ChatGPT became publicly available in November 2022, and Claude followed in March 2023. It's year 4. It's foolish to think we know who the big names will be 20 years down the line, which is the kind of timeline bond investors are thinking about. A lot of people probably wish they could have invested (or invested more) in Google or Facebook in 2006, but they are probably very glad they did not invest in Yahoo or MySpace (or Friendster or Tumblr .. you get the idea) at the time, despite looking very promising (MySpace was beating Google on pageviews on some days in '06 and '07). Instagram and Snapchat and TikTok and WeChat didn't even exist yet.
My favourite example is amazon.com and pets.com. In hindsight amazon looks like an obviously good investment and pets.com doesn't, however amazon was heavily invested in pets.com itself.
But the idea in early e-commerce was sane. Undercut retail on cost, increase profit. The ones that failed, failed on execution. Pets.com failed because you can't ship massive bags of food below cost and expect to make a profit.
Setting fire to cash to have the best model that anyone else can also do isn't a business model. They're never going to be able to stop setting fire to cash or they'll fall behind immediately. Their user base will not pay the bills. It's an obviously bad investment.
I don't see anything obvious about that at all for amazon in the book selling days.
dog food is heavy, and a repeat purchase that often requires its own store and brings no joy. Its the ideal thing to get delivered to your house on a schedule.
books are something you choose differently everytime, and the people that like them read recreationally.
Bezos was on another level, launching a successful company(online books) that was effectively just a way to boot strap into an everything store.
I heard that's why Washington state was chosen. Close to tech talent (Microsoft was there) and fewer people living there than other tech-heavy states such as California, so the US tax-exempt market was larger from Washington state.
I believe ChatGPT or Anthropic are both trying to be the "everything store" - whatever that may look like - of this brave new world. Call it a "business OS" and/or "life OS", they're clearly aware that research alone won't win dominance, and signs have been cropping up for the last year: Cowork, Research to Artifact pipelines, Dots/Instinct/Muse-esque personal assistant alternatives.
But the counterpoint still translates here: Amazon did not start with 1000 warehouses (i.e. Anthropic's debt obligations) and _then_ race to build a marketplace that would fill them with everything under the sun. It started lean and mean, and I suspect the Anthropic-killer will do the same.
CHWY is -7% per year since Sep 2019, while SP500 is +16% per year, and CHWY has 2% profit margins, give or take. I don’t know what success means, but I would not have wanted to buy CHWY stock anytime after IPO.
your point is well-taken enough, but the idea that a sharp investor was investing in Yahoo or Myspace in 2006 is a bit silly. Myspace was bought out in 2005, and it had been dead for years before that. and I don't think I had gone to yahoo.com for any reason other than testing my internet connection anytime in the prior 10 years before that. also, investing in Google or Facebook back then would be enough to retire on, much less had you invested in a basket of tech-but-not-obviously-dead-tech stocks. from what I recall from that era, the one things I knew for sure at the time were (1) Google is gonna be around in 20 years, bigly and (2) Facebook might be DoA in terms of social media endgame, but operationally they are very strong and executing.
what I really push back on is the idea that there are magically going to be all sorts of entrants into the space that meaningfully compete with Anthropic or OpenAI in the near or even medium term. for as much as the reddit set continues to put their collective heads in the sand while screaming "there's no moat"... there is a moat, and one so prominent as to be self-evident. not once has an open-source model come even close to genuinely reaching frontier performance for anything other than cost, and cost is by definition the easiest thing to improve on-- just distill bigger model into smaller model: boom, cheaper!
>Myspace was bought out in 2005, and it had been dead for years before that.
Myspace's peak popularity (in terms of monthly unique pageviews) came in 2007. Yahoo was ahead of Google for several years, though I am struggling to find good data on the specifics there. Your understanding seems to be based in your own personal experience of those sites, which is fine, but I don't think it quite matches the big picture.
Your proposal that there won't be any new entrants is puzzling to me because, in the grand scheme of things, OpenAI and Anthropic ARE new entrants. The entire space is new. It feels like ChatGPT became public eons ago, but it hasn't even been 4 years. Imagine saying in 2008 that no photo-sharing site would ever surpass Flickr - it's been dominant for 4 years! Why would you go to something new when everyone's photos are already on there?
One absolutely enormous advantage new entrants have in the AI space is that they can start on third base. Anthropic had to spend billions and billions of dollars to get to Opus 4.6. A new entrant could distill its way into a model roughly on par with that far more cheaply, and spend its VC cash on things like marketing and price cutting rather than burning it on actually creating a good model. The existing players need to service mountains of debt and are locked into deals for compute based on outdated assumptions about efficiency. I'm not saying it would be EASY for a new entrant to waltz in, especially to the enterprise market. But it's not impossible especially on the consumer side: Meta was essentially a non-factor until Muse came out, and now most people think of Muse when they think of an AI agent (as opposed to Claude, which is just a chatbot in the public imagination, because that's what it was when it became popular).
It's not even clear that the "frontier AI lab" is going to be the best, most profitable business model to evolve out of this tech. Especially if models are easy to distill, a new lab can start on 3rd base, and China open weights aren't far behind.
The winning business model may be 1 or 2 abstractions removed from "build a really good LLM model", and yet to be revealed in the marketplace.
I disagree, Yahoo was actually seen as a technological leader and, unlike Google that was purely a search engine in its early years, Yahoo was a full platform that seemed poised for a while to become a landing page for the internet.
When it comes to AI there simply isn't a moat - Anthropic would be my best guess out of the current entrants due to them trying to construct one via value-adding code assistance tools that might create enough friction to prevent customers from jumping ship but the models themselves are a commodity that depreciates extremely quickly. If you want to retain the frontier premium you need to constantly be improving due to the ease of distillation.
Your comment makes the timeline seem very different from the way I remember it. Yahoo was dominant much earlier than Google, and was one of a handful of big search engines. Google was a relative newcomer at the time, which had the benefit of being way better at search, which is why it eventually dominated the competition.
If open source models stay consistently <12 months behind the frontier, that shrinks the moat considerably; not for all use cases, but maybe enough to scupper profitability
That argument swings both ways. There's no real reason to bet on OAI or Anthropic. Not with the rise of local models and the increasingly desperate attempts at regulatory capture.
That's what I'm saying. Either one could be Facebook. Or it could be Friendster. The rise of local models raises the even more daunting possibility that nobody becomes Facebook, but you have to imagine somebody will win the consumer market for people who just don't want to bother figuring out a local setup. That market is orders of magnitude less lucrative than the enterprise market, but a company that dominated it would still be one of the hundred or so largest in the world.
Someone can always optimize a model down to running on a typical 32 GB M6 or M5 Mac.
It doesn't need to be the best. It just needs to be good enough. I'm not smart enough, but if I was I'd be working on a locally ran interface engine. That's where you charge say 100$ a year. Said engine would allow you to use a large open source model locally to the point where it's good enough to rival Haiku.
> Someone can always optimize a model down to running on a typical 32 GB M6 or M5 Mac. It doesn't need to be the best.
Two observations - one, there is some set of tasks where intelligence is already saturated, and sure, that threshold is rising by the month. So, sure, for some tasks you don’t need the best model. But there are other tasks that need models smarter than what we have now - like “make a cure for <disease>” that will yield billions of dollars of value, but likely require quintillions of tokens and higher IQ/token than anything we have now.
As long as we have non-saturated economically valuable tasks there will be incentive to train new and smarter models, and the frontier will always be on racks of GPUs not local distilled versions.
Two, it’s not clear that you can distill all the way down to 32GB even for the current frontier models. IIRC Karpathy has speculated that you can get an AGI into GPT-3 size, if you drop all the memorized facts and keep the reasoning core. But this is highly speculative! The best 32GB models right now are substantially dumber than even the large open-weight models, let alone the frontier closed models.
Perhaps I am missing something, what are you imagining of “a locally ran inference engine” that doesn’t already exist in a DGX workstation, Framework desktop, MacBook, etc?
>Perhaps I am missing something, what are you imagining of “a locally ran inference engine” that doesn’t already exist in a DGX workstation, Framework desktop, MacBook, etc?
Someone much smarter than me would figure this out.
One option would be an alternative bare metal OS which could allocate ram more carefully.
I'm sure with billions upon billions in VC funding a solution could be found.
Someone much smarter than me would figure this out.
Imagine if we had some kind of really intelligent AI model that we could pose problems like this to. It's obviously not the kind of thing an open weight model running on your local machine could do, but if we had some kind of really large, closed source model running in the cloud that could solve really difficult problems like this, I wonder if there'd be a market for it...
One way would be to generate a new model for every request. Currently we create an entire universe for each step when training if we predetermined what needed to be created it could be much smaller.
> like “make a cure for <disease>” that will yield billions of dollars of value
I think it's important to qualify that while I will very much appreciate such an innovation and I think it'll provide significant value to society... I remain doubtful of the ability for any company to actually capture such an astronomical yield. Either there's enough of an investment required by the company to help get that to market that the yield will be significantly lower or everyone and their brother will just launch competing products two days later. And, as a society, I don't think it's logical for someone writing "Hey Alex, cure cancer" with billions of dollars so I doubt there's going to be much appetite for creating such a moat via legislation.
This gets to the same point of "If ChatGPT can make you a successful million dollar business in a few prompts why is OpenAI sharing it" paradox. It very well may be that AI tools can accomplish the labor that used to be required to create such a business, and I'm sure a few people will land a jackpot and get rich in the transitional phase but if it's so cheap to build it why would someone pay you money for yours instead of just running the same prompting themselves? For most people that's because they lack the knowledge that they can but you'll still have a significant number of instant impersonators.
> This gets to the same point of "If ChatGPT can make you a successful million dollar business in a few prompts why is OpenAI sharing it" paradox
I agree fully, to be clear o was using that prompt as a shorthand, I don’t think _you_ can type that prompt and get $1B back.
It can’t do this now, obviously. You need a lot of scaffolding and domain expertise to get the most out of these models. (Eg Harvey.)
But eventually, the labs can just run the business themselves.
I’ve long held that as we see models become more capable, they will be less available to the public. It seems pretty obvious that vertical integration on private models (perhaps a per-industry fine-tune, eg a legal model) licensed to subsidiaries will let the labs capture more value.
Why would you publish your model, enable competitors, and enable distillation of your hard-won intelligence? Only antitrust law can prevent this outcome IMO, the business pressure is extremely strong.
The thing is, would the top AI minds rather charge end users 40 to 300$ a month for AI usage of sell a 100$ a year license to run it locally.
I'm far from an expert, but I don't think it's impossible. Future models won't give out vacation ideas, they'll focus on one or two things. This might allow us to get the size down.
Would AI companies rather have $100/yr of license revenue or $480/yr of token revenue at 50% margin? Obviously the latter, by more than 2x? And even larger dollar profit from the higher sub levels you mention.
The constraints will work against them long term. All this is doing is forcing people to look at how to run more with less. These companies are bloated beyond reality and have no incentive to optimize anything. They're starting out with access to everything and still producing very little tangible value comparative to the inputs. OAI and Anthropic represent the pure gluttony of the US tech bro market and it's not going to be the long term winner. It can't be lest the masses are cool at crowning these narcissists as kings.
Rise of local models? what rise? I run qwen3.8 flash next on my 200GB/s 128gb Strix Halo and even that is barely acceptable, big models require so much more. Nobody's running a local model on their power sipping phone. It's just a nerd's wet dream.
That's true today, but what will it look like in 20 years? Over time, algorithms and hardware always gets faster and more efficient. Like if I wanted to run a fluid dynamics simulation, what would have required a supercomputer 20 years ago can now run on a single desktop computer.
barely acceptable for all use cases? like a speed issue?
Regardless, looking back even a year at SOTA local models that fit on a non-absurd system creates a crazy trendline toward the future. And I think model routing is the gap filler that can make/break now vs. Oct 2027
I suspect Anthropic is of this general thinking as well; they've clearly switched from a thoughtful progression toward a natural IPO to "omg sprint for it". Along with the regulatory capture attempts, it's hard to imagine what is breathing down their neck so hard outside of this.
Totally agree local inference of any major scale is a nerd's wet dream short of many stacked improvements in efficiency. But again, auto-routing smooths over the rough edges if you are ok with a little bit of API spend when needed.
Better comparison is probably the dotcom bubble, and companies like pets.com, webvan, kozmo, etoys, worldcom, global crossing, etc. It was not entirely clear to most investors in 2001 that google and amazon would make it.
I don't think pets.com, an online retailer, is an apt comparison to Anthropic, an bleeding edge research lab doing things that most people would consider science fiction a few years ago.
anthropic has come up with a lot of the core successful consumer facing tooling/standards around ai.
they invented mcp, terminal coding agent, context files like claude/agents.md.
I have no real clue whos going to win the model race but they have repeatedly shown that their internal taste is on point, reminds me a lot of early google.
With one huge difference: you could never run the Google index on your local PC. But you can run a 'good enough' AI on your local PC. Good enough that Sam Altman thinks you shouldn't do it because he has better uses for your RAM than you do (oh, and besides, because it may hurt his IPO chances).
I’ve discussed with many people at work, all engineers, they’re weirdly oblivious. Most are shocked when I say things like OpenAI and Anthropic are gonna crater.
There’s a notable difference between saying those companies may crater, and predicting confidently that they will crater. The wording of your comment makes it seem like a foregone conclusion.
I can’t imagine being so overleveraged that I run into economic thermodynamics. It’s like interstellar travel: there’s not enough energy available to do it quickly and at scale and efficiently. There’s not enough monetary potential energy to satisfy the buildout.
We've been hearing this since DeepSeek was released in Jan 2025, and in that time, Anthropic's revenue has grown 100x. And they're still growing like crazy.
So...why hasn't that "essentially free" competition taken them down yet?
Mostly politics combined with nobody actually knowing how to utilize AI yet. We’re just now finding a singular use-case for AI, which is coding. Every other use-case has been a complete dead end. Cool gimmicks for demos, but nothing anyone uses on any regular basis, and definitely not value creating.
Everything is very unstable right now. This is still a very new tool and we’re very much experimenting on a product level.
And, when they do, it’s because AI can write code. Take for example financial analysis. AI sucks ass at that, but it’s good at writing code. You’re using it to write code to do the analysis.
When you ask AI a math question, it’s not answering the question. It’s writing a program to answer the question. Because AI is basically only useful for writing code.
I guess the mathematicians are all freaking out over nothing, since AI can’t do math. Weird how it’s been solving these problems that have stumped humans for decades or more…just coincidence I guess.
I’m also quite sure that when AI is doing someone’s job by writing code to do it, they’ll be comforted that it’s not really doing it, it’s just creating and using tools from scratch to do it.
The desperate grasping for any kind of position to hang onto that AI isn’t already more capable than most experts in many fields is…sad to watch.
The customer support reps find the AI assistance hamfisted and laughable. AI call transcriptions get big and small details wrong, often leaving out critical details in favor of generic and high level "customer and agent discussed an ongoing issue with their home appliance." AI "let me direct your call" bots are constantly sending people to essentially random departments. AI bots trying to directly be the customer service serve only to piss people off before they get to a human, and I've run into a few businesses that are only staffed by bots.
I still think OpenAI holds on as a Google competitor and Anthropic does fine as a business tool. Do they match their outsized spending with future growth? Well, that I'm not very confident about. A lot of this really comes down to what is the business model of integrating AI with 3rd party systems and I don't see that anyone has truly cracked that yet.
I also speculate that OpenAI and Anthropic will crater, but I also expect that the market will remain irrational much longer then anyone shorting it today will remain solvent.
So yes, I believe it's going to leave a smoking hole in the ground, but I won't be betting money on it.
