Is Data Really a Barrier to Entry?

The rise of generative artificial intelligence (AI) technology has raised concerns that a few large companies or digital platforms will corner the market. By controlling vast troves of data necessary to train foundation models, these firms could raise entry barriers, stifle competition and innovation, and ultimately harm consumers. Contrary to this fear, thriving data markets allow developers to access training data on an open-source or commercial basis, making it so that data access is not currently a barrier to entry in the generative AI market. In fact, tech giants with access to high volumes of user data face stiff competition from independent firms and startups that can outcompete larger rivals through superior algorithms or user experiences. Technological advancements, such as synthetic data and engineering workarounds that lower data demands and training costs, also suggest that concerns about data scarcity limiting AI innovation are overblown. Additionally, the industry’s shift towards more specialized foundation models and tailored AI applications also demonstrates that access to the “right” kind of data rather than vast troves of user data is paramount, further eroding the purported advantage of large tech giants. Conversely, preemptive regulations and remedies proposed to combat future data scarcity or monopolization would likely harm AI development and innovation. Current US antitrust law instead provides a flexible, pragmatic framework for policing exclusionary conduct by firms controlling key data inputs should these concerns materialize in the future.

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