Master the forces that decide which economies win, given demography and AI

If you are reading this, you are probably already aware that the standard toolkit for thinking about economic growth is not keeping pace with what is actually happening in the world.

Population aging is not a background condition anymore — it is an active fiscal crisis in Japan, a structural labor constraint in Germany, and an impending shock in China. AI is not a future scenario — it is already inside the production function, reshaping task structures and wage distributions in ways that the standard macro models were not built to handle. And in Africa and South Asia, a demographic dividend of extraordinary scale is unfolding at the exact moment that automation may be closing the traditional path from low-wage manufacturing to sustained development. These are not trends you can understand by reading headlines, or even by reading a few good papers in isolation. They require integrated analytical architecture — the kind that connects demographic biology & mechanics, growth theory, and frontier AI economics into a single, coherent framework.

That is what I built this school to provide. The curriculum moves deliberately: we start with the empirical/experiential and theoretical foundations you need to think clearly — growth accounting, the demographic transition, competing development theories from Rostow to Acemoglu, and how to read World Bank, UN, and OECD data systems as working instruments rather than background citations. We then go deep into the technical machinery of demography: cohort-component projection models, Lee-Carter stochastic mortality forecasting, dependency ratio analysis, and subnational disaggregation. Not as academic exercises, but because these are the tools that serious analysts at the IMF, World Bank, and national planning ministries actually use — and you should know how to use them too.

From there, we engage the AI and automation literature head-on. I have no interest in the hype cycle, and I have equally little patience for dismissiveness. The task-based models developed by the Nobelist Acemoglu and others give us the precision to ask the right questions: which tasks are being automated, in which economies, with what consequences for labor share, wage distribution, and productivity? The measurement problem — why AI's productivity impact has been so difficult to observe in aggregate data — is itself a major analytical topic, not a footnote.

The school culminates in applied country diagnostic work, using the Hausmann-Rodrik-Velasco binding-constraints framework to construct the kind of structured policy brief that actually informs decisions at serious institutions. This is where the frameworks become instruments. I designed the capstone specifically so that what you produce is something you could take into a policy conversation, a research proposal, or a board-level strategy discussion and defend with evidence.

My goal throughout is PhD-seminar standards with clear, direct prose — demanding without being obscure, technical without being inaccessible. If you are ready to build the analytical foundation for understanding the most consequential structural forces shaping the global economy over the next half-century, I would be glad to have you in the school.