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Authors: John Eichlin, Sebastian Plötz, Tara Rudra, Bhavishya Barbhaya
As the use of AI and algorithms across industries continues to rise, authorities are actively monitoring – and increasingly investigating – AI-powered pricing tools, amid concerns about their potential to facilitate collusion.
Many competition authorities, including in Germany and China, have emphasised algorithmic pricing as a key enforcement priority and numerous authorities have published reports on the topic. The UK’s CMA has since set out its views on algorithmic collusion and AI, detailing its active screening for algorithmic collusion, and its expectations in terms of steps businesses should take to mitigate risk after announcing an investigation on 2 March 2026 into the suspected sharing of competitively sensitive information among three competing hotel chains, using a third party data analytics provider. The European Commission has also announced that several cases targeting algorithm-based anticompetitive behaviour are in the pipeline.
To date, however, the US DoJ’s landmark settlement with RealPage Inc. – a provider of revenue management software for the multifamily rental housing industry – over the facilitation of anticompetitive coordination among landlords through algorithmic pricing software, provides one of the first concrete precedents for where the boundaries are being drawn in the context of algorithmic pricing.
In this post, we consider the impact of these latest developments and what they mean for businesses using algorithmic pricing tools.
Authorities are clear that the use of algorithms or AI in pricing is not inherently problematic. However, various factors can raise the risk of coordinated outcomes between companies, including the relevant market, design of the algorithm and software, use of a shared data provider and overall governance of the use of the algorithm.
Competitors don't need to communicate directly for the use of an algorithm to be problematic. Enforcement agencies have demonstrated their willingness to apply the "hub-and-spoke" conspiracy theory to algorithmic pricing – as seen in the EU's Eturas case and the US RealPage action. In these scenarios, the "hub" is the AI platform or software provider that collects and processes competitively sensitive information, and the "spokes" are the competing businesses that submit their data and receive pricing recommendations.
This arrangement can facilitate the same anticompetitive harm as traditional collusion – particularly when the algorithm uses real-time competitor data to generate coordinated pricing outputs, or where the tool monitors and incentivises adherence to what authorities could characterise as an unlawful pricing agreement.
Even without human communication or explicit agreement, algorithms that react predictably to certain market conditions may be problematic, e.g. where there is a shared understanding between businesses of how the algorithm operates. Regulators are also increasingly exploring (and prioritising - see e.g. new research and guidance published by the CMA) how agentic AI systems, which operate with greater autonomy, could learn to reach coordinated outcomes – including in setting or influencing prices – even in the absence of human intent to collude.
The proposed US settlement between the DoJ and RealPage gives insight into how algorithmic pricing cases may be approached going forward. RealPage's software collected non-public data from competing landlords – including rental prices from executed leases, lease terms, and future occupancy – and uses algorithms to generate rent pricing recommendations for all users.
The DoJ filed a civil antitrust lawsuit against RealPage in August 2024, alleging that this arrangement enabled a hub-and-spoke conspiracy between competing property managers and landlords. They argued that the pricing recommendations from RealPage’s software was akin to price-fixing when combined with technical measures monitoring and incentivising their compliance with these recommendations.
The settlement leaves RealPage’s business model intact but severely restricts its collection, use and sharing of data, including a prohibition on:
Further, the settlement restricts RealPage’s ability to incentivise users to abide by its generated pricing recommendations. It requires RealPage to change some of its software features (amongst others for features on auto-accept and automated price recommendations) to encourage independent pricing decisions by users.
Although the agreement settles the DoJ’s litigation against RealPage, the case continues against the property managers and landlords. The RealPage settlement will not be the final word on algorithmic pricing and AI-facilitated coordination in the US. California and other states have passed laws restricting the use of algorithmic pricing tools (predominantly relating to the rental housing market).
In terms of sectors affected, algorithmic pricing enforcement extends far beyond real estate – or indeed, hotels as we see in the UK. In the US, claims have arisen in healthcare, agriculture, and hospitality. Ride-hailing, retail and the broader platform economy are equally in the frame, with investigations in Spain targeting Uber, Cabify, and Bolt, and Germany's FCO extracting EUR 59 million from Amazon over its automated price control mechanism. No sector that relies on algorithmic or AI-driven pricing tools is immune.
Authorities, including the UK’s CMA, are clear that “businesses remain responsible for the outcomes of AI-driven pricing and commercial decisions” and must proactively “understand, test and govern” the tools they deploy.
While precedent on algorithmic collusion and automated pricing is still rare, the following principles established in the RealPage settlement will likely serve as foundational elements of antitrust compliance going forward:
We set out three key takeaways for businesses when using AI or algorithm-powered pricing tools going forward:
16 March 2026
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