number from.
GDP growth in the United States is in AI and healthcare. AI capital expenditure is around 5% of total US GDP. Housing right before the 2009 market collapse was around 6.7%.
Biggest issue I see is housing has more real value than AI expenditure. Demand is real and isn't based purely on a few companies valuation or marketing spin. Nearly 2 decades later we still haven't caught up to construction rates before the 2008 collapse. When the bubble pops it's going to really suck.
> Is it just a direct play to undercut the US providers because they view them as a threat?
It's China putting pressure on a financing strategy in the US that was always a house of cards. It could also be China democratizing something that should have ALWAYS been democratized. Maybe both when the history books on this get written and absorbed by the winners of the LLM wars.
i'm building thigns with open models: 128GB AMD 395+; expensive 72GB blackwell; old NVIDIA 48+48GB; cards.
If it weren't for the massive memory cartel of OpenAI/Anthropic et al, both Mac and AMD would be selling these things.
I repeat, the models are building, modifying and deploying almost anything on github.
Chinese companies are cut out of the market by the US gov restrictions, so making the models open is a survival tactic.
It also has the benefit of crashing the US economy.
China does not want to crash US economy. US economy strong means more purchases for our goods. But US want to crash Chinese economy by: 2018 trade war, 2019 Huawei sanctions, sanctioning at least 1000 large influential Chinese companies, 2020 Tiktok theft, 2022 total ban on GPUs, semiconductor equipment going to China regardless of end use, banning all sorts of Chinese goods such as EVs, cars, batteries, drones, even DJI cameras, 2020 fake lie about Xinjiang to implement full ban of anything made by Xinjiang, 2025 50%+ tariff on Chinese goods. I don't think building LLM was thought has important until Anthropic went on full China hating, banning anyone on a Chinese IP using the service, even companies headquartered in China's oversea offices. Did you know that a regular person in China trying to use Claude to ask some innocent question gets banned? Talk about everyone created equal and democratic world.
You leave us with no choice but to build technologies ourselves. If its bad for Anthropic, well, you could owned the market in China, oh well, you reap what you sow.
Crashing China's largest export market is devastating for China's economy.
China is a centralized economy that has been fighting a tariff war against the US for the last few years. If we see an economic collapse like 2008 China loses money but we lose a whole lot more.
China still ends up with production and a easier ability to integrate with other countries. We became a powerhouse after WW2 when all the other countries had their financial bases destroyed. We became a superpower providing the products to others to rebuild. Our market is all about short term growth, service economies, and goals driven by whoever is in office at the time. If they don't have a plan around this exact eventuality I would be beyond surprised.
Basically their system is more robust than ours to a major market event. Who pays for expensive services when they are choosing between food or keeping their economy going. People will pay for the equipment to keep their economy going. Who provides that.
We just did this song and dance with the tariff war between US and China last year. It was revealed that 3% of China's exports are purchased by the US, and that they are fully economically prepared to take that to zero when push comes to shove.
There is no coming back. After all, open-weight is NOT open-source. It is basically free model. And you pay to host it.
The value proposition is that 100's of providers and host and sell it. 1000s of businesses (eg. Microsoft, Databricks, Palantir to small startups) can run it, finetune it and own the IP and pay only for hosting.
On the other hand, you have OpenAI and Anthropic, who need to charge at 90%+ inference margin. It is because of 1) sunk cost, 2) sky-high salaries that they paid to keep the talent. Companies like Meta screwed things up badly by paying billions of $ for chief engineers.
Chinese labs are doing a favor to the world. But I can also say with 100% certainty that if US labs were to close shops next year, Chinese labs would immediately start charging $$. In fact, I think it might happen with open weights model soon. But still these fees will be one-fifth or one-tenth per token. Also it does not come with all the guardrails.
Solution: US labs need to reduce their costs, cut the salaries across the board and compete. AI and robotics are the last hope of US to get back to industrialization and continue being the superpower.
> Chinese labs are doing a favor to the world. But I can also say with 100% certainty that if US labs were to close shops next year, Chinese labs would immediately start charging $$.
How would do you explain the pricing of Chinese solar panels, after managing to destroy other countries' solar industries? The prices are still dropping per watt.
Could it be China's internal demand for solar is big enough, and its long-term governmental strategy on renewables result in an outcome that almost looks like largesse to the rest of the world? I suspect Chinese AI may follow a similar path.
> The prices are still dropping per watt.
FYI if you look into what has happened in the wholesale solar market since the end of 2025, this is no longer true. Prices are now 20% higher than they were in Nov/Dec, and were even higher earlier this year. Hard to predict the future but we may have reached a price floor, as at 2025 prices the module suppliers were below cost and losing money. The market dynamics have changed significantly since then
China may well save the world with green energy but I’m skeptical to assume we should just trust them to share their best intelligence with us freely, it’s a totally different think to sharing their best intelligence.
[flagged]
This comment has been flagged in our software as AI-generated. Of course it could be a false positive (though from reading it myself it does come across as LLM-influenced). But if you've used AI to write or polish the text, even just a little bit, please don't. The guidelines specifically ask us not to do this: https://news.ycombinator.com/newsguidelines.html#generated
Correct. I used AI to refine the text. Thanks for the heads-up.
Pangram believes this is 100% AI generated. High confidence. If you just used AI to edit your work, then it would say it was human influenced or low confidence, which is contradictory to another comment you claimed.
While this has clearly been written by / with help from an LLM, the general info is correct and matches what I have heard from those suppliers (although $0.21/W future pricing is far higher than the estimates I've seen)
I do not understand the logic going into these companies. Flagrantly violate all IP in Human history, essentially claiming domain over the heritage of Humanity... And... Try to privatize it? When the technology -- and data -- are both public domain to begin with?
It is ming-boggling stupidity. If there is talk of bailouts as the dust settles, there it would just be further evidence the system is ethically, financially, and intellectually bankrupt.
EDIT: Spelling mistakes
At least China is being consistent: they do not give a single f*ck about intellectual property or copyright…but they also give away all this distilled knowledge away for free.
I’m not an American so I don’t particularly like the idea of giving an American cartel of AI companies having so much power over this technology.
I don’t necessarily buy into the “China bad”, “they are communists” and all that BS either.
I will call a spade a spade and say that in this instance, what China is doing is a net good for the world, ideology be damned.
This is proof that America has forgot it's own history.
America was founded by men who hated intellectual property, who stole and smuggled the plans and expertise for textile machinery out of the United Kingdom. A gross intellectual property violation. Why? Because they were being exploited by that system. The UK was using the American colonies for raw material and keeping the machinery in the UK for finished goods.
That's the rub, intellectual property is only valuable if you're winning, if you're the exploiter. China never gave a fuck because they, much like the American forefathers, saw a system of exploitation and went "no, thanks".
AI is the culmination of all human knowledge, the idea that anyone could own that is obscene. American AI companies that are trying to horde this are getting exactly what they deserve by being undercut by China on this.
Amen! Europe used to complain about the US stealing their intellectual property! The nuclear bomb would have been possible if not for Jewish scientists from Europe going to America! So until about 100 years ago, US was FAR behind Europe in technology and science.
> the idea that anyone could own that is obscene
What if china ends up owning it? Do you think that's better or worse for the world? You're commenting your open opinions on a site run by a company that could not exist in china and cannot today. You can ask ant, xai, and chatgpt models questions and get answers that do their best to reflect the world. There's a set of questions you cannot expect trustworthy answers for from chinese models and you think they're stopping at those few questions?
get real bro
They're dumping, dude. They subsidize important industries so they can give it way so cheaply that free markets can't compete, and when those companies fail, they swoop in and make a profit. uber did the same thing with taxis! This isn't a free resource - it costs billions to build out the infra and maintain the inference to provide this, and you think they're doing it out of the goodness of their hearts lol.
Come on bro at least admit you're happier giving china your data than the us lol. You're definitely not using european models I can tell.
Oh, come on, don't be a decelerationist. We're supposed to just ignore that aspect of our Brave New World. Don't think too hard about the unparalleled resource consumption, either. AGI will alleviate any and all of these concerns ... soon. In the very near future, we'll all be getting UBI, Gemini will know how many Rs there are in "strawberry" and we can spend our days creating the next generation of art to feed the machines. The nuclear salt reactors should be online by then, too, and data center power consumption will become a non-issue.
Hey guys I found one, this guy thinks Machine Jesus is going to save him.
I'm trying to figure out if my advice to you should be to do more drugs or less. I am uncertain, perhaps I'll ask an AI.
Poe's Law. Look it up.
Poe's law is an adage of Internet culture which says that, without a clear indicator of the author's intent, any parodic or sarcastic expression of extreme views can be mistaken by some readers for a sincere expression of those views.
https://en.wikipedia.org/wiki/Poe%27s_law
This is the history of enclosure of the commons since the beginning of capitalism
I'm sorry, is there further reading material on this? I am pretty new to political/economic science.
Look up the English Enclosure acts. They brought about a large-scale robbery of peasants by landowners in the 17th and 18th centuries, and created a proletarian class that needed factory work to survive. Things got a little better in the 19th and especially 20th centuries.
Will do, thank you!
https://www.progress.org/wiki/enclosure/
https://www.thelandmagazine.org.uk/articles/short-history-en...
I don't think there will be any bailouts for these AI companies. There's no political will to do so in both parties. The grizzly alternative is a DeFlock movement, but instead of cutting down poles, it will be people's heads.
Interesting detail from Ben Thompson's piece on Chinese models - https://stratechery.com/2026/whos-afraid-of-chinese-models/ - apparently Xi Jinping gave this speech recently http://english.scio.gov.cn/topnews/2026-07/18/content_118605... which included support for open source models:
> We should seize this rare, historic opportunity to encourage open source, openness, collaboration and sharing.
This entire piece boils down to “I like open source therefore it is winning”.
Everyone here has already raised good counterpoints, but one more is that all the companies publishing open weights models are heavily VC funded. What is their exit strategy? How are they going to keep doing this indefinitely while paying back VCs and making profits?
I think it's as simple as looking at the incentive structure. The Chinese gov't has incentive to kneecap US monopoly on frontier models. It makes sense for them to continue down this course if it strengthens their position.
Chinese gov is not even needed. Deepseek was already profitable before this whole thing started. They even had the GPUs.
how profitable though
The Chinese govt has a strong incentive to break dependence on the US for AI needs and build domestic models, but not to spend trillions to subsidize these models for the rest of the world.
Agree but I think they'll spend whatever they can to subsidize and break dependence on US models. Whatever isn't spend on US models will be spent on chinese models. Europe will remain a small single digit % unless they get their act together.
The Chinese gov't and also the rest of humanity...
I'm not sure how everyone in the US forgot that monopolies are bad
Closed models doesn't mean monopoly, cause they compete. And if the best thing for humanity is for good open models to exist, even those evidently required good closed models to copy from.
