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Artificial Intelligence, Quantum and Beyond

Artificial Intelligence, Quantum and Beyond explores how technologies, principally computing, advance and how these generate prosperity. Improvements in computing have been a powerful driver of prosperity, but we should not take it for granted that this will continue. Many drivers of improvements in computing have slowed or stopped, while other new drivers have emerged. Which will matter most, and how, is not well understood. As a research group of computer scientists, engineers, and economists we use an interdisciplinary lens to understand progress in computing and identify the most important trends, impacts, and opportunities. By exploring key computing technologies –  Artificial Intelligence, specialized chips, quantum computers, and algorithms – we generate insights that can help leaders to use these technologies to sustain and promote greater prosperity. Neil Thompson leads this Research Group. Neil

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Research Papers Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts

Alexander K. Saeri

Jess Graham

Michael Noetel, et al.

 

This paper reports results from a three-round Delphi study conducted late 2025 with 272 international AI experts who rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern.

Working Papers Is there “Secret Sauce” in Large Language Model Development?

Natalia Fischl-Lanzoni

 

Do leading LLM developers possess a proprietary “secret sauce,” or is LLM performance driven by scaling up compute? Using training and benchmark data for 809 models released between 2022 and 2025, the authors estimate scaling-law regressions with release-date and developer fixed effects.

Research Papers The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence

 

Artificial intelligence (AI) is reshaping society, from video generation to medical diagnosis, coding agents to autonomous vehicles. Yet researchers, policymakers, and technology companies lack shared terminology for discussing AI risks. This paper addresses this challenge by creating a comprehensive catalog of AI risks. 

Working Papers Crashing Waves vs. Rising Tides: Preliminary Findings on AI Automation from Thousands of Worker Evaluations of Labor Market Tasks

Adam Kuzee

Harry Lyu

Jonathan Rosenfeld

Meiri Anto

 

The authors propose that AI automation is a continuum between: crashing waves where AI capabilities surge abruptly over small sets of tasks, and rising tides where the increase in AI capabilities is more continuous and broad-based.

Working Papers Economics of Human and AI Collaboration: When is Partial Automation More Attractive than Full Automation?

Atin Aboutorabi

Harry Lyu

Kaizhi Qian

Brian C. Goehring

 

This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation.