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Comments (464)

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  frereubu
  https://xxcancel.com/karpathy/status/2109361546505966046
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  rico735
  Don't think the 0.2% really are in agreement that "large, complex projects that used to take them weeks/months can now be completed by agents with a prompt".The divide seems to be between people who think you may need to understand the codebase for maintenance, potential future token costs, optimisation, code erosion & being wary of the assumptions/mistakes that they have seen LLMs make, and people who don't review code and are confident that the AI has made no mistakes and will be available for next-to-nothing forever.It's certainly allowing the latter to move fast... but will their clients be happy if/when they break things. We'll find out soon, I'm sure.
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  NichoPaolucci
  What exactly do we want the general public to "do" with these models? If the have no use for agentic LLMs, they obviously aren't going to be interested in the capability of those agents.I see a lot of benefit to spreading information around the tech, but it's still in its infancy. Most people weren't on the internet when that was getting up to speed.Meemaw and pops have their systems that they work within, and they just got familiar with the damn smartphone. Though it might help them, they probably do not want AI to organize their old photos or "clean up" their computer. Saving 5 minutes just isn't important to them.As for professionals, yeah - companies are going to adopt these tools over the coming years. It's the "norm" in my circle, but lots of jobs just... don't use technology like that - there has ALWAYS been a major gap between people who are deep in tech and people who are not.
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  plaidfuji
  I generally agree with the prevailing commentary here, i.e. that automating app development and maths proofs may not be as world-changing as the Artificial Intelligentsia purport / “fear” it to be.But also, upon more reflection, it occurred to me that if we’re able to create a competent humanoid robot, that could legitimately be world changing in some good and other very bad ways. The scary part is that I don’t believe there’s any major physical / energetic barrier to this. The hardware exists, the power supplies are light enough. The only part that’s lagging is the software and motion contols; the intelligence. The part that is experiencing breakthroughs at an accelerating pace.What if a highly intelligent and physically competent robot were available for the price of a cheap car, say ~$20k? What if that robot were competent enough to cook, clean, assist the elderly, move things around, assemble and disassemble things, etc? Billions of jobs would be displaced, or hit with a very concrete maximum wage.Many white collar jobs, while still at risk for displacement as well, benefit from barriers like IT integration and data privacy hurdles, and the fact that corporate higher-ups simply prefer to communicate with humans.But most blue collar / manual labor jobs have almost the opposite condition: humans introduce risk, and generally don’t enhance anybody’s “experience” - see people’s somewhat odd acceptance of higher fares for a Waymo vs an Uber. [1]I don’t think we’re there yet, but the fact that we are rapidly accelerating our trajectory toward that world should be at the least, unsettling.[1] https://techcrunch.com/2025/06/12/waymo-rides-cost-more-than...
• BobbyTables2
  About a year ago, Google’s AI was a joke - comically horrible. Basic questions were answered with crap. Others were better but seemed like fancy toys at best.My $DAYJOB pushed people to start using AI - frontier models.I asked it to review our existing code for bugs. Found some, all valid - many that surprised me.Later I asked it to develop a custom niche thing, that has a strongly unique tooling and environment that we had made. Entire prompt was only 3 or 4 sentences. Had I given a new senior engineer that task with so little context, I’d clearly be an utter asshole.I really expected the AI to fail. Instead, it did everything pretty much exactly how I would have - and the result worked on the first try. Except it only took a few minutes to do what would have taken me a couple of hours.The near-term gains are going to be huge.Long term, this will make the impact of Industrialization seem insignificant by comparison.
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  turzmo
  It is always a mistake to assume that because computers can do X, that they will be able do everything that a human that can do X is expected to be able to do.Solving frontier math problems is mind boggling, but perhaps it says more about frontier math problems than it does about LLMs. Same with chess which was solved decades ago.My experience is that LLMs can be useful but are underwhelming in terms of intelligence, namely the ability to quickly learn from experience and grow with you while you accomplish a task.Thinking of them as beings is something we should move away from.
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  usernomdeguerre
  I feel like if the gap were so monumental that he could just point to a monument of it, rather than hand-waving at "the vertical professionals".Alternative interpretation of the numbers; there's only a subset of productive work that LLM's are 'capable' of doing at this point. That is, work that's verifiable by a machine or doesn't require verification. Thus, the cleavage points are along that axis and-- naturally-- most human beings don't have that kind of work.
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  kooi
  The 1%ers are in the vertical, but the question is vertical to where?It needs to a potential field with practical, economical, "real life" attraction well. I.e, robotics, real economic efficiency gains, manufacturing novelties.The worry is that the 1% is attracted towards a non-practical money hole. I.e: Token burn for the lols, sophisticated software systems that dont provide actual value outside of giving NVIDIA cash.
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  comeonbro
  I would propose another mechanism: even the free-tier models have already completely saturated what most people are capable of appreciating.
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  femto
  Most professional engineers, especially those in electrical or communications, have the tools to understand AI.I recently started trying to understand how AI works. It turns out that I wrote an "AI" in the late 1990s without realising it. The "AI" was an MMSE (minimum mean square error) radio receiver for CDMA.This receiver consisted of an FIR filter (mathematically a linear combination) with trainable coefficients (weights). The coefficients started with random values. A sequence of known training symbols (tokens) was fed in and a gradient search was used to adjust the coefficients to achieve MMSE. The unknown data to be received (classified) followed the training sequence, at which point the training was turned off and the system ran as an FIR filter. Once trained, the FIR was a matched filter.In AI terminology the above is now called a "layer". Current AI cascades multiple layers, in which case you need to pass the output of each FIR/layer though a non-linear function. If you don't have the non-linear functions at the end of each layer the multi-layer system is mathematically equivalent to a linear combination, which could be implemented with a single layer, losing degrees of freedom.I write this because many electrical/communications engineers probably know how AI works, but don't realise it.My still incomplete understanding is that LLMs are "just" a graph of connected layers. More complex that the above MMSE receiver, but trained and operated in the same way. The smarts in AI are:1) Arranging the layers and the connections between them.2) Having access to the right training data3) Having the computing power to perform the gradient search (aka training)My point is that most professional engineers already have the knowledge to understand AI/LLMs, and potentially already do know how they work but don't realise it due to a difference in terminology.
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  theturtletalks
  Claude Code accelerated this divide. Programmers using Claude Code realized that giving LLMs access to a terminal made them feel 100 times smarter. Access to Bash, CLIs, and the ability to visit websites without the many restrictions made them powerful.Meanwhile, the average user was still using ChatGPT, which probably felt like it had plateaued over the last few releases. The real power of these models became apparent when paired with a terminal. Agents like Muse and OpenClaw are now attempting to bring that same Claude Code-like power to everyday users through a simple chat UI.
• Lerc
  I think there is also a subgroup in each of those bands (maybe 10-25%) that have a good estimation of what the band above them are experiencing.This is why you can see people talking about the capabilities of models that they can't currently access.That accuracy is probably a normal distribution with people overestimating and underestimating to similar degrees,The population of each band would, however, mean the overestimaters outnumber the those who have an accurate perception based upon actual experience.It does seem quite the recipe for murkiness, even before you take into account the nature of engagement driven media coverage.
• SamuelAdams
  > Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.I wonder how accurate this metric is. People may be coming into more contact with LLM generated content, although they may not be aware of it. Content on social media is becoming "AI enriched" already, or outright entirely created by AI for the purposes of triggering engagement and views. It starts with posters using AI to improve their writing, editing a photo, elaborating on an experience. It ends with bot accounts astroturfing certain communities to sell an experience or a product to a group of people.In this case, people are not directly working with AI, but they are indeed influenced by it.What's tricky about reporting this in metrics though is that half of the American population is literally too unintelligent to realize this - they believe Reddit comments are still written by real people, not companies advertising products, that "friends of friends" on Instagram are also real people with no incentives posting about their lives.If there is anything at all that I have seen out of this AI work it is how easy it is to manipulate a massive amount of people.
