What I Learned Evaluating AI Models for Anthropic

Earlier this year I participated in Anthropic's Model Safety program — evaluating frontier models for harmful behaviors, subtle misalignments, and edge cases that automated testing misses. It changed how I think about the software I build.

What the Work Actually Involves

Most people imagine AI safety as a philosophical exercise — debating trolley problems with robots. The actual work is more like adversarial QA. You're constructing prompts specifically designed to elicit unwanted behaviors, then carefully documenting what you find and why it matters.

The nuance is everything. A model refusing a clearly benign request is a safety failure just as much as a model complying with a clearly harmful one. Over-refusal erodes trust, makes the product useless, and teaches users to work around guardrails rather than with them.

The Alignment Gap

The most surprising thing I learned: alignment isn't binary. It's not "aligned" or "misaligned" — it's a spectrum of subtle biases that compound over a conversation. A model might give perfectly safe individual responses while still steering a long conversation in a direction that wasn't intended.

This is why most engineers underestimate alignment work. They test single prompts, see reasonable outputs, and conclude the model is fine. Real evaluation requires extended interactions, adversarial personas, and thinking about second and third-order effects.

What It Changed For Me

I think differently about interfaces now. Every UI I build that involves AI is also a channel through which the model's behaviors — intended and unintended — reach real people. That's a design responsibility, not just an engineering one.

The engineers most likely to build safe AI products aren't the ones who've read every alignment paper. They're the ones who've spent enough time with these models to develop genuine intuition for where they break — and enough humility to know that intuition is never complete.