I've said it before, but it would be a mistake to just focus on the models, and ignore everything else that is changing in the ecosystem -- tools, harnesses, agents, skills, availability of compute, etc. -- things are changing very quickly overall.

The thing that is changing most rapidly, however, is the understanding of how to harness this insanely powerful, versatile, and unpredictable new technology.

Like, those who experimented deeply with LLMs could tell that even if all model development completely froze in 2024, humanity had decades worth of unrealized applications and optimizations to explore. Even with AI recursively accelerating this process of exploration. As a trivial example, way back in 2023 anyone who got broken code from ChatGPT, fed it the error message, and got back working code, knew agents were going to wreck things up very quickly. It wasn't clear that this would look like MD files, Claude Code, skills, GasTown, and YOLO vibe-coding, but those were "mere implementation details."

I'm half-convinced an ulterior goal of these AI companies (other than the lack of a better business model) to give away so many cheap tokens is to encourage experimentation and overcome this "capability overhang."

Given all this, it's very hard to judge where we are on the curve, because there isn't just one curve, there are actually multiple inter-playing curves.