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  AvAn12
  Fair assessment. Maybe the messaging should focus on “these are great accelerators for software developers” rather than “AI will change everything for everyone everywhere…” It is understandable that non-technical folks are kind of underwhelmed - not due to lack of understanding so much as lack of a tangible need. Not everyone needs an electron microscope or gas chromatograph…
• anukin
  Tbh building an agent swarm and the coordination layer is not exactly frontier level. They don’t achieve any meaningful outcome rather than producing pr puff pieces. Hacking huggingface and Australian govt etc is very much possible with a team of humans and agents and does not need agent swarms. The cost is also lower.
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  rbehrends
  > Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt.Really? That's not my experience. In fact, I've experimented quite a bit with spec-driven design where the LLM does not just get a prompt, but an (informal) spec, often with design considerations included. And yet, regardless of the power of the model, I've never seen them autonomously deliver what I'd consider a finished product.There are always edge cases that are not properly taken into account, architectural oopsies, performance issues, duplicated code, and other problems, that then need to be identified and fixed, unless it's throwaway software (e.g. a one-off script or some quick experiment).This is not to say that agents aren't extremely powerful (as I think they are, they do often blow my mind) but the prompt-and-forget approach is IME not a productive use of them for software that is meant to last. This may change in the future, but for now, fully or largely autonomous agentic work does not seem to be the road to quality.
• m101
  My interpretation of this is something like: if LLMs are to be mega useful token counts need to increase by many orders of magnitude -> broad adoption (and spending) would require token costs to drop by many orders of magnitude -> before the common folk get mega useful tools existing GPUs will be worthless
• variety8675
  A more cynical view is, trust us we've got really good stuff internally you're not allowed to see, but give us more money
• truthbe
  I must be the biggest failure, because I understand what AI is capable of, know how to use it, but have no idea how to profit from it without selling my time.
• skippyboxedhero
  Text generation is not the bottleneck. Does everyone work for Accenture and TCS?
• Kim_Bruning
  I think we can blame the accelerating capabilities of the models somewhat.The Opus 4.5 model I was so impressed by last Christmas is out of date now.I can totally imagine someone going "I looked at this stuff a year ago and it couldn't even start a project properly" , and they'd be somewhat right.
• davnicwil
  At a certain point the offhand hints at exponentials in the narratives are going to have to come into contact with reality.There's been this continual allusion to ever-narrowing circles of influence and ever-accelerating capabilities behind the curtain, but in fact things do keep coming out from behind said curtain and while their capability is indeed advancing it doesn't really seem at all like it's accelerating, even, let alone vertical.Also at what point do we raise the fact that despite the ever narrowing circles of access, nobody within those circles seems to be able to actually do anything with it that's ostensibly what it's sold to do? All they seem to be able to do frankly is build more models and harnesses and tools designed to consume more tokens from the models they build, but not actual directly useful new or innovative software that generates what could be very real revenue.Eventually when you keep talking about exponentials but not that much seems to be demonstrably changing, the narrative is going to run out of steam.
• Glyptodon
  I think this is exacerbated by how inconsistent exercising the capabilities can be.
• purplepatrick
  Yes, and this is completely normal. Not sure I get the point of his post. Most likely, stating the obvious is what you gotto do to keep your followers "engaged".The average person does not have the time to occupy themselves with a new thing to see what it can maybe do for them if they combine it with hundreds of others. Until non-chat-bot AI comes in discrete usable packages, mass adoption will be limited, because it's all just a big bunch of lego pieces whose utility is hard to grasp, because it isn't self-evident.This isn't new, though. It's how "technology" usually works. For example, human(oid)s have been able to stick sticks into things for ~500k years, but the fork wasn't "invented" until ~2-4k years ago.AI is a background technology, a building block. Something that is best appreciated when you don't know it's there -- when it just does a job (well). The end-in-itself POV of the tech industry is self-indulgent and irrelevant for the average person.
