Dentons US LLP

01/10/2025 | News release | Distributed by Public on 01/10/2025 10:38

AI trends for 2025: Competition and antitrust

January 10, 2025

In 2025, several trends are anticipated in the realm of competition law enforcement as it relates to artificial intelligence (AI), including:

Continued scrutiny from global competition regulators and emergence of AI regulations

Aside from attempts to catch or call in "killer acquisitions", regulators increasingly scrutinize "killer collaborations" between tech giants and start-ups with foundational AI (large language) models, suspecting a risk to block rivals from accessing new critical AI inputs (e.g. data, cloud infrastructure and GPUs) ("foreclosure"). Some regulators even try to assert jurisdiction over the hiring of "key personnel".

After decades of politically neutral and methodologically consensual antitrust enforcement, regulators around the globe are increasingly subject to political pressure or beginning to deviate from orthodoxy to pursue industrial policy goals or protectionist objectives (e.g. "national champions").

Resources for classic ex-post enforcement of abusive conduct being scarce, the EU has introduced a series of ex-ante regulations that include provisions on the competitive conduct of the companies in scope, in particular "gatekeepers" (DMA, DSA, AI Act, etc.). The designation of companies as gatekeepers and other regulatory threshold features is expected to trigger litigation.

Data-rich companies with dominant positions or significant market power that resort to conduct such as discriminatory self-preferencing or biased targeted pricing, or that breach privacy/data rules, may become subject to ex-post enforcement even beyond the scope of the ex-ante regulations mentioned above.

Algorithmic collusion

Algorithmic collusion is a growing concern among regulators and lawmakers. Competition law historically distinguishes between unlawful collusion and lawful parallel conduct (bizarrely called "tacit collusion"). Adapting own prices to those of competitors based on independent intelligence is lawful, while a collusive understanding between competitors to align prices is unlawful. Algorithms that monitor and adjust prices push that distinction to its limits.

The US Preventing Algorithmic Collusion Act of 2024 aims to address gaps in existing laws by banning the use of algorithms trained on non-public competitor data, imposing disclosure and auditing requirements and establishing presumptions of illegal price-fixing in certain algorithmic contexts.

US and EU regulators are scrutinizing cases where competitors use shared algorithms to align prices. US lawsuits like those against RealPage and Yardi Systems involve allegations that algorithms were used to fix rental prices by analyzing and sharing non-public competitor data, with regulators claiming that algorithms enable or enforce a tacit agreement between competitors without explicit communication.

The DOJ has emphasized that even tacit agreements facilitated by algorithms, such as adhering to pricing recommendations based on competitors' shared data, can violate antitrust rules. Less radical EU guidelines stipulate that the shared use of algorithms relying on sensitive pricing information could be an "object" infringement and even algorithm providers could be held liable if their tools foreseeably facilitate collusion.

Companies using advanced AI systems should proactively prevent them from independently developing collusive behaviors, raising questions about liability in the absence of direct human contact. This has prompted calls for more proactive auditing and transparency measures to prevent inadvertent breaches ("looking under the hood").

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  1. "M&A in AI: 2022-2024," Aventis Advisors ↩