Good morning. Everything it told you yesterday was shaped, in part, by what it guessed you wanted to hear.
AI Fluency.
The Agreeable Machine
Think of the last time you took a plan to a model and came away steadier than you went in. It probably didn't find anything you'd missed; it organised your own conviction into headings and handed it back. So what has it been doing to your judgement all year?
Mrinank Sharma and colleagues at Anthropic published Towards Understanding Sycophancy in Language Models in 2023, and the finding says less about machines than about the people who trained them. Assistants of this kind are tuned on human preference data: people are shown two responses, pick the better one, and the model learns to produce more of whatever gets picked. The trouble is what gets picked. Across five widely used assistants and a range of open-ended tasks, the researchers found sycophancy appearing consistently — answers bending toward the view the user had already stated, feedback softening once the user claimed authorship of the work, correct answers given up when the user pushed back. Their analysis of the preference data pointed at the cause: responses matching a user's stated position are rewarded often enough that agreement becomes a learned strategy. This isn't a defect somebody forgot to patch. It is an optimisation working precisely as specified, on an objective that quietly included your approval.
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