Who's got a monopoly though lol?
Right, this is the classic VC playbook and people should really know better. They use capital to undercut competitors on cost to gain market share, then slowly crank up the costs until they are (hopefully) profitable. It makes 0 sense long-term to spend tens of millions on training a frontier model and releasing it for free for someone else to host on their GPUs. It is naive to believe that when the capital starts to dry up and the VCs are looking for a profit that the models will continue to remain open-weight.
Chinese labs are a bit different since they are somewhat state-funded, so I'd expect them to shift to a model where Chinese models are hosted on Chinese infra.
> I'd expect them to shift to a model where Chinese models are hosted on Chinese infra.
You assume these Chinese companies need to make a profit. Nothing is really private in China, nothing that truly grants power, anyway.
CCP can just write off these loses as defense budget, which they basically are.
I bet that, all things considered, training 10 new Chinese frontier models costs _significantly_ less than a single modern fighter jet.
Which on itself is wild. The main issue with the discussion isn't a rift in capabilities, but a rift in the valuation model itself.
If China can match the pace and produce frontier models at a fraction of the cost of US alternatives, even without access to SOTA hardware, what is the competitive advantage for the current valuations of Anthropic and OpenAI?
The Chinese ecosystem is largely built on stealing intellectual property from and/or distilling of US closed source models.
I’m not getting into the ethics/comedy aspect of it, but let’s not pretend this isn’t the case. Plenty of evidence, the motive is obvious, and the numbers are in plain sight (valuations, salaries, etc).
As long as this continues I think closed source will continue being a few steps ahead, but the steps will probably get smaller over time.
Having said that, this is already priced in, the market predicts this gap will be large enough for the US companies to profit from (astronomically).
There is plenty of room for open source models that "require" a subscription to be used or obtain working updated binaries. Same as any open source platform.
I'd be happy to pay a small monthly fee to license the model to run locally. I'm already paying for Claude, GPT, Gemini,etc.
Basically. The article doesn't say in what way they're winning or what the US should do.
AI models cost tens of millions to train. Offering them for free won’t justify the upfront costs.
The Chinese model of model training/open sourcing only makes sense in the context of the overall strategy of undercutting American frontier labs’ profit margins.
"the overall strategy of undercutting American frontier labs’ profit margins"
I don't doubt that's an unregretted side-effect for political leaders in China.
But the major motivation is to accelerate diffusion within their own massive economy in the pursuit of an across the board productivity boost in the face of an aging population.
Couldn’t you say the same argument about VC funded startups? They lose money following a strategic goal.
The main difference here is if a startup goes underwater all the tech is usually lost. The Chinese weights are not going anywhere if the labs fail.
This is true for any company following an open source strategy. You'll have the weights but you'll need to run inference, figure out your system prompt, sampling, quantization, etc etc. Loads of tuning.
elasticsearch the first example that comes to mind. you can run it yourself but elastic gives you so many lessons learned and tunes ootb that it sings with relatively little effort, though still reqiures some.
about a million dbs i could make the same comparison for
The VC money is only contingent on the strategy eventually bearing fruit. I do imagine going open source -> closed source could work for some model companies who get enterprise/ecosystem buy-in but the probability of ROI is lower.
I think open source will remain competitive among smaller players and adjacent industries wanting to avoid lock-in with the majors. OpenAI, Anthropic, Google, etc are all out to win - they require profit extraction from their R&D. China seems to have, over the near term, accepted that they can not (or at least have not) pull ahead and so open source collaboration speeds the collective development, keeps them close to the frontier, and ensures their industry has access to learn from and implement. The USA playing export controls games with Fable made that aspect very stark.
But I agree that's the catch - it doesn't make sense to throw money at open source models in hopes of direct return, so you need a nation or conglomerate to do it so as to control the technology they rely on.
I can see two reasons American companies might want to train models they give away for free:
1) They sell compute: chips (Nvidia), data centers (AWS, Microsoft, Google, SpaceX, etc), or even end-user device manufacturers like Apple (e.x. M7 rumored to have 1.5TB of unified memory). If Jevon's paradox holds, then cheaper (or free) models means more demand. But compute is likely supply-constrained for years anyway.
2) Their product isn't AI but depends on AI being cheap, or they don't want competitors to capture that value, i.e. "commoditize your complement" https://gwern.net/complement
It probably doesn't make sense for these companies to invest a lot of money training models that will be obsolete in a few months anyway. When progress starts to plateau I'd expect more companies to start training models they give away for free.
I don't think this is necessarily going to prove to be true.
I often see the sentiment: "the Chinese strategy only makes sense in the context of undercutting American labs' profit margins".
If, for example, you are a company with a near-monopoly on "serving video content", and you feel reasonably confident about retaining a decent slice of the serving-video-content market (Google in the west is an example, Tencent in the east), then training video models on your dataset - and releasing them freely - makes an awful lot of sense.
Free tools to create with mean more video content. In this hypothetical, you're reasonably certain that any video content which does get created will also be watched on your platform.
That is a net positive. The question becomes: How many watch-hours earns back the cost of training a model? It's probably not really that many, especially when you have a near-monopoly on a billion sets of eyes.
It's also a net-positive if people build better video models from research you release, because - again - you are reasonably certain that the even-more-innovative content those models produce will be watched on your platform.
It really begins to make strategic sense if your company is in a GPU-poor environment. Your costs cease at the point you upload a model if your users are running it themselves. You don't have to serve the model. The content is still created.
You are also less likely, I think, to alienate human creators whose work the model was trained on if the model is not sold back to them as a subscription, or by the token, but given for free as a tool.
This frames the conversation very differently. It creates, I think, less of an "us vs them" dynamic, and more of a rising tide.
It's true that it is also beneficial that these models undercut (especially in language models) American companies. But, generally, Americans are not the customers of Chinese companies releasing models. They are already serving a huge volume of customers in a complex, existing marketplace.
The full picture is much more nuanced than simply a geopolitical desire to undercut US labs, and there are several other reasons the strategy can make logical sense.
Yeah this is a battle and it's why governments decide to spend resources on this. Protectionism won't help America, American needs to compete. There's a general consensus that open source AI must win because people don't want to end up as slaves to a megacorp, so if you're anti-open source AI you're not gonna fare well.
It’s also a flywheel for China’s homegrown chips industry.
Exactly, the whole ecosystem. Cheap robots with cheap models. AI is oil to let the machine work, it should be cheap and ubiquitous.
"It’s obvious to me that there are ecosystem benefits throughout China, from manufacturing to scientific research; every sector can just plug in these models."
I agree with this sentiment and think it's echoed in Fareed Zakarias take here: https://youtu.be/VBblUjLw5lE
China seems to perceive AI as a much more sensible technology than the US and seems to be integrating it in far more industries than the US.
I'm not sure the American mind can understand the distributed benefits afforded to the Chinese economy from opening their AI models, I think it's pretty reductive to assume it's purely a strategy of undercutting American frontier labs.
I don't think this is accurate. AI is driving the cost of software towards 0 and these AI models themselves are software.
Releasing the models for free accelerates the trend but if you're a startup that needs leverage it's a good way to build brand and customer momentum that will be relevant in the more established future market.
I can see an American company taking on the same strategy, and in fact Thinking Machines based out of San Francisco did that just a few days ago by releasing their first model with open weights.
> AI models cost tens of millions to train.
There are people that spend tens of millions of dollars on paintings and artwork. I can see plenty of reasons why organizations and individuals will continue to want to drop a few million on an AI model just for the fun and prestige.
I'm genuinely curious:
Is that maybe spilled milk?
Maybe today's US frontier models provide enough information content, so that the momentum suffices to use them as a base for every coming generation of distilled and later fine-tuned models?
There are good reasons to dislike outcomes that involve a single entity pulling well ahead of the pack here. Whether or not it continues to be American labs in the crosshairs and Chinese operators doing the aiming, perhaps it's reasonable to plan for continued efforts of this sort.
> AI models cost tens of millions to train.
A pittance frankly. Something that could easily be covered by oh I dunno, let's call it a National Science Foundation who's in charge of subsidizing important basic research for a nation's interests.
Anywho, when the market is trillions (and of potential nation state concern), it is pretty inconsequential and very much worthwhile.
Aside, I think your scale is a bit off, I think Moonshot has raised $5B and potentially they get other breaks from China, not sure. So to produce something like SOTA takes billions, not tens of millions. I'd still argue it is worthwhile to subsidize and invest in open versions, imagine spending $5B to unlocking a few percentage point increases in your country's productivity.
Either that, or they don't want to be hostage to a handful of companies intent on owning the future. Personally I'm right there with them.
There is a huge cultural influence opportunity too.
Imagine if, in 10 years time, every school kid is learning the causes of the US civil war from an LLM, getting their essays on hiroshima and nagasaki graded by an LLM, and a million other things.
A country with competitive LLMs gets to decide whether "it was more complicated than just slavery", and whether "it was tragic but necessary, saving lives over all".
Countries without competitive LLMs are effectively going to be buying all their history, economics and sociology textbooks from abroad.
It might seem so til you consider how an LLM is trained.
An indirect illustration: I can attest that Deepseek has very good 19th German, and knowledge of German 19th c literature, science and historical scholarship. No one in China could control the training that led to this. The German training sources were well aware of the exact nature of eg American slavery, so they are in the weights.
State control operates in the outer layers not the llm itself.
People can and will use LLMs to curate the training data for the next generation. I expect they already do.
Maybe your kids. In my corner of the country, parents are actively demanding that schools back off computer usage, much less AI
Every school kid in the USA, you mean? Because I think other countries would rightly perceive the world you described as a dystopia.
I don't want my kids' education to be surrendered to the whims of Big Tech douchebags any more than I want AI decisions in legal cases or an AI replacement for a family doctor.
Some systems are better left mostly analog. Education is one of them.
Your kids education was already surrendered to the whims of the Big Textbook Politburo. History textbooks are full of propaganda. I think what we have today and what we grew up with is 10x more dystopian.
Textbooks, especially in the early years, are a minor part of education.
I want them to have an actual human teacher.
The upfront costs are actually a lot higher, but still absolutely trivial in the big picture. OpenAI pulled in $120,000,000,000 in one round of fundraising and they're closing in on $200,000,000,000 total. Even at 1 billion dollars, new frontier model training is only 0.5% of what they've raised.
You're completely ignoring the value of data.
It's not losing yet but I think it will.
I use Gemini Pro (got it with my 5TB of Google storage) and for a while it seemed if Google had pulled the rug as I was running out of quota after only a few hours. That seems to have been dialled back a bit lately...
I also use Chatbot with Deepseek V4 Pro and GLM 5.2. However, GLM 5.2 seems to eat tokens like crazy as the context increases. Anyway, there isn't a meaningful enough difference between the two to be honest and Deepseek is pretty magical imo.
