Shishene Jing

Shishene Jing argues that the fair use doctrine's central question—whether a use is sufficiently transformative to avoid licensing requirements—breaks down when applied to AI. Transformativeness worked as a test because transformative uses rarely competed financially with the originals. AI training severs that relationship, as it is both the most transformative use of copyrighted material and the use best equipped to displace their markets.

Although merger review now acknowledges potential harms to labor markets, the analytical tools remain underdeveloped. Shishene Jing proposes identifying “labor market mavericks” as companies essential to maintaining competition among employers and preventing mergers that could reduce wages and other worker benefits.

Responding to Mark Lemley and Jacob Noti-Victor, Shishene Jing argues that licensing deals struck by incumbent artificial intelligence firms with content owners will not disadvantage smaller AI rivals. Even if such deals establish a legal precedent requiring licenses, content owners have little incentive to actually sue small, cash-poor AI startups, making the precedent toothless in practice.

Supporters of more robust antitrust policy have pointed to the subsequent success of Figma after authorities blocked Adobe’s acquisition of it. Skeptics, including venture capitalists, have argued that the one case reveals nothing systematic about the benefits of stronger merger review. Venture capitalists happen to be the one party with the data and resources to fund the studies to show any systematic correlation one way or the other. They should do so, writes Shishene Jing.

The competitiveness of the artificial intelligence market at first glance masks how investment arrangements and partnerships between the largest players risks undermining their incentives to compete. Regulators must continue to monitor these arrangements for anticompetitive effects, writes Shishene Jing.