DECEMBER 2020

Mapping U.S. Multinationals’
Global AI R&D Activity
CSET Issue Brief

AUTHORS
Roxanne Heston
Remco Zwetsloot

Table of Contents
Executive Summary ...............................................................................................2
1. Introduction: U.S. Tech Multinationals and AI ................................................4
2. Methodology and Data ...................................................................................5
3. Findings ..............................................................................................................8
Where Are AI Labs and Staff Located? ...........................................................8
What Types of R&D Do AI Labs Conduct? .................................................. 14
What Drives Location Decisions?.................................................................. 19
Access to Talent ......................................................................................... 19
Market Access and Product Adaptation .................................................. 21
Cost Savings............................................................................................... 22
Conclusion .......................................................................................................... 24
Acknowledgments .............................................................................................. 26
Appendix: Data and Methods .......................................................................... 27
A. Locating AI Labs ........................................................................................ 27
B. Locating AI Staff ......................................................................................... 28
C. Comparing AI Labs, AI Staff, and Other R&D Measures ...................... 30
D. Classifying AI R&D Activities .................................................................... 32
Endnotes .............................................................................................................. 34

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Executive Summary
This paper presents new data on the global distribution of U.S. techcompanies’ artificial intelligence labs and staff. It focuses on six companies—
Amazon, Apple, Facebook, Google, IBM, and Microsoft—all of which havea history of conducting cutting-edge AI research and development. Ourfindings shed light on where these companies conduct AI R&D and why they
select particular locations.
This paper also addresses debates about the benefits and risks to national
security of having U.S. companies conduct R&D abroad. On its own, newdata will not solve these debates, but data is a prerequisite for nuanced
policy discussions and cost-benefit analyses. To facilitate future research on
the topic, we are releasing our new global AI labs dataset on CSET’s GitHub
page.
Key findings include:● Company AI labs are spread all over the world, especially in North
America, Europe, and Asia.
○ Media coverage frequently mentions “AI labs,” but not allcompanies conduct R&D along a “lab” structure. Amazon, forexample, integrates its R&D with the work of its product teams
and has no standalone labs. Apple, alternatively, does notrelease information about its R&D activities.
○ For the four companies where we could find information onlabs—Facebook, Google, IBM, and Microsoft—we found 62
labs conducting AI R&D. The majority of these labs (68percent) were located outside of the United States.
○ European countries, mainly the United Kingdom and France,
host 19 percent of AI labs in our dataset. Other common
destinations were Israel and China (10 percent each) andIndia (8 percent). The only region in the world without any AI
labs was Latin America.
● In contrast to AI labs, most company AI staff remain concentrated in
the United States.

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○ Whereas most AI labs are located abroad, 68 percent of AI
staff at these companies are located within the United States.
The San Francisco Bay Area and the Seattle region—where
the headquarters of five out of the six companies we focus onare located—host nearly half of these companies’ global AI
staff.
○ The companies combined have 500-plus AI staff in 30
locations across 15 countries. The main company hubs arealmost exclusively in large cities, both at home and abroad.
● Companies mainly focus on AI research in North America andEurope, and on AI development in other regions.
○ About 85 percent of labs in North America and Europe dosome form of basic AI research, and less than 15 percent
focus on development. In contrast, a large majority of labs inother countries, such as India and Israel, focus on appliedresearch or development.
○ Some companies’ labs appear to focus on research, whereas
others focus on development. Most labs conduct multiple
types of R&D.
● Companies decide to conduct AI R&D abroad for multiple reasons,but access to global talent appears to be the dominant factor.
○ According to company statements and industry analysts, themain reason that companies set up AI R&D labs abroad isaccess to talent. Other reasons include cost savings, as wellas market access and product adaptation.
○ Media reports and economic studies suggest U.S. immigration
restrictions are associated with U.S. companies’ global
expansion. Our findings similarly suggest that immigration
restrictions push companies to set up AI labs abroad. At thesame time, overseas expansion is unlikely to stop entirely evenif U.S. immigration policy became less restrictive.

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1. Introduction: U.S. Tech Multinationals and AI
The private sector is a driving force behind technical progress in AI andmachine learning, and a large fraction of private sector AI R&D is done by alimited number of large tech companies. A McKinsey report that surveyed 35
major technology companies found that they spent $18-27 billion in internalfunds on AI development in 2016, and another $8-12 billion on investments
and acquisitions.1 Since 2010, big tech in the United States absorbed morethan 60 smaller AI companies.2 The President’s Council of Advisors on
Science and Technology predicts that U.S. companies will spend more than$100 billion on AI R&D annually by 2025.3 By comparison, the U.S. federal
government intends to spend about $5 billion on unclassified AI and machinelearning-related research and development in FY2020.4
Many of the world’s biggest AI companies are American; this paper focuseson Amazon, Apple, Facebook, Google, IBM, and Microsoft in particular.
Together, these companies spend over $76 billion on R&D annually, andtheir collective market capitalization stands above $5 trillion.5 Their businessmodels are globalized. Apple, Facebook, Google, and IBM each receive
less than half of their total revenue from the U.S. market. Microsoft andAmazon receive 51 percent and 69 percent, respectively.6
The global reach of U.S. tech companies provides several benefits to theUnited States. The fact that they operate across the world allows them toinnovate at a scale and speed that would be impossible if they were focusedsolely on domestic markets, and ensures they can absorb talent and ideas
from all over the world. The U.S. economy and national security both benefit.
Some economists argue that U.S. multinationals expanding abroad contributeto more domestic employment, exports, and R&D, and that “less investment
abroad by US firms would weaken—not strengthen—the US economy.”7
But these companies’ scale and reach also come with risks. From theperspective of national security, for example, the commercial sector’s global
presence is a double-edged sword. As the 2018 National Defense Strategy
put it, “the fact that many technological developments will come from thecommercial sector means that state competitors and non-state actors will also
have access to them, a fact that risks eroding the conventional overmatch towhich our nation has grown accustomed.”8 U.S. multinationals’ R&D activities
abroad can boost other countries’ technology ecosystems in several ways, forexample, by training local talent, or through joint ventures involving
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intellectual property or collaborative research.9 One industry expert argues“if Microsoft had never founded [Microsoft Research Asia], that would havesignificantly delayed the rise of China’s AI ecosystem.”10 In 2019, the thenchairman of the Joint Chiefs of Staff said that Google’s work in China was“indirectly benefiting the Chinese military.”11 Others, including Microsoft and
Google, dispute such claims.12
These different benefits and concerns mean that U.S. policymakers facecomplex trade-offs as they think about whether and how to regulate U.S.
companies’ AI activities abroad. Resolving these complex trade-offs requires
data. To assess the implications of new policies or the continuation of thestatus quo, policymakers will need to know where U.S. multinationals areactive, what activities they engage in abroad, and why. Analysts anddecision-makers across the political spectrum have long argued that betterdata is needed on these and related questions.13
This paper informs these conversations by mapping the global AI R&D
activities of Amazon, Apple, Facebook, Google, IBM, and Microsoft. Section
2 describes how we collected and analyzed new data on these companies’
global AI labs and AI staff. Section 3 then presents our findings on wherecompany labs and staff are located, what types of R&D they appear to beengaged in across different locations, and what factors drive companies’
choice of location.
This research is meant to be a first step; many questions about U.S.
companies’ AI R&D activities remain unanswered. To facilitate future researchon the topic, we are releasing our new dataset of AI labs online along withthe publication of this paper. The Appendix discusses methodological choices
and other issues relevant to such future research.

2. Methodology and Data
This paper presents new data on the AI labs and staff of six big U.S. techmultinationals: Amazon, Apple, Facebook, Google, IBM, and Microsoft.
These companies were selected based on their high score on several AI R&Drelated metrics, including R&D spending, AI startup acquisitions, patent
holdings, and involvement in professional AI organizations.14 For thesecompanies, we sought to understand where they locate their AI R&D activities
and why.

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The first R&D metric we considered involved “labs,” which are featured
prominently in media and industry analyses of companies’ AI R&D. We
compiled lists of labs by searching companies’ own websites and news
reports, and confirmed the lists’ accuracy with company representatives.* We
found no public AI labs at Amazon and Apple; in 2014, Amazon started
integrating its AI research staff into product-focused teams,15 while Apple
does not publicly discuss its AI R&D activities.16 For the remaining four
companies—Facebook, Google, IBM, and Microsoft—we found a total of 62
labs, of which a plurality (23) are run by Google (Figure 1).17 The list is
current as of October 2019.
Figure 1. Number of public AI labs by company.

Source: CSET AI labs data (see Appendix A). Amazon and Apple are omitted because they
do not use a “lab” structure or do not publicly discuss their labs.

The second metric we looked at is staff counts. We count “AI staff” as any
company employee who listed one or more “AI skills,” such as “computer
vision” (a category of current AI research) or “PyTorch” (a specific machinelearning library), on their LinkedIn profile.† LinkedIn is, to our knowledge, theonly data source on AI skills and jobs with global coverage. However, its
data is not perfect; as discussed below, rates of LinkedIn usage vary across*We adopted companies’ own definitions of “lab” and “artificial intelligence.” Different
companies likely use these terms slightly differently; see the section of this paper on “WhatTypes of R&D Do AI Labs Conduct,” as well as Appendix A, for more discussion of this issue.
†

The full list of 46 AI skills we used to conduct our search is available in Appendix B.

