Because ML/AI are feature enablers. They make a good product better, but they won't make a product successful. It's a signal that people are more interested in solving technical problems than solving business/product problems.
Also, a large percentage of ML/AI projects are scams. And even the people who aren't scammers tend to massively underestimate the amount of work required to make something good. It doesn't surprise me at all that interest in these technologies could be a red flag on multiple levels.
Sure there are plenty of great startups built with these technologies, but both also tend to be the 'and then a miracle occurs' of the tech industry.
Use cases with ML out front are rare. I would argue that "self-driving" is just a feature of an automobile; the expensive part is building and delivering a half-ton hunk of precision-engineered metal. People were buying cars long before they were self-driving; and I doubt that self-driving will add a whole lot to the cost of a vehicle. Even then, the hard part of building a business around autonomous cars is obtaining safety certification and improving public perception of autonomous driving. All the major self-driving algorithms will be largely public domain before that happens.
Likewise with Google; there were search engines long before Google. Hell, Google first appeared as the search technology powering Yahoo! long before they had their own presence. Granted; in this case ML enabled the "killer app" of generating relevant results and allowing ad targeting, but use cases where ML is as critical to the product as Google are rare. More typical are things like Netflix's recommendation engine - the value of the service is in the video library, the recommendation engine is just another avenue for content discovery. It is also being increasingly curated as opposed to automated for promotional reasons.
All of this matters. ML is great, but ML results are often so narrowly scoped that you need to identify your product scope first, then find an ML solution that helps. And even then, at small scale you can often "fake" the impact of ML via manual labor or "doing things that don't scale" (i.e. operating the service via manual labor at a loss with the hopes of adding an AI component to handle that function later in a scalable fashion). If the product doesn't resonate with the market, all the ML in the world won't help it succeed.
