AI

In new research, Dragan Filimonovic, Christian Rutzer, and Conny Wunsch find that generative artificial intelligence not only enhances the productivity of scientific researchers, but also lowers barriers to entry for early-career scholars and scholars who are not fluent in English. Rather than attempting to prohibit GenAI’s use, institutions should develop disclosure guidelines to facilitate trust and support adoption.

Jérémie Haese and Christian Peukert present new empirical findings on core open source technologies for the web and AI. Open source holds promise for making AI systems more transparent and secure, but it risks masking continued centralized control under the guise of openness.

The largest artificial intelligence firms are able to afford access to quality data from content producers like the New York Times, while smaller startups are being left out. This dynamic risks concentrating markets and creating unassailable barriers to entry. Compulsory licenses offer one solution to lower barriers to entry for nascent AI firms without harming content producers and consumers, writes Kristelia García.

Christian Peukert argues that the market for licensing content from copyright owners like newspapers or online forums requires a standardized regime if access to this data, used to train artificial intelligence models, is to remain available for more than just the largest AI firms. A failure to maintain non-discriminatory access will result in the consolidation of both the AI and content production markets.

Is there a world where AI developers could get the training data they need through content licensing deals? Matthew Sag argues that content licensing deals between developers of artificial intelligence and content owners are only possible for large content owners and cannot feasibly apply to the bulk of producers and owners of content on the internet.

Large AI firms like OpenAI and Amazon are licensing content to train their models that they might otherwise have been able to access for free under the fair use doctrine. Mark A. Lemley and Jacob Noti-Victor write that this behavior may constitute anticompetitive acquiescence—where large firms agree to license content they don’t have to in order to raise rivals’ costs.

In new research, Claire Liu and Jared Stanfield examine how relationships between corporate leaders and the United States president enable firms to capture regulation and avoid antitrust scrutiny.

Stigler Center Assistant Director Matt Lucky reviews Khan Academy CEO Sal Khan’s recent book, Brave New Words: How AI Will Revolutionize Education (and Why That’s a Good Thing). The book presents an optimistic vision for the educational and pedagogical role of AI-assisted chatbots as personal tutors and teaching assistants. Khan discusses his book with Bethany McLean and Luigi Zingales on this week’s Capitalisn’t episode, which you can listen to here.

The artificial intelligence market is rapidly developing but antitrust regulators are failing to update their policies, write Tennessee Attorney General and Reporter Jonathan Skrmetti and Kevin Frazier. Regulators’ passiveness risks repeating what happened to social media markets, where a few tech giants were able to acquire nascent competitors and dominate the market. The authors propose three policies to help maintain a competitive AI market.

The United States power grid is increasingly strained by the surging electricity demand driven by the AI boom. Efforts to modernize the power infrastructure are unlikely to keep pace with the rising demand in the coming years. Barak and Eli Orbach explore why competition in AI markets may create an electricity demand shock, examine the associated social costs, and offer several policy recommendations.