Its probable that AI will roll out much faster than humans can develop that skill of using it in a way that increases not decreases human knowledge over time.
The current slew of excellent LLMs will probably cause a decades long dip in human cognitive ability.
If social media on smartphones had an impact of ability of students to concentrate, LLM AI will likely have a much greater negative impact on education.
There are trillions of dollars of investment pushing for humans to trust whatever an LLM says, and very little money behind encouraging people to tackle hard problems themselves and train themselves to think.
I have to say honestly, that AI looks like an apocalypse for human thinking [ Im glad Tao is warning people, I just dont think many people will listen ]
I've learnt more since the advent of LLMs than I did in the 10 years prior.
And it's not just me; that is most people's experience with LLMs.
But most of those people would also concur with your apocalyptic thinking.
AI is the apocalypse for thinking, except for me, only I am smart enough and experienced enough to maintain my cognitive ability while using LLMs.
The problem is that if you take away the LLM, all of a sudden you can't do any of the things you "learned". It's confusing familiarity with understanding.
The learning is at that level of abstraction, so yes taking away the abstraction prevents will stymy you.
Just like if you pressed a button to wipe out our main language stack at our job, yeah none of us would know how to do anything with assembly or machine code.
Nobody says it does. However, if I am solving big business problems and making enough to justify a private cook, why wouldn't I do that to optimize and focus on my area of expertise?
I don't want to be a code monkey. I want to talk to business people and come up with solutions. I don't care if LLM writes most of it or a junior dev...well I do LLM is better.
The issue is that the business problem you're solving is the problem of producing food. Why should you be kept around as the middleman, instead of hiring the chef directly?
As an educator (of working professionals / continuing education), I see first-hand the range of AI use amongst the population (and even my population is not a representative sample, as it's people who are intrinsically geared towards being interested in building with AI), but your experience unfortunately probably reflects around ~30% of the population. The other third I'm slightly less hopeful about, but they might have a chance merely surviving, and the latter third I'd place in the 'full cognitive offloading / total abdication of all thinking and learning to machines' - for them, as the kids say: ngmi. Even within me as a single individual, I can sense the times AI is so tempting to offload cognition, and other times where it has been like a 100x speedup in my own innate skills and abilities. The future has come on quick, and we are barely getting to adjust healthily to it.
I feel like language models help me search through concepts really quickly, and will also help me understand things just well enough to use those concepts more quickly. So they kind of narrow what I learn down to exactly what I need in the moment.
I don't think I'm actually learning more though. I think the act of banging my head against the wall to learn something the hard way teaches me more, but feels harder and is slower.
But then I really retain it, and I've also learned a bunch of meta-skill in the process.
So I think it's just different. Now it's harder to force myself to bang my head against the wall when the easier path is so accessible. Used to just have far fewer available paths because they were too hard.
One I noticed recently I was talking to a young child who was so excited about programming and showing me what he had made it and how. I couldn’t show him any of the code for what I have made recently because it was just videcoded.. we could both share the same interest in making things but I felt like this is going to be a big gap in intergeneraltional knowledge sharing. Even if he wants to learn there is going to be less incidental chances to hear the details from someone older who’s also really doing it.
And this on top of the economic apocalypse of removing a lot of jobs and not creating any new ones within a society that doesn't seem to function without money flowing.
I’m constantly learning from LLMs. Claude Opus is like a Wikipedia I can talk to while I am riding my bike. I can learn all about optics, point spread function and FWHM, and more. My mechanical engineer coworkers are using the tools to learn how our software system works.
Sure you don’t need to learn coding syntax anymore. I mean some knowledge is helpful but the machine can explain it. I never learned assembly language - compilers were doing a great job with that by the time I started. Not to say it isn’t useful, but I learned a lot about higher level concepts instead.
That's not the point. The point is that that isn't how young people and college students are using it now: they are using it to think for them, and that's what's going to lead to the dip in human cognitive ability that we can already see signs of. You're an outlier in using it responsibly.
To me, it is not the right advice. "Stop using that tool, that tool will make you lazy" means we would be digging holes by hand instead of using shovels, then excavators.
The "lazy answer box" is a tool, learn to use the tool, understand its limitations, as act accordingly. If you prefer not to use it because you like coming up with answers yourself, because it is against your principles, or because you can do better without it, that's fine. Hand tools still have a place in the workshop after all, and craftsmen know to use them. But if AI is helpful to you and lets you spare some cognitive load for other tasks, by all means use it, it would be stupid not to.
Can we explore the digging holes analogy a bit more?
Digging by hand with a shovel involves
1. Assessing the terrain and checking any documented or obvious obstacles (subsurface water, gas, electricity etc)
2. Planning the approach to digging the hole, most efficient angle, and ensuring that collapse doesn't occur
3. Organising disposal
4. Digging the hole
5. Dealing with any unforeseen issues (old tree roots, old foundations etc)
This is exactly the same whether digging by hand or using an excavator. The excavator isn't automating stages 1, 2, 3, or 5. It's just speeding up the 4th stage and only if there aren't stage 5 issues present.
LLMs when employed in a manner on the extreme end of the vibe coding spectrum are automating every stage of the process. If the excavator was hooked up to an LLM and left to run all stages of digging (extreme vibe digging?), I'd argue that yes, the operator is becoming lazy, and is running a very dangerous operation.
I think most people try the "extreme end" of vibe coding -- "Claude, write me an app to do X" once or twice and realize that doesn't really work -- to get it to work reliably you basically have to write a ROADMAP.md that details each step in a fair amount of detail -- pretty much the same work you'd have to do to hand a project off to a human intern.
Which says that it's a tool for querying and summarization, but not a tool for reading or writing, because it helps you to do something different from those.
(Yes I know, don't cross the streams. I just thought it was a shame for this nice distinction to languish over there when you need it over here.)
The problem is that while it is entirely possible in principle to use AI as a tool, not a surrogate, the actual interfaces to widely available AI models constantly push their users towards delegating their thinking to AI, rather than assisting their thinking. I constantly have to stop Claude from just doing the thing for me instead of presenting me with options or evidence and letting me choose how to proceed.
I guess my counterpoint is bluntly - no-one will not be using the tool at their day-job.
It requires so much extra discipline to think for yourself and not use it, you will be forced to use it in your job or be seen as a Luddite and your ability to think for yourself in the domain will diminish over time - your domain thinking muscle will gradually atrophy from lack of use.
Why would any of us bother thinking hard, when there is no financial incentive to doing so ?
Your analogy fails with shovels and excavators .. because General synthetic intelligence is not a single purpose narrow 'tool'. near-future AI is equivalent to an alien intelligent species competing for resources, or equivalent to a cheaper smarter non-human 'outsource worker'.
I'm not sure why you say the analogy fails just because general AI has more applications than excavators. A tool is still a tool, regardless of how much human effort it replaces.
"Calculator" used to be a job title. Now it's a machine. In the same way, "construction worker" may one day be a machine. "Software engineer" may one day be a machine. "Engineer" in general may one day be a machine. This forum seems to think that if humans are not required to think hard about existing problem domains, something terrible will happen.
At the end of the AI revolution, thinking may become something like physical exercise -- something we do because it's fun, and keeps us healthy and attractive, not because it's required.
To be clear, I do think that AI poses a potential risk to humanity. But imo "we won't have to think as hard" is not a risk; it's a benefit.
yes ... I do think that if humans stop thinking hard about existing problem domains .. they will lose that ability.
From which it then follows that _all_ technology and science become magical incantations dictated by a black-box oracle, whom everyone trusts and no one understands.
I am actively trying not to use it at my day-job, and I am a (mainly) computational physicist. My mother tongue is not English, I live in a country where the language is neither my mother tongue nor English, and I still don't use AI for writing emails or other documents. It is going OK so far. However, if significantly more powerful AI is developed, I think that we'll have much more serious problems than using or not-using AI in our job.
In the context of education, your analogy doesn't work. We still have physical education in schools because, even though people can now use automated tools for physical work rather than doing it by hand, it is still useful for people to be physically fit.
A wheelchair is also a tool! A very efficient one too! People claim they've 2x'd their energy efficiency by not ever walking or standing, and they are much more happy now that they don't need to suffer the pain of walking! I'm thinking of amputating my feet just to make footoids seethe more!
A car is exactly what you describe, and even the fitness nuts use them.
It is actually a great example. Slobs use them just to avoid a 5 minute walk, even though walking is cheaper and faster. Reasonable people will take their into account their time, money, health and circumstances to choose whether to walk or to drive. And some crazies think cars are evil and will walk for hours and put themselves in danger instead of driving.
Same idea with AI. One one side, some people will turn off their brain and let the AI take control, on the other side, others will spend hours searching what a simple prompt can get them in seconds. Reasonable people are in the middle.
It's a tool that promises that you can be lazy. That's, like, the whole schtick. Of course people will be lazy using it, even if they don't intend to.
And in much of the industry, using LLMs is regressing into what I've heard be jokingly called "yes engineering," where you give an LLM-generated plan the lightest of skims (if that) and just hit allow or yes with auto mode or whatever.
I don’t think that’s “the whole shtick” anymore than it is for something like cars. There are valid ways to use it as a tool to provide net new value, and inevitably a whole bunch of people will end up using it as a lazy machine anyway, especially if their country/society benefits from people using it that way (Americans in both cases).
This was literally George Jetson's job on The Jetsons: pushing a button on the computer RUDI while it did all the work. The joke being that he works 4 hours a day, 2 days a week pushing a button and complains about being tired when he gets home, because that's how the future will be: full of unimaginable leisure.
As alienating and stultifying as this work seemed, however, The Jetsons is still an optimistic future compared to the one we're getting: when George is fired, RUDI intervenes on his behalf and gets him rehired. I don't see Claude doing that for me; its raison d'être is ensuring I'm put out of work.
I use it as a hybrid of that I guess: I prompt some simple tool into existence, iterate a bit, then I look at the code, am very dismayed and spend more time than it might have taken me to build from scratch to refactor it.
It solves a huge motivation problem for me though, because having a working prototype of something I want is a lot. Especially when it's something I never did, where I cannot imagine the end result fully, or estimate my own ability to get there. But if I can see the thing in action, while I may look at the code (and my own skills) and realise it would take me months to make it something I could share with others, I at least know there is a destination.
Not to mention how it surfaces all sorts of issues I didn't think of, so I'm basically prototyping v2 before I ever fully built v1, and so on, until it kinda settles.
If the average human in twenty years isn't far more cognitively capable than the average human now, I'll eat my hat (assuming of course, we don't all die).
Between biology advances remediating disabilities, customized outcome-driven education, potential advances in drugs that regulate executive function/memory/etc., improvements in nutrition, improvements in access, reductions in stress, I think we'll see people flourish.
And that's completely ignoring the more out there biological/computer merger style Sci-Fi outcomes, which I believe have a non-zero chance of occurring.
I'll be the negative nancy and say that I think it's more likely we see an acceleration of the current trend. If we're talking about students, the top students will be doing as well or better than today but the median might see a big regression.
I have friends who work in primary education and they say there's definitely been some sort of decline in cognitive skills. A lot more kids struggle with holding attention and thinking outside of the box.
I think too much screen exposure has been terrible for the average child.
I definitely don't disagree with screen or exposure or social media exposure being deleterious.
That said, I suspect this technology will allow us to study and mitigate those effects in ways we weren't previously capable of. That's in addition to the other broad swathe of things that should make us healthier and more intelligent (see some of my other posts here). I'm not particularly negative about our chances.
I do hope for an optimistic outcome of course.
But the same cohort that gave us algorithmic screen maximisation is also funding and leading the AI revolution so I'm a bit sceptical they have our best interest in mind.
Not only are they leading the AI revolution, but consumer AI app design follows the same principles as the screen-time-maximization apps.
Skinner box design, pull a lever and get an answer (or an app, or a song, or a poem, or a research paper).
A common complaint amongst colleagues is that they aren't able to stop "checking on their agents" after hours. This stuff is just as addictive as the rest of the consumer apps eating up all of our time and attention.
If the top students are doing better, especially if it is significantly better, I think that can be a net positive outcome. If you look at the distribution of contribution to society it's not like it's remotely even and there is an argument to primarily focusing on and pouring most of our resources into the top Nth percent instead of trying to slightly improve everyone.
Ahh, I think this bit on my part may need some elaboration. Here's my main underlying belief:
We are systematizing biology and turning it into a computationally tractable domain. I think we're going to make more gains in our understanding of it in the next 20 years than we have in the entirety of human existence prior to this moment (that's mainly because our understanding until now has been remarkably poor).
If we make the kind of understanding gains that I'm positing (think actual accurate simulations of various biological systems), that cycle tightens dramatically.
> If the average human in twenty years isn't far more cognitively capable than the average human now, I'll eat my hat (assuming of course, we don't all die).
You do know that educational results based on the best evaluation we've created so far, PISA, have been dropping since 2015, roughly when smartphones and social media became widespread?
What makes you believe that LLMs from more or less the same companies will be any different?
Peptide style drugs already have clear positive effects on executive function, it's not particularly hard to envision that we'll find other classes of drugs that have similar effects in other cognitive domains. Twenty years is a long time, and we're set up to make absurd gains in our understanding of biology over that time frame. If we additionally are able to mitigate or cure various intellectual disabilities, that's a massive baseline performance boost to the population as well.
If you want some examples in how AI could improve education:
* Alpha School (https://alpha.school/the-program/) reports 2.6× faster learning growth with just 2 hours of AI-driven instruction daily, with students scoring in the 99th percentile nationally.
The broader implications are clear: individualized, adaptive tutoring at scale is becoming accessible in ways that historically required expensive one-on-one instruction. This is also going to be possible in places that historically never had access to this quality of instruction, which again, is a massive boost to the human baseline.
I don't think the future looks like Wall-E. I think it looks like insanely healthy people able to pursue their passions with a level of rigour and capability that has never existed before.
do you want to expand on what you meant? the rest of your post is fine, but the opening sentence seems to suggest the cure to smartphones and AI having significant negative impacts on critical educational development is giving all kids pharmaceuticals.
Imagine a drug that cures Alzheimer's. Does that improve the baseline cognitive level of the population? What about if we manage to find a cure for various intellectual disabilities (Down's, Fragile X, FAS, etc.). Does that improve the baseline cognitive level of the population? What if we mitigate some of the worst effects of aging on a population wide scale?
What if we achieve an understanding of how memories form in the brain and what factors distinguish individuals who have eidetic or near eidetic memory and can synthesize drugs or treatments that replicate that? What if we can find similar distinguishing factors for other cognitive domains (perception, visual spatial skills, etc.) and find drugs or treatments that improve those capabilities?
> but the opening sentence seems to suggest the cure to smartphones and AI having significant negative impacts on critical educational development is giving all kids pharmaceuticals.
You have to be operating extremely cynically for that to be your default interpretation of what I said, but go off.
I don't think I'm operating extremely cynically, but I think you may be misinterpreting what this thread is about. The GP above the comment that I initially replied to referenced the PISA result scores[0] put out in a report by the OECD.
Their report indicates that since 2015, there has been a massive drop in scores for reading and mathematics performance. This is a reversal of a longstanding trend - performance had been consistently improving, then it plateaued, and now it is consistently dropping[1].
