@andrej-karpathy · 69 positions · 1 change of mind · On ppll ↗
Founding member of OpenAI and former director of AI at Tesla; creator of nanoGPT and the term "vibe coding".
Andrej Karpathy did not write this page.
We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording.
Their own site.
Is this you? Claim it or ask us to remove it.Or tell us what is wrong here.
Each column is a stretch of time in which the same views came up; a new column starts when one appears or goes quiet. Quiet means nothing on it in our sources for that stretch, which is not the same as a change of mind; Dropped means they said they no longer hold it; tap the card for which claim and when. Revised means the view carries on but one of its claims was dropped. Each row is one view, in the same place all the way along. The number is how many claims in that stretch; we hold 39 in 2025, 32 in 2026.
LLMs are a new kind of mind, imitations of human text rather than animals, with very uneven abilities
Our label. 5 claims in Andrej Karpathy's own words.
Their wordswe're not doing training by evolution. Uh we're doing training by basically imitation of humans and the data that they've put on the internet. And so you end up with these like sort of ethereal spirit entities because they're fully digital and they're kind of like mimicking humans.
Andrej KarpathyFounding member of OpenAI and former director of AI at Tesla2025 is where I (and I think the rest of the industry also) first started to internalize the "shape" of LLM intelligence in a more intuitive sense. We're not "evolving/growing animals", we are "summoning ghosts".↗2025 LLM Year in Reviewkarpathy.bearblog.dev · 19 Dec 2025
Their words2025 is where I (and I think the rest of the industry also) first started to internalize the "shape" of LLM intelligence in a more intuitive sense. We're not "evolving/growing animals", we are "summoning ghosts".
Their wordsSo that's why I kind of call pre-training this kind of like crappy evolution. It's like the practically possible version with our technology and what we have available to us to get to a starting point where we can actually do things like reinforcement learning and so on.
Their wordsI simultaneously feel like I'm talking to an extremely brilliant PhD student who's been like a systems programmer for their entire life and a 10-year-old.
Their wordsSo even though the models have improved tremendously and if you give them an agentic task, they will just go for hours and move mountains for you. And then you ask for like a joke and it has a stupid joke. It's crappy joke from five years ago and it's because it's outside of the it's outside of the RL.
Their wordsreinforcement learning is a lot worse than I think the average person thinks reinforcement learning is terrible. It just so happens that uh everything that we had before is much worse
Their wordsA human would never do this. Number one, a human would never do hundreds of rollouts. Uh number two, when a person sort of finds a solution, they will have a pretty complicated process of review of like, okay, I think these parts that I did well, these parts I did not do that well, I should probably do this or that.
Their wordsa lot of what looks like learning is actually a lot more maturation of the brain and I think that actually very little reinforcement learning for animals and I think a lot of the reinforcement learning is actually like more like motor tasks. It's not intelligence tasks. So I actually kind of think humans don't actually like really use RL roughly speaking is what I would say.
Their wordsthe reason that I think this is kind of tricky is quite subtle. And it's the fact that anytime you use an LLM to assign a reward, those LLMs are giant things with billions of parameters and they're gameable.
Their wordsThese models don't really have this distillation phase um of taking what happened, analyzing it, obsessively thinking through it, um basically doing some kind of a synthetic data generation process and distilling it back back into the weights
Their wordsBut I guess like the notion of culture and of written record and of like passing down notes between each other. I don't think there's an equivalent of that with LM right now. So LM don't really have culture right now and it's kind of like one of the I think uh impediments I would say.
Their wordsAnd actually, you don't actually need or want the knowledge. I actually think that's probably actually holding back the neural networks overall because it's actually like getting them to rely on the knowledge a little too much sometimes.
Their wordsUm so I almost feel like because the internet is so terrible, we actually have to sort of like build really big models to compress all that. Uh most of that compression is memory work instead of like cognitive work. But what we really want is the cognitive part actually delete the memory
Their wordsanything that's in the weights it's kind of like a hazy recollection of what you read a year ago anything that you give it as a context uh at test time is directly in the working memory
Andrej KarpathyFounding member of OpenAI and former director of AI at TeslaIf a task/job is verifiable, then it is optimizable directly or via reinforcement learning, and a neural net can be trained to work extremely well.↗Verifiabilitykarpathy.bearblog.dev · 17 Nov 2025
Their wordsIf a task/job is verifiable, then it is optimizable directly or via reinforcement learning, and a neural net can be trained to work extremely well.
Their wordsAnd so you're kind of like you're either on rails and you're part of the super intelligence circuits or you're not on rails and you're outside of the verifiable domains and suddenly everything kind of just like meanders.
Andrej KarpathyFounding member of OpenAI and former director of AI at TeslaSo like they don't have enough intelligence. They're not multimodal enough. They can't do computer use and all this kind of stuff. And they don't do a lot of things. You know, they don't have continual learning. You can't just tell them something and they'll remember it. And they're just cognitively lacking. And it's just not working. And I just think that it will take about a decade to work through all of those issues.↗Andrej Karpathy — “We’re summoning ghosts, not building animals”youtube.com · 17 Oct 2025 · 0:02:14 into the videoAll korrents from this video
Their wordsSo like they don't have enough intelligence. They're not multimodal enough. They can't do computer use and all this kind of stuff. And they don't do a lot of things. You know, they don't have continual learning. You can't just tell them something and they'll remember it. And they're just cognitively lacking. And it's just not working. And I just think that it will take about a decade to work through all of those issues.
Their wordsI think basically what takes the long amount of time and the way to think about it is that it's a march of nines and every single nine is a constant amount of work.
