To trust an agentic AI, users need to see its work, just as a manager would with a new intern. Design patterns like "stream of thought" (showing the AI reasoning) or "planning mode" (presenting an action plan before executing) make the AI's logic legible and give users a chance to intervene, building crucial trust.

Just as biology deciphers the complex systems created by evolution, mechanistic interpretability seeks to understand the "how" inside neural networks. Instead of treating models as black boxes, it examines their internal parameters and activations to reverse-engineer how they work, moving beyond just measuring their external behavior.

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.

For AI systems to be adopted in scientific labs, they must be interpretable. Researchers need to understand the 'why' behind an AI's experimental plan to validate and trust the process, making interpretability a more critical feature than raw predictive power.

With AI, designers are no longer just guessing user intent to build static interfaces. Their new primary role is to facilitate the interaction between a user and the AI model, helping users communicate their intent, understand the model's response, and build a trusted relationship with the system.

Instead of pure academic exploration, Goodfire tests state-of-the-art interpretability techniques on customer problems. The shortcomings and failures they encounter directly inform their fundamental research priorities, ensuring their work remains commercially relevant.

Even when a model performs a task correctly, interpretability can reveal it learned a bizarre, "alien" heuristic that is functionally equivalent but not the generalizable, human-understood principle. This highlights the challenge of ensuring models truly "grok" concepts.

Goodfire AI defines interpretability broadly, focusing on applying research to high-stakes production scenarios like healthcare. This strategy aims to bridge the gap between theoretical understanding and the practical, real-world application of AI models.