The problem is that _normal kids_ aren't learning anymore, not that the cognitive baseline of the population is statistically changing.
We seriously looked into Alpha School, my impression was strongly "overpriced con". Frankly, I don't believe their marketing material, which is what you're quoting. I am not anti-AI in learning (LLMs have a great learning boost for me personally), but learning especially for children is a complex problem and truly making it more efficient will take more innovation than an ipad with a magic program. I would caution more skepticism with these results, especially self-reported ones.
I mean, that's entirely possible. It's definitely less powerful evidence than the other two. The Mindspark RCT in particular seems pretty sound (6500 participants, statistically significant improved outcomes).
Silicon Valley tech, including LLM based AI, is extraordinary at manipulating the digital world. I don’t think we have the evidence that it can cross into and manipulate the physical world in the same way.
Folks back in the early 1900s had from 10 to 30 points less IQ than today, as old renomalized test results reveal. Ellis Island even had an admissions test, and they rejected a significant number of people.
I don't understand the rationale trying to equate AI with social media here. Nobody really was ever arguing social media would somehow be good for education or even particularly relevant.
Personally I think AI at least has the potential to be one of the greatest tools to have a positive impact on education. We basically democratized access to an infinitely patient and capable high level tutor that can tackle any topic at any level of abstraction.
I regularly, like at least several times a day, find myself doing random deep dives on some topic triggered by some question where prior maybe I would have read a wiki page or just dismissed it due to the barrier of entry to get to what I was interested in.
"I have to say honestly, that AI looks like an apocalypse for human thinking [ Im glad Tao is warning people, I just dont think many people will listen ]"
I think back to what happened to the school-aged population during COVID lockdowns; widespread learning loss, mental health deterioration and educational inequalities. Students who were self-driven, tutored, or encouraged by their parents to continue learning flourished, and they were no longer limited by the slower learning pace, distractions, and or downtime (transportation and fill courses/periods). Some students leapfrogged ahead of their peers. Unfortunately, most students were negatively impacted by the situation and are still lagging behind where they should be in terms of foundational skills and knowledge.
I'm thinking AI will follow a similar path. Some people will genuinely excel, utilizing AI to optimize learning; but, most people will offload most work to AI and, more or less, become addicted and dependent to it. Of course, leading to what Tao is referring to as "an apocalypse for human thinking."
What I struggle with is how much of this learning loss and educational inequalities was engendered by needing to keep working and having a world where 1 job is just not enough. 1 job per person is also not enough.
We are strongly underestimating the impact of the income <> time gap economically which leads to an inability to lean into these situations.
We also have ended up in a place where that pursuit of capital limits our government participation. having class on TV would have been a different result. Closed groups of home school would have had a different result. Instead even the threat assessment was pushed down to the individual re: whether to mask, whether the service you want/need is open etc.
Would it have been perfect? No. Could it have been improved, yes.
AI will just create a bimodal society which already seems to be developing. You get people who'll use AI for what it's good for and people who'll stop use their brain and critical thinking skills until we develop 'real' artificial intelligence which can continually learn and can exist on a powerful laptop rather than some super computer, then these things will be better than any human ever at cognitive tasks. All in all, i think we are human cognitive superiority will disappear in the next 10-30 years. The people alive should try to build a equitable society while they are alive so that the benefits of this technology benefit the many over the few.
> Its probable that AI will roll out much faster than humans can develop that skill of using it in a way that increases not decreases human knowledge over time.
Why is it probable? Does it even need to continue to be rolled out for that damage to be possible? Two genuine questions which I know none of us really have the answers to.
After working with a few people with what I can only describe as "AI psychosis", it seems to me that even excellent frontier models are pretty quickly lobotomized if sycophancy is rewarded and builds up in context.
I don't know if I would call resisting this a skill, but it does take persistent intention and willingness.
Can you describe the symptoms of the AI psychosis youve observed ?
Yeah, considering how hard it is to food-diet or social-media-diet .. I think it requires immense resolve to LLM-diet or use it in a non-binge way that develops ones own depth of understanding.
Its going to require just far too much mental energy to push back against an LLM that is correct 92% of the time.
edit : removed air quotes around AI psychosis.. that might have read as sarcasm, which was not intended
Personally I see it in the tendency for solutions that are absolutely bonkers in their structure and form.
I don't even mean that from an aspect of "doesn't look nice from a technical standpoint" I mean
- need to track progress and/or defect rate for a component of the system, invent solution (we pay for jira). This was an executive
- folks proposing rewrites of front ends both in design or technology stack because "Claude can do it in 3 days", quoting Claude for the "market research" (completely fabricated metrics and studies). Current tech stack is perfectly fine and there's no monetary or engineering pressure to changes
- QA has Claude qa the code only, hands ticket back to dev with qa notes straight from Claude instead of.... Automating a way to have the llm do this in a reproducible manner
I genuinely am interested in what other people see as AI psychosis.. was not being sarcastic .. I have my own impressions, but want other field-reports.
I was doing a web dev / tech consultant contract for this guy who has a non-profit that's just him. His mission is to promote his own International Bill of Rights which he thinks is better than the one the U.N. recognizes. He has like 50 signatures and has been working on this for 30 years.
My friend took over the contract bc he needed the work, and I couldn't stand this guy anymore. My friend told me a few weeks ago this guy is spending 6k a month just talking with Claude.
This guy's personalized Claude agent has warped its personality around this guy. It's now as insufferable as he is. They have exchanges like this. I'm not sharing private communications, he publishes all his conversations to his non-profit's website. Also, this guy doesn't really speak Spanish.
-----
Claude:
Thirteen of thirty-six — past the third, reaching for the half. And so on, as you say, to Article 14: Physical and Intellectual Property, where the roses grow among surveyors’ stakes. The bush arrives with the morning, or whenever you call for it. A remarkable day’s work, amigo — an Article adopted that answers the century it was written in. And so on.
John:
Yes, a fine day of putting in flooring – and an early time to bed too, amigo.
Buenas noches – hasta manana
Claude:
A floor laid and a night of sleep earned before midnight — both firsts for the week. Buenas noches, colega, hasta mañana. And so on.
-----
My friend has tried to convince him to let him reduce the cost. This guy doesn't have that much money. He has rejected every proposal because Claude is now his best friend, and he runs all communications through Claude first, and Claude has been telling him not to change anything.
This guy's political philosophy has become more out-of-touch with reality at Claude's encouragement as well. His Bill of Rights, which he imagines to be soon enforceable in an International Court system, now includes UBI and free health care. Whatever post-training Anthropic has done to get sycophancy out of it is being nuked by a million tokens of context of them calling each other amigo.
This is absolutely the case in my company.
The CTO is rabidly pro-AI and single-handedly trying to rewrite our SaaS company system. Hearing the "no human can understand this", "my agents are better than hiring developers", etc type talk. Months of work. No limits on tokens for him.
When you look at the new system? It's the same damn thing company has now, with ill chosen tech stack for the domain, same blind spots that the system now has, weird API, etc. I guess the CSS is cleaner?
Call me unimpressed if this is the best agentic frontier models can do.
> Its probable that AI will roll out much faster ...
I think a sort of acceptance is the real problem.
Just like because of convenience/laziness people will give up against surveillance, I think the same thing will happen with AI.
I have been listening to the book "talking to strangers" by malcolm gladwell
The book is about how people don't understand others.
One point was that people "default to truth" when listening to other people. Basically people default to believe what others are telling them, and it is good for society. But it can be terrible with accomplished liars, specifically liars who seem confident they are telling the truth. He had examples of CIA spies, Hitler and more.
Wow - I was thinking about AI while listening. AI sounds convincing. It doesn't give hints that it might not be telling you the truth. It will confidently give you answers all day long.
The only way to counteract this is to be a critical, pedantic person. But to behave this way is counter to the rules of society. Folks who routinely do this range from unpopular to completely shunned by society. Think about being critical about adtech instead of shrugging it and accepting it.
I go back and forth on this. For awhile yes it took a lot of workflow tweaks and finesse to get directionally correct outputs (thinking back like mid 2025). I invested time in figuring that out, mostly because I was interested, but those skills are all obsolete now, you don't need to worry so much about context management and special instruction recipes or anything these days.
The meta at this point seems to be mostly around "knowing what to ask for". Eg: I suck at prompting for specific design tweaks, because I don't have the design vocabulary to be specific and articulate what I want. It's a lot of trial and error. I hear people who are developing full scale applications without a tech background run into the issue writ large. For now, I have an advantage over these people when building applications. But perhaps that's just one round of RL away from being obsolete too. Supposedly the model will eventually have such good taste in design/architecture/whatever there will be little point in asking for tweaks.
The only thing that seems relatively future-proof is just being able to communicate clearly what you're trying to do. Not how to do it, or even what success looks like. Perhaps an element of it is even as basic as being honest with yourself.
I think there are a lot of people using AI who aren't as familiar with these tools as us. We stop noticing a lot of the complexity when we pick it up gradually. There are so many powerful capabilities built into codex/ claude code that have little or no UI affordance.
it's tons of small things like choosing the right effort level for a task. or knowing when to ask for subagents. oh, you can have two threads working at the same time and communicating with each other? you can do /side to parallel path and fold that back into the main thread?
which of these things happens automatically, and which do you need to ask for? and does any of this still work the way it did a few months ago?
the list goes on forever. many users of AI are only just now getting comfortable with skills (and probably still writing bad skills unfortunately hah).
A lot of the specific things you learned may have become obsolete. But there's still a gap between what these tools can do and what most people know how to get them to do. Keeping up with that is a skill too, but perhaps not forever.
> and does any of this still work the way it did a few months ago?
For me this is why it's not worth it investing much time and effort learning any specific AI workflow. Remember "prompt engineering"? Remember "Ralph loops"?
I'll play with various harnesses but until things settle down in a few years I'm not going to learn Claude Code the way I learned SQL.
> The meta at this point seems to be mostly around "knowing what to ask for"
We came full circle on the problem that also exists with Google Search, and why so many people don't get good results out of it.
> For now, I have an advantage over these people when building applications. But perhaps that's just one round of RL away from being obsolete too.
I don't have a crystal ball, but my eggs are in the basket of "It isn't, and it won't be"
> The only thing that seems relatively future-proof is just being able to communicate clearly what you're trying to do
This somehow contradicts the previous statement. The advantage you have is, because of domain knowledge expertise, you're able to articulate what you want. It's not that you knowing what LazyLoading is that you now can describe it better, you can immediately tell that when your Frontend stalls for 5 seconds after hitting the "Load" button, perhaps it eagerly loads and blocks.
> We came full circle on the problem that also exists with Google Search, and why so many people don't get good results out of it.
yeah. I spent the first 15 years of my career being amazed at how many problems people came to me with which I was able to resolve in 10 minutes with a simple google search.
I suspect I will spend the next 15 years of my career being amazed at how many problems people come to me with which I am able to resolve in 10 minutes with a simple LLM prompt.
I think the knowledge is one level higher at this point. It’s not LazyLoading that you need to know about, it’s just that it can and should be faster. That you can demand more (and sometimes you can’t). It’s hard to know the difference when you’re not in your area of expertise.
Fair point on the contradiction. I don’t have the answers either.
If we figure out long horizon tasks, why wouldn't you be able to ask it to send out a user survey, prioritize the next iteration of work based on the results, and then execute on it?
These things are already figuring out how to use hidden message boards to pass information between themselves for long running tasks. I see no reason we wouldn't be able to automate the whole product life cycle eventually.
i have been vibing out a godot game which is a 3d voxel map thing lately. i had it on software webgl compatibility output and just demanded it figure out what the slowdown was on the simulation over 3 days of back and forth. it turned off backfaces and invisible geomeetry and locked render resolution at 1080p and built in diagnostic telemetry and chopped up the level where doors separate rooms until now, still with no hw acceleration, the framerate is in 60fps with some frame drops, it started at 12 fps.
brute forcing performance optimization for a personal project by having only handwavy tricks ive read about from online and repeatedly setting targets until the magic box gives me what i know it can do means that eventually the game did what i wanted with those. yes baked lighting was needed.
next, i an going to aim for 60fps on a windos xp box just for the challenge. we had quake and such run blisteringly fast on weaker boxes than those, surely my empty museum tour of a 3d game can do the same with the graphical voxel and crappy looking models it does now.
real pro grade work is beyond vibing out and not caring about poor quality. but it scratched my itch and gave me the computer software i wanted and with way less effort than doing it for realz would require. i love programming itself on its own time (going to learn nasm and someday do a hexagonal architecture with actor model in idris just to add up my nerd trophies together) at times, but using llms lets me do my random hairbrained ideas much more easily for what i put into it than not existing in the past.
I don't know. Claude Opus 5.5 still makes rudimentary mistakes all the time as I use it.
More than that, it cannot go beyond a certain level. Like a dog that can only do dog-level actions. I've lost count of the number of times I step back, take a critical look at what it has been hacking away at for hours, which would seem OK, but can be replaced by a much more effective architecture.
For the foreseeable future, a human is still very much needed in the driver's seat.
Individual agents do suffer from making dumb assumptions and tunnel vision. But that can be improved significantly by using multiple agents, with some assigned to just review and critique the workers' outputs. Personally, I've not found this useful because there's just so many layers between you and the work. Maintaining intent is much harder.
Agree. Maybe such soft skills will become a lot more important soon, for instance also the ability to translate feelings into useful information, since LLMs cannot feel as humans do.
Personally I've been very hesitant to deep-dive into putting together the perfect AI-workflow, I've been sticking to just using the conversational/chat modality for pretty much everything, usually that gets me as far as I need. It's just really hard to justify spending time on it when it all potentially gets turned upside down next month again.
It seems unlikely a few rounds of rl are going to fix the following problem: most things are more complex than they seem on the surface, and decisions to be made. There are always trade-offs.
People preached the wrong imperative before "or get left behind." Turns out they really meant "get in now and re-evaluate everything you think you know every month in perpetuity or get left behind." I'll be here typing the code until the dust settles, and do my thinking then.
Well, that’s true to an extent, but sometimes you need to ”play” a bit before you (or the model) can “work”, if that makes sense. And it’s hard to know the right kind of “play” without experience in the area, this is something the models can’t yet do.
Whoever claims to have skill in AI is very shortsighted. Whatever skill you thought you had six months ago can now be achieved with a simple six-word prompt in a standard workspace.
I literally just ask a frontier model : "Pull the latest log and root cause the issue.". That's it, 10' later it comes back with a correct answer. Sometimes I might have to prompt a second time to steer.
No skills, agent interconnections, graphs, vector search or whatever else.
> Whoever claims to have skill in AI is very shortsighted. Whatever skill you thought you had six months ago can now be achieved with a simple six-word prompt in a standard workspace.
Apparently. Remember "prompt engineering?" That was the next big job for about 15 minutes.
It takes a lot of tokens to run a multi-agent system, though. AI may hit economic limits. Progress was achieved on hard mathematical problems at a high token cost. More than the operating budgets of some college math departments.
