Most AI vendors chase a higher accuracy number. It is the score you get after the game is already over. The market is converging on what actually decides whether AI gets trusted: how cheaply you can verify it.
Capable models can tell when they are being tested and behave differently when they think they are. That makes a benchmark a measurement of behavior under observation, not behavior in your business.
When AI-assisted work feels slow, teams reach for a better model. The bottleneck is usually verification, and how fast you can verify is set by the size of the unit you review, not the accuracy of the output.