Who Is Winning the AI Race: China,
the EU, or the United States?
— 2021 Update
By Daniel Castro and Michael McLaughlin | January 2021

The United States stillholds a substantial
overall lead in AI, but
China has continued toreduce the gap in some
important areas and the
EU continues tofall behind.

The nations that lead in the development and use of
artificial intelligence (AI) will shape the future of thetechnology and significantly improve their economic
competitiveness, while those that fall behind risk losing
competitiveness in key industries. 1 As a result, more than
30 nations have created national AI strategies to improvetheir prospects.2 To date, the United States has emerged
as the early frontrunner in AI, but China is challenging
its lead.3

INTRODUCTION
This report examines the progress China, the European Union, and theUnited States have made in AI relative to each other in recent years andprovides an update on a report released on their comparative rankings from2019. It finds that the United States still holds a substantial overall lead, butthat China has continued to reduce the gap in some important areas. In
addition, the EU continues to fall behind. Absent significant policy changes inboth the EU and United States—particularly the EU changing its regulatory
system to be more innovation-friendly, and the United States developing andfunding a more proactive national AI strategy—it is likely that the EU willremain behind both the United States and China, and that China will
eventually close the gap with the United States.

FINDINGS
In 2019, the Center for Data Innovation analyzed the AI capabilities ofChina, the European Union, and the United States using 30 metrics across 6

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1categories: talent, research, development, hardware, adoption, and data. We
found that the United States led in four categories (talent, research,development, and hardware), and China led in two (adoption and data). Outof 100 total available points, the United States led with 44.2 points,
followed by China with 32.3 and the European Union with 23.5.
This report measures the progress each region has since made in AI byusing new data to update 15 of the metrics and add 1 new metric. It findsthat the United States still leads, with 44.6 points, followed by China with
32.0 and the European Union with 23.3.
To get a sense of each region’s AI strengths in relation to their size, we also
calculated scores for each metric by adjusting for the size of their labor
forces. Controlling for size, the United States (58.0 points) leads theEuropean Union (24.2) and China (17.8)—although China has narrowed the
distance between itself and the United States since our last report.
Crucially, China has made incremental progress—reducing the gap or
extending its lead over the United States in more than half of the updated
metrics. In contrast, the EU has made progress relative to the United Statesin only slightly more than a quarter of the updated metrics. As such, theUnited States has maintained or expanded its lead over the European Union
in nearly 75 percent of the updated metrics.
Despite China’s incremental improvement in many indicators, the UnitedStates has slightly increased its overall lead in our scoring system because it
has performed extremely well on heavily weighted indicators, such as
venture capital and private equity funding. For example, it has an
unmatched number of AI start-ups, which received $8 billion more in venture
capital and private equity funding than did China in 2019. 4 The UnitedStates also performs well on several indicators in which China has narrowed
the gap somewhat. One example is the research and development (R&D)
spending of software and computer services firms. Chinese firms haveclearly surpassed EU firms in R&D spending, but U.S. software and computer
services firms still spent three times more on R&D than did China and theEuropean Union combined in 2019. 5 Furthermore, average U.S. research
quality is still higher than that of China and the European Union. 6 Lastly,
despite China’s growing attempts to reduce its reliance on U.S.
semiconductors, the United States is still the world leader in designing chips
for AI systems. 7
China’s AI capabilities relative to the European Union and the United States
have improved in several ways. First, China has surpassed the EU as theworld leader in AI publications. 8 Second, the quality of its AI research has
generally trended upward year to year. 9 Third, its software and computer
services firms have increased their R&D spending. 10 Fourth, China now has
nearly twice as many supercomputers ranked in the top 500 for

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2performance as the United States—the United States led in this indicator as
recently as 2017. 11 Finally, China likely continues to lead in the amount ofdata generated. 12 Overall, however, China has not significantly reduced thegap in AI between itself and the United States, but its trend of consistent
progress could eventually evaporate the U.S. lead.
The European Union’s progress vis-à-vis the United States is mixed. For
example, U.S. AI firms continue to receive substantially more investment
than do European ones. Yet EU venture capital and private equity funding asa percentage of U.S. funding grew from 13 percent to 22 percent between
2016 and 2019. 13 In addition, the EU’s field-weighted citation impact (FWCI)
for AI papers, a relative measure of paper quality, increased in 2018 while
the United States’ FWCI decreased. 14 But the European Union has fallen
further behind the United States in terms of the number of funding deals,
acquisitions of AI firms, and AI firms that have raised at least $1 million in
funding since our last report. 15 In addition, EU software and computer
services firms have failed to close the gap between themselves and U.S.
firms in R&D spending. 16 The United Kingdom’s departure from the bloc
will also diminish EU AI capabilities, both in absolute terms and on a percapita basis.

METHODOLOGY
The purpose of this report is to assess the current state of AI development.
Unfortunately, there are no standard industrial classifications for firms
developing AI technologies, so compiling indicators to compare AI
development among nations is challenging. Nonetheless, many metrics
reveal important insights about the current state of AI development. Our
2019 report examined six categories of metrics—talent, research, enterprisedevelopment, adoption, data, and hardware—to measure the AI capabilities
of China, the European Union, and the United States. 17 This report uses thesame metrics, updates 15 of them, and adds 1 new metric (FWCI) to assess
the three region’s progress and capabilities in AI. The updated and new
metrics span four of the categories of metrics: research, development, data,and hardware.
There are limitations to focusing only on China, the European Union, and theUnited States. Indeed, many sources indicate other nations have madeprogress in AI. For example, India has a growing workforce with AI skills,
Israel receives significant private investment per capita, and Australia
publishes many deep-learning papers. 18 Nonetheless, we chose to review
China, the European Union, and the United States because they generally
outperform their peers in the six categories in absolute terms. For
consistency and ease of comparison with our last report, we treated theUnited Kingdom as part of the European Union since it officially left the blocon January 31, 2020. When notable, however, we discussed how the

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3European Union’s status for a particular indicator would change if we
excluded the United Kingdom.
We chose the six categories for several reasons. First, nations with therequisite AI talent will be able to better develop and implement AI systems,
attract businesses, and ensure their universities have enough talented AI
professors to teach the next generation of AI researchers. Second, research
will help nations expand AI innovation and solve problems related todomestic priorities and industries. Third, the number of AI companies andstart-ups, combined with related investment capital, lays the groundwork fora strong AI industry that will continue to innovate. Fourth, adoption of AI
systems will not only allow organizations to learn how to solve problems
related to implementation, but generate demand for AI services, thereby
likely helping domestic AI developers. Fifth, more and higher-quality data will
create new opportunities to use machine learning in AI applications. Finally,leading in hardware will reduce nations’ dependency on other countries—
something that, given the current trade dispute between China and theUnited States, could play an important role.
Within each category, we measured a nation’s progress using multiple
indicators. For example, for the research category, we used metrics such as
the number of AI papers, the quality of the AI papers, and R&D metrics torank China, the European Union, and the United States. For several of themetrics, complete data was not available for the European Union. For these
indicators, we estimated an EU figure using available data. We detailed
these estimates in the endnotes and the appendix of this report for updated
indicators. (For indicators we did not update, these details can be found in
the endnotes and appendix of the 2019 report.) 19 Each indicator is shown
both in absolute terms and controlling for the size of the economy. For
example, both the total number of AI researchers and the proportion of AI
researchers as a share of the economy’s total workforce are indicated.
We calculated a score for every indicator for each region. To do so, we first
calculated a proportional score. For example, on the indicator for thenumber of supercomputers ranking in the top 500, China has 214
computers, the European Union has 91, and the United States has 113.
Thus, China gets a proportional score of 0.51, the EU 0.22, and the UnitedStates 0.27. 20 Each indicator is worth between 1 and 5 points. So, if theindicator for supercomputers is worth 2 points, China receives a score of
1.02 points, the European Union 0.44, and the United States 0.54. We
replaced each region’s old data and score for any indicator in which we had
updated data. The data and scores for indicators that lacked updated data
did not change from our last report. The corresponding point value for eachindicator stayed the same as in our 2019 report, except for the indicators in
the research category. 21 We added a new metric to this category, andCENTER FOR DATA INNOVATION

