When AI Gets Cheaper, Do Prices Follow? Information Delays and Algorithmic Pricing
Authors: Krish Tadigotla
Abstract
We examine whether lower costs of running AI models lead to lower prices, and whether delays in observing competitors' prices and limits on price changes affect how much consumers benefit. We study a simulated market with two differentiated sellers, logit demand, and tabular Q learning. A factorial experiment varies the age of rival price information, how often sellers can revise prices, whether production costs fall, and differences in firm quality and costs. We evaluate policies trained on 60 paired market seeds in each of 16 conditions, for 960 training runs, and measure expected consumer surplus after learning ends. In the main setting with identical firms and falling costs, the estimated interaction between information delay and repricing frequency on consumer surplus is -0.00025 normalized units, with a 95% paired seed bootstrap interval of [-0.0402, 0.0393]. The interval includes zero, as do intervals for tests of whether the interaction changes with the cost regime or differences between firms. With the shortest information delay and the ability to revise prices every period, average price reductions equal 55.6% and 44.0% of the imposed cost reduction for the two firms. Mean consumer surplus under the learned policies is below the corresponding static grid Nash benchmark in all 16 conditions. The findings show that simulated cost savings need not be fully passed on to buyers. They do not establish collusion or describe pricing by actual AI providers.
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Closest papers
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Abstract
Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. We find that the algorithms consistently learn to charge supracompetitive prices, without communicating with one another. The high prices are sustained by collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand, changes in the number of players, and various forms of uncertainty. (JEL D21, D43, D83, L12, L1…
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Abstract
This study examines, within an analytical and holistic framework, how artificial intelligence (AI) and machine learning-driven algorithmic pricing systems are reshaping pricing behavior in international trade, firms’ profit margins (markups), and the broader dynamics of global competition. It focuses on three main research questions: the forms of market power generated by algorithmic pricing in export markets, the ways in which international organizations address the risks of digital monopolization and tacit collusion associated with autonomous pricing systems, and the regulatory gaps embedded in existing global trade rules. Methodologically, the study adopts a qualitative research design ba…
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Abstract
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Abstract
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Abstract
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The Hidden Cost of Convenience: How Algorithmic Pricing Hurts Vulnerable ConsumersOluwafemi Akinwande · 2026 · WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY · Not summarized yet
Abstract
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Abstract
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The Emerging Market for Intelligence: How Firms Buy and Sell AIMert Demirer, Andrey Fradkin, Nadav Tadelis · 2026 · Journal of Economic Perspectives · Not summarized yet
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:…
Prior work it builds on
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Algorithmic Collusion Without Deterrence: Auditing the Punishment Mechanism in Q-Learning Pricing AgentsKauê L. P. de Moraes, Rodrigo De-Losso · 2026 · Not summarized yet
Abstract
Independent tabular Q-learning agents repricing in a simulated Bertrand duopoly converge to supracompetitive prices, and the seminal evidence attributes this to a learned reward-punishment scheme: undercut your rival and it retaliates, so deviation never pays. This mechanistic reading is what turns high prices into a cartel, and into a regulatory target. We replicate the environment of Calvano et al. (2020) and confirm their impulse-response pattern within co-trained pairs. We then audit the pattern on frozen policies, with learning off, and find that on its own it does not identify a mechanism: the return is over-determined rather than selective. In nine of ten converged pairs (914 of 1,024…
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Algorithmic collusion under sequential pricing and stochastic costsGonzalo Ballestero · 2026 · International Journal of Industrial Organization · Not summarized yet
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Abstract
We develop a framework for the optimal pricing and product design of LLMs in which a provider sells menus of token budgets to users who differ in their valuations across a continuum of tasks. Under a homogeneous production technology, we show that users'high-dimensional type profiles are summarized by a scalar index, reducing the seller's problem to one-dimensional screening. The optimal mechanism takes the form of committed-spend contracts: buyers pay for a budget that they allocate across token classes priced at marginal cost. We extend the analysis to environments with multiple differentiated models and to competition between a proprietary leader and an open-source fringe, showing that co…
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Updated on 31 October 2026.