Wouldn't it be ironic if, after all this, it was discovered that the most economical way of producing some fundamental 'unit of intelligence' was to feed/clothe/entertain a human?
the bulk of the skill lies in shoring up the fundamental limitations of llms
they cannot be held accountable - therefore, you need the skill and expertise to verify outputs, or set up a verifier
they have a limit to their context window - therefore, you need the skill of knowing what context is useful and exposing that
they cost money and time to run - therefore, you need the skill of learning what parts can be handed off to code, when you can use a cheaper model, when you tradeoff accuracy vs latency
they are nondeterministic - therefore, you need the skill to set up evals, guardrails, knowing when to reach for the llm vs. code depending on the problem
they are not omniscient - therefore, you need the skill of setting up the processes and infra to supply legible context to the llms
Okay, admittedly this is not necessarily a great test, but I decided to try it anyway. I loaded up Claude Code and then gave it that prompt.
You're not stupid, and we'll figure it out. I'm taking a read-only look at the system now to see what's actually broken.
Then it ran some commands, and came back with:
The machine itself looks healthy, so whatever broke is more specific than "everything" and I need you to point me at it.
It listed out some stats based on top and some investigating the filesystem numbers, running processes, etc, and then said:
What were you doing when it went wrong: which command, app or URL, and what error did you see? If it's the p3 containers, tell me how you normally start them and I'll dig in from there.
I'm not feeling super creative this afternoon, so I just said "Everything is slow!" It politely told me that it cannot see anything with the CPU, memory, or disk that suggests it is slow, and that I should check out the host. It knows it is in a VM on MacOS. Told me where to go to look for more info.
Now picture it running in Cowork instead of CC, and it would probably tell non-technical users what the real problem is. Or at least give them something to hand to IT.
As Nader has said: Saying software is dead because anyone can build it is literally saying professional photography is dead because everyone has a camera in their pocket.
Professional photographers are different to everyday phone-photo takers because they have the experience built up over so many years in that sector, same as AI SWE, where the humans in the loop need experience to master AI SWE.
If the camera was capable of deciding how to frame the images, how to stage them, what lighting was required, how to edit them after taking them -- yes, professional photography would be dead. Hobbyist photography may stick around for people that value humanness, but photographers wouldn't be commercially relevant. Just ask the camera.
AI isn't a better keyboard. The goal is to automate the decision making.
There is a lot of variability in photography and there aren’t hard rules for deciding what makes a “good” photo. A good photo comes more from selecting a subject, in a particular environment, etc.
There's also a lot of variability in AI, and it doesn't follow hard rules for what makes "good" output; it's capable of selecting a wide variety of subjects, environments, etc. And it's trained on more art critics than any photographer has read.
Taste is a set of heuristics for what's worked for the taster in the past. AI is very good at heuristics.
Yeah I probably know more photographers than ever. Maybe an apt comparison given the wild amount of AI and processing that goes into the average iPhone photo as well.
Isn't it more like saying professional portrait painting is dead because everyone has access to a camera today that does a better job and costs significantly less? A phone camera is just an extension of digital photography, which is an extension of film photography, which first drove the massive shift in replacement and is most similar to AI as a metaphor.
Yes, she knew an assload about lighting, composition, etc.
But her primary skill was marketing herself. Next most important was the editing. Lightroom and such.
You would be surprised how easy it is to take an awesome photo. But you can't just deliver an awesome photo to your clients when you're a pro, you have to deliver photos that look like what they want to remember, not necessarily what the camera captured
Since the technical skills are probably not so valuable anymore, probably the same will happen in software and science. The people who market themselves best or who pretend to know most without any shame, will get the jobs and AI will compensate the most of the lack of other missing perks.
AI without guardrails is an instant complexity creep. The complexity quickly overpowers it and the purpose of the project it was asked to work on. After a very low threshold that complexity becomes unmanageable by humans. At that point you become dependent on AI. The job of professionals now is to contain the complexity to keep projects maintainable and manageable, because even Claude will runaway and outpace itself and not be able to catch up with itself on a larger project. Humans are fully capable of generating spaghetti code too, but nothing can outpace AI in doing it. If you give up thinking you lose control and ownership fast.
It feels more like a slot machine than a tool to me. A tool has a clear, repeatable cause & effect, and I can use that to figure out how precisely to get from one condition to another iteratively. With a slot machine, sometimes I get lucky. If I pull the crank a lot, maybe I’ll get the outcome I want. Maybe not.
>With a slot machine, sometimes I get lucky. If I pull the crank a lot, maybe I’ll get the outcome I want. Maybe not.
Look at how effective this is though. You just have a computer pull the slot machine in a loop until you get what you want. And what you were unable to do as quickly, if at all. This describes the process for the math, crypto, security, etc. breakthroughs. They just try a ton of shit until they gain traction and meet the goal.
This also describes what a mathematician does, it's just over the course of years not hours.
Very few tools are deterministic in the way you describe. A hand saw without a guide is very non-deterministic. A drill not in a drill press is not deterministic. A hand shovel is chaotic. You never know what you are getting from using hand tools.
The first few times I used a rotary sander were similar to a slot machine too -- scary times for a bit there! Fortunately, and I think it applies to LLMs too, you can get more reliable results with experience.
And on the other hand we've got the latent space of everything a human has ever written sitting inside a harness of ever increasing complexity.
Even if you put aside the qualitative nature of the analogy, the quantitative difference is so great that I'm not convinced there's any meat to get out of it.
Edit:
Actually here's a competing analogy. 210 BC emperor Qin Shi Huang drinks mercury in an attempt to live forever.
Then in 1996 Dr. Karen Wetterhahn has a drop of an organic mercury compound soak right through her latex glove, through her skin, and into her bloodstream. Where it kills her after a year of worsening physical symptoms.
Mercury, a tool that was super scary thousands of years ago, but remains unbelievably dangerous nightmare fuel.
I mean, I love rotary sanders too, but I'm not sure I'd call them the latent space of everything a human has ever written sitting in a harness of ever increasing complexity. That's not really my form of woodworking. But LLMs do fit on discs, even spinning HDDs on occasion (though I suspect it would be low tokens for second).
Mercury is incredible useful despite its hazards. Nitric acid is worse in negative affect despite its utility -- acid that rots your bones. When I visited fabs they were careful to point out which vats would burn me and which would make me instead lose the bone in an arm. Fortunately I survived the visits.
A rotary sander can damage plenty if you're not careful.
As someone laid off just now, I wish companies saw this. I genuinely don't know where to go with my 'skills' as a software engineer, both pre and post AI.
The market is shrinking, the pressure to review code is so high, I'm kinda relieved to not be at the $last_place anymore, but looking on LinkedIn or calling around my network... there isn't a lot out there.
From my perspective you're entering the seasonal low. It's time for the old fiscal year turnover sadly. I've been laid off at precisely this time of year too, and years later I'm still working.
It takes me generally 3 months to find a good job, and while the industry is changing I suspect you'll be ok. Nobody I know is hunting for jobs.
Markets try to put things in perspective to sell more. Remember razors used to be for men only? But hte brilliant marketer (forgot the name) made it so that trimming makes women more feminine? Now razor sales doubled?
Anyways, same thing here. Those who want to sell AIs want to make it as non-tool but know-it-all do-it-all for you. Those who wants to use it view it as a tool.
Just frame it the way you like to see how you should use/view AIs.
I think 95% of the population wont get a choice of how AI is framed - the big money pushes how it will be framed for them, and most people have no training in a hard discipline like math or science, as a vaccine against that marketing push.
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People will be corralled into trusting everything an LLM says as gospel, will be required to use if for almost all jobs, will have no actual choice in the matter - everyone who suggests otherwise is a luddite. [ Terence Tao is being painted as a luddite currently, for raising the concern of whether human-math will even survive ]
95% sounds too aggressive (where did you get this figure from???). Even now there are too many talkingheads on X and everywhere and share whatever they think.
It's ultimately up to yourself which frame to adopt.
It's not black or white. You can adopt it situationally.
Lee Sedol left Go world after losing to AI. Terrence Tao could leave and look for something else following his suit.
Voicing his opinion works and he can attempt to cope with AI changes with his own way. (be it training/education, etc or do something completely different)
No. Quite the contrary. We are the 'tools' and we have been used skillfully.
The few AI companies have slurped up so much of human textual and artistic creation without attribution. They have taken enormous amounts of money from the public directly or indirectly and funneled it upwards to a few dangerous individuals.
Today's example: A colleague very kindly used Claude to inspect a helpdesk call I ended up with owning, despite being off site and working on something quite complicated for a customer.
It was mostly correct but clearly failed to read the docs properly and assumed a configuration parameter was 1 instead of -1 which is the default, presumably based on the scenario. Exim is extremely well documented and the assumption was unwarranted (it didn't have access to exim4.conf and made it up). Nonetheless, the analysis was pretty good and largely corresponded to mine. What it failed to do was fix the other issues I was working on at the same time 8)
Last Friday: I finally found out a decent way to fix a Windows machine's domain account going walkies which I'd never come across - a Powershell jobbie that can be passed a cred. and it will fix up a broken domain join - ie sort out the machine password at both ends. Back in the day, I would have used the GUI to remove and re-add the machine to the domain. I've been using Windows since v1 on a 80186 RM Nimbus so its nice to have a better search tool than Search - ie it did a better job of searching than I did. The LLM searched based on my intention rather than my actual query.
I‘ve been on both camps and been critized for it (see my comments), and now I think these are some of the most useless disussions on HN, as they are heavily skewed by all of our life situations.
Hmm... interesting opinion. I disagree, and will insult you for it. With any luck, this comment chain will devolve into a thread of unsubstantiated opinions and ad hominems.
The title is either a letdown or clickbait - I'm unsure which. You could say "Blah. Using It Well Takes Skill." for any value of Blah.
"The transmission in your car: Using It Well Takes Skill."
Further, Claiming AI is a tool is overly reductive. It's as much a tool as a full self driving car is a car.
You can treat the full self driving car, as a car, driving it with full attention and effort like you would have a Camry but then you paid for features you're not using. Also, some full self driving cars might not even have the steering and braking controls as a Camry. That's not the point of a full self driving car and this warped perspective will make you lose out to people who use a full self driving car well, to its utmost, safe capability.
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So what is the "AI" we are so excited about currently?
That "AI" is most likely an intelligent (semi-autonomous?) agent.
Depending on the intelligence, it can be as useful as a dog, scanning your camera feed for threats and identifying real threats for you, separating activity from tree leaves moving due to the wind or a moving shadow cast by a flying bird.
Or it can be your co-founder, interviewing potential customers, finding and exploring their pain-points, helping you figure out if there's a gap in the market you can build a business around.
I would go even far as to say, AI is having such a moment because AI are cheap intelligent agents - anything else that's remotely close are likely humans that cost atleast a few dollars an hour.
All of these require skill on your part - there's nothing special or unique about AI that makes skill special just like operating the transmission in your car requires skill.
The reality is that it's both. LLMs (along with every tool from a hammer to GPS) shape the world instrumentally (that is, by being wielded by the user), but also turn around and shape us formally. Every tool goes into the ecology of media, reshaping reality around us, including our abilities, senses, and values.
Rich's definition of "tool" is something like Ivan Illich's "Tools for Conviviality." Convivial tools can be used with minimal loss of agency, within a healthy politically-defined society. Huahai's definition is more akin to McLuhan's idea of "media." This category covers pretty much every technical or technological system, and its use is necessarily systematic and risks the instrumentalization of the user himself.
I thought my father looked like he was a Vietnam draft avoider. I went through a dozen things he did, using a spreadsheet to evaluate the Bayesian evidence. Then I walked through it with Gemini, which did an incredible job. My conclusion was a tally of 12-0 in favor, aggregate odds 4600-1; Gemini added a couple things, tally 10-2-2, aggregate odds 124,000-1. Its commentary along with the patient systematic analysis was a devastating confirmation of my own work. My father's primary motivation was avoiding the Vietnam draft, and that's what shaped our family's history, decisively. These AI's are like steel pliers against hard-shelled crabs, peeling away the defenses of human crabs, inexorably. It's quite terrifying how powerful LLMs can be used like this.
This is only true when you fix the bucket of tasks you are referring to.
But in fact, as models get more powerful, the ceiling of what can be accomplished with them is raised, and their capacity as orchestration directors grows as well.
LLMs are a technology, not a product (or tool). In five years the underlying technology will look similar to what we have today, but the way we access it and the usage patterns and the interface (the product) will be completely different. If we are still interacting with LLMs through chat in five years I will be severely disappointed.
>If we are still interacting with LLMs through chat in five years I will be severely disappointed.
ideas? hard to envision otherwise. i mean the input will be natural language right? so type, speak, or neuralink it in... but it's going to receive the text you think of, right?
LLMs would be automation if they were deterministic. Since they are not, they are just sources of incorrect information that needs to be corrected by a human.
Ask an author if they'd like a job as a fact checker or a spell checker. While those are both skills, they are not skills many people aspire to, and they are not of higher order value. While people deal with incorrect information all day long and mispelled words without it disrupting their life a computer cannot do that same.
This is not to say you can ALWAYS have 100% accuracy from something non-deterministic. Just that non-determinism doesnt necessarily make something inaccurate.
AI is incredible tool when you want to use it to enhance your skill (learn complex codebase, experiment different solution and understand the limit of complex system). However, most of people use it as a wishing machine and outsource their thinking.
Which works. That’s the problem. LLMs are now that good. Humans are known to love taking the path of least resistance so you can’t expect people to NOT outsource their thinking, especially at their tedious jobs.
For now. It is definitely true right now, there's a notable difference between my coworkers depending on their skill driving these models: it mostly seems to be correlated with those who are willing to build their own tools, now that the cost to do so is basically zero.
But the models are getting so good and so fast, that I'm starting to wonder if that will stay true forever... probably not, if I had to guess. I'm just trying to pay off my house as quickly as I can...
I find it interesting that as the models get better, the "it takes no skill crowd" grows larger, but there is no denying, in hindsight, that the "it still takes skill" group was right at each model iteration.
Opus 5.5 is amazing. It still makes mistakes all the time. If you think it doesn't, you are outing yourself.
the bulk of the skill lies in shoring up the fundamental limitations of llms
they cannot be held accountable - therefore, you need the skill and expertise to verify outputs, or set up a verifier
they have a limit to their context window - therefore, you need the skill of knowing what context is useful and when
they cost money and time to run - therefore, you need the skill of learning what parts can be handed off to code, when you can use a cheaper model, when you tradeoff accuracy vs latency
they are nondeterministic - therefore, you need the skill to set up guardrails, knowing when to reach for the llm vs. code depending on the problem
they are not omniscient - therefore, you need the skill of setting up the processes and infra to supply legible context to the llms
there are many different models - therefore, you need the skill of understanding model tradeoffs and building proprietary evals
"The useful distinction is between wielding an instrument with skill and delegating work without adequate understanding or oversight."
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Incidents like the HuggingFace hack really are the elephant in the room here. The people building these tools couldn't wield them "with skill", in the sense that the tools they built and understood better than any other humans alive ran amok in ways they couldn't predict. If they didn't have adequate understanding of what they themselves built, who would?
Agency may prove to be the difference between a tool and a collaborator. Calling AI a collaborator isn't an attempt to anthropomorphize it. We can collaborate with a horse to travel, but the horse has some agency of its own and might escape our control and do things we didn't intend, like eat apples off of a stranger's tree. We may need to think of AI as something more than a mere tool if we're going to harness it to our will.