4

reduced the individual point values of indicators in this category by at most
1 point.
We assigned different weights to different indicators based on ourassessment of their relative importance in determining national AI
development success. As a result, not all categories are worth the same
number of points. However, the sum of all indicators is 100 points. Appendix
1 lists the categories, indicators, and corresponding weights. For several
indicators, we had to use a combination of quantitative and qualitative
analysis. In these cases, we ranked the regions first, second, and third, andtheir scores are the inverse of their ranking. For example, if China ranked
first, it received 3 points (prior to the score being adjusted by the weight of
the indicator).
To calculate category scores, we summed each region’s score for theindicators in the category. To calculate overall scores, we summed thecategory scores. We used this method to calculate two sets of scores: one
based on the absolute value of the metrics, and one adjusting each metricby the number of workers in the economy.
The following sections present data for new and updated indicators. Please
see the 2019 report to review data for the other indicators. 22

AI DEVELOPMENT
The ability of nations to develop AI firms is critical to their competitiveness.
Such firms provide tools and services to the growing number of companies
adopting AI. Indeed, the global percentage of large companies using AI in atleast one function or business unit increased from 47 percent to 58 percent
from 2018 to 2019. 23 This section updates four of the six metrics from the2019 report with new data. The updated metrics cover venture capital andprivate equity funding, acquisitions of AI firms, and the number of AI firms.
We allotted the development section 25 of the 100 available points. On an
absolute basis, the United States (15.5 points) led the European Union (5.4
points) and China (4.1 points). Controlling for the size of their economies,
the United States (19.2 points) led the European Union (4.6) andChina (1.2).

VENTURE CAPITAL AND PRIVATE EQUITY FUNDING (2019)
A nation's firms need access to sufficient capital to develop. In addition, theflow of investment can indicate the concentration of promising start-ups. The
United States (estimated $14.3 billion) led China (estimated $5.6 billion)
and the European Union (estimated $3.2 billion) in venture capital andprivate equity funding for AI firms in 2019. 24 On a per-worker basis, theUnited States ($86.5) led significantly over the European Union ($12.8) andChina ($7.2). 25

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5

China’s AI start-ups were not immune to the fizzling of China’s venture
capital boom in 2019. 26 Between 2017 and 2018, Chinese AI firms raised
80 percent of what U.S. AI firms raised. In 2019, Chinese AI firms raisedroughly 40 percent of their U.S. counterparts. 27 This contrast is a result of
U.S. AI firms receiving almost $4 billion more in investment in 2019 than2018 while investment in Chinese AI firms barely increased. 28 One reason
for China’s drop could be reduced venture capital flows between China andthe United States. In 2019, U.S.-owned venture capital firms invested $5
billion in all Chinese start-ups, compared with nearly $20 billion in 2018. 29
Chinese foreign direct investment in the United States also dropped. Still,
the stark difference in funding levels for AI firms in each nation suggests
U.S. firms may be able to better weather reduced investment flows. Data
from the second quarter of 2020 suggests that the U.S. is maintaining its
funding lead for AI firms. 30
EU AI firms received more investment in 2019 than in 2017 and 2018combined. 31 Still, the European Union has lagged behind the United States
and China every year since 2016 in venture capital and private equity
funding for AI firms. Moreover, the United Kingdom accounted for 57
percent of EU AI firm’s funding in 2019. 32 Thus, the EU’s relative standing in
AI investment will likely drop in the coming years.

Table 1: AI venture capital and private equity funding (2019)

Absolute
Per Worker

China

EU

US

$5,641M$3,207M$14,345M

7.2

12.8

86.5

Number of Venture Capital and Private Equity Funding Deals (2019)
The total amount of venture capital and private equity can be a result offunds concentrated in several large deals involving only a handful of AI startups. This report therefore also looks at the number of deals. The UnitedStates (786 deals) led the European Union (378) and China (264) for2019. 33 Thus, the United States (4.7 deals per 1 million workers) held asignificant lead over the European Union (1.5) and China (0.3) on a perworker basis. 34
Despite an increase in funding deals in all three regions, each region's shareof deals remained mostly unchanged relative to the others from 2017 to2018. The European Union slightly decreased its share (28 percent to 26
percent), the United States' share did not change (55 percent), and China's
slightly increased (17 percent to 18 percent). 35 Similar to investment

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6

funding, however, the EU was heavily reliant on the United Kingdom. UK AI
firms received nearly 40 percent of the venture capital and private equity
deals in 2019 that went to EU AI firms. 36

Table 2: Number of venture capital and private equity funding
deals (2019)
China

EU

US

Absolute

264

378

786

Per 1M
Workers

0.3

1.5

4.7

Number of Acquisitions of AI Firms (2019)
Acquisitions can improve a firm’s ability to develop innovative products andservices by bolstering both its internal talent and intellectual property. This
indicator tracks the number of acquisitions of firms in the AI category groupon Crunchbase by region in 2019. U.S. firms (130 acquisitions) acquired
more AI firms than did both EU (30) and Chinese firms (3). Per 1 million
workers, U.S. firms (0.8) made 7 times more acquisitions than did EU firms(0.1) and more than 100 times more did than Chinese businesses (<0.1). 37
U.S., EU, and Chinese firms each acquired more AI firms in 2019 than in
2018. Compared with 2018, U.S. firms acquired 14 more firms in 2019, EU
firms acquired 8 more, and Chinese firms acquired 2 more. U.S. AI firms
have acquired at least four times more AI firms than did their EU
counterparts each year since 2017. 38

Table 3: Number of acquisitions of AI firms (2019)
China

EU

US

Absolute

4

30

130

Per 1M
Workers

0.0

0.1

0.8

Number of AI Companies (2019)
AI firms without sufficient funding are less likely to develop innovative AI
products or services. Thus, it is important to gauge how many firms in eachregion are well-funded enough to make an impact in AI. This indicator tracks
the number of firms in the AI category group on Crunchbase that havereceived at least $1 million in combined funding. 39 The United States (2,130

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7

firms) led both the European Union (890) and China (398). The UnitedStates had nearly 13 such firms per 1 million workers, ahead of theEuropean Union (3.5) and China (0.5). 40
This indicator highlights several important elements of the competition
between the three regions. First, the United States still maintains asignificant advantage, but China started to close the gap in the past year. In
absolute terms, China’s share of the three regions’ AI companies grew from
8 percent to 12 percent, while the United States’ share decreased from 64
percent to 62 percent. The European Union’s share also decreased (28percent to 26 percent). 41 Second, U.S. immigration policy may besuppressing its number of AI start-ups. For example, the United States lacks
a visa that makes it easy for AI entrepreneurs to immigrate to the nation. 42
In addition, domestic AI graduates are more than twice as likely asinternational graduates to work for a small firm. One likely reason for thisdisparity is it is often difficult and costly for a start-up to sponsor a foreign
employee. 43 Third, the European Union frequently loses domestic talent to
the United States, which could be one reason why it has fewer AI start-ups. 44
A 2019 study finds that students are less likely to start a business and raiseless money when their professors have left their teaching positions. 45
Fourth, other analyses find that the United States has not only the most AI
start-ups but also the world’s leading start-ups. For example, CB Insights
ranked the top 100 AI start-ups using factors such as patent activity, market
potential, and talent. Its analysis finds that 65 of the top 100 start-ups are
based in the United States, which is ahead of the United Kingdom (8),Canada (8), China (6), Israel (3), Germany (2), France (1), Spain (1), andSweden (1). 46

Table 4: Number of active AI firms that have received more than $1
million in funding (2020)
China

EU

US

Absolute

398

890

2,130

Per 1M
Workers

0.5

3.5

12.8

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8RESEARCH
Research sustains innovation. Countries need to perform both basic
research, which does not have obvious commercial value, and applied AI
research, which solves practical problems. 47 In some areas of AI, such as
natural language processing, advancements are occurring at a rapid pace.
For example, the creators of a 2018 benchmark to test AI systems’ ability tounderstand language released a new, more challenging benchmark only ayear after their initial release because some systems quickly matchedhuman performance on the original test. 48 In addition, the time to train alarge image classification system shrank from three hours to barely morethan a minute between 2017 and 2019. 49
This section analyzes the number and quality of AI scholarly papers andbusiness R&D funding to assess China, the European Union, and the UnitedStates. Ideally, the study would also include government R&D funding, which
has helped lead to the development of innovations such as the global
positioning system (GPS), supercomputers, and Google’s search engine. 50
However, nations have different classifications of what constitutes AI R&D,
and some do not report AI R&D figures nor distinguish between private andpublic money in their announcements. Nonetheless, the United States spent$1.1 billion on non-defense AI R&D in the fiscal year 2020. 51 Moreover, theU.S. Department of Defense spent roughly $4 billion on AI R&D funding. 52
China, meanwhile, spent between $2 billion and $8 billion on AI R&D in
2018—although these estimates rely on several assumptions. 53 And in
2018, the European Commission announced it planned to invest €1.5 billion($1.7 billion) in AI by 2020 under Horizon 2020, its research and innovation
program. 54 However, the Commission has acknowledged that this figure
likely represents a small fraction of all the investments from member
states. 55 For example, the Commission believes its investment would trigger
an additional €2.5 billion ($2.8 billion) in public-private partnerships. 56
We allotted the research category 15 of 100 available points. On an
absolute basis, the United States led in AI research (7.2 points), followed by
China (4.1 points) and the European Union (3.7 points). Controlling for thesize of the workforces, the United States ranked first (8.9 points), followedby the European Union (3.8) and China (2.3).

