Evaluate legislative proposals funding national job guarantees via targeted AI compute and corporate revenue taxes. Dynamic dynamic trade-offs between labor market absorption, capital flight, productivity, and fiscal solvency.
| Year | Tax Revenue ($B) | Program Cost ($B) | Net Fiscal ($B) | Public Jobs (M) | Displaced Labor (M) | Deadweight Loss ($B) | Capital Flight Index |
|---|
Automation displacement absorbs private sector workers into federal public employment. High public wage floors incentivize voluntary transition from low-wage service sectors into public work, crowding out marginal private hiring.
Targeted compute and corporate AI taxes generate diminishing marginal returns beyond critical rate thresholds. Higher elasticity amplifies capital flight into international low-tax compute havens.
Diverting capital from AI R&D reduces long-term private sector TFP (Total Factor Productivity) growth, offset partially by aggregate demand stabilization from public employment wages.
In this macroeconomic simulation, policy makers levy taxes on AI compute clusters to fund a guaranteed public employment buffer. Raising tax rates generates revenue but increases capital flight, driving mobile compute infrastructure offshore according to investor elasticity. Concurrently, annual displacement shifts workers into public employment, while higher wage floors pull additional labor from private markets. Adjusting tax rates and wage floors instantly recalculates the ten-year cumulative balance, revealing the tipping point between solvency and deficit.