Aug 2026· Journal of Economic Perspectives· Vol 40, pp. 23-46· 0 citations· 4 references
TL;DR
The emerging business-to-business market for large language model (LLM) inference is described and key empirical patterns in its supply, pricing, and dynamics are document, using data from OpenRouter.
Abstract
We describe the emerging business-to-business market for large language model (LLM) inference and document key empirical patterns in its supply, pricing, and dynamics, using data from OpenRouter. First, supply has expanded rapidly: the number of commercially available models, model creators, and inference providers has grown sharply, driven heavily by opensource entrants. Second, the price of intelligence has fallen roughly a thousandfold, and opensource models now cost about 90 percent less than comparable closed-source ones. Third, the market is highly dynamic, with frequent turnover among leading models and creators. Fourth, we document substantial horizontal and vertical differentiation: no single model dominates across use cases, and demand for intelligence varies widely across applications. We place these patterns in historical perspective alongside earlier general-purpose technologies.
It is found that following LLM supply shocks, unverified reviews shift toward greater negativity: more 1-stars, fewer 5-stars, and lower ratings, suggesting that strategic AI use may reshape platform competition dynamics.
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