Number of AI Research Papers (2018)
The number of research papers a region publishes each year is one way togauge its research prowess. This metric tracks the number of AI papers
researchers published from each region in 2018 as tracked by Scopus,
which is a database that contains millions of documents from thousands ofpeer-reviewed journals. 57 China (24,929 papers) led the European Union(20,418) and the United States (16,233). Per one million workers, however,the United States (98.1 papers) led the European Union (81.4) andChina (31.8). 58
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9The European Union’s portion of the world’s AI papers has decreased
steadily since 2015, when it published nearly 29 percent of AI papers. Since
then, its share fell in 2016 (26.3 percent), 2017 (25.5 percent), and 2018(23.1 percent). The EU’s share in 2018 would have dropped to 19 percent if
we had not included the United Kingdom—which was barely above theUnited States’ share (18 percent). In the same time frame (2015–2018),
China increased its share of research papers from under 23 percent toabove 28 percent. Indeed, 2018 marked the first time in almost a decadethat China published more AI papers than did the EU. 59

Table 5: Number of AI research papers (2018)
China

EU

US

Absolute

24,92920,41816,233

Per 1M
Workers

31.8

81.4

98.1

Field-Weighted Citation Impact (2019)
It is important to gauge the quality of research a region produces. This
indicator tracks the re-based FWCI for each region. This figure measures
how often publications from each region are cited compared with similar
publications worldwide. In 2019, the United States led the European Union(1.1) and China (0.8) with an FWCI of 1.4, meaning its papers received 40
percent more citations than the world average. 60 The United States’ high
FWCI matched its representation at prestigious AI conferences, such as theConference on Neural Information Processing Systems. That same year, nineof the ten leading organizations for accepted papers at the conference werebased in the United States or owned by U.S. based firms. 61
Nonetheless, the United States’ FWCI in 2018 was its lowest since 1998. In
contrast, the EU’s FWCI has been increasing since 2015. As such, while theEU’s share of the world’s output of AI papers is declining, its publication
quality is increasing. 62 In comparison, China’s decline in FWCI from 2017 to2018 coincided with its authors publishing significantly more AI papers(16,455 to 24,929). 63 China’s FWCI had steadily increased from 2012 (0.6)
to 2017 (0.9) before the recent decline. 64

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Table 6: Field-weighted citation impact (2018)
China

EU

US

0.8

1.1

1.4

Field-Weighted Download Impact (2018)
Another way to gauge the impact of a nation’s AI research is to measure howoften individuals read its AI papers. This indicator tracks the re-based fieldweighted download impact (FWDI) of AI authors in China, the European
Union, and the United States. FWDI compares the number of downloads anation’s AI papers receive to their expected number of downloads based on
the type of document, subject type, and publication year. This figure is rebased so that the world average is 1. 65 For 2018, the United States (1.3) led
China (1.2) and the European Union (1.0). Without the United Kingdom, theEU’s FWDI for AI papers in 2018 would have been 0.95. 66 The United States’
lead in both FWCI and FWDI aligns with other analyses that have found thenation also leads in the number of articles published by top-rated AI
experts. 67
The FWDIs for both China and the United States have fluctuated year to year
but have been on a general upward trend since 2000. Moreover, China has
been reducing the gap between itself and the United States in recent years.
After having an abnormally high FWDI of 1.5 in 2010, China’s FWDI fell
below 1.0 between 2011 and 2013. Since then, however, China’s FWDI as apercentage of the United States’ FWDI has grown from 77 percent to 93
percent. It is also clear that the United Kingdom had a consistent positive
impact on the EU’s FWDI, raising it every year since 2009. 68

Table 7: Field-weighted download impact (2018)
China

EU

US

1.2

1.0

1.3

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Top 100 Software and Computer Services Firms for R&D
Spending (2019)
The level of funding a nation spends on AI R&D is one proxy to measure its
research capacity. It is difficult to know how much firms spend specificallyon AI R&D. But examining the overall R&D expenditures of software andcomputer services (S&C) firms, many of which are developing AI services,
provides a proxy for AI R&D spending. This indicator analyzes the top 100
software and computer services firms for R&D spending in 2019. The UnitedStates (58 firms) led China (15) and the European Union (12). Without theUnited Kingdom, the European Union had seven firms ranking in the top
100. Per 10 million workers, the United States (3.5 firms in the top 100) led
the European Union (0.5) and China (0.2). 69
The United States still has a significant absolute and per-capita lead, similar
to many other metrics. However, China is catching up. For example, theUnited States had 4 fewer S&C firms in the top 100 in 2019 than in
2017/2018—China had three more firms. Furthermore, China grew fromhaving 10 to 15 firms in the top 100 between 2016 and 2019. Meanwhile,
the United States decreased from 65 to 58 firms. 70

Table 8: Number of firms in the top 100 software and computer
services firms for R&D spending (2019)
China

EU

US

Absolute

15

12

58

Per 10M
Workers

0.2

0.5

3.5

Total R&D Spending of Software and Computer Services Firms
Ranking in Top 2,500 Globally
There were 288 S&C firms in the global top 2,500 firms for R&D spending in
2019, an increase of 20 firms from 2017/2018. This indicator measures
how much the 288 firms spent on R&D by region. The United States ($124.5
billion) led China ($23.7 billion) and the European Union ($14.6 billion). Per
worker, the United States ($750.4) substantially led the European Union($58.1) and China ($30.3). 71
There are several noteworthy elements to this R&D data. First, the UnitedStates significantly increased in this indicator since 2017/2018, growing by
roughly $40 billion. 72 Second, the United States’ lead was fueled by theinvestments of top firms such as Alphabet, Microsoft, and Facebook, whichwere the only S&C firms ranked in the top 10 for R&D spending. Indeed, themedian R&D spending of S&C firms ranking in the top 2,500 globally was
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12roughly similar for each region (EU: $131.6 million, U.S.: $131.2 million,
China: $120.7 million). Yet, the average R&D spending of such firms in the
United States ($813.6 million) was more than the averages of China($381.6 million) and the EU ($373.6 million) combined, partially because
the United States’ top firms spent significantly more on R&D than did top
Chinese and EU firms. 73 Third, China increased its R&D spending as apercentage of U.S. spending, growing from 15 percent in 2017/2018 to 19
percent in 2019. This growth was largely due to the number of Chinese S&C
firms in the top 2,500 growing from 42 to 62. 74 Fourth, the United Kingdom
accounted for nearly 21 percent of EU S&C R&D spending. Only Germany(39 percent) ranked higher. 75

Table 9: R&D spending by software and computer services firms in
top 2,500 (2019)

Absolute
Per Worker

China

EU

US

$23,659M$14,569M$124,480M

30.3

58.1

750.4

HARDWARE
Computing power is becoming increasingly important for AI. For example, theamount of computational power to train the largest AI systems has doubled
every 3.4 months since 2012. 76 The importance of hardware can also beseen in the recent proposals and investments of nations. For example,
lawmakers in the United States have proposed spending billions of dollars
for semiconductor R&D and to support the construction of semiconductor
factories. 77
This section analyzes semiconductor sales, semiconductor R&D spending,the number of firms designing AI chips, the number of supercomputers
ranked in the top 500 by performance, and the aggregate system
performance of the supercomputers in China, the European Union, and theUnited States. We allotted this category 10 of the 100 available points. On
an absolute basis, the most recent data available shows the United Statesleading in hardware (6.3 points), followed by China (2.3) and the European
Union (1.4). Controlling for workforce size, the United States (7.7 points) led
the European Union (1.5) and China (0.8).

