You're missing the point, there are specific reasons why these stacks have grown in complexity - even if you introduce "API for AI interface" as a requirement, you still have to balance that with performance, reliability, interfacing with other systems, and providing all of the information necessary to debug when AI gets it wrong. All of the same things that humans need apply to AI - the claim for AI isn't that it deterministically solve every problem it can comprehend.

So now we're looking at a good several decades of us even getting our human interfacing systems to amend themselves to AI will still requiring all the current complexity they already have. The end result is more complexity not less.

Based on what I've seen so far, I'm thinking a timeline more like 5-10 years where anything involving at least frontend has all but evaporated. What value is there in having a giant app team grind for 2 years on the perfect Android app when a user can simply ask for the display they want, and 5 variants of it until they are happy, all in a couple of seconds while sitting in the back of a car. What happens to all the hundreds of UI frameworks when a system as a widespread as Android adopts a technology approach like this?

Backend is significantly murkier, there are many tasks it seems unlikely an AI will accomplish any time soon (my toy example so far is inventing and finalizing the next video compression standard). But a lot of the complexity in backend derives from supporting human teams with human styles of work, and only exists due to the steady cashflow generated by organizations extracting tremendous premiums to solve problems in their particular style. I have no good way to explain this - what value is a $500 accounting system backend if models get good enough at reliably spitting out bespoke $15 systems with infinite customizations in a few seconds for a non-developer user, and what of all the technologies whose maintenance was supported by the cashflows generated by that $500 system?