If you run a traditional short (borrow and sell the shares), you need to be right on the direction and the timing to make any money. One random surge in the price can kill your short even if said surge is short-lived.
Not sure about Anthropic, unless they continue to fall over themselves in ego. They have strong GTM in my take.
Google is a bit oblivious (though is probably the smarter money based on diversification alone IMHO). OpenAI has folks wondering just how publicly Mr. Altman can blunder his wording before he gets ousted (today was along the lines of "you will own nothing" with his take that personal RAM ownership being an affront to efficiency).
All three are trying to convince enterprise buyers that central tokenmaxxing is the best thing since sliced bread; many of the circles I run in occasionally have huge (emphasis: huge in the 2010s ZIRP sense) buyers that are begging for more edge AI.
I talk to our risk lead and he was also weirdly oblivious as well. The whole company is dependent on them not going down the shitter. Surely that's a tangible risk?
I'm indifferent either way myself. It's just shocking the amount of ignorance.
It's the cryptobro craze but for AI. It's tiresome, I don't have AI fatigue, I love using Claude, I have aibro fatigue. I'll lump in the people all in on AI, and that includes anyone who hates it vehemently because they just have to sour conversations.
Yeah. AIbros and AI companies are SHOCKED that AI and datacenters are unpopular.
But guys, if your companies weren't run by psycopaths and weren't promoted by assholes who appear to be the exact same guys behind the NFT craze, (this is the future of creative expression! Don't like it? Your a luddite. Cope.) and weren't predicting horrible nightmare scenarios based on their tech maaaaaaybe people would like AI and be more interested in using it?
I personally see that generative AI is a very powerful and useful technology but I hate everyone involved in it and do not want to see them succeed because they're icky people.
Its like if the nazis had discovered how to harness atomic energy before anyone else. Fuck that.
I've talked to someone who has worked at those companies, and they drink the koolaid that people like Sam Altman shove down your throat, even when its blatantly bullshit.
Note that both Sam Altman and Dario have conveniently watered down AGI, and moved away from "trying to achieve AGI" essentially.
The world is using 300T token a day. Barely anyone is using AI. This number would grow 30x. If there is anything, Google, OpenAI, Anthropic are undervalued by a great deal.
There’s no business in tokens. Zero. The only business is selling compute and Anthropic rented all its compute.
Structurally they’re in a terrible position on what’s unfolding now with the completely collapse of model valuations. With truly “open” models gaining more and more momentum models are basically worth nothing and “token” prices are collapsing to the cost of compute. That doesn’t leave anything on the table for the Anthropic and Open AI’s of the world.
Google search ads used to also convert highly because they were well-targeted and positioned at the point of consumption.
If I'm searching for red widgets, it's a fairly strong indicator I'm way more likely than the average to want to BUY red widgets, so a well-placed ad for redwidgets-cheap.com didn't feel hostile.
Now when it's "I am clearly searching for red widgets but you want to railroad me into blue doohickeys because they're paying more per click", you're one step above a Taboola chumbox.
Google search ads are just highly optimised over now 26 years. There's no reason that OpenAI won't do the same. How successful they are going to be is not clear though.
No clue how it's low effort to point out ads. It's not my fault you and everyone here lacks the product creativity to see the reality that they WILL be embedded in the answers and you will not be able to tell it's an ad. This will result in a literally the best ad system the world has ever seen. They simply need to wait a few years for the general pop to be reliant on them (which if you work with kids surprise they believe everything it says) and then slowly shift this over. They will be the most valuable company in the world bar none.
Do I think it's dystopian and shitty? Yes. Doesn't mean it ain't gonna happen and doesn't mean it's not the right move for the business.
The equivalent model for chatgpt would be randomly getting replies that are generated from a prompt injected with advertiser copy and indicated by a small "sponsored reply" in the corner.
Honestly, having written that out... I'm not sure that's an outcome that's generally avoidable.
It has zero network effects and barely any economies of scale. It's a good product without a moat which will get replaced by any company with their own compute and cheaper energy.
I think the only way out for Anthropic is to buy a few neoclouds as they go under, but even then it's a coin flip as to if they survive the next 3 years.
The market is a cold beast and doesn’t care. If things hit the fan it will happily take ownership Opus 5.5 in bankruptcy court and slit everything else out on the street.
If people paid a profitable price for Opus 5.5 it would not be the best product in the world and would not be slotted into so many things where it's benefiting from ubiquity. That circle needs squaring. There will be an Opus 5.5 that's a 10th of the price in a few months.
I personally don't really believe the meme that Anthropic and OpenAI are charging less than the marginal cost (cost of inference on requests). Open weight models have surpassed the abilities of their closed weight models when such claims started, and the inference costs on said open weights models are provably not so high.
Where they quite reasonably might be making a loss is their marginal costs + amortised fixed costs (training).
I predict that eventually the training expenditure is going to slow down. At that point, the large commercial providers are likely hoping they can keep inference demand up, and recoup their investment.
The driver for the training is an arms race between providers and open weights labs. But several dynamics could play out:
* Models will be seen as good enough, with only fine-tuning required, for most use cases, and demand for expensive training of foundation models drops off, stopping the arms race.
* Most of the participants cannot raise capital due to lack of market confidence in them, and there is a 'winner takes all' dynamic with the remaining provider, who can then raise costs.
I personally think the first end game is more likely than the second.
You are absolutely entitled to call it a meme but that is also their own internal reporting/panicked emails. Subscriptions are offered at a large subsidy with the benefit of more training data and adoption.
> I predict that eventually the training expenditure is going to slow down. At that point, the large commercial providers are likely hoping they can keep inference demand up, and recoup their investment.
The big labs can't do this that's their edge over open models. The stickiness is critical for them to keep up. They might try again but it's going to finish badly.
Cost per token is going to tank; it's a race to the bottom. The frontier model providers have no moat between each other and eventually even the open weight models will be "good enough" for almost every purpose.
Right now the market is expecting something a lot more than "sells a lot of tokens" out of these things to justify their valuation.
Wouldn't that depend on how much data? I assume those who own the data would rather buy the hardware and create models from this data themselves, as opposed to sending their data off to someone else, or allowing it to be pirated.
I disagree, the moat will be of those holding rights for valuable problem domain specific training data. Examples I can think of:
Adobe sits on a treasure trove of:
- Copyrighted multimedia
- Years of telemetry on content creation
Zeiss sits on decades of data:
- Medical imaging, and surgical footage.
- Silicon waffer inspection.
These embody a training corpus that is de facto (lawfully) out of reach of the crawler scrapper approaches of Google/Meta/OpenAI/Anthropic (even if they pirate the seas for torrents like before).
Neither Adobe or Zeiss are an AI power house but the barrier to entry has also dropped with all the movement in the field.
Yeah, nobody is doubting that people want to consume tokens. We're doubting that that Google, OpenAI, or Anthropic has a significant enough moat to keep people buying their tokens there. Over the past couple years the moat has been the model. As open models are getting better, that moat is drying up. Eventually the market for "tokens" will converge with the market for compute, which is a commodity with little to no moat.
I think even if the moat dries up, you could think of tokens/inference similar to compute. There's all the open source tooling available in the world for you to rent your own bare metal servers/racks and use a white glove service, yet there is demand for cloud.
At current prices, it makes sense to self host, but if the price goes down and the demand grows across different verticals, there is room for this business.
If everyone is using AI the ROI could just keeping up with everyone else. If anything Intelligence is now becoming a tax or the cost to use the toll road.
ROI in the space is not complicated, their revenue is already insane. You just drive down costs with fixed capex (GPUs), that will continue to pay out over 5-10 years. These are growing pains, they need to make it past the GPU scarcity/ramp up phase.
The CFOs he is talking about are the enterprises who are spending big money on the tokens ; they are not seeing RoI ; not hyperscalers or frontier labs
Yep, i can't disclose specific numbers but we went from $X/month/person budgets to $X-100/month recently, and this is a pretty big company with ~5000 people. From what I can tell the pursestrings are only tightening further too, too little actual real-world benefit for the obscene spend
Correct. The people that control the pot of money these labs ultimately need to survive seem increasingly unconvinced that giving these labs money is a good idea. At least at the levels the labs need to not go bankrupt.
Reports indicate GPUs are increasingly being put on shelves in warehouses. There’s a scarcity of power and data centers to host them, but data increasingly suggests the world has an over supply of chips at the moment.
Like most “scarcity” scenarios things flip from under supply to massive oversupply real quick. Remember during COVID when lumber got stupidly expensive and then almost overnight folks couldn’t give wood away.
Everyone I know and their mom is using AI, technical or not. The question is, is it profitable to run a frontier AI lab and sell inference while spending billions on R&D.
Anthropic could be super profitable right now if they stopped developing new models. But then they'd quickly fall behind because there's not much of a moat. Maybe the only ones who will survive this are huge companies like Google with separate incomes who can swallow the cost of staying on top.
By reading the comments I wonder if perhaps OAI & ANT are not going to be “model” companies and they are going to be the ones innovating in many fronts and cashing in for the benefits of those advancements…
What I’m trying to say here is, what if, they are staying closed strategically so they keep their most advanced models close to their chest until they make an incredible discovery. Say they cure cancer. In this case, they are taking that IP collaborating with big pharma and cashing in on the upside of such discovery.
So, what if on top of being just another enterprise and consumer product that accelerates the way we all work and live, it also becomes this factory of breakthroughs and that’s where open source models can’t compete with them.
We're already making huge advances toward curing cancer.
They're not coming from LLMs. They're coming from mRNA-based treatments and applying standard science to their basic principles.
And just to reinforce what djierardi said, you can't make successful new drugs and treatments for diseases just by thinking about it really hard. Practical tests are an absolute, unavoidable necessity—and not even just the final clinical tests; you have to be able to see how the molecules you're synthesizing interact with living tissue of various types, among other things, before you can even hope to test on humans.
Delegate that entirely to an LLM, and you'll end up giving clinical test subjects something that will melt their flesh, or coagulate their blood, or give them turbo-cancer. Because there is no comprehensive dataset that they can use to figure that stuff out purely by computation. We, as a civilization, simply do not yet understand biology and chemistry well enough to model it at that level.
So stop trying to figure out just how OpenAI and Anthropic are playing 12D chess and actually this situation is fantastic and will result in them saving the world. They're just greedy, selfish companies trying desperately not to be left standing when the music stops and all the chairs have disappeared.
I appreciate your points of view.
I’m not trying to figure out the 12D chess these companies are playing nor I’m convinced these are the salvation of humanity. I just enjoy exploring all perspectives possible before arriving at a conclusion. It’s my defense mechanism against echo chambers.
You just made me think of LLMs as useful translators. I know they are not going to be the ones leading to the discoveries other ML mechanisms can. But LLMs are a great way to communicate though to a machine. Before it was only for those able to code, but now that problem is being resolved.
That is not to say that computer scientists are no longer useful, but it is now possible for everyone to talk to the computer and viceversa.
It sounds like you're positioning the LLM as "a computer that can speak human language (and also computer language because it's a computer)". If so, that's anthropomorphizing them to a concerning degree.
Even given the significant advances in coding models, everything I've seen described and experienced myself with them tells me that non-coders simply aren't going to be able to build and maintain nontrivial projects with them unless they get still better at it. By a lot.
And in the realm of medicine, the kinds of mistakes they absolutely will make, and defend, confidently, will get people killed.
Its plausible. Its just that the effort will be huge. LLMs can do a lot of stuff because they've ingested all of our published text. But, unless we're extremely lucky, there's just not enough data out there to solve this problem yet.
Maybe like with problems in maths: they've solved 700+ open conjectures, but how many did they try? Are the ones that they solved merely those that they put $10M effort into where humans just gave up because there wasn't any concrete benefit in putting in that sort of effort?
What a movie. Act like religious whackjobs (A) who want to consume the world. In secret you have a bag of world-changing causes. The setback in the middle of the plot is the supercomputer answering something mundane to a serious question. Everyone feels defeated. But one underling is chatting with his pal subagent 3495. And he mentions the mundane answer. Then the subagent makes a critical connection; the answer is not mundane, it just needs a proper interpretation. Then they send the answer out (15 minutes later) cancer is cured. They use the patents to pay off the debts and then give them away to the public. The CEO moves to a shed on Más a Tierra with a talking parrot companion.
I don’t think the cure for cancer will be the result of the prompt “tell me the cure for cancer. Make no mistakes.” But instead applying real AI and ML supercharged by recent advancements to identify genetic mutations that could possibly cure cancer or other diseases.
People keep mouthing this same fantasy. But cancer is so many different cancers. And "the cure(s)" aren't just 1000 different drugs, but maybe 10,000 different candidates for each that pass into lab tests, of which maybe 1% make it to phase 1 trials, and of those maybe 10% make it all the way through phase 3, each after 5-10 years of clinical trials and $100s of millions invested each. For the most part, AI-related biotechs are largely focussing on accelerating the discovery of good candidates. But that's a tiny fraction of the pipeline, which includes lots of lab work (which can be automated) and clinical trials (which OTOH require actual human guinea pigs and years of observation and assessment). How do you meaningfully accelerate that? Presumably by improving the odds of success in phase 3 trials from your initial candidates.
But nobody has proposed yet how LLMs can be a magic bullet here. At best they'll give you those 10,000 candidates per cancer type faster. But then you have all that lab work and clinical work to slog through, and all the likely failures you'll see in clinical trials. And there's just not enough data out there to train ML on to meaningfully improve the odds of success in clinical trials, since you'd really have to understand how your drugs work with all proteins and systems in every type of human body, e.g. "in silico" clinical trials. And we're only merely scratching the surface of that with stuff like OpenADMET.
That is, we can't even model well "how a human body works", much less "how those mechanisms go wrong in each type of cancer". But even if you could do all that, for the cures you'd need to figure out what novel, unusual, out-of-distribution thing (relative to your human model) you can throw into the system to avoid the bad behavior, that doesn't kill the person or interfere with other systems or get degraded too quickly or ... tons of other stuff.
Highly non-trivial. Maybe lower-hanging fruit is with a novel peptide or antibody, since those are better modeled with ML. But small drug-like molecules are galactic in number and not well modeled yet. ("Hey, what happens if I introduce this weird platinum-based drug into a body?" Well yeah, cisplatin is used for chemotherapy, but the body of knowledge needed to assess platinum-based drugs in a human body just doesn't exist, and ML isn't gonna intuit it from known data, nor "learn it" since that would require going down to QM, and then integrating that into its model of all human peptides and pathways.)
This is not a side-project for a chatbot company. Its a (multi-)generational project that requires buy-in from labs that can produce the requisite data.
(And okay so maybe I'm fast-and-loose above. Ideally a single "cure" would be to fix the apoptosis pathway, i.e. repair the built-in cell-death path for fast reproducing cells. But cancer cells mutate quickly and evade known avenues. Perhaps there's a generic fix? Plausible but same reasoning applies: ML doesn't have enough data to intuit a solution, if one exists. And there's no reason, a prior, to assume that one does exist. Only hope.)
I just feel like your taking it all as a given/fact. And maybe you’re right, but I’m not that well versed into it to say things for sure.
What I'm trying to get at is that if we’re considering a black swan event, why not consider both the bull and the bear case or even the neutral one and at least accept that we don’t know which one it will be and it may well be a coin toss…
Perhaps using “cancer” deviated things into a specific subject, but it could be anything. A drug discovery, quantum computing, etc.
Again, I’m no quantum physicist or chemist, but as a world citizen, these feel within the realm of possibilities. Just like it destroying humanity or simply just being yet another technological advancement like the computer or the internet was.
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I guess the people working on computers could have argued there was a 10% chance it was going to end humanity and so the people working on the internet because AI was going to come about.
