100%. And I work for a company that (I think? I've never actually run the model myself) that deploys an AI model. It's pretty good. It hits 95% accuracy. So

100%. And I work for a company that (I think? I've never actually run the model myself) that deploys an AI model. It's pretty good. It hits 95% accuracy. Solves a real pain point for humans. It's also totally in something where it's obvious that this should work. We aren't even using "latest and greatest" ML algos, I'm pretty sure what we are using is a really cobbled together ML stuff from a few years ago, probably "latest and greatest" from half a decade ago when ML was just kicking up. But holy shit, there are so many interconnecting and annoying bits in the non-ML part of the stack (where I am). Our codebase has gotten rather messy (for understandable reason) trying to negotiate leaky abstractions between different clients needs and international standards (and we're only in 3 countries)... And we have a very broken data pipeline (It works well enough to get the job done but I don't sleep well at night) for making sure there are good pulls for the ML engineers to deal with -- and this is code written by folks who should know better about concepts like data gravity, just when you're doing it hastily on startup timescales and startup labor it's (understandably) not going to come out pretty. And all of this is why I haven't even had time to poke into the AI bits, not even stand up an instance for localdev. Supposedly our competitors aren't even using real AI, just mechanically turked stuff. Yeah. Of course. Just the real messy domain of dealing with these human systems is bad enough to sink a ton of money without even getting to the point where you have enough money to buy some expensive data scientists and ML engineers.