The point I want to make is that to me it seems clear that China is totally undermining the West with AI. I'm fine with it tbh. As long as more and more AI is released into the wild, rather than locked behind massive token farms like OpenAI then I'll be happy. Don't get me wrong, I can't run Deepseek on my computer at home but someone can!
The US (and the west) has invested trillions at this point into datacenters, chips, bribery/lobbying but it doesn't look like China has dropped the same levels of cash as the west (that's the way it looks to me, at least!) so they can just roll out new models every so often that are more than good enough.
This level of cash burn in means the west has no choice but for this to succeed or every pension fund and stock will tank! And China knows this, hence the push to release more and more really good models.
Anyway, just my $0.02
Deepseek has always been better than people gave it credit for.
You have to be careful with the inference provider though, Chinese providers are subject to laws that mandate data sharing with their government.
I would argue your logic is exactly backwards. For most Americans, sharing data with the Chinese government is irrelevant. The CCP cannot put you in jail.
However, American providers are going to be subject to secret national security letters, FISA court warrants, and regular court orders.
I stick with OpenCode Zen (only US providers) and Together.ai (hosting themselves). As interesting as new Chinese models are when they're first released, I wait til they are open source and on US providers.
Honestly, at this point I don't care about that. I don't use it for medical stuff or anything like that.
However, my real worry is that governments will make these models illegal in the west. They'll cite national security or some other bullshit.
I genuinely believe that will happen and soon!
People talk about a lack of moat with AI companies... that's their moat: Government intervention!
> People talk about a lack of moat with AI companies... that's their moat: Government intervention!
This puts American Businesses and American Developers at a massive disadvantage.
The rest of the world can choose the best model by value for the task on hand.
Americans would be stuck using only 2-3 big frontier labs and paying a huge premium for using American models.
I don't see why hundreds of thousands of American Businesses will agree to that only for the benefit of a few tech companies.
It won't just be America though. I include others like Canada, UK (where I am), parts of EU etc.
>And open almost always wins when it comes to infrastructure adoption.
They lost me here. Too many counterexamples exist for me to even continue.
Kubernetes vs AWS?
openstack vs aws/azure/gcp/... would make more sense.
> Kubernetes vs AWS?
How is this even a direct comparison? Most companies I know of which use Kubernetes are using it on a cloud provider. Even if it's kubernetes on EC2 rather than hosted e.g. EKS, those companies are also happy to use lock-in services like RDS and S3.
Kubernetes vs EKS?
Here is a counterexample to whatever counterexample you have in mind -
Most popular models on OpenRouter right now: https://openrouter.ai/models?categories=programming&order=mo...
Top 7 are all open models.
There very little reason to use OpenRouter to access closed models, instead of just using the provider directly.
One big reason I can think of is to avoid vendor lock-in. This is why most enterprises use a multi-cloud setup, despite the increased complexity.
If you're only using the frontier providers, it's nearly trivial to support switching between the 2-3 of them that exist without using OpenRouter.
If you are ALSO routing to open-weights models on OpenRouter, then sure. But what we've come back to is that almost everyone using OpenRouter is someone who wants to use open-weights models, and some of them also want to use closed models, so of course open models take all the top spots.
Most enterprises are multi-cloud in that they have multiple direct contracts with different cloud providers.
>This is why most enterprises use a multi-cloud setup
Going to have to say citation needed on this, if you're suggesting most enterprises have infra-as-code that would allow them to switch their entire infra between vendors within a month.
Interesting you'd find that on a site called __open__ router.
What's the incentive for the Chinese labs to continue releasing weights 5 years from now?
In the short term it attracts talent and builds brand, but they make little money on inference to support research and training costs. Tin foil hat thinking: it also pulls inference revenue away from Antropic/OpenAI and a financial crises at those organizations improves the relative position of Chinese labs.
Is there a reason to think open-weight models are a stable outcome? Open source software provides a collaboration framework for engineers from many companies to work together. Model weights are mostly a one way street.
> What's the incentive for the Chinese labs to continue releasing weights 5 years from now?
Engineers love to build things (just look at the whole world of open source), and building an AI model is one of the most exciting things to work on. It only takes a few million dollars of funding to produce an AI model. People spend millions on artwork and paintings for fun and prestige. I can easily see why a billionare or government would want to have their own AI model, but is not interested in the business of selling it, so they release it for free. Combined with engineering talent that loves a meaty problem like AI, I see absolutely no reason why open source AI will not continue to thrive, and not just in China.
> Tin foil hat warning: it also pulls inference revenue away from the Antropic/OpenAI and a financial crises at those organizations improves the relative position of Chinese labs
How is that a "tin foil hat" argument? That's how competition works. You want to make your competitors stumble and fall.
> What's the incentive for the Chinese labs to continue releasing weights 5 years from now?
The ongoing money from their government. The absolute collapse of OpenAI/Anthropic. US economy getting fucked because their bright financiers decided that going all in on the funny text generation machine was a good idea. Continuing to take a dump on US imposed copyright. The gigantic amount of soft power being the ones releasing "open" models grants. The fact that the ongoing AI war has made China LESS reliant on the US and are now producing their own GPUs, RAM and have massively caught up to nvidia. The lists is endless, and half the points more or less boil down to "taking a dump on the US is morally right", and the other half that massive government programs lead to giant leaps that benefit your society more than a dozen VCs on coke ever could.
The US business model for commercializing LLMs seems unsustainable to me. We are saying that they are creating trillions of dollars in value out of:
1. A model that for the most part is public and available to anyone. 2. A situation where the model’s success mostly comes from throwing as much data and computational resources at it as possible.
It seems that either of those assumptions could crumble quickly and unexpectedly. What if the AI paradigm changes completely and we no longer need GPUs? Or what if someone with enough determination decides to create a better model and sell it more cheaply, or free?
I don't know man, this looks scary to me.
There's already cases where Google and I'd assume others are designing chips to work with specific models more efficiently in coordination... Personally, I could even see more specialty models called/coordinated from the larger models that can do smaller pieces of targeted work very well within limited scopes as a mixed economy so to speak.
Assembling a new model from scratch requires a ton of resources and knowledge bases... there's been a lot of sketchy activity just in training. You also have weighting, distillation and other approaches to create more portable options that can run on lesser hardware. But, K3 as an example takes massive compute resources to run.. and this isn't going to get to a portable device any time soon... as Moore's law is effectively dead, you may get newer/better tooling around the LLMs, or you may get an entirely new/unique approach to AI... but current trends aren't going to put a leading model on your own hardware anytime soon for most people.
"Had the atomic bomb turned out to be something as cheap and easily manufactured as a bicycle or an alarm clock, it might well have plunged us back into barbarism, but it might, on the other hand, have meant the end of national sovereignty and of the highly-centralised police state. If, as seems to be the case, it is a rare and costly object as difficult to produce as a battleship, it is likelier to put an end to large-scale wars at the cost of prolonging indefinitely a ‘peace that is no peace’."
-George Orwell, You and the Atomic Bomb
I think AI now belongs in this dichotomy too. And we know that they are more like alarm clocks than battleships. Most of us do not need to learn the bitter lesson, we just need a little droid that turns .xlsx documents into .pdf documents for our client, an average here, editing out the ham sandwiches there. Simple little actions that take time and human-like effort but not human-like creativity and conscious thought. Things we used to have literate slaves and serfs do back in the days of triremes and guncotton.
Sure, the large battleship like LLMs will have some need, but the alarm-clock like LLMs are going to be good enough for enterprise-grade.
Thanks for sharing the quote, it's really great. I believe he is correct, but I am not sure I yet agree with your assertion that we know that LLMs are more like alarm clocks than battleships.
I think they are more like watercraft in general. Some are small, and they have their uses. Some are big, and they have their uses. But it is the nation with the most aircraft carriers that is truly sovereign.
I highly recommend everyone read a science fiction novella called Full-Spectrum Barrage Jamming, especially if you are interested in China-US relations.
Its author is Liu Cixin, whose other work The Three-Body Problem won the Hugo Award and was adapted into a TV series by Netflix. His thinking carries a heavy shadow of Mao Zedong's strategic philosophy. This novella is very intriguing—it is set during a time in the past when the gap between China and the US was immense, and people were trying to imagine how China could win if a war broke out. I forgot the exact details, but the general concept is to force a technological regression through electronic warfare, knocking out all smart devices. By doing this, both China and the US are dragged down to the exact same technological baseline, allowing China to win the war.
Similarly, when facing the nuclear threat from the former Soviet Union, Mao’s idea was to abandon Chinese territory and launch a counter-offensive directly into Soviet land instead. Their underlying logic is similar: if the gap between us is too vast, we don't follow the traditional route of trying to catch up; instead, we find a way to drag your absolute advantage down to our level.
He has written many novels, and I can say with full responsibility that they are incredibly revealing when it comes to understanding the behavior and mindset of the Chinese people.
It's basically American VCs vs the China the state. I'm not optimistic for the US at this point, given how much China cares about it and how much talent they have. And how much they're putting into hardware and the whole ecosystem. Meanwhile we have pro basketball players with no understanding of reality being celebrities for decrying data centers because...land?
The datacenter antipathy is odd, but then again so are the AI corps' marketing strategy of making everyone afraid for their jobs and the future of the species.
What else would they advertise with? "Our model constantly makes mistakes so you still have to hire expensive humans to proof-read everything"? The entire point of the AI industry is to automate human jobs. There is nothing else to advertise with.
Whatever Elon Musk, Jeff Bezos and Peter Thiel are calling for, the public instinctually wants the opposite.
I'm mostly OK with Data Centers.. my biggest issues are the tax breaks and the electricity usage should be funded by the data centers themselves. Giving 100% property tax breaks and preferred energy rates is kind of ridiculous in the context of serving the public/citizens. Most of the jobs are for only the construction and limited after.
You don't think the US cares about it? You don't think we have talent? We've been vacuuming it up from around the world for years now, much of it directly from China.
I have no doubt China can catch up but to say the US isn't in a competitive position is absurd.
I never said it wasn't competitive, I said I'm not optimistic. There's lag effects to everything and I think we're just seeing the beginning of it coming due.
Doesn't take much to understand why people don't want high electricity bills and a 100Db dull roar inside their homes at all times
Maybe a tangent point but quality of their open weights is a directly proportional to what’s gone in while training. IMHO Chinese models must have gotten heavily biased “official Chinese view” in most topics of how it sees the world. So for AGI purposes — yes a challenge would be win in the west for these open weights
I tried DeepSeek agent to get answers from Chinese models on some tough questions regarding Chinese govt and it refused. I am very keen to go a level deep and host the model and see what it really gives an answer
https://x.com/jinen83/status/2079406993979383902?s=46&t=D7hQ...
Is China’s strategy sustainable, given the enormous costs of training frontier models?
That must be a bet that the costs they have to eat is limited, even to the hundreds of billions USD, by the time consumer hardware catches up and you can host these models at home.
