AI robots playing chess on a cityscape chessboard.

Algorithmic Pricing: Are AI Price Wars Inevitable?

Rhea Morgan in Business & Economy July 2025 • 4 min read.

"Discover how AI pricing algorithms can lead to unintended collusion and what it means for consumers and competition policy."

Imagine a world where the prices of everyday items fluctuate not based on supply and demand, but on the silent calculations of artificial intelligence. This isn't science fiction; it's the reality of algorithmic pricing, where companies use AI to set prices in real-time. The rise of these pricing algorithms has sparked both excitement and concern, particularly regarding their potential to mimic collusion, even without any explicit agreement between competitors.

Senator Amy Klobuchar introduced the Preventing Algorithmic Collusion Act. Her statement encapsulates this worry: price fixing, traditionally a human endeavor, might now occur through the automated actions of algorithms. These algorithms, designed to maximize profits, could inadvertently lead to inflated prices for consumers, impacting everything from rent to rideshares.

While regulators and lawmakers grapple with adapting antitrust laws to this new reality, a deeper question emerges: Can algorithms, acting independently, truly create collusive outcomes? Recent research suggests the answer is a resounding yes, challenging the very foundation of competition policy.

When Algorithms Act Like Cartels: Understanding the Dynamics

AI robots playing chess on a cityscape chessboard.

Collusion, traditionally, requires communication, agreement, and the ability to punish those who deviate. Yet, recent economic models demonstrate that algorithmic pricing can generate similar results even when these conditions are absent. In these models, firms use algorithms that adapt to market conditions, learning from each other's pricing strategies and adjusting their own accordingly.

These pricing algorithms often involve a parameterized family of model specifications, meaning they can adapt to different ways of understanding the market. Firms update these parameters and the weights they place on different models to adapt endogenously to market outcomes. The outcome? Markets experience recurrent episodes where firms set prices at levels that resemble collusion.

Communication Breakdown: There is no explicit or implicit communication between firms.
Agreement-Free Zone: Firms do not need to agree on a target outcome or punishment mechanism.
Short-Sighted Strategies: Firms are myopic, focusing only on immediate profits rather than long-term gains.
Simple Rules: Algorithms are simple, avoiding complex strategies or historical analysis.

The key to this "algorithmic collusion" lies in the combination of endogenous algorithmic selection and adaptation with an endogenous data generating process. Each firm independently chooses an algorithmic pricing specification that it updates over time. They treat the price process of competing firms as an exogenous factor and adjust to reach the competitive outcome. But as each firm updates its pricing strategy based on independent parameters, they begin to correlate their pricing behaviors.

The Future of Competition in an Algorithmic World

The implications of algorithmic pricing are far-reaching. As AI becomes more sophisticated, the line between independent adaptation and tacit collusion may blur even further, presenting a significant challenge to competition policy. Lawmakers and regulators must consider these new dynamics to ensure fair markets and prevent unintended consequences that harm consumers. This challenge highlights the need for continuous adaptation and vigilance in the face of rapidly evolving technology.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

Everything You Need To Know

What is algorithmic pricing, and how does it work?

Algorithmic pricing involves companies using AI to set prices in real-time. These AI algorithms analyze market conditions and adjust prices automatically. This dynamic pricing contrasts with traditional methods, where prices are determined by supply and demand or human decision-making. The goal of the pricing algorithms is to maximize profits.

How can AI pricing algorithms lead to unintended collusion, and what does that mean for consumers?

AI pricing algorithms can inadvertently mimic collusion, even without explicit agreement between competitors. The algorithms adapt to market conditions, learning from each other's pricing strategies. This leads to correlated pricing behaviors, which result in inflated prices for consumers. This algorithmic collusion challenges competition policy, as traditional definitions of collusion require communication and agreement, which are absent in this scenario. Consumers might end up paying higher prices for everyday items like rent and rideshares.

What are the key features that allow algorithmic pricing to function like collusion?

Algorithmic collusion can occur even without explicit communication, agreement, or long-term strategic thinking by the firms. Several factors are key: no explicit communication between firms; no need for firms to agree on a target outcome; firms focusing on immediate profits rather than long-term gains. The use of simple rules and independent adaptation of the algorithmic pricing specifications helps create correlated pricing. Firms treat the price process of competing firms as an exogenous factor and adjust to reach the competitive outcome.

How does endogenous algorithmic selection contribute to algorithmic collusion?

The key to algorithmic collusion lies in the combination of endogenous algorithmic selection and adaptation. Each firm independently chooses an algorithmic pricing specification that it updates over time. As these algorithms adapt to market conditions and each other's pricing strategies, they start to correlate their pricing behaviors. The models utilize a parameterized family of specifications to adapt to different ways of understanding the market. Firms update these parameters and the weights they place on different models to adapt endogenously to market outcomes. The outcome? Markets experience recurrent episodes where firms set prices at levels that resemble collusion.

What are the implications of algorithmic pricing for competition policy and market regulation?

The implications of algorithmic pricing are far-reaching, presenting significant challenges to competition policy. As AI becomes more sophisticated, the line between independent adaptation and tacit collusion may blur even further. Lawmakers and regulators must adapt antitrust laws to address this new reality to ensure fair markets. Policymakers face the difficult task of preventing unintended consequences that could harm consumers. Senator Amy Klobuchar introduced the Preventing Algorithmic Collusion Act to address these concerns. This highlights the need for continuous adaptation and vigilance in the face of rapidly evolving technology.