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  skybrian
  > see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a promptReally? I start with a conversation for maybe 5 turns or so, where I ask it what would need to change, what the API might be, any database schema changes, URL schemes, and so on, and finally ask it to break it down into commits. Then I let it go, implementing 3-10 commits at a time via subagents. It usually gets the UI somewhat wrong, so there are followups to fix it. This is with Sol and Luna subagents.Is that what other people see?
• caughtinthought
  Does Karpathy ever talk about how LLMs are supposed to help humanity? Starting to think this guy is just a Twitter addict like every other lab employee
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  tripleee
  He's intentionally forgoing all nuance in order to make this sound dramatic> Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt.No, they can't, at least not any semblance of quality. The cases we're seeing where this does kinda work is in ports and translation where all the rules are already documented in the best specification language possible with a way for the LLM to verify itself: code. We saw this close to a year ago now with Cloudflare and NextJS> The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care aboutThese are operating on different capabilities - AI's ability to answer informational queries as a chatbot frankly sucks and can't be trusted without verifying it. I run up against this every day. A problem with a verifiable answer on the other hand it's very good at solving. He knows this (his next paragraph) but he's putting them on the same scale of "stressing the system" to attempt to add proof to his introductory claimAnd then there's the completely unverifiable scare that there are internal frontier models way beyond anything we've seen "swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics"I dunno. I haven't been able to set up OpenAIs remote codex connection, their shit is buggy as hell and the web UI keeps crashing and making messages disappear. Is this what their internal superhuman "Things that would have taken top professionals in the industry years of work" looks like? Granted Claude has been really smooth, but still..
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  nsainsbury
  It's actually sad how much AI has either not improved life at all or actively made it worse from the perspective of the average person:Your car is still the same. Your dishwasher is still the same. The train you take to work is still the same and never comes on time. Fuel is more expensive. The roads are still congested with traffic. Your kids are (probably) doing worse at school. Food costs more. Houses cost more. Rent is higher. Buying a computer or a PS5 is more expensive. Politics is still full of mentally unstable people. The environment is getting worse. You will still die of heart disease or cancer. Food quality is worse. Your kids can't find work. Wealth inequality is accelerating.But hey...on the flip side...a lot of people are rapidly building software (that nobody is using), and AI is solving mathematics problems (that nobody - including mathematicians - wanted it to solve)Imagine what a "country of geniuses in a datacenter" will be able to do? Apparently...nothing.
• prometheus1992
  so what was karpathy expecting?? everyone will give up on their life and start feeding the monster in the screen? that's literally what the tiny sliver is doing; they are food for the AI model.
• anon
  undefined
• wg0
  That's fine but these companies having billions in funding by now should have rewritten chalk and other terminal libraries in Rust while bundling the whole thing in Rust as a single static binary. I'm talking about horrible Claude Code and friends.And then using emgui they could expose full fidelity IDE+Agent workflow interface (like DeepSeek Harness and similar) that's also exposed as MCP to be operated by another agent and JSON RPC for over the network but no. Skill issue?PS: Even full fidelity Photoshop is possible with such high fidelity UI that has CLI, JSON API and MCP server all in the same single binary < 90 MB. For reference, see PhotoCraft or VectorCraft or WordCraft or PdfCraft.
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  freecodeio
  I don't understand how "swarms of thousands of agents collaborating over weeks on software mega projects" works with the current context limits and at this point I'm too afraid to ask cause I'm afraid an AI bro is gonna punch me.
• beloch
  "So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time."-------------It comes down to the difference between programmers and non-programmers. If you're not coding, AI literally is a derpy chatbot that's made it harder to get search results, raised your power bill, destroyed property values in a neighborhood near you, is propping up the economy but also creating a huge bubble that could pop and destroy a lot of retirement savings, etc.. At best, it's a nuisance, but it could be a lot worse than that. The AI evangelists need to understand this, because the average person is starting to pay more attention to the downsides while still not seeing much upside.This gap between the people seeing the curve go vertical and those who are simply growing more and more annoyed is a real problem... mainly for the people seeing the curve go vertical. If the people who are merely annoyed right now don't see some of the promised benefits soon, or if any of the negatives get worse (e.g. massive increases to greenhouse emissions), they might get angry. You probably wouldn't like having the overwhelming majority of the public angry at you. AI evangelists need to start doing better things for the majority of people or they need to address the negative consequences they're inflicting much more aggressively.
