Where the Numbers Come From

The economic model, the data it uses, and who to blame.

Real Economic Data

SOURCED

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The Economic Model

This site runs a live simulation of “The AI Layoff Trap”, an economic paper that formalizes why companies automating with AI end up destroying their own market.

The model imagines N companies competing in an industry. Each can replace some fraction of its workers with AI, saving on wages. But each layoff eliminates a customer — and that lost spending splashes across all N companies, not just the one that automated.

What Each Number Means

Competitive Adoption Rate

(Cost Savings − Spending Loss ÷ Firms) ÷ Integration Cost

Where competition pushes every firm. Each firm only feels 1/N of the demand it destroys, so it automates too much.

Sustainable Adoption Rate

(Cost Savings − Spending Loss) ÷ Integration Cost

What firms would do if they cooperated. They fully count the demand they destroy, so they stop at the level that maximizes everyone's profit.

Spending Lost Per Automated Job

Local Spending × (1 − Income Replacement) × Wage

Each job lost reduces wages. How much of that lost income would have been spent in this industry?

Over-Automation Gap

Spending Lost × (1 − 1/Firms) ÷ Integration Cost

How much extra automation happens because of competition. Grows with more firms (more competition = bigger trap).
Shrinks with harder automation.

How the Countdown Works

We start from current AI adoption rates for each sector (from Stanford AI Index, CNBC, McKinsey surveys)
Each month, adoption inches forward — faster in sectors with more competition and bigger cost savings
At every step, we compute total profit using the actual adoption rate (not the theoretical Nash rate)
The collapse date is when total profit across all sectors falls below a critical threshold
The “Adoption Pace” slider on the dashboard lets you speed up or slow down how fast companies adopt AI

Sector Calibration

Sector	Companies (N)	Income Replacement (η)	Cost Savings (s)	Friction (k)	Data Source
customer Support	50	0.25	0.55	1.2	CNBC (2025c) Salesforce 4K layoffs; industry fragmentation estimates
software	30	0.5	0.65	1.5	CNBC (2025a) Goldman Sachs autonomous coder; Infosys-Cognition partnership
finance Backoffice	15	0.4	0.5	1	Banking sector concentration; Block 4K layoffs (CNBC 2026b)
retail	100	0.2	0.4	0.8	BLS retail employment; McKinsey automation potential
manufacturing	25	0.35	0.45	1.3	Acemoglu & Restrepo (2020) robot adoption; BLS manufacturing
legal Professional	40	0.55	0.6	1.8	Eloundou et al. (2024) exposure estimates; professional services concentration

Important

This is an educational tool built on an economic model. The countdown is a simplified projection — real outcomes depend on policy changes, new job creation, international coordination, and technological shifts. The value here is identifying a structural risk markets cannot self-correct, not predicting a specific date.