How Cloud-AI Partnerships Are Rewriting Antitrust Playbooks
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How Cloud-AI Partnerships Are Rewriting Antitrust Playbooks

ByJon Dykstra

AI is no longer built inside a single corporate perimeter. Foundation models now emerge from sprawling arrangements that knit together cloud providers, chip makers, and specialist developers. The same handful of companies trade equity, compute, and distribution, then tell enforcers that nothing resembling a merger has occurred. Regulators, in turn, are starting to ask whether these arrangements simply reflect the cost of training large models, or whether they lock up infrastructure and tilt the market before most users ever see a competing system.

Contents hide
Anatomy of a Modern AI Alliance
Why AI Demands Partnerships
How Partnerships Evolve Over Time
Where Enforcers See Leverage
U.S., U.K., and EU Chart Different Paths
What Deal Lawyers Should Guard Against
Signals To Watch In The Next Year
Sources

Anatomy of a Modern AI Alliance

The clearest example of this new architecture arrived in Nov. 2025, when Microsoft, Nvidia, and Anthropic announced linked strategic partnerships. Anthropic committed to purchase $30 billion of compute capacity on Microsoft Azure, with an option to scale up to one gigawatt of infrastructure, while Microsoft and Nvidia pledged up to $15 billion in combined investment to support Claude’s expansion. Microsoft’s announcement framed the deal as a way to broaden enterprise access to Claude on Azure. Reporting in the technology press, including coverage in TechRadar, underlined a second point: Claude is now positioned as the only frontier model available across all three major clouds.

The structure of the Anthropic deal is emblematic. Rather than a simple acquisition, it layers long-term compute commitments, strategic investment, and tight technical integration. OpenAI, Mistral, and other developers have also relied on deep partnerships and reseller arrangements to reach enterprise customers at scale. For counsel, the relevant question is not whether any single deal is large. The question is how the pattern of overlapping partnerships affects entry, switching costs, and the ability of smaller firms to reach customers on competitive terms.

Why AI Demands Partnerships

Regulators are not surprised that the industry looks like this. The UK Competition and Markets Authority’s work on foundation models describes a sector in which a few firms control critical inputs: high-end chips, global cloud infrastructure, proprietary datasets, and distribution through operating systems and productivity suites. Its 2024 AI Foundation Models update paper and technical annex map dozens of partnerships and investments linking the same small group of large technology companies to most significant model developers.

The economics behind those graphs are straightforward. An OECD background note on competition in the provision of cloud computing services and a CERRE report on competition policy for cloud and AI both stress that compute and storage are highly concentrated, with strong economies of scale and scope. Cloud providers can offer cheap incremental capacity because they already serve large enterprise and public sector workloads. Foundation model developers, by contrast, often lack both capital and established customer pipelines, but they have high-profile models that cloud providers want to integrate into flagship products. These arrangements give each side something the other cannot easily build alone.

The US Federal Trade Commission’s Office of Technology captured this dynamic in a Jan. 2025 staff report on partnerships between cloud service providers and AI developers. The report describes a recurring pattern of equity stakes, multi-year cloud credits, privileged access to new models, and deep technical integration into developer tooling and productivity software. It emphasizes that, taken together, these elements can create durable relationships that look more like joint ventures than ordinary vendor contracts, even when the parties insist they are not merging.

Beyond hardware, these alliances are also driven by the intense scarcity of human capital and proprietary data, two inputs that cannot be commoditized. Cloud providers and large technology firms use partnerships to secure exclusive or prioritized access to highly specialized AI research talent and the developers who deploy models. Similarly, the deals often include privileged access to the incumbents’ vast, model-specific datasets. These proprietary data pools are critical for fine-tuning and deployment-time learning, creating a unique form of value exchange that strengthens the competitive moat around the partnership.

