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.

Human wisdom derives from a single lifetime of experience. AI will achieve a superior form of wisdom by simulating billions of potential future scenarios and identifying the statistically optimal paths. This predictive power, already matching elite human forecasters, will be its core advisory function.

Emad Mostaque proposes that the math behind generative AI can describe economic systems. In this framework, Adam Smith's theories map to "gradient flows" (scarcity), Marx's to "circular flows" (compounding intelligence), and Hayek's to "harmonic flows" (structural rules).

AI's greatest impact on economics will be the ability to run complex, agent-based simulations. This allows economists to model the dynamic, equilibrium responses of millions of economic actors to policy changes—like a Fed balance sheet reduction—providing a much richer understanding than traditional, static models allow.

Demis Hassabis foresees AI enabling new scientific disciplines. He suggests that highly accurate AI simulations could transform fields like economics into hard sciences by allowing for the kind of repeated, controlled experiments that are currently impossible in the real world.

Instead of debating which AI future will occur, a more productive approach is using scenarios to ask, 'What would we do in this future?' This shifts the conversation from arguing over predictions to identifying 'no-regrets' policies that are beneficial across multiple potential outcomes.

By creating AI agents with distinct roles (CEO, CFO, Sales), individuals can simulate an executive team meeting. These agents argue from their perspectives, stress-test ideas, and collaboratively develop a robust business strategy that a single person might miss. This moves beyond simple content generation to complex strategic planning.

Most decision-makers don't want a passive prediction of a negative future. They want to know the causal levers—the actions they can take now to avoid that outcome and create a better one. Simulation provides this by modeling causal mechanisms.

Unlike traditional automation that follows simple rules (e.g., match competitor price), AI agents optimize for a business goal. They synthesize data from siloed systems like inventory and finance, simulate potential outcomes, and then recommend the best course of action.