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19
Artifi cial Intelligence and
International Trade
Avi Goldfarb and Daniel Trefl er
The last 200 years have produced a remarkable list of major innovations, not
the least of which is artifi cial intelligence (AI). Like other major innovations,
AI will likely raise average incomes and improve well- being, but it may also
disrupt labor markets, raise inequality, and drive noninclusive growth. Yet,
even to the extent that progress has been made in understanding the impact
of AI, we remain largely uninformed about its international dimensions.
This is to our great loss. A number of countries are currently negotiating
international agreements that will constrain the ability of sovereign gov-
ernments to regulate AI, such as the North American Trade Agreement
(NAFTA) and the Trans- Pacifi c Partnership (TPP)- 11. Likewise, govern-
ments around the world are freely spending public funds on new AI clusters
designed to shift international comparative advantage toward their favored
regions, including the Vector Institute in Toronto and the Tsinghua- Baidu
deep- learning lab around Beijing. The international dimensions of AI inno-
vations and policies have not always been well thought out. This work begins
the conversation.
China has become the focal point for much of the international discus-
sion. The US narrative has it that Chinese protection has reduced the ability
Avi Goldfarb holds the Rotman Chair in Artifi cial Intelligence and Healthcare and is professor of marketing at the Rotman School of Management, University of Toronto, and a research associate of the National Bureau of Economic Research. Daniel Trefl er holds the J. Douglas and Ruth Grant Canada Research Chair in Competitiveness and Prosperity at the University of Toronto and is a research associate of the National Bureau of Economic Research.
The authors thank Dave Donaldson and Hal Varian for their thoughtful feedback. Trefl er acknowledges the support of the “Institutions, Organizations and Growth” Program of the Canadian Institute for Advanced Research (CIFAR). For acknowledgments, sources of
research support, and disclosure of the authors’ material fi nancial relationships, if any, please see http:// www .nber .org/ chapters/ c14012.ack.
463
464 Avi Goldfarb and Daniel Trefl er
of dynamic US fi rms such as Google and Amazon to penetrate Chinese mar-
kets. This protection has allowed China to develop signifi cant commercial
AI capabilities, as evidenced by companies such as Baidu (a search engine
like Google), Alibaba (an e-commerce web portal like Amazon), and Ten-
cent (the developer of WeChat, which can be seen as combining the func-
tions of Skype, Facebook, and Apple Pay). While no Chinese AI- intensive
company has household recognition outside of China, everyone agrees that
this will not last. Further, a host of behind- the- border regulatory asymme-
tries will help Chinese fi rms to penetrate Canadian and US markets.
Even the Pentagon is worried. Chinese guided- missile systems are suffi
-
ciently sophisticated that they may disrupt how we think of modern warfare;
large and expensive military assets such as aircraft carriers are becoming
overly vulnerable to smart weapons.1 This may do more than transform the
massive defense industry; these AI developments may radically shift the
global balance of power.
As international economists, we are used to hype and are typically dis-
missive of it. Despite AI’s short life—Agrawal, Gans, and Goldfarb (2018)
date its commercial birth to 2012—AI’s rapid insinuation into our daily
economic and social activities forces us to evaluate the international impli-
cations of AI and propose best- policy responses. Current policy responses
often rest on a US narrative of a zero- sum game in which either the United
States or China will win.2 Is this the right premise for examining AI impacts
and for developing AI policies? Further, calls for immediate action by
prominent experts such as Bill Gates, Stephen Hawking, and Elon Musk
will likely encourage governments to loosen their pocketbooks, but will
government subsidies be eff ective in promoting broad- based prosperity or
will subsidies become yet another form of ineff ective corporate welfare?
What specifi c policies are likely to tip the balance away from ineff ective
corporate handouts?
Using comparative advantage theory, trade economists have thought
long and hard about the right mix of policies for successfully promoting
industry. Many of our theories imply a laissez- faire free- trade approach.
However, since the early 1980s our theories have shown that certain types of
government interventions may be successful, for example, Krugman (1980),
Grossman and Helpman (1991), and the more informal theories of Porter
(1990). These theories emphasize the role of scale and the role of knowledge
creation and diff usion. Unfortunately, the precise policy prescriptions pro-
duced by these theories are very sensitive to the form of scale and the form
1. New York Times, Feb. 3, 2017. See also Preparing for the Future of Artifi cial Intelligence, Offi
ce of the President, Oct., 2016.
2. For example, https:// www .economist .com/ news/ business/ 21725018-its- deep- pool- data
- may- let- it- lead- artifi cial- intelligence- china- may- match- or- beat- america and http:// www
.reuters .com/ article/ us- usa- china- artificialintelligence/ u- s- weighs- restricting- chinese
- investment- in-artifi cial- intelligence- idUSKBN1942OX?il=0.
Artifi cial Intelligence and International Trade 465
of knowledge creation/ diff usion. And competition can play an important
role too, for example, in Aghion et al. (2001, 2005) and Lim, Trefl er and
Yu (2017).
We therefore start in section 19.2 by identifying the key features of AI
technology in regard to scale and knowledge. To date there are no mod-
els that feature the particular scale and knowledge characteristics that are
empirically relevant for AI. In section 19.3 we use these features (a) to off er
some suggestions for what an appropriate model might look like, and (b) to
draw implications for policy. This leads to high- level thinking about policy.