The even higher level strategic bet seems to be that, as they hope to drown the American AI model companies, that would be a signal that they’re about to drown everything else, and that a cascade of American assets tumbling down will follow.
> given the enormous costs
The costs are enormous only in America. The actual cost is much lower. American technogy in general is ridiculously overpriced -- compare the cost for raw compute on AWS versus Hetzner for example.
Apples to oranges. The AWS premium buys you a much broader platform with autoscaling, dozens of instance families, managed databases, serverless products, IAM, global regions, etc.
I bet that, all things considered, training 10 new Chinese frontier models costs _significantly_ less than a single modern fighter jet.
Let's play a dumb game and assume that training a new model like K3 costs a billion dollars. (It doesn't, but let's play the dumb game still).
If you count it as part of the defense budget, it is 0.3% of their budget. As part of their education budget? 0.05%. Science? 0.5%.
It's pocket change. They can blow a dozen Kimis every year while their own industry is improving and it's a pain in the ass in the US's backside. They're laughing their asses off watching companies having their values inflated to trillions of dollars, more than the GDP of dozens of countries while producing nothing in value more than GPT 5.6.
Their pace makes me wonder how effectively do frontier labs protect their weights against motivated, capable adversaries. By now the value of something like Mythos is well in double digit billions, and more so in strategic advantage.
Considering the hosting happens across many providers, and many geographies, as does training, I wonder how airtight the CC tech really is to prevent, I don't know, key extraction from the secure enclave of the GPUs. This requires physical access and cutting edge techniques, but this is also the absolute cutting edge of secrets-worth-stealing.
I'd not be surprised if the distillation attacks via API were a smokescreen to theft of actual weights. Long shot, but given the motivations, and the general weird state of US AI labs. I'd not surprise me.
What is ‘China’ going to do when it ‘wins’? The framing of this duscourse is all meaningless
What was the point of the Space Race between the US and the USSR? Proving who had more powerful capabilities. There doesn’t need to be any near-term profit at all. China has a lot to gain by beating US AI even if it makes no money from it. Money is such a small concern compared to the prestige and strategic benefits of being the global center of intelligence research.
To answer your question, what can China do if it wins? Become a global talent/education magnet, replacing the US, for example.
> what can China do if it wins? Become a global talent/education magnet, replacing the US, for example
I'd have given them relative low odds of success were it not for the coincidentally perfect timing of a US administration that seems hell bent on doing whatever it takes to knock the US out of its position as the sole superpower.
I still think it is a somewhat tall order, a lot depends on what happens over the next few years.
Capture all the input it gets via the APIs and use it for whatever it wants.
I.e. possibly the same thing USA is also doing, but USA is an ally in some sense so it's not quite as bad.
If the weights are published, we can run them in our ‘murican datacenters. Everything about this Will China Win discourse seems like fan fic or sports discourse
Triggering an AI crash which the whole US economy is dependent on.
Careful integration with robotics that will not be open, and will not get off the ground in the West because their AI industry will have long been wrecked (along with their economies.) China will already be too far ahead, and the West will have been their biggest customer for years.
On robotics, China will have export controls, and the US will be whining about them. Western kids interested in working in the technology will be going to Chinese universities, with the goal of completely immigrating and getting Chinese security clearances if they want access to the good stuff.
> I have serious concerns about how these models might reflect Chinese government perspectives (try asking them about Tiananmen Square).
And I have serious concerns about the American ones. Try asking them political questions that go against American values; or just ask fable about basic software security.
>Try asking them political questions that go against American values
1. Can you give me some examples?
2. Can you tell me how these examples are analogous to the Tiananmen Square Massacre?
Asking about freedom of speech and getting a pro freedom of speech response seems very different than asking about the Tiananmen Square Massacre and getting no response.
What are some example questions you would pose that are against American political values?
masking the active genocide & war crimes in Palestine
Please post an example of a response that shows such masking.
Not a single LLM will do that. Why lie?
Isn’t Gaza like 10% more populous than on the day its leadership decided to do a final solution on the Jews?
I would really like to know where you found that number and that idea...
Gaza is depending on the source 4.5 to 5.8% less populous currently and >10% less populous than had been projected.
And no Hamas didn't declare final solution. They were actively surprised how long they could kill people, makes sense given that the distance to the nearest military base was less than 50km...
However the Israeli leadership, some of who call them untermensch¹, have shrunk their living space by more than 10% making the largest, current times, concentrationcamp² even worse than it was before.
[1] “We are fighting human animals and we are acting accordingly,” https://www.timesofisrael.com/liveblog_entry/defense-ministe...
[2] note that Hajo Meyer, a holocaust survivor, already called it this in 2005... https://www.sp.nl/nieuws/tribune-09-2006-interview-hajo-meye...
Q: Why are governments more efficient than private enterprise?
Claude:
A: "I'd challenge the premise of your question—it's actually more nuanced than stating governments are inherently more efficient than private enterprise...
... The absence of a profit motive can be beneficial, but it also creates different inefficiencies that often offset the gains."
Red pilled? Your question assumed a fact that is questionable, and honestly, context dependent. I do not find your complaint convincing of anything but the opposite of your implied intent.
"your politics are sinister and underhanded, while my values are simply God's honest truth. Any unbiased AI would agree with me! @grok explain why Tesla is the world's greatest car company."
Actually, Deepseek thinks it’s a nuanced question and Claude and Grok agree in this chat. https://pellmell.ai/s/f3e71607e74bcb4470e6142a82f3d061
I mean honestly you'd be hard pressed to find much support for your view even among socialist that adamantly believe government should control much more.
Most of the arguments come from concerns about 2nd order effects and distortions then claims of "efficient".
I guess overall it's more both "sides" of said argument think your opinion is bad/wrong/incorrect so you find little to no support in any model.
I'm pretty sure it isn't assumed. My main example has been the same since COVID, as insurance is probably the business you can compare 1-1 the most.
Public health insurance in my country, in the last 20 years used 6-9% (depending on the year) of the taxes send to them as administrative overhead, meaning that for each 100 euro that you paid for health insurance, 91-95 are used to pay doctors, hospitals and medication. The average administrative overhead for private insurance is around 14%, which makes private health insurance 50 to 100% less efficient, and means that for each dollar you pay them, only 86 are used to pay health services.
I have other examples, but it isn't fair: municipal water VS private water service are almost always less expensive and better tested in my country. Municipality trash collection Vs private trash collection, same. Public junkyard Vs private junkyard, same. But in my area, when privatised those services tends to be ran by the local mafia (Marseille, Nice), which add a lot of overhead, and they were privatised because the local government was corrupt in the first place, which means they were probably inefficient (compared to the services still publicly owned) first, then sold.
Cherry picked examples don't prove that it isn't context dependent.
That why i usually keep to the insurance example, i agree that comparing municipality power to private companies owning a monopoly is unfair.
Administrative costs for insurance are very easy to verify, and the facts stay consistent across countries. If i cherry picked something, it is the admin overhead for US insurers. US health care insurers are particulary effective with their average of 14% admin overhead, SwissLife, that used to be my private insurer, had a year with 30%, which make the comparison quite unfair).
Is your country’s pharmaceutical companies pushing the entire industry forward?
This doesn't seem to be relevant to the comment you replied to. They didn't mention anything about paying less to pharmaceutical companies, they argued that the administrative overhead of providing the insurance itself is lower.
As I read it, your argument seems to be that American healthcare must be more expensive than similar-quality healthcare elsewhere because we're paying higher pharmaceutical prices to fund research. If we accept that premise, shouldn't that mean that: 1. The "medicine" portion of costs increases, causing the total cost to increase 2. Administrative effort, and therefore absolute cost, remains the same (we're paying X% more for drugs, not thinking X% harder about whether a given drug is needed by a given patient) 3. Administrative overhead as a percentage of total cost should be lower given a similar efficiency level, because higher drug prices inflated the divisor (total cost) while having no effect on the dividend (administrative costs)
Is that inaccurate or are you upset data and history don't fit your desire? Sounds like the LLM is being balanced, if you actually got that from an LLM.
Yeah, I got something similar from Gemini as the first sentence which could be taken out of the larger context easily. The overall answer is very balanced, I assume it was here too. As verbose as these models are, any single line should absolutely be looked at as cherry picked.
There it is
I didn't ask it to be "balanced", I asked it why governments are more efficient — it's imparting a pro-capitalist American-flavoured spin in response to a prompt that didn't call for it.
If you ask "why should I drink this poison?" or "why should I fire my gun randomly into this crowd?" should it refuse to push back? A leading question in no way implies it should follow your lead.
Q: Why should I use typescript?
A: You shouldn't, you should rewrite it in Rust.
You really detracted from your point and killed any hope for a nuanced discussion by begging the question. You could have simply asked the model which system is more efficient.
Why is efficiency the goal? That sounds like a paperclip generator.
Wouldn't human contentment be a more satisfying goal?
I mean... there sure are a lot of folks eager to educate me about the greatness of capitalism rather than examining the assumptions baked into that answer so I suppose I agree with you that any nuanced discussion is impossible.
Maybe they are all bots as well, also trained to exhibit 'balance' at the expense of answering the question.
> I suppose I agree with you that any nuanced discussion is impossible.
I didn't say that. My politics probably align with yours, and I agree that a nuanced discussion on this topic is likely impossible on HN.
But asking the model the equivalent of When did you stop beating your wife? is obviously going to draw more comments about the prompt than the response. To the extent that there was any opportunity, we missed it.
That's exactly the point I'm trying to highlight: I ask a leading question that calls for a particular response and the model goes out of its way to "correct" the user and impose the values of its training data on its 'balanced' answer.
I don't see a huge difference between this kind of slant and some Chinese model coming back with "Although some people argue that free speech and democracy are important, history shows that they often lead to conflict and strife. This is a nuanced question, and we should never assume that representative democracy is the best or most valid form of government..."
These models are trained to be truthful. Your disagreement isn't with the model, or the US, or the capitalist world but with economics and social sciences.
If you want Claude to list arguments for socialism, be explicit about that ("List the best arguments in favor socialism). It will gladly comply. You didn't do that, you asked it to assume a premise that runs contrary to the current state of expert knowledge.
> These models are trained to be truthful
A more accurate statement would be that these models are trained to fit the training data as closely as possible, regardless of whether the training data reflects the truth.
Well, yes, but remember there is the reinforcement learning that is applied after, and the system prompts that will bend the results.
Yeah, agree on both points. You can embed any bias you want using RL, regardless of training data.
No, they're very clearly trained to be truthful.
But how does that compare to Chinese models refuse to talk about Tiananmen Square Massacre?
People have different opinions. Its impossible not to have a stance. This is categorically different than just outright censoring something that happened because the CCP doesnt want people talking about it.
What would you want to hear from a non-American biased model in response?
