>But by-and-large, it targets existing use-cases, for the simple reason that you need training data to build the AI in the first place. This doesn't strike me

>But by-and-large, it targets existing use-cases, for the simple reason that you need training data to build the AI in the first place. This doesn't strike me as a true statement. People are embracing the term "AI" to represent recent ML advancements because modern Large Models often generalize to unseen tasks without retraining. The main gold rush so far is in consumer and productivity applications for high cost knowledge work. However for a tech that's only been around for a year... that's to be expected. There will be a next shoe. Consider the disruption that could emerge in the near future if things like the following occur. - AI Agents which actually work and generalize across many domains. - AI Agents which can communicate to solve complex challenges spanning multiple companies/problem domains. - Large multi-modal models which can be used on stock consumer hardware thanks to hardware/software improvements - Large Multi-modal models which can effectively operate robotics to complete tasks specified via demonstration/natural language. - Large Models/Agents which can complete extremely large design tasks in CAD/code spanning multiple days of inference with equivalent performance to an average expert in those fields e.g. 10-100s of millions of tokens in context. I could go on... In my opinion, we are at best in the 1992 internet phase - or the 1960s of the computer revolution. Those markets both saw large early firms emerge to solve existing problems, then saw those firms displaced as market leaders by subsequent innovations.