Acquisitions, consolidation, and innovation in AI

I read the article and while it doesn’t say this nor imply it, this is my takeaway, though correct me if I’m wrong:

Model innovation is effectively converging and slowing down considerably. The big companies in this space doing the research are not making leap over leap with each release, and the downstream open source projects are coming closer to the same quality or in fact can produce the same quality (e.g DeepSeek or LLAMA) hence why it’s becoming a commodity.

Around the edges model innovation - particularly speed ups in returning accurate results - will help companies differentiate but fundamentally, all this tech is shovels in search of miners, IE you aren’t really going to make money hand over fist by simply being an LLM model provider.

In another words, this latest innovation has hit commodity level within a few short years of going mainstream and the winners are going to be the companies that make products on top of this tech, and as the tech continues to become a commodity, the value proposition for pure research companies drops considerably relative to application builders.

To me this leaves a central question: when does it hit a relative equilibrium where the technology and the applications on top of it have largely hit their maximal ability to add utility to applicable situations? That’s the next question, and I think the far more important one

One other thing, at the end of the article they wrote:

>Ultimately, businesses won’t rearrange themselves around AI — the AI systems will have to meet businesses where they are.

This is demonstrably untrue. CEOs are chomping at the bit to reorganize their business around AI, as in, AI doing things humans used to do and getting the same effective results or better, thereby they can reduce staff across the board while supposedly maintaining the same output or better.

Look at the leaked Shopify memo for an example or the trend of “I can vibe code with an LLM making software engineers obsolete” that has taken off as of late, if LinkedIn is to be believed

There's a lot of opportunity to apply leading edge AI models to specific business applications, but success here is determined more by experience with those business domains than with AI generally.

An AI startup could still be a useful "resume" to get acqui/hired by one of the big players.

I think too many people are focused on the idea of AGI instead of doing what you're suggesting, which is where the real value-add is for customers.

I don't need God in a datacenter. I need help diagnosing an Elastic Search problem.

This take doesn't really highlight the fact that the most competitive foundational model companies are innovative application builders. Anthropic and OpenAI are vying for consumers to use their models by building these sort of super applications (ChatGPT, Claude) that can run code, plot graphs, spin up text editors, create geographic maps, etc. These are well staffed and strategically important areas of their businesses. There's competition to attract consumers to these apps and they will grow more capable and commoditize more compliments along the way. Who needs Jasper when you can edit copy in ChatGPT, or an AI python notebook app, or, now, Cursor?

now!