AI

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.

Google built and maintains its AI leadership from cash, compute, and data accumulated illegally from its monopoly in internet search. Its control over internet search, advertisement, mobile phone operating systems, and cloud computing continues to give it an advantage in AI that its competitors lack. The U.S. Court of Appeals for the District of Columbia Circuit must consider this entrenched and vertically integrated market position when it revisits the lower court’s lax remedies, write Asad Ramzanali and Joel Thayer.

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.

In a new working paper, Benjamin Verschuere and Angus Cameron argue that the wide dispersion in economists' forecasts for the impact of artificial intelligence on the economy stems from two gaps. The first is that estimates for growth, jobs, and prices are each built in isolation, with no single framework to reconcile them. The second is that models fixate on AI’s current capabilities, rather than on how fast it spreads and how much of a given job it can eventually reach. The authors build a unified framework that predicts roughly $2 trillion in long-run output gains, the loss of about 20 million American jobs, and falling prices.

Investors have poured billions into using artificial intelligence to discover new drugs, and 2026 is the first real test of whether AI-designed medicines actually helps patients. The boom has genuinely transformed the search for molecules — but that was never the costly, failure-prone part of making a medicine, and there AI has so far had little to add. Capital, and the public subsidies have not yet priced the difference, writes Michael A. Santoro.

Microsoft CEO Satya Nadella’s argument that businesses need to be able to easily switch between artificial intelligence models is correct but elides the fact,...

Finbar Curtin and Matthew Burgess’ recent article analyzing the relationship between the climate and economy has been interpreted as a study proving that climate change’s impact on economic growth is weak. Garvin Jabusch argues that this interpretation is wrong. Rather, the article concludes that statistical estimates of this relationship are limited by data and future capital allocation should favor a ‘no-regrets’ approach anchored in observable cost curves and productive capacity.

In a new working paper, Magnus Lodefalk, Lydia Löthman, Michael Koch, and Erik Engberg examine how generative AI is reshaping the labor market. They find little evidence that AI has cut the total number of jobs, but show that it has slowed hiring for the youngest workers, especially in the AI-exposed occupations where young women are concentrated. Over time, AI’s effect on entry-level roles risks thinning the next generation’s ability to build the skills and networks that careers are made of. 

Across three studies, Jana Friedrichsen, Julia Schwarz, and Michel Clement explore how generative AI will change the music industry. They find that while consumers enjoy and even prefer AI-generated music, preferences shift upon learning that the song was AI-generated.

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.