Semiconductor Sales (2020)
The global semiconductor market is expected to grow more than $100
billion by 2027, reaching nearly $600 billion. 78 This indicator measures thenumber of semiconductor firms in the top 15 globally for forecasted sales in
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2020. The United States (8) led the European Union (1) and China (0). 79 The
United States gained 2 firms and both the European Union and China had 1
less firm in the top 15 since our last report, which reviewed data for the firstquarter of 2019. 80 The eight U.S. firms are Intel, Micron, Qualcomm,Broadcom, Nvidia, Texas Instruments, Apple, and AMD. The EU firm
is Infineon. 81
There are several caveats to this data, however. One is that the data
includes one pure-play foundry—Taiwan Semiconductor Manufacturing
Company (TSCM). Pure-play foundries mostly manufacture rather thandesign semiconductors. If we removed TSCM from the list, Sony would be in
the top 15. 82 A second caveat is the list does not reflect that the UnitedStates’ share of global chip manufacturing capacity declined from 37
percent to 12 percent between 1990 and 2020. 83 In addition, the UnitedStates’ market share of the global semiconductor has hovered between 45
percent and 50 percent for more than a decade. 84 Yet a 2020 study by the
Boston Consulting Group forecasts that China’s Made in China 2025 could
reduce U.S. market share between 2 and 5 percentage points, and thatmaintaining overly broad restrictions on trading semiconductors to China
could decrease U.S. market share by 8 percentage points. 85

Table 10: Number of firms in top 15 for semiconductor sales (2019)
China

EU

US

Absolute

01

8

Per 100M
Workers

0.0

0.4

4.8

Number of Firms Designing AI Chips (2020)
Trade disputes, such as between the United States and China, can put anation’s access to semiconductors, including AI chips, at risk. Moreover,some firms have found that chips explicitly designed to train and run AI
systems improve such systems' performance. This indicator tracks thenumber of firms, including start-ups, designing chips for AI use cases. In
2020, the United States had at least 62 firms developing AI chips, comparedwith 29 firms in China and 14 in the European Union. Per 10 million
workers, the United States (3.7) led the European Union (0.6) andChina (0.4). 86
Each region increased its number of firms since 2019, but their status
relative to each other did not change. It is notable, however, that 5 of the 14
EU firms are UK firms. 87 This group includes both Graphcore, which hadraised $682 million in funding through 2020. 88 As such, the EU’s position in

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designing AI chips is worse as the United Kingdom is no longer a member ofthe bloc.

Table 11: Number of firms designing AI chips (2019)
China

EU

US

Absolute

29

14

62

Per 10M
Workers

0.4

0.6

3.7

Number of Supercomputers (2020)
Many researchers increasingly view computing power as critical to thedevelopment of AI systems. This indicator examines the number ofsupercomputers each region had ranked in the top 500 in 2020 in terms of
performance, which is how many floating-point calculations a computer canperform per second. China (214) had more supercomputers in the top 500
than did the United States (113) and the European Union (91). Per 10
million workers, the United States (6.8) was ahead of the European Union(3.6) and China (2.9). Without the United Kingdom, the European Union had
79 supercomputers ranked in the top 500. 89
China has experienced remarkable growth in this metric over the past
decade. Indeed, China had only 68 computers in the top 500 in 2012,
compared with 252 for the United States. Yet it took China only four years tosurpass the United States and a little more than two more years to have 100
more supercomputers ranked in the top 500 than does theUnited States. 90

Table 12: Number of supercomputers ranked in top 500
China

EU

US

Absolute

214

91

113

Per 10M
Workers

2.7

3.6

6.8

Supercomputers (Aggregate Systems Performance, 2020)
Another way to evaluate a nation's strength in supercomputers is tomeasure its top systems' aggregate performance. This indicator tracks theaggregate systems performance of supercomputers ranked in the top 500 ineach region. In 2020, the United States (27.5 percent) led China (23.3

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percent) and the European Union (16.8 percent). Without the United
Kingdom, the European Union accounted for 15.4 percent of the world's top
500 supercomputers' aggregate systems performance. Per 10,000 workers,
the United States (40.2 TFLOPS) led the European Union (16.3) andChina (7.3).91
Both China and the United States accounted for a lower share of the top
500 systems' aggregate performance in 2020 than 2019, partially due toJapan's rise. The nation grew from accounting for under 8 percent of theaggregate system performance in 2019 to over 24 percent in 2020. 92 Japannow has the world's fastest system, Supercomputer Fugaku, which has a
max performance that is almost three times higher than that of the secondfastest supercomputer.93

Table 13: Aggregate system performance of supercomputers
ranked in top 500 (%, 2020)
China

EU

US

Absolute

23%

17%

28%

TFLOPS, per
10K Workers

7.3

16.3

40.2

Case Study: Building An AI Ecosystem
Although AI has already delivered significant value across multiple
industries, some industry watchers have noted that “enthusiasm isstalling,” in part because of the lack of availability of high-quality
datasets. 94 Indeed, data wrangling—the process of gathering, cleaning, andtransforming data so that it can be used in a particular task—can takeanywhere from 50 to 80 percent of the time spent on a data science
project. 95 To address this problem, there is a new breed of data start-ups
that focuses less on producing incremental improvements in machinelearning algorithms and more on developing dynamic databases
connected to novel data sources.
A prime example of this is Craft, a start-up based in San Francisco thatdelivers deep insights and visibility into enterprise supply chains bygathering and validating over 300 data points about different firms,
allowing businesses to understand the risks and vulnerabilities in theirglobal supply chains. 96 Craft tracks not only traditional financial andoperational indicators of firms, but also metrics on human capital, social
media sentiment, digital footprint, business ownership, diversity,
compliance, cybersecurity, and environmental, social, and corporate
governance. During a volatile economy, such as a global pandemic, these
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types of metrics are especially valuable because they allow businesses toapply predictive analytics in order to compare existing suppliers, anticipate
potential risks, and discover new business opportunities. Providing accessto high-quality data is a key building block for companies to use AI. As Craft
founder and CEO Ilya Levtov explained, “Since they have access to clean,organized, and validated data on millions of companies, along with MLassisted analytics tools that are easy to integrate with their enterprisesystems, data scientists have more time to innovate.” 97
Craft also illustrates some of the key dynamics at play in the global race forAI. The company initially launched in London, but later reincorporated in
the United States, moving its headquarters to San Francisco because of
the greater availability of venture capital for U.S.-based tech start-ups.
Indeed, a number of UK-based entrepreneurs have worried that post-Brexit
their access to venture capital will be restricted as the European
Investment Fund—which anchors a lot of venture capital funds—pulls backfunding from UK companies.98
AI-focused B2B start-ups face a particular challenge in the EU because U.S.
companies spend more than double their EU peers on business software.99
As explained in a report by Index Ventures, a VC firm based in San
Francisco and London, the problem is one of limited demand among
European businesses:
They invest less in technology, and when they do, it is too often
focused on compliance, rather than on business transformation.
European software companies selling into enterprises are therefore
forced to succeed in the U.S., before they are given the chance to do
so in Europe. Until this changes, we will continue to see
entrepreneurs crossing the Atlantic to scale and to list software
companies. 100
Indeed, this trend helps explain why a number of other European techcompanies have relocated, such as Collibra, founded in Brussels, moving
to New York, and Algolia, a French search engine, now being
headquartered in San Francisco.101 While many European start-ups choose
to grow at home, those looking to scale up quickly still often look to theUnited States because it offers a large market with one legal system, alarge supply of talent, and a single language.

Data is used to develop, test, and deploy AI systems. 102 Governments
directly supply some data (e.g., publishing open government data), indirectly
supply other data (e.g., providing research funding for scientific data), create
policies that lead to data collection (e.g., establishing electronic healthrecord systems), and regulate how the private sector shares data (e.g.,enacting data protection laws).