How Partnerships Evolve Over Time

The arrangements themselves are not static. In Oct. 2025, OpenAI completed a restructuring that clarified its relationship with Microsoft. The AI developer converted its for-profit arm into a public benefit corporation, with Microsoft holding a 27 percent stake valued at $135 billion. The agreement extended Microsoft’s intellectual property rights through 2032 and included models that achieve artificial general intelligence, but it also lifted Microsoft’s exclusive cloud rights. As part of the revised terms, OpenAI committed to purchase $250 billion in Azure services, while Microsoft relinquished its right of first refusal to be OpenAI’s compute provider.

The restructuring illustrates how initial arrangements can shift as market conditions and regulatory pressures evolve. What began as exclusive cloud rights became a more flexible but still deeply interconnected relationship. Both parties gained flexibility to work with third parties on certain products, while maintaining core technical and financial ties. These iterative adjustments raise questions about how enforcers should assess arrangements that change over time, particularly when initial exclusivity gives way to modified terms that still preserve significant competitive advantages.

Where Enforcers See Leverage

Enforcers are converging on three clusters of concern. The first is control without formal acquisition. The CMA’s foundation model work and separate inquiries into Microsoft’s hiring from Inflection and its partnership with OpenAI argue that antitrust analysis cannot focus solely on share purchases. Control can also arise from veto rights, exclusive deployment clauses, or de facto dependence on a single provider’s infrastructure. The CMA’s decision on the Microsoft and OpenAI partnership concluded that it did not meet UK merger thresholds, but the 15-month review highlighted how governance rights and technical dependence can shift over time.

The second concern is foreclosure. The European Commission’s Competition Policy Brief on generative AI and virtual worlds warns that incumbents in cloud, search, and productivity software may use partnerships to steer demand toward preferred models and restrict access to datasets, developer tools, or deployment channels. The brief, and commentary that followed, treat chips, cloud infrastructure, data licensing, and key application markets as interconnected layers where control over one can reshape competition in the others.

The third concern is feedback loops across the stack. Recent OECD work on competition in artificial intelligence infrastructure notes that the same firms now dominate advanced chips, cloud capacity, and foundational models. These arrangements can reinforce that position if they make it harder for competing clouds or regional providers to attract promising developers, or if model developers feel they must align with one of a few global platforms to reach enterprise customers at all. For regulators, that raises questions about both short-term foreclosure and long-term structural power. While such arrangements can enable smaller AI developers to access infrastructure they could not build alone, potentially increasing competition, they also create dependencies that may limit future market entry and innovation.


U.S., U.K., and EU Chart Different Paths

The challenge for regulators is that the same partnerships are landing in legal systems that use different jurisdictional triggers and control tests. In the United States, the FTC and the Department of Justice have used their information-gathering powers, congressional pressure, and targeted probes rather than traditional merger challenges. The FTC’s Jan. 2024 orders to Microsoft, Amazon, Alphabet, Anthropic, and OpenAI produced the Jan. 2025 staff report and a public warning that certain AI partnerships could undermine competition by raising switching costs and providing privileged access to sensitive technical and business information.

In parallel, the Justice Department has opened an antitrust investigation into Google’s agreement with Character.AI. That deal gives Google access to Character.AI’s models and talent without a classic acquisition. Reporting on the probe indicates that investigators are asking whether the structure was designed to bypass merger review while still neutralizing a potential rival. The case is at an early stage, but it is one of the first clear examples of US enforcers testing how far existing tools reach into AI alliances.

Political scrutiny has followed. In April 2025, Senators Elizabeth Warren and Ron Wyden sent detailed letters to Microsoft, Google, OpenAI, and Anthropic, asking how much had been paid for AI cloud services, whether the deals granted exclusive licensing rights, and whether the companies planned to acquire their partners. The accompanying press release framed AI alliances as a potential way to circumvent merger law while consolidating power in a market that is still forming.

The UK has taken a different route. Rather than treating each partnership as a free-standing merger question, the CMA has folded AI alliances into a broader strategy for digital competition. Its foundation model update paper and accompanying technical report identify three risks that cut across the sector: powerful incumbents shaping access to key inputs, partnerships that entrench positions across the value chain, and closed ecosystems that limit user choice. Subsequent commentary, including a Macfarlanes analysis of the Microsoft and OpenAI case, argues that even where individual deals fall short of merger thresholds, they may still inform conduct cases, market investigations, or digital markets enforcement under new UK legislation.

