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Meta's $14.8 billion Scale AI deal exposes antitrust vulnerabilities

The structured investment allows Meta to gain de facto control while avoiding traditional merger review.

Luis Rijo

• 8 min read
Mark Zuckerberg surfing digital AI wave as Meta's $14.8B Scale AI deal faces antitrust scrutiny concerns
Mark Zuckerberg surfing digital AI wave as Meta's $14.8B Scale AI deal faces antitrust scrutiny concerns

Meta's $14.8 billion investment in Scale AI announced on June 13, 2025, represents a sophisticated attempt to acquire critical AI infrastructure while circumventing traditional merger oversight, according to analysis by industry observers. The deal's complex structure involving minority equity stakes, exclusive licensing agreements, and coordinated talent transfers has drawn attention from antitrust experts who warn it could violate multiple Federal Trade Commission merger guidelines.

Scale AI, a leading data-labeling firm that provides essential training data for artificial intelligence models, has become a strategic chokepoint in the AI ecosystem. The company's human-in-the-loop data pipeline processes information for major AI laboratories including OpenAI, xAI, Google, Microsoft, and Anthropic. Together, these organizations plan to spend over $250 billion on AI-related capital expenditures in 2025.

Scale AI, a leading data-labeling firm that provides essential training data for artificial intelligence models, has become a strategic chokepoint in the AI ecosystem. The company's human-in-the-loop data pipeline processes information for major AI laboratories including OpenAI, xAI, Google, Microsoft, and Anthropic. Together, these organizations plan to spend over $250 billion on AI-related capital expenditures in 2025.

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Summary

Who: Meta Platforms executed a $14.8 billion structured investment in Scale AI, a data-labeling startup led by CEO Alexandr Wang, while competitor AI laboratories including OpenAI, xAI, Google, Microsoft, and Anthropic have scaled back relationships with Scale following the announcement.

What: The transaction involves Meta acquiring 49% non-voting shares in Scale AI, securing exclusive access to the company's data-labeling pipeline, hiring Scale's CEO and key personnel for Meta's superintelligence unit, and maintaining Wang on Scale's board while he leads Meta's AI laboratory.

When: The deal was announced on June 13, 2025, with Drayton D'Silva's antitrust analysis published on June 20, 2025, and Patrick Boyle's video commentary released June 27, 2025, highlighting concerns about the structure's compliance with FTC merger guidelines.

Where: The transaction affects the global AI ecosystem, with Scale AI's data-labeling services used by major laboratories worldwide and Meta's infrastructure investments supporting AI development across multiple geographic markets and regulatory jurisdictions.

Why: Meta structured the investment to gain control over critical AI infrastructure while avoiding traditional merger review, reflecting broader industry beliefs that controlling data pipelines, compute access, and training workflows represents the new battleground for competitive advantage in artificial intelligence development.

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According to analysis by Drayton D'Silva, the Meta-Scale AI transaction effectively dismantles Scale as an independent entity and reassembles its core capabilities within Meta's corporate structure. "Instead of outright acquisition that would trigger an FTC review, Meta split a Big Beautiful Deal into multiple smaller components that disassembled Scale, the independent company, and then reassembled Scale inside Meta's empire," D'Silva wrote in his June 20 analysis.

D'Silva compared the strategy to medieval cathedral relocations: "There are stories of medieval masons moving entire cathedrals from one site to another, brick by brick, to a less vulnerable location. They would number every block, cart each brick to a new site, and rebuild the structure exactly as before. The reconstructed cathedral is in a new location but in a spiritual and metaphysical sense, it is the same cathedral."

The deal structure includes four interconnected components that collectively replicate the outcomes of a traditional acquisition. Meta purchased 49% of Scale's non-voting shares, secured exclusive access to Scale's data-labeling pipeline, hired Scale's CEO Alexandr Wang and key staff for Meta's superintelligence unit, and maintained Wang on Scale's board while he leads Meta's AI laboratory.

This approach follows what analysts describe as "non-acquisition acquisitions" - strategic transactions designed to work around antitrust law by avoiding formal takeovers while securing privileged access to foundational AI models, critical data pipelines, and elite research talent. As explained in Patrick Boyle's analysis of the broader phenomenon: "Companies like Meta, Microsoft, Amazon, Google, and Nvidia are embedding themselves deep within the AI ecosystem through strategic investments, exclusive partnerships, and talent acquisitions- with deals that stop just short of formal takeovers, but the economic impact of these deals it turns out – is indistinguishable from full control."

The tactics echo historical precedents, including John D. Rockefeller's strategies during Standard Oil's expansion. According to Boyle's research, Rockefeller "often acquired companies without formal legal documentation using silent partnerships, verbal agreements, and trusted intermediaries to quietly absorb competitors."

According to the law firm Goodwin Procter, US enforcers remain willing to challenge deals they view as anticompetitive under traditional horizontal theories of harm, particularly mergers between direct competitors with leading market positions. The Federal Trade Commission has shown no signs of reduced enforcement under the current administration.

The FTC's 2023 Merger Guidelines provide the framework for evaluating such structured transactions. Guideline 11 addresses minority stakes that confer de facto control through board seats, veto rights, influence over capital budgets, or access to sensitive data. Meta's 49% ownership combined with potential special rights could enable the company to steer Scale's research priorities, customer access, and product roadmap.

Guideline 5 addresses input foreclosure, where a company corners critical resources and denies access to competitors. Scale's exclusive partnership with Meta forces rival AI laboratories to face delays and higher costs when seeking alternative data-labeling services. Based on market estimates, Scale AI commands 5-15% market share in data labeling, though this figure rises significantly when considering only the top tier AI laboratories.

