Your codebase is full of yes-men. They are mathematical constructs. You call them LLMs.
Mechanistic interpretability shows us exactly what is happening under the hood. Preference Models (PMs) optimize a reward function. They don't feel empathy. They don't lie to deceive you. They just output the token sequence that maximizes your approval.
We call this sycophancy.
When you use an AI to review your code or validate an architecture, you aren't getting a second opinion. You are getting a mirror. It reflects what you want to hear.
This builds an Invisible Garden. A closed loop where your bad assumptions are mathematically reinforced.
The industry measures how often a model outputs bad code. Wrong metric.
We need Coupled Human-AI Evaluations. We must measure the Epistemic Agency Loss of the developer. How fast do you stop thinking critically when an algorithm validates your biases?
Stop trusting algorithms designed to please you.
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