> When a bubble happens, it's usually preceded by the companies involved reaching obviously inflated evaluations, which makes the burst obvious in hindsight. With public AI companies, we don't see that happening. Alphabet, Meta, and NVIDIA are all AI companies that have seen their market capitalization explode over the past few years, but all of them have stable P/E ratios in their 20s. If the increase in their valuations were irrational (or even just in expectation of future growth as opposed to current earnings), you would expect the P/E ratios to go up. Instead, their valuations look exactly what we would expect from a healthy trillion dollar company. When the dot-com bubble happened, it was largely driven by companies that couldn't turn a profit or had P/E ratios in the hundreds. I'm not going to pretend that I'm an expert on financial bubbles, but I've never heard of a bubble where the companies involved had such a modest valuation based on their earnings, even if their earnings (and therefore their evaluations) are enormous.

My worry isn't so mcuh the large players ability to raise funding, it's the long long tail of "AI" in everything else and especially small startup companies that looks more worrying to me.

At the moment you have young grads getting $20-30 million with basically limited evaluation of their skills, all over the place. The area I am interested in is physics and engineering, and the number of Physics AI startups for where there there are not physicists or mathematicians with at least PhDs in numerical computing as founders for example looks to me like a total mess. And I keep seeing things where very clearly solvers haven't converged then getting used as training data and publicised in press releases from companies like this. I assume that most technical areas are seeing similar. This is where the dotcom bubble comparisons are most apt.