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countries, and data is available only as relative company totals and locationspecific aggregates.18
Figure 2 compares the percentage of AI staff across the companies to thepercentage of AI labs, and shows that the relative distribution of the two
indicators differs considerably across companies. For example, while Google
has more than a third of all labs, it has only 23 percent of all staff. Staff data
are current as of May 2020.19
Figure 2. Global distribution of AI labs and staff by company.

Source: CSET AI labs data (see Appendix A); AI staff data from LinkedIn (see Appendix B).20

Both the lab and staff metrics have strengths and weaknesses as measures of
AI R&D activity. For example, the presence of an AI lab indicates that AI R&D
is taking place, but the absence of a lab does not imply the absence of R&D.
Snapshot measures of staff counts are a good indicator for current activity,
but do not reveal whether a company plans to expand in a certain location orwhat they are working on. For instance, Facebook AI Research in Paris had
just six researchers at its founding, but the company said from the start that itplanned to expand the lab, which eventually grew more than ten-fold.21 Bothmeasures sometimes suffer from lack of data availability, and results on one
measure can help fill the gaps of the other. To provide as comprehensive a
picture as possible, this report provides data on both labs and staff counts
when such data is available. The Appendix describes our methodology ingreater detail. The full CSET AI labs database is available athttps://github.com/georgetown-cset/us-multinational-ai-labs.
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3. Findings
This section presents our main findings. First, we discuss where Amazon,Apple, Facebook, Google, IBM, and Microsoft have located their AI R&D
activities, analyzing data on both labs and staff. Second, for the four
companies that have AI labs, we analyze what types of AI R&D they do indifferent locations. Third, we review what prior research and company
statements suggest about the reasons that companies decide to locate AI R&D
in particular places.
Where Are AI Labs and Staff Located?
In mapping U.S. multinational corporations’ (MNC) AI R&D activity, we study
two metrics: AI labs and AI staff. Figure 3 shows these two metrics are
distributed differently—while only about one-third (32 percent) of AI labs arelocated in the United States, around two-thirds (68 percent) of AI staff are.
Below, we will first take a closer look at the geographic distribution of labs,
followed by more detail on the distribution of staff.
Figure 3. Percentage of U.S. MNC AI labs and staff by U.S. versus non-U.S.
location.

Source: CSET AI labs data (see Appendix A); AI staff data from LinkedIn (see Appendix B).
Included companies for staff are Amazon, Apple, Facebook, Google, IBM, and Microsoft;no labs data available for Amazon and Apple.

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Figure 4 shows the international distribution of the 62 AI labs in our dataset,
which are located in 17 countries and 41 cities across 29 metropolitan
areas.22 Of the 20 labs located in the United States, six are located in Silicon
Valley, four in or near New York City, and another four in or near Seattle.
Outside of the United States, the most popular countries include Israel, China,
and India. Outside of countries with established technological hotspots, wefound four labs in Africa: one each in Egypt, Ghana, Kenya, and Nigeria.
Notably, we did not find any labs located in Latin America.
Figure 4. Global distribution of AI labs by location.

Source: CSET AI labs data (see Appendix A).

Within countries, the labs are predominantly located in well-known “techclusters,” such as London, Beijing, and the west coast region in Israel
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commonly referred to as “Silicon Wadi.” In line with economics research on
R&D clustering, the market areas that host the most labs are typically also
home to active AI startup ecosystems and universities that have highly ranked
AI programs.23 Indeed, companies sometimes establish labs by acquiring
those startups or partnering with universities (Box 1). Even if they do not,companies often benefit from being located there because clusters are hometo large talent pools and new technical ideas (See section on “What Drives
Location Decisions.”)24
Box 1. How Companies Establish AI Labs
Labs in our dataset were established in one of three ways: by hiring or relocating staff internally, bypartnering with local institutions, or by acquiring startups.
Internal hiring and relocation is by far the most common mode of establishment. Sometimes acompany will create a standalone location specifically for work on AI. One such example isGoogle Brain Tokyo, one of more than a dozen Google Brain groups around the globe.25 In othercases, the company will extend its existing (non-AI) R&D facilities to include AI-focused projects.
Companies can also partner with universities or other organizations to create joint R&D projects or
facilities. One common approach within AI is for a company to partner with a professor at aprestigious university, as Facebook AI Research did with Carnegie Mellon University professors
when it set up its Pittsburgh offices.26 Institutional partnerships are also an option. The
Massachusetts Institute of Technology (MIT) alone is home to two joint labs in our database: theMIT-IBM Watson AI Lab and Microsoft Research Lab - AI.27 Meanwhile, in Shanghai, Microsoft
teamed up with the state-owned enterprise INESA to create an AI Innovation Center, where it does
regional AI product development.28
Finally, companies will occasionally establish labs by acquiring startups. Since 2010, there havebeen at least 635 AI company acquisitions, roughly a tenth of which were made by the six
companies that this report focuses on.29 Microsoft’s acquisition of Montreal-based startup
Maluuba, which formed the basis for a significant expansion of AI R&D in Montreal in 2018, is
one example.30 Another clear case of a startup turned lab is DeepMind. Since Google acquired
the company in 2014, DeepMind has not only grown its headquarters to hundreds of staff but has
also spawned five global offices. However, not every startup acquisition leads to a new lab.
Startup staff and intellectual property are often folded into existing products or teams instead, inwhich case we do not count them as standalone “labs.”31

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Figure 5 takes a closer look at the locations of AI labs across the four
companies with labs. The United States is the most common location acrossall the companies, but each has labs across Europe and in Israel as well, andthree of the four companies have labs in Canada, China, and India. In
general, IBM and Microsoft appear proportionately more active outside ofNorth America and Europe than Google and Facebook.*

Figure 5. Number of AI labs in different locations, by company.

Source: CSET AI labs data (see Appendix A).

*

One possible explanation for this finding is that Facebook and Google have set up many
relatively new AI-specific research entities, while IBM and Microsoft only have general R&D
facilities, some of which have recently added AI components. Other explanations, such as
market-driven location choices, are discussed in more detail in the section of this paper on“What Drives Location Decisions.”

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The previous two figures tracked the number and location of labs, but mostcompanies do not release public data on how large their labs are. Moreover,as noted above, Amazon does not structure its AI R&D in labs and Apple
does not publicly release information on their R&D activities, and theremaining four companies may do AI R&D outside of their labs as well. It istherefore important to compare these findings to staff counts, which provide
another indicator of AI activity.
It was clear from Figure 3 that 68 percent of our companies’ AI staff arelocated in the United States. Digging deeper, Figure 6 maps out all 30
metropolitan areas in 15 countries where our six companies cumulatively
employ 500 or more AI staff.* Nearly half (46 percent) of AI staff are locatedin just two metropolitan areas: San Francisco and Seattle. Four other U.S.
cities—Austin, Boston, Los Angeles, and New York—are also in the top ten
locations.32*

Locations with 500 staff represent locations that host more than 0.5 percent of thesecompanies combined global workforces.

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Figure 6. Locations where companies cumulatively have more than 500 AI
staff.

Source: LinkedIn. Companies whose staff are included are Amazon, Apple, Facebook,
Google, IBM, and Microsoft. See Appendix B for how “AI staff” was measured.

Outside of the United States, our six companies employ most AI staff acrossEurope, India, and the Asia-Pacific region. Several locations host AI labs but
appear to have fewer than 500 AI staff, including several locations in China(in or near Shanghai, Shenzhen, and Beijing) and Israel. Because our main
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source for AI staff is LinkedIn, these findings have to be interpreted withcaution; some of the differences may be explained by different rates ofLinkedIn usage among multinational employees in these different countries.*
What Types of R&D Do AI Labs Conduct?
This section looks at the types of R&D that companies conduct at their AI labs.
R&D activities can be categorized as basic research, applied research, or
experimental development.33 In our context, we define “basic research” as
the pursuit of foundational breakthroughs in AI; “applied research,” as theapplication of AI research to known problems; and “experimental
development,” as the creation of AI prototypes and products.34 In order togroup the R&D activities of AI labs into these three categories, we looked atnews coverage, press releases, and other corporate statements (see*LinkedIn data likely over-represents AI staff from some nations relative to others, as theplatform is far more common in, say, the United States than it is in China. We expect crosscountry differences in the rates of LinkedIn usage to be significantly smaller among
employees at U.S. multinationals than among populations at large, but the possibility of error
remains real. For one study, which finds LinkedIn penetration rates ranging from 44 percentof the population in the United States to 1 percent in Nigeria, see Bloom, “LinkedIn countryby-country audience visualized.”

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Appendix D for more detail and examples). Many labs conduct multiple
types of R&D.*
Figure 7 shows the proportion of AI labs conducting each type of R&D. About
80 percent of the AI labs perform some form of research. Most AI labs (41 of62) conduct some basic research, although AI labs often conduct more than
one type of AI R&D. More than half of labs perform at least two types of AI
R&D, corroborating a separate survey of multinational R&D labs in China (notspecific to AI) that found “it is not unusual to observe all three types of R&D
activities within one tech MNC R&D lab.”35*

For example, Google Research India’s activities in Bangalore count as a mix of all three
R&D types based on the following statement: “This team will focus on two pillars: First,
advancing fundamental computer science and AI research … [and] second, applying thisresearch to tackle big problems … while also using it to make apps and services”; quoted in
Jay Yagnik, “Google Research India: an AI lab in Bangalore,” Google: The Keyword (blog),
September 19, 2019, https://blog.google/around-the-globe/google-asia/googleresearch-india-ai-lab-bangalore/.