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I’d say that my final conclusion is that we don’t really know and the signals we’re getting from founders, investors, the market, here, and other people we talk to is that NOBODY KNOWS. All we can do is to continue to speculate and make bets based on hunches.
Yeah. What I think is deceptive is something like solving Navier-Stokes with $10M of compute. The bigger question is how many conjectures and hypotheses they tried, and what fraction of those actually succeeded. If they expended say $1B on various open problems, and only 1% succeeded, is that the kind of investment that one will make? (The $10M alone is more than what would have been devoted to academics investigating these issues. But realistically one has to quantify the cost of the entire enterprise, and the probability of success vs failure.
From OAI we're hearing only about 700+ successes. Relative to what failure rate? The cost of success isn't merely the cost of each success, but rather the overall cost of all experiments, of which -- I'd guess -- a huge fraction failed.
I'm all in on that, and I own Moderna stock ... so there's that. But we sort of understand the pathway there: we want to introduce a protein that induces an immune system reaction. Traditionally you re-engineer a virus that produces that protein that produces the RNA that gets transcribed into that protein. But instead, with better tech, you introduce that RNA directly. Simpler and faster. And perhaps you can use that mechanism to introduce other proteins for other ends (as recently with its personalized melanoma treatments).
I'm all for the notion that ML can improve 1% to 5%. But I haven't seen anyone suggesting HOW that happens. So far, it looks like taking 1000s of candidates to 10,000 candidates, which just further clogs the pipeline. Using ML to automate lab tests seems like a win there. But still, pushing those through phase 1, phase 2, phase 3 clinical trials is the bottleneck. 90% of what enters that pipeline dies, and each involves many years and many $100s of millions of investment.
But as I noted: the quicker path would seem to be through peptides rather than small drug molecules. ML does pretty well with proteins/peptide chains and mRNA provides a way to introduce them (or injection, like GLP1's). Still requires all the testing, though, but presumably we know more about how they degrade, etc. over time.
Oh, I see it. I’m just thinking 5–10 years down the line not short-term volatility.
Could there be more ChatGPT moments down the line in other areas like science, genetics, space and something we haven’t considered? I think there’s a chance.
I also think AI is here to stay and there may be some confirmation bias from the community regarding open source models. It’s like someone saying back then that Linux was going to take over… (?) Open source software seems to be served for technical people. Not for the average Joe looking for an answer or help generating a social media post or help writing an email.
Indeed, ML is here to stay. But there's a difference between biology/chemistry and math, e.g. There isn't enough data out there on which to train MLs for the former.
I'm thinking here of LLMs as a lossy compression of all our published knowledge. And then agents doing some smart/heuristic search relative to that. They can surely find things that we have missed -- just because they'll tireless try all avenues of investigation that humans might have given up on -- but if the underlying data is not there they can't manufacture it.
LeCun has made this point elsewhere wrt world models and more ordinary physics. This issue is exaggerated in the case of biophysics and quantum mechanics.
Maybe they can’t keep the discoveries secret… idk.
But what they can do with them can make a difference. Especially with the access to compute and resources they have compared to anyone else. So, they still have a competitive advantage.
The one current example I can think of is Google. They have Alpha Fold and Quantum Computing. I believe they’ve seen advancements to the way they work and an improvement to the speed of progress. It’s just that it’s not what it’s talked about.
Yes. I doubt they’d be able to patent those discoveries without serious litigation from the healthcare sector. The models are all “polluted” with others’ IP.
IMO once AI can flywheel discovery, the entire international ip/patent system would simply break down - world is not going to allow US companies to automate innovation pipeline and horde / extract at scale, unless US somehow maintains full leading edge AI monopoly. Closed model discovering something a few months before open model and getting years of exclusivity rights is just not going to happen / last. If anything expect mass invalidation of IP which will disproportionately fuck over US incumbent IP leadership position.
An IPO is where the investors cash out to spend money on something else. It’s where the founders and early employees get rich enough to start chasing other interests, or just retire outright.
My understanding is that the underwriting bank and early investors and institutional investors win first, then founders, after that early employees. Usually offloaded onto the robinhood crowd who excitedly buys in.
According to this analysis of Anthropic's S-1, the founders own about 10% of the company, which at $2T valuation would mean they each make about $28B. The employees collectively own about 15% which results in some staggering math - thousands of employees making ~$100M each, even those hired as recently as last year.
It truly is. And, looked at that way, it is an indictment of the systems that allow this, and yet another piece of incontrovertible evidence that we need to change those systems to bring money, and power, back from the ultra-mega-wealthy to the populace at large.
It just, y'know, isn't a good argument for having everyone work for Anthropic ^_^
In contrast, back when RedHat went public they reached out to everyone on the open source mailing lists, and gave everyone a few shares as thanks for the work they were benefiting from. I got some shares even though all I had done was post some free video format converters.
It's also often also part of the employee retention plan. If you discover your boss has lied to you shortly after an IPO or a merger, then odds are good they were trying to hold onto you by any means necessary until their bonus was paid out.
I've had several people I trusted destroy our relationship this way over the years.
In general I’d agree with you but where else would you go to take the money that’s more exciting than the forefront of AI in the world? Retirement makes sense, though if they wanted to retire they all could and own multi-million dollar homes and do so now, pre-IPO. Post ipo I guess you get a few super yachts and some land in Hawaii? Idk at a certain point I think these guys like the chase more than the result.
That certainly comes with the territory. However, being a public company offers a lot of opportunity for the company - easier access to capital, to enterprise/government contracts, and attracting talent that want less risky compensation.
There's also a pretty big morale hit for the people who fall below the remuneration cutoff of peers they are almost as good as or feel they are better than. I've seen a few different people lose almost all of their fucks because some dweeb is buying fancy cars and they're still paying their mortgage every month.
That makes sense, anthropic is in a rapidly changing market. Revenue could crater if a better model is released by a competitor. AI demand could fall behind. Doesn't seem like that will happen, but its definitely not investment-grade debt worthy either.
Even if Anthropic invents a godlike frontier model that can do anything, it feels like they won't have a moat for very long before competitors like DeepSeek offer something almost as good for a fraction of the price.
If AGI is created, then it will be used by someone else to create a cheaper AGI, making the finances of the person who borrowed trillions to create it in question.
An extremely hypothetical experiment but what if this is some sort of MAD (Mutually Assured Destruction) and in our timeline, we have accepted it with AI.
Deepseek can do MAD on Anthropic and release open weights model (which means that they might not earn the most or basically burn millions/billions to then give us all essentially the same thing and we would all be on equal footing, oh they also released their inference engine right now.)
This might explain why the stock market got into an bubble and is fearing itself right now.
Mutually Assured Commoditization. (Credit where it's due I pasted my comment to Chatgpt and it gave me this term ironically and it seems to fit well to the comment that I am writing, I might write a blog about it someday.)
MAD / MAC assumes everyone has the same leverage on each other- that they are all as heavily invested in capital.
Where it falls apart is when you get companies that take open models, rent compute and sell tokens at a profit, without heavy investment into research or training. They spend way less, and if they can keep distilling the frontier models, they can produce reasonable products without the massive losses of subscription plans. In your example, DeepSeek et al aren't nearly so vulnerable. There's nothing mutually assured.
It is much more of a classic race to the bottom. Every new frontier model shifts the goal posts a bit, but eventually the house of cards will crumble - or at least, that's what the market seems to be thinking will happen.
Are you trying to say "the Anthropic API surely cannot be highly marked up! Therefore subscriptions surely are selling at a loss!"? If not, I'm not sure how your comment is particularly relevant to the discussion?
I mean, it's worth pointing out because either API pricing is crazy marked up and thus subscriptions are still profitable if they are used at full amount or subscriptions are subsidized and API pricing is more true cost. Sure, it could be second one but due to average usage, it's profitable overall.
Right. Either way, there's nothing at all to support that subscription plans are "massive losses" except the original commenter's imagination. A subscription/API price delta does not a massive loss make.
This moat issue comes up a lot, but look at what's been happening so far; open models are consistently 6-12 months behind. What if their moat is simply staying ahead?
And once their models start to accelerate their progress, which may already be happening, this would further solidify their lead.
Demand is currently not sustainable because they - ant and OAI - refuse to raise prices.
Infact they’re going to end up in price competition unless they field an army of ‘FDE’ who go to every large firm and reorganise the operations around their models and displace labour.
I was under the impression this was only on the consumer side? I have heard of contracts expiring and them wanting a lot more money now (forcing from generously subsidized enterprise seat plans to all API usage). Specifically Anthropic in this case.
GitHub copilot then comes in and offers multi provider support including open source models, and gets the contract due to Microsoft’s existing establishment and lower price point.
Anthropic can’t raise their prices without loosing market share of Claude code
Amazon didn’t borrow anywhere near this much. They also had the sales tax loophole that let them outprice the competition and take their market share for over a decade.
Meh rookie numbers you give me $250B too and keep doing that every year I will generate revenue $125B revenue easy. I only have to sell a dollar for 50 cents .
Same Amazon to develop AWS to profitable business spent only 30B in all capex (including e-commerce) over 12 years between 2003-15 .
Right, OK, but then that's the interesting or newsworthy claim, not the company's ratings status --- junk status for companies that later went on to become among the most successful in the entire marketplace is not unprecedented.
As someone who uses Claude Code daily for work, I fail to see how they lack a real product. It provides tangible value on a day-to-day basis, something my workplace is happy to pay for.
The more interesting questions aren’t about whether they have a real product (they do). It’s whether they can defend that product from competitors, such that their future revenues can justify the massive investments made.
The consumer product is losing them money and the possibly profitable enterprise product (API) is a smaller % of revenue and in an increasingly competitive niche. The constant disingenuous baiting and switching between the two whenever convenient is very tiresome.
Perhaps I am just blind to consumer markets, but it is not self evident for me, at least. When I think of successful consumer products, I think of stuff like Coca Cola, the light bulb, the CD, acrylic paint. Are you saying that Claude is as successful consumer products as the Ford model T, and Nike sneakers? I am not buying it.
I think speed to market and revenue of the company are nonsensical measures of successful consumer products. Like did Theranos have a successful consumer product? How about Juul?
Anthropic's S-1 (IPO doc) says they will spend >$0.5T. Where is that money going to come from? The gap between the frontier and the open models isn't large enough to generate anything like that in operating profits. Debt markets are getting tapped out with AI debt. We are about to find out how much stomach equity investors have remaining. The fact that they might have trouble servicing their current and expected future debt comes as no surprise.
The S1 actually said it has $855B of commitment. Above $1T of you take in account some that don’t have fixed prices like extra TPUs and the Broadcom agreement. And that they will also build their own infrastructure, on top of that.
It would make sense that they'd be considered high risk borrowers.
But that won't stop them from issuing bonds or more convertible notes as they have and the market may or may not bite.
It does add pressure to their IPO proceedings being as high as possible as their funding mechanism tho. But reduces pressure on them if shares crash a bit after that, still high risk borrowers.
"Hey Mr. Inference Provider, you can sell tokens for $0.03 that cost you $0.02, to PRC end users who are too poor to use Claude or don't like that it refuses their use case, which is spam, scams and cheating on homework.
Or, you can trade $0.03 of tokens that cost you $0.02 for $1.00 in ANTHROPIC SHARES, which makes the thing that everyone uses for like, math discoveries and curing cancer."
Average Hacker News commenter (not you): "Nooooooo it's not profitable Anthropic is going to crater nobody will take their convertible notes"
Many companies IPO'd in the late 1990s and did not exist 10 years later.
Let's say that the house of cards crashes down and takes the market with it who will shutdown, survive or have no choice but to get acquired for a 90% discount.
Some will be just alive long enough for the early investors to pull out enough before the market crashes.
And who will be the Amazon and grow 2500x over the next 30 years?
Now, I'm not saying that's going to happen -- but if you take analogies from the dot-com era and assume they're going to play out in the same way, you can't just cherry-pick the bad ones.
Amazon had valuation of $60M at their first venture round, and it took them 19 years to 3500x it to $200B.
Anthropic had valuation of $550M at their first venture round, and assuming it IPOs at roughly $2T, it will take them 5 years to give this 3500x return to their initial investors.
Companies IPO much later now because the laws changed. They used to be forced to IPO much earlier because employees shareholders pushed them over the threshold for # or private shareholders.
So you're absolutely correct about the comparison.
That said, I bet whatever happens over the next 20 years goes beyond what any of us can imagine. I wouldn't be surprised if the winners of this paradigm are worth hundreds of trillions. Sounds ridiculous until you look backwards and valuation a and consider this is an exponential curve and time is compressing.
Well sure..., but what might be less obvious is that a can of beans, a tank of gas, a wig and a chair can all inflate at 2% a year while stocks and land inflate much faster.
The way we measure inflation is focused on consumption goods that everyone depends on. It doesn't take into account the stock market.
In other words we could stay around 3% in measured inflation but see ~30% annual equity inflation...
So my point is that the most valuable companies in 20 years could be worth hundreds of trillions while the cost of a can of beans is like $5, not $200.
Zimbabwe type inflation happens when the economy doesn't get more efficient at producing stuff as fast as the government prints money. If the economy increases efficiency very quickly and the government prints money to peg at 2% inflation, the products we consume only increase a little bit while scarce assets increase a lot.
This is why in the time I took a can of beans to increase in price 3x a house increased 20x.
> And who will be the Amazon and grow 2500x over the next 30 years?
Almost certainly nobody if you're tracking post-IPO growth.
And not because there won't be survivors/winners when the next crash comes (there probably will be) but because of the market changes (in the wake of legislation like the JOBS act) that result in these companies having their significant growth periods prior to IPO instead of after it.
IPOs used to be a launch pad to growth, now they are just a way for private investors to finally cash out when the company is already close to peak valuation.
None of the current vintage of IPOs are going to 2500x in our lifetimes (inflation adjusted at least..). The VC to IPO process is totally different now.
Companies are staying private longer, raising many orders of magnitude more private funding, and IPOing straight into "top 10 biggest companies in history" size.
If you IPO at $1.5T you are already equivalent to ~1.5% of World GDP.
Thus is just for scale, as comparing GDP vs a company market cap doesn't make total sense since GDP is 1 year economic activity and market caps represent discounted future cashflows of future economic activity...
Let's say Anthropic IPOs at $1.5T and grows even 1000x over 20 years, it would be worth $1.5P (petabillion?). If it had a 20x earnings multiple like google this would imply Net Income of $75T. Mag7 total Net Income for 2025 was $500B or $0.5B.
World GDP is $125T, and has grown less than 5%/year before/thru/after DotCom. So lets say in 20 years its $250B and Anthropic is capturing 30% of world GDP in earnings (not revenue, that would be even higher)!!
You can cherry pick the bad ones if the point you want to make is that it's risky. Nobody is saying Anthropic is definitely going to 0, just that there is a risk.
But from an economics perspective OAI/Antropic are more similar to Amazon than to Pets.com. Pets.com always had a heavily negative COGS - they were in the business of selling $20 bills for $15. On the flip side, Anthropic appears to be positive on COGS, and OAI could get positive by cutting limits on free and $20 plans. OAI/Anthropic are negative on the books mostly because of massive R&D expenses. This compares well to Amazon circa 1999, which was positive on COGS but negative overall due to massive buildout R&D costs.
Note that I'm not saying OAI/Anthropic are destined for success - because the rate of change combined with competition with cheap Chinese models is pretty novel in the business case history. But I am saying that pessimistic dot-com-boom comparisons don't seem to be all that deep.
"they are way more expensive to the company than people buying metered tokens" is true, but that doesn't mean they're a massive loss as you imply. It could be that plans are effectively breakeven but tokens are highly profitable.
But this is further to the point that the economics are more Amazon than Pets.com. If the plans are losers, great, when they need to be profitable they kill the plans and focus on metered tokens.
