The math used for training AI—minimizing the gap between an internal model and external reality—also governs economics. Successful economic agents (individuals, companies, societies) are those with the most accurate internal maps of reality, allowing them to better predict outcomes and persist over time.

Instead of seeking financial advice, focus on understanding the underlying mechanics of the economy. By mapping the sequence of cause and effect for concepts like money creation or market forces, you can build a robust mental model that allows you to evaluate any new information or prediction on your own.

To an expert mathematician, an equation can be beautiful because they can imagine its power to explain phenomena. This reveals that mastery isn't just knowledge; it's the ability to see abstract concepts aesthetically and connect them to a wider, meaningful context.

To truly understand complex systems like the economy, one should focus on the 'physics' of cause and effect. This approach helps build a robust mental model, making it clear where your understanding breaks down and what specific questions you need to research.

A key sign of mastery is the development of a unique vocabulary to describe nuanced observations. While apprentices often parrot the language of icons like Warren Buffett, true experts coin their own terms. This indicates they are seeing the world through an original lens, not a borrowed one.

Unlike most professions where deep specialization is crucial, legendary investors like Warren Buffett and Charlie Munger have thrived by being generalists. Their success comes from applying broad mental models across various industries, a stark contrast to the specialist approach that dominates other fields.

Contrary to popular belief, economists don't assume perfect rationality because they think people are flawless calculators. It's a simplifying assumption that makes models mathematically tractable. The goal is often to establish a theoretical benchmark, not to accurately describe psychological reality.

Milton Friedman's 'as if' defense of rational models—that people act 'as if' they are experts—is flawed. Predicting the behavior of an average golfer by modeling Tiger Woods is bound to fail. Models must account for the behavior of regular people, not just theoretical, hyper-rational experts.

If a highly successful person repeatedly makes decisions that seem crazy but consistently work, don't dismiss them. Instead, assume their model of reality is superior to yours in a key way. Your goal should be to infer what knowledge they possess that you don't.