Ajay Agrawal – författare

Named one of "The five best books to understand AI" by The EconomistThe impact AI will have is profound, but the economic framework for understanding it is surprisingly simple.Artificial intelligence seems to do the impossible, magically bringing machines to life—driving cars, trading stocks, and teaching children. But facing the sea change that AI brings can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many either cower in fear or predict an impossibly sunny future.But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this masterful stroke, they lift the curtain on the AI-is-magic hype and provide economic clarity about the AI revolution as well as a basis for action by executives, policy makers, investors, and entrepreneurs.In this new, updated edition, the authors illustrate how, when AI is framed as cheap prediction, its extraordinary potential becomes clear:Prediction is at the heart of making decisions amid uncertainty. Our businesses and personal lives are riddled with such decisions.Prediction tools increase productivity—operating machines, handling documents, communicating with customers.Uncertainty constrains strategy. Better prediction creates opportunities for new business strategies to compete.The authors reset the context, describing the striking impact the book has had and how its argument and its implications are playing out in the real world. And in new material, they explain how prediction fits into decision-making processes and how foundational technologies such as quantum computing will impact business choices.Penetrating, insightful, and practical, Prediction Machines will help you navigate the changes on the horizon.

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines can help you prepare.Artificial intelligence (AI) has impacted many industries around the world—banking and finance, pharmaceuticals, automotive, medical technology, manufacturing, and retail. But it has only just begun its odyssey toward cheaper, better, and faster predictions that drive strategic business decisions. When prediction is taken to the max, industries transform, and with such transformation comes disruption.What is at the root of this? In their bestselling first book, Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction, they go deeper, examining the most basic unit of analysis: the decision. The authors explain that the two key decision-making ingredients are prediction and judgment, and we perform both together in our minds, often without realizing it. The rise of AI is shifting prediction from humans to machines, relieving people from this cognitive load while increasing the speed and accuracy of decisions.This sets the stage for a flourishing of new decisions and has profound implications for system-level innovation. Redesigning systems of interdependent decisions takes time—many industries are in the quiet before the storm—but when these new systems emerge, they can be disruptive on a global scale. Decision-making confers power. In industry, power confers profits; in society, power confers control. This process will have winners and losers, and the authors show how businesses can leverage opportunities, as well as protect their positions.Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policymaker on how to make the coming AI disruptions work for you rather than against you.

Advances in artificial intelligence (AI) highlight the potential of this technology to affect productivity, growth, inequality, market power, innovation, and employment. This volume seeks to set the agenda for economic research on the impact of AI. It covers four broad themes: AI as a general purpose technology; the relationships between AI, growth, jobs, and inequality; regulatory responses to changes brought on by AI; and the effects of AI on the way economic research is conducted. It explores the economic influence of machine learning, the branch of computational statistics that has driven much of the recent excitement around AI, as well as the economic impact of robotics and automation and the potential economic consequences of a still-hypothetical artificial general intelligence. The volume provides frameworks for understanding the economic impact of AI and identifies a number of open research questions.Contributors:Daron Acemoglu, Massachusetts Institute of TechnologyPhilippe Aghion, Collège de FranceAjay Agrawal, University of TorontoSusan Athey, Stanford UniversityJames Bessen, Boston University School of LawErik Brynjolfsson, MIT Sloan School of ManagementColin F. Camerer, California Institute of TechnologyJudith Chevalier, Yale School of ManagementIain M. Cockburn, Boston UniversityTyler Cowen, George Mason UniversityJason Furman, Harvard Kennedy SchoolPatrick Francois, University of British Columbia Alberto Galasso, University of TorontoJoshua Gans, University of TorontoAvi Goldfarb, University of TorontoAustan Goolsbee, University of Chicago Booth School of BusinessRebecca Henderson, Harvard Business SchoolGinger Zhe Jin, University of MarylandBenjamin F. Jones, Northwestern UniversityCharles I. Jones, Stanford UniversityDaniel Kahneman, Princeton UniversityAnton Korinek, Johns Hopkins UniversityMara Lederman, University of TorontoHong Luo, Harvard Business SchoolJohn McHale, National University of IrelandPaul R. Milgrom, Stanford UniversityMatthew Mitchell, University of TorontoAlexander Oettl, Georgia Institute of TechnologyAndrea Prat, Columbia Business SchoolManav Raj, New York UniversityPascual Restrepo, Boston UniversityDaniel Rock, MIT Sloan School of ManagementJeffrey D. Sachs, Columbia UniversityRobert Seamans, New York UniversityScott Stern, MIT Sloan School of ManagementBetsey Stevenson, University of MichiganJoseph E. Stiglitz. Columbia UniversityChad Syverson, University of Chicago Booth School of BusinessMatt Taddy, University of Chicago Booth School of BusinessSteven Tadelis, University of California, BerkeleyManuel Trajtenberg, Tel Aviv UniversityDaniel Trefler, University of TorontoCatherine Tucker, MIT Sloan School of ManagementHal Varian, University of California, Berkeley

A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system.In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.

An authoritative look at how artificial intelligence both shapes and is shaped by the political and economic forces of the modern world.As the effects of artificial intelligence are felt across economies and societies, many of its ramifications are still emerging. This volume brings together economists and political scientists to examine how AI intersects with regulation, military power, and political identity—offering analytical frameworks and identifying key open questions for future research. The contributions address topics such as the allocation of property rights for AI inputs, trade-offs among alternative regulatory regimes, and the role of interest groups in shaping the technology’s trajectory. They explore how AI-related capabilities influence military effectiveness, resource allocation, and bargaining power among nations, and consider AI’s effects on political preferences, from the influence of AI-curated information on polarization to the implications of targeted political advertising and personalized education for national identity formation. The volume highlights key trade-offs that arise in AI’s political economy, and points toward empirical strategies and theoretical models that can advance understanding in this emerging field. Drawing on diverse disciplinary perspectives, the collection provides a foundation for rigorous inquiry into how AI both shapes and is shaped by political and economic forces.

