Think Clearly When Everyone Else Is Panicking About Technology!

If you work anywhere near the intersection of technology, economic growth, labor, and public argument, you already know the feeling. A new study drops — "X% of jobs at risk from automation" — and within 72 hours it's everywhere: op-eds, US Senate testimony, your LinkedIn feed, your board meeting, etc. And somewhere in the back of your mind, a quiet alarm goes off. Is this real? Is this the same thing we said about computers in 1980? How would I even know?

That alarm is the beginning of analytical honesty. This course is what you do with it.

I built The Luddite Fallacy because I kept watching smart, well-intentioned people — economists, journalists, policymakers, senior managers — make the same category errors under pressure. Not because they lacked intelligence, but because they'd never been given a structured theory cum praxis way to distinguish the recurring logical fallacies from the legitimate empirical concerns. The lump-of-labor assumption. Fixed-pie thinking. Naturalistic panic. These aren't obscure academic constructs. They're the operating system underneath nearly every technology-panic headline you'll read this year, and once you can name them, you can't un-see them.

The course takes you through two centuries of evidence — not to reassure you that everything always turns out fine (it doesn't, not for everyone, not evenly), but to give you the actual employment and wage record so you can reason from facts rather than from the narrative of the moment. We'll sit inside the 1811 Luddite uprisings, inside the 1960s automation hearings in Detroit, inside the NAFTA debate in Washington, inside a 2016 newsroom being asked to write a robot-apocalypse cover story. The experiential method is deliberate: the only way to develop genuine fallacy-spotting instincts is to practice reasoning under the same “incomplete-information” conditions the original actors faced.

I want to be direct about what this course is not. It is not a defense of any technology company or a dismissal of real displacement pain — the China Shock distributional evidence, for example, deserves far more serious treatment than it usually gets in optimist circles. And it is not a polemical brief for technological pessimism, either. It is an evidence-first discipline. The productivity paradox gets examined with the same rigor as the job-apocalypse studies. The goal is that you leave capable of applying that rigor yourself, to whatever controversy arrives next.

By the time you reach the capstone, you'll have a five-question Disruption Diagnostic, practice writing under pressure, and — perhaps most valuably — a durable habit of asking what would the evidence actually need to show for this claim to be true? That question is worth more than any single answer I could give you about any particular technology. It's the skill that compounds.

I'd be glad to have you in the room — or rather, in 1811, 1960, 1993, and 2016 before we get to the present.