> OAI/Anthropic are negative on the books mostly because of massive R&D expenses. This compares well to Amazon circa 1999, which was positive on COGS but negative overall due to massive buildout R&D costs.
Isn’t the difference that ai labs can’t stop the R&D spend? They have to continue training new models.
There's a bunch of arguments you could make there, varying from "yes, to stay ahead of the competition" to "yes, but it'll get a lot cheaper" to "no, things will plateau eventually". All those are datapoints that IMO this isn't a great analogue to the dotcom boom.
A) they’re all getting acquired by these IPO seeking ones
B) nobody is IPOing at $400 million like Amazon did except SPAC share dilution scams, that you have to be willing to pick up at $30 million a year later
C) private secondary markets solve that, for some and are pretty great
D) tokenized synthetic and SPV backed private secondary markets in crypto ecosystems like Hyperliquid give you that exposure too, so now everyone can replicate the 90s
Anthropic won't let people make video games. They are on the wrong side of history.
They are so going to get disrupted. Chinese open weights models will have no such limitations, and there's no way we'll let Anthropic outlaw or ban them. (And even if they did, they can't police the whole world.)
There is no moat. Anthropic should be playing nice, not trying to create a god while holding engineers under their thumb. We will route around it.
People got upset that Anthropic added rejection rules for people saying "Make Pokemon in Elden Ring" . You can still make games, as long as you aren't telling Claude to recreate a specific, IP-protected game.
If they can't raise capital then yes that will have a huge impact on a company that's a massive tower of financial engineering as coreweave have been learning.
Same thing as with SpaceX. If there is that much of an equity buffer, it should be investment grade.
That it isn't means that the bond market doesn't trust the valuations at all.
No offense to them, but in general, investment grade companies should have a stable future and not have easily-switchable, competing products that cost a fraction as much to use come out regularly. This is almost laughable that I have to say this.
> Wall Street banks earlier this week closed the North Star deal, which provides the financing for Anthropic to lease 1.5 gigawatts of Google’s semiconductors.
This line is certainly eyebrow-raising to the educated layman.
Apparently it means that anthropic is leasing a Google data center whose constituent servers draw 1.5 GW of power running representative workloads.
It's interesting you imagine nine shares would suffice for a large site like HN. No, the link doesn't work for me. Honestly, this kind of "sharing" seems a bit disingenuous at this point.
He is the article:
Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.
Blackstone told investors Anthropic may not receive an investment-grade rating immediately after going public, a reversal from its previous guidance that would complicate the AI lab’s ability to finance its massive spending.
The private capital giant told investors in Anthropic’s latest $63bn chip financing deal that the start-up is expected to receive a “high” junk rating from major credit rating agencies after its initial public offering, according to people familiar with the matter.
Blackstone is acting as an anchor investor on the deal, known as “Project North Star”, and is working with banks that stumped up financing on selling the debt on to other investors.
Anthropic filed to go public on Monday, setting up what could be the largest listing of all time next month. The AI lab’s prospectus sketched out its “bold but sane” plans to spend more than $800bn on computing infrastructure between now and 2045.
While Blackstone had told its investment partners in recent months that it was expecting an investment-grade rating for Anthropic before year-end, it has recently signalled that a rating in the double B range or above is more likely given the guidance it has received from major credit agencies, the people said.
Such a designation would limit the pool of capital that Anthropic could tap because many insurance and pension funds are restricted from taking large positions in junk-rated borrowers. As a result, the company might need to continue to rely on its suppliers with high credit ratings to support its debt-raising efforts.
US tech stocks slid to session lows on Friday following the FT’s report on Anthropic’s rating, with the Nasdaq 100 down 3.7 per cent in afternoon trading. Markets were sharply lower prior to the report after earnings from Amazon and Intel worsened fears about the sustainability of the AI boom.
“It’s all about access to credit, it really matters . . . if [Anthropic] isn’t investment grade, the cost of credit goes higher, that’s not good for anyone,” said a US tech-focused portfolio manager.
“It’s a bloodbath out there,” they added, referring to the sell-off in stocks.
Wall Street banks earlier this week closed the North Star deal, which provides the financing for Anthropic to lease 1.5 gigawatts of Google’s semiconductors. More than $42bn of the senior loans is guaranteed by Broadcom, which co-develops the chips and has an investment-grade rating, while $18bn of the junior debt is backed by the AI start-up’s lease payments.
Institutional investors covet senior debt because it takes priority over junior loans in the case of a default. Broadcom’s high A-tier credit rating was an important factor in allowing Anthropic to secure the financing.
Blackstone’s funds invested in $9bn of the junior debt at a lucrative yield of more than 10 per cent, the FT previously reported. Bankers for Anthropic and rival OpenAI have also lobbied the largest rating agencies to grant the AI labs investment-grade ratings after their IPOs.
Project North Star’s $18bn junior debt, which exposes lenders more acutely to Anthropic’s credit risk, has been assigned a double B plus rating with positive outlook from Kroll Bond Rating Agency, a slight improvement from stable outlook in a similar deal known as Project “Big Sky” four months ago. It also received a double B rating from Morningstar DBRS, the people added.
The big three credit agencies — Fitch, Moody’s and S&P — which have bigger influence on companies’ borrowing costs, have not officially issued any credit ratings for Anthropic.
Anthropic, Blackstone, KBRA, Morningstar DBRS and S&P declined to comment. Fitch and Moody’s did not respond to requests for comment.
The problem is sharing it to a group of people who mostly aren't paying for it. It's a good way to get a thread that's just people reacting to the title, which drives HN toward just being a discussion board instead of a place to find good content.
Most news articles are not worth paying for. From leaving out details to serving an agenda; a lot of news has lost it's credibility. WSJ is a good example. It had really great long articles backed by real research before it was bought by Murdock.
There are some similarities between Anthropic (or OpenAI) and WeWork. Obviously the large difference is software but there is a whole lot of leasing hardware going on (neither frontier lab owns any infrastructure as far as I can tell), long term capital commitments, short term monthly subscriptions, no real moat for the frontier labs (or a hip office rental space), etc.
I think 2027 will be when a lot of companies revisit their AI spend and ROI. Have there been measurable business benefits reported by the media outside of programming?
Nothing solid. All the reporting I see is that numbers are measurable but not on the level many people expected, and that these systems are proving incapable of wholesale replacing employees, which clicks with my own knowledge of some large layoffs accompanied by press releases about streamlining corporate structures using AI and the outgoing employees asking “what AI?” Because they didn’t train an offshore team or document their department responsibilities before being let go.
My suspicion (and many others as well) is that AI is used as a way to cover for large corporate restructuring in the face of money no longer being available for “free” due to interest rates.
Anthropic is in the worst position of the AI labs. As far as investors and "the market" are concerned, the only AI that matters is the one built into your phone, car, your TV, your smart home and appliances. Anthropic has none of that share. It's all Google, Apple, and OpenAI powering Siri.
Personally, I don't think that tracks. If you are measuring "share" as share of vibes or "AI adoption" sure, but having AI in a phone, TV, smart home, etc doesn't make anyone any significant money. Hyperscaling isn't happening because there is an insatiable demand for retail AI usage. People are subscription'd out. A significant portion of people don't want AI in a lot of these things, another portion don't think they need it, and the ones that are eager to adopt it can use AI to bootstrap AI tools they use through a token router. You will have some revenue from retail AI but that isn't surprising considering I quite often have to provide tech support to people to help them cancel their subscription to a calculator app they've been paying $20 a month for.
Personally I view it like this - did ant and OAI push the industry forward yup? Will they fail as businesses though? Yup. Why? Business economics - as long as cost efficient Chinese replica’s exist it’s fine. Unless the us govt artificially makes the frontier market large then I see no chance of a large enough market that sustains current valuations.
More intelligence alone != more progress. And more intelligence in the context of business != more productivity that shows up in financial performance.
That's very true but unfortunately Anthropic have locked in contracts for so much compute that they absolutely need to expand beyond the corporations currently paying them.
Realistically they need to 3x their revenue to pay for their compute. While that's plausible, I'm not convinced that it can all come from developers. Many many developers are already using Claude. I think they need consumer, or some other market too.
> These new capabilities are powered by the next generation of Apple Foundation Models, custom-built in collaboration with Google and its Gemini models for deeply integrated Apple Intelligence experiences.
https://wallhop.io/https://www.ft.com/content/59383c7f-8d82-...
ChatGPT became publicly available in November 2022, and Claude followed in March 2023. It's year 4. It's foolish to think we know who the big names will be 20 years down the line, which is the kind of timeline bond investors are thinking about. A lot of people probably wish they could have invested (or invested more) in Google or Facebook in 2006, but they are probably very glad they did not invest in Yahoo or MySpace (or Friendster or Tumblr .. you get the idea) at the time, despite looking very promising (MySpace was beating Google on pageviews on some days in '06 and '07). Instagram and Snapchat and TikTok and WeChat didn't even exist yet.
My favourite example is amazon.com and pets.com. In hindsight amazon looks like an obviously good investment and pets.com doesn't, however amazon was heavily invested in pets.com itself.
But the idea in early e-commerce was sane. Undercut retail on cost, increase profit. The ones that failed, failed on execution. Pets.com failed because you can't ship massive bags of food below cost and expect to make a profit.
Setting fire to cash to have the best model that anyone else can also do isn't a business model. They're never going to be able to stop setting fire to cash or they'll fall behind immediately. Their user base will not pay the bills. It's an obviously bad investment.
I don't see anything obvious about that at all for amazon in the book selling days.
dog food is heavy, and a repeat purchase that often requires its own store and brings no joy. Its the ideal thing to get delivered to your house on a schedule.
books are something you choose differently everytime, and the people that like them read recreationally.
Bezos was on another level, launching a successful company(online books) that was effectively just a way to boot strap into an everything store.
It was actually even smarter than that. People also forget about "media postage" rates that existed at the time. (They still exist, just fyi)
In 1999. To ship a book using media mail cost of $1.13 for the first pound and $0.45 for each pound up to 7lbs. It got cheaper per pound above that.
This meant even heavy books were relatively cheap to ship. Way cheaper than random goods.
Wasn’t one of Amazon’s biggest advantages over brick and mortar stores the fact that you could effectively avoid sales tax for a lot of the purchases?
I heard that's why Washington state was chosen. Close to tech talent (Microsoft was there) and fewer people living there than other tech-heavy states such as California, so the US tax-exempt market was larger from Washington state.
I believe ChatGPT or Anthropic are both trying to be the "everything store" - whatever that may look like - of this brave new world. Call it a "business OS" and/or "life OS", they're clearly aware that research alone won't win dominance, and signs have been cropping up for the last year: Cowork, Research to Artifact pipelines, Dots/Instinct/Muse-esque personal assistant alternatives.
But the counterpoint still translates here: Amazon did not start with 1000 warehouses (i.e. Anthropic's debt obligations) and _then_ race to build a marketplace that would fill them with everything under the sun. It started lean and mean, and I suspect the Anthropic-killer will do the same.
And despite all of that ... Chewy is successful.
My favorite example is WebVan vs. Instacart. What a difference a decade makes.
Uber / gig economy seems to be the biggest difference
In some ways, the biggest "new form of business/commerce" that ubiquitous smartphones lead to was the entire gig economy.
CHWY is -7% per year since Sep 2019, while SP500 is +16% per year, and CHWY has 2% profit margins, give or take. I don’t know what success means, but I would not have wanted to buy CHWY stock anytime after IPO.
your point is well-taken enough, but the idea that a sharp investor was investing in Yahoo or Myspace in 2006 is a bit silly. Myspace was bought out in 2005, and it had been dead for years before that. and I don't think I had gone to yahoo.com for any reason other than testing my internet connection anytime in the prior 10 years before that. also, investing in Google or Facebook back then would be enough to retire on, much less had you invested in a basket of tech-but-not-obviously-dead-tech stocks. from what I recall from that era, the one things I knew for sure at the time were (1) Google is gonna be around in 20 years, bigly and (2) Facebook might be DoA in terms of social media endgame, but operationally they are very strong and executing.
what I really push back on is the idea that there are magically going to be all sorts of entrants into the space that meaningfully compete with Anthropic or OpenAI in the near or even medium term. for as much as the reddit set continues to put their collective heads in the sand while screaming "there's no moat"... there is a moat, and one so prominent as to be self-evident. not once has an open-source model come even close to genuinely reaching frontier performance for anything other than cost, and cost is by definition the easiest thing to improve on-- just distill bigger model into smaller model: boom, cheaper!
>Myspace was bought out in 2005, and it had been dead for years before that.
Myspace's peak popularity (in terms of monthly unique pageviews) came in 2007. Yahoo was ahead of Google for several years, though I am struggling to find good data on the specifics there. Your understanding seems to be based in your own personal experience of those sites, which is fine, but I don't think it quite matches the big picture.
Your proposal that there won't be any new entrants is puzzling to me because, in the grand scheme of things, OpenAI and Anthropic ARE new entrants. The entire space is new. It feels like ChatGPT became public eons ago, but it hasn't even been 4 years. Imagine saying in 2008 that no photo-sharing site would ever surpass Flickr - it's been dominant for 4 years! Why would you go to something new when everyone's photos are already on there?
One absolutely enormous advantage new entrants have in the AI space is that they can start on third base. Anthropic had to spend billions and billions of dollars to get to Opus 4.6. A new entrant could distill its way into a model roughly on par with that far more cheaply, and spend its VC cash on things like marketing and price cutting rather than burning it on actually creating a good model. The existing players need to service mountains of debt and are locked into deals for compute based on outdated assumptions about efficiency. I'm not saying it would be EASY for a new entrant to waltz in, especially to the enterprise market. But it's not impossible especially on the consumer side: Meta was essentially a non-factor until Muse came out, and now most people think of Muse when they think of an AI agent (as opposed to Claude, which is just a chatbot in the public imagination, because that's what it was when it became popular).
It's not even clear that the "frontier AI lab" is going to be the best, most profitable business model to evolve out of this tech. Especially if models are easy to distill, a new lab can start on 3rd base, and China open weights aren't far behind.
The winning business model may be 1 or 2 abstractions removed from "build a really good LLM model", and yet to be revealed in the marketplace.
Most people do not think of Muse as an the ai agent. Most think OpenAI or Claude. The people using Muse are using it as a facebook chatbot.
> Most people do not think of Muse as an the ai agent. Most think OpenAI or Claude. The people using Muse are using it as a facebook chatbot.
Yeah, I was with the case bring made, until that point. A very bizarre claim, especially given the questions around safety:
https://www.malwarebytes.com/blog/privacy/2026/10/metas-muse...
I disagree, Yahoo was actually seen as a technological leader and, unlike Google that was purely a search engine in its early years, Yahoo was a full platform that seemed poised for a while to become a landing page for the internet.
When it comes to AI there simply isn't a moat - Anthropic would be my best guess out of the current entrants due to them trying to construct one via value-adding code assistance tools that might create enough friction to prevent customers from jumping ship but the models themselves are a commodity that depreciates extremely quickly. If you want to retain the frontier premium you need to constantly be improving due to the ease of distillation.
Your comment makes the timeline seem very different from the way I remember it. Yahoo was dominant much earlier than Google, and was one of a handful of big search engines. Google was a relative newcomer at the time, which had the benefit of being way better at search, which is why it eventually dominated the competition.
If open source models stay consistently <12 months behind the frontier, that shrinks the moat considerably; not for all use cases, but maybe enough to scupper profitability
I was sad on the day when my.yahoo.com shut down. It had a bunch of RSS feeds, and news, I have not found a good replacement for it.
That argument swings both ways. There's no real reason to bet on OAI or Anthropic. Not with the rise of local models and the increasingly desperate attempts at regulatory capture.
