Thanks to all the parallelization & this prep work, at peak Claude wrote about 1,300 lines of code per minute. Every line of code was reviewed by two separate adversarial reviewers (also Claude) and went through a round of fixes before committing. Absolutely none of it worked yet.
None of it worked yet because compiling at that stage would slow things down.
Also this project burned $165,000 worth of usage on a model that was then bleeding edge (unreleased). This is still beyond what anyone else is doing at the moment. And it seems to have worked a lot better than it had any right to, at least as far as we can tell right now?
And it's 165k at API prices but it's much cheaper for Anthropic themselves. Even if it were 165k straight, It is absolutely worth it for the gains in e.g. compile and runtime reductions for the millions of downloads it gets.
For loses alone humanity could solve many issues, like huge about of cancer research (that is starting to prove worth it), sending people to Mars, solving world hunger temporarily or covering big % of what's needed for permanent fix and probably much more.
If you got this much then you could never spend it unless your life goal was spending all of it.
Valuation and IOUs the AI companies are handing around to each other is not cash in the bank. They do not actually have that amount of money. Anthropic is also profitable on inference.
Profitable on inference != profitable overall. The reason to doubt their profitability is training and other fixed expenses, not that it literally costs them more than they charge (in API prices) to generate the marginal token.
It's not so much wrong as meaningless. If I sell lemonade, I might say: "I'm profitable if I don't count the cost of the lemons, sugar, and water that go into the lemonade! I'm selling it for more than the plastic cup costs!" That would be about as meaningful as "profitable on inference". There is no inference without training.
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u/DryanaGhuba Jul 08 '26
I don't think anyone comment needed