The company wants to make building AI models less like alchemy and more like a science. Sure, LLMs like ChatGPT and Gemini can do amazing things. But when it comes to building them, best practice is often based on rules of thumb rather than theory. Nobody really knows exactly how or why they work, and that can make it hard to fix their flaws or block unwanted behaviors.

“We saw this widening gap between how well models were understood and just how widely they were being deployed,” Goodfire’s CEO, Eric Ho, tells MIT Technology Review in an exclusive chat ahead of Silico’s release. “I think the dominant feeling in every single major frontier lab today is that you just need more scale, more compute, more data, and then you get AGI [artificial general intelligence] and nothing else matters. And we’re saying no, there’s a better way.”

Goodfire is one of a small handful of companies, including industry leaders Anthropic, OpenAI, and Google DeepMind, pioneering a technique known as mechanistic interpretability, which aims to understand what goes on inside an AI model when it carries out a task by mapping its neurons and the pathways between them. (MIT Technology Review picked mechanistic interpretability as one of its 10 Breakthrough Technologies of 2026.)

Goodfire wants to use this approach not only to audit models—that is, studying those that have already been trained—but to help design them in the first place.