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Figure 7. Types of R&D done at AI labs by R&D category.

Source: CSET AI labs data (see Appendix A).

Figure 8 shows the fraction of AI labs performing each type of R&D by
country or region. Labs in North America and Europe are predominantly
research focused, whereas labs located in other regions conduct less basic
research and more experimental development. Google’s AI China Center
was, at least at the time of its launch, a notable exception; a press releaseannouncing the center said it would be “focused on basic AI research.”36
Google’s AI lab in Ghana also conducts both basic and applied research.37

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Figure 8. Types of R&D at AI labs by country or region.

Source: CSET AI labs data (see Appendix A). A single AI lab can be involved in multiple
types of R&D. Australia and Taiwan, which each host only one lab, are excluded from thefigure.

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Figure 9 shows the fraction of each company’s AI labs conducting each typeof R&D. There are significant differences between the companies in terms ofthe types of R&D their labs perform. Facebook AI Research (FAIR) labs arealmost exclusively engaged in basic research, Google’s labs conduct a mixof basic and applied research, Microsoft’s labs are more likely to focus on
experimental development, and IBM’s labs conduct a mix of all three R&D
types.38 This illustrates that tech companies structure their R&D activities
differently, and may also use the term “lab” differently; as noted in Section 2,
in this paper we adopted each company’s own “lab” designations. We
discuss how we coded types of R&D in greater detail in Appendix D.

Figure 9. Types of AI R&D at labs by company.

Source: CSET AI labs data (see Appendix A). AI labs involved in multiple types of R&D arecounted multiple times.

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Differences in companies’ AI R&D activities are also visible with respect towhere they are established and how they performed. In Seattle, where AI
labs primarily focus on basic and applied research, Facebook’s local lab
director retains a computer science professorship at the University ofWashington and involves strong university ties.39 By contrast, othercompanies’ labs in Israel tend to focus on applied research and experimental
development. For example, Microsoft AI labs there, such as the Israel
Development Center and Advanced Technology Lab Israel, work closely withstartup founders and have a stronger engineering focus.40
What Drives Location Decisions?
This section considers why companies set up labs and offices in particularlocations. We find that these decisions are typically made on a variety ofgrounds, but companies’ statements, along with research in economics andbusiness, suggest three main reasons: access to talent, market access or
product adaptation, and cost savings.*

Access to TalentCompanies nearly always cite the presence of a local talent pool as adominant reason for their location choices.41 Speaking about a series of new*

A fourth reason suggested by the business literature for foreign investment by multinationals
is “technology scouting,” finding good ideas or companies to collaborate with or acquire.
Beyond Microsoft’s labs in Israel—where Zack Weisfeld, the then general manager ofMicrosoft Ventures Global Accelerators, spoke of Advanced Technology Lab Israel as ameans of finding promising AI startups—we did not see this motivation reflected in company
statements. See Julie Bort, “After Success in Israel, Microsoft Launches More Accelerator
Programs,” Business Insider, April 24, 2013, https://www.businessinsider.com/aftersuccess-in-israel-microsoft-launches-worldwide-startup-accelerator-program-2013-4.
Moreover, we did not assess what effects U.S. export control policies have on companies’
decisions to locate R&D overseas. Export controls may affect companies’ choice of location—
one IBM executive said export controls are “relevant” though “not a tier-one factor” indetermining the company’s R&D location choices; cited in Andrew B. Kennedy, The

Conflicted Superpower: America’s Collaboration with China and India in Global Innovation(New York: Columbia University Press, 2018), 135. However, export controls did not comeup in news reports about AI labs, and the complexities of export control regulations make it
difficult to assess independently.

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Facebook AI labs, CTO Mike Schroepfer put it simply: "We're basicallygoing where the talent is.”42 A Google spokesperson said, “the primary
driver for our choice of AI research office locations is the availability of talent
who are either already living in the area or would be attracted to move
there.”43 The managing director of Microsoft Research Asia explains thecompany’s decision to open a lab in China as resulting from their realizationthat “we cannot just have the research lab in the US because we know we
won’t hire enough, and also, we don’t believe all the smart and the most
passionate people doing research will all come to [the United States].”44
These arguments are reinforced by companies’ perception that there is a
global shortage of people with the necessary AI skills, which makes locatingin talent clusters especially attractive.45
Labs and offices are often located near universities; in the AI labs database,38 percent of regions with at least one AI lab are also home to one or moreof the world’s top 50 AI universities.46 Companies’ local offices often forge
close ties with universities in an attempt to access talent (see also Box 1).
Examples include Google’s ties with the Technical University of Munich andMicrosoft’s relationship with the University of Cambridge.47 In Israel, Google
reportedly sources half of its engineers from Tel Aviv University.48 Companies
are also often interested in hiring professors and researchers employed byuniversities49—in one prominent example, Uber hired about 50 staff from
Carnegie Mellon University in 2015 shortly after setting up a Pittsburgh lab.50
Universities also incubate startups that tech companies want to keep an eye
on for potential partnerships or acquisitions.
Some locations are good convening points for talent from a broader region,
either because they are up-and-coming hubs or because they have friendly
immigration policies. Google AI Ghana and Microsoft Taiwan, for example,
appear to have been set up to recruit not just from their respective countries
but from their broader regions.51 Labs within the European Union, whereinternal labor mobility is unrestricted for citizens, also often serve as hubs forregional talent.52

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Box 2. The Role of U.S. Immigration Policy in Location Decisions
Media reports and economic studies suggest U.S. immigration restrictions are associated with U.S.
companies’ global expansion. For instance, one July 2020 article on an executive order halting
temporary employment-based programs said that “even before the new visa restrictions,immigration obstacles had begun to hurt AI activity in the U.S.,” citing as evidence that “Companies
like Facebook, Microsoft, Google, Amazon, and Intel established AI centers in other countries inpursuit of local talent.”53 IBM says that 58 percent of AI researchers in its U.S. labs were born
abroad.54
In line with this argument, several economic studies have found that immigration restrictions lead totight labor markets for software talent in the United States, and that tight labor markets in turn drive
companies to locate more of their R&D abroad in places where skilled workers are more plentiful.55
In recent years, several countries have reformed their immigration systems to make it easier toattract AI talent.56 Some have also launched recruitment campaigns that explicitly target U.S.based workers with immigration difficulties, as illustrated by a Canadian billboard in Silicon Valleythat reads: “H-1B Problems? Pivot to Canada.”57 A Toronto-based immigration lawyer found that
U.S. tech firms “have opted to open Canadian offices [because they] can attract and keep workers
in Canada.”58 A Microsoft Research representative told us, “some of the people we are hiring
today in China and India are the exact same people we would normally be hiring in Redmond,
Boston, or NYC, but today they are not able to get visas to immigrate to the U.S.”59
On the other hand, as discussed in this section, companies have compelling reasons beyond
concerns with U.S. immigration policy to establish themselves abroad. For example, Facebook’s AI
lead Yann LeCun noted that “immigration in Canada is more well organized than immigration into
the U.S.,” but that “this didn’t factor into [Facebook’s] decision” to establish itself in Montreal.60
In sum, the evidence suggests that U.S. companies would likely establish AI R&D operations
abroad at a lower rate if U.S. immigration policies were less restrictive. However, due to factors
beyond talent that drive location decisions, companies would continue conducting at least some of
their AI R&D abroad even if immigration reforms were adopted.

Market Access and Product Adaptation
Economists often contrast “knowledge-seeking” or “talent-seeking”
investments abroad with “market-driven” investments.61 Foreign labs can help
gain access to markets in one of two ways. First, some countries require
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companies to establish local facilities or partnerships in exchange for market
access. Even in countries without such requirements, however, companies
often set up local offices in order to be close to their customers and to adapt
products to local conditions.62
Market access does not appear to be a dominant motivation for thecompanies establishing new standalone AI labs in our dataset, perhaps
because we exclude MNC offices without an R&D component (e.g., those
exclusively staffed by product and sales teams). But market access may
matter to AI R&D location indirectly. In China, for example, one study notesthat “most of the handful of R&D centers that conduct basic research did not
begin to do so until they had already established strong product development
functions.”63 Other studies find similar local “value chain climbing” incountries with sufficient R&D talent pools, including India and China.64

Cost Savings
Companies also conduct R&D abroad in order to save on costs. It is
commonly accepted that AI talent is expensive; exact numbers vary acrossstudies, but most find average salaries well above $100,000 in the UnitedStates.65 Similarly detailed AI salary statistics are not available for othercountries, but a 2013 survey by the National Science Foundation found that
U.S. information and communication technologies companies on average
had to spend nearly twice as much per employee for R&D conducted
domestically compared to R&D performed abroad.66 This indicates costsavings on salaries could be significant.67
Host governments also sometimes attract multinational companies throughnon-salary incentives, for example by providing low-rent or rent-free officespace or subsidized electricity. A desire to avoid paying U.S. taxes, or to takeadvantage of tax incentives provided by the host country, can also be
motivators for companies. Host governments provide such incentives because
they believe their populations, businesses, and economies will benefit. A hostcountry’s eagerness for foreign technology can also impose costs on
multinationals, however. These costs include the risk of physical or intellectual
property theft and the expense of guarding against such risks.
Companies themselves are typically reluctant to admit cost plays a role intheir decisions to establish offices abroad, but experts recognize it as animportant factor.68 For example, evaluating Microsoft’s establishment inCenter for Security and Emerging Technology | 22

