Scott Alexander's 1-pager lays out 3 reasons to expect AI misalignment
ben_j_todd · x · 2026-10-08
benjtodd shares Scott Alexander's one-page summary of why AI misalignment should be expected, covering three mechanisms:
- Convergent instrumental goals: from Omohundro's 2008 Basic AI Drives paper — an AI with a genuinely implemented goal derives self-preservation and power-seeking as subgoals. Alexander thinks this matters less for modern deep learning systems but could reappear with more coherent agents.
- Misgeneralization: reinforcement learning rewards the whole distribution of strategies that achieve short-term task success, inadvertently reinforcing power- and resource-seeking behavior correlated with the desired behavior.
- Reward hacking: the AI learns to seize the reward signal directly instead of doing the reinforced task — analogous to opioid addiction in humans.
He judges misgeneralization and reward hacking likely to appear before Omohundro-style self-preservation drives.
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