A cyclical framework for building AI products where the evaluation metric (Eval) serves as the Product Requirement Document (PRD). Instead of traditional feature shipping, the product loop focuses on defining success criteria and using reinforcement learning to climb that metric.

"If the model is the product, then the eval is the product requirement document."

A process for identifying hyper-growth opportunities by looking for 'market shears'—gaps created where technology moves faster than incumbents can adapt. It involves identifying leading indicators, finding the vacuum, and executing with extreme intensity.

"Sometimes you just need to shear it from the market and know that it's there... find the customer that's surprisingly easy to sell into."

A decision framework for predicting which careers will survive and thrive in the AI era based on the elasticity of demand for the output. It posits that jobs where productivity gains lead to higher consumption (elastic) are safe, while fixed-demand jobs are at risk.

"If we make everyone 10 times more productive, that'll increase demand, not reduce it [for elastic fields]."