In my last blog, I said that I’d attended the Summit on Law and Innovation at Vanderbilt Law in Nashville. I try to write at least one blog a month, but something wonderful happened as I went to the Summit, and I have to report it to you here.
On the plane going to the Summit, I finally had time to read a book. On the plane going back to Seattle, I re-read it.
What an eye-opener.
So the book is Prediction Machines, subtitled, The Simple Economics of Artificial Intelligence, written by Professors Ajay Agrawall, Joshua Gans, and Avi Goldfarb, all three of whom are economists and Professors at the University of Toronto’s Rotman School of Management.
But these three academics also had some hands-on experience for their teachings. They built the Creative Destruction Lab, a seed-stage program to support science-based startups. As they put it, on page 2 of their book, CDL’s most exciting ventures were AI-enabled companies and that, as of September of 2017, the CDL had (for the third year in a row) interfaced with the largest cohort of AI startups of any program on the planet.
From that advantaged perch, the authors launch their book with their “first key insight,” which is that “the new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence—prediction.” (Italics in the original.)
And they were still on page 2.
At the end of Chapter 1, they provide Key Points in bullet-point fashion. They do this with every chapter. In other words, they take notes for you.
In Chapter 2, there was a second major insight. Prediction, they say, is “the process of filling in missing information.” Cheaper predictions will mean more predictions because, they say, when the cost of something valuable falls, we will do more of it.
That puts us on the road to disruption. Predictions are being used to solve traditional problems now, but they will be used to solve non-traditional problems in the future. And then something else happens: the value of other things, which they and other economists call “complements,” increases. As examples, they write that if the cost of coffee goes down, and we drink more of it, the demand for and value of sugar and cream goes up. When autonomous vehicles make highly accurate predictions, the value of sensors to capture the data representing the oncoming surroundings goes up.
In fact, they write, “Some AIs will affect the economics of a business so dramatically that they will no longer be used to simply enhance productivity in executing against the strategy; they will change the strategy itself.”
Now what do they mean by that? They mean that, for Amazon, the current strategy is to enable “shop, then ship.” But they also mean that if the processes of delivery and handling returns are so well predicted that their respective costs go down significantly, then a new model might emerge: “ship, then shop.”
And that’s just the end of Chapter 2.
By the time these brilliant authors reach the end of their book, they are explaining why the likes of the AI-enabled tech companies—Google and Microsoft—have seen the future and, having seen it, transformed their companies from “mobile-first” to “AI-first.”
In Chapter 17, they’re explaining that such a shift “means compromising on other goals such as maximizing revenue, user numbers, or user experience.”
Why? What’s the explanation? Here it is, on p. 194:
“AI can lead to disruption because incumbent firms often have weaker economic incentives than startups to adopt the technology. AI-enabled products are often inferior at first because it takes time to train a prediction machine to perform as well as a hard-coded device that follows human instructions rather than learning on its own. However, once deployed, an AI can continue to learn and improve, leaving its unintelligent competitors’ products behind….”
Without reservation and with sincere (and highest) compliments, I recommend Prediction Machines.
Published by the Harvard Business Review Press. 2018.
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