Should Developers Care about Interpretability?

Interpretability research is often framed as an alignment problem: we need to understand models so we can verify they’re safe. That framing is correct but undersells it. Even if you’re not working on AI safety, interpretability has something to offer developers building with models today.

Debugging a model you can’t read is archaeology. You sift through outputs looking for patterns, form hypotheses, and test them — all without any ground truth about what’s happening internally. Interpretability tools change that dynamic. Sparse autoencoders, probing classifiers, and attention visualization aren’t just research artifacts; they’re slowly becoming debugging infrastructure.

This post makes the case that developers — not just researchers — should be paying attention to what the interpretability community is building, and why it will matter sooner than most expect.

Full write-up coming soon.