When an AI lab cannot trust its data, it spends more compute evaluating and ablating against that data, particularly as tasks become longer-horizon.

“It's a ton of researcher time. It's, you know, compute is now, uh, uh, compute and researcher time are probably the two most valuable resources at a lab. And so if you can't trust the data, you're spending way more compute on trying to, you know, ablate against that data in eval, because the tasks are longer and longer horizons.”