To Compete Or Not to Compete: A Legal Question
Jay Fort
Associate Editor
Loyola University Chicago School of Law, JD 2026
Today, federal and state antitrust laws are as important as ever. However, modern courts struggle to apply the traditional interpretation and application of antitrust law to modern technology and related anti-competitive practices. This is particularly true in the realm of emerging technologies, where algorithms, automation, and artificial intelligence increasingly dominate. As a result, regulators face a host of unique challenges in an increasingly interconnected, data driven, and automated era. From business to finance, healthcare to housing, the importance of antitrust and competition law cannot be easily understated.
Historical purpose of competition law
Traditionally, U.S. (and subsequent international) antitrust laws trace their historical roots and regulatory authority to the Constitution, particularly Article I, Section 8, Clause 3, the “Commerce Clause.” Additionally, the Sherman Act (1890) , and later the Clayton Act and FTC Act of 1914 were enacted to support, and extend pro-competitive goals. Congress recognized the necessity of reigning in the power of established and rapidly emerging industry giants, whose tendency towards monopolistic and oligopolistic market consolidation severely distorted competition, harming markets, consumers, and competitors alike.
High-tech consolidation: modern competition challenges
In 2026, modern forms of consolidation not only persist, but are rapidly approaching critical mass. From traditional challenges of corporate mergers and acquisitions; monopolistic or conspiratorial behavior of industry giants, algorithmically driven social media platforms, personalized “smart” technology, and continuous data exchanges, the wave of nonstop consolidation presents ever evolving, and novel, challenges for today’s regulators.
For better or worse, today’s digital marketplace(s) is vastly different than the markets and backdrop in which antitrust statutes were enacted. As a result, modern regulators grapple with a difficult challenge: how to adapt the U.S.’s historic antitrust statutory frameworks, which may be ill-equipped to address the modern, competitive dynamics of the digital marketplace.
Powers of prediction: dynamic and algorithmic pricing
One area which has become the subject of increasing relevance and review in modern federal and state antitrust scrutiny is algorithmic and dynamic pricing. Dynamic pricing is defined as pricing which fluctuates depending on certain conditions, set by an algorithm using personal data.
Broadly, dynamic pricing can be differentiated from algorithmic pricing, in which models vary greatly based on general, non-personalized data, including supply and demand, time of day, or general market conditions. As algorithms become increasingly integrated across the landscape of commercial transactions and industries, prohibited practices, like price-fixing, collusion to distort prices, and other anti-competitive practices appear to be implicated.
RealPage case study
Perhaps the most notable modern case at the intersection of algorithm and antitrust concerns is U.S. v. Real Page. In this case, RealPage, a property management software and analytics company, managed twenty-four million units across North America, Asia, and Europe. Powered by AI, Real Page utilized a rent-setting software for landlords, real-estate companies, and independent firms adopting third-party revenue-management tools. Consequently, the Department of Justice (DOJ) raised concerns of rent gouging and rental market manipulation, later filing suit along with nine state Attorneys General, claiming Real Page violated Sections 1 and 2 of the Sherman Act.
The DOJ also alleged that RealPage’s software facilitated a series of illegal actions, including: coordinating rent increases between competing landlords; forecasting optimal rent pricing; and recommending a price-change synchronization scheme which inflated rental prices for properties.
Further, the DOJ alleged that the RealPage’s software system was essentially a “hub-and-spoke cartel,” with RealPage functioning as the hub and the landlords as spokes. It suggested that this held true despite its modern, aberrant features, in relation to traditional signs of anticompetitive coordination, such as RealPage’s lack of “express intent” to price fix, as the system’s algorithm performed the coordinating function in its stead. The complaint further found that RealPage’s own express admissions – stating that its tools were intended to encourage increased pricing across the industry at large – were further evidence of the company’s unlawful intent.
In November 2025, the DOJ announced a proposed settlement. Arguably, the consent decree was comprehensive, including: banning the use of competitors’ nonpublic data for pricing; capping model training with active lease data; geographic restrictions on model granularity; redesign/removal of features that align pricing or limit price decreases; ban on market surveys and sharing sensitive data in meetings; appointment of a court monitor; and RealPage’s cooperation in ongoing litigation against property management companies. However, several of the suit’s state plaintiffs have yet to declare their own next steps.
Unfortunately, there remains an insufficient baseline of judicial understanding, interpretation, and consistent application. Courts continue to cite the challenges of applying older statutes to modern technology, including novel algorithmic pricing. Following RealPage, factually similar cases have been filed challenging the use of pricing algorithms, with courts reaching mixed conclusions regarding how to evaluate the application of algorithmic data practices, as a matter of law.
Federal and state collaboration: a modern approach
Today, some states have picked up the mantle where the federal courts continue to struggle. For example, California, New York, and Connecticut passed statutes which appear intended to expand the definition of collusion more broadly than the Sherman Act. Although diverging in some areas, the statutes share a common objective: to legally ban certain algorithmic pricing and commercial practices which may elude the scope of current antitrust jurisprudence. Additionally, New York passed an unprecedented statute requiring business entities to disclose when algorithms are used to set personalized “dynamic” prices for consumers based on the consumer’s personal data, commonly known as “surveillance pricing.
These are a few examples of states rising to meet the demands of the moment. Such insights reveal important possibilities for a new era of competition and consumer protection based on federal and state partnership. In light of today’s challenges, we should embrace a new era of regulatory enforcement, rooted in a strong legacy of U.S. antitrust law and jurisprudence, updated for the demands of a modern economy and the challenges of ever emerging technology. The tools are here if we choose to use them well. I hope we will.