That's what I'm saying. Either one could be Facebook. Or it could be Friendster. The rise of local models raises the even more daunting possibility that nobody becomes Facebook, but you have to imagine somebody will win the consumer market for people who just don't want to bother figuring out a local setup. That market is orders of magnitude less lucrative than the enterprise market, but a company that dominated it would still be one of the hundred or so largest in the world.
Someone can always optimize a model down to running on a typical 32 GB M6 or M5 Mac.
It doesn't need to be the best. It just needs to be good enough. I'm not smart enough, but if I was I'd be working on a locally ran interface engine. That's where you charge say 100$ a year. Said engine would allow you to use a large open source model locally to the point where it's good enough to rival Haiku.
If I had to guess someone is working on this
> Someone can always optimize a model down to running on a typical 32 GB M6 or M5 Mac. It doesn't need to be the best.
Two observations - one, there is some set of tasks where intelligence is already saturated, and sure, that threshold is rising by the month. So, sure, for some tasks you don’t need the best model. But there are other tasks that need models smarter than what we have now - like “make a cure for <disease>” that will yield billions of dollars of value, but likely require quintillions of tokens and higher IQ/token than anything we have now.
As long as we have non-saturated economically valuable tasks there will be incentive to train new and smarter models, and the frontier will always be on racks of GPUs not local distilled versions.
Two, it’s not clear that you can distill all the way down to 32GB even for the current frontier models. IIRC Karpathy has speculated that you can get an AGI into GPT-3 size, if you drop all the memorized facts and keep the reasoning core. But this is highly speculative! The best 32GB models right now are substantially dumber than even the large open-weight models, let alone the frontier closed models.
Perhaps I am missing something, what are you imagining of “a locally ran inference engine” that doesn’t already exist in a DGX workstation, Framework desktop, MacBook, etc?
>Perhaps I am missing something, what are you imagining of “a locally ran inference engine” that doesn’t already exist in a DGX workstation, Framework desktop, MacBook, etc?
Someone much smarter than me would figure this out.
One option would be an alternative bare metal OS which could allocate ram more carefully.
I'm sure with billions upon billions in VC funding a solution could be found.
Someone much smarter than me would figure this out.
Imagine if we had some kind of really intelligent AI model that we could pose problems like this to. It's obviously not the kind of thing an open weight model running on your local machine could do, but if we had some kind of really large, closed source model running in the cloud that could solve really difficult problems like this, I wonder if there'd be a market for it...
One way would be to generate a new model for every request. Currently we create an entire universe for each step when training if we predetermined what needed to be created it could be much smaller.
I stumbled upon this neural net compressor by Bellard that beats gzip on compression benchmarks
https://bellard.org/nncp/
> like “make a cure for <disease>” that will yield billions of dollars of value
I think it's important to qualify that while I will very much appreciate such an innovation and I think it'll provide significant value to society... I remain doubtful of the ability for any company to actually capture such an astronomical yield. Either there's enough of an investment required by the company to help get that to market that the yield will be significantly lower or everyone and their brother will just launch competing products two days later. And, as a society, I don't think it's logical for someone writing "Hey Alex, cure cancer" with billions of dollars so I doubt there's going to be much appetite for creating such a moat via legislation.
This gets to the same point of "If ChatGPT can make you a successful million dollar business in a few prompts why is OpenAI sharing it" paradox. It very well may be that AI tools can accomplish the labor that used to be required to create such a business, and I'm sure a few people will land a jackpot and get rich in the transitional phase but if it's so cheap to build it why would someone pay you money for yours instead of just running the same prompting themselves? For most people that's because they lack the knowledge that they can but you'll still have a significant number of instant impersonators.
> This gets to the same point of "If ChatGPT can make you a successful million dollar business in a few prompts why is OpenAI sharing it" paradox
I agree fully, to be clear o was using that prompt as a shorthand, I don’t think _you_ can type that prompt and get $1B back.
It can’t do this now, obviously. You need a lot of scaffolding and domain expertise to get the most out of these models. (Eg Harvey.)
But eventually, the labs can just run the business themselves.
I’ve long held that as we see models become more capable, they will be less available to the public. It seems pretty obvious that vertical integration on private models (perhaps a per-industry fine-tune, eg a legal model) licensed to subsidiaries will let the labs capture more value.
Why would you publish your model, enable competitors, and enable distillation of your hard-won intelligence? Only antitrust law can prevent this outcome IMO, the business pressure is extremely strong.
Great comment, I'm working on my own Hermes/Mac Mini & the amount of sec ops/setting up its arms is a good challenge as a non-technical.
Building the iPhone-platform equivalent of this would be interesting.
The thing is, would the top AI minds rather charge end users 40 to 300$ a month for AI usage of sell a 100$ a year license to run it locally.
I'm far from an expert, but I don't think it's impossible. Future models won't give out vacation ideas, they'll focus on one or two things. This might allow us to get the size down.
Would AI companies rather have $100/yr of license revenue or $480/yr of token revenue at 50% margin? Obviously the latter, by more than 2x? And even larger dollar profit from the higher sub levels you mention.
The constraints will work against them long term. All this is doing is forcing people to look at how to run more with less. These companies are bloated beyond reality and have no incentive to optimize anything. They're starting out with access to everything and still producing very little tangible value comparative to the inputs. OAI and Anthropic represent the pure gluttony of the US tech bro market and it's not going to be the long term winner. It can't be lest the masses are cool at crowning these narcissists as kings.
I think the hardware makers are going to have the last laugh, but not sure if that would be NVIDIA
Rise of local models? what rise? I run qwen3.8 flash next on my 200GB/s 128gb Strix Halo and even that is barely acceptable, big models require so much more. Nobody's running a local model on their power sipping phone. It's just a nerd's wet dream.
That's true today, but what will it look like in 20 years? Over time, algorithms and hardware always gets faster and more efficient. Like if I wanted to run a fluid dynamics simulation, what would have required a supercomputer 20 years ago can now run on a single desktop computer.
256x speedup on FAT32 just earlier today; I expect we'll have a nice bump in performance from domain cross-pollination for a good while now.
barely acceptable for all use cases? like a speed issue?
Regardless, looking back even a year at SOTA local models that fit on a non-absurd system creates a crazy trendline toward the future. And I think model routing is the gap filler that can make/break now vs. Oct 2027
I suspect Anthropic is of this general thinking as well; they've clearly switched from a thoughtful progression toward a natural IPO to "omg sprint for it". Along with the regulatory capture attempts, it's hard to imagine what is breathing down their neck so hard outside of this.
Totally agree local inference of any major scale is a nerd's wet dream short of many stacked improvements in efficiency. But again, auto-routing smooths over the rough edges if you are ok with a little bit of API spend when needed.
Also no telling that AI will be more lucrative than some "boring" industry like power generation.
Why would ASI still have companies around? Seems like an inefficient way to turn the planet into computronium.
Better comparison is probably the dotcom bubble, and companies like pets.com, webvan, kozmo, etoys, worldcom, global crossing, etc. It was not entirely clear to most investors in 2001 that google and amazon would make it.
I don't think pets.com, an online retailer, is an apt comparison to Anthropic, an bleeding edge research lab doing things that most people would consider science fiction a few years ago.
anthropic has come up with a lot of the core successful consumer facing tooling/standards around ai.
they invented mcp, terminal coding agent, context files like claude/agents.md.
I have no real clue whos going to win the model race but they have repeatedly shown that their internal taste is on point, reminds me a lot of early google.
With one huge difference: you could never run the Google index on your local PC. But you can run a 'good enough' AI on your local PC. Good enough that Sam Altman thinks you shouldn't do it because he has better uses for your RAM than you do (oh, and besides, because it may hurt his IPO chances).
Is this a surprise?
By any sane normal sense of business and financials these AI labs look terrible on paper.
Any loan would be speculative at best, and the rating would simply reflect that.
Investing in these companies is like going wild at the casino. Sure you might make a fortune but strong odds are you’re gonna lose your shirt.
I’ve discussed with many people at work, all engineers, they’re weirdly oblivious. Most are shocked when I say things like OpenAI and Anthropic are gonna crater.
There’s a notable difference between saying those companies may crater, and predicting confidently that they will crater. The wording of your comment makes it seem like a foregone conclusion.
Because it is, there isn’t enough money to be made to cover their valuation and their competition are essentially free by comparison.
I can’t imagine being so overleveraged that I run into economic thermodynamics. It’s like interstellar travel: there’s not enough energy available to do it quickly and at scale and efficiently. There’s not enough monetary potential energy to satisfy the buildout.
We've been hearing this since DeepSeek was released in Jan 2025, and in that time, Anthropic's revenue has grown 100x. And they're still growing like crazy.
So...why hasn't that "essentially free" competition taken them down yet?
Mostly politics combined with nobody actually knowing how to utilize AI yet. We’re just now finding a singular use-case for AI, which is coding. Every other use-case has been a complete dead end. Cool gimmicks for demos, but nothing anyone uses on any regular basis, and definitely not value creating.
Everything is very unstable right now. This is still a very new tool and we’re very much experimenting on a product level.
Tell that to the mathematicians, translators, paralegals, financial analysts, customer support reps, etc, etc.
Virtually all of those have no use for AI.
And, when they do, it’s because AI can write code. Take for example financial analysis. AI sucks ass at that, but it’s good at writing code. You’re using it to write code to do the analysis.
When you ask AI a math question, it’s not answering the question. It’s writing a program to answer the question. Because AI is basically only useful for writing code.
lol, this couldn’t be more false and reductive.
I guess the mathematicians are all freaking out over nothing, since AI can’t do math. Weird how it’s been solving these problems that have stumped humans for decades or more…just coincidence I guess.
I’m also quite sure that when AI is doing someone’s job by writing code to do it, they’ll be comforted that it’s not really doing it, it’s just creating and using tools from scratch to do it.
The desperate grasping for any kind of position to hang onto that AI isn’t already more capable than most experts in many fields is…sad to watch.
I never said AI isn’t capable or more capable than experts, just that it’s only good at writing code. It’s really, really good at coding.
The customer support reps find the AI assistance hamfisted and laughable. AI call transcriptions get big and small details wrong, often leaving out critical details in favor of generic and high level "customer and agent discussed an ongoing issue with their home appliance." AI "let me direct your call" bots are constantly sending people to essentially random departments. AI bots trying to directly be the customer service serve only to piss people off before they get to a human, and I've run into a few businesses that are only staffed by bots.
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for real i hold the opposing view i think openai will be a 5-10T dollar company in the next 5 years, but im speculating just as much as he is.
Just a tad bit more ; it like saying either an event will happen or not so probability is 50%.
Your scenario is certainly possible, that doesn’t make both predictions equally (un)likely.
I still think OpenAI holds on as a Google competitor and Anthropic does fine as a business tool. Do they match their outsized spending with future growth? Well, that I'm not very confident about. A lot of this really comes down to what is the business model of integrating AI with 3rd party systems and I don't see that anyone has truly cracked that yet.
I'm also speculating that they'll kindly donate at least 1 T of that to me out of the kindness of their heart.
At least you admit you're speculating (we all are). They confidently just say its a foregone conclusion.
If they're actually that confident I hope they short the stock long term with basically all their money.
I also speculate that OpenAI and Anthropic will crater, but I also expect that the market will remain irrational much longer then anyone shorting it today will remain solvent.
So yes, I believe it's going to leave a smoking hole in the ground, but I won't be betting money on it.
If you run a traditional short (borrow and sell the shares), you need to be right on the direction and the timing to make any money. One random surge in the price can kill your short even if said surge is short-lived.
The complete opposite is the generally held perspective among engineers where I work. It’s the management that think they’re not going to crater.
"It is difficult to get a man to understand something, when his salary depends on his not understanding it."
-Upton Sinclair
It's ironic that this describes both the engineers and the management, depending on where you stand.
> when I say things like OpenAI and Anthropic are gonna crater
But how do you know that? (And no I'm not shocked by this statement, it's just odd to say it with conviction.)
Two companies valued at 3 trillion dollars who sell a product anyone can make that falls in cost by 50% every single quarter.
I don't understand how anyone could argue that doesn't crater with any conviction.
Not sure about Anthropic, unless they continue to fall over themselves in ego. They have strong GTM in my take.
Google is a bit oblivious (though is probably the smarter money based on diversification alone IMHO). OpenAI has folks wondering just how publicly Mr. Altman can blunder his wording before he gets ousted (today was along the lines of "you will own nothing" with his take that personal RAM ownership being an affront to efficiency).
All three are trying to convince enterprise buyers that central tokenmaxxing is the best thing since sliced bread; many of the circles I run in occasionally have huge (emphasis: huge in the 2010s ZIRP sense) buyers that are begging for more edge AI.
I talk to our risk lead and he was also weirdly oblivious as well. The whole company is dependent on them not going down the shitter. Surely that's a tangible risk?
I'm indifferent either way myself. It's just shocking the amount of ignorance.
It's the cryptobro craze but for AI. It's tiresome, I don't have AI fatigue, I love using Claude, I have aibro fatigue. I'll lump in the people all in on AI, and that includes anyone who hates it vehemently because they just have to sour conversations.
Yeah. AIbros and AI companies are SHOCKED that AI and datacenters are unpopular.
But guys, if your companies weren't run by psycopaths and weren't promoted by assholes who appear to be the exact same guys behind the NFT craze, (this is the future of creative expression! Don't like it? Your a luddite. Cope.) and weren't predicting horrible nightmare scenarios based on their tech maaaaaaybe people would like AI and be more interested in using it?
I personally see that generative AI is a very powerful and useful technology but I hate everyone involved in it and do not want to see them succeed because they're icky people.
Its like if the nazis had discovered how to harness atomic energy before anyone else. Fuck that.
I've talked to someone who has worked at those companies, and they drink the koolaid that people like Sam Altman shove down your throat, even when its blatantly bullshit.
Note that both Sam Altman and Dario have conveniently watered down AGI, and moved away from "trying to achieve AGI" essentially.
1929 all over again.
Robber Barrons are back !
Question is: Who is gonna be the Carnegie that builds libraries vs the assholes who just hoarded
The model employee must live (or pretend to) inside the company's information bubble.
Words are cheap. Put your money and short Anthropic (when it IPOs) if you are convinced it will crater.
Are you one of those people who lost half their money to spacex? You sound like you are.
I agree with this sentiment entirely. Too much cheap talk. If you want to talk then you should be taking a position.
There is no position to take. The market will remain irrational longer than I remain solvent. But I'm not sure how long that will be
https://en.wikipedia.org/wiki/Hyman_Minsky#Financial_theory
How so? I thought Anthropic was very capital efficient. They still have more than 100B cash, and their inference margin is 80%.
The world is using 300T token a day. Barely anyone is using AI. This number would grow 30x. If there is anything, Google, OpenAI, Anthropic are undervalued by a great deal.
There’s no business in tokens. Zero. The only business is selling compute and Anthropic rented all its compute.
Structurally they’re in a terrible position on what’s unfolding now with the completely collapse of model valuations. With truly “open” models gaining more and more momentum models are basically worth nothing and “token” prices are collapsing to the cost of compute. That doesn’t leave anything on the table for the Anthropic and Open AI’s of the world.
openai has a billion users. Ads and they know it.
The math doesn’t math here. There isn’t enough spent on online ads to dig them out of this hole.
Is that potential source of revenue? Maybe but folks are verbally very negative on AI ads.
Is that a viable business plan for Open AI? No.
What’s with the low quality posts?
Google search ads are highly converting because they are camouflaged.
OpenAI ads are nothing of the sort!
Google search ads used to also convert highly because they were well-targeted and positioned at the point of consumption.
If I'm searching for red widgets, it's a fairly strong indicator I'm way more likely than the average to want to BUY red widgets, so a well-placed ad for redwidgets-cheap.com didn't feel hostile.
