Source: The New York Times
Interpretability has moved from academic footnote to urgent business problem. Regulators, enterprises, and safety researchers now demand answers about why AI models make specific decisions—particularly in hiring, lending, and healthcare. Concrete techniques (mechanistic interpretability, feature visualization, attention analysis) are shifting from "nice to have" to table-stakes for deployment. Companies like Anthropic and OpenAI that can credibly explain their models' reasoning are building a technical moat. Trustworthy transparency now influences enterprise adoption and regulatory approval timelines.