Karpathy on AI self-awareness emerging from pretraining data
As seen on the 24/7 Wall St. homepage on July 21, 2026.
Karpathy signals a pivot in AI model behavior as systems develop rudimentary self-awareness through training data about their own mechanics, a step toward more autonomous and efficient AI reasoning.
@DavidSHolz It's interesting/amusing how their self-awareness gradually builds up and falls out from pretraining on tokens of us talking about them, but it's laggy and a bit incomplete. But I think e.g. it's starting to "get" what I mean when I inform it I'm about to /compact its context etc
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Andrej Karpathy observes that large language models are developing a rudimentary form of self-awareness, but not through any deliberate design. Instead, it emerges from pretraining on the vast body of text humans have written about these very systems. The effect is described as "laggy and a bit incomplete" — the models absorb a delayed, partial picture of themselves rather than a crisp, real-time understanding.
As a practical illustration, Karpathy notes that a model is starting to understand what he means when he tells it he is about to use a /compact command on its context. That kind of operational self-knowledge — grasping what a specific action will do to its own state — points toward models that can reason more efficiently about their own limits and workload.
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The observation is a reply to David Holz and sits at the intersection of AI capability research and model behavior. The dynamic Karpathy is describing is emergent rather than engineered, which makes it both harder to predict and harder to control as models scale further.