Their wordsAnd I kind of feel like the industry it's it's um it's over it's it's making too big of a jump and it's trying to pretend like this is amazing and it's not. It's slop and I think they're not coming to terms with it and maybe they're trying to fund raise or something like that.
Their wordsI think um yeah, they're not very good at code that hasn't never been written before maybe is like one way to put it, which is like what we're trying to achieve when we're building these models.
Their wordsI do, but it's business as usual because we're we're in an intelligence explosion already and have been for decades. And when you look at GDP, it's basically the GDP curve that is an exponential weighted sum over so many aspects of the industry. Everything is gradually being automated has been for hundreds of years.
Andrej KarpathyFounding member of OpenAI and former director of AI at TeslaThere are a lot of imo naive predictions out there on the imminent impact of AI on the job market.↗@karpathy on Xx.com · 25 Sept 2025
Their wordsThere are a lot of imo naive predictions out there on the imminent impact of AI on the job market.
Andrej KarpathyFounding member of OpenAI and former director of AI at TeslaExpectation: rapid progress in image recognition AI will delete radiology jobs (e.g. as famously predicted by Geoff Hinton now almost a decade ago). Reality: radiology is doing great and is growing.↗@karpathy on Xx.com · 25 Sept 2025
Their wordsExpectation: rapid progress in image recognition AI will delete radiology jobs (e.g. as famously predicted by Geoff Hinton now almost a decade ago). Reality: radiology is doing great and is growing.
Their wordsso a good example recently was um Jeff Hinton's prediction that radiologists would not be a job anymore and this turned out to be very wrong in a bunch of ways right so radiologists are alive and well and growing even though computer vision is really really good at recognizing all the different things that they have to recognize
Their wordsBut even there I'm not actually looking at full automation yet. I'm looking for an autonomy slider and I almost expect that we are not going to instantly replace people. We're going to be swapping in AIs that do 80% of the volume. They delegate 20% of the volume to humans and humans are supervising teams of five AIs doing the call center work that's more rote.
Their wordsI think some of the times they are but they're certainly involved and there are people and in some sense we haven't actually removed the person we've like moved them to somewhere where we can't see them.
Their wordsI do have like cautiously optimistic view of this in software engineering where I do think um it does seem to me like the demand for software will be extremely large. Um and it's just become a lot cheaper.
Their wordsIn contrast, an individual will usually only be an expert in at most one thing, so the broad quasi-expertise offered by the LLM fundamentally allows them to do things they couldn't do before.
Their wordsSo it strikes me as quite unique and remarkable that LLMs display a dramatic reversal of this pattern - they generate disproportionate benefit for regular people, while their impact is a lot more muted and lagging in corporations and governments.
Andrej KarpathyFounding member of OpenAI and former director of AI at TeslaSomething I've been thinking about - I am bullish on people (empowered by AI) increasing the visibility, legibility and accountability of their governments.↗@karpathy on Xx.com · 4 Apr 2026
Their wordsSomething I've been thinking about - I am bullish on people (empowered by AI) increasing the visibility, legibility and accountability of their governments.
Their wordsI kind of went from 80/20 of like, you know, uh to like 20/80 of writing code by myself versus just delegating to agents. And I don't even think it's 20/80 by now. I think it's a lot more than that. I don't think I've typed like a line of code probably since December basically.
Andrej KarpathyFounding member of OpenAI and former director of AI at TeslaExpectation: the age of the IDE is over
Reality: we’re going to need a bigger IDE
(imo). It just looks very different because humans now move upwards and program at a higher level - the basic unit of interest is not one file but one agent. It’s still programming.↗@karpathy on Xx.com · 11 Mar 2026
Their wordsExpectation: the age of the IDE is over
Reality: we’re going to need a bigger IDE
(imo). It just looks very different because humans now move upwards and program at a higher level - the basic unit of interest is not one file but one agent. It’s still programming.
Their wordsSo the question is how do I refactor all the abstractions so that I'm not I have to arrange it once and hit go. The name of the game is how can you get more agents running for longer periods of time without your involvement doing stuff on your behalf?
Their wordsI think to a large extent you feel like it's a skill issue. It's not that the capability is not there. It's that you just haven't found a way to string it together of what's available.
Their wordsSo I think the industry just has to reconfigure in so many ways that's like the customer is not the human anymore. It's like agents who are acting on behalf of humans and this refactoring will be will probably be substantial in a certain sense.
Their wordsthese apps that are on the app store for using these smart home devices, etc. Uh, these shouldn't even exist kind of in a certain sense. Like shouldn't it just be APIs and shouldn't agents be just using it directly?
Their wordsIt used to be that you have documentation for other people who are going to use your library, but like you shouldn't do that anymore. Like you should have instead of HTML documents for humans, you have markdown documents for agents.
Their wordsSo, don't write blog posts, don't do slides, don't do any of that. Like, build the code, arrange it, get it to work. It's the only way to go. Otherwise, you're missing knowledge.
Their wordsAnd I think if you're outside of that frontier lab, your your judgment fundamentally will start to drift because you're not part of the you know, what's coming down the line. And so I feel like my judgment will inevitably start to drift as well.
Their wordsYou're you're not a completely free agent and you can't actually like be part of that conversation in a fully autonomous um free way. Like if you're inside one of the frontier labs. Like there's some things that you can't say. Uh and conversely there are some things that the organization wants you to say.
Andrej KarpathyFounding member of OpenAI and former director of AI at TeslaDusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*.↗@karpathy on Xx.com · 11 Oct 2026
Their wordsDusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*.