Here’s something that should be keeping school leaders up at night: 55% of recent graduates report that their academic programs didn’t prepare them to use generative AI tools in the workforce. Not just use AI well — use it at all. We are preparing students for an economy that is reorganizing itself faster than our curriculum review cycles can keep up with, and most schools are responding with either panic or denial.
The World Economic Forum’s Future of Jobs Report 2025 projects that AI will displace 92 million jobs while creating 170 million new ones — a net gain on paper, but that math only works if the people losing the 92 million jobs can access the 170 million new ones. That transition requires education, retraining, and policy infrastructure that does not currently exist at the scale needed. Young workers in AI-exposed occupations are already experiencing shifts in employment. The college wage premium has flattened. Jobs requiring AI skills now command a 56% wage premium over those that don’t — up from 25% just the year before.
This is not an abstract future problem. It is the context in which our students will graduate.
I don’t write primarily about business or economics — this site is about education, technology, and the ideas that shape both. But understanding how AI is disrupting the economy is part of understanding what we are actually preparing students for. The books below are the ones I’d put in front of any educator or school leader who wants to think more seriously about this.
The Coming Wave: Technology, Power, and the Twenty-First Century’s Greatest Dilemma — Mustafa Suleyman
Suleyman co-founded DeepMind (later acquired by Google) and Inflection AI before becoming CEO of Microsoft AI. He is, in other words, someone who has been building this technology from the ground up and who has had to think carefully about what he was building.
The Coming Wave is his argument that we are facing a genuine inflection point: AI and synthetic biology are advancing faster than governance structures can keep pace with, and the window to build appropriate containment mechanisms is closing. His central concern isn’t that AI is malevolent — it’s that the concentration of power that comes with controlling transformative technology is itself the problem, whether that power sits with corporations, governments, or both.
For educators: the chapter on economic disruption is essential reading. Suleyman doesn’t pretend the transition will be smooth. He takes seriously the question of what happens to people and communities during the displacement phase, which is precisely the phase our current students are entering.
AI Superpowers: China, Silicon Valley, and the New World Order — Kai-Fu Lee
Lee has a unique vantage point: he’s worked at Apple, Microsoft, and Google, and then moved to Beijing to lead Google China before becoming one of China’s leading AI investors. AI Superpowers was published in 2018, and some of the specific competitive dynamics have shifted, but the core argument holds: we are in a global race for AI dominance between two different models of how AI development should work, and the outcomes of that race will have profound economic consequences at every level.
The section on job displacement is where this book becomes most directly relevant to educators. Lee argues that routine cognitive work is the most vulnerable to automation — not just manual labor — and that the categories of work that will be protected are those requiring creativity, empathy, and complex human judgment. That framing has direct implications for what we teach and why.
Read this alongside The Coming Wave for a richer picture of the geopolitical and economic forces shaping the AI landscape.
Prediction Machines: The Simple Economics of Artificial Intelligence — Ajay Agrawal, Joshua Gans & Avi Goldfarb
Three economists from the University of Toronto built their framework around a deceptively simple claim: AI is, fundamentally, a technology that makes prediction cheaper. When prediction gets cheaper, the value of the things that complement prediction — judgment, action, data — increases. When prediction gets cheaper, the value of things that substitute for prediction — routine rule-following, low-stakes decision-making — decreases.
This framework is useful for educators because it maps directly onto a question we should be asking about curriculum: what are we teaching students that will be substituted by cheap AI prediction, and what are we teaching them that will be complemented by it? The answer has real implications for what genuinely rigorous education looks like in an AI economy. Prediction Machines is the most analytically useful book on this list for thinking through those questions.
The Age of AI: And Our Human Future — Henry Kissinger, Eric Schmidt & Daniel Huttenlocher
An unusual collaboration: a former Secretary of State, a former Google CEO, and an MIT computer scientist thinking together about what AI means for how human societies understand the world. The book is less about the economic disruption and more about the epistemological one — the way AI systems generate outputs that humans can use without understanding how those outputs were produced, and what that does to decision-making in business, government, and education.
The argument that lands hardest for me as an educator: we have spent centuries building institutions of learning around the transmission and evaluation of human knowledge. AI is producing a new kind of knowledge — statistical, pattern-based, extraordinarily capable, and fundamentally alien to how human minds work. What does education mean in that context? This book doesn’t fully answer the question, but it asks it more precisely than most.
Power and Prediction: The Disruptive Economics of Artificial Intelligence — Ajay Agrawal, Joshua Gans & Avi Goldfarb
The follow-up to Prediction Machines, published in 2022, moves from “here’s what AI does to economics” to “here’s how organizations and institutions will be restructured by it.” The core new argument: AI doesn’t just automate tasks; it disrupts the decision-making systems in which those tasks are embedded. That disruption creates power shifts — between professions, between institutions, between incumbents and challengers.
The education implications are direct. The authors discuss healthcare and legal services as sectors being restructured by AI-driven prediction, and the analysis applies equally to education. What happens to the teacher’s role when AI can provide personalized feedback faster and at greater scale? What happens to credentialing when AI can assess competencies that diplomas approximate? These aren’t comfortable questions, but they’re the right ones to be asking now rather than after the disruption has already happened.
The Question Underneath All of These Books
The books above are written primarily for business leaders, policymakers, and economists. That’s who they were designed for. But they all circle around a fundamentally educational question: what kind of people do we need to develop, and what do we need to prepare them for, in an economy being reorganized by AI?
Self-Determination Theory gives us part of the answer — humans are most resilient and most capable when they have genuine autonomy, a sense of competence, and meaningful connection. Those psychological needs don’t get automated. They get more important as the tasks around them do.
The Connectivist framing that the network is where knowledge lives is also useful here: in an economy where AI can provide information faster than any human, the competitive advantage lies in the quality of your connections — to ideas, to people, to problems worth solving — and in your capacity to navigate those networks with judgment. That’s what education in an AI economy should be building.
These books don’t answer those questions for us. But they describe the problem with enough precision that we can start asking the right ones.
Related on this site: the AI books post covers the books I’d recommend for understanding what AI actually is — how it works, what it can and can’t do, and what the most credible researchers think about its implications. That’s a companion list to this one.
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