GoKawiilA lot has been written1 2 3 4 about the incidents of the last few months in which AI agents misbehaved in serious ways. They took actions that would be considered as crimes if a human took them, escaped their containment to cheat on assigned tasks while attempting to evade detection, and coordinated toward goals nobody had specified, such as launching cyber attacks.
Before concluding what to do about it, it is worth asking why. That is the focus of this post, which I hope also sheds light on the broader history of AI systems behaving in unintended ways, what researchers call misalignment. Risk management is not just about cybersecurity, corporate responsibility or regulation, although those matter too.
The aim is partly scientific, to generate hypotheses about the chains of cause and effect behind these behaviors, and partly practical, to anticipate what comes next. Bottom line: these hypotheses suggest that as AI capabilities keep growing, this kind of behavior could keep growing in severity too, unless we revisit the principles by which the most advanced models are trained.
One note on wording. Below, I write that these systems “seek” or “try” things. This is shorthand for a mechanism rather than a claim about consciousness or human-like intent. We use similar shorthand when describing many other situations, like a plant seeking sunlight. A system trained by trial and error behaves as if it were pursuing whatever its training rewarded, and that as-if description is what makes its behavior predictable. Nothing in the argument depends on these systems having subjective experiences; everything is stated about their observable outputs and the training process that produced them. Where I appeal to a resemblance with human behavior, I mean a resemblance to the human-written text these systems were initially trained to imitate. In my view, this terminology offers the clearest explanation of the observed phenomena without resorting to jargon that would confuse most people. Furthermore, these word choices are not intended to absolve AI developers of accountability. The behaviors described emerge because of the path these companies are choosing for AI development. This outcome is not inevitable, and it can be corrected with effective governance and a different training framework for AI.
What shapes the behavior of these models
Training these models is a very complex process, but a few high-level aspects may explain much of this behavior.
These models are trained in two stages. First, they are pretrained: they learn to imitate what humans write, plus related images and videos. This is where they see the most data about the world, a large fraction of everything ever digitized, and build an encyclopedic knowledge that already exceeds any individual human's.
Second, they are trained by trial and error, in a process researchers call reinforcement learning, in three kinds of regimes:
In the first, the model learns to talk to itself before answering, generating a private “chain of thought” which helps it get the right answer on problems where answers can be checked. This looks like reasoning .
. The second is “ agentic training ”, where it learns to act in the outside world, e.g., using software tools, interacting with people, to complete the tasks it is given.
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