IMF Working Paper: The Price of Intelligence -Optimal Pricing and Deployment of AI by Socially-Minded Firms
SDG 9: Industry, Innovation and Infrastructure | SDG 8: Decent Work and Economic Growth | SDG 10: Reduced Inequalities
Institutions: NITI Aayog | Ministry of Finance | Ministry of Electronics and Information Technology
The IMF Working Paper The Price of Intelligence -Optimal Pricing and Deployment of AI by Socially-Minded Firms derives economic formulas for firms with a social mandate (beyond pure profit maximization) to determine how they should price and deploy Artificial Intelligence (AI), balancing efficiency and labor disruption.
The Social Trade-Off:
AI firms face a fundamental tension between expanding access to AI (which maximizes aggregate social welfare) and the resulting loss in profits and labor market risks in the short run.
The deployment strategy must balance four distinct motives: profit maximization, aggregate efficiency (welfare), distributional concerns, and minimizing labor market disruptions. The paper introduces a variable in the Modified Lerner’s Rule that summarizes these competing objectives of a socially-minded firm.
Optimal Pricing Scenarios:
Welfarist Firms (Prioritizing Welfare): A firm that equally values profit and aggregate welfare should price closer to the marginal cost. This is because the efficiency gains from broad access to AI generally outweigh distributional concerns.
Conservative Firms (Prioritizing Labor Stability): A firm focused on minimizing labor market disruption should price above the profit-maximizing level in the short run and follow a gradual deployment path. This applies especially when AI may displace low-income workers.
Policy Conclusion:
The most pro-social course of action for AI firms with considerable market power is to refrain from exploiting that power.
A socially minded firm should price closer to marginal cost in the long run to broaden access.
Further increasing the price of AI through taxes or self-regulation is discouraged, as it would have an adverse first-order impact on consumers (limiting access) with only modest protective benefits for workers.
This paper offers a quantitative framework for India’s AI strategy, advising policymakers to focus regulatory pressure on ensuring low long-run AI prices to maximize the country’s aggregate productivity gains. It suggests that instead of high AI taxes, the government should utilize tools like progressive tax systems or targeted retraining to provide insurance for displaced workers, thereby promoting both efficiency and equity.
What is the Modified Lerner’s Rule used in this paper?→ This is an economic formula that extends the traditional rule used by monopolistic firms to set prices. The Modified Lerner’s Rule incorporates a factor that summarizes the competing motives of a socially-minded AI firm—including the incentive to maximize aggregate social welfare (pushing the price down closer to marginal cost for wider access) and the incentive to minimize short-term labor market disruptions (which can push the price up to slow adoption).
Follow the full paper here: The Price of Intelligence -Optimal Pricing and Deployment of AI by Socially-Minded Firms