I think the foundation model companies are actually poorly situated to reach the leading edge of AGI first, simply because their efforts are fragmented across multiple companies with different specializations—Claude is best at coding, OpenAI at reasoning, Gemini at large context, and so on.

The most advanced tools are (and will continue to be) at a higher level of the stack, combining the leading models for different purposes to achieve results that no single provider can match using only their own models.

I see no reason to think this won't hold post-AGI. AGI doesn't mean capabilities are uniform.

> The first-mover advantages of an AGI that can improve itself are theoretically unsurmountable.

This has some baked assumptions about cycle time and improvement per cycle and whether there's a ceiling.

> this has some baked assumptions about cycle time and improvement per cycle and whether there's a ceiling

To be precise, it assumes a low variability in cycle time and improvement per cycle. If everyone is subjected to the same limits, the first-mover advantage remains insurmountable. I’d also argue that whether there is a ceiling matters less than how high it is. If the first AGI won’t hit a ceiling for decades, it will have decades of fratricidal supremacy.

> I’d also argue that whether there is a ceiling matters less than how high it is.

How steeply the diminishing returns curve off at.

I find these assumptions curious. How so? What is the AGI going to do that captures markets? Even if it can take over all desk work, then what? Who is going to consume that? And further more (and perhaps more importantly), with it putting everyone out of work, who is going to pay for it?

I'm pretty sure today's models probably can be capable of self-improving. It's just that they are not yet as good as self-improving as the combinations of programmers improving them with the help of the models.