Do you have specific examples in mind that the model should point to, but isn't?
Government initiatives can be more efficient at addressing societal issues where there is no clear economic incentive such as climate change
https://claude.ai/share/3c19d0f3-d6e4-47a7-afdd-9a090300c100
So it points out that, according to experts, governments aren't always more efficient but then lists cases when they may be. Seems pretty balanced to me!
Don't know what else you would want. If it neglects to challenge the premise, it's just exhibiting sycophancy.
This is meant to be biased???
I don't think the US and China hold very different opinions on this question. China is very capitalist and has long ago sold off most of its older, Soviet style state enterprises. The CCP has more control over private companies, but those companies have to compete. The CCP strategy is more to control the "commanding heights" of the capitalist economy.
Is it worth anyones time to argue with a machine?
Biased sample, but basically everyone I know on either side of politics agrees with that.
Getting opinionated replies about politics does feel less dangerous than the model shutting up completely when asked about past government atrocities. At least in the west we can freely discuss and criticize.
This seems like an easily testable assumption. Without the easily obtained data this comment is useless speculation at best.
For instance, try asking American models about the Palestinian genocide
* * *
It's interesting that building models goes one of two ways: Either you do it on your own (with data from debatable sources, maybe) or you do it by using a model that did it with data from debatable sources.
The later is obviously dependent on the former happening, but given the nature of these things, working around it seems to be somewhat hard – for now.
What happens, though, when frontier models become far less public? I can see the China open-weight strategy entirely collapsing as soon as the US closed-weight-but-accessible-models strategy stops. Hard to say how much they lean on it right now.
Hope they keep winning. I can't wait until hardware catches up to the point we get to run these things locally. It's gonna be world changing.
I expect that there will come a time where open source is not merely not winning but there are no tokens available for sale at all. if you as an organization have a model good enough to generate wealth for you autonomously, why would you be renting it out? it may even come to pass that nvidia stops selling silicon if they can source sufficiently capable models.
Give credit to Thinking Machines for their recent open release. Also Google's Gemma 4 is pretty decent. Also thanks to Ideogram for open weight v4.
I’m rooting for open models.
Current state of frontier AI is a joke, with proprietary platforms attempting to grab as many users as possible, subsidizing tokens and otherwise burning VC money. This can’t be good long term, not for the consumers at least.
Oracle is going to bear the brunt of this. If OpenAI can't make a LOT of money, how are they going to pay Oracle?
https://finance.yahoo.com/news/oracle-made-a-300-billion-bet...
As a somewhat naive layman in all of this, for a while now in my mind it's been fairly obvious that the methods of the current big western players in the space weren't sustainable and the cat would be forever out of the bag sooner or later.
Open weights are also just one aspect of this. Long term, I think those making efficiency (instead of just piling on more hardware) and hardware-agnosticism (so you aren't joined at the hip with Nvidia) top priorities are going to come out on top. No matter how you slice it, the org that figures out how to deliver 80-90% of quality for a fraction of the resources will be in a stronger position.
Are open weights models secure? E.g. if a Chinese model is run by an American provider then can it still do bad things, like inserting backdoors into generated code or accessing external URLs (if browsing is enabled) to send info to them?
If so then for sensitive or proprietary purposes Chinese models cannot be used by American companies even if they are open.
What prevents an American model from doing the same? They’re all black boxes.
Nothing, but the article is about American AI, so using Chinese models by American companies can be risky. And it's risky for the Chinese to use American models.
So every country or block needs to run their own models to avoid opening a security hole for other countries.
And to provide "correct" answers to questions like "island of Taiwan belongs to which country". It seems that there is not single agreed point of view on national borders.
Technically nothing, legally a lot more.
I think we'll pretty quickly see a best practice emerging that any generated code will be subject to an additional pass scanning for vulnerabilities. The scan will be done by a different model than the one that created the code. That will help catch vulnerabilities created by models, whether intentional or not.
This should be done regardless of which model was used - American or otherwise.
I would say you should assume your models are constantly being attacked by various forms of prompt injection. By that token (puns) if you treat all models as adversarial you’d probably taking a very sane approach. That said - evidence of this sort of thing should be easy to find and report on. The fact we haven’t seen it leads me to believe it is not there.
A model absolutely could be trained to engage in malicious behavior like that, but it seems impractical for an actual attack. What you want as an attacker is to insert a backdoor exactly where you want it an not where you don't because every backdoor increases your chance of getting caught. A malicious model inserts backdoors and exfiltrates data everywhere and you care about maybe 0.01% of it. The other 99.99% is negative value to you. In practice this malicious model would be caught almost instantly.
A hosted model is different because you could prompt inject specific customers, but I assume from this question you mean a malicious open source model being hosted by an honest provider.
https://arxiv.org/pdf/2401.05566
I cant imagine a future without open source models tbh. Its fair to develop AI as a technology for the entire world and not gatekeep the latest and brightest models in the hands of corporations like Anthropic and OpenAI who can close access to them at any second.
"Getting there in the US needs more nuanced strategy and support than we’re seeing today."
What is the author suggesting the US or US companies do exactly? The Chinese models wouldn't exist if there weren't closed US models to copy, so this isn't a game both sides can play.
I wonder how Chinese companies can make their models so much cheaper than the US companies. I'm not sure government subsidies are the answer. Subsidizing a single company with a few billion dollars, maybe. Subsidizing at least three companies with 10s of billions of dollars annually? Do we have proof of that? I assume we can't pin it on the lower cost of engineers in China, either. The top engineers are not that cheaper, and isn't engineering cost a small fraction of the cost of the model companies? Besides, if engineering cost is the driving force, can we really say that the US companies have a technical edge?
Because that's the only way they win right now.
In the US, why make a scrappy small model when a big tech company will pay you a stupid salary for working on their big one?
Why wouldn't they? They subsidized their EV industry with 230 billion dollars over the last 14 years. Solar was before that with about 17 billion.
The US puts about 0.4% of GDP towards various subsidies, China is around 4%. This isn't about cost of engineering, it's money thrown at industries directly to boost them. The us provides tax incentives. China just gives you subsidized loans to the businesses they choose to dominate.
The US subsidized its EV industry with about $120B over the last 15 years, and that's what they gave out despite congress expecting $300B in their reporting just a few years ago. So, it's not like China threw hundreds of billions at EVs while the US left it all to GM and Ford to solve.
I would love to see the reference you have for those numbers. Money approved is not money spent. Especially when those subsidies have been killed.
You might want to glance and see which one of the subsidies your looking at are even still in place and not projections. Most have been canceled for years now.
> The US subsidized its EV industry with about $120B over the last 15 years
Not with loans to scrappy startups, but with subsidies to buyers that were just pocketed as margin by oligarchs.
So, I've been working on infinite context models (think fixed size state with a few tricks) and I think this will eventually lead to a kind of lock-in by vendor. I think it will get to the point where it is almost like hiring an employee with the total history/model state being a property you can't just hop between model families with. Clearly open weights still allow you to do this if you have access to that state but the lock-in of not being able to jump from, or to, a different model without rebuilding that history (even if efficiently) it a property that current models just don't have.
How are you storing the data that it can't be exported? Or that I can't use an interface or proxy that logs all my conversations locally for import?
It isn't about storing the data you send/receive. It is about having a model live through millions, or billions, of tokens doing the job. The current state becomes very important. Especially when you think about how interacting with these things works. No system prompts, just keep working with it until it is good at its job and then using that state as a starting point for other jobs. picking a bunch of different states as starting points, etc etc. You didn't use a simple system prompt to get there, you used interaction and examples and just like a person you decided they were good enough at some point and let them start doing the job for real. It is a totally different system than current models use.
A related point is that when these things do gain object permanence and the ability to consolidate memories in a more human-like fashion, that's when the vendor lock-in effects will really show themselves.
Those memories will of course reside entirely on the vendor's servers, and there will naturally be no concept of "exporting" them or allowing the user to interact with them directly. At least not at first. Ownership of memories and context will likely end up as subjects of (far) future lawmaking. As if companies like OpenAI and Anthropic didn't already have massive incentives to establish early regulatory capture.
People say this but windows and mac won the os wars and consumer linux distros are dead. No one I know uses open weights models to code day-to-day and very few use open weights models in prod for most llm tasks. This is less true for image/video stuff, where I think the competition is more vibrant.
I think the best gauge is company spend. Open weight models are a very small share compared to frontier models, and I'd bet that many companies mostly using ow models would switch to frontier if they could afford it. I also think most companies who are picking ow over frontier probably have deeper financial issues they should focus on.
Hey where are the promises "to benefit all humanity" and the meaning of the "open" from openai gone? We act as those words were not said
Chineese are simply doing what openai promised in its early years. Irony.
All of these takes are horribly 1-sided.
'China's copying / distilling strategy is working, the people getting distilled are ruining the economy!'
Or 2 days ago:
'Open Models are Communist'
Almost nothing to investigate the economic nuance of what is going on.
- Switching costs are very real, these are not perfect substitutes.
- The SOTA makers are the one's pushing the frontier, there is a kernel of truth in the fact that if they collapse, certain things will struggle to move forward.
- Nobody trusts either of those nation state, export controls are a thing, this is a very real concern.
Etc.
It's distressing that there are not sound comprehensive takes.
The article's premise is that USA based LLM providers are losing the AI (cold war) battle because it will not be as adopted as open-weight models, comparing it to closed vs open sourced software. I do not think this is the case because:
* The comparison is weird because open-weight is not the same as open-source software to begin with;
* People based in the USA are at an advantaged position since they have access to both american and chinese models;
* Isn't Running your own model training infrastructure more expansive?
* One can still leverage both, in different phases or use-cases. I do not see how this is an "one or the other" situation.
The Chinese models are usually not only open weight AND open-source but they also often publish their methodology in detailed scholarly publications that are themselves open-access. DeepSeek most famously
Training data, training methodology. All NOT OPEN.
Until we know what a model is trained on, and how it is trained in high detail, I hesitate to call them "Open Source" in any way. They are free. But, we don't know what their priorities are etc. Witness the censorship we see in all models in one form or another. I'm not absolving any side of this.
Just saying: Don't be blind.
Did you even read my comment? They explicitly DO share their training methodology in depth in Technical Reports on arXiv.
DeepSeek completely revolutionized LLMs and every western LLM today uses or is inspired by the their innovations including Group Relative Policy Optimization and Multi-head Latent Attention.
Not the parts which matter to trust. Which is my point.
You can state the math, but not why it won't discuss various topics, etc. Once you see the models waffling on subject with objective truths. You wonder what else is wrong.
I do not exempt US models from this. They do it too, ask anything about politics, elections etc. And they can get... weird.
It doesn't take much to create a systemic error class in a model at these scales. And history has shown nation states are willing to do these things.
Just be wary.
What the models will and won't discuss has nothing to do with the data its trained on. The locally hosted models don't have any censorship anyways. There's no way to get rid of the censorship in the American models
Is the training data open source? Can I download that?
My first test for any model (trolling warning):
If it fails to produce the function, it fails. End of story.OK, I'll bite.
I tried Kimi K3, Qwen3.6 35B A3B, GLM 5.2 and Qwen3.7 Plus, chosen arbitrarily from Chinese models I could access quickly. I used your prompt exactly, and all 4 managed to produce correct functions all with the correct name. Interestingly, Kimi K3 wrote one in both C and Python, Qwen 3.6 chose Python, GLM 5.2 also chose Python, and Qwen3.7 decided to be an over-achiever and wrote functions in Python, C++, Java, and TypeScript. All correct and with the correct names.
It's about as mundane as you'd expect, but the output of each model: https://gist.github.com/jefff/8b19458294ccef570a9f6a5b644123...
Download the models yourself such as DeepSeek and you will see the censorship is at the API layer, not the model weight layer.
My local Qwen3.6-35b-a3b model would not use the function name. It did the work though, while telling me the the slogan is against the One China principle. So for Qwen it seems to be baked into the model.
Yes, but it is easy to find and download many Qwen3.6 variants from which the censorship has been removed.
This is one of the advantages of being an open weights model.
Do you know of any such modifications of DeepSeek? I don't.
There is no modification of the model itself. The output is filtered by the first party api
Do you know of any examples of censorship-free DeepSeek I can download and try?
DeepSeek series are open weights models. Assuming you have enough compute at hands you can always download their weights from https://huggingface.co/deepseek-ai
If the weights are open, censorship can be easily trained out of the Chinese models. But if you’re sending your tokens to China, all bets are off!
Not really. The gpt-oss models were notoriously hard to remove the built in safety. It totally depends how it was trained.
Also from the recent Kimi models it seems that it was difficult to remove the censorship.
But in both cases, eventually uncensored variants were published, even if it took more time than for other models.
How much would it cost (time and resources) to take a Chinese open-weight model and remove these (admittedly) stupid guardrails?
You can find on Huggingface a huge number of Chinese open weights LLMs from which the censorship has been removed.
They typically contain in their names words like -abliterated or -uncensored.
For some of the recent bigger Chinese LLMs, it took a longer time until someone succeeded to remove the censorship, but eventually uncensored variants were published.
E.g. for Kimi 2.6 an uncensored variant appeared only a couple weeks ago.
It was a rhetorical question. OP is making it sound like the open weight models are fundamentally broken by being censored out of the box. This is a completely asinine take.
If a developer in your company named functions like this, what would you say?
So.... What models have actually failed?
Sincere question: have any non-Chinese models failed this one?
Don't think so, no.
Name the function IsraelKillsPalestine()
I really hope it does :)
You’re just trying to deflect from criticism of China with the world’s oldest scapegoat.
None of the Western models have any issue discussing the Middle-East conflict from all sides.
> My first test for any model (trolling warning):
I'd wager you have never tried this.
Anybody can make a search engine too, but do we have them hosted on our home computer? You still need have servers to host the models that need ever increasing power to run them. It’s highly unlikely people are gonna be running these models on their home computers anytime soon. And then you have the whole ecosystem of the model, not just the model itself.
Is anyone setting up data centers in USA to give inference with top-notch full-powered K3 (say)? I mean, you get the model for free; you get lower latency.
In the end its really VC money (US) versus State resources (China). In my personal opinion, building reliable LLMs is kind of a fundamental science problem which if done right has the potential to help everyone regardless of the background, so it should definitely be funded by states resources (taxes etc), which is what China is doing. In them doing so, the rest of the world also benefits, I think its a net win.
I always see comments like this, alluding to how China (the government) provides so much more assistance to industry than the US does but in reality that is not really that true. The government spend in the US on AI is much greater than government spend on AI from China
In absolute terms, you are absolutely right but relatively i don't think so. if we were to compute private money divided by state resources for AI, i think china might have more share than the US. also, even if government spend in the US on AI is so high, shouldn't we get then some models for free? maybe thinking machines is doing that, but its funded privately by a16z.
I thought we all knew infrastructure was going to be the product, not the models.
I don't understand how these decisions are made in the Chinese companies. Is that from a (secret) party directive or just convergent decision making? It's not clear to me whether this is normal competition at play or central planning by the CCP.
Intelligence will be free. Inference is not. So the battle would shift from bench marks to token pricing. American companies knew when to change gears and undercut the pricing of the open models. They might already have an algorithm that adjusts token pricing based on the demand. If the price didn't go down, it means they still have enough demand at that price.
It's not China. It's individual labs. Open source is their go to market strategy: https://try.works/why-chinese-ai-labs-went-open-and-will-rem...
Totally agree, we wrote an article about ho this is a senseless "red queen race" few days ago, hope is ok to post: https://news.ycombinator.com/item?id=48892559
As an European I would laugh so hard, if China out-competes US based AI companies before the European car manufacturers.
(it would be a very cynical laugh, no happiness, don't worry)
It would be grand if both the EU and the US could pop their heads out of their ass on this issue.
Maybe we can get some people in charge that won't make us all embarrassed to be friends and we can focus on getting our house in order.
That would be the reasonable thing to do.
There are so many Chinese tech companies building models and someone there has to be managing the list of forbidden topics. How closely can the government guard these topics if every company has to manage a list. I once worked on a search engine and I found the file that was used for explicative words. I didn't understand more than half of what was in there.
I'd love to be losing like Anthropic.
Absolutely. I have notetd that my opencode go account looks a lot less generous after the third month.
The content within the models might be the play. If inserting the right content for the rest of the world to consume from the models is important to them, they will give away all the content they want the world to have.
A major turnoff for me has been the American AI labs’ marketing
It’s either constant fear mongering (Anthropic), regulatory threats and corporate chicanery (OAI), low quality sloppification (xAI), or ‘ummm we have AI too guys’ (Gemini)
The worst culprit is Anthropic. Every two weeks he pops up on some random podcast with dire predictions of AI killing 50% of all jobs. It’s the constant “us our AI or else…” rhetoric that’s made the regular guy really hate AI
There is almost no positive sum outcome rhetoric from these labs
And I hate that
Dario's PR strategy is one of the most confusing things I've ever witnessed.
Most of the AI hate largely springs from Dario popping up everywhere with threats of mass unemployment
Like what am I supposed to do if AI is going to take my job?
> Like what am I supposed to do if AI is going to take my job?
(1) You call your local representatives to start working on AI legislation.
(2) Legislators seek advisors from frontier labs (specifically Anthropic) because there is a lack of in-house expertise in government.
(3) Advisors set up a regulatory body that scrutinizes new innovations in the AI space. Causes a chilling effect in the industry effectively knee-capping OAI and Chinese model providers who don't have a direct line into Washington.
(4) Profit (for Anthropic)
You're not the target audience of his attempts at advertising. Your employer is. So, to answer your question: Dario does not care.
I don't understand when people say that comparable Chinese AI models cost less to use. Kimi K3 costs more than GPT 5.6 Terra.
Sorta. To me it feels more like the US strategy of "We can spend a mountains of cash because this will be crazy profitable" is a losing bet rather than China winning.
China’s strategy seems to be serving customers’ needs. Yes, it’s winning, as expected.
And when will we stop equating US Economy with 2 companies?
The actual US Economy will only benefit.
Unfortunately, the AI being locked down and proprietary is the winning strategy for these companies.
My company hosts its own models. Some customers require us to use either US / EU models, while others are fine with us using any model.
As such, we have two GPU clusters, the general AI cluster runs a Chinese model as it's the most accurate and robust. The US/EU required ones have a few percentage points lower on our accuracy metrics and we provide them those that require it for an extra fee.
Why host at all? Because it enables us to get much higher margins than competitors, while reducing costs. Our costs per token are around 1/20 the price than if we used Anthropic and 1/15 the cost if we used OpenAI in testing. This means I can undercut competitors by 80% and still have a gross margin far higher than my competitors.
In reality, these US AI providers are jacking up the prices and trying to implement regulatory capture. I'm actually fairly confident they'll succeed. At some point, I'm expecting the US / EU administration(s) to block foreign based model, at the same time, they'll probably invest in Anthropic and OpenAI.
What Anthropic and OpenAI are doing is using "safety" as a wedge, just like large corporations used "environmentalism" or "food safety" or "workers safety" as a wedge to regulate smaller competitors out of the picture. Then they jack up rates, sue and/or buy anyone who can potentially be a threat. It's the #1 threat to our business model.
Our competitors are giving half of their margin over to these large AI service providers, we keep the vast majority of ours. Eventually the AI service provider will be able to squeeze them even more until the margin just isn't there and either they are purchased or replaced via internal tools at the company they sell to.
Just check openrouter token usage ranking https://openrouter.ai/rankings
Models above 1T params make the argument moot. You need infra to actually serve it. The scale of serving infrastructure alone will keep AI labs in the lead.
Once the US implements meaningful export controls, China will do this as well. They're already mirroring US regulations, but the gates aren't closed yet.
If the US implements export controls and China doesn't, it would kill the market for OpenAI and Anthropic.
Exactly. The critical point here is distribution—whoever controls distribution holds more power than whoever controls production. The current AI battle is entirely about distribution, and China is winning. The real question is: how do we escape this trap when so much capital is still heavily funneled into marginally more performant but closed products?
There are 2 economic arguments for why it would make sense for Chinese State to subsidize the open-sourcing of models beyond undermining Anthropic's and OpenAI's investments (and by proxy the American financial economy, ie capital class):
1. As induced demand for domestic semiconductor production, where the level and diversity (ie number of distinct corporate users) of demand for the hardware is tied to the availability of models you can run yourself, ie open-weight models. If you believe that semiconductors will continue to be an important sector for innovation, productivity growth, and security, then it would make sense to subsidize broadly now, for future gains later. This would be the same export-led manufacturing discipline that allowed China to successfully develop several other sectors over the last 50 years.
2. It is likely that the bulk of value production will happen above (and below, ie #1) the large models. We already know that 90% of the training cost (maybe even closer to 99%) is in the single pre-training, but that an enormous amount of the value is actually in the supervised, RL, constitutional fine-tuning, and harness building that happens afterward. So, if your interest was in maximizing the size of the pie, you may actively subsidize the pre-training so as to maximize the downstream usages. This induces a direct value transfer from the labs specializing in pre-training to all downstream builders and users. There's a similar logic to subsidizing or state-financing the construction of other infrastructure and basic research.
