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.
> 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.
Why is this being down voted? It's not meant as a negative question, but honest curiosity.
It's been on my mind since watching a vintage episode of "Computer Chronicles" recently on the coming AI boom, in the 80s. I'm not aware of any of those companies being still around, so I could not help but wonder what's different now.
Real talk, to he fair, I've helped architect, design and bring to market several successful "AI" products in the medical space. There are good, useful, value add applications for the tech out there. But also, to be fair, I've always seen companies that don't call themselves AI companies be successful. For example, a surgical robotics company that calls itself a surgical robotics company, that uses some AI to enable certain value add features, I've seen success there. A company that calls itself an "AI company" that does robotic surgery, I've not seen those types of companies be successful.
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.