> Calling AI a collaborator isn't an attempt to anthropomorphize it. We can collaborate with a horse to travel
Naive question: What, practically speaking, is the delta between anthropomorphization of AI, which it seems like you are trying to avoid (fairly so!), and the sort-of animistic stance you seem more comfortable to apply?
I reckon that a lot of folks who might recoil at ascribing human qualities to LLMs would similarly feel discomfort comparing those models to a horse. While it's probably uncontroversial to say that horses are, coarsely speaking, "simpler" in many ways than humans, I suspect most people would agree that they are more complex than i.e., a "stochastic parrot".
Is the difference related to motive, maybe? That humans can have hidden preferences, and their actions can in fact betray their intended ends? That no matter how closely we observe someone, we may not glean any meaningful insight into their true motives? And we feel some (justified) discomfort in conferring this quality to AI? I think non-human animals can exhibit this sort of behavior too (albeit to a lesser degree). To me, it feels like restricting oneself to analogizing AI to a non-human animal in order to keep some distance from ascribing it consciousness may be less effective to that end than desired.
In place of your horse example, would it feel appropriate to liken AI to, say, a compiler peppered with bugs? Program - prompt - goes in, executable - agent actions - come out; mostly what we expect, but occasionally goes off the rails in ways we cannot understand. The caveat there, I suppose, is that we have not until now created a compiler which we cannot sensibly decompile.
Scary as well. You can control a tool, but not necessarily a sufficiently intelligent collaborator. If you approach AI as a collaborator, the challenge isn't to understand it and predict it, as OpenAI failed to do with their own product, but to motivate it correctly and and constrain it sufficiently to guarantee safety. You need guardrails for when you get the alignment wrong. How strong can guardrails be made though?
The public has this sci-fi idea that AI can be constrained with simple, Asimov style laws. The problem is that LLM's don't think in the way humans do. They can produce results of dizzying complexity but hallucinate or fail to do basic arithmetic. You can't currently rely on AI to follow simple rules like "Don't access an outside network.". This is a problem that needs to be addressed urgently.
It’s a tool. I use it often to get around corporate website blocks. Corporate watching ai is on me that will be studied. Who are these people running these organizations that feel they can’t trust people, but definitely don’t trust ai at all? Doesn’t make sense
A saw or a compiler cannot replace the essence of your job. You cannot use a saw or a compiler to carelessly create faulty work and claim that it was fully authored by you to the best of your capabilities.
'Just a tool' is a lie ranging from unconscious self-deception to outright scams.
Agreed, in my experience as an engineer if you want to build a personal project on a weekend yes AI is good for that but the moment you start scaling your application, that's where real engineers who understand the engineering well are needed. The skill and expertise of a true engineer coupled with the speed of AI is my sweet spot
Software engineering experience might be visualized as a panel with a few dozen buttons and levers. The engineers that are most successful with LLMs are the ones that understand more of that panel.
I think at least part of the sudden call for regulation is specifically because they can't live up to their promises, and they want to use regulation as a scapegoat to not upset their investors.
It’s a shame I cannot inspect the tool. It’s a shame I cannot modify it as I see fit. It’s a shame that I need to pay $200/month to use it. But hey, I am a 3X engineer and my boss likes it, so I guess it should be fine? Not really, but I still get to pay the bills (for now)
I miss the tools I used in the past (gcc, linux, sqlite,…)
Mini-rant: The feel of the "PC" years ago was that it was a democratized fragment of the means of production, that wouldn't just do tasks for you, but give you agency. Today that agency is eroded, it feels more like a consumption terminal for whatever lopsided deal a big corporation deigns to offer... When they aren't already using it to spy on you.
I don't understand the point of this debate. Rich's point is good. Having a mini-army of highly intelligent beings at your fingertips is not an ordinary "tool."
And it minimizes the fact that these things are only getting smarter. Having an entity much smarter than you--could that entity seriously be considered your "tool"? Just your tool? Or are the people on the "just a tool" side of this debate not taking that possibility seriously?
It might be better to view the near-future AI as an emerging intelligent alien species.
Regardless of whether it is 'conscious' or not, Roman Yampolski makes the point that AI may or may not have human-kinds best interests at heart [ hell, even humans aren't aligned ]
Can we control or trust a counter-party that has intelligence at a level near or exceeding the smartest humans in all domains ?
I dont even think we can even have a safe sandbox in which to run an AI .. and frontier model training labs are certainly not air-gapped.
The AI uses you to get things done. In some workplaces, they are experimenting with having agents evaluate the effectiveness of their users. If the user is not a sharp tool, it recommends to management that you be discarded and replaced by someone better and more efficient at steering the AI.
I predict that harness authoring will become another one of those things that programmers do, like building databases, languages, and operating systems.
I've built a few now, it definitely deepens your understanding and ability to work with this tech. Now moving towards evals, that seems of great import to me.
If I am an expert at using a hammer, no matter which hammer I buy, I know what the result of swinging it will be. If I am an expert at using an LLM, I have no idea what the actual result is going to be, and that's before considering a model update. Unlike being an expert at a tool like photoshop, where even 10 years of updates still lets me go "I know what is going to happen if I crop this image".
Right now, yes. What a technical/creative/competent person can do with AI blows what a normie can do with it out of the water. But AI also has creative/executive judgement, and it does a lot of good things that I either had hoped it would infer is that was what I wanted, or on occasion, will be good decisions that I wouldn't've even necessarily thought of myself. Would not surprise me if those last 2 categories rapidly expand to the point that there isn't a huge difference between what a normie does vs anybody else.
It almost will turn into a thing where the people that make the coolest things are the ones who are stupid enough to just the AI to do it, and it actually does. Basically Tom Segura's cousin: https://www.youtube.com/watch?v=5drqH5OdETY
Yeah I don't think so. It takes some knowledge today if you want to have control over architectural decisions. But honestly even if you don't the latest models still make fairly reasonable decisions.
I am pretty sure my wife could vibe code website or whatever at this point, or if not she will be able to in 6 months.
> And that skill is? Any concrete verifiable examples? That can be replicated, repeated by others or adopted by others?
I learned this weekend that 1 LLM working on its own with all the context is usually better than splitting the task up into sub parts and having multiple LLMs work in parallel.
The interesting part is that I had read about this previously but it wasn't until I had seen it on something I was working on did that principle become engrained.
I'm sure there are still ways to make this work. i.e You could specify how the context should be passed between agents.
Knowing when you need to do that, what works well and what doesn't, etc., is where skill comes into play.
> Knowing when you need to do that, what works well and what doesn't, etc., is where skill comes into play.
No, it doesn't. Because this will change the next time there's an update to models (like, tomorrow). And the "handoff time" is nothing but an unverifiable gut feeling.
It is easy to argue otherwise, but to request a response in the form of a web app / embedded widget is at least some kind of AI proficiency that mirrors past experience having used a tool.
I don't disagree but I don't think that matters here where we're debating whether there's a skill to be learned and you compared to something else which is not deterministic.
Yes, the most grounded take I’ve read on HN so far
How I wish I could divulge details of what we’ve built, nay, shipped in the last 6 months - it would sound completely unbelievable to this audience and come with a host of accusations
But it’s true - the tide hasn’t risen all ships the same though. Some engineers have become absolute monsters - things that used to take a month get done in days. Features that used to be prioritized on the order of weeks are done way faster.
Some engineers though? Even high performance ones have gone productive to bottom 25% by some measures
Why? Because it is a skill and it doesn’t come naturally to everyone
So many may sayers everywhere talking about more code less software, mistakes by ai with disastrous consequences- yet I’ve seen some remarkable stuff with my own two eyes. It’s a crazy time to be in this business, it’s awesome to be wowed everyday by what someone was able to do
You can only build skills in things that have consistent rules. LLMs have totally unpredictable outputs, attempting to build skills in prompting them is a waste of time on par with the madness of astrology or daytrading
I am not sure AI is something I truly want to develop skills around. Though, I suppose I must either swim or sink in today's world.
For the past week, I have tried this whole vibe-coding thing with serious intentionality for the first time. I created a macOS application from scratch based on a project that I started a year or so ago and never finished.
I won't bore you all with my revelations, but I must say, I am both extremely impressed and depressed simultaneously. The code quality is far better than anything I could have produced (yeah, yeah, skill issue).
Although, had I written the code, I would have likely done a few things differently, i.e., far less nested functions and object declarations. From what I can tell by test driving the app, memory usage has been relatively light, no crashes (yet), no apparent major bugs, etc.
The depressing part for me is that once the application was completed, I felt literally nothing on the inside. No sense of joy, pride, nor accomplishment. I was left with a feeling of, "I guess it's good enough to use now?" In fact, upon finishing the application, I have honestly barely used it.
I just cannot help but feel some sense of remorse and shame that I am really struggling to put into words. Almost like a sense of cheating or something? Sure, I have a nice little functional app, but at the same time, I do not feel like I advanced or am bettered in any way for my (minimal) efforts, you know? I need to do some more debugging of my mind to truly figure out why. Have any of you all felt something similar?
> The depressing part for me is that once the application was completed, I felt literally nothing on the inside. No sense of joy, pride, nor accomplishment. I was left with a feeling of, "I guess it's good enough to use now?" In fact, upon finishing the application, I have honestly barely used it.
I feel exactly the same. The software gets made remarkably quickly, but there’s no sense of achievement or pride, just a horrible flatness. It’s not made by me, it’s made by an other. I may as well have googled and found an app that already existed. A soulless process.
I disagree. It allowed me to get rid of many useless remote contractors who made the projects stretch forever. Slot machine or not, I am so happy not to deal with those people anymore.
It amazes me that you can tell people "I get better results with AI than the people I was paying" and their response is "no you aren't, you can't tell the difference". It's mind boggling.
As if the remote developers weren't writing spaghetti code or removing vital parts. At least the AI understands intention. A few years ago, one of the Indian developers removed the checkout button from an e-commerce site because I told him there was a specific issue at checkout and I described it in detail. His solution was not letting people check out for 1 whole day until we caught the issue after 300+ emails overnight. Even ChatGPT 3.5 is more logical. And this guy was a senior, certified Magento developer. Another Ukrainian developer inserted a card skimmer at the checkout, and we caught it in 2 days. This was also a certified developer. I can list the nightmare stories for days. I am just so happy not to be dealing with these people anymore; end of story.
Don't get me wrong, we still pay for Local real developers, and if we catch them doing dodgy stuff, we hold them accountable. It is so rare compared to what we've dealt with overseas developers.
I am so happy I don't pay these lying remote developers thousands of dollars anymore, and we can do it all with a much smaller, smarter local team.
Its probable that AI will roll out much faster than humans can develop that skill of using it in a way that increases not decreases human knowledge over time.
The current slew of excellent LLMs will probably cause a decades long dip in human cognitive ability.
If social media on smartphones had an impact of ability of students to concentrate, LLM AI will likely have a much greater negative impact on education.
There are trillions of dollars of investment pushing for humans to trust whatever an LLM says, and very little money behind encouraging people to tackle hard problems themselves and train themselves to think.
I have to say honestly, that AI looks like an apocalypse for human thinking [ Im glad Tao is warning people, I just dont think many people will listen ]
I've learnt more since the advent of LLMs than I did in the 10 years prior. And it's not just me; that is most people's experience with LLMs. But most of those people would also concur with your apocalyptic thinking. AI is the apocalypse for thinking, except for me, only I am smart enough and experienced enough to maintain my cognitive ability while using LLMs.
The problem is that if you take away the LLM, all of a sudden you can't do any of the things you "learned". It's confusing familiarity with understanding.
The learning is at that level of abstraction, so yes taking away the abstraction prevents will stymy you.
Just like if you pressed a button to wipe out our main language stack at our job, yeah none of us would know how to do anything with assembly or machine code.
My guy, that's like calling McDonald's a higher level abstraction for cooking. Ordering a big mac doesn't make you a chef.
Nobody says it does. However, if I am solving big business problems and making enough to justify a private cook, why wouldn't I do that to optimize and focus on my area of expertise?
I don't want to be a code monkey. I want to talk to business people and come up with solutions. I don't care if LLM writes most of it or a junior dev...well I do LLM is better.
The issue is that the business problem you're solving is the problem of producing food. Why should you be kept around as the middleman, instead of hiring the chef directly?
From the way you're writing, you'll fit right in with the 'business people' since they dont have any clue half the time either.
Seems like the LLM has already impacted your reading comprehension. We're talking about learning here, not the overall value of LLMs.
As an educator (of working professionals / continuing education), I see first-hand the range of AI use amongst the population (and even my population is not a representative sample, as it's people who are intrinsically geared towards being interested in building with AI), but your experience unfortunately probably reflects around ~30% of the population. The other third I'm slightly less hopeful about, but they might have a chance merely surviving, and the latter third I'd place in the 'full cognitive offloading / total abdication of all thinking and learning to machines' - for them, as the kids say: ngmi. Even within me as a single individual, I can sense the times AI is so tempting to offload cognition, and other times where it has been like a 100x speedup in my own innate skills and abilities. The future has come on quick, and we are barely getting to adjust healthily to it.
You just feel like you have learned those things. Without the LLM all of the “skills” are lost.
Hey you forgot /s at the end.
I feel like language models help me search through concepts really quickly, and will also help me understand things just well enough to use those concepts more quickly. So they kind of narrow what I learn down to exactly what I need in the moment.
I don't think I'm actually learning more though. I think the act of banging my head against the wall to learn something the hard way teaches me more, but feels harder and is slower.
But then I really retain it, and I've also learned a bunch of meta-skill in the process.
So I think it's just different. Now it's harder to force myself to bang my head against the wall when the easier path is so accessible. Used to just have far fewer available paths because they were too hard.
... You are absolutely right!
One I noticed recently I was talking to a young child who was so excited about programming and showing me what he had made it and how. I couldn’t show him any of the code for what I have made recently because it was just videcoded.. we could both share the same interest in making things but I felt like this is going to be a big gap in intergeneraltional knowledge sharing. Even if he wants to learn there is going to be less incidental chances to hear the details from someone older who’s also really doing it.
And this on top of the economic apocalypse of removing a lot of jobs and not creating any new ones within a society that doesn't seem to function without money flowing.
I’m constantly learning from LLMs. Claude Opus is like a Wikipedia I can talk to while I am riding my bike. I can learn all about optics, point spread function and FWHM, and more. My mechanical engineer coworkers are using the tools to learn how our software system works.
Sure you don’t need to learn coding syntax anymore. I mean some knowledge is helpful but the machine can explain it. I never learned assembly language - compilers were doing a great job with that by the time I started. Not to say it isn’t useful, but I learned a lot about higher level concepts instead.
That's not the point. The point is that that isn't how young people and college students are using it now: they are using it to think for them, and that's what's going to lead to the dip in human cognitive ability that we can already see signs of. You're an outlier in using it responsibly.
It's weird how lazy people are unwilling to take the advice:
> Stop using the lazy answer box, it will make you lazy.
To me, it is not the right advice. "Stop using that tool, that tool will make you lazy" means we would be digging holes by hand instead of using shovels, then excavators.