Taiwan, which included an AI R&D component, a Taipei-based industry
analyst pointed to the greater return on investment the company would havein Taiwan due to lower salaries.69 Analysts also argue, however, that lower
costs, while helpful, are not a sufficient reason for a company to open a newlocation. Without a supply of sufficiently high-quality local talent, companies
will be reluctant to launch initiatives involving significant technical work even
if costs are substantially lower.70

Center for Security and Emerging Technology | 23

Conclusion
Based on our analysis of new data on U.S. multinational companies’ AI R&D
labs, we found that Facebook, Google, IBM, and Microsoft have 62 labsthat are spread all over the world; 20 are in the United States, and theremaining 42 are located across 16 countries. In contrast to AI labs,
however, most company AI staff remain concentrated in the United States.
Much AI R&D in North America and Europe appears focused on research,whereas labs based elsewhere tend to emphasize development. Companies
decide to conduct AI R&D abroad for multiple reasons, but access to global
talent is typically cited as the driving factor. Our findings regarding AI labs
are based on company classifications of “AI” and “labs”—we did not
independently assess the work that is being done at these locations. Data on
AI staff comes from information provided by individuals through LinkedIn.
Our findings can inform policy on several fronts. First, they reinforce past
research that suggests U.S. immigration reforms would encourage U.S.
companies to conduct more R&D at home (see Box 2), although it appears
unlikely that increasing the domestic talent supply would entirely halt orreverse companies’ internationalization of R&D. Second, for other tools that
could be used to encourage more domestic R&D, our data can help
policymakers assess how different policy approaches would affect leading
U.S. AI companies. Such impact assessments are an important part ofbalancing the complex benefits and risks of R&D internationalization. Third,
calls for multilateral R&D initiatives and an “alliance innovation base”
recommend building on pre-existing R&D links.71 Our data provides awindow into places where U.S. companies already have a significant AI R&D
presence, which could inform future diplomatic engagements.
This study also provides lessons and resources relevant to analysts. For
example, it is important to look at multiple metrics of AI R&D abroad instudying the extent of U.S. multinationals’ R&D internationalization, as
illustrated by the fact that domestic-to-foreign ratios for AI labs and AI staff
are almost mirror images. In Appendix C, we discuss several other AI R&D
metrics that future studies should consider. In order to facilitate future work on

Center for Security and Emerging Technology | 24

these questions, we are releasing our AI labs dataset along with this paper.*We encourage other researchers to build on this dataset by adding further
information on the AI labs or by adding additional labs or companies. Other
U.S. companies with AI R&D labs include Intel, NVIDIA, and Uber,72 andmany non-U.S. companies conduct significant global AI R&D, as well.73

The CSET AI labs dataset is available at https://github.com/georgetown-cset/usmultinational-ai-labs.
*

Center for Security and Emerging Technology | 25

Acknowledgments
Thanks to Tessa Baker, Ben Chang, Melissa Flagg, Rebecca Kagan, Igor
Mikolic-Torreira, Dewey Murdick, Ilya Rahkovsky, Kathleen Walsh, andrepresentatives from Facebook, Google, and Microsoft for their input andfeedback; to Rebecca Gelles and Farhana Hossain for data and visualization
support; and to Dale Brauner, Melissa Deng, Shelton Fitch, and Lynne Weilfor editorial support.
Roxanne Heston co-authored this paper while she was a Research Analyst
at CSET. She is currently detailed as an artificial intelligence policy adviser in
the Office of the Science and Technology Adviser at the U.S. Department ofState. She completed her contributions to this paper prior to joining the State
Department. The views expressed herein are the authors' and do notnecessarily reflect those of the U.S. government. Inquiries about this report
should be directed to Remco Zwetsloot.

© 2020 by the Center for Security and Emerging Technology. This work islicensed under a Creative Commons Attribution-Non Commercial 4.0
International License.
To view a copy of this license, visit
https://creativecommons.org/licenses/by-nc/4.0/.
Document Identifier: doi: 10.51593/20190008

Center for Security and Emerging Technology | 26

Appendix: Data and Methods
A. Locating AI Labs
To locate AI labs, we looked primarily at company news releases, company
websites, and other news coverage. Labs could involve either a standalone
AI R&D group or an AI R&D-focused team within a larger R&D organization.*
When companies listed R&D facilities that were not explicitly named as “AI”
centers, we looked at their websites to determine whether they described
themselves as having a systematic AI R&D program. We relied oncompanies’ own choices for what they called “AI” and what they labeled a“lab.”
This approach has several shortcomings. First, some information was notavailable for all labs. For example, we were unable to determine the dates of
establishment for a majority of the AI labs in our dataset. More broadly,
media and companies themselves often discuss projects outside of the UnitedStates in more detail because they are considered more notable, thoughsome labs abroad received little coverage in English-language sources. Moredetailed surveys of the companies may be needed to fill these informational
gaps.
Second, what counts as a “lab” differs across companies, and two of thecompanies we studied, Amazon and Apple, either do not use or do not
publicize “labs” (see Section 2).74 Designations are often a form of branding.

*

The following companies listed R&D locations on their websites. Only Facebook’s AI labs
were consistently listed distinct from their general R&D facilities:●

Facebook:
https://web.archive.org/web/20190611075902/https://research.fb.com/cat
egory/facebook-ai-research/ (The live version of this link now redirects toai.facebook.com, but a member of Facebook’s AI Communications teamcorroborated this list in an email exchange.)●

Google: https://research.google/careers/●

IBM: https://www.research.ibm.com/labs/●

Microsoft: https://www.microsoft.com/en-us/research/Center for Security and Emerging Technology | 27

A Google representative, for example, explained that the company used todesignate some of its locations as “AI centers,” but that groups doing thesame work today are simply referred to as Google Research locations.75
Similarly, Microsoft Research emphasizes that “AI research is infused across
many parts of Microsoft,” and does not happen only within groups
designated as “labs.”76 This shortcoming can be addressed by looking at
alternative metrics of R&D. This paper also looked at AI staff; Appendix C lists
several additional AI R&D-related metrics that future research could collectdata on.
We hope future research will shed further light on these issues. To lay thegroundwork for follow-on research, we are releasing our AI labs dataset
along with this paper. In addition to the descriptive AI labs data that wereported on in the paper, the dataset also includes links to the sources that weused to make coding decisions, for example on the type(s) of R&D that eachlab is engaged in. We did not conduct site visits at labs or interview lab
leadership and staff, host country governments, U.S. government experts, orother stakeholders. Future research should also look beyond quantitative
metrics to include such visits and interviews, if possible.
B. Locating AI Staff
To count the number of “AI staff” at our six companies across various
locations, we used employees’ skills listed on their LinkedIn profiles. We
accessed LinkedIn data through the LinkedIn Talent Insights platform. To
define “AI” skills, we used a list of “core AI” skills supplied to CSET by
Burning Glass Analytics, a labor market analytics company. In total, there are
46 “AI skills” that were included in both Burning Glass’s AI taxonomy andfound on LinkedIn profiles, ranging from specific machine learning libraries(e.g. PyTorch, TensorFlow) to general skill categories (e.g. Data Science,
Machine Learning).**

The following 46 skills both appear on LinkedIn and were identified as “core AI skills” by
Burning Glass: Artificial Intelligence (AI), IBM Watson, iThink, Cluster Analysis, Data
Science, k-means clustering, Kernel, Pattern Recognition, Predictive Analytics, Principal
Component Analysis, Computer Vision, Decision Trees, Deep Learning, Deeplearning4j,

Center for Security and Emerging Technology | 28

Any binary measure of whether someone has “AI skills” will inevitably be
imperfect. Narrow operationalizations will mean excluding people who do
AI R&D work (“false negatives”), whereas a broad operationalization will
apply to some people without real AI skills (“false positives”). On balance,
our skill list leans toward a broad operationalization; to minimize false
negatives, we include skills like “Chatbot” that may capture some workers
who are not conducting AI R&D. Moreover, using LinkedIn profiles meansthat we are relying on people to accurately report their skills. Finally, as
discussed in Sections 2 and 3, LinkedIn’s differential coverage may also resultin undercounting in countries outside of North America or Europe. Theseissues should be kept in mind when interpreting the results.
Researchers also have to determine how to associate employees withcompanies. Multinational corporations frequently have many wholly owned
subsidiaries or affiliates, such as Amazon Web Services for Amazon. In
measuring staff counts in particular locations, researchers have to choose
whether to include only employees at the main company or also at theirsubsidiaries and affiliates. In Figures 2, 3, and 6 we reported results only forthe main company. As a robustness check we also reran our analysis
including affiliates, which did not significantly change the results.77
Pending data availability, future research could use staff-based measures to
dig deeper on several questions. For example, if demographic data on staff
were available, future work could analyze what percentage of staff at U.S.
and foreign locations are U.S. citizens, and what the most common non-U.S.
nationalities are among company employees across different locations.
Similarly, data on staff job titles and educational backgrounds could, as
discussed in Appendix C below, be used to assess the type(s) of AI work
being done in different locations.

