Understand human flourishing before automation rewrites the rules!

If you have spent time thinking seriously about AI and the future of work, you have probably noticed a persistent frustration: most of the conversation moves very fast and stays very shallow. Commentators oscillate between utopian visions of liberated leisure and dystopian warnings of mass purposelessness — and neither camp spends much time with the empirical literature that could actually discipline the debate.

That gap is what this school is designed to fill. The psychological, sociological, and economic research on work and wellbeing is substantial. It is also genuinely complicated. Paid work is simultaneously one of the strongest predictors of wellbeing and, for many people, a significant source of strain. Unemployment is harmful not primarily because of lost income but because of lost structure, identity, and social connection — and yet voluntary retirement, which also removes paid work, is often associated with sustained or improved wellbeing. Lottery winners do not, on average, become lastingly happier. These findings are not paradoxes; they are data points that, taken together, reveal the mechanisms underneath. Learning to read those mechanisms clearly is the prerequisite for any serious thinking about what AI-induced economic transformation will actually mean for human lives.

What I have tried to build here is a school that treats you as a serious thinker. The curriculum does not flatten these complexities or rush toward reassuring conclusions. It walks through the evidence carefully — across populations, across methods, and across very different cultural and institutional contexts — and it is honest about what we do and do not yet know. The three-factor framework that organizes the final units is not a proprietary invention; it is a synthesis of what the comparative literature consistently points toward, made explicit and applicable so that you can use it in your own analysis.

Whether you are a policy researcher trying to think through universal basic income (UBI) proposals, an AI ethics practitioner evaluating deployment decisions, a social scientist looking to connect your domain expertise to automation debates, or simply someone who wants to think more clearly about one of the defining challenges of our time — this school is built for the quality of thinking you bring to hard problems.

The empirical evidence will not tell you what to do. But it will make you much harder to mislead, and much better at building recommendations that stand up to scrutiny. That seems like a worthwhile place to start.