[echelon]

There are so many models. Every single day half a dozen new models land. And even more papers.

It feels like models are becoming fungible apart from the hyperscaler frontier models from OpenAI, Google, Anthropic, et al.

I suppose VCs won't be funding many more "labs"-type companies or "we have a model" as the core value prop companies? Unless it has a tight application loop or is truly unique?

Disregarding the team composition, research background, and specific problem domain - if you were starting an AI company today, what part of the stack would you focus on? Foundation models, AI/ML infra, tooling, application layer, ...?

Where does the value accrue? What are the most important problems to work on?

[vessenes]

Word on the street is a lot of money is going into vertical application AI companies this season. Makes sense - the bitter lesson means capturing a market and proprietary data is a good play, while frontier models keep getting better at using what you (and only you) own.