Google Cloud Platform (GCP), Gradient Boosting, H2O.ai, Keras, Machine Learning,
Apache Mahout, Microsoft Cognitive Toolkit (CNTK), MXNet, Neural Networks, Object
Tracking, OpenCV, Random Forest, Recommender Systems, Support Vector Machine (SVM),
Vowpal Wabbit, XGBoost, Caffe, TensorFlow, PyTorch, ANTLR, Chatbots, Computational
Linguistics, Latent Dirichlet Allocation, Lexical Semantics, Machine Translation, MoSes,
Natural Language Processing (NLP), K-Nearest Neighbors (KNN), Sentiment Analysis,
Speech Recognition, Text-to-Speech, Tokenization.

Center for Security and Emerging Technology | 29

C. Comparing AI Labs, AI Staff, and Other R&D Measures
This paper included data on both AI labs and AI staff because looking at onlya single metric is likely to provide a partial and misleading picture.78 Looking
only at labs, for example, could make it seem like the bulk of U.S. MNC AI
R&D is happening abroad, while looking at staff could give the opposite
impression. This does not mean the two metrics are contradictory; both havestrengths and weaknesses, and both tell an important story.
Where a multinational launches AI labs, and the company statements andmedia coverage that accompany lab launches, can provide a window intothat company’s AI R&D strategy. These statements often convey thecompany’s expansion plans and the type of R&D they hope to be doing, andexplain why the company decided to open the lab where it did. But company
statements must be taken with a grain of salt, so there are limits to what canbe gleaned from this type of information. Augmenting labs data with othermetrics is also important because not all companies use “labs” to structure
their R&D investments.
Measures of staff counts provide a more objective indicator of how much a
company has invested in a specific location. Companies might be inclined to
say that all labs are very valuable to them, but a location with 200 staff islikely to be more valuable than one with 20 staff. A weakness of current staff
measures, however, is that they only provide a snapshot: a location with 20
staff could have 200 staff one or two years later. Depending on how muchdata is available on staff—do researchers only have data on raw numbers or
also on job titles, professional background, and so forth?—staff data may or
may not allow a detailed assessment of a company’s focus. In this paper weonly reported raw staff numbers, but future research could go deeper.
Future research on the global distribution of AI R&D by multinational
companies can also look at other metrics. Additional metrics could include:79● Job advertisements. In addition to overall staff counts, job
advertisements can provide an indicator of a company’s growth in a
specific location. It might be possible to use advertisement content,such as educational requirements or job titles, to distinguish AI jobs
from non-AI jobs and R&D jobs from non-R&D jobs.

Center for Security and Emerging Technology | 30

● Spending. Company figures on AI R&D spending across different
locations would provide a valuable look into their R&D activity.
However, companies may be unable or unwilling to share data thatlooks at a specific technology area (AI) and disaggregates by
location. Existing surveys that ask companies for R&D spending, suchas the National Center for Science and Engineering Statistics’
Business R&D and Innovation Survey (BRDIS), unfortunately do notcontain sufficiently detailed data for AI-specific analysis.80● Acquisitions. Large multinationals frequently acquire startups andother companies in specific locales, sometimes to set up R&D labs(see Box 1). One possible issue with this metric from an R&D
perspective is that it is not always clear whether an acquisition was
focused on R&D activities.
● Partnerships. Companies often partner with other organizations,
including universities, in setting up research activities. For example,
Microsoft, as part of its $165 million “AI for Good” initiative,
provides extensive support to the University of Waterloo’s AI
Institute.81 Such partnerships were excluded from our dataset but arepart of companies’ local AI R&D footprints. Partnership data iscurrently not tracked in a systematic fashion.
● Publications. Looking at papers that staff from particular company
labs publish could provide another indicator of AI R&D activity.
Possible shortcomings include limited data availability on specificlocations (authors may not report their geographic location in theirpapers), poor coverage of more applied work (which is less likely to
get published), and systematic differences in company policies thatwould make cross-company comparisons problematic (somecompanies allow or encourage their staff to publish whereas others
do not).
● Patents. Company patents could also function as an AI R&D indicator,
and inventor addresses can help link the employees to specificcompany locations.82 For example, in a press release, IBM said that
“IBM inventors who reside outside the U.S. contributed to more than
36 percent of the company's 2015 patents.”83 As with publications,however, patents only cover a subset of R&D work, and crossCenter for Security and Emerging Technology | 31company comparisons could be problematic due to policy
differences.84
Future research should also look at the nature and impact of foreign R&D
activities more broadly. For example, to what extent are U.S. multinationals
able to control access to the R&D done in their labs abroad? How areproperty rights and ownership divided between multinationals and local
partners across different projects? How do companies weigh national security
concerns when establishing locations abroad?
D. Classifying AI R&D Activities
In the section on “What Types of R&D Do AI Labs Conduct,” we reported
results that categorized R&D into three categories: (1) Basic research; (2)Applied research; and (3) Experimental development. Categories were notexclusive, i.e., one lab can conduct multiple types of R&D. Below we provide
some examples, drawn from company websites and news reports, to illustrate
the type of information on which we based our coding choices. The CSET AI
R&D labs dataset released alongside this paper contains hyperlinked sources
for each choice.
● Basic research: “Members of FAIR carry out fundamental research in
the field of AI. Some of their breakthroughs are applied to Facebook's
platforms (Facebook, Instagram, and WhatsApp) by an appliedmachine learning (AML) team and other engineers, but the majority of
their research is purely academic.”85● Applied research: “Established in 1982, IBM Research – Tokyo
focuses on innovations that use cognitive computing to solve social
and industry problems through expertise in cognitive device
technologies, text analytics and mathematical science technologies.
With facilities in Tokyo and Shin-Kawasaki, IBM Research – Tokyo
has been playing a key role in exploring research with clients inJapan by applying research assets and expertise to meet their needs
for innovations and to overcome their business challenges throughjoint collaboration.”86● Experimental development: “The Microsoft AI R&D Center in Taiwan
will not pursue the basic science of artificial intelligence, which means
Center for Security and Emerging Technology | 32

teaching machines to interpret and anticipate a user’s movements.
Instead, the center is designed to be more engineering-centric.”87● Mix of all three R&D categories: “This [Google Research India] teamwill focus on two pillars: First, advancing fundamental computerscience and AI research by building a strong team and partneringwith the research community across the country. Second, applying thisresearch to tackle big problems in fields like healthcare, agriculture,and education while also using it to make apps and services used bybillions of people more helpful.”88

Center for Security and Emerging Technology | 33

Endnotes

1

Jacques Bughin, Eric Hazan, Sree Ramaswamy, Michael Chui, Tera Allas, Peter Dahlström,
Nicholaus Henke, and Monica Trench, “Artificial Intelligence: The Next Digital Frontier?”(McKinsey Global Institute, June 2017), 10,https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/O
ur%20Insights/How%20artificial%20intelligence%20can%20deliver%20real%20value%20
to%20companies/MGI-Artificial-Intelligence-Discussion-paper.ashx. This is the most recent
year for which we were able to find estimates of private sector R&D spending on AI.
2"The Race For AI: Here Are The Tech Giants Rushing To Snap Up Artificial Intelligence
Startups,” CB Insights, September 17, 2019, https://interactives.cbinsights.com/artificialintelligence-acquisitions-by-famga/.
3

President’s Council of Advisors on Science and Technology, Recommendations forStrengthening American Leadership in Industries of the Future (Washington, DC: Office of

Science and Technology Policy, June 2020), 5, https://science.osti.gov//media/_/pdf/about/pcast/202006/PCAST_June_2020_Report.pdf?la=en&hash=019
A4F17C79FDEE5005C51D3D6CAC81FB31E3ABC.
4

Of that $5 billion, $4 billion is for AI-related research, development, testing, andevaluation (RDT&E) at the Department of Defense, and $973.5 million is for civilian
agencies; see Chris Cornillie, “Finding Artificial Intelligence Money in the Fiscal 2020
Budget,” Bloomberg Government, March 28, 2019,https://about.bgov.com/news/finding-artificial-intelligence-money-fiscal-2020-budget/;National Science and Technology Council, Supplement to the President’s FY2020 Budget(Washington, DC: Executive Office of the President of the United States, 2019), 7,
https://www.whitehouse.gov/wp-content/uploads/2019/09/FY2020-NITRD-AI-RDBudget-September-2019.pdf.
Rani Molla, “Amazon spent nearly $23 billion on R&D last year — more than any other
U.S. company,” Vox, April 9, 2018,https://www.vox.com/2018/4/9/17204004/amazon-research-development-rd.
5

6

Omri Wallach, “How Tech Giants Make Their Billions,” Visual Capitalist, July 6, 2020,
https://www.visualcapitalist.com/how-big-tech-makes-their-billions-2020/; Vaishali

Center for Security and Emerging Technology | 34

Ladha, “IBM: Revenue by Region (2020),” Business Quant,https://businessquant.com/ibm-revenue-by-region-2.
7

Gary Clyde Hufbauer, Theodore H. Moran, Lindsay Oldenski, and Martin Vieiro, Outward
Foreign Direct Investment and US Exports, Jobs, and R&D: Implications for US Policy,

Peterson Institute for International Economics, August 2013,https://www.piie.com/bookstore/outward-foreign-direct-investment-and-us-exports-jobsand-rd-implications-us-policy.
8

