The (Surprisingly) Simple Economics of Artificial Intelligence
How will AI affect your business strategy? In an excerpt from their much-anticipated new book, the authors argue that the answer will depend on how prediction affects your industry.
How will AI impact your business? In an excerpt from their new book, the authors argue that the answer depends on how prediction affects your industry.
— or will soon have — an AI moment. We have EVERYONE HAS HAD grown accustomed to a media saturated with stories of new technologies that will change our lives. We are so used to the constant drumbeat of technology news that we numbly recite that ‘the only thing immune to change is change itself ’. Until we have our AI moment. Then we realize that this technology is different.
Some computer scientists experienced their AI moment in 2012, when a student team from the University of Toronto delivered such an impressive win in the visual object-recognition competition Imagenet that the following year, all top finalists used the then-novel ‘deep learning’ approach to compete. These scientists recognized that object recognition is more than just a game; it actually enables machines to ‘see’.
Some technology CEOS experienced their AI moment when they read the headline in January 2014 that Google had paid more than $600 million to acquire Uk-based Deepmind, even though the start-up had generated negligible revenue relative to the purchase price but had demonstrated that its AI had learned — on its own, without being programmed — to play Atari video games with super-human performance.
Some regular citizens experienced their AI moment later that year, when renowned physicist Stephen Hawking emphatically explained, “Everything that civilization has to offer is a product of human intelligence. Success in creating AI would be the biggest event in human history.”
Our own AI moment came in 2012, when a trickle and then a surge in the number of early-stage AI companies employing state-of-the-art machine-learning techniques applied to the Creative Destruction Lab at the Rotman School of Management. The applications spanned industries — drug discovery, customer service, manufacturing, quality assurance, retail, medical devices. The technology was both powerful and general purpose, creating significant value across a wide range of applications.
We set to work understanding what this meant in economic terms. We knew that AI would be subject to the same economics as any other technology. The technology itself is, simply put,
amazing. Early on, famed venture capitalist Steve Jurvetson quipped: “Just about any product that you experience in the next five years that seems like magic will almost certainly be built by these algorithms.” We understand and sympathize with Jurvetson’s characterization of AI applications as ‘magical’; but as economists, our job is to take seemingly-magical ideas and make them simple, clear and practical.
Cutting Through The Hype
Economists view the world differently than most people. We see everything through a framework governed by forces such as supply and demand, production and consumption, prices and costs. Although economists often disagree with each other, we do so in the context of a common framework. We argue about assumptions and interpretations but not about fundamental concepts, like the roles of scarcity and competition in setting prices. This approach to viewing the world gives us a unique vantage point. On the negative side, it doesn’t make us popular at dinner parties. On the positive side, it provides useful clarity for informing business decisions.
Let’s start with the basics: prices. When the price of something falls, we use more of it. That is simple Economics, and it is happening right now. AI is everywhere — packed into your phone’s apps, optimizing your electricity grid, and replacing your stock portfolio managers. Soon it may be driving you around or flying packages to your house. Where others see transformational innovation, we see a simple fall in price. But it is more than that. To understand how AI will affect your organization, you need to know precisely what price has changed and how that change will cascade throughout the broader economy. Only then can you build a plan of action.
Economic history has taught us that the impact of major innovations is often felt in the most unexpected places. Consider the story of the commercial internet in 1995. While most of us were watching Seinfeld, Microsoft released Windows 95, its first multitasking operating system. That same year, the U.S. government removed the final restrictions to carrying commercial traffic on the internet, and Netscape — the browser’s inventor — cel- ebrated its initial public offering (IPO). This marked an inflection point when the Internet transitioned from a technological curiosity to a commercial tidal wave that would wash over the economy.
Netscape’s IPO valued the company at more than $3 billion, even though it had not generated any significant profit. Venture capital investors valued start-ups in the millions even if they were ‘pre-revenue’. Freshly-minted MBAS turned down lucrative traditional jobs to prospect on the web. As the effects of the Internet began to spread across industries, technology advocates stopped referring to it as a new technology and began referring to it as the New Economy. The Internet had transcended the technology and permeated human activity at a fundamental level, and everyone from politicians to corporate executives, investors, entrepreneurs and major news organizations started using the term.
