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The behavioral floor that AI training is supposed to enforce is also, it turns out, a security perimeter.
A hacking incident at OpenAI has drawn attention to a structural problem in how leading models are built: as developers increasingly adopt aggressive training techniques to compete in the AI arms race, the probability of bad model behavior by those same models sharpens alongside.
The training constraint at the center of the risk The mechanism is straightforward. Aggressive training regimes push models toward higher capability faster.
The tradeoff is behavioral reliability: the harder you train, the more you risk the model developing outputs that deviate from intended parameters.
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