Toronto – When electricity was introduced in the 1880s, not everyone immediately switched from candles to lightbulbs, but there was a rapid shift in subsequent years. The same is true for artificial intelligence, warn Ajay Agrawal, Joshua Gans, and Avi Goldfarb, economists and professors at University of Toronto's Rotman School of Management. In their bestselling book, Prediction Machines, they focused on the economic benefits of AI for cheaper, faster, better predictions. In Power and Prediction: The Disruptive Economics of Artificial Intelligence they turn their attention to explaining the ways this transformative technology will impact us and how to identify the threats and opportunities.
They introduce several key concepts:
The Between Times – We have entered a unique moment in history—after witnessing the power of AI and before its widespread adoption, the authors argue. Some industries have been quick to embrace AI, while others are slower. The reason for this uneven approach is because we have not fully considered the effect of the larger systems in which we operate, the authors argue, and different solution types provide different opportunities to gain market share.
The AI bullwhip – The challenge is building reliable AI systems to enable new ways of serving people. However, the authors caution, the AI bullwhip happens when it improves the quality of one decision but harms others in the system by lowering reliability for other decisions. For example, more accurate forecasting at a restaurant for vegetables may mean wildly fluctuating orders for farmers used to consistency, which throws off their harvest. Like a bullwhip, a small change in one place can lead to a big crack elsewhere. Such unforeseen consequences can be protected by designing the optimal balance of coordination where possible and building a wall of modularity where necessary.
When rules should become unglued – Rules are good to help reduce error and ensure reliability. However, some rules have been created to mask decisions in an organization that have a degree of uncertainty. “Ungluing” a decision from the related policies and procedures enables AI to make predictions. However, changing one task is not worth implementing an expensive AI system. The real value is in combining multiple decisions. In some cases, this means newer companies will have an advantage over established corporations.
Power and disruption – As old systems are displaced, there is a shift in economic power as the reward for innovation. AI gives an advantage to early adopters. This flywheel explains why some in the venture capital community are investing so aggressively in seemingly nascent AI projects. Some assume AI will provide valuable “insights” but the authors argue this is the wrong way to think. Instead, they say AI only has value if it leads to better decision-making. Others worry that machines will hold all the power. However, the authors show that humans still control the decisions being made. The challenge is that the humans and organizations that control decision-making can change.
Bias and reducing discrimination – Another big worry is bias. However, the authors argue that AI can actually detect discrimination and reduce it. But, it requires overcoming the legal and regulatory challenges of a world designed for humans unaided by algorithms as well as navigating resistance from those who benefit from bias.
AI is already beginning to have an impact on decision making and the power balance within organizations. Insightful and provocative, Power and Prediction shows how this will happen.
About the Authors
Ajay Agrawal is Professor of Strategic Management and Geoffrey Taber Chair in Entrepreneurship and Innovation at the University of Toronto's Rotman School of Management. He is founder of the Creative Destruction Lab, cofounder of Next 36 and Next AI, and cofounder of Sanctuary, an AI/robotics company. Ajay conducts research on the economics of innovation and is a research associate at the National Bureau of Economic Research and faculty affiliate at the Vector Institute for Artificial Intelligence.
Joshua Gans is the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship and Professor of Strategic Management at Toronto's Rotman School of Management. He is Chief Economist of the Creative Destruction Lab, department editor (Strategy) at Management Science, and cofounder and managing director of Core Economic Research. Joshua has published numerous books on innovation, disruption, entrepreneurship, and most recently, pandemic economics. He is a research associate at the National Bureau of Economic Research, a research affiliate at MIT, a senior academic fellow at the e61 Institute, a distinguished fellow of the Luohan Academy, and a fellow of the Academy of Social Sciences in Australia.
Avi Goldfarb is the Rotman Chair in AI and Healthcare and Professor of Marketing at Toronto's Rotman School of Management. Avi is also Chief Data Scientist at the Creative Destruction Lab, a fellow at Behavioral Economics in Action at Rotman, a faculty affiliate at the Vector Institute for Artificial Intelligence, and a research associate at the National Bureau of Economic Research. A former senior editor at Marketing Science, Avi conducts research on privacy and the economics of technology.
The book will be launched at the Rotman School during a hybrid event on November 23 and further information on the book is online.
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