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Andrej Karpathy

What Andrej Karpathy thinks about LLMs

@andrej-karpathy · 66 positions · 0 changes of mind

Founding member of OpenAI and former director of AI at Tesla; creator of nanoGPT and the term "vibe coding".

Everything they publish, on ppll ↗

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.

10 dated positions, 2025 to 2026, in their own words. Our reading of what Andrej Karpathy has said — not written or endorsed by them.

  1. Our readingPeople disagreeing about AI capability are speaking past each other, because they are using models of very different tiers on very different kinds of task.

    Their wordsTLDR the people in these two groups are speaking past each other.

    ↗@karpathy on Xx.com

    Interesting
  2. 12 days earlier

  3. Our readingBecause an LLM can argue almost any direction competently, the right way to use one for forming an opinion is to make it argue every side.

    Their wordsThe LLMs may elicit an opinion when asked but are extremely competent in arguing almost any direction. This is actually super useful as a tool for forming your own opinions, just make sure to ask different directions and be careful with the sycophancy.

    ↗@karpathy on Xx.com

    Interesting
  4. 3 days earlier

  5. Our readingLLM memory as currently built is a distraction to the model: one old question keeps resurfacing as if it were a lasting interest.

    Their wordsOne common issue with personalization in all LLMs is how distracting memory seems to be for the models. A single question from 2 months ago about some topic can keep coming up as some kind of a deep interest of mine with undue mentions in perpetuity.

    ↗@karpathy on Xx.com

    Interesting
  6. 5 days earlier

  7. Our readingA swarm of untrusted volunteers on the internet could improve models and run circles around the frontier labs, because the earth has far more compute than they do.

    Their wordsa swarm of agents on the internet could collaborate to improve LLMs and could potentially even like run circles around frontier labs. Like who knows, you know? Um yeah, like maybe that's even possible. Like frontier labs have a huge amount of trusted compute but the earth is much bigger and has huge amount of untrusted compute.

    ↗Skill Issue: Andrej Karpathy on Code Agents, AutoResearch, and the Loopy Era of AIyoutube.com 13th of 20 in this recording

    Interesting
  8. 5 weeks earlier

  9. Our readingA tiny language model inventing a plausible-sounding name is the same phenomenon as a large one confidently stating a false fact.

    Their wordsmicrogpt “hallucinating” a name like “karia” is the same phenomenon as ChatGPT confidently stating a false fact.

    ↗microgptkarpathy.github.io

    OpenAI

    Interesting
  10. 8 weeks earlier

  11. Our readingLLM intelligence should not be understood through an animal analogy; it is a different kind of entity altogether

    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".

    ↗2025 LLM Year in Reviewkarpathy.bearblog.dev

    Interesting
  12. 2 months earlier

  13. Our readingProcess supervision by an LLM judge cannot be run for long, because a judge with billions of parameters is gameable and RL will find its cracks.

    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.

    ↗Andrej Karpathy — “We’re summoning ghosts, not building animals”youtube.com 14th of 30 in this recording

    reinforcement learning

    Interesting

  15. Our readingCoding is the first job AI took because coding has always been text, which is the one thing these models are built to process.

    Their wordsso I think there's there's an interesting point here because I do believe coding is like the perfect first thing for uh for a for uh these LLMs and uh agents and that's because coding has always fundamentally uh worked around text.

    ↗Andrej Karpathy — “We’re summoning ghosts, not building animals”youtube.com 21st of 30 in this recording

    Interesting
  16. 6 months earlier

  17. Our readingLLMs reverse the usual pattern of technology diffusion: they benefit ordinary individuals far more than they benefit corporations and governments.

    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.

    ↗Power to the people: How LLMs flip the script on technology diffusionkarpathy.bearblog.dev

    Interesting

  19. Our readingBecause an individual is an expert in at most one thing, an LLM's broad shallow expertise lets them do things they could not do before, whereas an organisation only gets better at what it already did.

    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.

    ↗Power to the people: How LLMs flip the script on technology diffusionkarpathy.bearblog.dev

    Interesting