The most ambitious vision for interpretability has long been to reverse-engineer a model well enough to answer an arbitrary question about how it works. For years, that’s seemed out of reach. Now, we think it’s possible. Neural networks are cleaner than we thought: we can now understand most neurons in models up to trillions of parameters. And agents can rapidly accelerate our progress. But to get there, we need a concerted effort to unlock scaling laws for interpretability, where our understanding of a model grows predictably with compute. That’s why we’re committing $10M to start a Human Genome Project for AI. Our ultimate goal is to reverse-engineer a frontier scale model and publish a living encyclopedia explaining how it works. We don’t know exactly what we’ll find, and we can’t do it alone. We hope you’ll join us. Our chief scientist @banburismus_ lays out the vision here:

Biosecurity is the next frontier of AI security. We built SOTA monitors so agents can do more biology, safely. Our monitors outperform frontier model safeguards with fewer refusals on dual-use tasks. They’re fast, real-time, and robust to adversarial attacks. 🧵

Are SAEs dead? Will they save us from neuralese? Should I just use a probe? We get these questions all the time. Part 2 of our educational series on applied interpretability explains what SAEs are good for, when *not* to use them, and what to use instead. 🧵