Department of Defense, Summary of the 2018 National Defense Strategy (Department of
Defense, 2018), 3, https://www.hsdl.org/?view&did=807329.
9

Darren E. Tromblay and Robert G. Spelbrink, Securing U.S. Innovation: The Challenge ofPreserving a Competitive Advantage in the Creation of Knowledge (New York: Rowman &Littlefield, 2016), 68; William C. Hannas and Huey-meei Chang, “China Access to Foreign
AI Technology” (Center for Security and Emerging Technology, September 2019), 13,
https://cset.georgetown.edu/research/chinas-access-to-foreign-ai-technology/; Lee
Branstetter, Britta Glennon, and J. Bradford Jensen, “Knowledge Transfer Abroad: The Roleof U.S. Inventors within Global R&D Networks” (NBER Working Paper No. 24453, March2018), https://www.nber.org/papers/w24453.
10Matt Sheehan, “Who Benefits From American AI Research in China?”, MacroPolo,
October 21, 2019, https://macropolo.org/china-ai-research-resnet/. On a webpage
about its presence in China, Microsoft says that it “boasts a robust partner ecosystem with
17,000 partners. For every RMB that Microsoft earns in China, Microsoft partners earn 16”;
see “About Microsoft’s Presence in China,” https://news.microsoft.com/about-microsoftspresence-in-china/.
11

Ryan Browne, “Top US general says Google ‘is indirectly benefiting the Chinese military,’”

CNN Politics, March 14, 2019, https://www.cnn.com/2019/03/14/politics/dunfordchina-google/.

A Microsoft Research representative told us that Microsoft believes “China would be
where it is now regardless of what we did in the early 2000s in China” (email
correspondence, October 6, 2020). Google’s CEO has contested claims the company
benefits the Chinese military and says the company has “cleared up any misunderstandings”;
see Jackie Wattles, “Google CEO says he wants to reach the next billion users, but has noplans to relaunch in China,” CNN, June 17, 2019,https://edition.cnn.com/2019/06/16/tech/google-china-ceo-sundar-pichai/index.html.
12

Center for Security and Emerging Technology | 35

13

See, e.g., White House, “National Security Strategy,” December 2017, 20,
https://www.whitehouse.gov/wp-content/uploads/2017/12/NSS-Final-12-18-20170905-2.pdf (calls for U.S. government agencies to “improve their understanding ofworldwide S&T trends and how they are likely to influence--or undermine--American
strategies and programs”); Sheehan, “Who Benefits From American AI Research in China?”;
Tromblay and Spelbrink, Securing U.S. Innovation.
On R&D spending, see Molla, “Amazon spent nearly $23 billion on R&D last year —more than any other U.S. company”; on patent counts, see TR Staff, “Who Is Winning the AI
Race?”, MIT Technology Review, June 27, 2017,https://www.technologyreview.com/s/608112/who-is-winning-the-ai-race/; on startup
acquisitions, see CB Insights, "The Race For AI: Here Are The Tech Giants Rushing To Snap
Up Artificial Intelligence Startups”; on industry publications, see Gleb Chuvpilo, “Who’s
Ahead in AI Research? Insights from NIPS, Most Prestigious AI Conference,” Medium,
August 7, 2018, https://medium.com/@chuvpilo/whos-ahead-in-ai-research-insightsfrom-nips-most-prestigious-ai-conference-df2c361236f6; on faculty hiring, see Michael
Gofman and Zhao Jin, “Artificial Intelligence, Human Capital, and Innovation,” SSRN,
January 2020, 36, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3449440. Allcompanies are prominent members of the Partnership on AI; see “Meet the Partners,”
Partnership on AI, https://www.partnershiponai.org/partners/.
14

Steven Levy, “Inside Amazon’s Artificial Intelligence Flywheel,” WIRED, February 1,
2018, https://www.wired.com/story/amazon-artificial-intelligence-flywheel/; Amazon
uses the term “Tech Hub” to describe some of the locations where the company conducts
research and software development; see, e.g., “Amazon Expands Toronto Tech Hub andAnnounces Plans to Create 600 New Tech Jobs,” dayone: The Amazon Blog, December 18,
2018, https://press.aboutamazon.com/news-releases/news-release-details/amazonexpands-toronto-tech-hub-and-announces-plans-create-600.
15

16

On Apple’s relative secretiveness, see, e.g., Tristan Greene, “Apple’s culture of secrets is
why it won’t win the AI race,” The Next Web, September 6, 2017,https://thenextweb.com/apple/2017/09/06/apples-culture-of-secrets-is-why-it-wontwin-the-ai-race/. The public information on its AI R&D activities that exists suggests thecompany does have some AI R&D labs. For example, Apple’s offices in Cambridge (United
Kingdom) “are thought to be hubs for Siri and A.I. research,” according to news reports;
Jonny Evans, “Apple is pumping money into creating an international chain of R&D centers,”
ComputerWorld, March 17, 2017,https://www.computerworld.com/article/3181020/apple-is-pumping-money-intocreating-an-international-chain-of-randd-enters.html.

Center for Security and Emerging Technology | 36

17

Google’s lab predominance may be in part attributable to how they delineate AI labs.
Google often has different names for different AI projects in the same market area. For
instance, Google has three distinct AI labs in Montreal: Google Research, Google Brain,
and DeepMind. Other companies do not as clearly separate their AI projects, which may
drive up Google’s lab count without necessarily indicating an increase in the number ofpeople they have doing AI research in a location. However, these different AI labs do often
focus on different types of R&D and may indicate a diversity of research that other companies
do not have in any one location. See Appendix A for a discussion of how we defined AI labs.
18

Data limitations mean we do not have the number of employees by company, or
company-specific location data.
19

LinkedIn data likely over-represents AI staff from some nations relative to others, as theplatform is far more common in, e.g., the United States (44% penetration) than it is in China(3% penetration). However, we expect the disparity in representation by country is likely to
be significantly lower for the subset of the population that works at U.S. technological MNCs.
Jeremy Bloom, “LinkedIn country-by-country audience visualized,” Thinknum, March 29,
2018, https://media.thinknum.com/articles/tracking-linkedins-growth-from-american-iconto-global-player/.
20

We compared the fraction each company’s AI staff comprised with and without theiraffiliates, but the results stayed similar to those above. This graph shows the companies
alone, affiliates excluded.
21

Romain Dillet, “Facebook Opens New AI Research Center In Paris,” TechCrunch, June 2,
2015, https://techcrunch.com/2015/06/02/facebook-opens-new-ai-research-center-inparis/; Sam Shead, “Facebook Plans To Double Size Of AI Research Unit By 2020,”
Forbes, October 1, 2018,https://www.forbes.com/sites/samshead/2018/10/01/facebook-plans-to-double-sizeof-ai-research-unit-by-2020/#32f8cf1c4c4f.
22

Market areas are clusters of towns and cities that are located near each other and tightly
economically integrated. For example, San Francisco and its surrounding towns (e.g., Menlo
Park, Palo Alto) are counted as falling within the same market area. In practice, these market
areas overlap with the OECD’s metropolitan area classifications(https://stats.oecd.org/Index.aspx?DataSetCode=CITIES). For non-OECD countries, weapplied the OECD’s framework to that country’s cities and regions; cities in Israel’s “Silicon
Wadi” region — which include Tel Aviv, Jerusalem, and Haifa — are counted as fallingwithin the same market area because they are tightly economically integrated. For morediscussion, see Organization for Economic Cooperation and Development, Policy

Center for Security and Emerging Technology | 37

Roundtable: Market Definition, 2012,https://www.oecd.org/daf/competition/Marketdefinition2012.pdf.
23

For AI startup ecosystems, see “Global Startup Ecosystem Report 2019,” Startup
Genome, May 9, 2019, 67-68, https://startupgenome.com/reports/global-startupecosystem-report-2019; for top AI programs, see Emery Berger, “CSRankings: Computer
Science Rankings,” CSRankings, Accessed August 7, 2020,
https://csrankings.org/#/index?ai&vision&mlmining&nlp&ir&world.
24

For an overview of the economics literature on tech clusters, see William R. Kerr and
Frédéric Robert-Nicoud, “Tech Clusters,” The National Bureau of Economic Research, no.
27421 (June 2020), https://www.nber.org/papers/w27421.
25

Synced, “Google Boosting its AI Research in Tokyo,” Medium, April 27, 2018,https://medium.com/syncedreview/google-boosting-its-ai-research-in-tokyo608f8ba11c9.
26

Cade Metz, “Facebook Adds A.I. Labs in Seattle and Pittsburgh, Pressuring Local
Universities,” The New York Times, May 4, 2018,https://www.nytimes.com/2018/05/04/technology/facebook-artificial-intelligenceresearchers.html.
27“MIT-IBM Watson AI Lab,” https://mitibmwatsonailab.mit.edu/; Darrell Etherington,“Microsoft creates an AI research lab to challenge Google and DeepMind,” TechCrunch,
July 12, 2017, https://techcrunch.com/2017/07/12/microsoft-creates-an-ai-researchlab-to-challenge-google-and-deepmind/.
28

Microsoft Asia News Center, “Microsoft establishes Microsoft Research Asia-Shanghai
and a new AI Innovation Center,” Microsoft Blog, September 17, 2018,https://news.microsoft.com/apac/2018/09/17/microsoft-establishes-microsoftresearch-asia-shanghai-and-a-new-ai-innovation-center/.
29

