We live in a digital economy powered by data and algorithms, and two federal courts have found Google monopolizing fundamental digital markets. These courts now face a rare opportunity: to remedy Google’s monopolization conduct, and to shape the rules of fair competition in data-driven markets—including those underpinning artificial intelligence (AI). Last week, one of these courts required Google to share part of its data to address its monopolization of data-driven markets—data it had also collected through antitrust violations. U.S. District Judge Amit Mehta recognized that Google engaged in anticompetitive agreements that enabled it to amass an unparalleled scale of data, and ruled that the company should be denied “the fruits of its violation.”

The antitrust regulatory effort should focus precisely on decentralizing today’s data economy, which is increasingly shaped by algorithms, rather than pushing to break Google into pieces as the government argued—a cure that risks being worse than the disease. That means requiring Google to share its vast amount of data (also collected through anticompetitive behavior) with rivals, ensuring fairer competition in the markets that depend on it, and using algorithms to implement this remedy consistently.

The real threat is not the size of Google. Decades of economic studies have dispelled the myth that “Big is Bad,” demonstrating that large corporations can benefit consumers by lowering costs and providing a larger interconnected network and more advanced services. What really threatens today’s competition and consumers stems from the fact that the more we use AI models like ChatGPT, the more these models absorb information without providing new, publicly available data to run critical digital services. Mandating access to Google’s data and data facilities emerges as one of the most promising solutions to ensure competition and protect American consumers in a data-driven economy. But sharing obligations have traditionally raised hard questions, including who should be granted access to the shared monopoly resources and how that access can be provided fairly and on a non-discriminatory basis.

One way to address these challenges within a digital framework is to use the same tools that companies are adopting to compete aggressively. Algorithms that drive competition and shape our choices can inform courts on how to enforce antitrust law and regulate tech giants effectively.  By using algorithmic remedies, we can truly engage with a digital framework, rather than remain passive products of it.

We are not technologies. We build them to address problems. So why not leverage those very tools to tackle today’s challenges, like regulating monopolies that threaten to control us without limits?

The option of breaking monopolies is always there, but the truth is more complex – and the wrong remedy could backfire on consumers, innovation, and the very idea of an open democratic internet. Rather than breaking up large networks into pieces, sharing key assets such as data and data facility seems to be the cure that a data-driven economy really needs. Data is the essential fuel to feed AI algorithms and run essential services, including online search and the Ad Tech markets where Google has been found violating antitrust law. A data-sharing remedy can be designed and operated, whether to remedy specific conduct in antitrust litigation or as part of a new general statutory scheme for tech regulation.

Before we waste more time, let us use algorithms to establish a regulatory framework for sharing data facilities that matches the realities of the digital economy—while drawing on lessons from more than a century of antitrust enforcement. The real game changers were not the few break-ups that occurred in the century-long history of antitrust, but remedies that implied the sharing of critical technologies and resources for competitors to compete like AT&T’s sharing of the transistor technology in 1956. The transistor is a breakthrough technology that is present in all our digital devices. Millions of transistors are present in our smartphones and computers, and its rapid adoption was facilitated by the antitrust relief. According to Intel’s co-founder Gordon Moore, the AT&T antitrust consent decree of 1956 was “one of the most important developments for the commercial semiconductor industry.” Similarly, IBM’s decision in 1969 to unbundle its hardware from software to address monopolization concerns over the emerging computer industry had significant pro-competitive effects. This remedy changed the software industry almost overnight. A start-up company named Microsoft became a key IBM software supplier and when Microsoft faced a federal antitrust lawsuit, the case (concluded in 2001) required the company to share technical information about its Windows operating system, enabling competitors to build compatible software. The proposed breaking up of the company was ultimately rejected.

Presently, data is the critical resource that powers AI systems and fuels competitive advantage in online search and ad tech—markets where Google has already been found violating antitrust law. The most effective remedy isn’t breaking Google into pieces, but rather requiring it to share the unique amount of data that the company acquired while monopolizing key markets.

Digital markets pose unique challenges but also new opportunities for courts and regulators. Sticking with remedies that failed in the railroad era won’t solve the present challenges. History offers valuable lessons, but the future requires new solutions. If we fail to regulate the digital economy effectively, don’t blame Google for running the show while consumers are left in the audience.

Giovanna Massarotto is a Lecturer at University of Pennsylvania Carey Law School. She is an affiliate of the University of Pennsylvania Carey Law School’s Center for Technology, Innovation & Competition (CTIC), the Penn Program on Regulation, and University College London’s Centre for Blockchain Technologies (UCL CBT).