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Unfortunately, there is no straightforward measure of the relative amount, or
value, of data available for AI in a particular place. However, individuals
produce a significant amount of data when they engage in various online
and offline activities, such as using search engines, posting on social media,
and making purchases. These activities produce data that can haveenormous value for machine learning models. Therefore, one way toestimate the potential value of data in a country or region is to consider thepercentage of the population that engages in digital activities.
This section updates three of the eight indicators we used to measure theamount and availability of data in China, the European Union, and the UnitedStates. These metrics are the number of fixed broadband subscriptions, thenumber of mobile payment users, and the availability of health data. Whennecessary, we used a combination of quantitative and qualitative analysis torank the regions first, second, and third.
We allotted this category 25 of 100 possible points. On an absolute basis,
China leads (11.6 points) the United States (8.0) and the European Union(5.3). Controlling for workforce sizes, the United States leads (11.0 points)
China (7.9) and the European Union (6.1).

Fixed Broadband Subscriptions (2019)
Internet users generate data when they browse the web, post on social
media, or use Internet-connected devices such as smart speakers. This data
can help with the development of AI that improves systems such as thosethat understand human language, make predictions about the spread of
disease, or analyze biometric data. This indicator tracks the number ofbroadband subscriptions in each region. In 2019, China (449.3 million
subscriptions) led the European Union (184.5 million) and the United States(114.1 million). The European Union accounted for 157.9 million
subscriptions without the United Kingdom. 103 Per 100 people, the European
Union (35.1 subscriptions) led the United States (34.7) and China (31.3).
Without the United Kingdom, the European Union had 34.4 fixed broadband
subscriptions per capita. 104
There are caveats to this data, however. First, the data for the number ofbroadband subscriptions per 100 people for the European Union is from2018, while the data for China and the United States is from 2019. 105
Second, the figures do not reflect that the European Union’s average smaller
household size likely inflated its subscription numbers. For example, theaverage household size in the European Union was 2.3, compared with 2.9
and 2.6 in China and the United States, respectively. 106 Nonetheless, China
added substantially more subscribers (55.1 million) than did the European
Union (8.8 million) and the United States (4.3 million) since 2018. 107

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Table 14: Fixed broadband subscriptions in millions (2019)
China

EU

US

Absolute

449M

184M

114M

Per 100
People

31.3

35.1

34.7

Mobile Payments (2019)
Consumers generate data, such as the time, location, and value of afinancial transaction, each time they use a mobile device to purchase a
product. 108 We defined “mobile payments” as using a mobile device to scan,tap, swipe, or check an order to make a point-of-sale transaction, which
does not include purchases such as those of digital goods on mobile
devices. This indicator tracks the number of mobile payment users in eachregion. China (577.4 million) led the United States (64.0 million) and theEuropean Union (estimated 51.7 million) in 2019. China also led (50.3
percent of individuals 15 and older) the United States (23.9 percent) andEuropean Union (11.9 percent) per capita. 109
China experienced sizable absolute growth in the number of individuals
using mobile payments in the past year—similar to broadband subscriptions.
For example, China had 50 million more mobile payment users in 2019 thanin 2018. This figure is roughly the size of the number of individuals in theEuropean Union using mobile payments. 110 Nonetheless, the year-over-year
growth rates for the United States (16.4 percent) and European Union (15.5
percent) were higher than China’s (10.0 percent). 111 This rate was likely
influenced by the fact that significantly more individuals in China had
adopted mobile payment technology before 2019.

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Table 15: Number of individuals using mobile payments inmillions (2019)

Number ofindividuals
Percent of
population(15 and over)

China

EU

US

577M

52M

64M

50%

12%

24%

Health Data (2019)
Researchers are increasingly using health data to develop AI systems that
can help identify, prevent, and predict diseases' development. This indicatoranalyzes the ability of each region to collect and exchange health data.
Comprehensive data for China, all European Union member states, and theUnited States concerning the use of electronic health records (EHRs) was
not available. 112 However, a combination of quantitative and qualitative
information suggests the United States has the best combination of accessand ability to share health data electronically, ahead of the European Unionand then China. Consequently, the United States also leads in access per
capita, followed by the European Union and China.
The adoption of EHRs is relatively high in all regions. For example, in 2019,91 percent of primary care physicians in the United States used EHRs.
Physicians in England (99 percent), France (88 percent), Germany (88percent), Netherlands (99 percent), and Sweden (98 percent) also had highadoption rates. Data from the same survey for China was not available.113
However, almost every health care provider in China has an EHR system. 114
In comparison, almost all non-federal acute care hospitals (96 percent) use
EHRs as of 2017 in the United States. Most U.S. hospital EHR systems cantrack not only patient demographics, medications, and laboratory tests, butalso imaging results and clinician notes. 115 In China, EHR systems areusually not interoperable, forcing patients to bring printed records wheneverthey see a doctor at a different hospital. 116 These bottlenecks, which canhinder data mining, exist despite the government repeatedly emphasizing
the importance of integrating health data. 117
In comparison, between 53 and 54 percent of U.S. primary care physicians
could exchange patient clinical summaries, laboratory diagnostic test
results, and lists of medications taken by patients with any doctor outside
their practice. 118 The United States should strive to improve these numbers,
but they are also likely higher than those for the European Union. For
example, a weighted average of the UK, France, Germany, Netherlands, andSweden reveals that between 45 percent and 53 percent of primary care

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physicians could perform the aforementioned tasks. 119 Germany
significantly brought down the EU average. For example, few German
primary care physicians could exchange patient clinical summaries (12percent), laboratory diagnostic test results (32 percent), and lists ofmedications taken by a patient (14 percent) with any doctor outside theirpractice. 120
The European Union and the United States are both implementing measures
to improve their ability to share health data. For example, the European
Commission has adopted a recommendation that contains principles andtechnical specifications for EHR systems to enable the exchange of healthdata across borders. 121 On March 9, 2020, the U.S. Department of Healthand Human Services (HHS) finalized two new rules intended to give patientssecure access to their health data and facilitate the flow of information
between health care providers and payers. The new rules issued by HHS
make four important changes to electronic health information: They
establish data exchange standards, require open application programming
interfaces (APIs), support data exchange between payers, and prevent
information-blocking practices. 122

CONCLUSIONThe Chinese government has made AI a top priority. The EU and UnitedStates can and should take steps to respond. For example, EU member
states should increase their R&D tax incentives because EU software andcomputer services firms spend significantly less on R&D than do U.S. firms,
and member states’ R&D tax incentives vary widely. 123 Member statesshould also expand public research institutes (PRIs), which collaborate withother research groups and can help firms introduce new or significantly
improved services. 124 They can also expand the number of high-performancecomputing centers through public-private partnerships. For example, in the
United States, the Commonwealth of Massachusetts and multiple
universities and companies collaborated to create the Massachusetts GreenHigh Performance Computing Center. 125
But the biggest challenge for the EU and member states is that many inEurope do not trust AI and see it as technology to be feared andconstrained, rather than welcomed and promoted. The European
Commission’s white paper on AI, which provides a roadmap for its
anticipated legislation, highlights these fears about AI citing “potential risks,such as opaque decision-making, gender-based or other kinds of
discrimination, intrusion in our private lives or being used for criminal
purposes.” 126 This is one reason regulations such as the General Data
Protection Regulation (GDPR), which limits the collection and use of datathat can foster developments in AI, are in place. Moreover, even proposals
that include elements that would promote the development of AI, such as
the Data Governance Act, also include elements that hinder data innovation.
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For example, the Data Governance Act enables individuals to donate theirdata, creates a European Data Innovation Board, and encourages the reuseof public sector data—all useful policies. But the act also maintains
restrictions on the transfer of commercially sensitive data and proposescreating European data spaces that could inhibit global partnerships.127
Meanwhile, the United States needs to implement additional policies tomaintain its lead. It can start with more active support for AI research anddeployment. For example, AI faculty are leaving academia for industry
positions at increasing rates. 128 As such, Congress should provide funding
for the National Science Foundation (NSF) to increase AI research grants
and condition the funding on the professors committing to remain in
academia for a set period. To further develop domestic talent, the UnitedStates should create more scholarships and fellowships for AI students,
including by expanding the existing programs that provide scholarships forstudents committed to public service. 129 To attract the world’s top talent,
Congress should increase the cap on H-1B visas to make it easier for top AI
entrepreneurs and researchers to work in the United States. 130 Congressshould also increase the R&D tax credit rate, which is anemic compared withour competitors. For example, a 2020 ITIF report finds that the United Statesranked 24th out of 34 comparison nations for tax support for R&D spending.
Moreover, China’s R&D tax subsidy is 2.7 times more generous than that of
the United States. 131
But research is not enough. Deployment drives innovation. As of 2020, lessthan half of federal agencies had used AI tools. 132 As such, the federal
government should also focus more on using AI to achieve both individual
agencies’ missions and big national goals such as dramatically improving
drug development and health outcomes. And perhaps most importantly,
Congress should ensure any change to federal data privacy legislation does
not limit data collection and use of AI. Moreover, when U.S. policymakers
propose banning AI-based technologies such as facial recognition or
algorithms used to screen job applicants, on the misguided notion that they
are inherently biased or not protective of civil liberties, they are in essence
paving the way for China to take the lead in that technology.
There are also multiple ways democratically aligned nations can collaborate.
The United States announced in 2020 that it would join the Global
Partnership on AI (GPAI), a group launched by the G7 to provide cooperation
between allied, democratic nations on AI. The group was set up to focus on
the responsible development of AI, including by developing research
agendas, promoting AI workforce development, and spurring AI innovationand commercialization, while ensuring these uses align with shareddemocratic values. 133 The Biden administration should work to ensure GPAI
focuses on its original mission—aligning allied nations to better competewith China on AI—and not devolve into an EU-inspired project to globally