A thought-provoking examination of how AI might either spur or harm human economic progress.What happens to an economy when machines can think as well as, or even better than, humans? The Economics of Transformative AI tackles this issue, which is one of the most consequential economic questions of our time. This book brings together sixteen research studies from top economists that look closely at how transformative AI reshapes everything from innovation and market structure to employment, inequality, and human purpose. They explore both opportunities, such as personalized algorithmic assistance, accelerated scientific discovery, and new forms of organization, and profound challenges, including potential labor displacement, rising concentration of power, changes in the information ecosystem, and even possible existential risks to humanity.The studies in this volume develop economic frameworks for understanding the conditions under which AI might enhance or undermine human flourishing. They offer policymakers, researchers, and business leaders the analytical tools needed to prepare for the potential economic transformations ahead.

The next generation of AI is here—use it to lead your business forward.If you read nothing else on artificial intelligence and machine learning, read these 10 articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand the future direction of AI, bring your AI initiatives to scale, and use AI to transform your organization.This book will inspire you to:Create a new AI strategyLearn to work with intelligent robotsGet more from your marketing AIBe ready for ethical and regulatory challengesUnderstand how generative AI is game changingStop tinkering with AI and go all inThis collection of articles includes "Competing in the Age of AI," by Marco Iansiti and Karim R. Lakhani; "How to Win with Machine Learning," by Ajay Agrawal, Joshua Gans, and Avi Goldfarb; "Developing a Digital Mindset," by Tsedal Neeley and Paul Leonardi; "Learning to Work with Intelligent Machines," by Matt Beane; "Getting AI to Scale," by Tim Fountaine, Brian McCarthy, and Tamim Saleh; "Why You Aren't Getting More from Your Marketing AI," by Eva Ascarza, Michael Ross, and Bruce G. S. Hardie; "The Pitfalls of Pricing Algorithms," by Marco Bertini and Oded Koenigsberg; "A Smarter Strategy for Using Robots," by Ben Armstrong and Julie Shah; "Why You Need an AI Ethics Committee," by Reid Blackman; "Robots Need Us More Than We Need Them," by H. James Wilson and Paul R. Daugherty; "Stop Tinkering with AI," by Thomas H. Davenport and Nitin Mittal; and "ChatGPT Is a Tipping Point for AI," by Ethan Mollick.HBR's 10 Must Reads paperback series is the definitive collection of books for new and experienced leaders alike. Leaders looking for the inspiration that big ideas provide, both to accelerate their own growth and that of their companies, should look no further. HBR's 10 Must Reads series focuses on the core topics that every ambitious manager needs to know: leadership, strategy, change, managing people, and managing yourself. Harvard Business Review has sorted through hundreds of articles and selected only the most essential reading on each topic. Each title includes timeless advice that will be relevant regardless of an ever‐changing business environment.

How to stay ahead in a world transformed by AI.If you read nothing else on artificial intelligence, read this book. We've chosen a new selection of current and classic Harvard Business Review articles that will help you make sense of the evolving generative AI landscape, manage regulation and risk, and develop a strategy that keeps pace with technological change.This book will inspire you to:Help your people and technology work better togetherRedesign roles, workflows, and capabilitiesKeep your AI projects on trackAddress gen AI's quality control and waste problemsHarness the potential of agentic AIEmbed AI in the core of your businessThis collection of articles includes "For Success with AI, Bring Everyone on Board," by David De Cremer; "Bring Human Values to AI," by Jacob Abernethy, Francois Candelon, Theodoros Evgeniou, Abhishek Gupta, and Yves Lostanlen; "From Prediction to Transformation," by Ajay Agrawal, Joshua Gans, and Avi Goldfarb; "How AI Can Help Managers Think Through Problems," by Elisa Farri and Gabriele Rosani; "The Gen AI Playbook for Organizations," by Bharat N. Anand and Andy Wu; "Agentic AI Is Already Changing the Workforce," by Jen Stave, Ryan Kurt, and John Winsor; "Keep Your AI Projects on Track," by Iavor Bojinov; "How to Marry Process Management and AI," by Thomas H. Davenport and Thomas C. Redman; "The Hidden Penalty of Using AI at Work," by Oguz A. Acar, Phyliss Jia Gai, Yanping Tu, and Jiayi Hou; "Heavy Machinery Meets AI," by Vijay Govindarajan and Venkat Venkatraman; "AI Is Changing the Structure of Consulting Firms," by David S. Duncan, Tyler Anderson, and Jeffrey Saviano; "Stop Running So Many AI Pilots," by Goutam Challagalla, Mahwesh Khan, and Fabrice Beaulieu; "Addressing Gen AI's Quality-Control Problem," by Stefan Thomke, Philipp Eisenhauer, and Puneet Sahni; "AI's Growing Waste Problem—and How to Solve It," by Samsurin Welch, Khaled Soufani, and Eva Morales; and "Don't Let an AI Failure Harm Your Brand," by Julian De Freitas.HBR's 10 Must Reads are definitive collections of classic ideas, practical advice, and essential thinking from the pages of Harvard Business Review. Exploring topics like disruptive innovation, emotional intelligence, and new technology in our ever-evolving world, these books empower any leader to make bold decisions and inspire others.This Updated and Expanded edition features new, breakthrough articles, additional short-form pieces, and a detailed discussion guide to give you and your team the tools you need for sustained success.