Cognitive marginal cost

Cognitive marginal cost is a microeconomic measure that quantifies the expenditure required to produce one additional unit of intellectual work, whether an analysis report, a legal review, a contract audit or any other information-intensive task. The concept sits within the framework of the theory of the firm initiated by Coase: the boundaries of an organization are determined by comparing the cost of performing a task in-house (human marginal cost, including gross salary, social charges, error and supervision costs) against the cost of outsourcing it to the market (API, foundation model, AI provider). The emergence of large language models creates a major economic discontinuity: the human's marginal cost remains proportional to the time and expertise mobilized, while the AI's marginal cost converges towards zero once the model or subscription is amortized. This asymmetry generates a break-even volume beyond which algorithmic internalization is mathematically optimal under the Coasian criterion. In the insurance and reinsurance domain, this mechanism applies directly to assisted underwriting, claims analysis, regulatory monitoring and portfolio surveillance, where the ability to process large volumes at decreasing marginal cost alters the economics of technical departments and service providers. The counterpart is a dependency risk: if all firms optimize toward the same AI providers, the concentration creates a single point of failure of systemic reach.

A reinsurance broker uses an AI agent to summarize the 200 pages of a cession bordereau in under two minutes, where a junior analyst would spend two hours. At 50 euros per hour, the human marginal cost is 100 euros per bordereau; the API call costs a few cents. Beyond a certain monthly volume, internalizing a local model becomes economically optimal.

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