The price of intelligence: How should socially-minded firms price and deploy AI?

Mon, 01 Jan 0001 00:00:00 +0000

Leading AI firms such as OpenAI and Anthropic publicly claim dual mandates of profit and social welfare, raising the question of whether—and how—a social mandate should change their pricing and deployment decisions. This paper provides a framework to answer this question, deriving a Modified Lerner Rule for socially minded AI firms that extends the standard profit-maximizing Lerner Rule to incorporate incentives for aggregate efficiency, distributional concerns, and labor market stability. Using U.S. data on 525 detailed occupations, the paper evaluates optimal pricing and deployment paths for an AI capable of replacing human labor in each job at 50% of the cost. The main finding is that a welfarist firm (one that values profits and social welfare) should price closer to marginal cost because, for the jobs considered, efficiency gains outweigh distributional concerns—AI does not primarily displace low-income workers. A conservative firm focused on labor market stability should price above the profit-maximizing level in the short run, but not in the long run. The paper concludes that the most pro-social course of action for AI firms with market power is to refrain from exercising that power, and that proposals to tax AI to protect labor markets miss the counteracting role of market power.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is the Modified Lerner Rule and what motives does it capture?

The Modified Lerner Rule states (P − MC)/P = M/ε, where ε is the demand elasticity and M is a modifier that summarizes the motives of the socially minded firm; M = 1 corresponds to the standard profit-maximizing Lerner Rule. The modifier M reflects four distinct considerations: (1) profit motives push M toward 1; (2) aggregate efficiency considerations push M toward 0 (marginal-cost pricing, which maximizes the “size of the pie”); (3) distributional concerns (who benefits from AI) can be positive or negative depending on whether AI substitutes for high- or low-income workers; and (4) the incentive to minimize labor market disruptions pushes M above 1 in the short run, because the cost of labor disruption is higher while workers are still adjusting, but not in the long run. The formula is derived in a general equilibrium model where the AI firm has a monopoly over an AI capable of replicating human skills.

Q2. What does the welfarist case imply for pricing?

A firm that values both profits and aggregate social welfare should price closer to marginal cost than the profit-maximizing firm, because for all 525 occupations considered, the aggregate efficiency gains from AI adoption outweigh the distributional costs. This finding reflects the structure of AI’s labor market effects: since AI does not primarily displace low-income workers in the US occupational data used, distributional concerns do not push toward restricting AI access. The welfarist firm therefore faces a dominant efficiency motive to expand access by pricing down toward marginal cost, accepting lower profits in exchange for greater welfare gains from AI adoption.

Q3. What does the conservative case imply?

A firm focused solely on labor market stability should price above the profit-maximizing level in the short run, because restricting AI deployment reduces the speed of worker displacement; but this above-profit-maximizing pricing is optimal only temporarily, and converges toward profit-maximizing pricing in the long run as workers adjust. The intuition is that the cost of disrupting the labor market is highest when workers have not yet adjusted—their human capital is not yet redeployed—so a conservative firm acts as a gradual deployer. This conservative pricing is distinct from the welfarist case: the conservative motive restricts access more than a welfarist mandate, since it is willing to sacrifice efficiency to slow disruption.

Q4. Why does the paper argue against taxing AI?

The paper argues that proposals to tax AI firms to protect workers from displacement overlook the fact that AI firms may already exercise significant market power, which protects workers by restricting AI supply below the efficient level. Adding a tax on top of an already-restricted supply would harm consumers (who face high AI prices and limited access) without providing meaningful additional protection for workers (since output is already suppressed by market power). The paper’s analysis implies that the first-order social priority is to have AI firms refrain from exercising their market power—by pricing closer to marginal cost—rather than further restricting supply through taxation.

Key concepts

Modified Lerner Rule : (P − MC)/P = M/ε, where M captures a socially minded firm’s weighting of profit, aggregate efficiency, distributional, and stability motives; the paper’s key pricing formula, derived from a GE model with a monopoly AI firm.

welfarist vs. conservative firm : two polar cases: the welfarist firm maximizes a weighted sum of profits and aggregate welfare (implying near-marginal-cost pricing); the conservative firm prioritizes labor market stability (implying above-profit-maximizing pricing in the short run to slow displacement).

labor disruption cost : the welfare cost to workers of being displaced by AI, which is higher in the short run when workers must reallocate across jobs or sectors and lower in the long run after adjustment; the paper’s formal treatment of this cost motivates the conservative firm’s gradual deployment strategy.