Pigouvian Automation Tax & The AI Layoff Trap

import numpy as np

def compute_pigouvian_automation_tax(
    wage_rate: float = 45.0,        # Human wage per hour ($)
    ai_compute_cost: float = 2.50,  # AI execution cost per equivalent hour ($)
    mpc: float = 0.85,              # Displaced worker Marginal Propensity to Consume
    num_competing_firms: int = 12   # Market fragmentation factor
) -> dict:
    """
    Formulates the Falk & Tsoukalas (2026) Optimal Pigouvian Automation Tax.
    tau* = MPC * (w - c_AI) * (1 - 1/N)
    """
    private_cost_differential = wage_rate - ai_compute_cost
    uninternalized_externality_ratio = 1.0 - (1.0 / num_competing_firms)
    
    # Optimal Pigouvian tax per unit of automated labor
    optimal_tau = mpc * private_cost_differential * uninternalized_externality_ratio
    
    # Net post-tax cost for automating firm
    effective_firm_cost = ai_compute_cost + optimal_tau
    
    return {
        "optimal_tax_per_hour": round(optimal_tau, 2),
        "private_cost_savings": round(private_cost_differential, 2),
        "effective_automated_cost": round(effective_firm_cost, 2),
        "status": "SPEED_GOVERNOR_ACTIVE",
        "demand_preservation_ratio": round(1.0 - (private_cost_differential - optimal_tau) / wage_rate, 3)
    }

if __name__ == "__main__":
    result = compute_pigouvian_automation_tax()
    print(f"Optimal Pigouvian Speed Governor Tax: ${result['optimal_tax_per_hour']}/hr")