Prediction Machines: The Simple Economics of Artificial Intelligence     
Ajay Agrawal, Joshua Gans, and Ami Goldfarb
Harvard Business Review Press (2018)

How to identify the best tradeoffs, compare/contrast benefits, and make the best possible decisions

Ajay Agrawal, Joshua Gans, and Ami Goldfarb examine the simple economics of artificial intelligence. “AI is a prediction technology, predictions are inputs to decision making, and economics provides a perfect framework for understanding the trade-offs underlying any decision. So, by dint of luck and some design, we found ourselves at the right place at the right time to form a bridge between the technologist and the business practitioner. The result is this book.”

More specifically, they examine the nature and extent of AI’s impact on management and decisions. They provide a framework for thinking about the evolution of jobs and the careers of the future. They offer a structure around which to develop investment theses. They recommend guidelines for understanding how AI is likely to change society and how policy might shape those changes for the better.

The information, insights, and counsel are carefully organized within five Parts: First they lay the foundation by explaining how machine learning makes [begin italics] prediction [end italics] better. Next, they describe the role of prediction as an input into [begin italics] decision-making [end italics] and explain the importance of another component that the AI community has so far neglected: judgment. Then they explain how AI [begin italics] tools [end italics] make prediction machines useful and are implementations of prediction machines designed to perform a specific task. In Part Four, they shift their attention to strategy, noting that  “some AIs will have such a profound effect on the economics of a task that they will transform a business or industry.”  Finally,  they “apply their economist’s tool kit to questions that affect [begin italics] society [end italics] more broadly, examining five of the most common debates.” For example, “Will there still be jobs?” Their answer is “Yes.”

Agrawal, Gans, and Goldfarb immediately establish a personal rapport with their reader: “If you’re lost in the fog trying to figure out what AI means for you, then we can help you understand the implications of Ai and navigate through the advances of this technology, even if you’ve never programmed a convolutional neural network or studied Bayesian statistics.” I also commend them on their brilliant use of several reader-fiendly devices, notably a set of “Key Points” at the end of each chapter.

These are among the passages of greatest interest to me, also listed to suggest the scope of the co-authors’ coverage:

o Cheap Changes Everything (Pages 7-20)
o Cheap Creates Value (13-15, 23-30, 31-41, and 58-650
o Predicting Churn (32-36, 45-47, and 187-188)
o Data Is the New Oil (43-51 and 174-1760
o The New Division of labor (53-69)

o Biases (55-58 and 204-205)
o Unknown Unknowns, and, Unknown Knowns (60-65)
o Unpacking Decisions (73-82)
o The Anatomy of a Decision, and, The AI Canvas (74-76 and 134-138)
o Should You Take an Umbrella? (78-81, 84-87, 95-102, and 173-174)

o Judging Fraud (84-87)
o Will Humans Be Pushed Out? (98-102)
o Taming Complexity (103-110)
o Deconstructing Work Flows (123-131 and 131-133)
o Binding (135-138)

o Job Redesign (141-151)
o How AI Can Change Business Strategy (156-158 and 167-178)
o Impact of AI: Data (174-176 and 195-199)
o Is This the End of Jobs?, and, Will Inequality Get Worse? (210-214)
o The End of the World as We Know It? (221-223)

These are among Ajay Agrawal, Joshua Gans, and Ami Goldfarb’s concluding thoughts: “The rise of AI presents society [and its citizens]  with many choices. Each represents a trade-off. At this stage, while the technology is still in its infancy, there are three particularly salient trade-offs at the society level”: productivity versus distribution, innovation versus competition, and performance versus privacy. “For all three trade-offs, jurisdictions will have to weigh both sides of the trade and design policies that are most aligned with their overall strategy and the preference of their citizenry.”

I have two suggestions to offer while you’re reading Prediction Machines  First, highlight key passages. Also,  document your comments, questions, action steps (preferably with deadlines), and page references as well as your responses to the questions posed and to lessons you have learned. (Pay close attention to the set of “Key Points” at the end of chapters.) These two simple tactics will facilitate, indeed expedite frequent reviews of key material later.