We are likely in a situation where the most powerful models in the world are internal models, and, here's a key distinction, potentially even models that are never intended to be released to the public (i.e. a model designed to provide internal uplift on capabilities during training but that is not intended to be / not particularly suitable for commercial purposes.)

Internal-external information and capability gaps can distort both public and regulatory perception of frontier capabilities and dangers. If the internal frontier is three or six months ahead, such a gap is worrisome if the labs are close to automating various bottlenecks to increased capabilities development, like automating AI R&D.

Some ways of mitigating these visibility gaps and communicating the existing data we do have are implementable over the short term and confer minimal cost for many actors relative to the advantages of visibility for policymakers.

What is a "holy shit" graph?

Many of my intuitions about the coming impacts of AI are shaped by "Holy shit" graphs. The METR time horizon is one such graph, but has been discussed to death, so let's talk about another three: