It’s become common in late 2018 to hear that the United States and China are locking themselves into an Artificial Intelligence ‘arms race’. But what does that really mean, and why (apart from hype) should anyone think of competition in this particular set of technologies as an arms race and not just — well — plain old commercial and economic competition?

The answer lies in the long term economic and security consequences of general purpose technologies, and in distinctive characteristics of the technologies that fall under the AI umbrella (I prefer the term ‘machine learning’ because it carries fewer connotations and science-fiction expectations). General purpose technologies are technologies that sweep across the economy and impact what is possible in many sectors, like a wind of change that shakes up how companies and governments do what they do in the broadest sense. Machine learning is a general purpose technology because it can (and will) be applied in just about every economic production process you can imagine, from retail management to autonomous driving to drug discovery and beyond.

But the most important characteristic of machine learning as a technology is that it has strong first mover advantages and positive feedback loops. My recent paper published in Business & Politics explains in detail why that is the case (it’s not just about the technology itself, but also about people, about regulation, about intellectual property, and other ‘auxiliary’ elements of the machine learning economy) and why that matters so much for how countries will seek economic growth and national security in the next decade.

The big underlying question is how countries should think about data flows in the context of globalization and economic growth. Growth theory has long grappled with the consequences of cross-border flows of goods, services, ideas, and people. But the most significant growth in cross-border flows now comes in the form of data. And — just as they did with flows of goods and services — countries are starting to express concern about the nature of those data flows, including imbalances in ‘imports’ and ‘exports’, as well as the differences between ‘raw’ data (like raw materials) and high value data products.

I argue in this paper that they are right to do so, in contrast to perspectives that see data flows as a rising tide that lifts all boats. At the other extreme, it’s just as wrong to portray data flows as a zero-sum game.  Data flows have unique characteristics in relation to the machine learning economy, and those characteristics lie at the heart of a new ‘high development theory’ that I propose for the modern era. Countries need a clear perspective here in order to make successful and adaptive policy choices on issues like data localization, subsidies for national data champions, foreign direct investment restrictions, and other controversial issues of the day.

As challenging as these decisions are for the US and China, they are even tougher problems for advanced industrial economies that are behind the leaders in machine learning (like many European states) and for developing countries, who will have to find a new growth and development ladder to climb now that the low-wage manufacturing window is nearly shut.  This paper lays out a logic for those choices in a series of thought experiments that I believe just about every economic development agency in the world will have to confront in the next several years.

This paper was runner-up for the inaugural Business & Politics David P Baron Prize.

 

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