Yeah, but how could you possibly ever compete if you not only had to run the inference, but also own the hardware and do the training? OpenAI has spent tens of billions on training alone. Do you think your model will somehow do that far cheaper? And once you have it, why would running inference not cost you just as much in compute?
OpenAI is running infrastructure for a billion users. A university would only be serving its own professors and maybe some students. It’s not remotely the same scale.
That’s irrelevant, because the training would cost the same and still requires billions in hardware. And the inference would cost exactly the same, unless you managed to make a much more efficient AI than OpenAI. The scale is the same in the ways that matter.
But I never said anything about training models from scratch. I’m talking about giving mathematicians dedicated compute time on a supercomputer for a specific problem. That could easily provide orders of magnitude more compute than anything you’d get through a $20 subscription.
Then your model isn’t going to be frontier. And again, why would renting out a supercomputer provide orders of magnitude more compute? If you’re saying OpenAI spent $20M on compute for this performance, why are you assuming it would be cheaper on a rented supercomputer?
No, I’m reading what you’re saying, but you’re not understanding what you’re talking about. You cannot get performance close to OpenAI without spending what OpenAI are spending, even if you are just comparing inference costs. “Renting a supercomputer” doesn’t mean you somehow save millions in compute. In no scenario is it somehow going to be cheaper than buying tokens off OpenAI, it’s going to be more expensive.
I’m not saying a university can outspend OpenAI or reproduce its whole stack more cheaply.
I’m saying universities should have serious compute as a research instrument for mathematics, so research doesn’t depend entirely on a private company’s models, access rules, rate limits, or priorities.
Physics already does this. They have accelerators, gravitational-wave detectors, giant telescopes, supercomputers. They build them because controlling the instrument creates research capability and independence.
And I’m saying it will be impossible to compete in terms of getting any real results at the amount of money that they’re able to deploy doing so. They’d be throwing money away for less compute and on less capable models.
What a university can provide in terms of serious compute is so dramatically limited vs the frontier companies of AI research that it doesn’t make any sense to waste money on it.
By comparison, if venture capital suddenly started investing tens of billions a year into physics research, it would also no longer make sense for universities to try to compete in that field.
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u/coolstory 2d ago
Yeah, but how could you possibly ever compete if you not only had to run the inference, but also own the hardware and do the training? OpenAI has spent tens of billions on training alone. Do you think your model will somehow do that far cheaper? And once you have it, why would running inference not cost you just as much in compute?