By Sauntharya Manikandan, J.D. Candidate, 2026
Introduction to Algorithmic Pricing
If you are a consumer in today’s world, you have likely encountered AI algorithmic pricing. Think about the times when your Uber ride cost more due to surge pricing or when Southwest Airlines notified you about discounted tickets for a specific week or month. But how exactly does algorithmic pricing work?
Unlike traditional human-driven pricing strategies or spreadsheet-based analysis, AI algorithmic pricing leverages real-time data and automation to respond dynamically to market conditions. The algorithm is designed to “link” with the pricing strategies of the company’s competitors and adopt a “tit for tat” approach —whenever a competitor decreases their price, the algorithm adjusts the company’s own prices accordingly, and vice versa. At first glance, this may seem beneficial, especially if it leads to lower prices for consumers. However, a closer look reveals a different reality: such algorithms can facilitate collusion, whether explicit or tacit.
Explicit collusion, also known as the “messenger model, is when representatives from competing companies agree to use an algorithm to coordinate their pricing strategies. Tacit collusion, also known as the “hub and spoke model,” occurs when competitors use a shared pricing algorithm to align prices. Instead of explicitly coordinating, firms independently contract with a third-party service that sets prices on their behalf. This indirect arrangement enables competitors to adopt similar pricing strategies without formal agreements.
Legal Implications of Algorithmic Pricing
While algorithmic pricing is not inherently illegal, it can facilitate forms of collusion that raise significant legal concerns. Antitrust enforcement plays a crucial role in addressing explicit algorithmic collusion, particularly under Section 1 of the Sherman Act. This provision prohibits agreements that unreasonably restrain trade, allowing enforcement agencies and plaintiff-side lawyers to take legal action against companies involved in such practices.
However, tacit collusion poses a far greater challenge. Unlike explicit collusion, tacit collusion does not involve direct communication or agreements between competitors. Since the Sherman Act requires proof of an explicit agreement to establish liability, companies that achieve tacit collusive outcomes through algorithmic pricing may evade traditional antitrust enforcement. This gap in legal oversight has prompted regulatory bodies to take a more proactive stance, seeking new ways to address the competitive risks posed by algorithmic pricing.
In a 2024 joint statement, the U.S. Department of Justice (DOJ), the Federal Trade Commission (FTC), the U.K. Competition and Markets Authority, and the European Commission pledged to remain “vigilant” about “the risk that algorithms can allow competitors to share competitively sensitive information, fix prices, or collude on other terms or business strategies in violation of our competition laws.” Regulatory bodies have also recently taken legal action against companies employing tacit algorithmic pricing strategies.
In September 2023, the FTC, along with 19 states, filed a lawsuit against Amazon, alleging that the company used three different algorithmic pricing models to sustain its monopoly power. A key focus was on a secret algorithm known as Project Nessie, which Amazon used to manipulate pricing. According to the complaint, the algorithm raised Amazon’s prices to test whether competitors would follow suit. If competitors matched the increase, Amazon maintained the higher prices; if they did not, the algorithm automatically lowered prices again. The FTC claims that this strategy allowed Amazon to generate over $1 billion in additional revenue by influencing market-wide pricing trends. The lawsuit is scheduled to go to trial in October 2026.
In August 2024, the DOJ, along with 8 other states, filed a lawsuit against RealPage Inc., a property management software company, alleging that competing landlords entered into a vertical agreement with RealPage and agreed to share their nonpublic sensitive data for use in RealPage’s algorithmic pricing model. This arrangement allowed landlords to coordinate and artificially inflate rental rates. The DOJ amended the complaint in January 2025 to include the names of six major landlords who participated in this scheme with RealPage.
The recent decision in U.S. v. Google provides a significant precedent that may influence the outcomes of these cases. Judge Mehta ruled that Google had illegally maintained monopoly power through exclusive agreements that prevented competition. This ruling suggests that courts are willing to scrutinize algorithmic pricing practices that contribute to market dominance and restrict competition. If the courts follow similar reasoning, both Amazon and RealPage may face heightened legal risks. The remedies proposed in the Google case, including potential divestitures, indicate that structural changes could be considered in these cases as well, potentially forcing Amazon or RealPage to alter their pricing models significantly.
Conclusion
The widespread adoption of algorithmic pricing has significant implications for businesses, consumers, and policymakers. Although these algorithms provide efficiency and market responsiveness, they also pose new challenges for antitrust enforcement. Without updates to antitrust laws, increased regulatory enforcement, and enhanced monitoring of algorithmic pricing systems, the risks of collusion and anti-competitive behavior will continue to escalate, potentially harming consumers and market competition.