I think our little corner of the world has a distorted view of AI in that it is actually proving useful for us. Once they passed a certain level of usefulness... I remember when they were still struggling just to output syntactically correct code, you know, like, 18 months ago or so... they became a useful tool that we can incorporate.

But there's a lot of things playing out to our advantage. Vast swathes of useful and publicly available training data. The rigorous precision of said data. Vast swathes of data we can feed it as input to our queries from our own codebases. While we never attained the perfect ideal we dreamed of, we have vast quantities of documentation at differing levels of abstraction that the training can compare to the code bases. We've already been arguing in our community about how design patterns were just level of abstraction our coding couldn't capture and AI has access now to all sorts of design patterns we wouldn't have even called design patterns because they still take lots of code to produce, but now for example, if I have a process that I need to parallelize it can pretty much just do it in any of several ways depending on what I need at that point.

It is easy to get too overexcited about what it can do and I suspect we're going to see an absolute flood of "We let AI into our code base and it has absolutely shredded it and now even AI can't do anything with it anymore" in, oh, 3 to 6 months. Not that everyone is going to have that experience, but I think we're going to see it. Right now we're still at the phase where people call you crazy for that and insist it must have been you using the tool wrong. But it is clearly an amazing tool for all sorts of uses.

Nevertheless, despite my own experiences, I persist in believing there is an AI bubble, because while AI may replace vast swathes of the work force in 5-20 years, for quite a lot of the workforce, it is not ready to do it right this very instant like the pricing on Wall Street is assuming. They don't have gigabytes of high-quality training data to pour in to their system. They don't have rigorous syntax rules to incorporate into the training data. They don't have any equivalent of being guided by tests to keep things on the rails. They don't have large piles of professionally developed documentation that can be cross-checked directly against the implementation. It's going to be a slower, longer process. As with the dot-com bubble, it isn't that it isn't going to change the world, it is simply that it isn't going to change the world quite that fast.

Leaving aside the economic shitshow and other things.

I think you're right but for the wrong reasons wrt sustainable profit.

Specifically, overcounting how much it will cost in 5 years to run AI because you're extrapolating current high prices, and at the same time undercounting how the demand will drive efficiency gains.

> but the US economy is currently buoyed by promises of AI replacing the workforce across the board.

Still don't understand what's the end goal here. Assuming they don't deliver, then there are billions of investments that will go bust. Assuming they deliver, millions lose their jobs and there's going to be a bloodbath on the streets.