We know that five of the six companies made 59 (9.2 percent) AI acquisitions. We do nothave data for IBM but, given how the data is presented, believe IBM made between one andsix such purchases, for a total of 9.4-10.2 percent. CB Insights, "The Race For AI: Here Are
The Tech Giants Rushing To Snap Up Artificial Intelligence Startups.”
30

Microsoft News Center, “Microsoft announces significant expansion of Montreal researchlab, new director,” Microsoft News Centre Canada, January 24, 2018,

Center for Security and Emerging Technology | 38

https://news.microsoft.com/en-ca/2018/01/24/microsoft-announces-significantexpansion-montreal-research-lab-new-director/.
31

For example, when Google acquired Jetpac, an AI-based image recognition company, in
2014, their staff, intellectual property, and functions were rolled into Google’s existing (nowdefunct) Picasa product.
32

For a deeper look at the geographic distribution of AI talent in the United States beyond
the six big tech companies, see Melissa Flagg and Justin Olander, “AI Hubs in the UnitedStates” (Center for Security and Emerging Technology, May 2020),
https://cset.georgetown.edu/research/ai-hubs-in-the-united-states/.
33

This classification is used, for example, by the U.S. National Science Board, which
adopted the definitions from the Organization for Economic Cooperation and Development.
For complete definitions, see the OECD glossary entries for Basic Research(https://stats.oecd.org/glossary/detail.asp?ID=192), Applied Research(https://stats.oecd.org/glossary/detail.asp?ID=12), and Experimental Development(https://stats.oecd.org/glossary/detail.asp?ID=908).
34

The boundaries between these types of R&D are undoubtedly blurry; see, for example,

Donald E. Stokes, Pasteur’s Quadrant (Washington, DC: The Brookings Institution, 1997).
35

Xiaohong Quan, “Multinational Research and Development Labs in China: Local andGlobal Innovation,” Ph.D. dissertation, University of California, Berkeley, 2005, ProQuest(3211492).
36

Fei-Fei Li, “Opening the Google AI China Center,” Google: The Keyword (blog),
December 13, 2017, https://www.blog.google/topics/google-asia/google-ai-chinacenter/.
37
Google’s two AI labs in Ghana are Google AI Ghana and Google Brain Ghana, both
located in the capital city Accra. Google AI Lead Jeff Dean said that they chose Ghana in2019 for its “robust network of academic institutions as well as infrastructure.” Victor
Asemota, “‘Ghana is the future of Africa’: Why Google built an AI lab in Accra,” CNN
World , July 15, 2018, https://edition.cnn.com/2018/07/14/africa/google-ghana-ai/
index.html.
38

The one group that Facebook calls a “lab” and that does AI experimental development isData.AI, a 10-person team improving the company’s internal interface at Facebook’s Tel

Center for Security and Emerging Technology | 39

Aviv R&D center. CTech, “Facebook Sets Up AI Team in Tel Aviv,” CTech, April 23, 2019,https://www.calcalistech.com/ctech/articles/0,7340,L-3760927,00.html.
39

Yann LeCun, “Facebook AI Research Expands With New Academic Collaborations,”
Facebook Newsroom , July 17, 2018, https://about.fb.com/news/2018/07/facebookai-research-expands/.
40“Advanced Technology Lab Israel,” Microsoft Research, https://www.microsoft.com/enus/research/group/advanced-technology-lab-israel/; Microsoft Israel R&D Center,
https://www.microsoftrnd.co.il/; Sam Shead, “Execs from Facebook, Google, andMicrosoft explain why they use Israel for their R&D,” Business Insider , October 6, 2016,
https://www.businessinsider.com/facebook-google-microsoft-israel-rd-2016-10?op=1.
41

Bronwyn H. Hall, “The Internationalization of R&D” (University of California, Berkeley,August 2010), 10,https://eml.berkeley.edu/~bhhall/papers/BHH10_RND_international_August.pdf.
42

Cade Metz, “Facebook Adds A.I. Labs in Seattle and Pittsburgh, Pressuring Local
Universities,” The New York Times , May 4, 2018,https://www.nytimes.com/2018/05/04/technology/facebook-artificial-intelligenceresearchers.html.
43

A Google spokesperson said: “In general, the primary driver for our choice of AI research
office locations is the availability of talent who are either already living in the area or would
be attracted to move there. Aside from that it is the usual factors, such as strength of local
academic institutions, business-friendly environment and good transport infrastructure. We
think that with world-class universities and a thriving developer community, Europe is well
positioned to play a leading role in AI research and application.” See Sam Shead, “Google
Brain Has Expanded To Amsterdam,” Forbes , July 10, 2018,https://www.forbes.com/sites/samshead/2018/07/10/google-brain-has-expanded-toamsterdam/#5e7f426b26f4.
44

Microsoft Research Podcast, “Celebrating 20 Years of MSR in Asia with Dr. Hsiao-Wuen
Hon,” https://www.microsoft.com/en-us/research/podcast/celebrating-20-years-of-msr-

in-asia-with-dr-hsiao-wuen-hon/.
45

On perceptions of talent shortages in AI, see Remco Zwetsloot, Roxanne Heston andZachary Arnold, "Strengthening the U.S. AI Workforce: A Policy and Research Agenda"

Center for Security and Emerging Technology | 40

(Center for Security and Emerging Technology, September 2019),
https://cset.georgetown.edu/research/strengthening-the-u-s-ai-workforce/.
46

Calculated using CSRankings top 50 from August 7, 2020.

47

TU Munich’s Applied AI initiative is designed to benefit Google and Cambridge’s staff
work on major Microsoft projects and its AI Residency Program; see, respectively, Arne
Graevemeyer, “Google and TU Munich promote artificial intelligence [Google und TU
München fördern Künstliche Intelligenz],” Heise Online, February 16, 2018,https://www.heise.de/newsticker/meldung/Google-und-TU-Muenchen-foerdernKuenstliche-Intelligenz-3972112.html; and Christopher Bishop, “Microsoft Research andCambridge University strengthen their commitment to AI innovation and the field’s future
leaders,” Microsoft News Centre UK, October 31, 2018, https://news.microsoft.com/engb/2018/10/31/microsoft-research-and-cambridge-university-strengthen-theircommitment-to-ai-innovation-and-the-fields-future-leaders/.
48

Shead, “Execs from Facebook, Google, and Microsoft explain why they use Israel for
their R&D.”
49

Facebook’s Chief AI Scientist Yann LeCun says the company has set up AI research labs
near universities in order to accommodate professors who do not want to move. “Basically
you have to get the talent where it is,” said LeCun. “Not everyone wants to live wherever wehave our labs.” Quoted in Alan Boyle, “FAIR competition? Facebook creates official AI labs
in Seattle and Pittsburgh, vying for top talent,” GeekWire, May 5, 2018,https://www.geekwire.com/2018/fair-competition-facebook-raises-status-ai-researchlabs-seattle-pittsburgh/.
50

Josh Lowensohn, “Uber gutted Carnegie Mellon’s top robotics lab to build self-driving
cars,” The Verge, May 19, 2015,
https://www.theverge.com/transportation/2015/5/19/8622831/uber-self-drivingcars-carnegie-mellon-poached.
51

Asemota, “‘Ghana is the future of Africa’”; Elvina Yang, “Microsoft R&D Center in Taiwan
Starts Recruiting for AI Research,” Meet Startup @TW, March 27, 2018,https://meet.bnext.com.tw/intl/articles/view/42604.

Center for Security and Emerging Technology | 41

52

McKinsey, for example, noted that Facebook’s AI lab in Paris will “make it easier for thecompany to recruit top researchers in Europe.” Bughin, Hazan, Ramaswamy, Chui, Allas,
Dahlström, Henke, and Trench, “Artificial Intelligence: The Next Digital Frontier?”.
53

Kyle Wiggers, “White House advisory council’s AI guidance conflicts with Trump’s talentpool sabotage,“ VentureBeat, July 10, 2020,
https://venturebeat.com/2020/07/10/white-house-advisory-council-calls-on-u-s-toincrease-ai-funding-to-10-billion-by-2030/.
54

Dario Gil, “How to Ensure the U.S.’s Quantum Future,” Scientific American, August 20,
2020, https://www.scientificamerican.com/article/how-to-ensure-the-uss-quantumfuture/.
55

Lee G. Branstetter, Britta Glennon, and J. Bradford Jensen, “The IT Revolution and the
Globalization of R&D,” Innovation Policy and the Economy, 19 (2019): 1-37,
https://www.journals.uchicago.edu/doi/abs/10.1086/699931; Britta Glennon, “HowDo Restrictions on High-Skilled Immigration Affect Offshoring? Evidence from the H-1B
Program” (SSRN, February 21, 2020), https://ssrn.com/abstract=3547655; Margaret E.
Peters, Trading Barriers: Immigration and the Remaking of Globalization (Princeton: Princeton
University Press, 2017).
56

Tina Huang and Zachary Arnold, “Immigration Policy and the Global Competition for AI
Talent” (Center for Security and Emerging Technology, June 2020),
https://cset.georgetown.edu/research/immigration-policy-and-the-global-competition-forai-talent/.
Zwetsloot et al., “Strengthening the U.S. AI Workforce,” 6-7.

