This article is part of a monthly column that considers the significance of recent Federal Trade Commission antitrust announcements. In this installment, we discuss AI and navigating innovation, concentration and scrutiny in the new tech frontier.
In today's digital age, artificial intelligence has become a household utility.
It is hard to imagine anyone who hasn't heard of ChatGPT — from the pundit lamenting the impending obsolesce of the human workforce; to the chatbot's influence on pop culture, serving as the inspiration for movies like "Her" and star-studded episodes of "Black Mirror;" or that one parent-teacher conference concerning the ghostwriter of your child's book report.
But beyond the headlines lies a more complex story that has also captured the attention of the Federal Trade Commission and other regulators.
At the center of this story is a persistent and difficult question: How should the government approach innovation in tech markets that tend, almost inevitably, toward concentration?
That question, once raised in the context of Microsoft Corp. and Google LLC, is now being asked anew in the age of AI.
The First-Mover Effect and the Economics of AI
AI technologies exhibit a classic pattern familiar to antitrust scholars and enforcers. Breakthrough innovations tend to be introduced by a handful of pioneering firms that move fast, scale rapidly and thus dominate emerging markets.
These first movers often enjoy outsized returns and accumulate durable competitive advantages through brand recognition, user base growth and technical superiority.
With AI, this dynamic is amplified. Building competitive generative models requires enormous resources: cutting-edge chips, specialized engineering talent, immense training datasets and access to global-scale cloud infrastructure.
The capital costs alone can pose a daunting barrier to entry. As with prior waves of innovation, network effects are also at play. The more users engage with a platform like ChatGPT, the better the model becomes — entrenching the lead of the dominant firm.
Even at the hardware level, we are seeing this trend. Nvidia Corp. has become synonymous with AI computing power, and even long-established semiconductor manufacturers have struggled to gain a foothold.
The result is a technology stack — from chips to training data to foundation models — that is once again tending toward concentration.
Regulatory Interest and Active Investigations
This rapid consolidation has not gone unnoticed. The U.S. Department of Justice is currently investigating whether Google's partnership with Character.AI, an AI chatbot company, was structured to avoid the antitrust scrutiny typically triggered by mergers and acquisitions.
The inquiry echoes past concerns about dominant tech firms using strategic alliances, rather than outright purchases, to corner emerging markets without tripping regulatory alarms.
Meanwhile, in the landmark U.S. v. Google LLC monopolization case, the U.S. District Court for the District of Columbia found, on May 9, 2024, that Google had unlawfully maintained its search monopoly through exclusionary contracts with smartphone manufacturers and web browsers.
AI featured prominently in that case. The court specifically cited Google's integration of generative AI into its search engine as a competitive differentiator and noted that the practice had "implications in search advertising."
Testimony from AI executives underscored the extent to which emerging technologies were already reshaping the competitive landscape — and how entrenched incumbents might seek to co-opt or foreclose them.
A Changing Regulatory Climate Under Ferguson
But the regulatory winds may be shifting. In a significant reversal of the prior administration's approach, President Donald Trump recently issued an executive order rescinding the Biden-era AI directive and calling for a comprehensive review of federal regulations deemed burdensome to AI innovation.
The order reflects growing concerns, especially among conservatives, that aggressive antitrust enforcement may chill technological advancement or punish success.
Andrew Ferguson, Trump's FTC chair and a former solicitor general of Virginia, is widely expected to steer the commission in a more restrained direction.
Yet his public remarks suggest that the FTC will not abandon the field entirely. In a Jan. 17 statement, Ferguson said the agency must remain "a vigilant competition watchman," particularly in rapidly evolving sectors like artificial intelligence.
His approach may emphasize economic rigor and procedural restraint, but large tech firms can still expect scrutiny — especially when exclusionary conduct or vertical entrenchment is suspected.
This recalibration of enforcement priorities raises important questions for companies navigating the AI ecosystem. While the risk of bold new rulemakings or sweeping complaints may decline under Ferguson, the legal and reputational risks of regulatory missteps remain.
Firms will need to monitor not just market share, but also the structure of their partnerships, licensing arrangements, and data access policies.
What Comes Next?
The AI market is in its infancy, but antitrust lawyers are already facing a familiar challenge: distinguishing between innovation and entrenchment.
At what point does a company's scale or first-mover advantage cross the line into anticompetitive dominance?
Are strategic alliances between foundational model developers and smaller startups pro-competitive collaborations or stealth acquisitions in disguise?
How should regulators weigh the benefits of vertical integration — faster product improvement, better user experience — against the risk of foreclosing rivals?
AI is a revolutionary force, but it is also replaying an antitrust film that we have seen before: innovation leading to dominance, dominance triggering regulation, and regulation shaping the next stage of innovation.
The FTC's role in this story is still unfolding. But the groundwork — antitrust doctrine, enforcement history and regulatory posture — is already being laid. Several emerging themes that should guide firms' conduct and compliance planning are discussed below.
Transparency matters.
Whether it's data acquisition, algorithmic training, or model deployment, companies should document their processes and be prepared to explain how they promote — not harm — competition.
Partnerships should be scrutinized.
Strategic collaborations should be reviewed not just for business value, but for how they could be perceived by antitrust enforcers. Agreements that limit a startup's ability to partner with others or mandate exclusivity may trigger scrutiny.
Vertical integration is not immune.
The FTC and DOJ are increasingly willing to investigate nonhorizontal mergers and alliances, especially where one party controls critical inputs like cloud infrastructure, graphics processing units or training datasets.
Litigation risk is real.
The Google decision may embolden private plaintiffs and state enforcers, even as federal regulators adjust their strategies. AI leaders must consider that their conduct today may become the basis for lawsuits tomorrow.
Conclusion
While we can't say exactly how the antitrust enforcement efforts will play out, AI's continued growth is inevitable. Just ask ChatGPT.
Nevertheless, tech companies seeking to establish and grow a presence in the field should remain cognizant of the lessons past antitrust litigation offers while navigating this new terrain.
Reproduced with permission. Originally published July 3, 2025, "FTC Focus: Enforcers Study AI Innovation And Entrenchment," .
