But they're not. That's just the excuse. The real truth is somewhere along pandemic over hire and bad / unstable economy.
https://newrepublic.com/article/178812/big-tech-loves-lay-of...
For example say you have a complex medical problem. It can be difficult to do a direct Internet search that covers the history and symptoms. If you ask AI though, it'll be able to give you some ideas for specific things to search. They might be wrong answers, but now you can easily search specific conditions and check them.
Sort of P vs. NP for questions.
Or you go to a doctor instead of imagining answers.
As another example, you can give the AI a photo of something to have it name what that thing is. Then you can check the thing by its name on Google to see if it matches. Much easier than describing the thing (plant, tool, etc) to Google.
Imagine the lambda person confronted with this:
sudo rm -rf /
1. You have a complex or vague question that you can't search easily via Google etc
2. You ask the AI and it converts that to concrete searchable suggestions (in this case "sudo rm -rf /")
3. You search "sudo rm -rf /" to check the answer.
Step 3 is designed to (hopefully) catch this kind of problem.
This has basically been why it's a non-starter in a lot of (most?) business applications.
If your dishwasher failed to clean anything 20% of the time, would you rely on it? No, you'd just wash the dishes by hand, because you'd at least have a consistent result.
That's been the result of AI experimentation I've seen: it works ~80% of the time, which sounds great... except there's surprisingly few tasks where a 20% fail rate is acceptable. Even "prompt engineering" your way to a 5% failure/inaccuracy rate is unacceptable for a fully automated solution.
So now we're moving to workflows where AI generates stuff and a human double checks. Or the AI parses human text into a well-defined gRPC method with known behavior. Which can definitely be helpful, but is a far cry from the fantasized AI in sci-fi literature.