The economy splits into diverging trajectories: those who own capital or work with AI see incomes rise, while displaced workers see purchasing power collapse. Luxury goods thrive as essentials-only spending grows.
This simulation uses a system dynamics approach with feedback loops between AI adoption → task automation → job displacement → wage effects → consumer demand → GDP. Sector-specific automation ceilings are calibrated to McKinsey/Goldman Sachs task-level exposure estimates. Reabsorption follows a logistic adoption curve. The model includes second-order effects: reduced consumer spending feeds back into corporate revenue, partially offsetting profit gains from labor cost savings.
Goldman Sachs 2024-26 300M global jobs exposed, 15% productivity gain potential
McKinsey Global Institute 2024-25 30% of US work hours automatable by 2030, $13T GDP uplift
WEF Future of Jobs 2025 2025 92M displaced, 170M created by 2030
Moody's Analytics Feb 2026 Macro model with hysteresis and demand-side risks
EU AI Alliance 2025 Seven feedback loops; only 3-7% of productivity gains reach workers
Dallas Fed Feb 2026 AI simultaneously aiding and replacing workers
This is an illustrative simulation, not a forecast. Real economic outcomes depend on countless factors this model cannot capture: policy decisions, technological breakthroughs, geopolitical events, social adaptation, and emergent industries we can't predict. The model deliberately shows tensions and tradeoffs to aid thinking—not to make definitive claims. Historical technology transitions (electricity, computing) suggest both significant disruption AND eventual adaptation.