Setting The AI Standard For Algorithmic Pricing In The U.S.: Per Se Or Rule Of Reason?

While algorithmic pricing tools can enhance market efficiency and competition, concerns persist about their potential to facilitate collusion. Antitrust enforcers in the United States are increasingly focusing on the use of algorithmic pricing mechanisms by competitors. This article examines the aggressive stance of the Department of Justice Antitrust Division (DOJ) and the Federal Trade Commission (FTC) on algorithmic price-fixing, which seek to treat such practices as per se illegal under the Sherman Act. Through an analysis of early key civil cases like RealPage and Cendyn, the article explores the government's arguments for considering algorithmic pricing as concerted action, and argues that courts should continue applying the Rule of Reason to assess the nuanced impacts of these technologies.

I. Introduction

Artificial intelligence (AI) is poised to profoundly transform the global economy. While AI may spur a technological revolution that has the potential to supercharge productivity,¹ AI can also present an array of risks.² Government enforcers around the world have begun to grapple with the challenges presented by AI, and antitrust and competition enforcers are no exception. In the United States, dual competition regulators, the Department of Justice Antitrust Division (DOJ) and the Federal Trade Commission (FTC), are taking particular aim at competitors' use of algorithmic pricing mechanisms. Claiming that "[p]rice fixing using AI is still price fixing," the agencies have promised that prosecutors will seek even harsher penalties where AI technology is used to advance collusive crimes.³ Even groups of private plaintiffs are filing civil lawsuits, alleging that competitors who act on the suggestions of algorithmic pricing programs are no more than AI-assisted colluders.

The DOJ and FTC have responded to these lawsuits by filing aggressive Statements of Interest, asserting that mere use of pricing algorithms by competitors should be treated as illegal per se. But courts seem justifiably skeptical of this approach. After all, using algorithmic pricing mechanisms and agreeing with competitors to price according to those mechanisms are distinct factual scenarios, and algorithmic pricing tools can have a host of procompetitive benefits. While it should not be legal for competitors to "task" AI with otherwise collusive activities, the use of AI should not be allowed to per se supplant the legal prerequisite of a conspiratorial agreement.

II. Innovative Algorithmic Pricing Models

The term "algorithmic pricing practices" generally refers to the use of predefined, rule-based algorithms that can analyze market data (such as cost, competitor prices, and demand) and suggest or even automate a company's pricing decisions. Algorithms can use different parameters to achieve different ends, and can analyze and adjust pricing recommendations based on real-time competitive conditions.⁴ While some algorithmic models are capable of utilizing only a company's own or public data sources to generate strategies, many models rely on the sharing of competitors' information through a third-party algorithm. Typically, these third-party programs will not provide their users with raw competitive data, but only with recommendations based on their completed analyses.

AI and algorithmic pricing practices are proliferating and evolving quickly, often for procompetitive reasons. These tools can aid in price discovery, which allows companies to better engage in price competition. Computers are capable of ingesting and analyzing vastly larger quantities of data than a person attempting to conduct similar analysis. Algorithms are also capable of quickly adjusting recommended prices in response to market fluctuations, which can ensure that pricing is more competitive. And algorithms are also potentially able to reduce human error or bias in the analysis of market conditions, leading to more accurate pricing.⁵

Yet, competition enforcers are concerned that algorithmic pricing mechanisms could be used to facilitate explicit or tacit price-fixing. Some believe that algorithm use will lead to higher, more uniform market prices as computers signal and engage in parallel pricing moves. And as advancement in generative AI technology merges with algorithmic models, the fear is that collusion will inevitably occur autonomously - that AI-enabled algorithms will independently "conspire" without human instruction or intervention.⁶

III. Traditional Legal Framework in the U.S

Pure information exchanges between competitors, even those involving competitively sensitive pricing data, have traditionally been analyzed under the Rule of Reason standard.⁷

This is due to the courts' long-standing recognition⁸ that the sharing of "information about prices, costs, capacity and availability can benefit [market participants], by allowing markets to function more efficiently, intelligently and competitively[.]"⁹

But courts also recognize that information exchanges (particularly those involving competitively sensitive information) can be used to effectuate the kinds of agreements between competitors traditionally treated as illegal per se (including price fixing agreements).¹⁰ Where no direct evidence of an agreement exists, plaintiffs can nevertheless invite an inference of conspiracy by proving a pattern of parallel competitive conduct supported by various "plus factors." Under certain conditions, courts have considered exchanges of price information among competitors to be a plus factor supporting the inference of an anticompetitive agreement. In determining whether such an inference is appropriate, courts have considered, for example, the role and responsibility of the persons engaging in the exchanges,¹¹ the flow of information to decision-makers,¹² the temporal proximity of the information exchanges to pricing decisions,¹³ and proof that the exchanges had an impact on pricing.¹⁴

IV. Private Civil Cases

In the United States, the most prominent battle over algorithmic pricing software programs has taken place in real estate markets, starting with In re RealPage, Inc., Rental Software Antitrust Litig. (No. II)¹⁵ in 2023. There, renters of multifamily and student housing filed dozens of class action lawsuits against RealPage Inc., a company that developed and sold a suite of revenue management software ("RMS") to property owners, operators, and managers. RealPage's clients submitted their rental pricing and supply data to fuel RealPage's price optimization RMS algorithms, and RealPage produced unit-specific, daily price recommendations for their clients. RealPage promised its clients that implementing its recommendations would "outperform the market" by achieving "both short-and long-term goals of increasing revenues by raising rents."¹⁶ However, plaintiffs did not merely allege that competing market players used RealPage's software - plaintiffs alleged that...