Comment by beloch
2 days ago
Here's why I'm skeptical:
It may only be a short-term circumstance that you need a data centre to run AI for lucrative applications. Thirty years ago you needed a mainframe to beat Kaspersky at chess. Algorithm's got better, and now you can beat the mainframe that beat Kaspersky with a bot running on a desktop Pentium of the day[1]. You could still, today, devote a full hyperscale data centre to create a chess bot to beat all other chess bots, but nobody is doing that because there's no money in it.
The frontier is continually pushed outwards. At it's outer edge you need mainframes and data-centres. Once it pushes past, you don't. There wasn't a lot of money in Chess or protein folding, despite the later having huge potential for public benefit. There is a lot of money in coding and middle management, and that's what currently has tech-bros in a frenzy. However, there's no guarantee that what the frontier moves onto next will be lucrative. It might be more chess and protein folding. If, as Algorithm's and hardware improve, coding moves to local models (Sam Altman's nightmare) and nothing lucrative immediately replaces that, what are these companies going do do with their massive data-centres?
The scale of investment we're seeing these companies commit only makes sense if they have multiple decades to reap the returns. They may well not have that much time.
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[1]https://hackaday.com/2026/11/05/pitting-a-modern-chess-engin...
Kasparov, btw
No this is Ned Kaspersky, from Moscow (Idaho)
This must be 3HN1
2 replies →
Chess is a fixed target, so as a benchmark, it can be saturated.
Reality can’t be saturated. The set of things you can accomplish with more intelligence is unbounded. So while cheap models will continue to proliferate, there will always be a massive market at the frontier, and that won’t ever change.
Conversely, the TAM for tasks that cheap models can and have been able to one-shot for a while now is orders of magnitude larger than the TAM at the frontier. While it’s kind of cool that frontier models are able to solve Millennium Prize problems, there just isn’t a huge market for that sort of thing. Otherwise, professional mathematicians would be getting paid a lot more than they do now, and there would be more of them.
Nah there's plenty of TAM for frontier models even if just to take over market share from cheaper models. Fields like law or medical cn always use lower hallucination rates and smarter "human social" intelligence.
Intelligence is bounded by it's interface to reality. Until that problem is solved the set of what AI can accomplish in a reasonable timeframe is quite limited.
Until that problem is solved
Guess what could help solve it? :)
Seriously though, this “problem” will never be fully solved, we’ll just get better and better at connecting AI to the broader world, and to me that’s already the story of the last few years. Models get better, they’re able to build and drive tools to interact with both the digital and physical world better, which helps build the next generation of models, and so on.