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America's $1T AI Gamble

57 points by m-hodges

by vessenes

1 subcomments

This is a good analyst report - lots of data. Conclusion - firms are spending ahead of sustained revenues right now, and a lot of the money is going offshore to TSMC, basically.
I’m not certain of the conclusion - I think a lot depends on amortization schedules - if data centers are fully booked right now, then we don’t need very long amortization schedules at the reported 60+% margin on inference to see this capex fully paid off.
My prior is that we are seeing something like 1/10,000th or so of the reasonable inference demand the world has fulfilled. There’s a note in the analysis that might back this - it says that we are seeing one of the only times ever where hardware prices are rising over time. Combined with spot prices at lambda labs (still quite high I’d say), it doesn’t look like we’re seeing a drop in inference demand.
Under those circumstances, the first phases of this bet, cross-industry, look like they will pay off. If that’s true, as an investment strategy, I’d just buy the basket - oAI, Anthropic, GOOG, META, SpaceX, MSFT, probably even Oracle, and wait. We’ll either get the rotating state of the art frontier capacity we’ve gotten in the last 18 months, or one of those will have lift off.
Of those, I think MSFT is the value play - they’re down something like 20% in the last six months? Satya’s strategy seems very sensible to me - slow hyperscale buildouts in the US (lots of competition) and do them everywhere else in the world (still not much competition). For countries that can’t build their own frontier models, the next best thing is going to be running them in local datacenters; MSFT has long standing operational bases everywhere in the world, it’s arguably one of their differentiators compared to GOOG/META.

by mg

4 subcomments

My napkin-math approach to get a bird's eye perspective on the situation:
A $1T investment needs to produce on the order of $100B in yearly earnings to be a good investment.
Global GDP is about $100T.
So one way for things to work out for the AI companies would be if AI raises GDP by 1% and the AI companies capture 10% of the created value.

by throwaway1114

0 subcomment

At this year Davos it was said in open text: big LLM supplieers & labs don't have enough demand to have profit that will cover spending. They must compete and provide supply because who win race will get most of the rewards. Geopolitics makes this even worse. So they are over leveraged and now time is running out before house of cards will collapse. All the hype and paid marketing is target to make masses (and more important people who make decisions) believe their story and buy into it. I assume for a person without technical background it's hard to filter signal from noise. It's all about that is hard to see that LLMs are what they are because "they" make great illusion of intelligence and illusion of novel thinking when it fact they just do the math with big text database.

by fastball

0 subcomment

A significant part of the capex is just energy, so even if there is some sort of AI black swan event and the data centers become obsolete overnight (unlikely), energy is literally the root of all bounty so it is good that something is incentivizing increased resource allocation in that area.

by WarmWash

6 subcomments

AI plans are not going to stay at $20/mo.
People will go to alternative models, but it likely will be as popular as Linux.

by dfilppi

0 subcomment

[dead]

by xyst

6 subcomments

And this gamble is paid for by American taxpayers, increased cost of utilities, and multibillion dollar corporations receiving tax breaks/subsidies from the cities/counties they build in.
This country is so awful. Great if you are rich. Awful if you are not in this top 0.01-1%.
A massive $79T has been transferred from bottom 90% to top 1% since the 1970s. [1]
[1] https://www.rand.org/pubs/working_papers/WRA516-2.html