Publication Library
AIs Power Requirements Under Exponential Growth
Description: Larger training runs and widespread deployment of future artificial intelligence (AI) systems may demand a rapid scale-up of computational resources (compute) that require unprecedented amounts of power. In this report, the authors extrapolate two exponential trends in AI compute to estimate AI data center power demand and assess its geopolitical consequences. They find that globally, AI data centers could need ten gigawatts (GW) of additional power capacity in 2025, which is more than the total power capacity of the state of Utah. If exponential growth in chip supply continues, AI data centers will need 68 GW in total by 2027 — almost a doubling of global data center power requirements from 2022 and close to California's 2022 total power capacity of 86 GW.
Created At: 04 April 2025
Updated At: 04 April 2025
Strategic competition in the age of AI
Description: Artificial intelligence (AI) holds the potential to usher in transformative changes across all aspects of society, economy and policy, including in the realm of defence and security. The United Kingdom (UK) aspires to be a leading player in the rollout of AI for civil and commercial applications, and in the responsible development of defence AI. This necessitates a clear and nuanced understanding of the emerging risks and opportunities associated with the military use of AI, as well as how the UK can best work with others to mitigate or exploit these risks and opportunities.
Created At: 04 April 2025
Updated At: 04 April 2025
Brain-Computer Interfaces
Description: The U.S. Department of Defense (DoD) has invested in the development of technologies that allow the human brain to communicate directly with machines, including the development of implantable neural interfaces able to transfer data between the human brain and the digital world. This technology, known as brain-computer interface (BCI), may eventually be used to monitor a soldier's cognitive workload, control a drone swarm, or link with a prosthetic, among other examples. Further technological advances could support human-machine decisionmaking, human-to-human communication, system control, performance enhancement and monitoring, and training. However, numerous policy, safety, legal, and ethical issues should be evaluated before the technology is widely deployed. With this report, the authors developed a methodology for studying potential applications for emerging technology.
Created At: 04 April 2025
Updated At: 04 April 2025
Artificial Intelligence, Scientific Discovery, and Product Innovation
Description: This paper studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of "idea-generation" tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives. Together, these findings demonstrate the potential of AI-augmented research and highlight the complementarity between algorithms and expertise in the innovative process. Survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization.
Created At: 17 March 2025
Updated At: 17 March 2025
Generative AI at Work
Description: New AI tools have the potential to change the way workers perform and learn, but little is known about their impacts on the job. In this paper, we study the staggered introduction of a generative AI-based conversational assistant using data from 5,179 customer support agents. Access to the tool increases productivity, as measured by issues resolved per hour, by 14% on average, including a 34% improvement for novice and low-skilled workers but with minimal impact on experienced and highly skilled workers. We provide suggestive evidence that the AI model disseminates the best practices of more able workers and helps newer workers move down the experience curve. In addition, we find that AI assistance improves customer sentiment, increases employee retention, and may lead to worker learning. Our results suggest that access to generative AI can increase productivity, with large heterogeneity in effects across workers.
Created At: 17 March 2025
Updated At: 17 March 2025