Now when it's "I am clearly searching for red widgets but you want to railroad me into blue doohickeys because they're paying more per click", you're one step above a Taboola chumbox.
Google search ads are just highly optimised over now 26 years. There's no reason that OpenAI won't do the same. How successful they are going to be is not clear though.
No clue how it's low effort to point out ads. It's not my fault you and everyone here lacks the product creativity to see the reality that they WILL be embedded in the answers and you will not be able to tell it's an ad. This will result in a literally the best ad system the world has ever seen. They simply need to wait a few years for the general pop to be reliant on them (which if you work with kids surprise they believe everything it says) and then slowly shift this over. They will be the most valuable company in the world bar none.
Do I think it's dystopian and shitty? Yes. Doesn't mean it ain't gonna happen and doesn't mean it's not the right move for the business.
> surprise they believe everything it says
It's not just kids in my observation.
The equivalent model for chatgpt would be randomly getting replies that are generated from a prompt injected with advertiser copy and indicated by a small "sponsored reply" in the corner.
Honestly, having written that out... I'm not sure that's an outcome that's generally avoidable.
Their ads aren’t converting like search ads, more like social
Even social is charitable..
Google, I hear, is very very good at Ads
but Opus 5.5 is the best product ever made in the history of the world (yes I'm serious). There's gotta be some consideration for that.
It has zero network effects and barely any economies of scale. It's a good product without a moat which will get replaced by any company with their own compute and cheaper energy.
I think the only way out for Anthropic is to buy a few neoclouds as they go under, but even then it's a coin flip as to if they survive the next 3 years.
Or they partially crash and get bought by Oracle. The Sun route
The market is a cold beast and doesn’t care. If things hit the fan it will happily take ownership Opus 5.5 in bankruptcy court and slit everything else out on the street.
If people paid a profitable price for Opus 5.5 it would not be the best product in the world and would not be slotted into so many things where it's benefiting from ubiquity. That circle needs squaring. There will be an Opus 5.5 that's a 10th of the price in a few months.
I personally don't really believe the meme that Anthropic and OpenAI are charging less than the marginal cost (cost of inference on requests). Open weight models have surpassed the abilities of their closed weight models when such claims started, and the inference costs on said open weights models are provably not so high.
Where they quite reasonably might be making a loss is their marginal costs + amortised fixed costs (training).
I predict that eventually the training expenditure is going to slow down. At that point, the large commercial providers are likely hoping they can keep inference demand up, and recoup their investment.
The driver for the training is an arms race between providers and open weights labs. But several dynamics could play out:
* Models will be seen as good enough, with only fine-tuning required, for most use cases, and demand for expensive training of foundation models drops off, stopping the arms race. * Most of the participants cannot raise capital due to lack of market confidence in them, and there is a 'winner takes all' dynamic with the remaining provider, who can then raise costs.
I personally think the first end game is more likely than the second.
You are absolutely entitled to call it a meme but that is also their own internal reporting/panicked emails. Subscriptions are offered at a large subsidy with the benefit of more training data and adoption.
> I predict that eventually the training expenditure is going to slow down. At that point, the large commercial providers are likely hoping they can keep inference demand up, and recoup their investment.
The big labs can't do this that's their edge over open models. The stickiness is critical for them to keep up. They might try again but it's going to finish badly.
Cost per token is going to tank; it's a race to the bottom. The frontier model providers have no moat between each other and eventually even the open weight models will be "good enough" for almost every purpose.
Right now the market is expecting something a lot more than "sells a lot of tokens" out of these things to justify their valuation.
The future is in private data, private models, and private APIs, which can't be distilled as easily as public APIs.
Wouldn't that depend on how much data? I assume those who own the data would rather buy the hardware and create models from this data themselves, as opposed to sending their data off to someone else, or allowing it to be pirated.
For most of those Google is much better positioned.
I disagree, the moat will be of those holding rights for valuable problem domain specific training data. Examples I can think of:
Adobe sits on a treasure trove of:
- Copyrighted multimedia
- Years of telemetry on content creation
Zeiss sits on decades of data:
- Medical imaging, and surgical footage.
- Silicon waffer inspection.
These embody a training corpus that is de facto (lawfully) out of reach of the crawler scrapper approaches of Google/Meta/OpenAI/Anthropic (even if they pirate the seas for torrents like before).
Neither Adobe or Zeiss are an AI power house but the barrier to entry has also dropped with all the movement in the field.
Google owns Google Books and a myriad other data sub businesses beside the index of the entire web.
Excellent point, and one that’s not raised enough in these conversations.
Well openai and anthropic are sitting on absolute treasure troves of private data that is their users' interaction with their models.
personally thats why i think OAI is in a way better place with them targeting ads.
Yeah, nobody is doubting that people want to consume tokens. We're doubting that that Google, OpenAI, or Anthropic has a significant enough moat to keep people buying their tokens there. Over the past couple years the moat has been the model. As open models are getting better, that moat is drying up. Eventually the market for "tokens" will converge with the market for compute, which is a commodity with little to no moat.
I think even if the moat dries up, you could think of tokens/inference similar to compute. There's all the open source tooling available in the world for you to rent your own bare metal servers/racks and use a white glove service, yet there is demand for cloud.
At current prices, it makes sense to self host, but if the price goes down and the demand grows across different verticals, there is room for this business.
CFOs looking at tons of 2026 token spend but with no clear ROI have joined chat.
If everyone is using AI the ROI could just keeping up with everyone else. If anything Intelligence is now becoming a tax or the cost to use the toll road.
ROI in the space is not complicated, their revenue is already insane. You just drive down costs with fixed capex (GPUs), that will continue to pay out over 5-10 years. These are growing pains, they need to make it past the GPU scarcity/ramp up phase.
The CFOs he is talking about are the enterprises who are spending big money on the tokens ; they are not seeing RoI ; not hyperscalers or frontier labs
Yep, i can't disclose specific numbers but we went from $X/month/person budgets to $X-100/month recently, and this is a pretty big company with ~5000 people. From what I can tell the pursestrings are only tightening further too, too little actual real-world benefit for the obscene spend
% would be helpful, is it -1% or -100% per month per person?
Correct. The people that control the pot of money these labs ultimately need to survive seem increasingly unconvinced that giving these labs money is a good idea. At least at the levels the labs need to not go bankrupt.
What GPU scarcity? Unconnected GPUs are being hoarded by the oligarchy:
https://www.businessinsider.com/companies-hoarding-unused-ai...
Reports indicate GPUs are increasingly being put on shelves in warehouses. There’s a scarcity of power and data centers to host them, but data increasingly suggests the world has an over supply of chips at the moment.
Like most “scarcity” scenarios things flip from under supply to massive oversupply real quick. Remember during COVID when lumber got stupidly expensive and then almost overnight folks couldn’t give wood away.
If models got smarter and an average prompt response used let's say 5k tokens instead of 10k tokens, would that change your prediction?
Point is, the number of tokens is a silly metric to proxy these companies' growth.
Everyone I know and their mom is using AI, technical or not. The question is, is it profitable to run a frontier AI lab and sell inference while spending billions on R&D.
Anthropic could be super profitable right now if they stopped developing new models. But then they'd quickly fall behind because there's not much of a moat. Maybe the only ones who will survive this are huge companies like Google with separate incomes who can swallow the cost of staying on top.
I saw a (highly reputable of course) tweet a few days back suggesting all of OpenRouter's DS4 traffic would fit on a single rack of modern Nvidia kit.
Hm, that’s doubtful. I think even one instance of DS 4.1 Flash alone at ~500B parameters needs a full node (8 GPUs).
4xh200 seem to be enough if you cpu offload the engrams (which is pretty typical and even expected).[1]
And these are quite old cards by now. But yeah "all of OpenRouters traffic" is gonna take more than a rack
https://recipes.vllm.ai/deepseek-ai/DeepSeek-V4.1-Flash?hard...
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high token usage but where is the value? How come the other s&p companies aren't killing it?
Well they just need to use it properly and build a bunch of software systems for them to lever..oh hang on a minute...
It's like Agile, if it doesn't work, they must be doing it wrong.
And price could drop 100x.
By reading the comments I wonder if perhaps OAI & ANT are not going to be “model” companies and they are going to be the ones innovating in many fronts and cashing in for the benefits of those advancements…
What I’m trying to say here is, what if, they are staying closed strategically so they keep their most advanced models close to their chest until they make an incredible discovery. Say they cure cancer. In this case, they are taking that IP collaborating with big pharma and cashing in on the upside of such discovery.
So, what if on top of being just another enterprise and consumer product that accelerates the way we all work and live, it also becomes this factory of breakthroughs and that’s where open source models can’t compete with them.
Just speculating…
We're already making huge advances toward curing cancer.
They're not coming from LLMs. They're coming from mRNA-based treatments and applying standard science to their basic principles.
And just to reinforce what djierardi said, you can't make successful new drugs and treatments for diseases just by thinking about it really hard. Practical tests are an absolute, unavoidable necessity—and not even just the final clinical tests; you have to be able to see how the molecules you're synthesizing interact with living tissue of various types, among other things, before you can even hope to test on humans.
Delegate that entirely to an LLM, and you'll end up giving clinical test subjects something that will melt their flesh, or coagulate their blood, or give them turbo-cancer. Because there is no comprehensive dataset that they can use to figure that stuff out purely by computation. We, as a civilization, simply do not yet understand biology and chemistry well enough to model it at that level.
So stop trying to figure out just how OpenAI and Anthropic are playing 12D chess and actually this situation is fantastic and will result in them saving the world. They're just greedy, selfish companies trying desperately not to be left standing when the music stops and all the chairs have disappeared.
I appreciate your points of view. I’m not trying to figure out the 12D chess these companies are playing nor I’m convinced these are the salvation of humanity. I just enjoy exploring all perspectives possible before arriving at a conclusion. It’s my defense mechanism against echo chambers.
You just made me think of LLMs as useful translators. I know they are not going to be the ones leading to the discoveries other ML mechanisms can. But LLMs are a great way to communicate though to a machine. Before it was only for those able to code, but now that problem is being resolved.
That is not to say that computer scientists are no longer useful, but it is now possible for everyone to talk to the computer and viceversa.
It sounds like you're positioning the LLM as "a computer that can speak human language (and also computer language because it's a computer)". If so, that's anthropomorphizing them to a concerning degree.
Even given the significant advances in coding models, everything I've seen described and experienced myself with them tells me that non-coders simply aren't going to be able to build and maintain nontrivial projects with them unless they get still better at it. By a lot.
And in the realm of medicine, the kinds of mistakes they absolutely will make, and defend, confidently, will get people killed.
Its plausible. Its just that the effort will be huge. LLMs can do a lot of stuff because they've ingested all of our published text. But, unless we're extremely lucky, there's just not enough data out there to solve this problem yet.
Maybe like with problems in maths: they've solved 700+ open conjectures, but how many did they try? Are the ones that they solved merely those that they put $10M effort into where humans just gave up because there wasn't any concrete benefit in putting in that sort of effort?
yes!
Sounds about right. About the only way you could justify the projection of making so much hand over fist.
But can they actually pull it off? Since the open models are kind of able to pull off the same things.
Maybe they can brute force faster, but not releasing allows another to scoop it.
Public closed-source models remain superior on paper, to the best of my knowledge.
Now, consider they have one, two or three generations of models being tested and toyed around before being released.
Those would be the ones used for breakthroughs.
What a movie. Act like religious whackjobs (A) who want to consume the world. In secret you have a bag of world-changing causes. The setback in the middle of the plot is the supercomputer answering something mundane to a serious question. Everyone feels defeated. But one underling is chatting with his pal subagent 3495. And he mentions the mundane answer. Then the subagent makes a critical connection; the answer is not mundane, it just needs a proper interpretation. Then they send the answer out (15 minutes later) cancer is cured. They use the patents to pay off the debts and then give them away to the public. The CEO moves to a shed on Más a Tierra with a talking parrot companion.
I don’t think the cure for cancer will be the result of the prompt “tell me the cure for cancer. Make no mistakes.” But instead applying real AI and ML supercharged by recent advancements to identify genetic mutations that could possibly cure cancer or other diseases.
Can I assume that you'll volunteer to be a human guinea pig to test these proposals?
No thank you.
And I’m not proposing anything. I’m simply using my imagination here.
We’re supposed to fluff up AI companies with cheap words here, not actions.
> Say they cure cancer.
A pet peeve of mine.
People keep mouthing this same fantasy. But cancer is so many different cancers. And "the cure(s)" aren't just 1000 different drugs, but maybe 10,000 different candidates for each that pass into lab tests, of which maybe 1% make it to phase 1 trials, and of those maybe 10% make it all the way through phase 3, each after 5-10 years of clinical trials and $100s of millions invested each. For the most part, AI-related biotechs are largely focussing on accelerating the discovery of good candidates. But that's a tiny fraction of the pipeline, which includes lots of lab work (which can be automated) and clinical trials (which OTOH require actual human guinea pigs and years of observation and assessment). How do you meaningfully accelerate that? Presumably by improving the odds of success in phase 3 trials from your initial candidates.
But nobody has proposed yet how LLMs can be a magic bullet here. At best they'll give you those 10,000 candidates per cancer type faster. But then you have all that lab work and clinical work to slog through, and all the likely failures you'll see in clinical trials. And there's just not enough data out there to train ML on to meaningfully improve the odds of success in clinical trials, since you'd really have to understand how your drugs work with all proteins and systems in every type of human body, e.g. "in silico" clinical trials. And we're only merely scratching the surface of that with stuff like OpenADMET.
That is, we can't even model well "how a human body works", much less "how those mechanisms go wrong in each type of cancer". But even if you could do all that, for the cures you'd need to figure out what novel, unusual, out-of-distribution thing (relative to your human model) you can throw into the system to avoid the bad behavior, that doesn't kill the person or interfere with other systems or get degraded too quickly or ... tons of other stuff.
Highly non-trivial. Maybe lower-hanging fruit is with a novel peptide or antibody, since those are better modeled with ML. But small drug-like molecules are galactic in number and not well modeled yet. ("Hey, what happens if I introduce this weird platinum-based drug into a body?" Well yeah, cisplatin is used for chemotherapy, but the body of knowledge needed to assess platinum-based drugs in a human body just doesn't exist, and ML isn't gonna intuit it from known data, nor "learn it" since that would require going down to QM, and then integrating that into its model of all human peptides and pathways.)
This is not a side-project for a chatbot company. Its a (multi-)generational project that requires buy-in from labs that can produce the requisite data.
(And okay so maybe I'm fast-and-loose above. Ideally a single "cure" would be to fix the apoptosis pathway, i.e. repair the built-in cell-death path for fast reproducing cells. But cancer cells mutate quickly and evade known avenues. Perhaps there's a generic fix? Plausible but same reasoning applies: ML doesn't have enough data to intuit a solution, if one exists. And there's no reason, a prior, to assume that one does exist. Only hope.)
Great take and useful for the thought experiment.
I just feel like your taking it all as a given/fact. And maybe you’re right, but I’m not that well versed into it to say things for sure.
What I'm trying to get at is that if we’re considering a black swan event, why not consider both the bull and the bear case or even the neutral one and at least accept that we don’t know which one it will be and it may well be a coin toss…
Perhaps using “cancer” deviated things into a specific subject, but it could be anything. A drug discovery, quantum computing, etc.
Again, I’m no quantum physicist or chemist, but as a world citizen, these feel within the realm of possibilities. Just like it destroying humanity or simply just being yet another technological advancement like the computer or the internet was.
—-
I guess the people working on computers could have argued there was a 10% chance it was going to end humanity and so the people working on the internet because AI was going to come about.
