Two AI-first teams. Same model. Same stack. Same budget.
One improves every week. The other flatlines after launch.
The difference is not technical. It's the belief system embedded in how they evaluate performance.
Carol Dweck mapped it thirty years ago.
The deepest pattern:
Dweck proved that the word "yet" transforms a verdict into a status update.
"Our agent can't handle complex queries" β fixed endpoint.
"Our agent can't handle complex queries yet" β capability roadmap.
The teams that build "yet" into their architecture β versioned models, progressive capability rollout, evaluation loops that expect improvement β are the ones that compound.
The teams that treat launch accuracy as the final exam are the ones that flatline.
Your AI system has two failure modes.
One is fast, automatic, and invisible. The other is slow, expensive, and optional.
Daniel Kahneman described both β in 2011.
Every AI team is obsessed with model selection.
Almost none of them are obsessed with the decision loop that feeds the model.
Jeff Olson wrote the spec for this problem in 2005. He called it The Slight Edge.