What question did this study set out to answer?

To characterize the emerging business-to-business market for large language model inference and assess trends in supply expansion, pricing trajectories, and product differentiation.

August 1, 2026The Journal of Economic PerspectivesOpen Access

The Emerging Market for Intelligence: How Firms Buy and Sell AI

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Authors

MDMert Demirer AFAndrey FradkinMinistry of Economy NTNadav TadelisMicrosoft (United States)

Discussion

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Implication

Empirical analysis reveals plunging inference costs and high turnover in commercial artificial intelligence models, highlighting rapid commoditization across specialized enterprise use cases.

Key Points

To characterize the emerging business-to-business market for large language model inference and assess trends in supply expansion, pricing trajectories, and product differentiation.
Analyzed empirical transaction, model catalog, and pricing data from the AI inference platform OpenRouter.
Evaluated market trends across open-source and proprietary models to measure supplier entry, cost per token, and model churn over time.
The price of model inference declined approximately 1,000-fold, with open-source options priced roughly 90% lower than comparable closed-source models.
Market supply surged due to open-source model creators and providers, resulting in rapid leadership turnover among top-performing models.
Buyer demand exhibits substantial horizontal and vertical differentiation, preventing any single architecture or model from dominating all enterprise use cases.

Cite This Study

Demirer et al. (2026) studied this question.

synapsesocial.com/papers/6aad1180555e610345f4940c https://doi.org/10.1257/jep.20261506

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August 1, 2026The Journal of Economic PerspectivesOpen Access

The Emerging Market for Intelligence: How Firms Buy and Sell AI

View Full Paper

Ask AI

Bookmark

Authors

MDMert Demirer AFAndrey FradkinMinistry of Economy NTNadav TadelisMicrosoft (United States)

Discussion

Member takes

Implication

Empirical analysis reveals plunging inference costs and high turnover in commercial artificial intelligence models, highlighting rapid commoditization across specialized enterprise use cases.

Key Points

To characterize the emerging business-to-business market for large language model inference and assess trends in supply expansion, pricing trajectories, and product differentiation.
Analyzed empirical transaction, model catalog, and pricing data from the AI inference platform OpenRouter.
Evaluated market trends across open-source and proprietary models to measure supplier entry, cost per token, and model churn over time.
The price of model inference declined approximately 1,000-fold, with open-source options priced roughly 90% lower than comparable closed-source models.
Market supply surged due to open-source model creators and providers, resulting in rapid leadership turnover among top-performing models.
Buyer demand exhibits substantial horizontal and vertical differentiation, preventing any single architecture or model from dominating all enterprise use cases.

Cite This Study

Demirer et al. (2026) studied this question.

synapsesocial.com/papers/6aad1180555e610345f4940c https://doi.org/10.1257/jep.20261506

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