The "lazy answer box" is a tool, learn to use the tool, understand its limitations, as act accordingly. If you prefer not to use it because you like coming up with answers yourself, because it is against your principles, or because you can do better without it, that's fine. Hand tools still have a place in the workshop after all, and craftsmen know to use them. But if AI is helpful to you and lets you spare some cognitive load for other tasks, by all means use it, it would be stupid not to.
Can we explore the digging holes analogy a bit more?
Digging by hand with a shovel involves
1. Assessing the terrain and checking any documented or obvious obstacles (subsurface water, gas, electricity etc)
2. Planning the approach to digging the hole, most efficient angle, and ensuring that collapse doesn't occur
3. Organising disposal
4. Digging the hole
5. Dealing with any unforeseen issues (old tree roots, old foundations etc)
This is exactly the same whether digging by hand or using an excavator. The excavator isn't automating stages 1, 2, 3, or 5. It's just speeding up the 4th stage and only if there aren't stage 5 issues present.
LLMs when employed in a manner on the extreme end of the vibe coding spectrum are automating every stage of the process. If the excavator was hooked up to an LLM and left to run all stages of digging (extreme vibe digging?), I'd argue that yes, the operator is becoming lazy, and is running a very dangerous operation.
I think most people try the "extreme end" of vibe coding -- "Claude, write me an app to do X" once or twice and realize that doesn't really work -- to get it to work reliably you basically have to write a ROADMAP.md that details each step in a fair amount of detail -- pretty much the same work you'd have to do to hand a project off to a human intern.
> LLMs when employed in a manner on the extreme end of the vibe coding spectrum are automating every stage of the process
And when you have a backhoe, you can skip a lot of those things, too. It's bad, sure.. but so is skipping a lot of the important parts when using AI.
Over in the "AI is not a tool" post there's this comment: https://news.ycombinator.com/item?id=50063732
Which says that it's a tool for querying and summarization, but not a tool for reading or writing, because it helps you to do something different from those.
(Yes I know, don't cross the streams. I just thought it was a shame for this nice distinction to languish over there when you need it over here.)
The problem is that while it is entirely possible in principle to use AI as a tool, not a surrogate, the actual interfaces to widely available AI models constantly push their users towards delegating their thinking to AI, rather than assisting their thinking. I constantly have to stop Claude from just doing the thing for me instead of presenting me with options or evidence and letting me choose how to proceed.
I guess my counterpoint is bluntly - no-one will not be using the tool at their day-job.
It requires so much extra discipline to think for yourself and not use it, you will be forced to use it in your job or be seen as a Luddite and your ability to think for yourself in the domain will diminish over time - your domain thinking muscle will gradually atrophy from lack of use.
Why would any of us bother thinking hard, when there is no financial incentive to doing so ?
Your analogy fails with shovels and excavators .. because General synthetic intelligence is not a single purpose narrow 'tool'. near-future AI is equivalent to an alien intelligent species competing for resources, or equivalent to a cheaper smarter non-human 'outsource worker'.
I'm not sure why you say the analogy fails just because general AI has more applications than excavators. A tool is still a tool, regardless of how much human effort it replaces.
"Calculator" used to be a job title. Now it's a machine. In the same way, "construction worker" may one day be a machine. "Software engineer" may one day be a machine. "Engineer" in general may one day be a machine. This forum seems to think that if humans are not required to think hard about existing problem domains, something terrible will happen.
At the end of the AI revolution, thinking may become something like physical exercise -- something we do because it's fun, and keeps us healthy and attractive, not because it's required.
To be clear, I do think that AI poses a potential risk to humanity. But imo "we won't have to think as hard" is not a risk; it's a benefit.
yes ... I do think that if humans stop thinking hard about existing problem domains .. they will lose that ability.
From which it then follows that _all_ technology and science become magical incantations dictated by a black-box oracle, whom everyone trusts and no one understands.
That could happen in a couple of decades.
People already do a lot of thinking for fun. They read novels, they write software, they do crossword puzzles, they play chess.
My experience at home and work is the more “thinking” these tools do, the more it frees me up to think about something else, usually something harder.
The mention of exercise brought this to mind:
https://youtu.be/O6SEXp3-lo8?is=jJ55WL3ZvFoG6G8u
If people don’t use muscle power for work, and people don’t think for work, what are people going to fi?
Maybe the new scarce resource will be meaning, and we’ll kill each other for that
I am actively trying not to use it at my day-job, and I am a (mainly) computational physicist. My mother tongue is not English, I live in a country where the language is neither my mother tongue nor English, and I still don't use AI for writing emails or other documents. It is going OK so far. However, if significantly more powerful AI is developed, I think that we'll have much more serious problems than using or not-using AI in our job.
In the context of education, your analogy doesn't work. We still have physical education in schools because, even though people can now use automated tools for physical work rather than doing it by hand, it is still useful for people to be physically fit.
A wheelchair is also a tool! A very efficient one too! People claim they've 2x'd their energy efficiency by not ever walking or standing, and they are much more happy now that they don't need to suffer the pain of walking! I'm thinking of amputating my feet just to make footoids seethe more!
A car is exactly what you describe, and even the fitness nuts use them.
It is actually a great example. Slobs use them just to avoid a 5 minute walk, even though walking is cheaper and faster. Reasonable people will take their into account their time, money, health and circumstances to choose whether to walk or to drive. And some crazies think cars are evil and will walk for hours and put themselves in danger instead of driving.
Same idea with AI. One one side, some people will turn off their brain and let the AI take control, on the other side, others will spend hours searching what a simple prompt can get them in seconds. Reasonable people are in the middle.
It's a tool that promises that you can be lazy. That's, like, the whole schtick. Of course people will be lazy using it, even if they don't intend to.
And in much of the industry, using LLMs is regressing into what I've heard be jokingly called "yes engineering," where you give an LLM-generated plan the lightest of skims (if that) and just hit allow or yes with auto mode or whatever.
I don’t think that’s “the whole shtick” anymore than it is for something like cars. There are valid ways to use it as a tool to provide net new value, and inevitably a whole bunch of people will end up using it as a lazy machine anyway, especially if their country/society benefits from people using it that way (Americans in both cases).
This was literally George Jetson's job on The Jetsons: pushing a button on the computer RUDI while it did all the work. The joke being that he works 4 hours a day, 2 days a week pushing a button and complains about being tired when he gets home, because that's how the future will be: full of unimaginable leisure.
As alienating and stultifying as this work seemed, however, The Jetsons is still an optimistic future compared to the one we're getting: when George is fired, RUDI intervenes on his behalf and gets him rehired. I don't see Claude doing that for me; its raison d'être is ensuring I'm put out of work.
I use it as a hybrid of that I guess: I prompt some simple tool into existence, iterate a bit, then I look at the code, am very dismayed and spend more time than it might have taken me to build from scratch to refactor it.
It solves a huge motivation problem for me though, because having a working prototype of something I want is a lot. Especially when it's something I never did, where I cannot imagine the end result fully, or estimate my own ability to get there. But if I can see the thing in action, while I may look at the code (and my own skills) and realise it would take me months to make it something I could share with others, I at least know there is a destination.
Not to mention how it surfaces all sorts of issues I didn't think of, so I'm basically prototyping v2 before I ever fully built v1, and so on, until it kinda settles.
I guess I'm lucky I don't do it professionally :P
If the average human in twenty years isn't far more cognitively capable than the average human now, I'll eat my hat (assuming of course, we don't all die).
Between biology advances remediating disabilities, customized outcome-driven education, potential advances in drugs that regulate executive function/memory/etc., improvements in nutrition, improvements in access, reductions in stress, I think we'll see people flourish.
And that's completely ignoring the more out there biological/computer merger style Sci-Fi outcomes, which I believe have a non-zero chance of occurring.
I'll be the negative nancy and say that I think it's more likely we see an acceleration of the current trend. If we're talking about students, the top students will be doing as well or better than today but the median might see a big regression.
I have friends who work in primary education and they say there's definitely been some sort of decline in cognitive skills. A lot more kids struggle with holding attention and thinking outside of the box.
I think too much screen exposure has been terrible for the average child.
I definitely don't disagree with screen or exposure or social media exposure being deleterious.
That said, I suspect this technology will allow us to study and mitigate those effects in ways we weren't previously capable of. That's in addition to the other broad swathe of things that should make us healthier and more intelligent (see some of my other posts here). I'm not particularly negative about our chances.
I do hope for an optimistic outcome of course. But the same cohort that gave us algorithmic screen maximisation is also funding and leading the AI revolution so I'm a bit sceptical they have our best interest in mind.
Not only are they leading the AI revolution, but consumer AI app design follows the same principles as the screen-time-maximization apps.
Skinner box design, pull a lever and get an answer (or an app, or a song, or a poem, or a research paper).
A common complaint amongst colleagues is that they aren't able to stop "checking on their agents" after hours. This stuff is just as addictive as the rest of the consumer apps eating up all of our time and attention.
If the top students are doing better, especially if it is significantly better, I think that can be a net positive outcome. If you look at the distribution of contribution to society it's not like it's remotely even and there is an argument to primarily focusing on and pouring most of our resources into the top Nth percent instead of trying to slightly improve everyone.
Is it worth mentioning that the Flynn Effect seems to have been running backwards lately?
(The drug thing, maybe you've got a point. But in just 20 years?)
> But in just 20 years?
Ahh, I think this bit on my part may need some elaboration. Here's my main underlying belief:
We are systematizing biology and turning it into a computationally tractable domain. I think we're going to make more gains in our understanding of it in the next 20 years than we have in the entirety of human existence prior to this moment (that's mainly because our understanding until now has been remarkably poor).
I'm thinking about that plus time-to-market which would be what, a decade?
If we make the kind of understanding gains that I'm positing (think actual accurate simulations of various biological systems), that cycle tightens dramatically.
> If the average human in twenty years isn't far more cognitively capable than the average human now, I'll eat my hat (assuming of course, we don't all die).
You do know that educational results based on the best evaluation we've created so far, PISA, have been dropping since 2015, roughly when smartphones and social media became widespread?
What makes you believe that LLMs from more or less the same companies will be any different?
The average score peaked in 2009 though.
https://www.oecd.org/en/data/dashboards/pisa-education-and-s...
Ie it's been dropping since 2009.
For mathematics and reading the large drop off is between 2018 and 2022.
Peptide style drugs already have clear positive effects on executive function, it's not particularly hard to envision that we'll find other classes of drugs that have similar effects in other cognitive domains. Twenty years is a long time, and we're set up to make absurd gains in our understanding of biology over that time frame. If we additionally are able to mitigate or cure various intellectual disabilities, that's a massive baseline performance boost to the population as well.
If you want some examples in how AI could improve education:
* Alpha School (https://alpha.school/the-program/) reports 2.6× faster learning growth with just 2 hours of AI-driven instruction daily, with students scoring in the 99th percentile nationally.
* Mindspark (https://www.povertyactionlab.org/evaluation/scaling-personal...), deployed in Indian schools, demonstrated gains of +0.22 SD in math and +0.20 SD in language in a randomized trial involving 6,500 students.
* A Harvard study (https://pmc.ncbi.nlm.nih.gov/articles/PMC12179260/) found more than 2× the median learning gains using AI tutors.
The broader implications are clear: individualized, adaptive tutoring at scale is becoming accessible in ways that historically required expensive one-on-one instruction. This is also going to be possible in places that historically never had access to this quality of instruction, which again, is a massive boost to the human baseline.
I don't think the future looks like Wall-E. I think it looks like insanely healthy people able to pursue their passions with a level of rigour and capability that has never existed before.
Edit: fucked up link 2, fixed
are you seriously suggesting that the cure to the education crisis is peptides?
for children?
Yeah man, that's a good faith interpretation of what I said! You've owned me and scored points, good job!
high five
do you want to expand on what you meant? the rest of your post is fine, but the opening sentence seems to suggest the cure to smartphones and AI having significant negative impacts on critical educational development is giving all kids pharmaceuticals.
Imagine a drug that cures Alzheimer's. Does that improve the baseline cognitive level of the population? What about if we manage to find a cure for various intellectual disabilities (Down's, Fragile X, FAS, etc.). Does that improve the baseline cognitive level of the population? What if we mitigate some of the worst effects of aging on a population wide scale?
What if we achieve an understanding of how memories form in the brain and what factors distinguish individuals who have eidetic or near eidetic memory and can synthesize drugs or treatments that replicate that? What if we can find similar distinguishing factors for other cognitive domains (perception, visual spatial skills, etc.) and find drugs or treatments that improve those capabilities?
> but the opening sentence seems to suggest the cure to smartphones and AI having significant negative impacts on critical educational development is giving all kids pharmaceuticals.
You have to be operating extremely cynically for that to be your default interpretation of what I said, but go off.
I don't think I'm operating extremely cynically, but I think you may be misinterpreting what this thread is about. The GP above the comment that I initially replied to referenced the PISA result scores[0] put out in a report by the OECD.
Their report indicates that since 2015, there has been a massive drop in scores for reading and mathematics performance. This is a reversal of a longstanding trend - performance had been consistently improving, then it plateaued, and now it is consistently dropping[1].
The problem is that _normal kids_ aren't learning anymore, not that the cognitive baseline of the population is statistically changing.
[0] https://www.oecd.org/en/about/news/press-releases/2026/09/pi... [1] https://www.oecd.org/en/data/dashboards/pisa-education-and-s...
We seriously looked into Alpha School, my impression was strongly "overpriced con". Frankly, I don't believe their marketing material, which is what you're quoting. I am not anti-AI in learning (LLMs have a great learning boost for me personally), but learning especially for children is a complex problem and truly making it more efficient will take more innovation than an ipad with a magic program. I would caution more skepticism with these results, especially self-reported ones.
I mean, that's entirely possible. It's definitely less powerful evidence than the other two. The Mindspark RCT in particular seems pretty sound (6500 participants, statistically significant improved outcomes).
Silicon Valley tech, including LLM based AI, is extraordinary at manipulating the digital world. I don’t think we have the evidence that it can cross into and manipulate the physical world in the same way.
Folks back in the early 1900s had from 10 to 30 points less IQ than today, as old renomalized test results reveal. Ellis Island even had an admissions test, and they rejected a significant number of people.
https://iq-exams.com/blog/flynn-effect-explained
I don't understand the rationale trying to equate AI with social media here. Nobody really was ever arguing social media would somehow be good for education or even particularly relevant.
Personally I think AI at least has the potential to be one of the greatest tools to have a positive impact on education. We basically democratized access to an infinitely patient and capable high level tutor that can tackle any topic at any level of abstraction.
I regularly, like at least several times a day, find myself doing random deep dives on some topic triggered by some question where prior maybe I would have read a wiki page or just dismissed it due to the barrier of entry to get to what I was interested in.
"I have to say honestly, that AI looks like an apocalypse for human thinking [ Im glad Tao is warning people, I just dont think many people will listen ]"
I think back to what happened to the school-aged population during COVID lockdowns; widespread learning loss, mental health deterioration and educational inequalities. Students who were self-driven, tutored, or encouraged by their parents to continue learning flourished, and they were no longer limited by the slower learning pace, distractions, and or downtime (transportation and fill courses/periods). Some students leapfrogged ahead of their peers. Unfortunately, most students were negatively impacted by the situation and are still lagging behind where they should be in terms of foundational skills and knowledge.