Everyone, that is, except economists. To us, this looked like the regular old economy. To be sure, some important changes had occurred: Goods and services could now be distributed digitally; communication was easy; and you could find information with the click of a search button. But you could do all of these things before. What had really changed was that you could now do them cheaply. The rise of the Internet led to a significant drop in the cost of distribution, communication and search.
Re-framing technological advances as ‘a shift from expensive to cheap’ or ‘from scarce to abundant’ is invaluable for thinking about how they will affect your business. The first time you used Google, you may remember being mesmerized by its seemingly-magical ability to access information. From the economist’s perspective, Google made search cheap, and when that happened, companies that made money selling search through other means (e.g., the Yellow Pages, travel agents, classified ads) found themselves in a competitive crisis. At the same time, companies that relied on people finding them (e.g., selfpublishing authors, sellers of obscure collectibles, homegrown moviemakers) prospered.
This change in the relative costs of certain activities radically influenced some business models and even transformed industries. However, economic laws did not change: We could still understand everything in terms of supply and demand, and
Re-framing a technological advance as ‘a shift from expensive to cheap’ is invaluable for thinking about how it will affect your business.
could set strategy, inform policy and anticipate the future using off-the-shelf economic principles.
Cheap Means Everywhere
Chances are you are reading this under some kind of artificial light. Moreover, you probably never thought about whether using that light for reading was ‘worth it’. Light is so cheap that we use it with abandon. But, as economist William Nordhaus meticulously explored, in the early 1800s it would have cost you 400 times what you are paying now for the same amount of light. At that price, you would think twice before using artificial light to read. The subsequent drop in the price of light lit up the world: Not only did it turn night into day, but it allowed us to live and work in big buildings that natural light could not penetrate. Virtually nothing we have today would be possible had the cost of artificial light not collapsed to almost nothing.
What might be affected when a new technology makes something cheap is not always obvious, whether the technology is artificial light, steam power, the automobile or computing. Tim Bresnahan, a Stanford economist and one of our mentors, has pointed out that computers do arithmetic and nothing more, and that the commercialization of computers made arithmetic cheap. When arithmetic became cheap, not only did we use more of it for traditional applications, but we also used it for applications that were not traditionally associated with arithmetic — like music.
Heralded as the world’s first computer programmer, Ada Lovelace was the first to see this potential. Working under very expensive light in the early 1800s, she wrote the earliest recorded program to compute a series of numbers (called Bernoulli numbers) on a still-theoretical computer that Charles Babbage designed. Babbage was also an economist, and that was not the only time Economics and Computer Science intersected. Lovelace understood that arithmetic could ‘scale’ and enable so much more. She realized that applications of computers were not limited to mathematical operations:
“Supposing, for instance, that the fundamental relations of pitched sounds in the science of harmony and of musical composition were susceptible of such expression and adaptations, the engine might compose elaborate and scientific pieces of music of any degree of complexity.”
No computer had been invented yet, but Lovelace saw that an arithmetic machine could store and replay music. Of course, that is precisely what happened. When, a century and a half later, the cost of arithmetic fell low enough, there were thousands of applications for arithmetic that most had never dreamed of. Arithmetic was such an important input into so many things that, when it became cheap — like light before it — it changed the world. Reducing something to pure cost terms has a way of cutting through hype, although it does not help make the latest and greatest technology seem exciting. You would never have seen Steve Jobs announce Apple’s ‘new adding machine’ — even though that is all he ever did. By reducing the cost of something important, Jobs’s adding machines were transformative.
AI will be economically significant precisely because it will make something very important much cheaper. Right now, you may be thinking about intellect, reasoning or thought itself; you might be imagining robots all over or non-corporeal beings, such as the friendly computers in Star Trek, letting you avoid the need to think. Lovelace had the same thought, but quickly dismissed it. At least insofar as a computer was concerned, she wrote, it “had no pretensions to originate anything. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths.”
Despite all the hype and the baggage that comes with the notion of AI, what Alan Turing later called ‘Lady Lovelace’s Objection’ still stands: Computers still cannot think, so thought isn’t about to become cheap. However, what will be cheap is something so prevalent that — like arithmetic — you are probably not even aware of how ubiquitous it is and how much a drop in its price could affect the economy.
What will new AI technologies make so cheap? Prediction. Therefore, as Economics tells us, not only are we going to start doing more prediction, but we are going to see it emerge in surprising new places.