On the continent, the European Commission has framed AI partnerships through the lens of its broader digital competitiveness agenda. The generative AI policy brief and speeches from competition officials stress that enforcement will focus on gatekeeper behavior, data access, and interoperability obligations where the Digital Markets Act applies. They also signal that alliances involving designated gatekeepers will receive particular scrutiny, since those firms already control key distribution channels and app stores.

National authorities are moving in parallel. Germany’s Bundeskartellamt convened an expert group on AI and competition in June 2025 to discuss entry barriers around foundation models and lock-in effects in AI value chains. The authority’s contribution to international work on digital markets describes alliances as one factor in those barriers. The picture that emerges from these efforts is fragmented but overlapping: agencies are comparing notes, watching the same deals, and slowly sketching a shared vocabulary for AI-specific competition risks.

What Deal Lawyers Should Guard Against

All of this has direct consequences for how corporate and antitrust counsel approach AI partnerships. The FTC staff report and subsequent commentary flag several recurring contractual features that are likely to attract attention. Equity stakes combined with exclusive or preferred deployment on a single cloud platform will be examined closely, especially where the partner is a leading model developer. Long-term commitments to specific GPU architectures or data center footprints can also raise questions if they effectively prevent a developer from working with rival providers.

Information sharing is another fault line. Joint technical teams, shared safety tooling, or co-developed evaluation datasets can be pro-competitive if they deepen interoperability and improve security. They can also create risks if they provide a conduit for competitively sensitive information that would not otherwise be shared. Both the CMA and the European Commission have signaled that control of training data, user feedback logs, and usage analytics for foundation models will be important in future enforcement. Counsel who draft data access and use clauses as afterthoughts are likely to find those terms scrutinized in any subsequent investigation.

Multi-homing and exit rights deserve similar attention. The Microsoft and Anthropic partnership illustrates how a developer can, at least on paper, retain the ability to work with multiple clouds while receiving significant investment and technical support from one of them. If future agreements tie model improvements, safety features, or downstream developer access too tightly to a single platform, they may raise concerns even when nominal multi-cloud options remain. Deal lawyers should assume that enforcers will read those provisions alongside real-world deployment patterns when assessing competitive effects.

Finally, governance and documentation matter. The FTC report and the growing body of analytical work around it suggest that authorities will look beyond headline terms to how alliances are managed over time. Minutes, board presentations, and internal assessments can show whether a partnership is primarily about scale and efficiency or whether it has gradually become a way to neutralize a competitor or steer an ecosystem. Counsel who treat antitrust as boilerplate in these deals may discover that the most important evidence sits in their own board decks and planning documents.

Signals To Watch In The Next Year

Antitrust agencies are now testing how far existing tools can reach into AI alliances without new legislation. Any move by the FTC or DOJ to convert current inquiries into formal complaints would signal a shift from information gathering to active enforcement. In Europe and the UK, early cases involving cloud infrastructure, app stores, or gatekeeper obligations that touch AI deployments will help define how digital market tools interact with traditional competition law.

Industrial policy and export control will add another layer of complexity. Governments that promote domestic AI champions or restrict access to high-end chips for security reasons are, in effect, shaping the competitive landscape that antitrust agencies are trying to keep open. Work from OECD and CERRE suggests that close coordination between trade, industrial, and competition authorities will be necessary if those objectives are not to cut across one another. For practitioners, the practical lesson is clear. AI alliances are no longer purely commercial arrangements. They are becoming central to how regulators think about market power in the next decade, and they require the same level of antitrust discipline that major mergers have demanded for years.

Sources

This article was prepared for educational and informational purposes only. It does not constitute legal advice and should not be relied upon as such. All cases, sanctions, and sources cited are publicly available through court filings and reputable media outlets. Readers should consult professional counsel for specific legal or compliance questions related to AI use.

See also: Courts Tighten Standards as AI Errors Threaten Judicial Integrity

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