Authority of the Attorney General to Investigate Corporate Price Fixing

Price fixing, one of the most serious antitrust violations, occurs when competing firms agree to set prices rather than competing independently. Traditional price-fixing conspiracies involved direct communication between competitors, such as meetings, phone calls, or written agreements. However, the emergence of algorithmic pricing software has created new mechanisms through which competitors can achieve coordinated pricing outcomes without direct communication. Companies in the rental housing market, hotel industry, and grocery sector have increasingly adopted algorithmic pricing tools that use shared data, including competitors' pricing information, to optimize prices in real time.

The Minnesota Attorney General's investigation into algorithmic pricing practices was prompted by reports that major landlords in the Twin Cities area were using a common pricing algorithm, RealPage's YieldStar, which collects nonpublic pricing and occupancy data from competing landlords and recommends rental rates based on the aggregated data. Similar concerns have been raised about algorithmic pricing in the grocery market, where retailers use AI-powered tools to set prices based on competitor data and demand signals. These practices raise fundamental questions about whether the use of a common pricing algorithm constitutes an "agreement" or "conspiracy" sufficient to establish a price-fixing violation.

Minnesota's antitrust law, codified in Minnesota Statutes Chapter 325D, prohibits contracts, combinations, and conspiracies in restraint of trade. Like the Sherman Act, the statute requires an "agreement" between two or more entities. The central legal question in algorithmic pricing cases is whether the independent decision by multiple competitors to use the same pricing algorithm, which processes shared data and recommends similar prices, constitutes an agreement for antitrust purposes.

The traditional legal standard for agreement in antitrust law requires more than mere parallel pricing behavior or conscious parallelism. Under the Supreme Court's framework in Bell Atlantic Corp. v. Twombly (2007), plaintiffs must plead facts suggesting an actual agreement rather than independent but similar conduct. However, the algorithmic pricing context may satisfy the agreement requirement through several theories. First, the competitors' agreement to share nonpublic pricing data with a common intermediary (the algorithm vendor) may itself constitute a hub-and-spoke conspiracy, with the vendor as the hub. Second, the competitors' delegation of pricing authority to a common algorithm may function as a tacit agreement to coordinate prices, particularly if the competitors know that their competitors are using the same algorithm and that the algorithm's recommendations are based on shared data.

The Attorney General's investigative authority includes the power to issue civil investigative demands (CIDs) under Minnesota Statutes Section 8.31. CIDs compel the production of documents, answers to interrogatories, and oral testimony, and are a critical tool for gathering evidence of price-fixing arrangements. The opinion analyzes the scope of CID authority, concluding that it extends to algorithmic pricing investigations and that the Attorney General may compel production of the algorithm's source code, input data, pricing recommendations, and adoption rates among competitors. The opinion also addresses the parens patriae authority under federal antitrust law (Section 4C of the Clayton Act), which allows state attorneys general to bring actions on behalf of natural person consumers for treble damages.

The Attorney General has robust investigative and enforcement authority to address price-fixing arrangements facilitated by algorithmic pricing tools. The use of a common pricing algorithm that processes shared competitor data and recommends coordinated prices may constitute an agreement in restraint of trade under state antitrust law. The Attorney General should pursue investigations through civil investigative demands, coordinate with other state attorneys general and federal agencies, and develop enforcement strategies tailored to the unique characteristics of algorithmic price coordination.

This opinion signals an aggressive enforcement posture toward algorithmic pricing practices. Companies that use pricing algorithms should evaluate whether their tools incorporate competitor data in ways that could facilitate coordination and should consult antitrust counsel before sharing nonpublic pricing information with algorithm vendors. Algorithm vendors should assess whether their products function as mechanisms for price coordination and should implement safeguards to prevent the aggregation and dissemination of competitively sensitive data. Tenants, consumers, and their attorneys should be aware that algorithmic pricing practices may violate antitrust law and that state enforcement actions may provide avenues for recovery. The opinion also contributes to the growing national conversation about the intersection of artificial intelligence and antitrust enforcement.