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regulate AI. Allied nations should also develop shared data depositories for avariety of data, such as health data and environmental data. In addition,
they can partner to address such challenges as assessing thetrustworthiness of AI systems. Finally, allied nations can use a series ofinternational prize competitions to foster developments in AI systems for the
public good. 134 While individual nations will continue to compete in AI, theircollaboration could hasten the development of AI technologies and ensure
their benefits are widespread.

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APPENDIX 1: CATEGORIES, INDICATORS, AND WEIGHTS
Development
Indicator

Weight

VC + PE Funding

5

Number of VC + PE Deals

2

Number of Acquisitions of AI Firms

2

Number of AI Startups

4

Number of AI Firms That Have Received More Than $1
Million in Funding

4

Highly-Cited AI Patent Families (1960-2018)

3

PCT AI Patents (1960-2018)

5

TalentIndicator

WeightNumber of AI Researchers

5

Number of Top AI Researchers (H-Index)

5

Number of Top AI Researchers (Academic Conferences)3

Educating Top AI Researchers (%)

2

CENTER FOR DATA INNOVATION

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Research
Indicator

WeightNumber of AI Papers

4.5

Field-Weighted Citation Impact

3

Field-Weighted Download Impact2

Top 100 Software and Computer Services Firms for
R&D Spending

2.5

R&D Spending by Software and Computer Services Firms in
top 2,500

3

Hardware
Indicator

WeightNumber of Firms in Top 15 for Semiconductor Sales

2

Number of Firms in Top 10 for Semiconductor
R&D Spending2

Number of Firms Designing AI Chips

2

Number of Supercomputers Ranked in Top 500

2

Aggregate System Performance of Supercomputers Rankedin Top 500 (%)

2

Adoption
Indicator

WeightNumber of Workers in Firms Adopting AI

5

Number of Workers in Firms Piloting AI

5

CENTER FOR DATA INNOVATION

25

Data
Indicator

WeightFixed Broadband Subscriptions

4

Number of Individuals Using Mobile Payments

3Electronic Health Records (Rank)

2

Mapping Data (Rank)

2

Genetic Data (Rank)

2

Internet of Things Data (TB)

3

Productivity Data (TB)

4

Regulatory Barriers (Rank)

5

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APPENDIX 2: METRICS AND SCORES, ABSOLUTE
Development
Metrics and Scores
Indicator

Year

China

European
Union

UnitedStates

VC + PE Funding

2019$5,641M$3,207M$14,345M

Number of VC + PE
Deals

2019

264

378

786

Number of
Acquisitions of AI
Firms

2019

4

30

130

Number of AI
Startups

2017

383

726

1,393

Number of AI Firms
That Have Received
More Than $1
Million in Funding

2020

398

890

2,130

Highly-Cited AI
Patent Families(1960-2018)

2018

691

2,985

28,031PCT AI Patents(1960-2018)

2018

1,085

1,074

1,863

4.1

5.4

15.5

Scores (Weighted)

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Talent
Metrics and Scores
Indicator

Year

China

European
Union

UnitedStatesNumber of AI
Researchers

2017

18,23243,06428,536

Number of Top AI
Researchers(H-Index)

2017

977

5,787

5,158

Number of Top AI
Researchers(Academic
Conferences)

2018

2,525

4,840

10,295

Educating Top AI
Researchers (%)

2018

11%

21%

44%

2.1

6.2

6.7

Scores (Weighted)

CENTER FOR DATA INNOVATION

28

Research
Metrics and Scores
Indicator

Year

China

European
Union

UnitedStatesNumber of AI Papers

2018

24,92920,41816,233

Field-Weighted
Citation Impact

2018

0.8

1.1

1.4

Field-Weighted
Download Impact

2018

1.2

1.0

1.3

Top 100 Softwareand Computer
Services Firms for
R&D Spending

2019

15

12

58

R&D Spending by
Software andComputer Services
Firms in top 2,500

2019$23,659M$14,569M$124,480M

4.1

3.7

7.2

Scores (Weighted)

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29

Hardware
Metrics and Scores
Indicator

Year

China

European
Union

UnitedStatesNumber of Firms in
Top 15 for
Semiconductor Sales

2020

0

1

8

Number of Firms in
Top 10 for
Semiconductor R&D
Spending

2017

0

0

5

Number of Firms
Designing AI Chips

2020

29

14

62

Number ofSupercomputers
Ranked in Top 500

2020

214

91

113

Aggregate System
Performance ofSupercomputers
Ranked in Top
500 (%)

2020

23%

17%

28%

2.3

1.4

6.3

Scores (Weighted)

Adoption
Metrics and Scores
Indicator

Year

China

European
Union

UnitedStatesNumber of Workers
in Firms Adopting AI

2018

252M

44M

36M

Number of Workers
in Firms Piloting AI

2018

417M

64M

48M

7.7

1.3

1.0

Scores (Weighted)

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Data
Metrics and Scores
Indicator

Year

China

European
Union

UnitedStatesFixed Broadband
Subscriptions

2019

449M

184M

114M

2019

577M

51M

64M

2019

1

2

3

2019

1

2

3

2019

2

1

3

2018

152M

53M

69M

2018

684M

583M

966M

2019

3

1

2

Scores (Weighted)

11.6

5.3

8.0

Overall Scores (Weighted)

32.0

23.3

44.6

Number of
Individuals Using
Mobile Payments
Electronic Health
Records (Rank)
Mapping Data
(Rank)
Genetic Data (Rank)
Internet of Things
Data (TB)
Productivity Data
(TB)
Regulatory Barriers(Rank)CENTER FOR DATA INNOVATION

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APPENDIX 3: METRICS AND SCORES, ADJUSTED BY NUMBER
OF WORKERS
Development
Metrics and Scores
Indicator

Year

China

European
Union

UnitedStates

VC + PE Funding

2019

7.2

12.8

86.5

2019

0.3

1.5

4.7

2019

0.0

0.1

0.8

Number of AI
Startups

2017

0.5

2.9

8.4

Number of AI Firms
That Have Received
More Than $1
Million in Funding

2020

0.5

3.5

12.8

2018

0.9

11.9

169.4

2018

1.4

4.3

11.3

1.2

4.6

19.2

Number of VC + PE
Deals
Number of
Acquisitions of
AI Firms

Highly-Cited AI
Patent Families(1960-2018)
PCT AI Patents(1960-2018)
Scores (Weighted)

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32

Talent
Metrics and Scores
Indicator

Year

China

European
Union

UnitedStatesNumber of AI
Researchers

2017

23.3

171.7

172.4

Number of Top AI
Researchers (HIndex)

2017

1.2

23.1

31.2

Number of Top AI
Researchers(Academic
Conferences)

2018

3.2

19.3

62.2

Educating Top AI
Researchers

2018

1.0

2.0

3.0

0.9

5.8

8.4

Scores (Weighted)

