The Spring Meeting is the largest gathering of competition, consumer protection, and data privacy profes­sionals globally, with lawyers, academics, economists, enforcers, journalists, and students from around the world. During the 2026 Spring Meeting, Axinn associates attended thought leadership panels to capture key insights. Their report of the takeaways from four panels discussing enforcement and policy in specific sectors appears below. It covers the panels, “AI, Algorithms, and Antitrust - Enforcement Trends,” “Collusion by Code: Hub-And-Spoke 2.0?,” “Healthcare Pricing Algorithms: Novel or Collusive?,” and “Talk is Cheap? Trends in Information Exchange.”

To read all the articles in the series, click here.

Artificial intelligence (“AI”) is one of the most prominent topics of discussion in any forum. As more companies adopt AI in their day-to-day operations to optimize performance and stay ahead, antitrust enforcers and courts are grappling with AI’s potential impact on competition. AI issues are relevant across various aspects of antitrust, including collusion, merger review, and monopolization, according to the panels of the 2026 ABA Antitrust Spring Meeting.

Using Traditional Antitrust Tools to Address AI Collusion 

Across all four panels, one theme stood out: alleged collusion through AI algorithms and information exchange can be assessed using the same traditional antitrust tools. 

The “Collusion by Code: Hub-And-Spoke 2.0?” panelists noted that analysis under Section 1 of the Sherman Act remains applicable to alleged collusion where competitors supply confidential and non-public information to an algorithm. In discussing how Section 1 applies, the panelists referenced the “Bob” test: if an information exchange would be illegal when done by humans, it remains illegal when accomplished through an algorithm. This concept was reiterated by the “Talk is Cheap? Trends in Information Exchange” panel. There, panelists described the open issue as whether to (i) analyze pricing algorithms and information exchange as part of a larger collusive arrangement under the rule of reason or (ii) treat them as per se unlawful on their own.

Panelists explained that recent filings in the Agri Stats related litigation suggest that enforcers will pursue information exchange among competitors as a per se violation when enforcers believe the evidence shows collusive intent. They described enforcers taking a similar approach in the European Union (“EU”), where exchanges involving competitively sensitive information can be investigated as standalone “by object” (i.e., per se) infringements. Other jurisdictions, including Singapore and Brazil, have yet to establish clear precedent on this issue, but there are cases pending in those jurisdictions that may establish the relevant precedent.

The “Healthcare Pricing Algorithms: Novel or Collusive?” panelists had a similar debate. One panelist argued that the per se rule is applicable under a hub-and-spoke theory because, when competitors use the same algorithmic pricing service, they (as competing “spokes”) are using the service as the “hub” through which they agree to use the same pricing tool that provides the same price recommendations. Others argued that the rule of reason is the more appropriate framework, given that algorithmic pricing vendors are not traditional “hubs” coordinating agreement among competing “spokes,” and, as the courts have long recognized, information exchange can promote competition. The Hub-and-Spoke panel discussed this disagreement as well, with panelists observing that it was difficult to identify any bright lines uniformly recognized by courts and enforcers. Rather, they explained, courts and enforcers look to a number of factors, including the nature of the inputs, the method for generating recommendations, and the availability and use of overrides.

The Healthcare panel noted that enforcers have acknowledged that an updated framework is needed to tackle new issues arising in merger review and monopolization involving algorithms. Panelists cautioned against overly aggressive antitrust enforcement against algorithmic pricing, however, emphasizing that healthcare’s highly customized pricing makes these allegations more nuanced than in other industries. According to the panel, pricing in healthcare is complicated due to the third-party payor model and government regulations. As a result, algorithmic pricing tools offer significantly more value to healthcare companies trying to navigate pricing models and decisions. One panelist provided that such pricing complexities lead to greater price variation and likely more deviation than would be found in a true price-fixing agreement. 

Merger Review: The Rise of Acquihire in AI

AI brings about a new issue in merger analysis: the rise of “acquihire” type transactions. “Acquihire” refers to a transaction where the main purpose is to acquire key technical personnel or talents of a target company, rather than the target company’s products or assets. Acquihire transactions are not always included in merger-review regimes, and they are a rising concern for antitrust authorities. Panelists observed that, in the fast-growing field of AI, technology companies are looking to acquire talent rather than startups as a whole, but the antitrust laws have not caught up to these people-first transactions. 

In the EU, the Commission has faced challenges in the review of acquihire transactions as many have not met the relevant merger control thresholds. To ensure these transactions are reviewed, the Commission has encouraged member states to use their call-in powers to allow transactions not necessarily meeting the turnover thresholds to be reviewed by the EC. This was highlighted in the Microsoft/­Inflection transaction, where Microsoft hired the co-founders of the AI startup Inflection and most of its employees. Panelists of the “AI Algorithms and Antitrust - Enforcement Trends” noted that the transaction did not fall squarely within the jurisdiction of the EU Merger Regulation (“EUMR”) because Inflection’s turnover did not meet the EUMR thresholds. Seven member states referred the case to the European Commission (“EC”) for review, but they later withdrew their referrals. 

The United States has also struggled to review acquihire transactions under the traditional merger-review framework. Notification to the agencies is required only for acquisitions of assets or stock above set thresholds, but the structure of acquihire transactions is such that companies are acquiring talent and licensing technology.  Accordingly, panelists noted, these transactions do not trigger the Hart-Scott-Rodino (“HSR”) filing obligation.

In comparison, the panelists highlighted the powers of the antitrust authorities in the United Kingdom and Brazil to review acquihire transactions. The Competition and Markets Authority (“CMA”) of the United Kingdom can review such transactions through its “share of supply test,” when the businesses have a combined share of at least 25 percent of a good or service in the United Kingdom, and one of the businesses has a UK turnover of at least £10 million. The Administrative Council for Economic Defense (“CADE”) of Brazil can also review such transactions with their ability to call in transactions for review even if they don’t meet the turnover thresholds up to one year after closing. 

Enforcement Against Monopoly Using AI

Panelists also discussed antitrust authorities’ increasing attention to the use of AI to maintain monopoly power, and what tools the authorities may have at their disposal to investigate monopolization cases. 

“AI Algorithms and Antitrust - Enforcement Trends” panelists highlighted the Federal Trade Commission’s (“FTC”) 2023 lawsuit against Amazon, which alleged, among other things, that Amazon used “Project Nessie,” an internal pricing algorithm, to maintain its monopoly. Panelists noted the difficulty of using traditional tools and theories under Section 2 of the Sherman Act in cases challenging AI-related conduct. Accordingly, in the FTC-Amazon case, the FTC challenged Amazon’s conduct not only as a Section 2 violation, but also separately as an unfair method of competition in violation of Section 5 of the FTC Act. 

Panelists explained that, in Europe, authorities have more regulatory and enforcement tools to deploy against major technology companies. For instance, the EC’s Digital Markets Act (“DMA”) authorizes the EC to designate dominant platforms as “gatekeepers,” thereby subjecting them to specific rules of behavior. And the UK has the Digital Markets, Competition and Consumers Act, which authorizes the CMA to designate firms as having “strategic market status.” A designation of strategic market status allows the CMA to impose conduct requirements or intervene in actions by designated firms to preserve competition. For AI firms, this designation will require them to engage more closely with the CMA and ensure the actions they take in the market continue to foster competition, allow for fair dealing, and avoid creating barriers to entry.

In sum, panelists recognized, the rapid pace of AI development and deployment means antitrust rules and enforcement are also in flux. It is therefore critical for companies to actively monitor the changing landscape, continuously refine compliance guidelines, and seek legal counsel.