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I’d say that my final conclusion is that we don’t really know and the signals we’re getting from founders, investors, the market, here, and other people we talk to is that NOBODY KNOWS. All we can do is to continue to speculate and make bets based on hunches.
Yeah. What I think is deceptive is something like solving Navier-Stokes with $10M of compute. The bigger question is how many conjectures and hypotheses they tried, and what fraction of those actually succeeded. If they expended say $1B on various open problems, and only 1% succeeded, is that the kind of investment that one will make? (The $10M alone is more than what would have been devoted to academics investigating these issues. But realistically one has to quantify the cost of the entire enterprise, and the probability of success vs failure.
From OAI we're hearing only about 700+ successes. Relative to what failure rate? The cost of success isn't merely the cost of each success, but rather the overall cost of all experiments, of which -- I'd guess -- a huge fraction failed.
I think that’s what VCs do. Only a fraction of investments succeed and the ones that do pay off big time.
Now, imagine having a mechanism that will constantly improve, so you can go from 1% success rate to 5% success rate in say a year.
I see your point. What I think could be different is the speed at which things can be done compared to conventional ways of discovery.
Think of a COVID/MRNA like solution for other areas.
I'm all in on that, and I own Moderna stock ... so there's that. But we sort of understand the pathway there: we want to introduce a protein that induces an immune system reaction. Traditionally you re-engineer a virus that produces that protein that produces the RNA that gets transcribed into that protein. But instead, with better tech, you introduce that RNA directly. Simpler and faster. And perhaps you can use that mechanism to introduce other proteins for other ends (as recently with its personalized melanoma treatments).
I'm all for the notion that ML can improve 1% to 5%. But I haven't seen anyone suggesting HOW that happens. So far, it looks like taking 1000s of candidates to 10,000 candidates, which just further clogs the pipeline. Using ML to automate lab tests seems like a win there. But still, pushing those through phase 1, phase 2, phase 3 clinical trials is the bottleneck. 90% of what enters that pipeline dies, and each involves many years and many $100s of millions of investment.
But as I noted: the quicker path would seem to be through peptides rather than small drug molecules. ML does pretty well with proteins/peptide chains and mRNA provides a way to introduce them (or injection, like GLP1's). Still requires all the testing, though, but presumably we know more about how they degrade, etc. over time.
It seems you just don't wanna see the bubble. Or maybe there isn't a bubble and that's your call to invest more? Amodei would be happy.
Oh, I see it. I’m just thinking 5–10 years down the line not short-term volatility.
Could there be more ChatGPT moments down the line in other areas like science, genetics, space and something we haven’t considered? I think there’s a chance.
I also think AI is here to stay and there may be some confirmation bias from the community regarding open source models. It’s like someone saying back then that Linux was going to take over… (?) Open source software seems to be served for technical people. Not for the average Joe looking for an answer or help generating a social media post or help writing an email.
Indeed, ML is here to stay. But there's a difference between biology/chemistry and math, e.g. There isn't enough data out there on which to train MLs for the former.
I'm thinking here of LLMs as a lossy compression of all our published knowledge. And then agents doing some smart/heuristic search relative to that. They can surely find things that we have missed -- just because they'll tireless try all avenues of investigation that humans might have given up on -- but if the underlying data is not there they can't manufacture it.
LeCun has made this point elsewhere wrt world models and more ordinary physics. This issue is exaggerated in the case of biophysics and quantum mechanics.
if they can actually do it, they're in big trouble because no one is going to let them hoard truly novel discoveries, eg: new physics, curing cancer.
Maybe they can’t keep the discoveries secret… idk.
But what they can do with them can make a difference. Especially with the access to compute and resources they have compared to anyone else. So, they still have a competitive advantage.
The one current example I can think of is Google. They have Alpha Fold and Quantum Computing. I believe they’ve seen advancements to the way they work and an improvement to the speed of progress. It’s just that it’s not what it’s talked about.
Yes. I doubt they’d be able to patent those discoveries without serious litigation from the healthcare sector. The models are all “polluted” with others’ IP.
Probability of "if"? I'll give you 1000:1 odds, if you're game.
IMO once AI can flywheel discovery, the entire international ip/patent system would simply break down - world is not going to allow US companies to automate innovation pipeline and horde / extract at scale, unless US somehow maintains full leading edge AI monopoly. Closed model discovering something a few months before open model and getting years of exclusivity rights is just not going to happen / last. If anything expect mass invalidation of IP which will disproportionately fuck over US incumbent IP leadership position.
An IPO is where the investors cash out to spend money on something else. It’s where the founders and early employees get rich enough to start chasing other interests, or just retire outright.
My understanding is that the underwriting bank and early investors and institutional investors win first, then founders, after that early employees. Usually offloaded onto the robinhood crowd who excitedly buys in.
According to this analysis of Anthropic's S-1, the founders own about 10% of the company, which at $2T valuation would mean they each make about $28B. The employees collectively own about 15% which results in some staggering math - thousands of employees making ~$100M each, even those hired as recently as last year.
These are averages, but still!
https://x.com/AndrewBenson/status/2115458726396310008
Jesus. Why doesnt every man, woman, dog, chat and child work at Anthropic right now?
I was on my way but the train broke down again!!
Because if we did, we'd each get about 1000 bucks.
You do understand how individual slices decrease in size as you divide the pie more ways, right...?
That’s a rather astounding amount of cash to have extracted per capita.
It truly is. And, looked at that way, it is an indictment of the systems that allow this, and yet another piece of incontrovertible evidence that we need to change those systems to bring money, and power, back from the ultra-mega-wealthy to the populace at large.
It just, y'know, isn't a good argument for having everyone work for Anthropic ^_^
Don't we, though?
Yeah - maybe we get a free token on IPO day.
In contrast, back when RedHat went public they reached out to everyone on the open source mailing lists, and gave everyone a few shares as thanks for the work they were benefiting from. I got some shares even though all I had done was post some free video format converters.
Employee shares usually get put in a 3-6 month lockdown so they don't get to participate in the bubbly robinhood excitement.
It's also often also part of the employee retention plan. If you discover your boss has lied to you shortly after an IPO or a merger, then odds are good they were trying to hold onto you by any means necessary until their bonus was paid out.
I've had several people I trusted destroy our relationship this way over the years.
The s1 lays out the exact schedule and let me tell you it is not a normal schedule. Is that a surprise anymore though?
Oh wow, I looked it up. Employee's get to sell up to 10% on day 1. That's cool for them.
In general I’d agree with you but where else would you go to take the money that’s more exciting than the forefront of AI in the world? Retirement makes sense, though if they wanted to retire they all could and own multi-million dollar homes and do so now, pre-IPO. Post ipo I guess you get a few super yachts and some land in Hawaii? Idk at a certain point I think these guys like the chase more than the result.
That certainly comes with the territory. However, being a public company offers a lot of opportunity for the company - easier access to capital, to enterprise/government contracts, and attracting talent that want less risky compensation.
IPO = exit for a lot of people
There's also a pretty big morale hit for the people who fall below the remuneration cutoff of peers they are almost as good as or feel they are better than. I've seen a few different people lose almost all of their fucks because some dweeb is buying fancy cars and they're still paying their mortgage every month.
Initial Position Offload
Counter-examples: Amazon, Microsoft, Google, nVidia
That was a different time and that’s a very small sample of survivor bias.
That makes sense, anthropic is in a rapidly changing market. Revenue could crater if a better model is released by a competitor. AI demand could fall behind. Doesn't seem like that will happen, but its definitely not investment-grade debt worthy either.
Even if Anthropic invents a godlike frontier model that can do anything, it feels like they won't have a moat for very long before competitors like DeepSeek offer something almost as good for a fraction of the price.
If AGI is created, then it will be used by someone else to create a cheaper AGI, making the finances of the person who borrowed trillions to create it in question.
An extremely hypothetical experiment but what if this is some sort of MAD (Mutually Assured Destruction) and in our timeline, we have accepted it with AI.
Deepseek can do MAD on Anthropic and release open weights model (which means that they might not earn the most or basically burn millions/billions to then give us all essentially the same thing and we would all be on equal footing, oh they also released their inference engine right now.)
This might explain why the stock market got into an bubble and is fearing itself right now.
Mutually Assured Commoditization. (Credit where it's due I pasted my comment to Chatgpt and it gave me this term ironically and it seems to fit well to the comment that I am writing, I might write a blog about it someday.)
MAD / MAC assumes everyone has the same leverage on each other- that they are all as heavily invested in capital.
Where it falls apart is when you get companies that take open models, rent compute and sell tokens at a profit, without heavy investment into research or training. They spend way less, and if they can keep distilling the frontier models, they can produce reasonable products without the massive losses of subscription plans. In your example, DeepSeek et al aren't nearly so vulnerable. There's nothing mutually assured.
It is much more of a classic race to the bottom. Every new frontier model shifts the goal posts a bit, but eventually the house of cards will crumble - or at least, that's what the market seems to be thinking will happen.
Subscription plans are not massive losses. What are you on about?
They are simply less profitable than API pricing. They are on par with what sellers of open model tokens sell for.
If you price out Subscription limits against API pricing, subscriptions are 20x-100x less depending on model and so forth.
Yes, that's accurate.
Are you trying to say "the Anthropic API surely cannot be highly marked up! Therefore subscriptions surely are selling at a loss!"? If not, I'm not sure how your comment is particularly relevant to the discussion?
I mean, it's worth pointing out because either API pricing is crazy marked up and thus subscriptions are still profitable if they are used at full amount or subscriptions are subsidized and API pricing is more true cost. Sure, it could be second one but due to average usage, it's profitable overall.
Right. Either way, there's nothing at all to support that subscription plans are "massive losses" except the original commenter's imagination. A subscription/API price delta does not a massive loss make.
Agreed, but there is also no information to think subscriptions are profitable either. S-1 doesn't really explain it.
It would explain why Anthropic are anti open-weight too. They need the moat
This moat issue comes up a lot, but look at what's been happening so far; open models are consistently 6-12 months behind. What if their moat is simply staying ahead?
And once their models start to accelerate their progress, which may already be happening, this would further solidify their lead.
Demand is currently not sustainable because they - ant and OAI - refuse to raise prices.
Infact they’re going to end up in price competition unless they field an army of ‘FDE’ who go to every large firm and reorganise the operations around their models and displace labour.
I was under the impression this was only on the consumer side? I have heard of contracts expiring and them wanting a lot more money now (forcing from generously subsidized enterprise seat plans to all API usage). Specifically Anthropic in this case.
GitHub copilot then comes in and offers multi provider support including open source models, and gets the contract due to Microsoft’s existing establishment and lower price point.
Anthropic can’t raise their prices without loosing market share of Claude code
How unusual is this? Amazon spent over a decade with junk ratings.
Amazon didn’t borrow anywhere near this much. They also had the sales tax loophole that let them outprice the competition and take their market share for over a decade.
>> Amazon didn’t borrow anywhere near this much.
Amazon also did not go from ~$1B revenue to $16B in one year.
Meh rookie numbers you give me $250B too and keep doing that every year I will generate revenue $125B revenue easy. I only have to sell a dollar for 50 cents .
Same Amazon to develop AWS to profitable business spent only 30B in all capex (including e-commerce) over 12 years between 2003-15 .
Right, OK, but then that's the interesting or newsworthy claim, not the company's ratings status --- junk status for companies that later went on to become among the most successful in the entire marketplace is not unprecedented.
It is the combination of several facts what makes this unprecedented, junk rating being one of these
Amazon did not acquire debt. Period.
Huh? Yes they did. Why do you think they didn't?
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As someone who uses Claude Code daily for work, I fail to see how they lack a real product. It provides tangible value on a day-to-day basis, something my workplace is happy to pay for.
The more interesting questions aren’t about whether they have a real product (they do). It’s whether they can defend that product from competitors, such that their future revenues can justify the massive investments made.
Anthropic has one of the most successful consumer products of all time.
The consumer product is losing them money and the possibly profitable enterprise product (API) is a smaller % of revenue and in an increasingly competitive niche. The constant disingenuous baiting and switching between the two whenever convenient is very tiresome.
That is a different argument and not one I'm engaging with. The claim was made that Anthropic has no product, which is just obviously false.
Really? The most successful consumer product? of all time?
Among them, clearly.
Self evidently so, I suppose.
Perhaps I am just blind to consumer markets, but it is not self evident for me, at least. When I think of successful consumer products, I think of stuff like Coca Cola, the light bulb, the CD, acrylic paint. Are you saying that Claude is as successful consumer products as the Ford model T, and Nike sneakers? I am not buying it.
Anthropic, founded just 5 years ago, has revenue comparable to all of Nike.
I think speed to market and revenue of the company are nonsensical measures of successful consumer products. Like did Theranos have a successful consumer product? How about Juul?
No? Theranos generated under $1MM in total revenue. Juul is obviously a highly successful consumer product.
The anti-ai lab crowd is becoming increasingly cult like and putting their blinders up.
Anthropic's S-1 (IPO doc) says they will spend >$0.5T. Where is that money going to come from? The gap between the frontier and the open models isn't large enough to generate anything like that in operating profits. Debt markets are getting tapped out with AI debt. We are about to find out how much stomach equity investors have remaining. The fact that they might have trouble servicing their current and expected future debt comes as no surprise.
The S1 actually said it has $855B of commitment. Above $1T of you take in account some that don’t have fixed prices like extra TPUs and the Broadcom agreement. And that they will also build their own infrastructure, on top of that.
I’ve already stashed the popcorn.
It would make sense that they'd be considered high risk borrowers.
But that won't stop them from issuing bonds or more convertible notes as they have and the market may or may not bite.
It does add pressure to their IPO proceedings being as high as possible as their funding mechanism tho. But reduces pressure on them if shares crash a bit after that, still high risk borrowers.
"Hey Mr. Inference Provider, you can sell tokens for $0.03 that cost you $0.02, to PRC end users who are too poor to use Claude or don't like that it refuses their use case, which is spam, scams and cheating on homework.
Or, you can trade $0.03 of tokens that cost you $0.02 for $1.00 in ANTHROPIC SHARES, which makes the thing that everyone uses for like, math discoveries and curing cancer."
Average Hacker News commenter (not you): "Nooooooo it's not profitable Anthropic is going to crater nobody will take their convertible notes"
Many companies IPO'd in the late 1990s and did not exist 10 years later.
Let's say that the house of cards crashes down and takes the market with it who will shutdown, survive or have no choice but to get acquired for a 90% discount.
Some will be just alive long enough for the early investors to pull out enough before the market crashes.
And who will be the Amazon and grow 2500x over the next 30 years?
Now, I'm not saying that's going to happen -- but if you take analogies from the dot-com era and assume they're going to play out in the same way, you can't just cherry-pick the bad ones.
Amazon had valuation of $60M at their first venture round, and it took them 19 years to 3500x it to $200B.
Anthropic had valuation of $550M at their first venture round, and assuming it IPOs at roughly $2T, it will take them 5 years to give this 3500x return to their initial investors.
Companies IPO much later now because the laws changed. They used to be forced to IPO much earlier because employees shareholders pushed them over the threshold for # or private shareholders.
So you're absolutely correct about the comparison.
That said, I bet whatever happens over the next 20 years goes beyond what any of us can imagine. I wouldn't be surprised if the winners of this paradigm are worth hundreds of trillions. Sounds ridiculous until you look backwards and valuation a and consider this is an exponential curve and time is compressing.
With enough inflation, you and I might be worth hundreds of trillions. See: Zimbabwe.
Well sure..., but what might be less obvious is that a can of beans, a tank of gas, a wig and a chair can all inflate at 2% a year while stocks and land inflate much faster.
The way we measure inflation is focused on consumption goods that everyone depends on. It doesn't take into account the stock market.
In other words we could stay around 3% in measured inflation but see ~30% annual equity inflation...