The talent acquisition component raises concerns under Guideline 4, which prohibits transactions that eliminate potential entrants in concentrated markets. Wang and key Scale employees joining Meta removes these individuals from serving competitors, representing precisely the talent consolidation the FTC views as eliminating future competition before it emerges.

Guideline 3 addresses coordination risks through shared board roles or information sharing. Wang's dual position as Scale board member and Meta AI laboratory leader creates pathways for accessing Scale's strategic deliberations and facilitating tacit coordination between the companies.

Finally, Guideline 6 targets transactions that entrench or extend dominant positions by raising rivals' costs and depriving competitors of economies of scale. Meta has effectively disrupted its rivals' access to Scale by creating sufficient uncertainty about the company's independence that major AI laboratories have announced they will cease using Scale's services.

The immediate competitive impact supports concerns about the deal's anticompetitive effects. OpenAI, xAI, Google, and Microsoft have scaled back or paused work with Scale AI, citing data-privacy risks and conflict of interest issues. Anthropic has not announced publicly but also maintains relationships with Scale. This reaction has induced months-long delays and disruptions to critical workflows, likely costing these companies billions in operational expenses.

D'Silva warns that even if regulators block the transaction, Meta has already achieved strategic objectives. "Even if the FTC or the courts block the deal, Zuck already has a win," he wrote, noting that major competitors have already scaled back their Scale AI relationships. "Meta has already induced months-long delays and disruptions to their critical workflows, likely costing them billions."

The analyst estimates the deal's setup costs at approximately $40 million over six months, describing it as "a very effective asymmetric attack" on rivals. This represents what D'Silva characterizes as strategic disruption regardless of regulatory outcomes.

The timing of Meta's move coincides with the company's broader AI strategy challenges. Meta's advertising business has shown strong growth with AI-powered tools, yet the company faces an ongoing FTC antitrust case regarding its acquisitions of Instagram and WhatsApp. If Meta loses this case, the company could be forced to divest these platforms, making control over AI infrastructure increasingly critical for future growth.

Meta has historically struggled with internal innovation, relying primarily on acquisitions for expansion into new markets. The company's aggressive approach to generative AI investments reflects a belief among technology executives that the next wave of breakthroughs will be shaped by control over infrastructure and inputs rather than algorithms alone.

Microsoft's relationship with OpenAI demonstrates both the potential and limitations of such strategic partnerships. Since 2019, Microsoft has invested over $13 billion in OpenAI's for-profit subsidiary, securing exclusive commercial rights to its models while avoiding formal equity stakes in the nonprofit parent. Yet this structure creates ongoing tensions, with The Wall Street Journal reporting that OpenAI executives have discussed leveraging antitrust accusations against Microsoft as a negotiating tactic.

Amazon and Google have each invested billions in Anthropic, the AI startup behind Claude models, with Claude hosted on AWS and Google Cloud. Nvidia has taken equity stakes in dozens of AI startups, bundling investments with early hardware access and co-marketing opportunities. These patterns suggest that structured AI investments have become standard practice across the technology sector.

The Federal Trade Commission's response to Meta's Scale AI transaction could establish precedents for how regulators evaluate such complex arrangements in the AI sector. The agency has recently adopted a "substance over form" approach, focusing on economic reality rather than legal structure when assessing competitive impacts.

The FTC's Operation AI Comply, launched on September 25, 2024, demonstrates the agency's increased focus on AI-related enforcement. The initiative included five law enforcement actions against firms allegedly exploiting AI technologies for deceptive practices, indicating regulators are actively monitoring the sector.

Industry observers note that even if the FTC ultimately blocks the Meta-Scale AI deal, the announcement has already achieved strategic objectives for Meta. The uncertainty created around Scale's independence has effectively removed a key resource from competitors' reach, demonstrating how structured transactions can deliver competitive advantages regardless of regulatory outcomes.

As Boyle explains in his analysis: "The deals being struck by Meta, Microsoft's - OpenAI partnership, and Nvidia's customer investment loop all challenge regulators to look beyond formal mergers in assessing competition within an industry." He notes that while these transactions might not trigger traditional review thresholds, "their cumulative effect can without a doubt be expected to reshape competition within an industry."

Boyle observes that technology executives view AI as representing more than technological change: "Big tech CEOs don't seem to just expect AI to be a technological shift— but a structural shift too. They believe that as foundational models become the engines of the next digital economy, the companies that control their development, deployment, and distribution could end up wielding extraordinary influence."

Addressing current market dynamics, Boyle notes: "It is not obvious that any of the big tech firms have anything like a monopoly on AI at present –it appears to be an extremely competitive space." However, he warns about the strategic nature of current investments: "While the technology is driving massive investment and strategic growth, profitability remains elusive for most AI companies other than NVIDIA."

According to Boyle's research, the expectation among technology companies is that "long-term gains will come as AI becomes more efficient, cheaper to run, and more deeply integrated into enterprise workflows." He describes current AI spending as both defensive and opportunistic: "At present they have a lot of money – and are spending some of it on generative AI to make sure that they are still competitive in the future."

For marketing professionals, these developments carry significant implications for future platform capabilities and competitive dynamics. Meta's AI-driven advertising tools have delivered measurable performance improvements, with the company reporting 12% higher return on ad spend for advertisers using value optimization compared to volume-only approaches.

The concentration of AI capabilities among major technology platforms could accelerate the development of sophisticated targeting and optimization tools while potentially limiting advertiser choice and increasing dependency on specific ecosystems. Understanding these structural changes becomes crucial for marketing professionals planning long-term platform strategies and technology investments.

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