Goodfire frames interpretability as the core of the AI-human interface. One direction is intentional design, allowing human control. The other, especially with superhuman scientific models, is extracting novel knowledge (e.g., new Alzheimer's biomarkers) that the AI discovers.

Turing operates in two markets: providing AI services to enterprises and training data to frontier labs. Serving enterprises reveals where models break in practice (e.g., reading multi-page PDFs). This knowledge allows Turing to create targeted, valuable datasets to sell back to the model creators, creating a powerful feedback loop.

The most significant gap in AI research is its focus on academic evaluations instead of tasks customers value, like medical diagnosis or legal drafting. The solution is using real-world experts to define benchmarks that measure performance on economically relevant work.

The researchers' failure case analysis is highlighted as a key contribution. Understanding why the model fails—due to ambiguous data or unusual inputs—provides a realistic scope of application and a clear roadmap for improvement, which is more useful for practitioners than high scores alone.

Instead of a linear handoff, Google fosters a continuous loop where real-world problems inspire research, which is then applied to products. This application, in turn, generates the next set of research questions, creating a self-reinforcing cycle that accelerates breakthroughs.

As AI models are used for critical decisions in finance and law, black-box empirical testing will become insufficient. Mechanistic interpretability, which analyzes model weights to understand reasoning, is a bet that society and regulators will require explainable AI, making it a crucial future technology.

In partnership with institutions like Mayo Clinic, Goodfire applied interpretability tools to specialized foundation models. This process successfully identified new, previously unknown biomarkers for Alzheimer's, showcasing how understanding a model's internals can lead to tangible scientific breakthroughs.

Many users know about AI's research capabilities but don't actually rely on them for significant decisions. A dedicated project forces you to stress-test these features by pushing back and demanding disconfirming evidence until the output is trustworthy enough to inform real-world choices.

AI companies are pivoting from simply building more powerful models to creating downstream applications. This shift is driven by the fact that enterprises, despite investing heavily in AI promises, have largely failed to see financial returns. The focus is now on customized, problem-first solutions to deliver tangible value.