So my point is that the most valuable companies in 20 years could be worth hundreds of trillions while the cost of a can of beans is like $5, not $200.
Zimbabwe type inflation happens when the economy doesn't get more efficient at producing stuff as fast as the government prints money. If the economy increases efficiency very quickly and the government prints money to peg at 2% inflation, the products we consume only increase a little bit while scarce assets increase a lot.
This is why in the time I took a can of beans to increase in price 3x a house increased 20x.
> I wouldn't be surprised if the winners of this paradigm are worth hundreds of trillions.
And the winners may be not be the obvious choice.
And, more importantly, the dollar is inflating.
> And who will be the Amazon and grow 2500x over the next 30 years?
Almost certainly nobody if you're tracking post-IPO growth.
And not because there won't be survivors/winners when the next crash comes (there probably will be) but because of the market changes (in the wake of legislation like the JOBS act) that result in these companies having their significant growth periods prior to IPO instead of after it.
IPOs used to be a launch pad to growth, now they are just a way for private investors to finally cash out when the company is already close to peak valuation.
None of the current vintage of IPOs are going to 2500x in our lifetimes (inflation adjusted at least..). The VC to IPO process is totally different now.
Companies are staying private longer, raising many orders of magnitude more private funding, and IPOing straight into "top 10 biggest companies in history" size.
If you IPO at $1.5T you are already equivalent to ~1.5% of World GDP.
Thus is just for scale, as comparing GDP vs a company market cap doesn't make total sense since GDP is 1 year economic activity and market caps represent discounted future cashflows of future economic activity...
Let's say Anthropic IPOs at $1.5T and grows even 1000x over 20 years, it would be worth $1.5P (petabillion?). If it had a 20x earnings multiple like google this would imply Net Income of $75T. Mag7 total Net Income for 2025 was $500B or $0.5B.
World GDP is $125T, and has grown less than 5%/year before/thru/after DotCom. So lets say in 20 years its $250B and Anthropic is capturing 30% of world GDP in earnings (not revenue, that would be even higher)!!
So the math doesn't math.
For comparison, Amazon had a valuation of $500 million at IPO, roughly 4,000x less than Anthropic.
You can cherry pick the bad ones if the point you want to make is that it's risky. Nobody is saying Anthropic is definitely going to 0, just that there is a risk.
But from an economics perspective OAI/Antropic are more similar to Amazon than to Pets.com. Pets.com always had a heavily negative COGS - they were in the business of selling $20 bills for $15. On the flip side, Anthropic appears to be positive on COGS, and OAI could get positive by cutting limits on free and $20 plans. OAI/Anthropic are negative on the books mostly because of massive R&D expenses. This compares well to Amazon circa 1999, which was positive on COGS but negative overall due to massive buildout R&D costs.
Note that I'm not saying OAI/Anthropic are destined for success - because the rate of change combined with competition with cheap Chinese models is pretty novel in the business case history. But I am saying that pessimistic dot-com-boom comparisons don't seem to be all that deep.
AI subscription plans are selling $100 bills for $10, they are way more expensive to the company than people buying metered tokens.
"they are way more expensive to the company than people buying metered tokens" is true, but that doesn't mean they're a massive loss as you imply. It could be that plans are effectively breakeven but tokens are highly profitable.
But this is further to the point that the economics are more Amazon than Pets.com. If the plans are losers, great, when they need to be profitable they kill the plans and focus on metered tokens.
> OAI/Anthropic are negative on the books mostly because of massive R&D expenses. This compares well to Amazon circa 1999, which was positive on COGS but negative overall due to massive buildout R&D costs.
Isn’t the difference that ai labs can’t stop the R&D spend? They have to continue training new models.
There's a bunch of arguments you could make there, varying from "yes, to stay ahead of the competition" to "yes, but it'll get a lot cheaper" to "no, things will plateau eventually". All those are datapoints that IMO this isn't a great analogue to the dotcom boom.
Except their own filing shows it’s getting more expensive, not cheaper. It’s highly unlikely that they’ll ever be able to stop.
A) they’re all getting acquired by these IPO seeking ones
B) nobody is IPOing at $400 million like Amazon did except SPAC share dilution scams, that you have to be willing to pick up at $30 million a year later
C) private secondary markets solve that, for some and are pretty great
D) tokenized synthetic and SPV backed private secondary markets in crypto ecosystems like Hyperliquid give you that exposure too, so now everyone can replicate the 90s
> B) nobody is IPOing at $400 million like Amazon did
Exactly You can't wait to IPO until you're already one of the 10 biggest companies ever AND also grow 2500x post-IPO.
Retail is now exit liquidity for Big VC.
Not really comparable. An IPO in the 90s is equivalent to a Series C or Series D round today.
I'm so excited to see these companies crater.
Anthropic won't let people make video games. They are on the wrong side of history.
They are so going to get disrupted. Chinese open weights models will have no such limitations, and there's no way we'll let Anthropic outlaw or ban them. (And even if they did, they can't police the whole world.)
There is no moat. Anthropic should be playing nice, not trying to create a god while holding engineers under their thumb. We will route around it.
I hope so. I would never bow down to such a god.
We demand a second amendment right to access god in a first class manner.
If the people can't access god, then we are living under a dictatorship with aims to enslave us.
> Anthropic won't let people make video games.
Wait, what?
People got upset that Anthropic added rejection rules for people saying "Make Pokemon in Elden Ring" . You can still make games, as long as you aren't telling Claude to recreate a specific, IP-protected game.
Thankfully GPT-Sol is pretty cavalier with academic paper IP.
Sci-hub doesn't work anymore but I can at least ask GPT what a paper says
Check out Annas-archive for papers.
And this is why open weights matter.
If you can think a thing, you should be able to prompt a thing.
"Risky" doesn't mean "bad investment". All the AI companies are high risk, high reward plays, and their interest rates will reflect that.
Yes but it removes some groups of otherwise-investors, meaning there is less interest in stocks, which pushes them up less, if not down
This is a bond rating. It has no direct bearing on their stock.
If they can't raise capital then yes that will have a huge impact on a company that's a massive tower of financial engineering as coreweave have been learning.
That this is only "may not" is the shocker.
Same thing as with SpaceX. If there is that much of an equity buffer, it should be investment grade. That it isn't means that the bond market doesn't trust the valuations at all.
No offense to them, but in general, investment grade companies should have a stable future and not have easily-switchable, competing products that cost a fraction as much to use come out regularly. This is almost laughable that I have to say this.
What could go wrong?
How typical is this of tech ipos? Or ipos in general?
I realize that SpaceX only did slightly better. But not sure what that says
https://giftarticle.ft.com/giftarticle/actions/redeem/2d0b4d...
9 shares left
please comment if you have used it to ensure other people know if it could be read
and its limited - only click if you are interested
enjoy!
> Wall Street banks earlier this week closed the North Star deal, which provides the financing for Anthropic to lease 1.5 gigawatts of Google’s semiconductors.
This line is certainly eyebrow-raising to the educated layman.
Apparently it means that anthropic is leasing a Google data center whose constituent servers draw 1.5 GW of power running representative workloads.
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It's interesting you imagine nine shares would suffice for a large site like HN. No, the link doesn't work for me. Honestly, this kind of "sharing" seems a bit disingenuous at this point.
Do you not feel uneasy about demonstrating such entitlement?
Yeah, if you actually want to share the article, just copy/paste its content, under a throwaway if you have to.
Yeah, 9 shares and then 10k people click
He is the article: Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.
Blackstone told investors Anthropic may not receive an investment-grade rating immediately after going public, a reversal from its previous guidance that would complicate the AI lab’s ability to finance its massive spending.
The private capital giant told investors in Anthropic’s latest $63bn chip financing deal that the start-up is expected to receive a “high” junk rating from major credit rating agencies after its initial public offering, according to people familiar with the matter.
Blackstone is acting as an anchor investor on the deal, known as “Project North Star”, and is working with banks that stumped up financing on selling the debt on to other investors.
Anthropic filed to go public on Monday, setting up what could be the largest listing of all time next month. The AI lab’s prospectus sketched out its “bold but sane” plans to spend more than $800bn on computing infrastructure between now and 2045.
An investment-grade credit rating is central to those ambitions, enabling Anthropic to borrow at lower rates to finance its AI infrastructure projects. The group has cash and equivalents of more than $112bn, and investors anticipate it could raise about $100bn more in the IPO. Sign reading "Blackstone" outside the Blackstone headquarters in New York, with pedestrians and city lights reflected in the glass. Blackstone’s funds have invested in $9bn of the junior debt at a lucrative yield of more than 10% © Angus Mordant/Bloomberg
While Blackstone had told its investment partners in recent months that it was expecting an investment-grade rating for Anthropic before year-end, it has recently signalled that a rating in the double B range or above is more likely given the guidance it has received from major credit agencies, the people said.
Such a designation would limit the pool of capital that Anthropic could tap because many insurance and pension funds are restricted from taking large positions in junk-rated borrowers. As a result, the company might need to continue to rely on its suppliers with high credit ratings to support its debt-raising efforts.
US tech stocks slid to session lows on Friday following the FT’s report on Anthropic’s rating, with the Nasdaq 100 down 3.7 per cent in afternoon trading. Markets were sharply lower prior to the report after earnings from Amazon and Intel worsened fears about the sustainability of the AI boom.
“It’s all about access to credit, it really matters . . . if [Anthropic] isn’t investment grade, the cost of credit goes higher, that’s not good for anyone,” said a US tech-focused portfolio manager.
“It’s a bloodbath out there,” they added, referring to the sell-off in stocks.
Wall Street banks earlier this week closed the North Star deal, which provides the financing for Anthropic to lease 1.5 gigawatts of Google’s semiconductors. More than $42bn of the senior loans is guaranteed by Broadcom, which co-develops the chips and has an investment-grade rating, while $18bn of the junior debt is backed by the AI start-up’s lease payments.
Institutional investors covet senior debt because it takes priority over junior loans in the case of a default. Broadcom’s high A-tier credit rating was an important factor in allowing Anthropic to secure the financing.
Blackstone’s funds invested in $9bn of the junior debt at a lucrative yield of more than 10 per cent, the FT previously reported. Bankers for Anthropic and rival OpenAI have also lobbied the largest rating agencies to grant the AI labs investment-grade ratings after their IPOs.
Project North Star’s $18bn junior debt, which exposes lenders more acutely to Anthropic’s credit risk, has been assigned a double B plus rating with positive outlook from Kroll Bond Rating Agency, a slight improvement from stable outlook in a similar deal known as Project “Big Sky” four months ago. It also received a double B rating from Morningstar DBRS, the people added.
The big three credit agencies — Fitch, Moody’s and S&P — which have bigger influence on companies’ borrowing costs, have not officially issued any credit ratings for Anthropic.
Anthropic, Blackstone, KBRA, Morningstar DBRS and S&P declined to comment. Fitch and Moody’s did not respond to requests for comment.
"bold but sane" -> that sounds as if Claude wrote their prospectus?
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Shocking. /s
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What's wrong with paying for serious news?
The problem is sharing it to a group of people who mostly aren't paying for it. It's a good way to get a thread that's just people reacting to the title, which drives HN toward just being a discussion board instead of a place to find good content.
I would pay if its token based pay per read model. nobody want to subscribe to 10 news websites paying hundreds every month.
This, don't get why there was no model like this. Even understanding the complexity to reach consensus (and technology).
Nothing wrong with paying for serious news, but when most viewers can't read the article it probably won't get much useful discussion.
Most news articles are not worth paying for. From leaving out details to serving an agenda; a lot of news has lost it's credibility. WSJ is a good example. It had really great long articles backed by real research before it was bought by Murdock.
1. Can’t be bothered
2. Never going to visit this site again and I am not going to make an account for it
I am however, okay with turning off the ad blocking if they’re running reasonable ads (not every other paragraph).
Agree with the 2 siblings: the problem is HN readers at large unable to read it, hence no useful discussion.
I'd also add: from paid online news, I also expect a moderated forum/comment section, with a human to escalate to if automatic/AI moderation fails.
Should everyone pay for every news source?
Only the ones that ask for it, obviously. What kind of stupid question is this?
People who participate in forums share their insights for free and resent the friction that a paywall adds to information being diffused.
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I can see why you would think that based on the low quality commentary you provide here, but you’re wrong.
There are some similarities between Anthropic (or OpenAI) and WeWork. Obviously the large difference is software but there is a whole lot of leasing hardware going on (neither frontier lab owns any infrastructure as far as I can tell), long term capital commitments, short term monthly subscriptions, no real moat for the frontier labs (or a hip office rental space), etc.
I think 2027 will be when a lot of companies revisit their AI spend and ROI. Have there been measurable business benefits reported by the media outside of programming?
Nothing solid. All the reporting I see is that numbers are measurable but not on the level many people expected, and that these systems are proving incapable of wholesale replacing employees, which clicks with my own knowledge of some large layoffs accompanied by press releases about streamlining corporate structures using AI and the outgoing employees asking “what AI?” Because they didn’t train an offshore team or document their department responsibilities before being let go.
My suspicion (and many others as well) is that AI is used as a way to cover for large corporate restructuring in the face of money no longer being available for “free” due to interest rates.
Anthropic is in the worst position of the AI labs. As far as investors and "the market" are concerned, the only AI that matters is the one built into your phone, car, your TV, your smart home and appliances. Anthropic has none of that share. It's all Google, Apple, and OpenAI powering Siri.
Personally, I don't think that tracks. If you are measuring "share" as share of vibes or "AI adoption" sure, but having AI in a phone, TV, smart home, etc doesn't make anyone any significant money. Hyperscaling isn't happening because there is an insatiable demand for retail AI usage. People are subscription'd out. A significant portion of people don't want AI in a lot of these things, another portion don't think they need it, and the ones that are eager to adopt it can use AI to bootstrap AI tools they use through a token router. You will have some revenue from retail AI but that isn't surprising considering I quite often have to provide tech support to people to help them cancel their subscription to a calculator app they've been paying $20 a month for.
> People are subscription'd out
No, they're not. Maybe you are?
> A significant portion of people don't want AI in a lot of these things
Not true. People love AI. Specifically Android and iPhone AI. I doubt they even know or care about Anthropic or OpenAI.
> the ones that are eager to adopt it can use AI to bootstrap AI tools they use through a token router
Absolutely microscopic share of the overall market for "AI" users. We are hobbyists.
Don’t agree with this
Personally I view it like this - did ant and OAI push the industry forward yup? Will they fail as businesses though? Yup. Why? Business economics - as long as cost efficient Chinese replica’s exist it’s fine. Unless the us govt artificially makes the frontier market large then I see no chance of a large enough market that sustains current valuations.
More intelligence alone != more progress. And more intelligence in the context of business != more productivity that shows up in financial performance.
Life is more nuanced.
No, the only AI that matters is one that large corporations are paying for. And that is Claude.
That's very true but unfortunately Anthropic have locked in contracts for so much compute that they absolutely need to expand beyond the corporations currently paying them.
Realistically they need to 3x their revenue to pay for their compute. While that's plausible, I'm not convinced that it can all come from developers. Many many developers are already using Claude. I think they need consumer, or some other market too.
Wouldn't OpenAI be in the same boat? As far as I'm aware, they don't control any means of distribution either.
OpenAI models power Apple's Siri assistant
They don't?
https://www.apple.com/newsroom/2026/09/siri-ai-a-profoundly-...
> These new capabilities are powered by the next generation of Apple Foundation Models, custom-built in collaboration with Google and its Gemini models for deeply integrated Apple Intelligence experiences.
They powered the initial "Apple Intelligence". The newer version is Gemini.
https://www.cnbc.com/2026/01/12/apple-google-ai-siri-gemini.... ?