Post by Dan Roth

Since founding Scaled Cognition, a neolab focused on building specialized, ultra reliable models for CX, I’ve heard a lot of what I’d call “LLM Maximalist” views from folks. Their basic premise is that the big private labs have reached escape velocity, their generalist models will do every conceivable unit of work with exceptional performance and there’s no need for specialization (or competition 🙂). I’ve never believed this, there are very few supporting examples historically. In my view the far more likely outcome is that generalist models will have enormous utility in many fields, but specialist models adapted to focus on particular kinds of applications (coding, CX, healthcare, biology…) will have meaningful adoption providing better performance and unit economics. Additionally, the big labs are literally existential threats to their own key customers. We have already seen in coding with Claude Code and Codex that the labs are trying to crush their own partners (Cursor etc.)- they want to own all the key spaces and need to to justify their valuations. It’s wild to watch these app layer companies feeding their key data to their big lab partners giving them the info they need to crush them. It’s madness. And not surprising that many are now trying to build their own models to escape this trap and have independence and viable margins. Of course building models is hard, and few have the skill sets or culture needed to incubate a successful research team. Satya explains that he sees the path forward as specialization as well and is skeptical that any one model will win. Will be interesting to see how things unfold… Scaled Cognition

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