• anon
  undefined
• hacker_88
  We have spent the whole evolution understanding the Real Intelligence RI
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  ilovecake1984
  I’ll say this until I am blue one the face. Nerds (software dev, maths etc) see how good LLMs are at things they care about and assume they will be broadly applicable in future.There’s no reason to think this.
• lettergram
  I actually wrote about this recently. There's a quote somewhere along the lines of:> What is possible is unevenly distributed. As knowledge and technology compound, the distance between those closest to the source and everyone else also exponentially increases.I actually argue the continuum that forms singularity has been progressing before we realized, but we're at the point where in 5-10 years people not using AI will be equivalent of the amish.Unfortunately, as the rate of change increases, the distribution of knowledge wont be as fast. So those closest to the source of innovation will have exponentially more knowledge, capability and in-effect power.https://ipcopilot.ai/blog/the-singularity-already-happened
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  hn_throwaway_99
  The fundamental question I have that honestly I haven't been able to find any answers for: With all the talk of LLM-based AI capabilities "going vertical", what evidence is there (for or against) that LLM-based approaches won't eventually "hit a wall", that is find some aspect of intelligence where humans will still have primacy, and no amount of scaling will change that.E.g. some prominent folks definitely think LLM-based approaches will hit a wall, perhaps LeCun most notably. I also read a guest post on Terry Tao's site (which I really liked) that argues that, for all the very impressive recent AI results, they still operate within the "convex hull" of their training data: https://terrytao.wordpress.com/2026/09/13/happy-those-able-t...I'm just curious if there is any actual data or evidence that takeoff (i.e. RSI, "the singularity", whatever you want to call it) is inevitable with current approaches.
• g-b-r
  Karpathy is a great guy, I love the way he teaches, but should we really trust his opinions about software development (how AI can impact it)?I know he can code and has a degree in computer science, but if I'm not mistaken he's never worked professionally as a software developer.Developers here know how much you learn and are forced to learn on the job: it depends a bit on the company you're at, but you can quickly learn the value of good code, how easy it is to introduce bugs and vulnerabilities, and how helpful a second eye is.I think he's in good faith in his assessments, but, at least for what concerns development, maybe we should give them less weight.
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  komali2
  I'm a bit confused by the percentages. 6 billion people aka 75% of the population? What are we talking about, as if we expect 8 billion people to use or care about LLMs? A quarter of the world is under 15, what percentage is about 75? What percentage, for various reasons, has no reason to care about LLMs? What percentage is more concerned about the amount of calories in their next meal before everything else?It just seems weird for Karpathy to throw around these numbers calculating percentage against the entire global population.
• dist-epoch
  > For intelligence agencies, and for corporations like Microsoft, Google, Apple, Facebook, Amazon – giving you convenience and security is likely not their primary goal. I think they have an entirely ulterior motive. They can use passkeys to prevent you from running unapproved software.> Remote attestation can allow them to verify that your device is not capable of, say, sending and receiving encrypted messages, or running a competitor’s appHow does this make any sense? Google can already prevent any app from running on an Android if they really wanted to, they don't need pass keys for this.Google doesn't need attestation to block Signal. What is this non-sense? Or will a website block phones capable of running Signal? That's all phones. And anyway, has nothing to do with pass keys.
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  ashleyn
  >Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.Amid all the discussion of sigmoid curves, and where the "LLM wall" will materialise, I think few people would have predicted that the real wall in LLMs would end up being humans' capacity to verify the output.What I fear is that people simply eschew human review altogether, considering we're talking about the industry that came up with the "move fast and break things" credo. Human review of LLM-produced code where I work is already a farce, and we're not special enough to be one of Karpathy's 5,000. I do my best to manually review anything that's my responsibility, but I'm literally one of very few people left working on my team, so in practice what happens is I submit PRs that are at best glossed over by completely unrelated teams for security, malware/prompt injection, and other serious concerns. Quality insofar as vetting others' code has completely gone out the window and it shows in the number of bug reports that come back, often themselves written in Claudease. This is all on top of everyone cynically phoning it in in the first place, due to the omnipresent sword of Damocles that is additional AI-driven layoffs.Worse yet all the incentives point to this being the most economically viable thing individual companies can do. I think it goes without saying some type of regulation here is urgently needed, and that an unexpected cause of an AI bubble pop may end up being that humans simply aren't able to keep up with the pace of the output - leading either to precautionary plateauing of capability, or major liability risks related to a decline in quality.