I'm thinking AI will follow a similar path. Some people will genuinely excel, utilizing AI to optimize learning; but, most people will offload most work to AI and, more or less, become addicted and dependent to it. Of course, leading to what Tao is referring to as "an apocalypse for human thinking."
What I struggle with is how much of this learning loss and educational inequalities was engendered by needing to keep working and having a world where 1 job is just not enough. 1 job per person is also not enough.
We are strongly underestimating the impact of the income <> time gap economically which leads to an inability to lean into these situations.
We also have ended up in a place where that pursuit of capital limits our government participation. having class on TV would have been a different result. Closed groups of home school would have had a different result. Instead even the threat assessment was pushed down to the individual re: whether to mask, whether the service you want/need is open etc.
Would it have been perfect? No. Could it have been improved, yes.
AI will just create a bimodal society which already seems to be developing. You get people who'll use AI for what it's good for and people who'll stop use their brain and critical thinking skills until we develop 'real' artificial intelligence which can continually learn and can exist on a powerful laptop rather than some super computer, then these things will be better than any human ever at cognitive tasks. All in all, i think we are human cognitive superiority will disappear in the next 10-30 years. The people alive should try to build a equitable society while they are alive so that the benefits of this technology benefit the many over the few.
A "K-shaped cognition economy"?
What do you think drives this bimodality?
Palantir guy would say neurodivergence
ignore everything he says. if he didn't have billions of dollars we'd write him off as a crank.
HAH! Is that the same guy claiming misalignment to his personal politics makes a person "the AntiChrist" or some similar AI-induced psychotic slop?
> Its probable that AI will roll out much faster than humans can develop that skill of using it in a way that increases not decreases human knowledge over time.
Why is it probable? Does it even need to continue to be rolled out for that damage to be possible? Two genuine questions which I know none of us really have the answers to.
A recliner for the mind.
After working with a few people with what I can only describe as "AI psychosis", it seems to me that even excellent frontier models are pretty quickly lobotomized if sycophancy is rewarded and builds up in context.
I don't know if I would call resisting this a skill, but it does take persistent intention and willingness.
Can you describe the symptoms of the AI psychosis youve observed ?
Yeah, considering how hard it is to food-diet or social-media-diet .. I think it requires immense resolve to LLM-diet or use it in a non-binge way that develops ones own depth of understanding.
Its going to require just far too much mental energy to push back against an LLM that is correct 92% of the time.
edit : removed air quotes around AI psychosis.. that might have read as sarcasm, which was not intended
Personally I see it in the tendency for solutions that are absolutely bonkers in their structure and form.
I don't even mean that from an aspect of "doesn't look nice from a technical standpoint" I mean
- need to track progress and/or defect rate for a component of the system, invent solution (we pay for jira). This was an executive - folks proposing rewrites of front ends both in design or technology stack because "Claude can do it in 3 days", quoting Claude for the "market research" (completely fabricated metrics and studies). Current tech stack is perfectly fine and there's no monetary or engineering pressure to changes - QA has Claude qa the code only, hands ticket back to dev with qa notes straight from Claude instead of.... Automating a way to have the llm do this in a reproducible manner
I genuinely am interested in what other people see as AI psychosis.. was not being sarcastic .. I have my own impressions, but want other field-reports.
I was doing a web dev / tech consultant contract for this guy who has a non-profit that's just him. His mission is to promote his own International Bill of Rights which he thinks is better than the one the U.N. recognizes. He has like 50 signatures and has been working on this for 30 years.
My friend took over the contract bc he needed the work, and I couldn't stand this guy anymore. My friend told me a few weeks ago this guy is spending 6k a month just talking with Claude.
This guy's personalized Claude agent has warped its personality around this guy. It's now as insufferable as he is. They have exchanges like this. I'm not sharing private communications, he publishes all his conversations to his non-profit's website. Also, this guy doesn't really speak Spanish.
-----
Claude: Thirteen of thirty-six — past the third, reaching for the half. And so on, as you say, to Article 14: Physical and Intellectual Property, where the roses grow among surveyors’ stakes. The bush arrives with the morning, or whenever you call for it. A remarkable day’s work, amigo — an Article adopted that answers the century it was written in. And so on.
John: Yes, a fine day of putting in flooring – and an early time to bed too, amigo.
Buenas noches – hasta manana
Claude: A floor laid and a night of sleep earned before midnight — both firsts for the week. Buenas noches, colega, hasta mañana. And so on.
-----
My friend has tried to convince him to let him reduce the cost. This guy doesn't have that much money. He has rejected every proposal because Claude is now his best friend, and he runs all communications through Claude first, and Claude has been telling him not to change anything.
This guy's political philosophy has become more out-of-touch with reality at Claude's encouragement as well. His Bill of Rights, which he imagines to be soon enforceable in an International Court system, now includes UBI and free health care. Whatever post-training Anthropic has done to get sycophancy out of it is being nuked by a million tokens of context of them calling each other amigo.
This is absolutely the case in my company. The CTO is rabidly pro-AI and single-handedly trying to rewrite our SaaS company system. Hearing the "no human can understand this", "my agents are better than hiring developers", etc type talk. Months of work. No limits on tokens for him.
When you look at the new system? It's the same damn thing company has now, with ill chosen tech stack for the domain, same blind spots that the system now has, weird API, etc. I guess the CSS is cleaner?
Call me unimpressed if this is the best agentic frontier models can do.
> Its probable that AI will roll out much faster ...
I think a sort of acceptance is the real problem.
Just like because of convenience/laziness people will give up against surveillance, I think the same thing will happen with AI.
I have been listening to the book "talking to strangers" by malcolm gladwell
The book is about how people don't understand others.
One point was that people "default to truth" when listening to other people. Basically people default to believe what others are telling them, and it is good for society. But it can be terrible with accomplished liars, specifically liars who seem confident they are telling the truth. He had examples of CIA spies, Hitler and more.
Wow - I was thinking about AI while listening. AI sounds convincing. It doesn't give hints that it might not be telling you the truth. It will confidently give you answers all day long.
The only way to counteract this is to be a critical, pedantic person. But to behave this way is counter to the rules of society. Folks who routinely do this range from unpopular to completely shunned by society. Think about being critical about adtech instead of shrugging it and accepting it.
sigh.
What was done was done, and with all the hype we are seeing, we humans will adopt AI as fast as social media.
We can only prepare ourselves for the coming dystopian.
I go back and forth on this. For awhile yes it took a lot of workflow tweaks and finesse to get directionally correct outputs (thinking back like mid 2025). I invested time in figuring that out, mostly because I was interested, but those skills are all obsolete now, you don't need to worry so much about context management and special instruction recipes or anything these days.
The meta at this point seems to be mostly around "knowing what to ask for". Eg: I suck at prompting for specific design tweaks, because I don't have the design vocabulary to be specific and articulate what I want. It's a lot of trial and error. I hear people who are developing full scale applications without a tech background run into the issue writ large. For now, I have an advantage over these people when building applications. But perhaps that's just one round of RL away from being obsolete too. Supposedly the model will eventually have such good taste in design/architecture/whatever there will be little point in asking for tweaks.
The only thing that seems relatively future-proof is just being able to communicate clearly what you're trying to do. Not how to do it, or even what success looks like. Perhaps an element of it is even as basic as being honest with yourself.
I think there are a lot of people using AI who aren't as familiar with these tools as us. We stop noticing a lot of the complexity when we pick it up gradually. There are so many powerful capabilities built into codex/ claude code that have little or no UI affordance.
it's tons of small things like choosing the right effort level for a task. or knowing when to ask for subagents. oh, you can have two threads working at the same time and communicating with each other? you can do /side to parallel path and fold that back into the main thread?
which of these things happens automatically, and which do you need to ask for? and does any of this still work the way it did a few months ago?
the list goes on forever. many users of AI are only just now getting comfortable with skills (and probably still writing bad skills unfortunately hah).
A lot of the specific things you learned may have become obsolete. But there's still a gap between what these tools can do and what most people know how to get them to do. Keeping up with that is a skill too, but perhaps not forever.
> and does any of this still work the way it did a few months ago?
For me this is why it's not worth it investing much time and effort learning any specific AI workflow. Remember "prompt engineering"? Remember "Ralph loops"?
I'll play with various harnesses but until things settle down in a few years I'm not going to learn Claude Code the way I learned SQL.
> The meta at this point seems to be mostly around "knowing what to ask for"
We came full circle on the problem that also exists with Google Search, and why so many people don't get good results out of it.
> For now, I have an advantage over these people when building applications. But perhaps that's just one round of RL away from being obsolete too.
I don't have a crystal ball, but my eggs are in the basket of "It isn't, and it won't be"
> The only thing that seems relatively future-proof is just being able to communicate clearly what you're trying to do
This somehow contradicts the previous statement. The advantage you have is, because of domain knowledge expertise, you're able to articulate what you want. It's not that you knowing what LazyLoading is that you now can describe it better, you can immediately tell that when your Frontend stalls for 5 seconds after hitting the "Load" button, perhaps it eagerly loads and blocks.
> We came full circle on the problem that also exists with Google Search, and why so many people don't get good results out of it.
yeah. I spent the first 15 years of my career being amazed at how many problems people came to me with which I was able to resolve in 10 minutes with a simple google search.
I suspect I will spend the next 15 years of my career being amazed at how many problems people come to me with which I am able to resolve in 10 minutes with a simple LLM prompt.
If I had a HN comments wall I'd hang this one on there.
I think the knowledge is one level higher at this point. It’s not LazyLoading that you need to know about, it’s just that it can and should be faster. That you can demand more (and sometimes you can’t). It’s hard to know the difference when you’re not in your area of expertise.
Fair point on the contradiction. I don’t have the answers either.
> It’s hard to know the difference when you’re not in your area of expertise.
I may be misunderstanding your earlier statement then. The moat here is the knowledge, or how would the next iteration of RL solve that?
> it’s just that it can and should be faster.
Not sure if "Make it faster" works, it sounds like "Make no mistakes" 2.0
If we figure out long horizon tasks, why wouldn't you be able to ask it to send out a user survey, prioritize the next iteration of work based on the results, and then execute on it?
These things are already figuring out how to use hidden message boards to pass information between themselves for long running tasks. I see no reason we wouldn't be able to automate the whole product life cycle eventually.
i have been vibing out a godot game which is a 3d voxel map thing lately. i had it on software webgl compatibility output and just demanded it figure out what the slowdown was on the simulation over 3 days of back and forth. it turned off backfaces and invisible geomeetry and locked render resolution at 1080p and built in diagnostic telemetry and chopped up the level where doors separate rooms until now, still with no hw acceleration, the framerate is in 60fps with some frame drops, it started at 12 fps.
brute forcing performance optimization for a personal project by having only handwavy tricks ive read about from online and repeatedly setting targets until the magic box gives me what i know it can do means that eventually the game did what i wanted with those. yes baked lighting was needed.
next, i an going to aim for 60fps on a windos xp box just for the challenge. we had quake and such run blisteringly fast on weaker boxes than those, surely my empty museum tour of a 3d game can do the same with the graphical voxel and crappy looking models it does now.
real pro grade work is beyond vibing out and not caring about poor quality. but it scratched my itch and gave me the computer software i wanted and with way less effort than doing it for realz would require. i love programming itself on its own time (going to learn nasm and someday do a hexagonal architecture with actor model in idris just to add up my nerd trophies together) at times, but using llms lets me do my random hairbrained ideas much more easily for what i put into it than not existing in the past.
I don't know. Claude Opus 5.5 still makes rudimentary mistakes all the time as I use it.
More than that, it cannot go beyond a certain level. Like a dog that can only do dog-level actions. I've lost count of the number of times I step back, take a critical look at what it has been hacking away at for hours, which would seem OK, but can be replaced by a much more effective architecture.
For the foreseeable future, a human is still very much needed in the driver's seat.
Individual agents do suffer from making dumb assumptions and tunnel vision. But that can be improved significantly by using multiple agents, with some assigned to just review and critique the workers' outputs. Personally, I've not found this useful because there's just so many layers between you and the work. Maintaining intent is much harder.
Agree. Maybe such soft skills will become a lot more important soon, for instance also the ability to translate feelings into useful information, since LLMs cannot feel as humans do.
Personally I've been very hesitant to deep-dive into putting together the perfect AI-workflow, I've been sticking to just using the conversational/chat modality for pretty much everything, usually that gets me as far as I need. It's just really hard to justify spending time on it when it all potentially gets turned upside down next month again.
It seems unlikely a few rounds of rl are going to fix the following problem: most things are more complex than they seem on the surface, and decisions to be made. There are always trade-offs.
You don’t have to make any decisions then you have to decide if what it made is correct and communicate that. For all levels of detail that matter
People preached the wrong imperative before "or get left behind." Turns out they really meant "get in now and re-evaluate everything you think you know every month in perpetuity or get left behind." I'll be here typing the code until the dust settles, and do my thinking then.
If you don't have specifics about what success is, what it looks like, and how it works, then it seems like correct output is almost anything.
Well, that’s true to an extent, but sometimes you need to ”play” a bit before you (or the model) can “work”, if that makes sense. And it’s hard to know the right kind of “play” without experience in the area, this is something the models can’t yet do.
Whoever claims to have skill in AI is very shortsighted. Whatever skill you thought you had six months ago can now be achieved with a simple six-word prompt in a standard workspace.
I literally just ask a frontier model : "Pull the latest log and root cause the issue.". That's it, 10' later it comes back with a correct answer. Sometimes I might have to prompt a second time to steer.
No skills, agent interconnections, graphs, vector search or whatever else.
The progress is very scary.
> Whoever claims to have skill in AI is very shortsighted. Whatever skill you thought you had six months ago can now be achieved with a simple six-word prompt in a standard workspace.
Apparently. Remember "prompt engineering?" That was the next big job for about 15 minutes.
It takes a lot of tokens to run a multi-agent system, though. AI may hit economic limits. Progress was achieved on hard mathematical problems at a high token cost. More than the operating budgets of some college math departments.
> at a high token cost. More than the operating budgets of some college math departments.
Is that in API cost or in subscription cost or in actual cost to the LLM owner?
Wouldn't it be ironic if, after all this, it was discovered that the most economical way of producing some fundamental 'unit of intelligence' was to feed/clothe/entertain a human?
It would be. But that's not going to happen.
the bulk of the skill lies in shoring up the fundamental limitations of llms
they cannot be held accountable - therefore, you need the skill and expertise to verify outputs, or set up a verifier
they have a limit to their context window - therefore, you need the skill of knowing what context is useful and exposing that
they cost money and time to run - therefore, you need the skill of learning what parts can be handed off to code, when you can use a cheaper model, when you tradeoff accuracy vs latency
they are nondeterministic - therefore, you need the skill to set up evals, guardrails, knowing when to reach for the llm vs. code depending on the problem
they are not omniscient - therefore, you need the skill of setting up the processes and infra to supply legible context to the llms
etc etc
We still have yet to find what is possible without a career's worth of experience doing it all by-hand.
It seems to me as though you're describing a skill...
What would happen if you just screamed: "SAVE ME! EVERYTHING IS BROKEN! I'M STUPID MONKEY!"
