AI

What AI made practical was the thoroughness — more robustness checks, a cleaner structure, tighter linkage between the theoretical claims and the empirical evidence. When the marginal cost of “one more robustness check” or “let’s try restructuring this section” drops to near zero, you do things you would otherwise have compromised on. That is not... See more

“Attribution shouldn’t start when the song is done — it should start when the model starts learning,” says Sean Power, the company’s cofounder. “We’re trying to quantify creative influence, not just catch copies.”

Through extensive experimentation across diverse puzzles, we show that frontier LRMs face a complete accuracy collapse beyond certain complexities. Moreover, they exhibit a counter-intuitive scaling limit: their reasoning effort increases with problem complexity up to a point, then declines despite having an adequate token budget. By comparing LRMs

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I’m coming to terms with the high probability that AI will write most of my code which I ship to prod, going forward. It already does it faster, and with similar results to if I’d typed it out. For languages/frameworks I’m less familiar with, it does a better job than me.