
"Power and Prediction" reveals how AI transforms entire systems, not just tasks. Shortlisted for the Thinkers50 Digital Thinking Award, it's hailed as "the best book on AI" by industry leaders navigating what the authors call "the between times" - our crucial AI transition period.
Ajay Agrawal, the bestselling author of Power and Prediction, is a leading economist and a renowned authority on the business implications of artificial intelligence.
A professor at the University of Toronto’s Rotman School of Management, he is also the founder of the Creative Destruction Lab—the world’s largest AI startup incubator. This incubator is responsible for nurturing companies that have collectively generated over $29 billion in equity value.
Bridging academic rigor with real-world impact, Agrawal advises governments, including those of the U.S. and Japan, on AI strategy. He is also the co-author of the influential Prediction Machines. Agrawal specializes in demystifying how AI reshapes industries, spanning from healthcare to quantum computing.
In 2022, he was recognized with the Order of Canada for his contributions to advancing innovation. His insights are firmly grounded in decades of research and practical experience in commercializing cutting-edge technologies. Power and Prediction expands upon his pioneering framework for understanding AI’s systemic economic transformations.
Power and Prediction explores how artificial intelligence (AI) disrupts industries by transforming decision-making through cheaper, faster predictions. The book argues AI’s true potential lies in shifting from isolated "point solutions" to redesigned "system solutions," reshaping economic power dynamics and organizational structures. It emphasizes AI’s role in separating prediction from human judgment to unlock innovation.
Business leaders, policymakers, and tech strategists seeking to navigate AI-driven disruption will benefit most. The book offers actionable frameworks for integrating AI into organizational decision-making, making it essential for those in finance, healthcare, retail, and tech industries.
Yes—it provides a visionary yet practical roadmap for AI adoption, blending economic theory with real-world examples. Its insights into systemic innovation and AI’s role in reshaping industries remain highly relevant, especially post-ChatGPT.
Point solutions apply AI to optimize existing processes (e.g., fraud detection), while system solutions redesign entire workflows (e.g., AI-driven supply chains). The authors argue systemic change is crucial for unlocking AI’s full economic value.
AI excels at predictions (e.g., forecasting demand), allowing humans to focus on judgment (e.g., setting ethical boundaries). This decoupling speeds decisions and reduces costs, but requires reimagining organizational hierarchies.
The book foresees AI redistributing economic power by favoring early adopters of system solutions. It highlights value creation over cost-cutting and warns of feedback loops that entrench dominant players, akin to Google’s search algorithm.
While Prediction Machines introduced AI as a prediction tool, Power and Prediction delves into systemic disruption, addressing challenges like organizational resistance and the transition from incremental to transformative AI adoption.
These emphasize AI’s foundational role in modern strategy and the urgency of preparedness.
Post-ChatGPT, AI’s rapid evolution makes the book’s system-level strategies critical for businesses. Its lessons on balancing reliability and flexibility during AI integration address current challenges like ethical AI and workforce transitions.
Critics note its focus on large enterprises over SMEs and underemphasis on AI’s ethical risks. However, its actionable frameworks for disruption mitigation are widely praised.
The book advises healthcare leaders to redesign systems (e.g., diagnostic workflows) around AI predictions rather than retrofitting tools. This approach improves accuracy and reduces costs, as seen in AI-driven drug discovery.
For deeper dives into AI economics, pair with The Master Algorithm or AI Superpowers. For organizational change, combine with Leading Digital or Competing in the Age of AI.
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AI will only reach its potential when entire systems of decision-making adjust.
When using prediction machines, we must be explicit about judgment.
AI transforms rules into decisions, potentially disrupting system reliability.
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In 2013, Monsanto paid $1.1 billion for a company most people had never heard of. The Climate Corporation wasn't developing miracle seeds or new pesticides-it was replacing something farmers had relied on for generations: their own judgment. Using AI to analyze weather patterns, soil conditions, and crop data, the company could tell farmers exactly when to plant, when to harvest, and what would happen if they didn't. This wasn't just a helpful tool. It was a fundamental shift in who held the knowledge-and the power. This is the heart of what's happening with AI today. We're living in what might be called "The Between Times"-that awkward period after a technology proves it works but before it actually changes how we live. Think about electricity: Edison's light bulb debuted in 1879, yet only 3% of American homes had electricity twenty years later. It took another two decades to reach 50%. Those forty years weren't about perfecting the technology-they were about rebuilding entire systems around it. AI is following the same pattern, and understanding why reveals something profound about how transformation actually happens.