The Emergence of AI-Powered Pricing and its Associated Antitrust Risks

By Ekin Hizli

AI-Powered Algorithmic Pricing

Algorithmic pricing refers to an automation process in price setting for firms through the use of programmes. For many businesses, this automation is appealing as it simplifies their price response to dynamic market conditions, thus enhancing efficiency in an important business decision. As artificial intelligence tools increasingly permeate the workplace, they also contribute to automation processes in pricing decisions. In this context, the case of AI-powered algorithmic pricing arises, posing a unique antitrust challenge for authorities.

Differences in Algorithmic Pricing Models

The distinction between ‘price monitoring algorithms’, ‘dynamic pricing algorithms’, and ‘personalised pricing algorithms’ becomes an important consideration in understanding the potential antitrust risks algorithmic pricing cases pose.

‘Price monitoring algorithms’ operate by tracking prices from other companies. ‘Dynamic pricing algorithms’ produce recommendations or direct price adjustments according to prices from other companies and market conditions including market demand. ‘Personalised pricing algorithms’ base price recommendations on individual consumer characteristics, for example an individual’s willingness to pay for a particular good.

In particular, price monitoring and dynamic pricing algorithms are widely used by businesses as Alejandro Guerrero Perez and others outline in Global Competition Review that European Commission 2017 E-Commerce Sector Enquiry concluded ‘nearly one-third of all retailers analysed effectively the online prices of competitors using automatic software programs’. In comparison, the article suggests that personalised pricing algorithms are less commonly adopted, considering a 2021 OECD background note on algorithmic pricing. However, this trend continues to evolve considering ‘the increasing availability of consumer data and the development of technology’, which can be linked the integration of AI mechanisms and the further complexity this creates in algorithmic pricing processes.

Antitrust Risks

This section considers two important types of anti-competitive behaviour that can arise in relation to algorithmic pricing cases and particularly the integration of AI into these processes: ‘price collusion’ and ‘unlawful price discrimination’.

Price collusion could occur when pricing algorithms learn to coordinate prices in the absence of human intervention or clear agreement between firms, as Ai Deng explains in the DLA Piper Pricing Rules Podcast. This creates a unique antitrust concern as algorithms rather than humans are positioned as the decision makers in this context. While price collusion through AI-powered algorithmic pricing is a clear possibility, Deng emphasises that recent studies show limited empirical evidence that AI mechanisms truly collude pricing decisions without human input lead to collusion in pricing decisions ‘autonomously’. On the other hand, Avigail Kifer suggests that higher prices resulting from algorithmic pricing might indicate an elimination of human error within the pricing process rather than such anti-competitive conduct.

Unlawful price discrimination could result from personalised pricing algorithms where AI mechanisms generate recommendations ‘based on protected or opaque consumer attributes’, as recognised by Martin Spann and others in their ‘Algorithmic pricing: Implications for marketing strategy and regulation’ paper. In this case, the inputted consumer data is protected under data privacy law, creating a data privacy concern in addition to the antitrust risks. Accounting for distinct consumer characteristics, AI-powered personalised pricing mechanisms might present a non-uniform set of prices for consumers based on an opaque algorithm.

Recent US Cases

A particular case in the US that illustrates algorithmic collusion concerns involves MultiPlan, Inc. and numerous health insurance companies, where it was alleged that the firms colluded ‘to fix reimbursement rates for out-of-network healthcare services via MultiPlan’s algorithm’ as outlined by Brian Boyle and others at DLA Piper. According to plaintiffs, MultiPlan ‘collects confidential pricing data from insurers’, utilising the algorithm to set on reimbursement rates and resulting in insurers adopting these rates. Their suggestion was that this negatively impacted competition through inhibiting reimbursement rates, ultimately resulting in harm to healthcare providers.

More recently, having reached a preliminary solution, In re: RealPage, Inc. Rental Software Antitrust Litigation (No. II) exemplifies a class action in the US. In this case, the allegations surrounded the use of a shared algorithm by numerous property managers and landlords through the RealPage revenue management software. The claims suggested that the algorithms accessed ‘non-public, competitively sensitive data from participating landlords’ with resulting rent recommendations utilised by ‘competing apartment complexes’ and caused a coordinated rise in rents, discussed by Dr. Elena Wiese at Hogan Lovells.

Conclusion – Implications for the EU and the UK

To conclude, this article aimed to provide an overview on algorithmic pricing practices, integration of AI in pricing mechanisms, and relevant challenges in the antitrust domain. Moving forward, the US experience in algorithmic pricing cases carry implications for EU and UK regulatory priorities. As Tobias Klose at Freshfields highlights, Deputy Director-General Linsey McCallum affirmed ongoing investigations on the EU level related to algorithmic pricing, positioning this ‘as an enforcement priority’. The Competition & Markets Authority 2025 Policy Paper on dynamic pricing indicates that the UK will continue to monitor market practices on pricing, utilising decisions in consumer law and fines where possible harm for consumers and the larger economy arises. In its entirety, these algorithmic pricing cases represent a challenge for antitrust frameworks on an international scale, considering complexities created by the potential for AI. This illustrates the importance of rising policy and enforcement priorities in this domain.

Sources:

• https://competitionandmarkets.blog.gov.uk/2024/11/08/pricing-algorithms-and-competition-law-what-you-need-to-know/

• https://www.cliffordchance.com/insights/resources/podcast-library/all-podcasts/aba-spring-meeting-roundup-2025.html

• https://globalcompetitionreview.com/guide/digital-markets-guide/fifth-edition/article/eu-competition-authorities-zero-in-antitrust-risks-of-algorithmic-pricing

• https://www.sciencedirect.com/science/article/pii/S0167811625000473

• https://www.dlapiper.com/en/insights/podcasts/pricing-rules-podcast/2024/do-pricing-algorithms-collude

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• https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5515940

• https://www.dlapiper.com/en-gb/insights/publications/2025/11/antitrust-and-ai-plaintiffs-enforcers-and-legislatures-take-aim-at-alleged-ai-driven-collusion

• https://www.hoganlovells.com/en/publications/update-on-algorithmic-pricing-in-competition-law-what-you-need-to-know

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• https://www.oecd.org/en/publications/algorithmic-pricing-and-competition-in-g7-jurisdictions_f36dacf8-en.html

Edited by Artyom Timofeev