New Course
An honest map of a contested question
Five modules covering what work actually is, how we got here, what could go wrong, what could go right, and how to build your own view. Not doom. Not hype. The evidence as it stands.
What You'll Learn
The five modules
Each module stands on its own. Together they give you a working framework for thinking about automation and work — one you can test, push back on, and extend.
1. What We're Actually Talking About
The question, the definitions, and the four dimensions of human work.
2. How We Got Here
Tasks not jobs, general-purpose technologies, and the long-run data.
3. What Could Go Wrong
Demand contraction, tax-base erosion, inequality, and the household income stack.
4. What Could Go Right
Sovereign wealth funds, employee ownership, transfers, and the human exception.
5. Your Mindset
Self-audit, diversification, path dependency, and thinking for yourself.
Course Includes
Concept boardsFour concepts per lesson, explained clearly
FlashcardsFive key terms per lesson to review
Practice questionsCheck understanding as you go
Further readingCurated links to papers and books per topic
Timed final exam20 questions, 30 minutes
Printable certificateUnlocked by passing the exam
Ready To Start
Begin with Lesson 1
Start with the question that won't go away. From Keynes's 1930 forecast to today's AI wave, there is a single thread — and it is worth pulling on.
Who This Course Is For
No economics background required
This course is written for a general adult audience — no economics background assumed. Terms are defined where used; claims are sourced; and every lesson points you to the primary readings if you want to go deeper. If you have followed the AI debate and want something more grounded than most of the public conversation, this is for you.
Sources and Attribution
A synthesis, not a single voice
This course deliberately draws on a wide set of voices — Keynes (1930), Fisher (1933), Autor, Levy & Murnane (2003), Bresnahan & Trajtenberg (1995), Piketty, Brynjolfsson et al. (2023), Acemoglu & Restrepo, Frey & Osborne (2013), Paul David (1985), Diamandis & Kotler (2015), Darity & Hamilton (2010), Van Parijs, Karabarbounis & Neiman (2014), the ILO, the OECD, the IMF, and the NBER. It also draws on David Shapiro's popular writing, which has done much to bring post-labour economics to a wider audience. The point of the course is to let you read all of them — and then form your own view.
Privacy & Storage
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All Lessons
Course Map
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Module 1 — What We're Actually Talking About10 min
Lesson 1: The Question That Won't Go Away
For nearly a century, economists, philosophers, and workers have asked the same question: what happens to people when machines do most of the work? This course is one attempt at a measured answer.
Module 1 — What We're Actually Talking About10 min
Lesson 2: Two Definitions of Work
Before asking whether work will disappear, it helps to be clear about what work actually is. Economics has one answer. Physics has another. Both are useful.
Module 1 — What We're Actually Talking About12 min
Lesson 3: The Four Dimensions of Human Contribution
Any honest map of human work has to cover several very different kinds of capability. Carrying, manipulating, thinking, and relating are not the same thing, and they are not automatable at the same rate.
Module 1 — What We're Actually Talking About10 min
Lesson 4: What Gets Replaced and Why
A machine does not have to be better at everything. It just has to be good enough on a few dimensions to displace the human choice. Understanding those dimensions is the practical core of automation analysis.
Module 2 — How We Got Here11 min
Lesson 5: Tasks, Not Jobs
The most useful single idea in modern labour economics is that jobs are bundles of tasks — and machines buy tasks, not jobs. Everything that follows in this course depends on this shift of focus.
Module 2 — How We Got Here11 min
Lesson 6: General-Purpose Technologies
Some technologies change one industry. A few reshape the whole economy. Understanding which kind AI is — and why economists think it is the second kind — is the background for every claim in this course.
Module 2 — How We Got Here12 min
Lesson 7: What the Data Shows
Three macroeconomic trends have been running for forty to fifty years, long before the current AI wave. They are the underlying weather against which the AI debate takes place.
Module 2 — How We Got Here10 min
Lesson 8: Cheaper, Then Free
Technology makes things cheaper. Sometimes much cheaper. Sometimes cheap enough that an entire market disappears. Both directions matter, and both have names worth knowing.
Module 3 — What Could Go Wrong12 min
Lesson 9: The Self-Reinforcing Loop
Each individual decision to use a cheaper machine instead of a human is perfectly rational. Add them up across a whole economy, however, and you can get a contraction that no single actor wanted. This is the key macro risk of an automation wave.
Module 3 — What Could Go Wrong10 min
Lesson 10: Who Pays for the State?
Every modern government depends on an underlying assumption: people earn wages, and a large share of those wages comes back as tax. If the wage base shrinks, the tax base shrinks with it — and so does the state's ability to respond.
Module 3 — What Could Go Wrong11 min
Lesson 11: The Household-Income Stack
Every pound or dollar in your household budget comes from one of three sources. Which source dominates — wages, transfers, or capital — is a national fingerprint, and it is shifting.
Module 3 — What Could Go Wrong12 min
Lesson 12: Inequality and Within-Firm Compression
The most striking recent finding about AI is not that it erases jobs outright. It is that it reshapes who, inside a firm, captures the gains from the work — and the winners and losers are not who most people expected.
Module 4 — What Could Go Right12 min
Lesson 13: Shared Capital
Two countries have run large-scale, decades-long experiments in sharing the returns from natural-resource wealth with ordinary citizens. Their results are the clearest existing template for sharing AI-era capital returns.
Module 4 — What Could Go Right11 min
Lesson 14: Owning Where You Work
Not every way to broaden capital ownership requires a national fund. Employees can become owners of the firms they work for — and have, in significant numbers, through several well-tested institutional forms.
Module 4 — What Could Go Right12 min
Lesson 15: Stipends, Safety Nets, and Baby Bonds
If wages shrink, transfer income has to grow. The menu of ways to do this is larger than public debate suggests — and some of them have been running as live experiments for years.
Module 4 — What Could Go Right11 min
Lesson 16: The Human Exception
Not everything that could be automated will be. Several large domains will keep hiring humans because customers, courts, and cultures insist on it — and some of them will grow, not shrink.
Module 5 — Your Mindset12 min
Lesson 17: Auditing Your Own Work
Enough theory. Start with yourself. This lesson gives you a short, honest framework for looking at your own tasks and working out which are exposed, which are durable, and what to do about it.
Module 5 — Your Mindset12 min
Lesson 18: Designing Your Three Buckets
You already saw in Lesson 11 that household income has three streams. This lesson turns that national-accounts framework into a personal-finance one. Building a resilient income mix is one of the most useful things you can do for an uncertain future.
Module 5 — Your Mindset12 min
Lesson 19: Path Dependency and Policy Design
Every good policy idea lives or dies on whether it fits the country it is proposed in. Path dependency is the name for that constraint — and it is the single most neglected concept in most post-labour debate.
Module 5 — Your Mindset12 min
Lesson 20: Thinking for Yourself
This course has given you maps of other people's thinking. The last lesson is about building your own. Frameworks are better than prophets, and synthesis is a discipline. Here is how to keep going.