David A. Price interviews R. Preston McAfee in the most recent issue of Econ Focus from the Federal Reserve Bank of Richmond (Fourth Quarter 2018, pp. 18-23). From the introduction to the interview: "Following a quarter-century career in academia at the California Institute of Technology, the University of Texas, and other universities, McAfee was among the first economists to move from academia to a major technology firm when he joined Yahoo in 2007 as chief economist. Many of the younger economists he recruited to Yahoo are now prominent in the technology sector. He moved to Google in 2012 as director of strategic technologies; in 2014, he joined Microsoft, where he served as chief economist until last year. McAfee combined his leadership roles in the industry with continued research,
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"Following a quarter-century career in academia at the California Institute of Technology, the University of Texas, and other universities, McAfee was among the first economists to move from academia to a major technology firm when he joined Yahoo in 2007 as chief economist. Many of the younger economists he recruited to Yahoo are now prominent in the technology sector. He moved to Google in 2012 as director of strategic technologies; in 2014, he joined Microsoft, where he served as chief economist until last year. McAfee combined his leadership roles in the industry with continued research, including on the economics of pricing, auctions, antitrust, and digital advertising. He is also an inventor or co-inventor on 11 patents in such wide-ranging areas as search engine advertising, automatically organizing collections of digital photographs, and adding user-defined gestures to mobile devices. While McAfee was still a professor in the 1990s, he and two Stanford University economists, Paul Milgrom and Robert Wilson, designed the first Federal Communications Commission auctions of spectrum."Here are some comments from the interview that especially caught my eye--although the whole interview is worth reading:
On the antitrust and competition issues with the FAANG companies:
Of course, a lot of the discussion today is focused on FAANG — Facebook, Apple, Amazon, Netflix, and Google. ... First, let's be clear about what Facebook and Google monopolize: digital advertising. The accurate phrase is "exercise market power," rather than monopolize, but life is short. Both companies give away their consumer product; the product they sell is advertising. While digital advertising is probably a market for antitrust purposes, it is not in the top 10 social issues we face and possibly not in the top thousand. Indeed, insofar as advertising is bad for consumers, monopolization, by increasing the price of advertising, does a social good.
Amazon is in several businesses. In retail, Walmart's revenue is still twice Amazon's. In cloud services, Amazon invented the market and faces stiff competition from Microsoft and Google and some competition from others. In streaming video, they face competition from Netflix, Hulu, and the verticals like Disney and CBS. Moreover, there is a lot of great content being created; I conclude that Netflix's and Amazon's entry into content creation has been fantastic for the consumer. ...
That leaves Apple, and the two places where I think we have a serious tech antitrust problem. We have become dependent on our phones, and Apple does a lot of things to lock in its users. The iMessage program and FaceTime are designed to force people into the Apple ecosystem. Also, Apple's app store is wielded strategically to lock in users (apps aren't portable), to prevent competition with Apple services, and to prevent apps that would facilitate a move to Android. My concern is that phones, on which we are incredibly dependent, are dominated by two firms that don't compete very strongly. While Android is clearly much more open than Apple, and has competing handset suppliers, consumers face switching costs that render them effectively monopolized. ...
The second place I'm worried about significant monopolization is Internet service. In many places, broadband service is effectively monopolized. For instance, I have only one company that can deliver what anyone would reasonably describe as broadband to my house. The FCC says I have two, but one of these companies does not actually come to my street. I'm worried about that because I think broadband is a utility. You can't be an informed voter, you can't shop online, and you probably can't get through high school without decent Internet service today. So that's become a utility in the same way that electricity was in the 1950s. Our response to electricity was we either did municipal electricity or we did regulation of private provision. Either one of those works. That's what we need to do for broadband.Using "double machine-learning" to separate seasonal and price effects
Like most computer firms, Microsoft runs sales on its Surface computers during back-to-school and the December holidays, which are also the periods when demand is highest. As a result, it is challenging to disentangle the effects of the price change from the seasonal change since the two are so closely correlated. My team at Microsoft developed and continues to use a technology to do exactly that and it works well. This technology is called "double ML," double machine learning, meaning it uses machine learning not once but twice.
This technique was originally created by some academic economists. Of course, as with everything that's created by academic economists, including me, when you go to apply it, it doesn't quite work. It almost works, but it doesn't quite work, so you have to change it to suit the circumstances.
What we do is first we build a model of ourselves, of how we set our prices. So our first model is going to not predict demand; it's just going to predict what decision-makers were doing in the past. It incorporates everything we know: prices of competing products, news stories, and lots of other data. That's the first ML. We're not predicting what demand or sales will look like, we're just modeling how we behaved in the past. Then we look at deviations between what happened in the market and what the model says we would have done. For instance, if it predicted we would charge $1,110, but we actually charged $1,000, that $110 difference is an experiment. Those instances are like controlled experiments, and we use them in the second process of machine learning to predict the actual demand. In practice, this has worked astoundingly well.On the power of AI
AI is going to create lots of opportunities for firms in every industry. By AI, I mean machine learning, usually machine learning that has access to large volumes of data, which enables it to be very clever.
We're going to see changes everywhere: from L'Oréal giving teenagers advice about what makeup works best for them to airplane design to logistics, everywhere you look within the economy.
Take agriculture. With AI, you can start spot-treating farms for insect infestation if you can detect insect infestations, rather than what we do today, which is spread the treatment broadly. With that ability to finely target, you may be able to reduce pesticides to 1 percent of what you're currently using, yet still make them more effective than they are today and have them not deteriorate so rapidly in terms of the bugs evolving around them.For a recent article about "Economists (and Economics) in Tech Companies," interested readers may want to check the article by Athey, Susan, and Michael Luca in the Winter 2019 issue of the Journal of Economic Perspectives (33:1, pp. 209-30).