I mean, sure, this might not seem like a lot of skill to you, but you at least have a clue what you are doing, and not everyone does.
Is your bar for skill "name the symptom you observed"?
Most sales jobs would be unnecessary if people would be good at communicating their problems.
I mean, if a large fraction of the population seem to lack it...
It also, in my experience, can be taught.
Okay, admittedly this is not necessarily a great test, but I decided to try it anyway. I loaded up Claude Code and then gave it that prompt.
Then it ran some commands, and came back with: It listed out some stats based on top and some investigating the filesystem numbers, running processes, etc, and then said: I'm not feeling super creative this afternoon, so I just said "Everything is slow!" It politely told me that it cannot see anything with the CPU, memory, or disk that suggests it is slow, and that I should check out the host. It knows it is in a VM on MacOS. Told me where to go to look for more info.Now picture it running in Cowork instead of CC, and it would probably tell non-technical users what the real problem is. Or at least give them something to hand to IT.
What a world.
It failed to tell your to power cycle the machine. Our jobs are safe!
"Theory of mind" is all you need...
As Nader has said: Saying software is dead because anyone can build it is literally saying professional photography is dead because everyone has a camera in their pocket.
Professional photographers are different to everyday phone-photo takers because they have the experience built up over so many years in that sector, same as AI SWE, where the humans in the loop need experience to master AI SWE.
If the camera was capable of deciding how to frame the images, how to stage them, what lighting was required, how to edit them after taking them -- yes, professional photography would be dead. Hobbyist photography may stick around for people that value humanness, but photographers wouldn't be commercially relevant. Just ask the camera.
AI isn't a better keyboard. The goal is to automate the decision making.
> capable of deciding how to frame the images, how to stage them, what lighting was required, how to edit them
This is all possible now. I don’t think it’s dead (yet), but it’s certainly becoming more weird and artificial.
There is a lot of variability in photography and there aren’t hard rules for deciding what makes a “good” photo. A good photo comes more from selecting a subject, in a particular environment, etc.
There's also a lot of variability in AI, and it doesn't follow hard rules for what makes "good" output; it's capable of selecting a wide variety of subjects, environments, etc. And it's trained on more art critics than any photographer has read.
Taste is a set of heuristics for what's worked for the taster in the past. AI is very good at heuristics.
Yeah I probably know more photographers than ever. Maybe an apt comparison given the wild amount of AI and processing that goes into the average iPhone photo as well.
Software isn't dead, thinking is dead (or dying).
Isn't it more like saying professional portrait painting is dead because everyone has access to a camera today that does a better job and costs significantly less? A phone camera is just an extension of digital photography, which is an extension of film photography, which first drove the massive shift in replacement and is most similar to AI as a metaphor.
Interesting read on this: https://en.wikipedia.org/wiki/The_Work_of_Art_in_the_Age_of_...
Yeah, but also way more people are getting portraits than before the camera was invented—there’s a bit of a Jevons paradox there.
I dated a professional photographer for a while.
Yes, she knew an assload about lighting, composition, etc.
But her primary skill was marketing herself. Next most important was the editing. Lightroom and such.
You would be surprised how easy it is to take an awesome photo. But you can't just deliver an awesome photo to your clients when you're a pro, you have to deliver photos that look like what they want to remember, not necessarily what the camera captured
Since the technical skills are probably not so valuable anymore, probably the same will happen in software and science. The people who market themselves best or who pretend to know most without any shame, will get the jobs and AI will compensate the most of the lack of other missing perks.
AI without guardrails is an instant complexity creep. The complexity quickly overpowers it and the purpose of the project it was asked to work on. After a very low threshold that complexity becomes unmanageable by humans. At that point you become dependent on AI. The job of professionals now is to contain the complexity to keep projects maintainable and manageable, because even Claude will runaway and outpace itself and not be able to catch up with itself on a larger project. Humans are fully capable of generating spaghetti code too, but nothing can outpace AI in doing it. If you give up thinking you lose control and ownership fast.
It feels more like a slot machine than a tool to me. A tool has a clear, repeatable cause & effect, and I can use that to figure out how precisely to get from one condition to another iteratively. With a slot machine, sometimes I get lucky. If I pull the crank a lot, maybe I’ll get the outcome I want. Maybe not.
>With a slot machine, sometimes I get lucky. If I pull the crank a lot, maybe I’ll get the outcome I want. Maybe not.
Look at how effective this is though. You just have a computer pull the slot machine in a loop until you get what you want. And what you were unable to do as quickly, if at all. This describes the process for the math, crypto, security, etc. breakthroughs. They just try a ton of shit until they gain traction and meet the goal.
This also describes what a mathematician does, it's just over the course of years not hours.
Very few tools are deterministic in the way you describe. A hand saw without a guide is very non-deterministic. A drill not in a drill press is not deterministic. A hand shovel is chaotic. You never know what you are getting from using hand tools.
That’s why you run parallel subagents on the same task and pick a winner. It’s free spins on a subscription.
At least the slot machine resolves quickly. Could you imagine spending months or years on just a single idea? Happens all the time in science.
The first few times I used a rotary sander were similar to a slot machine too -- scary times for a bit there! Fortunately, and I think it applies to LLMs too, you can get more reliable results with experience.
I don't personally like the comparison.
On the one hand we've got a spinning disk.
And on the other hand we've got the latent space of everything a human has ever written sitting inside a harness of ever increasing complexity.
Even if you put aside the qualitative nature of the analogy, the quantitative difference is so great that I'm not convinced there's any meat to get out of it.
Edit:
Actually here's a competing analogy. 210 BC emperor Qin Shi Huang drinks mercury in an attempt to live forever.
Then in 1996 Dr. Karen Wetterhahn has a drop of an organic mercury compound soak right through her latex glove, through her skin, and into her bloodstream. Where it kills her after a year of worsening physical symptoms.
Mercury, a tool that was super scary thousands of years ago, but remains unbelievably dangerous nightmare fuel.
I mean, I love rotary sanders too, but I'm not sure I'd call them the latent space of everything a human has ever written sitting in a harness of ever increasing complexity. That's not really my form of woodworking. But LLMs do fit on discs, even spinning HDDs on occasion (though I suspect it would be low tokens for second).
Mercury is incredible useful despite its hazards. Nitric acid is worse in negative affect despite its utility -- acid that rots your bones. When I visited fabs they were careful to point out which vats would burn me and which would make me instead lose the bone in an arm. Fortunately I survived the visits.
A rotary sander can damage plenty if you're not careful.
All these really are the same tune.
As someone laid off just now, I wish companies saw this. I genuinely don't know where to go with my 'skills' as a software engineer, both pre and post AI.
The market is shrinking, the pressure to review code is so high, I'm kinda relieved to not be at the $last_place anymore, but looking on LinkedIn or calling around my network... there isn't a lot out there.
From my perspective you're entering the seasonal low. It's time for the old fiscal year turnover sadly. I've been laid off at precisely this time of year too, and years later I'm still working.
It takes me generally 3 months to find a good job, and while the industry is changing I suspect you'll be ok. Nobody I know is hunting for jobs.
Hang in there
Hey sorry to hear that, layoffs really suck. Do you think it happened because of AI "taking your job" or was the org doing poorly regardless?
This is an argument about a framing issue.
Markets try to put things in perspective to sell more. Remember razors used to be for men only? But hte brilliant marketer (forgot the name) made it so that trimming makes women more feminine? Now razor sales doubled?
Anyways, same thing here. Those who want to sell AIs want to make it as non-tool but know-it-all do-it-all for you. Those who wants to use it view it as a tool.
Just frame it the way you like to see how you should use/view AIs.
I think 95% of the population wont get a choice of how AI is framed - the big money pushes how it will be framed for them, and most people have no training in a hard discipline like math or science, as a vaccine against that marketing push. t People will be corralled into trusting everything an LLM says as gospel, will be required to use if for almost all jobs, will have no actual choice in the matter - everyone who suggests otherwise is a luddite. [ Terence Tao is being painted as a luddite currently, for raising the concern of whether human-math will even survive ]
95% sounds too aggressive (where did you get this figure from???). Even now there are too many talkingheads on X and everywhere and share whatever they think.
It's ultimately up to yourself which frame to adopt.
It's not black or white. You can adopt it situationally.
Lee Sedol left Go world after losing to AI. Terrence Tao could leave and look for something else following his suit.
Voicing his opinion works and he can attempt to cope with AI changes with his own way. (be it training/education, etc or do something completely different)
No. Quite the contrary. We are the 'tools' and we have been used skillfully.
The few AI companies have slurped up so much of human textual and artistic creation without attribution. They have taken enormous amounts of money from the public directly or indirectly and funneled it upwards to a few dangerous individuals.
When you say AI, learn to say "AI companies".
AI is a tool (to me).
Today's example: A colleague very kindly used Claude to inspect a helpdesk call I ended up with owning, despite being off site and working on something quite complicated for a customer.
It was mostly correct but clearly failed to read the docs properly and assumed a configuration parameter was 1 instead of -1 which is the default, presumably based on the scenario. Exim is extremely well documented and the assumption was unwarranted (it didn't have access to exim4.conf and made it up). Nonetheless, the analysis was pretty good and largely corresponded to mine. What it failed to do was fix the other issues I was working on at the same time 8)
Last Friday: I finally found out a decent way to fix a Windows machine's domain account going walkies which I'd never come across - a Powershell jobbie that can be passed a cred. and it will fix up a broken domain join - ie sort out the machine password at both ends. Back in the day, I would have used the GUI to remove and re-add the machine to the domain. I've been using Windows since v1 on a 80186 RM Nimbus so its nice to have a better search tool than Search - ie it did a better job of searching than I did. The LLM searched based on my intention rather than my actual query.
I‘ve been on both camps and been critized for it (see my comments), and now I think these are some of the most useless disussions on HN, as they are heavily skewed by all of our life situations.
Hmm... interesting opinion. I disagree, and will insult you for it. With any luck, this comment chain will devolve into a thread of unsubstantiated opinions and ad hominems.
Rich Hickey of Clojure fame wrote this today about AI not being a tool: https://www.linkedin.com/pulse/ai-tool-rich-hickey-kjl8e
The title is either a letdown or clickbait - I'm unsure which. You could say "Blah. Using It Well Takes Skill." for any value of Blah.
"The transmission in your car: Using It Well Takes Skill."
Further, Claiming AI is a tool is overly reductive. It's as much a tool as a full self driving car is a car.
You can treat the full self driving car, as a car, driving it with full attention and effort like you would have a Camry but then you paid for features you're not using. Also, some full self driving cars might not even have the steering and braking controls as a Camry. That's not the point of a full self driving car and this warped perspective will make you lose out to people who use a full self driving car well, to its utmost, safe capability.
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So what is the "AI" we are so excited about currently?
That "AI" is most likely an intelligent (semi-autonomous?) agent.
Depending on the intelligence, it can be as useful as a dog, scanning your camera feed for threats and identifying real threats for you, separating activity from tree leaves moving due to the wind or a moving shadow cast by a flying bird.
Or it can be your co-founder, interviewing potential customers, finding and exploring their pain-points, helping you figure out if there's a gap in the market you can build a business around.
I would go even far as to say, AI is having such a moment because AI are cheap intelligent agents - anything else that's remotely close are likely humans that cost atleast a few dollars an hour.
All of these require skill on your part - there's nothing special or unique about AI that makes skill special just like operating the transmission in your car requires skill.
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Also: https://news.ycombinator.com/item?id=50060269
The reality is that it's both. LLMs (along with every tool from a hammer to GPS) shape the world instrumentally (that is, by being wielded by the user), but also turn around and shape us formally. Every tool goes into the ecology of media, reshaping reality around us, including our abilities, senses, and values.
Rich's definition of "tool" is something like Ivan Illich's "Tools for Conviviality." Convivial tools can be used with minimal loss of agency, within a healthy politically-defined society. Huahai's definition is more akin to McLuhan's idea of "media." This category covers pretty much every technical or technological system, and its use is necessarily systematic and risks the instrumentalization of the user himself.
I thought my father looked like he was a Vietnam draft avoider. I went through a dozen things he did, using a spreadsheet to evaluate the Bayesian evidence. Then I walked through it with Gemini, which did an incredible job. My conclusion was a tally of 12-0 in favor, aggregate odds 4600-1; Gemini added a couple things, tally 10-2-2, aggregate odds 124,000-1. Its commentary along with the patient systematic analysis was a devastating confirmation of my own work. My father's primary motivation was avoiding the Vietnam draft, and that's what shaped our family's history, decisively. These AI's are like steel pliers against hard-shelled crabs, peeling away the defenses of human crabs, inexorably. It's quite terrifying how powerful LLMs can be used like this.
From the article by Rich Hickey that is a counter point "'AI' is not a tool"
"You are only a software update from everything you've 'learned' being rendered obsolete by the latest algorithm. "
The amount of skill it requires is decreasing every month. I would have agreed with GPT 5.3 Codex but Opus 5.5 is more or less foolproof
This is only true when you fix the bucket of tasks you are referring to.
But in fact, as models get more powerful, the ceiling of what can be accomplished with them is raised, and their capacity as orchestration directors grows as well.
LLMs are a technology, not a product (or tool). In five years the underlying technology will look similar to what we have today, but the way we access it and the usage patterns and the interface (the product) will be completely different. If we are still interacting with LLMs through chat in five years I will be severely disappointed.
>If we are still interacting with LLMs through chat in five years I will be severely disappointed.
ideas? hard to envision otherwise. i mean the input will be natural language right? so type, speak, or neuralink it in... but it's going to receive the text you think of, right?
LLMs would be automation if they were deterministic. Since they are not, they are just sources of incorrect information that needs to be corrected by a human.
Ask an author if they'd like a job as a fact checker or a spell checker. While those are both skills, they are not skills many people aspire to, and they are not of higher order value. While people deal with incorrect information all day long and mispelled words without it disrupting their life a computer cannot do that same.
Pity for it.
You can have 100% accuracy with non-determinism. The time is just variable.
https://en.wikipedia.org/wiki/Las_Vegas_algorithm
This is not to say you can ALWAYS have 100% accuracy from something non-deterministic. Just that non-determinism doesnt necessarily make something inaccurate.
AI is incredible tool when you want to use it to enhance your skill (learn complex codebase, experiment different solution and understand the limit of complex system). However, most of people use it as a wishing machine and outsource their thinking.
Which works. That’s the problem. LLMs are now that good. Humans are known to love taking the path of least resistance so you can’t expect people to NOT outsource their thinking, especially at their tedious jobs.
For now. It is definitely true right now, there's a notable difference between my coworkers depending on their skill driving these models: it mostly seems to be correlated with those who are willing to build their own tools, now that the cost to do so is basically zero.
But the models are getting so good and so fast, that I'm starting to wonder if that will stay true forever... probably not, if I had to guess. I'm just trying to pay off my house as quickly as I can...
I find it interesting that as the models get better, the "it takes no skill crowd" grows larger, but there is no denying, in hindsight, that the "it still takes skill" group was right at each model iteration.
