[kozikow]

I feel that really there are no more place for "AI startup" as there is for "database startup" - there are some exceptions, but it's hard to compete with open source and large corporations treating AI tech as loss leader. Of course there are exceptions.

I work on AI startup that with squinted eyes could be described "AI for pricing the insurance policy". As the company grows, we really add a lot of more non-AI pieces. A lot more goes into the frontend that keeps getting non-AI features. In a closed industry you can't get clean dataset for everything, so lots of heuristics and domain knowledge goes into some pieces of equation. Custom APIs and integrations for customers, etc.

My point is, any "AI startup" by the time of exit won't be AI startup, but "problem X startup", where AI was initially used to address X. It will have a lot more non-AI pieces than AI. Rare exceptions of AI base technology will get commoditized pretty soon anyway.

[fatnoah]

> I work on AI startup that with squinted eyes could be described "AI for pricing the insurance policy".

I work in a very similar domain, and my company is also in the business of "solving problem x" with AI as the means to do so. It's in an area where effectiveness in solving the problem can be clearly measured, so it's easy to calculate ROI for customers.

The main downside of the AI hype, IMHO, is the conflation with LLMs and the AI bubble. We do plan to leverage LLMs in some specific ways, but it's not the core of our business, just part of the solution.