For example, it provides a foundation for assessing recent proposals put
forward by AI researcher Geoff Hinton and others on the potential benefi t
of public investments in AI.3 However, these models are not suffi
ciently
fi ne- grained to directly capture existing regulatory issues that “go behind the
border” such as privacy policy, data localization, technology standards, and
industrial regulation. In section 19.4 we therefore review the many behind-
the- border policies that already impact AI and discuss their implications for
comparative advantage and the design of trade agreements. We begin with
a factual overview of the international dimensions of AI.
19.1 From Hype to Policy
Statistics about where AI is being done internationally and how it is dif-
fusing can be tracked in a number of ways, for example, the number of
basic research articles, patents and patent citations produced in a region;
the number of start-ups established in a region; or the market capitaliza-
tion of publicly traded AI- based companies in a region. We look at two of
these indicators: basic research and market capitalization. For the former,
we collected time- series data on the institutional affi
liation of all authors of
papers presented at a major AI research conference, namely, the Association
for the Advancement of Artifi cial Intelligence (AAAI) Conference on Arti-
fi cial Intelligence. In table 19.1, we compare the 2012 and 2017 conferences.
In 2012, 41 percent of authors were at US institutions, but by 2017 this was
down to 34 percent. The two other largest declines were recorded by Canada
and Israel. While these countries all increased their absolute number of par-
ticipants, in relative terms they all lost ground to China, which leapt from
10 percent in 2012 to 23 percent in 2017.
We have not examined patent numbers, but suggestive work by Fujii and
Managi (2017) points to weaker international diff usion of AI: US tech-
nology giants such as IBM and Microsoft remain far and away the world’s
dominant patent applicants.
Another indication of the economic future of AI comes from the largest
3. “Artifi cial Intelligence is the Future, and Canada Can Seize It” by Jordan Jacobs, Tomi Poutanen, Richard Zemel, Geoff rey Hinton, and Ed Clark. Globe and Mail, Jan. 7, 2017.
466 Avi Goldfarb and Daniel Trefl er
Table 19.1
Participants at a major AI conference
Country
2012 (%)
2017 (%)
Change (%)
United States
41
34
– 6
China
10
23
13
United Kingdom
5
5
0
Singapore
2
4
2
Japan
3
4
1
Australia
6
3
– 2
Canada
5
3
– 3
India
1
2
1
Hong Kong
3
2
– 1
Germany
4
2
– 1
France
4
2
– 2
Israel
4
2
– 3
Italy
2
2
– 1
Other
10
10
0
Notes: Participation rates at the Association for the Advancement of Artifi cial Intelligence (AAAI) Conference on Artifi cial Intelligence. For example, of the papers presented at the 2017 conference, 34 percent of authors had a US affi
liation.
public companies in the world by market capitalization. Table 19.2 lists the
twelve largest companies worldwide. What is striking about the table is the
number of companies that might subjectively be described as “AI intensive.”
Seven of the twelve companies are heavily engaged in AI (such as Alphabet/
Google), three are in fi nance (where the use of AI is growing rapidly), and
one has a substantial pharmaceutical presence (where AI is likely to soon
be reducing development costs). What makes table 19.2 relevant for inter-
national trade is the fact that two of the largest companies worldwide are
now Chinese AI- intensive fi rms (Tencent and Alibaba). It is truly remark-
able that two high- tech companies based out of China—private companies,
not state- owned enterprises—are among the largest companies in the world.
While we had to move beyond the round number of ten to make this point, it
&
nbsp; is striking nonetheless. It points to the major global shake-up that is coming.
Some would conclude from tables 19.1 and 19.2 that almost all of the
world’s largest companies will soon be competing directly against Chinese
companies when—not if—these Chinese companies go global. In 2000,
Robin Li signaled his agreement by moving to China to establish Baidu.
The fl ood of US- trained talent returning to China has continued. This year,
former Microsoft executive Qi Lu joined Baidu as chief operating offi
cer
(COO). In describing China, Lu writes, “We have an opportunity to lead
in the future of AI.”4 Not everyone agrees. Some have argued that China’s
AI- intensive companies will not be globally competitive until they compete
head-on in China with global leaders such as Google. This fl ies in the face of
4. The Economist, July 15, 2017.
Artifi cial Intelligence and International Trade 467
Table 19.2
World’s largest public companies and AI exposure
Company
Market value ($)
AI exposure
1. Apple
754
High
2. Alphabet
579
High
3. Microsoft
509
High
4. Amazon
423
High
5. Berkshire Hathaway
411
Rising
6. Facebook
411
High
7. ExxonMobil
340
Low
8. Johnson & Johnson
338
Rising
9. JPMorgan Chase
314
Rising
10. Wells Fargo
279
Rising
11. Tencent Holdings
272
High
12. Alibaba
269
High
Notes: Market capitalization of the largest public companies as of March 31, 2017, from PWC
(2017). “AI exposure” is our subjective assessment of the role of AI in company performance.
a long history of Chinese export successes in other fi elds. Indeed, Sutton and
Trefl er (2016) describe both theoretically and empirically how developing
countries such as China initially enter new markets at a low level of quality,
but over time develop the capabilities to deliver high- quality, internationally