I have met Ryan precisely once at a convening organized by Ajeya and Zico. At the time, I vividly remember listening to him about loss of control risks and how they would manifest. I struggled to keep pace with his thinking. I would generally say I am a fairly good reasoner but what he worried about seemed deeply incompatible with everything I thought about on the topic of risks of advanced AI, in spite of both of us nominally being experts on this topic. Yet in watching his work with Ajeya and others on the independent OpenAI-HF investigation, and many other things he has done publicly in the past few years, I would agree with many in acknowledging that he appreciated risks, and the mechanisms by which they would emerge, in ways I entirely did not back then. On top of that, I am heartened by the consensus that making information publicly available, and not just siloed within frontier AI companies, is a first-class priority across independent organizations working on frontier AI. From 2022-2025, this was the topic I spent the majority of my time on spanning research like FMTI and policy like the EU CoP/California Frontier AI Policy Report and many other efforts. I am also reminded by the piece from @deanwball @DKokotajlo that found consensus on this topic back in 2024 in the wake of a contentious SB 1047 debate. Back in 2022-3, I was often asked why I prioritized proactive disclosures as the mechanism to strengthen the public information base. In other words, while the goal was well-motivated, why prefer the method that I did? A common alternative given, especially from those had studied other policy domains, is reactive independent investigations. Why prefer disclosures to investigations for achieving the goal of increased public information (and reduced information gap)? I have many answers, but the simplest was that I thought the investigation channel was not promising. At the time, the model I and others had in mind was something like a Biden-era FTC-led investigation. I was skeptical because I thought these government entities that existed cerca 2022 were not adroit enough to actually know where to look or what to seek when running an independent investigation in the frontier AI space, even if they could do it in other areas. But I am a lot more optimistic about METR and others doing this, and so I look forward to seeing how powerful the channel of investigations -> information can be. And I am much more confident that an organization like METR will place a premium on fully externalizing the information publicly, and having meta-transparency when they cannot/the terms of their investigation, than a government actor that might more readily accept just being informed themselves.

University leaders should make sure they reason about the broader trend. Around 2020, Fei-Fei and others at Stanford identified the growing compute divide between industry and academia, and the need for policy response in pushing for the National Research Cloud. In 2021, the same point was made when applied to model training in the foundation models paper. In 2022, the same point was made when applied to model evaluation in HELM among other works. In 2026, the same point was made when applied to model use. OpenAI spent more than $6.5 million to resolve Navier Stokes (Astra prices for 130B output tokens only). The stakes are not just the academic computer scientists doing frontier research on how to build AI models. The stakes are about the whole university system doing frontier research. Coping with "scarcity is the mother of invention" won't cut it, and we should trust the "godmother of AI" on that.