AI

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.

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.