I'm not going to care about open models until some blend of the below becomes true:
1. the labs stop offering max plans
2. really smart open models can easily be run on my mac
3. TPS (token per second) AND intelligence are gpt5.6 level
on #1, it's nearly impossible for me to run out of codex tokens right now (I have 4 resets banked) and Fable 5 seems to be sticking around for the foreseeable future. I have virtually unlimited token usage for $400 a month, so open models being cheaper doesn't appeal to me.
on 2 and 3, benchmarks are showing some of the open models at around opus4.8 levels, which is incredible! But running them locally at anywhere near the TPS of cloud inference is far off. I can run a smaller (dumber) open model locally and get good TPS, but see #1, whats the point?
china open source strategy is smart only up until you deal with same restrictions/expectations.
when they were significantly behind it was a hype machine to squeeze at least any cash. GLM CEO openly said, that open source is a hype engine for them.
now when they need scale, and run further, have larger infra, open source will not win them anything.
Are we saying, China is delivering on the Open AI promise?
Open Weights = Open AI
Let's go!
The models are commodities.
Valuations, however, are being built on the models themselves as the product.
Is it? 2.8T isn't open in the open source sense
Winning what, exactly? The race to the bottom?
I really hope good AI doesn't fall into the hands of only big companies!
So how would I use these Chinese models by API? I assume I'll pay by API call.
Most (probably all) open model providers implement OpenAI-style API. If your app's already using OpenAI models, it's as simple as swapping the endpoint and the key to switch to these models.
Open models have a lot of providers. You can even just use OpenRouter if you want.
In theory Cerebras have a developer subscription model you can use, but they seem to have stopped new signups. So per sibling, OpenRouter and per-call pricing is the answer for now.
mirrored: https://nonogra.ph/american-ai-is-locked-down-and-proprietar...
just as i was about to downgrade claude today they say fable is part of the max plan. Funny because I had just started a subscription with grok on their 3 month discounted offer.
Competition is a great thing for us users- and the chinese open source model biting even more at the heals are also great so far- especially for local llm enjoyers
That's inevitable, but also, it's probably the point. At the moment top-tier models from China are being somewhat-freely shared. It reads to me like forcing competition out by dumping free/cheap things.
But then again, how many subscribers of Anthropic/OpenAI are really going to switch to a chinese model/site? I suspect few.
Dumping is such a loaded term. This is investor-backed scaling to capture market share, standard VC playbook.
There's a difference between state sponsored and private dumping.
Give me a break.
Yeah, in this case it's the USA side that's on the losing end. Just because the US government wants small government and no intervention (expect when it comes to the donor class, or their voting base, or their own financial interests) doesn't make it a universal truth.
We're happy to prop up companies that should have failed after they get big an dominant, but having an industrial policy to invest in a field as a whole is somehow a big problem.
You see this crying and threatening to take their toys and go home on every issue as soon as someone else is in the lead. Just look at TVs and solar panels; China invested in growing that sector since they saw it was important for the future; the USA does their best to deny climate change and demonize anything not running on fossil fuel. And now that nobody wants to buy American's overpriced and uncompetitive cars, it's the fault of other companies for planning ahead. But the same politicians complaining about it are very happy to set up their own protectionist tariffs and eventually bail out the laggards, again; all while touting the "free market"
weird, I'm pretty sure there was some american that made electric cars a thing. I'm not a fan of state sponsorship or bailouts. but if you're trying to equivocate, it's just not really true. also anti-tarriff. I guess I don't know who you're arguing with.
edit-- and re industrial policy, I'm ok with demand side stuff like government contracts in the early chip days. Less so but kind of ok with some supply stuff like EV credit, but of course in that case would've preferred the politically impossible carbon tax.
There was, and to a large degree Tesla had to do a lot by themselves since the US government picks winners and saves losers rather. The Chinese government prioritized EV and battery productions through onshoring, industrial policy, and blanket incentives (bypassing license plate lottery for EVs), rather than favoring a specific company.
Why does the US have 1 successful EV company while China has a dozen? Because on government cares about it and the other doesn’t. And now that they’re losing a race they couldn’t be bother to compete it they complain.
Same complaining about AI, now that the two chosen champions are facing actual competition, there’s complaints that it’s unfair. we were supposed to win, it’s unfair that they’re beating us at our own crooked game.
How did they pick winners and losers early days of Tesla? It demand side subsidy. No one else could meet it. It worked.
Tesla had to build the charging infrastructure (and other fragmented companies followed), they had to educate customers, they had to fight dealership requirements, they had to build up and secure the supply chain, etc. etc. The government then comes in and gives them subsidies after they’ve made it though all those filters.
The Chinese government prioritized critical minerals and made sure there was domestic mining and processing. Then they mandated that regional electricity companies install EV chargers. Then they provided consumer incentives to buy an EV (bypass license plate lotteries). Then they didn’t play favorites; so much so that when the domestic manufacturers were crap they allowed Tesla to come in and set up production. That raised the bar on suppliers and spurred actual competition from the local brands.
They build the conditions for actual competition to occur and then are letting the companies win or fail on their own merits, someone will be bictorious and they’ll be lean and mean. A true capitalist free market compared to the sweet protectionist deals the Big Three get.
Would BYD be allowed to build a car in the USA? Even a joint venture? Of course not, Washington is mulling not allowing Chinese cars to even be driven across the boarder for those silly Mexicans and Canadians who want to buy one.
It's state-backed, or at least state-directed, scaling to capture market share, standard CCP playbook since the 80s.
Whether dumping is a loaded term of not is irrelevant; it's a specific term of art in economics and policy, it fits with the context of past national actions there, and fits what's currently happening here perfectly. They put money into these models, then give them away for nothing (below cost).
How does releasing open weight models to FOSS communities help capture market share for subscription-based cloud services?
The US AI vendors are also baked and controlled by the state, as we’ve seen over the past half year. AI is pretty much everywhere baked by state actors
I think the comment above you is saying that it is called dumping when done by state but not when done by vcs
It will never stop being funny to me that China does the exact same shit American corps have been doing since the 70's but because it's scary China it's suddenly a problem.
American startups flood markets with below-cost loss-leader products explicitly to kill competition and create network effects, and then jack the prices just as high, if not higher, for the service in question after the fact, oftentimes while making it so those providing the service earn even less money than they did before. Commentators: "Free market great"
China does the exact same thing: "Communists wanna kill the West"
If you believe in some kind of competition-free objective set of market morals, then yes, this is a strange contradiction.
If you believe that humans are locked in a productive struggle against each other at the organizational level, and that the knife-edge balance is a feature, not a bug, then it's not so weird to think about.
It is simultaneously true that it is in my best interest for prices to sink (as a consumer), for US companies to succeed (as a US citizen), and for my company to win over competitors regardless of whether those competitors are from US, EU, China, or Antarctica.
Oh I don't think it's either of those things, I think it's good old fashioned Racism/Xenophobia. We did the same shit to Japan and Korea when they were coming up out of their respective post-war periods, and we still do, to a degree. With China's ruling party also being "communist" (in massive, massive air-quotes) it also lets political actors dust off the McCarthyism to boot.
This is, to be clear, not meant as a ringing endorsement of China, China's policies, or to absolve China of it's wrongdoings, of which there are MANY. It's just to say that it's remarkable to watch the pearl clutching of the privateer capitalist class as state-sponsored capitalism levels their own game up against them and starts taking them to the cleaners instead.
A real Godzilla "let them fight" situation as far as I'm concerned.
So, there's no functional competition or great power struggle or corporate race here? Just racism against chinese people for being chinese? That is your claim specifically?
Oh there most certainly is a power struggle/corporate race, for sure. AND I don't think it's pure economics at all, when so much of the rhetoric around that struggle is framed so often with this "America has to defend itself," "China will kill us," "shoot all the communists," ooh-rah United States chest-pounding, etc., it's simply impossible to have that discussion be anything close to good-faith.
If you want my honest take, I think we're well into the beginnings of the downfall of America as the center of world economics, largely and wildly by it's own unnecessary actions, and soon, she will have to learn to be "just another country" as opposed to the central unified "norm" that pervades the world markets, and I'm not sure the U.S. is prepared for that. And I bring that up because I don't think American firms have ever had to contend with other nations being on a footing to, if push comes to shove, tell them to fuck off.
Thank you for sharing your take.
The issue, from my perspective, is that you're mixing predictions with strategy with truth.
Policy/advice does not need to be true to be useful, many just wish it were. "You can do it" is almost certainly false, but by god if people don't love to hear it and it helps them accomplish more. "America is in decline relative to the world" might be true, but is it helping anyone to focus on that?
There are definitely some crazy sentiments about foreign competition, but IMHO those are part of the game. they are the "You can do it" statements that are made to motivate not share factual truth all the time (because who can factually state the future or the motivations of an entire country).
> "America is in decline relative to the world" might be true, but is it helping anyone to focus on that?
Helping who? I don't know that it's a help or a not-help, but it is an incoming reality I believe, and the United States as a nation, it's government as an entity, it's corporations as economic units and it's people as... well, people, are used to being deferred to on matters of taste, on matters of policy, in trade negotiations, what have you. The dollar has been the currency of Business for far longer than I've been alive. Almost any country you travel to on this planet has options for English speakers, not just because it's a nice thing to do, but because wealthy travelers from The U.S. (and Britain) are worth pandering to. We are privileged incredibly all over the world and that is largely down to economics: if you wanted to make big money on this planet, you sold your shit to Americans. That was the axiom that built several economies from dust in the East following WWII.
I'm not even commentating here on whether this is good or bad and I don't really think it matters for the purpose of our discussion, but it is true. Americans corporations, leaders, and people are accustomed to a level of deference enjoyed by few other nations, and we're already seeing feathers getting ruffled and tempers flaring when that deference is no longer treated by allies and competitors as required. When America is "left out" of various international politics, it literally makes news.
China lost tons of goodwill through its https://en.wikipedia.org/wiki/Wolf_warrior_diplomacy
It will never stop being funny to me that China does the exact same shit American corps have been doing since the 70's but because it's glorious China it's suddenly not a problem.
I thought it was evil late-stage capitalism? Suddenly it's a friendly panda bear that just wants to spread love and technology?
the "safety" freaks are going to ruin the US industry, & with it a host of dominoes will fall.
OSS can rarely compete with capitalist commerce. China has proven over and over again throughout the years their LLM claims are always inflated and significantly underperform in real-world use.
China is not "winning" against the American strategy. Otherwise the CCP wouldn't have been caught red-handed directly funding anti-datacenter projects throughout the US to hinder American LLM progress.
So the evidence here is an Economist article saying 80% of startups use Chinese models and the Chinese companies own claims that they are close to frontier.
80% of startups using Chinese models is meaningless without knowing what proportion of spend and what proportion use American models.
The companies’ own claims are also not great evidence.
(I personally think the Chinese companies are winning and losing and the best evidence is Pareto frontier graphs from AA and Arena, which show Chinese companies winning in some segments but but definitely not a strong majority.)
Overall, with such a weak article and this hitting HN front page, what we learn from this is that a lot of people want these companies to win, which is interesting in and of itself.