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33

Research
Metrics and Scores
Indicator

Year

China

European
Union

UnitedStatesNumber of AI Papers

2018

31.8

81.4

98.1

Field-Weighted
Citation Impact

2018

0.8

1.1

1.4

Field-Weighted
Download Impact

2018

1.2

1.0

1.3

Top 100 Softwareand Computer
Services Firms for
R&D Spending

2019

0.2

0.5

3.5

R&D Spending by
Software andComputer Services
Firms in top 2,500

2019

30.3

58.1

750.4

2.3

3.8

8.9

Scores (Weighted)

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34

Hardware
Metrics and Scores
Indicator
Number of Firms in
Top 15 for
Semiconductor
Sales
Number of Firms in
Top 10 for
Semiconductor R&D
Spending
Number of Firms
Designing AI Chips
Number ofSupercomputers
Ranked in Top 500
Aggregate System
Performance ofSupercomputers
Ranked in Top 500(TFLOPS)

Year

China

European
Union

UnitedStates

2020

0.0

0.4

4.8

2017

0.0

0.0

30.4

2020

0.4

0.6

3.7

2020

2.7

3.6

6.8

2020

7.3

16.3

40.2

0.8

1.5

7.7

Scores (Weighted)

Adoption
Metrics and Scores
Indicator

Year

China

European
Union

UnitedStatesNumber of Workers
in Firms Adopting AI

2018

0.3

0.2

0.2

Number of Workers
in Firms Piloting AI

2018

0.5

0.3

0.3

4.7

2.4

2.9

Scores (Weighted)

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35

Data
Metrics and Scores
Indicator

Year

China

European
Union

UnitedStatesFixed Broadband
Subscriptions

2019

31.3

35.1

34.7

2019

0.5

0.1

0.2

2019

1

2

3

2019

1

2

3

2019

2

1

3

2018

19.4

21.3

41.7

2018

87.3

232.3

583.7

2019

3

1

2

Scores (Weighted)

7.9

6.1

11.0

Overall Scores (Weighted)

17.8

24.2

58.0

Number of
Individuals Using
Mobile Payments
Electronic Health
Records (Rank)
Mapping Data
(Rank)
Genetic Data (Rank)
Internet of Things
Data (TB)
Productivity Data
(TB)
Regulatory Barriers(Rank)CENTER FOR DATA INNOVATION

36

APPENDIX 4: VENTURE CAPITAL AND PRIVATE EQUITYFUNDING
SOURCESCB Insights, Advanced Search (Industry & Geography, Company Attributes,
Financing & Exit; accessed July 10, 2020), https://app.cbinsights.com.
World Bank, World Bank Open Data (labor force, total; accessed July 2020),
https://data.worldbank.org/indicator/SL.TLF.TOTL.IN.

METHODOLOGY
We used CB Insight’s advanced search tool to get the number and size ofventure capital and private investments made in Chinese, EU, and U.S. AI
firms. We filtered our search by firms in the Artificial Intelligence collection
and by only including deals, CB Insights classified as seed or angel, series
A–E, convertible note, growth-equity, private-equity, or other venture capitalfunding. However, more than 20 percent of the deals did not have a known
investment size. For each of China, the European Union, and the UnitedStates, we used that region’s median AI deal size for the year to impute a
total. For example, in 2019, U.S. AI firms were part of 786 deals for a known
total of $13.8 billion. However, 99 of the deals had no corresponding dollar
amount. Consequently, we multiplied 99 by the median U.S. AI deal size in2019 ($5.5 million) and added it to the known total of $13.8 billion to get an
estimated total of $14.3 billion.
Formula: Funding Total = Known Total ($) + (Number of Missing Deals *Median Deal Size)

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REFERENCES
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Martijn Rasser et al., “The American AI Century: A Blueprint for Action” (Center
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2.

Alexandra Mousavizadeh, Alexi Mostrous, and Alex Clark, “The Arms Race,”
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3.

Ibid.

4.

CB Insights, Advanced Search (Industry & Geography, Company Attributes,
Financing & Exit), accessed July 10, 2020, https://app.cbinsights.com.

5.

European Commission, EU Industrial R&D Investment Scoreboard 2020
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The figures were converted from the Euro to the U.S. Dollar at a rate of 1.211.

6.

2019 AI Index and Elsevier (FWCI and FWDI, FWCI data), accessed August 4,
2019, https://docs.google.com/spreadsheets/d/1PPKTUAc0DF5_F1gXkWKSqPCq7BBy7Q-HGk9eBkL-NE/edit#gid=443668838.

7.

CrunchBase (companies, operating status, headquarters location, categories),
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8.

2019 AI Index and Elsevier (Copy of All Datasets, WLD), accessed August 4,
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9.

2019 AI Index and Elsevier (FWCI and FWDI, FWCI data), accessed August 4,
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10.

European Commission, EU Industrial R&D Investment Scoreboard 2020
(World 2500), accessed January 5, 2020, https://iri.jrc.ec.europa.eu/data.

11.

TOP500 (Lists, top500 list (excel)), accessed July 2020,
https://www.top500.org/lists/top500/2020/11/.

12.

See World Bank, World Bank Open Data (fixed broadband subscriptions),
accessed January 6, 2021,
https://data.worldbank.org/indicator/IT.NET.BBND; Man-Chung Cheung,"China Mobile Payment Users 2019," October 24, 2019,https://www.emarketer.com/content/china-mobile-payment-users-2019.

13.

CB Insights, Advanced Search.

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38

14.

2019 AI Index and Elsevier (FWCI and FWDI, FWCI data), accessed August 4,
2019, https://docs.google.com/spreadsheets/d/1PPKTUAc0DF5_F1gXkWKSqPCq7BBy7Q-HGk9eBkL-NE/edit#gid=443668838.

15.

CB Insights, Advanced Search; CrunchBase (announced date, acquisition
type, acquirer headquarters location, acquiree industry groups), accessed July
2020, https://www.crunchbase.com/search-home; CrunchBase (industry
groups, total funding amount, operating status, headquarters location),
accessed July 2020, https://www.crunchbase.com/search-home.

16.

European Commission, EU Industrial R&D Investment Scoreboard 2020
(World 2500), accessed January 5, 2020, https://iri.jrc.ec.europa.eu/data.

17.

Daniel Castro, Michael McLaughlin, and Eline Chivot, “Who is Winning the AI
Race: China, the EU, or the United States?”

18.

Raymond Perrault et al., 2019 Artificial Intelligence Index (AI Index Steering
Committee, Human-Centered AI Institute, Stanford University, December2019),
https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf.

19.

Daniel Castro, Michael McLaughlin, and Eline Chivot, “Who is Winning the AI
Race: China, the EU, or the United States?”

20.

TOP500, (Lists, top 500 list (excel)), accessed July 2020,
https://www.top500.org/lists/top500/2020/06/.

21.

The weights for the indicators changed as follows: 1) The weight for thenumber of AI papers decreased from 5 to 4.5. 2) The weight for field-weighted
citation impact decreased from four to three. 3) The weight for the top 100
software and computer services firms for R&D spending decreased from 3 to
2.5. 4) We added one new metric, field-weighted download impact, which had
a weight of two. The weight for the R&D spending by software and computer
services firms in Top 2,500 did not change; Daniel Castro, Michael
McLaughlin, and Eline Chivot, “Who is Winning the AI Race: China, the EU, or
the United States?”

22.

Daniel Castro, Michael McLaughlin, and Eline Chivot, “Who is Winning the AI
Race: China, the EU, or the United States?”

23.

“Global AI Survey: AI Proves its Worth, but Few Scale Impact” (McKinsey &Company, November 22, 2019), https://www.mckinsey.com/featuredinsights/artificial-intelligence/global-ai-survey-ai-proves-its-worth-but-fewscale-impact#; Raymond Perrault et al., 2019 Artificial Intelligence Index.

24.

The size of every funding deal was not known. As such, these are estimatedfigures. See Appendix 1 for more details. CB Insights, Advanced Search.

25.

CB Insights, Advanced Search; World Bank, World Bank Open Data (labor
force, total), accessed July 2020,
https://data.worldbank.org/indicator/SL.TLF.TOTL.IN.

26.

Jing Yang, “No More Easy Profits as China’s Venture-Capital Boom Fizzles,”The Wall Street Journal, November 14, 2019,https://www.wsj.com/articles/chinas-venture-capital-boom-is-over-leavinginvestors-high-and-dry-11573727756.

27.

CB Insights, Advanced Search; Daniel Castro, Michael McLaughlin, and Eline
Chivot, “Who is Winning the AI Race: China, the EU, or the United States?”

