Book Information

Book ID Asin: B075GXJPFS
Book Title: Prediction Machines: The Simple Economics of Artificial Intelligence
Book Author: Ajay Agrawal,Joshua Gans,Avi Goldfarb
Book Format and Price:
Book Format Name: Kindle
Book Format Price: $13.29
Book Format Name: Audiobook
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Book Format Name: Hardcover
Book Format Price: $16.59
Book Format Name: MP3CD
Book Format Price: $24.99
Book Price: $22.35
Book Category: Kindle Store, Kindle eBooks, Business & Money and unknown
Book Rating: 569 ratings

Book Product Detail

• Best Sellers Rank: #169,893 in Kindle Store (See Top 100 in Kindle Store)
  • #26 in Strategic Management
  • #56 in AI & Semantics
  • #111 in Business Systems & Planning

• Customer Reviews:
  4.5 out of 5 stars 569 ratings

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Book Review

Name: M Prywes
Rating: 3.0 out of 5 stars
Title: Covers basic concepts well, but lacks depth
Date: Reviewed in the United States on December 2, 2019
Review: Agarwal, Gans, and Goldfarb are professors at theUniversity of Toronto School of Management. Agarwal and Goldfarb also work in the School’s Creative Destruction Lab, a program for science-based start-ups. 

The authors effectively explain the basic economics of artificial intelligence (AI) and its evident consequences for business.  They write in simple and clear language that readers will understand and avoid convoluted technical digressions.  They discuss some of the lessons from cognitive psychology for the machine-learning approach to AI.  One theme is to understand human intelligence itself as a prediction mechanism, since our brains fill in much beyond what we knowthrough our senses.  The discussion of the consequences of AI for corporate strategy is useful, although the languageis general.  I would have appreciated a couple of in-depth case studies.

The overall weakness is that the bookstops at the elementary level.  The sub-title promises economic analysis, but authors barely deliver.  Their main theme is that machine learning has lowered the cost of prediction.  That implies an outward shift in the supply curve for goods & services that rely on prediction, which increases their consumption and lowers their price.   The most prominent services that rely on prediction are facial recognition and interpretation of radiological scans. The main goods are robots that rely on images to avoid obstacles andlocate work sites, and therefore, self-driving vehicles. 

The authors predict that AI will displace some workers and sustain the employment of others by increasing their productivity.  They compare productivity-augmenting AI to the introduction of spreadsheet programs (VisiCalc, Lotus 123, and Excel).  Clerks became more valuable because they could produce calculations more quickly and could run alternative scenarios, for example, to determine how changes in assumptions would change profitabilityof a project.  In a further example, AI might increase the demand for software engineers by increasing their productivity.

There’s little argument that AI will cause,at a minimum, a short-term surge of unemployment.  What’s unclear is whether there will be a net decline in unemployment once all the consequences of AI the technology have workedthrough the economic system.  There’ s been much published research on the likely impact of robots on employment.  The leading researcher is David Autor, of theMIT economics department.  He wrote that,

“…journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation andlabor that increase productivity, raise earnings, and augment demand for labor.”

Moreover, the increased income to owners of this new technology should raise spending (demand), inducing a compensatingboost to employment.  Most researchersare unsure whether the net and long-term effect of these economic processeswill be to decrease employment.  A morerealistic concern is that AI will lead to greater wage inequality between those with STEM education, creativity, and judgement on one hand and those with highschool or lower education on the other. Unfortunately, the authors have nothing to add to this central debate. 

The authors do write about the way AI should influence corporate strategy. If AI is central to the enterprise, such as Google’s Waymo, which develops self-driving cars, then the strategy could be AI-first.  This means developing AI even at the expenseof other objectives, such as customer satisfaction.  Securing enough data is a priorityfor development of AI.  Tesla’s strategyhas been to introduce AI software early so that that it can learn from data collected in real world use by customers. It has been releasing its self-driving car software in parts, rather than wait until it can produce a car approved to drive without human supervision.  

Name: fitzalling
Rating: 5.0 out of 5 stars
Title: Three Economists Demystify Artificial Intelligence
Date: Reviewed in the United States on July 9, 2018
Review: The authors, three economists from the University of Toronto, do a great job of demystifying artificial intelligence by examining it through the lens of standard economic theory. The authors are clear that they are not examining AGI (artificial general intelligence), but rather the artificial intelligence produced by algorithms in common, and ever-increasing, use today. When you go online and get a recommendation for a product or you ask a question of Alexa, Siri, or Google, the recommendation or answer that you get is produced by algorithms (prediction machines in the authors' definition).

The authors' basic premise is that these prediction machines have become, and are becoming, so cheap that their use has expanded, and will continue to expand, dramatically across a range of businesses. They analogize this expansion to the expansion in the use of electricity or cars during the early parts of the last century. The processes for how work was done and the skills needed to do it dramatically changed the number and type of jobs required by the economy. Jobs were both created and destroyed. It took time for this to occur. The authors expect the same effect from the prediction machines.

The book looks at the possible effect on the types of jobs at which humans will excel. Judgement will become more valuable to augment the input of artificial intelligence. Jobs will have to be redesigned. Work flows altered.

Strategy in the C-suite will be impacted by artificial intelligence. The occupants of top management positions will have to adjust. The book suggests how.

After reading this book, I read the July/August edition of MIT Technology Review, which states on the cover "AI and robots are wreaking economic havoc. We need more of them." There are a number of articles in the magazine that paint a cautionary picture of the prediction machines ("Confessions of an accidental job destroyer"). The authors of Prediction Machines recognize the potential adverse consequences and social risk that the current edition of MIT Technology Review addresses so the book and the magazine are not in conflict.

If you're interested in artificial intelligence and want to read a book that examines the topic dispassionately, then I recommend it highly. The authors did a fine job of making the topic highly accessible.

Name: N2009
Rating: 3.0 out of 5 stars
Title: I predict you will be satisfied if you approach book with low expectations
Date: Reviewed in the United States on March 17, 2022
Review: This quick read is a mildly interesting overview of AI as primarily a prediction tool. I agree with reviewers who felt there was little depth or new information. It just doesn't have that weighty, comprehensive feel of a book with a cohesive and well-developed theme. It reads and feels like a series of short, interesting articles. It reminds me of the 20 page papers I used to churn out at the last minute in college; full of many mildly interesting, but not very thought-provoking, points with a healthy sprinkling of somewhat-related anecdotes to fill the space.

There are quite a number of observations that are just stated and not further developed. For example, "One major benefit of prediction machines is that they can scale in ways that humans cannot." This assertion and its implied importance just hangs there. There is no further development or elaboration of this point or its ramifications.

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