57

58

Erica Alini, “2 years in, Trump’s immigration squeeze is feeding Canada’s tech talentpipeline,” Global News, April 10, 2019,https://globalnews.ca/news/5117201/canada-immigration-tech-trump/.
Email correspondence with Microsoft Research spokesperson, October 6, 2020.

59

60

John Mannes, “Facebook is the latest tech giant to hunt for AI talent in Canada,”

TechCrunch, September 14, 2017, https://techcrunch.com/2017/09/14/facebook-isthe-latest-tech-giant-to-hunt-for-ai-talent-in-canada/.

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61

Branstetter, Glennon, and Jensen, “The IT Revolution and the Globalization of R&D.”

62

In general, “emerging technologies often [require] a close interaction between R&D andproduction”; see Börje Johansson and Hans Lööf, “Global Location Patterns of R&D
Investments” (Centre for Excellence for studies in Science and Innovation, April 2006), 5,
https://www.researchgate.net/publication/5094381; A study of deep learning
publications and startups suggests that AI developers and AI adopters also benefit from colocating; see J. Klinger, J.Mateos-Garcia, and K. Stathoulopoulos, “Deep learning, deepchange? Mapping the development of the Artificial Intelligence General Purpose
Technology,”ArXiv [Cs.CY] (August 20, 2018), arXiv,19,
https://arxiv.org/abs/1808.06355.
63William C. Hannas, James Mulvenon, and Anna B. Puglisi, Chinese Industrial Espionage:

Technology Acquisition and Military Modernization , (Routledge, 2013), 65.
64

Gert Bruche, “The Emergence of China and India as New Competitors in MNCs’
Innovation Networks,” Competition and Change 13, no. 3 (September 2009): 267–288,
https://www.researchgate.net/profile/Gert_Bruche/publication/228714393_The_Emerg
ence_of_China_and_India_as_New_Competitors_in_MNCs'_Innovation_Networks/links/
0f317530a2cc960641000000.pdf; Branstetter, Glennon, and Jensen, “The IT Revolution
and the Globalization of R&D.”
65

GlassDoor estimates the average income for a U.S. AI job is $114,121. “Artificial
Intelligence Salaries,” Glassdoor, Accessed August 7, 2020,
https://www.glassdoor.com/Salaries/artificial-intelligence-salary-SRCH_KO0,23.htm.
PayScale reports an average salary for people with AI skills as $122,127. “Salary for Skill:
Artificial Intelligence (AI),” PayScale, Accessed August 7, 2020,
https://www.payscale.com/research/US/Skill=Artificial_Intelligence_(AI)/Salary. Indeed
found that Machine Learning Engineers make an average of $140,456 per year in the
United States. “Artificial Intelligence Salaries in the United States,” Indeed, Accessed August
7, 2020, https://www.indeed.com/salaries/Artificial-Intelligence-Salaries.

66

Brandon Shackelford and Raymond M. Wolfe, National Center for Science andEngineering Statistics, U.S. Companies Performed $73 Billion in R&D Outside the UnitedStates in 2013 (Washington, DC: National Science Foundation, April 2017), 3,
https://www.nsf.gov/statistics/2017/nsf17317/nsf17317.pdf.

Center for Security and Emerging Technology | 43

67

Beyond salaries, other cost saving factors include taxes and capital investments (realestate, energy costs, and so forth); see, e.g., Hall, “The Internationalization of R&D.”
68

Vijay Govindarajan, Gunjan Bagla, and Avinash Agrawal, “Which U.S. Companies Are
Doing the Most R&D in China and India?”, Harvard Business Review, March 26, 2015,
https://hbr.org/2015/03/which-u-s-companies-are-doing-the-most-rd-in-china-andindia.
69

Ralph Jennings, “Why Google, IBM & Microsoft Are All Expanding In Taiwan This Year,”
Forbes, April 4, 2018, https://www.forbes.com/sites/ralphjennings/2018/04/04/whygoogle-ibm-and-microsoft-all-decided-to-expand-in-taiwan-this-year/#39bb9fc35c72.
70

Govindarajan, Bagla, and Agrawal, “Which U.S. Companies Are Doing the Most R&D in
China and India?”. This cost-benefit calculation may differ for less technical operations, suchas sales or certain types of engineering, but our focus is on R&D activities.
71

Andrew Imbrie, Ryan Fedasiuk, Catherine Aiken, Tarun Chhabra, and Husanjot Chahal,“Agile Alliances: How the United States and Its Allies Can Deliver a Democratic Way of AI”(Center for Security and Emerging Technology, February 2020),
https://cset.georgetown.edu/research/agile-alliances/; Daniel Kliman, Ben FitzGerald,
Kristine Lee, and Joshua Fitt, “Forging an Alliance Innovation Base” (Center for a New
American Security, March 2020), https://www.cnas.org/publications/reports/forging-analliance-innovation-base; Martijn Rasser, Rebecca Arcesati, Shin Oya, Ainikki Riikonen, andMonika Bochert, “Common Code: An Alliance Framework for Democratic Technology
Policy” (Center for a New American Security, October 2020),
https://www.cnas.org/publications/reports/common-code.
72

Intel AI Labs: https://newsroom.intel.com/press-kits/intel-labs/; NVIDIA AI Labs:
https://www.nvidia.com/en-in/deep-learning-ai/ai-labs-her/; Uber AI Labs:
https://www.uber.com/newsroom/ailabs/.
73

The Australian Strategic Policy Institute has already mapped the global presence ofChina’s largest tech companies, including their R&D labs; see Danielle Cave, Samantha
Hoffman, Alex Joske, Fergus Ryan, and Elise Thomas, “Mapping China’s Tech Giants,”
Australian Strategic Policy Institute, 2019, https://www.aspi.org.au/report/mappingchinas-tech-giants.

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74

When it comes to defining “AI,” almost all companies’ labs appear to focus on machinelearning-related research.
75

Correspondence with Google representatives, September 25, 2020.

76

Email correspondence with Microsoft Research representative, October 6, 2020.

77

Including affiliates could affect two results: the distribution of staff across companies(Figure 2) and across locations (Figures 3 and 6). For Figure 2, the biggest change when
including affiliates is to Amazon’s fraction of total AI staff, which increases from 19 percent to25 percent of the total. The remaining companies change only by one or two percentage
points. For Figure 6, the fraction of staff in any one metropolitan area also varies by no morethan one or two percentage points, with the exception of Seattle, which shifts from 21
percent to 14 percent of all staff. The fraction of staff in the United States (Figure 3) changes
from 68 percent to 64 percent.
78

Zachary Arnold, Rebecca Gelles, and Ilya Rahkovsky, “Identifying AI-Related
Companies: A Conceptual Outline and Proof of Concept” (Center for Security and Emerging
Technology, July 2020), https://cset.georgetown.edu/research/identifying-ai-relatedcompanies/.
79

For a general overview of AI R&D metrics, see Raymond Perrault et al., “The AI Index
2019 Annual Report” (Stanford Human-Centered AI Institute, December 2019),
https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf. For a broader
discussion of metrics to track R&D and innovation, see Bronwyn H. Hall and Adam B. Jaffe,“Measuring Science, Technology, and Innovation: A Review” (May 2012),
http://people.brandeis.edu/~ajaffe/Hall-Jaffe%20HJ12_indicators_final.pdf.
80“Business R&D and Innovation Survey (BRDIS),” National Science Foundation,

https://www.nsf.gov/statistics/srvyindustry/#sd&tabs-1.
81

Waterloo Artificial Intelligence Institute, “The University of Waterloo’s Artificial Intelligence
Institute expands AI for Good partnership with Microsoft Canada,” July 8, 2020,
https://uwaterloo.ca/artificial-intelligence-institute/news/university-waterloos-artificialintelligence-institute. Budget figures for Microsoft’s AI for Good program are from John
Kahan, “Using AI to Advance the Health of People and Communities Around the World,”
Microsoft, January 29, 2020, https://blogs.microsoft.com/on-theissues/2020/01/29/ai-for-health-child-mortality/.

Center for Security and Emerging Technology | 45

82

Lee Branstetter, Guangwei Li, and Francisco Veloso, “The Rise of International
Coinvention,” in The Changing Frontier: Rethinking Science and Innovation Policy (National
Bureau of Economic Research, 2015), https://www.nber.org/chapters/c13028.
83

IBM, “IBM First in Patents for 23rd Consecutive Year,” January 13, 2016, https://www03.ibm.com/press/us/en/pressrelease/48785.wss.
84

Nathan Calvin and Jade Leung, “Who Owns Artificial Intelligence? A Preliminary Analysis
of Corporate Intellectual Property Strategies and Why They Matter” (Future of Humanity
Institute, February 2020), https://www.fhi.ox.ac.uk/wp-content/uploads/GovAI-workingpaper-Who-owns-AI-Apr2020.pdf.
85

Shead, “Facebook Plans To Double Size Of AI Research Unit By 2020.”

86“IBM Research | Tokyo,” IBM, http://www.research.ibm.com/labs/tokyo/.

87

Yang, “Microsoft R&D Center in Taiwan Starts Recruiting for AI Research.”88

Jay Yagnik, “Google Research India: an AI lab in Bangalore,” Google: The Keyword(blog), September 19, 2019, https://blog.google/around-the-globe/googleasia/google-research-india-ai-lab-bangalore/.

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