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?
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.
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?