The economist who formalized the AI doom scenario says its own conditions are too extreme to hold — here's what the math actually shows + the gap framework that changes your career calculus

A fictional memo from 2028 just wiped over $200 billion in real market cap. IBM had its worst day in 25 years because Anthropic wrote a blog post about COBOL. And the doom narrative driving all of it is probably wrong — not about the technology, but about the economics.

This piece steelmans both the bear and bull cases, walks through the formal academic math on whether AI can actually cause economic contraction, and introduces a framework you haven’t seen elsewhere: the Capability-Dissipation Gap — the widening distance between what AI can technically do and what the economy has actually reorganized around AI doing. That gap explains why the market is incoherent, why both doom and boom timelines are wrong, and why the economic rewards for early adopters are larger and more persistent than anyone is currently pricing.

The companion prompt kit operationalizes everything in this post. Use it to audit where you personally sit on the capability-dissipation curve, stress-test the AI narratives crashing your sector, build a Lütke-style eval framework for your domain, and generate the credible briefing that makes you the most valuable person in a room full of panicking executives.

Here’s what’s inside:

• The doom meme, at its strongest. Why the Citrini scenario is internally consistent, emotionally resonant, and probably too extreme to hold — and what the formal economic math actually says about the conditions required for AI to cause contraction.

• The bull case with purchasing power math. The mechanism neither side is discussing: when AI compresses service costs 40–70% and services are 70% of spending, a household earning less can afford more. Real numbers on what this means for median households.

• The market’s incoherent bet. Why Wall Street is simultaneously pricing AI as too weak to justify infrastructure spending and too strong for any incumbent to survive — and why the organizational damage from that paradox is already self-reinforcing.

• The four forms of societal inertia. Regulatory, organizational, cultural, and trust drag that neither narrative accounts for — and why this force changes every timeline, doom and boom alike.

• The capability-dissipation gap. A framework for the widening distance between what AI can technically do and what the economy has actually reorganized around AI doing — and why the people operating at the frontier are capturing outsized, persistent returns.

• Five prompts to operationalize all of it. A personal gap audit, a narrative stress test, a domain-specific eval builder, an executive briefing generator, and a 10-minute quick version.

I’m going to make the best case for both sides, walk through the math, and explain why the force nobody is modeling changes everything about the timeline.