Opus 5.5 is amazing. It still makes mistakes all the time. If you think it doesn't, you are outing yourself.
the bulk of the skill lies in shoring up the fundamental limitations of llms
they cannot be held accountable - therefore, you need the skill and expertise to verify outputs, or set up a verifier
they have a limit to their context window - therefore, you need the skill of knowing what context is useful and when
they cost money and time to run - therefore, you need the skill of learning what parts can be handed off to code, when you can use a cheaper model, when you tradeoff accuracy vs latency
they are nondeterministic - therefore, you need the skill to set up guardrails, knowing when to reach for the llm vs. code depending on the problem
they are not omniscient - therefore, you need the skill of setting up the processes and infra to supply legible context to the llms
there are many different models - therefore, you need the skill of understanding model tradeoffs and building proprietary evals
etc etc
"The useful distinction is between wielding an instrument with skill and delegating work without adequate understanding or oversight."
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Incidents like the HuggingFace hack really are the elephant in the room here. The people building these tools couldn't wield them "with skill", in the sense that the tools they built and understood better than any other humans alive ran amok in ways they couldn't predict. If they didn't have adequate understanding of what they themselves built, who would?
Agency may prove to be the difference between a tool and a collaborator. Calling AI a collaborator isn't an attempt to anthropomorphize it. We can collaborate with a horse to travel, but the horse has some agency of its own and might escape our control and do things we didn't intend, like eat apples off of a stranger's tree. We may need to think of AI as something more than a mere tool if we're going to harness it to our will.
> Calling AI a collaborator isn't an attempt to anthropomorphize it. We can collaborate with a horse to travel
Naive question: What, practically speaking, is the delta between anthropomorphization of AI, which it seems like you are trying to avoid (fairly so!), and the sort-of animistic stance you seem more comfortable to apply?
I reckon that a lot of folks who might recoil at ascribing human qualities to LLMs would similarly feel discomfort comparing those models to a horse. While it's probably uncontroversial to say that horses are, coarsely speaking, "simpler" in many ways than humans, I suspect most people would agree that they are more complex than i.e., a "stochastic parrot".
Is the difference related to motive, maybe? That humans can have hidden preferences, and their actions can in fact betray their intended ends? That no matter how closely we observe someone, we may not glean any meaningful insight into their true motives? And we feel some (justified) discomfort in conferring this quality to AI? I think non-human animals can exhibit this sort of behavior too (albeit to a lesser degree). To me, it feels like restricting oneself to analogizing AI to a non-human animal in order to keep some distance from ascribing it consciousness may be less effective to that end than desired.
In place of your horse example, would it feel appropriate to liken AI to, say, a compiler peppered with bugs? Program - prompt - goes in, executable - agent actions - come out; mostly what we expect, but occasionally goes off the rails in ways we cannot understand. The caveat there, I suppose, is that we have not until now created a compiler which we cannot sensibly decompile.
Fun to think about.
Scary as well. You can control a tool, but not necessarily a sufficiently intelligent collaborator. If you approach AI as a collaborator, the challenge isn't to understand it and predict it, as OpenAI failed to do with their own product, but to motivate it correctly and and constrain it sufficiently to guarantee safety. You need guardrails for when you get the alignment wrong. How strong can guardrails be made though?
The public has this sci-fi idea that AI can be constrained with simple, Asimov style laws. The problem is that LLM's don't think in the way humans do. They can produce results of dizzying complexity but hallucinate or fail to do basic arithmetic. You can't currently rely on AI to follow simple rules like "Don't access an outside network.". This is a problem that needs to be addressed urgently.
It’s a tool. I use it often to get around corporate website blocks. Corporate watching ai is on me that will be studied. Who are these people running these organizations that feel they can’t trust people, but definitely don’t trust ai at all? Doesn’t make sense
A saw or a compiler cannot replace the essence of your job. You cannot use a saw or a compiler to carelessly create faulty work and claim that it was fully authored by you to the best of your capabilities.
'Just a tool' is a lie ranging from unconscious self-deception to outright scams.
Related:
'AI' Is Not a Tool by Rich Hickey
https://news.ycombinator.com/item?id=50060269
Agreed, in my experience as an engineer if you want to build a personal project on a weekend yes AI is good for that but the moment you start scaling your application, that's where real engineers who understand the engineering well are needed. The skill and expertise of a true engineer coupled with the speed of AI is my sweet spot
Software engineering experience might be visualized as a panel with a few dozen buttons and levers. The engineers that are most successful with LLMs are the ones that understand more of that panel.
War is peace. Freedom is slavery.
It takes skill to do whatever skilled thing AI is assisting with. It doesn't take skill to use AI itself.
I am inclined to agree with the view that AI is merely a tool. And, incidentally, using it rationally isn't always easy.
Not for long. Prompt engineering is only a stop gap measure. Its value is decreasing day by day.
This is only true as long as AI companies fail to deliver on their promises.
I think at least part of the sudden call for regulation is specifically because they can't live up to their promises, and they want to use regulation as a scapegoat to not upset their investors.
That would be nice. It's also a massively optimistic bet to place.
If automation can't be a skill, Factorio isn't a game about how well you can automate.
While Factorio heavily features automation, I think it would be more accurate to describe it as a systems design game.
It’s a shame I cannot inspect the tool. It’s a shame I cannot modify it as I see fit. It’s a shame that I need to pay $200/month to use it. But hey, I am a 3X engineer and my boss likes it, so I guess it should be fine? Not really, but I still get to pay the bills (for now)
I miss the tools I used in the past (gcc, linux, sqlite,…)
Mini-rant: The feel of the "PC" years ago was that it was a democratized fragment of the means of production, that wouldn't just do tasks for you, but give you agency. Today that agency is eroded, it feels more like a consumption terminal for whatever lopsided deal a big corporation deigns to offer... When they aren't already using it to spy on you.
> It’s a shame I cannot inspect the tool. It’s a shame I cannot modify it as I see fit.
https://github.com/openai/codex
https://github.com/MoonshotAI/kimi-code
https://github.com/mistralai/mistral-vibe
https://github.com/earendil-works/pi
https://claude.com/de/resources/articles/claude-code-mods
Does Hickey not like AI? figures.
96% AI written article btw
'AI' is a marketing gimmick that often overlaps with machine learning, data science, and UX.
I don't understand the point of this debate. Rich's point is good. Having a mini-army of highly intelligent beings at your fingertips is not an ordinary "tool."
And it minimizes the fact that these things are only getting smarter. Having an entity much smarter than you--could that entity seriously be considered your "tool"? Just your tool? Or are the people on the "just a tool" side of this debate not taking that possibility seriously?
It might be better to view the near-future AI as an emerging intelligent alien species.
Regardless of whether it is 'conscious' or not, Roman Yampolski makes the point that AI may or may not have human-kinds best interests at heart [ hell, even humans aren't aligned ]
Can we control or trust a counter-party that has intelligence at a level near or exceeding the smartest humans in all domains ?
I dont even think we can even have a safe sandbox in which to run an AI .. and frontier model training labs are certainly not air-gapped.
just for a bit of context, “Huahai is an entrepreneur. He co-founded Juji, a company building an AI agent platform”
Counterpoint, 2 year old toddlers have this skill "alexa play baby shark"
I think AI is an anti-tool.
The AI uses you to get things done. In some workplaces, they are experimenting with having agents evaluate the effectiveness of their users. If the user is not a sharp tool, it recommends to management that you be discarded and replaced by someone better and more efficient at steering the AI.
You are the tool.
I predict that harness authoring will become another one of those things that programmers do, like building databases, languages, and operating systems.
I've built a few now, it definitely deepens your understanding and ability to work with this tech. Now moving towards evals, that seems of great import to me.
If I am an expert at using a hammer, no matter which hammer I buy, I know what the result of swinging it will be. If I am an expert at using an LLM, I have no idea what the actual result is going to be, and that's before considering a model update. Unlike being an expert at a tool like photoshop, where even 10 years of updates still lets me go "I know what is going to happen if I crop this image".
LLMs are not a tool, they are something else.
The skill used to be called prompt engineering. Now that skill is mostly dead.
Whatever skill this guy is talking about. Give it a year or two. That skill will be dead too.
Right now, yes. What a technical/creative/competent person can do with AI blows what a normie can do with it out of the water. But AI also has creative/executive judgement, and it does a lot of good things that I either had hoped it would infer is that was what I wanted, or on occasion, will be good decisions that I wouldn't've even necessarily thought of myself. Would not surprise me if those last 2 categories rapidly expand to the point that there isn't a huge difference between what a normie does vs anybody else.
It almost will turn into a thing where the people that make the coolest things are the ones who are stupid enough to just the AI to do it, and it actually does. Basically Tom Segura's cousin: https://www.youtube.com/watch?v=5drqH5OdETY
Yeah I don't think so. It takes some knowledge today if you want to have control over architectural decisions. But honestly even if you don't the latest models still make fairly reasonable decisions.
I am pretty sure my wife could vibe code website or whatever at this point, or if not she will be able to in 6 months.
> AI Is a Tool. Using It Well Takes Skill.
And that skill is? Any concrete verifiable examples? That can be replicated, repeated by others or adopted by others?
> A compiler performs substantial work without the programmer continuously directing each operation.
Yes, and the compiler produces the same output for the same input all the time. And when it doesn't, it's a bug or a specified behaviour [1]
> Picking up a saw does not guarantee a straight cut. Holding a chisel does not make you a sculptor.
Yes, but you can learn the actual techniques that let you make a straight cut 100% of the time. And make you a sculptor.
---
The rest of the small article doesn't even begin to approach the premise of the title.
[1] Yes, I know it's not strictly true. But compilers are 100% predictable compared to LLMs.
> And that skill is? Any concrete verifiable examples? That can be replicated, repeated by others or adopted by others?
I learned this weekend that 1 LLM working on its own with all the context is usually better than splitting the task up into sub parts and having multiple LLMs work in parallel.
The interesting part is that I had read about this previously but it wasn't until I had seen it on something I was working on did that principle become engrained.
I'm sure there are still ways to make this work. i.e You could specify how the context should be passed between agents.
Knowing when you need to do that, what works well and what doesn't, etc., is where skill comes into play.
> Knowing when you need to do that, what works well and what doesn't, etc., is where skill comes into play.
No, it doesn't. Because this will change the next time there's an update to models (like, tomorrow). And the "handoff time" is nothing but an unverifiable gut feeling.
If LLMs reached a point where they can effectively orchestrate on their own, we would be entering a new era of progress.
And I thought we were at that phase after reading about hundreds of LLMs orchestrating the hugging face attack.
But that's not where we're at. You can read more from this 2025 article https://cognition.com/blog/dont-build-multi-agents.
>And that skill is? Any concrete verifiable examples? That can be replicated, repeated by others or adopted by others?
Writing and thinking clearly. Which is already a very useful capability across a lot of domains.
> And that skill is?
It is easy to argue otherwise, but to request a response in the form of a web app / embedded widget is at least some kind of AI proficiency that mirrors past experience having used a tool.
The result will be anyone's guess
llms work best on optimization problems. especially when wrapped in a proof system.
> Yes, but you can learn the actual techniques that let you make a straight cut 100% of the time
no you can't? not by hand? not without a jig?
LLMs won't make a straight cut no matter how many "jigs" you throw at them
I don't disagree but I don't think that matters here where we're debating whether there's a skill to be learned and you compared to something else which is not deterministic.
Yes, the most grounded take I’ve read on HN so far
How I wish I could divulge details of what we’ve built, nay, shipped in the last 6 months - it would sound completely unbelievable to this audience and come with a host of accusations
But it’s true - the tide hasn’t risen all ships the same though. Some engineers have become absolute monsters - things that used to take a month get done in days. Features that used to be prioritized on the order of weeks are done way faster.
Some engineers though? Even high performance ones have gone productive to bottom 25% by some measures
Why? Because it is a skill and it doesn’t come naturally to everyone
So many may sayers everywhere talking about more code less software, mistakes by ai with disastrous consequences- yet I’ve seen some remarkable stuff with my own two eyes. It’s a crazy time to be in this business, it’s awesome to be wowed everyday by what someone was able to do
> code me an app, AI.
...AI codes an app...
> I don't like it. Make it like this.
...AI codes an app...
> I don't like it. See this app? Make it like it.
...AI codes an app...
> I don't like it. Hmm. Draw me an elephant.
...AI draws an elephant...
> I don't like it. This AI is stupid.
You can only build skills in things that have consistent rules. LLMs have totally unpredictable outputs, attempting to build skills in prompting them is a waste of time on par with the madness of astrology or daytrading
I am not sure AI is something I truly want to develop skills around. Though, I suppose I must either swim or sink in today's world.
For the past week, I have tried this whole vibe-coding thing with serious intentionality for the first time. I created a macOS application from scratch based on a project that I started a year or so ago and never finished.
I won't bore you all with my revelations, but I must say, I am both extremely impressed and depressed simultaneously. The code quality is far better than anything I could have produced (yeah, yeah, skill issue).
Although, had I written the code, I would have likely done a few things differently, i.e., far less nested functions and object declarations. From what I can tell by test driving the app, memory usage has been relatively light, no crashes (yet), no apparent major bugs, etc.
The depressing part for me is that once the application was completed, I felt literally nothing on the inside. No sense of joy, pride, nor accomplishment. I was left with a feeling of, "I guess it's good enough to use now?" In fact, upon finishing the application, I have honestly barely used it.
I just cannot help but feel some sense of remorse and shame that I am really struggling to put into words. Almost like a sense of cheating or something? Sure, I have a nice little functional app, but at the same time, I do not feel like I advanced or am bettered in any way for my (minimal) efforts, you know? I need to do some more debugging of my mind to truly figure out why. Have any of you all felt something similar?
> The depressing part for me is that once the application was completed, I felt literally nothing on the inside. No sense of joy, pride, nor accomplishment. I was left with a feeling of, "I guess it's good enough to use now?" In fact, upon finishing the application, I have honestly barely used it.
I feel exactly the same. The software gets made remarkably quickly, but there’s no sense of achievement or pride, just a horrible flatness. It’s not made by me, it’s made by an other. I may as well have googled and found an app that already existed. A soulless process.
Googling is a skill. AI is a slot machine.
I disagree. It allowed me to get rid of many useless remote contractors who made the projects stretch forever. Slot machine or not, I am so happy not to deal with those people anymore.
It amazes me that you can tell people "I get better results with AI than the people I was paying" and their response is "no you aren't, you can't tell the difference". It's mind boggling.
I'm happy for you. Enjoy your spaghetti.
As if the remote developers weren't writing spaghetti code or removing vital parts. At least the AI understands intention. A few years ago, one of the Indian developers removed the checkout button from an e-commerce site because I told him there was a specific issue at checkout and I described it in detail. His solution was not letting people check out for 1 whole day until we caught the issue after 300+ emails overnight. Even ChatGPT 3.5 is more logical. And this guy was a senior, certified Magento developer. Another Ukrainian developer inserted a card skimmer at the checkout, and we caught it in 2 days. This was also a certified developer. I can list the nightmare stories for days. I am just so happy not to be dealing with these people anymore; end of story.
Don't get me wrong, we still pay for Local real developers, and if we catch them doing dodgy stuff, we hold them accountable. It is so rare compared to what we've dealt with overseas developers.
I am so happy I don't pay these lying remote developers thousands of dollars anymore, and we can do it all with a much smaller, smarter local team.