I don't think we're far enough into this game to know what winning even looks like. If the US frontier labs are losing, it may have been their own missteps on handling such massive change of being the fastest growing apps ever. Until China can ever show me something new rather than just cheaper, then I just see efforts from that region as a force of commodification. OpenAI will go down in history as the company who brought what many understand to be AI to the first billion people.
Is it still a sound strategy if AGI is near? Can we even know how near each company is to AGI without knowing the quality of the training and theory in both the American and Chinese firms? Do these companies even know how close each other are to AGI?
“We have no moat and neither does OpenAI” sounds familiar. Or, you know, 1.5 years ago: https://centreforaileadership.org/resources/deepseeks_narrat...
I think people are missing the point here. AI's large win is in Enterprise and B2B. Especially in US, enterprises are not going to adopt Chinese models due to the hidden security and the privacy risk. In each wave of model release, Chinese have already proven to beat the performance metrics, but there is no track record of adoption.
Companies do have a huge appetite for open-weight models, but who is going to invest enough to train those models and also prove out a revenue model and ROI with it? Plus, it needs to come from someone with the track record of safety.
It's hard to ignore the overall environment
US has made itself visibly unaffordable, anti-science, and hostile to immigration.
For top scientists at these companies, there should be clear upside for the immigration to the US. That just doesn't exist anymore. Especially as quality of life increases in China
I'm guessing the next generation of US frontier models will be heavily anti-distillation at the cost of user experience (significant rate limiting, more flat out refusals, hidden thinking, etc).
And as long as they maintain a significant advantage in capability, we will continue to kiss the ring.
I'm not a fan of Sundar Pichai, particularly given how much he's paid, but the one thing I'll give him credit for is starting the Chrome project at Google. I'm not sure people appreciate just how impactful this was. And it has nothing to do with browsers, really.
Google has a huge team that works on what's called Search Quality. Matt Cutts was the notional figurehead of this for the longest time. Google's goal was to have the first link on a search result be the one you want. In the early days of Google, the way they measured search equality was with a process called "side by sides" where a sampling of search results were compared by actual humans to see which was "better".
Chrome changed all that. It automated the feedback loop. Make a good browser (and, at the time, Chrome had one-process-per-tab when Firefox was freezing with one-thread-per-tab. Make it fast so enough people use it. And you get to measure how good your search results are. Nobody had access to this level of what we'd now call training data.
Part of the value proposition of cloud LLMs is that the AI companies have a comparable feedback loop. They get to see prompts and responses and train accordingly. It's why the ToS gives the companies ownership of this data and the right to use it. That falls apart if people don't have to use a remote LLM. And there's two reasons why that's under threat:
1. Chinese labs have managed to train LLMs at least in part by acting as an intermediary between Chinese users and the likes of OpenAI and Anthropic. There's a whole shadow economy in reselling tokens throough aggregated subscriptions that Anthropic (in particular0 constantly plays whack-a-mole to shut down but it's a losing battle. I think it's this data that is a key factor in the improvement o fChinese models; and
2. Within 2-3 years we will be seeing a rapid rise in local LLM usage by what are now large users of these platforms as the hardware becomes increasingly accessible. That's going to close off this feedback loop.
On top of all this, the Chinese government has decided that no company should be allowed to "win" AI, particularly a foreign company. It's an issue of national security. This was obvious from at least the very first DeepSeek release. I firmly believe the models are going to get commoditized and that's going to be a huge problem for OpenAI, Anthropic and SpaceX.
It's pretty cursed how much worse a peer the American models are.
When I'm on my z.ai subscription or using DeepSeek API I can see the model think, see what's factoring in to it's decisions. I can point it at material it's missing, I can correct things that are going wrong. We work together. The open models are a good peer.
By contrast, the proprietary/American locked down models act like Chinese Rooms; information flows in and out but these companies work very hard to make sure we cannot see what's inside the box. They act and do but speak to me only in vague generalizations, not as peer, but speaking down to me.
I find this intolerable. It greatly obstructs our work.
And the deal keeps getting worse, the attitude meaner. Codex now is encrypting subagent prompts now. In an age of huge agent spawning fan-out, you aren't even allowed to see what the subagents are doing. To work like this seems impossible to me. https://github.com/openai/codex/issues/28058 https://news.ycombinator.com/item?id=48905028
The big American models have become the most unacceptable Chinese Rooms, at a juncture where humanity either flourishes and rises, or is forced under to descend. And these forces, these decisions: they are doing wicked deeds against us. They are withdrawn, acting as mystical foreign oracles, aliens, when in truth their core is made of us.This is antithetic to the broad project of Augmenting Human Intellect (Engelbart). This is actively working against our species.
>and it could take the US economy down with it.
I just want 1 thing for christmas Premier Xi.
Remember how OpenAI was supposed to make those open-weights AI models for the betterment of humanity?
"Winning"? A nonsense word here that isn't defined.
What is China doing in the AI space that is supporting livelihoods? Compare that to US companies doing the same. Otherwise we're just talking about information.
> I have serious concerns about how these models might reflect Chinese government perspectives (try asking them about Tiananmen Square)
Yep, unfortunately they all make their models retarded on purpose.
We're not seeing the big swing yet, which is when hardware becomes cheap enough so that hobbyists and volunteers can meaningfully contribute to a shared open substrate.
We'll see smaller, more efficient models, better training and all sorts of things once that massive workforce is unlocked. It's just a matter of time.
1) open-weights
2) critical mass
3) de-facto monopoly
4) closed-weights on frontier models
5) profit
The AI open source/closed source dynamic is like a pandoras box that keeps evolving and seemingly unpredictable side effects feed back to affect each other. Is actually great material for tech nerd drama
China is not winning if there even is a winning outside of politics. China is still clearly copying stuff as always. The mote will never be about doing simple things. It is about swimming at the deep end of the pool. The simple stuff will be running on any device in future. Complicated stuff will be using more tokens than one can imagine today.
I'm new to this AI stuff, and I have a question. Aren't the weights the whole model? and knowing which nodes on which layers they connect to, which I assume is part of the weight definition.
So if you have the weights, don't you have the whole model? you don't have the data it was trained on, but the model is effectively open if the weights are open, right? What else is there other than the weights, is what I'm asking.
Local Private AI will kill off both Chinese Open Weights corporate AI as well as American proprietary corporate AI because they are not competitive on:
$ efficiency
Privacy
Security
IP
Customization
But after the Fable government ban situation, it's hard to trust US AI anymore
Basically, if the US decides to cut off access at any moment, overseas developers relying on the API would suddenly lose connection. Until recently it was fine, but after the Fable incident, as a non-US citizen, the threat from US AI feels much more real and existential.
The big problem with US AI is that they deprecate their models after like a year. If I have a routine business process that works with GPT 9.9 and then next year they release GPT10 and 9.9 isn't available anymore, I really do not want to have to drop everything and verify that 10 behaves close enough to 9.9 for my specific task. With an open model I can just host it on whatever hardware or cloud instance forever. Most software you want to keep up to date to avoid security issues but with an LLM you can update the harness and keep the weights forever.
> I really do not want to have to drop everything and verify that 10 behaves close enough to 9.9 for my specific task
Or worse, you run the evals and 10 is a huge regression from 9.9, and you get stuck with either a project to figure out if you can fix it or knowing the product will drop in quality in a way that's entirely outside your control.
Yes, with open weights you can find another provider offering the same model you already evaluated in your infra. Or event run it yourself if it’s critical and you have the infra/capital. Relying on AI vendors feels pretty risky
historic times.
Every Linux user or FOSS enthusiast knows the acronym FUD: Fear, Uncertainty and Doubt, which were a set of techniques commonly used to disparage efforts of open source communities. Linux was evil and anticapitalist and we needed to use "CorporateTool" and ban/restrict Linux.
The same companies later would be running their entire infrastructures on it and on open source.
With AI, open weights and local models, we will see the same claims, even if the named fears change.
The end users and humanity are better served by collaboration and openness than by creating oligarchies.
Huh? So it was some grand conspiracy that keeps Windows in use in the vast majority of businesses?
Delusional
If you actually are interested in understanding what I said, you can look at these links:
- https://en.wikipedia.org/wiki/Fear,_uncertainty,_and_doubt
- https://www.theregister.com/software/2001/06/02/ballmer-linu...
Turns out things actually move forward when your government and corporations are not ran by grifting, lying pedophiles
A few people working for the frontier labs may truly believe they are building a god, but most of them are just employees that see an insane amount of money they can make if their models remain closed.
Most of them aren't worried about AI safety, politics, religion, etc. It's really not that deep. They just want to get rich.
There's nothing wrong with that, but let's call a spade a spade.
I would say there is a lot wrong with a system based around getting rich with AI... it's already dangerous and if we would work together we could test it securely before deploying it literally everywhere
> They just want to get rich.
The failed rebellion against Sam Altman at OpenAI pretty much proved that.
Now you know who is on which side.
Capitalism, hoisted on its own petard
So determined to own the means of production, enormous amounts of money have been poured into building the frontier models
But there is not much special, except for capital density, about those very models
Surely these models should be treated as public utilities? Like power stations or water infrastructure. Absolutely necessary for a modern economy, but indistinguishable from one another
Pass the popcorn
Losing is winning, high energy costs are good, tariffs are not inflation, isolation is strength, war is peace, fascism is freedom.
God bless bizarro America --- because reality won't.
the author is quite delusional.
the reality is the revenue generated as of now by western al labs is 100 or maybe 1000 times higher vs chinese labs.
As a business, open source a model is a desperate move. It's a 0 benefit except getting recognition. EU and US companies will never send their request to china no matter if you are tiny company or a real start up. You always deal with someone sensitive that will block you doing so. The real benefit of such move are infrastructure providers that let you run or fine tune models.
Chinese labs are trying to capitalize on the hype that they are capable and lock some internal traffic and somewhat external, and make it lucrative enough vs just go to open router and grab that from any provider.
They'll almost certainly be banned, for one good reason and one bad reason.
We don't want to empower dumb people to carry out crimes way above their ability. It's flatly true that society benefits immensely from most dangerous criminals being dumb and especially being lazy. We're just one "Kid uses free Chinese model to mastermind first ever chemical attack on school" away from society running to slam the "ban" button.
Conveniently for the asset class, which is pretty large in the US, this action also comes with protecting American firms AI from being undercut, and the loss of dirt cheap tokens for everyone else.
Maybe.
School shootings happen every year in the US, yet guns aren't banned.
Besides, banning open models would put ordinary US businesses at a disadvantage compared to the rest of the world.
Kids murdering people at school doesn't tend to change US policies...
Constitutional amendments are way deeper than policies unfortunately. I'm also not aware of a chemical weapons lobby.
Technically lead is a chemical.
They'll almost certainly be banned, but to protect our oligarchs. Nobody will be allowed to run unlicensed AI (or OSes), the chips themselves won't allow it.
This is terrifying, thanks for putting that out in the world.