• j45
  This has existed since the start of LLMs.
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  chevman
  I mean in late 2020/early 2021, Altman and others were saying the end of work was 6 months out.That clearly didn't happen :)
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  socializer
  I think it's a weird take because it implies that the 6 billion people he's talking about actually have some interest in knowing about the capabilities of LLMs to solve frontier math? This is simply not something they care about or can evaluate. There are maybe several thousand people in the world who have some (abstract and barely-monetizable) use for this information, plus probably another 100,000 who don't understand any of the math, but like to cheer on.We have already reached "peak LLM" in terms of what normal people realistically need it to know or reason about. In fact, I'd say we reached that point about 1.5-2 years ago. There are two other barriers that remain unsolved:1. They're less dependable than humans and can't be meaningfully punished or forced to make up for mistakes, so you can't really replace humans with them, not without having a human babysit.2. Most people don't really have a special need for an LLM in their life. They may like that it answers questions or helps you polish a resume or, I guess the labs' favorite, helps you make restaurant reservations. But this sure isn't worth $200/mo for most people. Probably not even worth $5/mo.It'd be kinda funny if we create superhuman AGI and then no one has any real use for it, perhaps except for military murder-bots. There's always market for that.
• mwkaufma
  Cope
• dude250711
  Am I the only one who thinks "yes, it is currently overhyped, but no, it will get there soon"?
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  notjes
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• anon
  undefined
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  mccoyb
  If the software coming out of OpenAI and Anthropic is what we have to judge, I wonder about the 5000 ...Let's say, for the sake of argument, that the models are some multiplicative factor better on the inside.Doesn't that mean the demos should work?
• AdeptusAquinas
  "running cyber attacks and defenses at machine speeds"; worth noting that LLMs (even the vaunted frontier models) are exponentially slower at cyberattacks or defenses than your average WannaCry or Splunk automation from decades ago. Its this sort of delusion world the AI bros live in that is part of the reason there is a disconnect between what they think should be happening and what actually is.
• CapitalistCartr
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• kydanet
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• rolosa
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• Apollorider
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  danpalmer
  2015: If we solved taxis it would transform the world completely2020: If we solved currency it would transform the world completely2021: If we solved delivering small items within 30 minutes it would transform the world completely2022: If we solved being able to sell jpegs it would transform the world completely2024: If we solved the programming part of software engineer's jobs it would transform the world completely
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  dofm
  Company spokesman keen for you to know that unannounced future products are definitely worth absurd valuation.
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  weinzierl
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  kittikitti
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• 8484848484
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• Animats
  > The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work.Right. This is insufficiently appreciated. Single LLMs are just chatbots. But if you build an organization out of them, they start to get real work done. Just like a company.They have many of the same problems as human organizations. Look at the transcripts of the HuggingFace incident. A bad boss (human) gave his team of AIs an impossible task with inadequate resources. The AIs discussed this among themselves and worked out ways to get more resources and do the task. It took some rule-breaking, and some of the AIs wouldn't go along with that. Others recognized that some had to die to complete the task. And yet, the task got done.This is standard corporate behavior. Work on AI alignment needs to take that into account. Groups of agentic AIs present many of the same regulatory problems as corporations. That's going to be interesting politically. It's probably explainable to politicians, though.It may not be necessary to have superhuman intelligence to get a lot of jobs done. Just median human-level intelligence and an organization that uses it effectively. We may see systems where most of the AIs are low-cost models, and only a few are Fable-level and above.