AI data centers that exist and are operational are running at maximum capacity. That's why you see things like the tiny little data center run by xai showing up as a valuable resource to xai (on the sale side) and anthropic (buy side). It is "only" 300 megawatts and there's a 1.25 billion rent on it per month.

If all these other data centers were anywhere near coming on line, that 300mw data center would be a rounding error not a line item as it is right now.

So someone's signed contracts for way more and way larger data centers, someone's purchased billions in hardware for these not yet operational data centers. I'm wondering how depreciation's going to work on all these assets...

Anyhow, I'm not really sure what "max capacity" is here, nor am I really aware when they're going to be delivering the operational assets that are currently levered to their eyeballs and consuming 1/3rd of the memory made on the planet.

As far as inference vs training, have new gotten radically better than old models or only marginally (at the cost of 10x or more the training costs)?

Very exciting stuff.

I imagine the trend for AI usage will go up over the very long term (5-10yrs etc.), but short term how much usage is being propped up by employer's forcing their employees to use it? Or by user's being curious about the novelty but ultimately abandoning it if it doesn't do what they want? It'll be interesting to see what changes as tokenmaxxing disappears.

I would day that the dotcom was directionally correct but the timing was wrong. For instance you had pets.com in 1999 but in 2020 you had chewy.com. It's like you had broadcast.com in 2000 but by 2020 you had YouTube that was making more in ad revenue than the next 4 largest competitors.

With investing timing matters a lot.