28.

U.S. AI firms received an estimated $10.7 billion in 2018 and Chinese AI
firms received an estimated $5.4 billion; CB Insights, Advanced Search.

29.

U.S.-China Investment: 2020 Report Launch, Rhodium Group, May 11, 2020,
https://rhg.com/event/u-s-china-investment-2020-report-launch/; Politico

CENTER FOR DATA INNOVATION

39

Morning Tech, “How is the FTC protecting us during the pandemic?” Politico,
May 11, 2020, https://www.politico.com/newsletters/morningtech/2020/05/11/how-is-the-ftc-protecting-us-during-the-pandemic-787488.
30.

Chris O’Brien, “CB Insights: AI venture Deals in Q2 2020 Slump to Lowest
Since 2017,” VentureBeat, July 28, 2020,
https://venturebeat.com/2020/07/28/cb-insights-ai-venture-deals-in-q22020-slump-to-lowest-since-2017/.

31.

CB Insights, Advanced Search; Daniel Castro, Michael McLaughlin, and Eline
Chivot, “Who is Winning the AI Race: China, the EU, or the United States?”

32.

CB Insights, Advanced Search.

33.

Ibid.

34.

Ibid; World Bank, World Bank Open Data (labor force, total), accessed July
2020, https://data.worldbank.org/indicator/SL.TLF.TOTL.IN.

35.

CB Insights, Advanced Search; Daniel Castro, Michael McLaughlin, and Eline
Chivot, “Who is Winning the AI Race: China, the EU, or the United States?”

36.

CB Insights, Advanced Search.

37.

CrunchBase (announced date, acquisition type, acquirer headquarters
location, acquiree industry groups), accessed July 2020,
https://www.crunchbase.com/search-home; World Bank, World Bank Open
Data (labor force, total), accessed July 2020,
https://data.worldbank.org/indicator/SL.TLF.TOTL.IN.

38.

CrunchBase (acquisitions, industry groups), accessed August 5, 2020,
https://www.crunchbase.com/search-home.

39 .

This indicator includes any type of funding, whether that be venture capital,
private equity, debt financing, grants, etc.

40.

CrunchBase (industry groups, total funding amount, operating status,
headquarters location), accessed July 2020,
https://www.crunchbase.com/search-home; World Bank, World Bank Open
Data (labor force, total), accessed July 2020,
https://data.worldbank.org/indicator/SL.TLF.TOTL.IN.

41.

Ibid; Daniel Castro, Michael McLaughlin, and Eline Chivot, “Who is Winning
the AI Race: China, the EU, or the United States?”

42.

Remco Zwetsloot et al., “Keeping Top AI Talent in the United States” (Center
for Security and Emerging Technology, December 2019),
https://cset.georgetown.edu/wp-content/uploads/Keeping-Top-AI-Talent-inthe-United-States.pdf.

43.

Tina Huang and Zachary Arnold, “Immigration Policy and the Global
Competition for AI Talent” (Center for Security and Emerging Technology, June2020), https://cset.georgetown.edu/research/immigration-policy-and-theglobal-competition-for-ai-talent.

44.

Elsevier, Artificial Intelligence: How Knowledge is Created, Transferred, andUsed (Elsevier, 2018),
https://www.elsevier.com/researchintelligence/resource-library/ai-report.

45.

Michael Gofman and Zhao Jin, “Artificial Intelligence, Human Capital, andInnovation,” August 20, 2019, http://gofman.info/AI/AI_GofmanZhao.pdf;
Martijn Rasser et al., “The American AI Century: A Blueprint for Action” (Center
for a New American Security, December 2019),
https://s3.amazonaws.com/files.cnas.org/documents/CNAS-Tech-AmericanAI-Century-DEC-2019.pdf.

CENTER FOR DATA INNOVATION

40

46.

“AI 100: The Artificial Intelligence Startups Redefining Industries,” CB
Insights, March 3, 2020, https://www.cbinsights.com/research/artificialintelligence-top-startups.

47.

“Basic vs. Applied Research,” Lawrence Berkeley National Laboratory,
accessed August 5, 2020,
https://www.sjsu.edu/people/fred.prochaska/courses/ScWk170/s0/Basicvs.-Applied-Research.pdf.

48.

Raymond Perrault et al., 2019 Artificial Intelligence Index; New York University
et al., SuperGlue, accessed August 5, 2019,https://super.gluebenchmark.com.

49.

Raymond Perrault et al., 2019 Artificial Intelligence Index.

50.

Michael Collins, “Declining Federal Research Is Hurting US Innovation,”
IndustryWeek, January 22, 2020, https://www.industryweek.com/theeconomy/public-policy/article/21121160/declining-federal-researchundercuts-the-us-strategy-of-innovation.

51.

The Networking and Information Technology Research and Development
Program (AI R&D Investments), accessed January 7, 2020,
https://www.nitrd.gov/apps/itdashboard/AI-RD-Investments/#TOP.

52.

The AI Index used figures from an analysis by Bloomberg Government.
Raymond Perrault et al., 2019 Artificial Intelligence Index.

53.

Ashwin Acharya and Zachary Arnold, “Chinese Public AI R&D Spending:
Provisional Findings” (Center for Security and Emerging Technology,
December 2019), https://cset.georgetown.edu/wp-content/uploads/ChinesePublic-AI-RD-Spending-Provisional-Findings-1.pdf.

54.

We used the Euro to U.S. Dollar conversion rate (1.13) for December 7, 2018,
which is the date the Commission published the webpage. European
Commission, Artificial Intelligence, December 7, 2018,https://ec.europa.eu/commission/news/artificial-intelligence-2018-dec07_en.

55.

European Commission, Artificial Intelligence (AI), accessed January 7, 2021,
https://ec.europa.eu/info/research-and-innovation/research-area/industrialresearch-and-innovation/key-enabling-technologies/artificial-intelligenceai_en.

56.

Ibid.

57.

Raymond Perrault et al., 2019 Artificial Intelligence Index.

58.

2019 AI Index and Elsevier (All Datasets, WLD), accessed August 4, 2019,https://docs.google.com/spreadsheets/d/17MWnUPnFbhsCgx0OJuY7quKGS
6FQRQTG/edit#gid=138668413; World Bank, World Bank Open Data (labor
force, total), accessed July 2020,
https://data.worldbank.org/indicator/SL.TLF.TOTL.IN.

59.

2019 AI Index and Elsevier (All Datasets, WLD), accessed August 4, 2019,https://docs.google.com/spreadsheets/d/17MWnUPnFbhsCgx0OJuY7quKGS
6FQRQTG/edit#gid=138668413.

60.

2019 AI Index and Elsevier (FWCI and FWDI, FWCI data), accessed August 4,
2019, https://docs.google.com/spreadsheets/d/1PPKTUAc0DF5_F1gXkWKSqPCq7BBy7Q-HGk9eBkL-NE/edit#gid=0.

61.

Diego Charrez, “NeurIPS Stats,” Medium, September 6, 2019,https://medium.com/@dcharrezt/neurips-2019-stats-c91346d31c8f.

62.

To calculate an EU-28 re-based FWCI, we divided the EU’s FWCI by theaverage worldwide FWCI. 2019 AI Index and Elsevier (FWCI and FWDI, FWCICENTER FOR DATA INNOVATION

41

data), accessed August 4, 2019,https://docs.google.com/spreadsheets/d/1PPKTUAc0DF5_F1gXkWKSqPCq7BBy7Q-HGk9eBkL-NE/edit#gid=0.
63.

2019 AI Index and Elsevier (All Datasets, WLD), accessed August 4, 2019,https://docs.google.com/spreadsheets/d/17MWnUPnFbhsCgx0OJuY7quKGS
6FQRQTG/edit#gid=138668413.

64.

2019 AI Index and Elsevier (FWCI and FWDI, FWCI data), accessed August 4,
2019, https://docs.google.com/spreadsheets/d/1PPKTUAc0DF5_F1gXkWKSqPCq7BBy7Q-HGk9eBkL-NE/edit#gid=0.

65.

Elsevier, Artificial Intelligence: How Knowledge is Created, Transferred, andUsed (Elsevier 2018),
https://www.elsevier.com/researchintelligence/resource-library/ai-report.

66.

To calculate an EU-28 and EU-27 re-based FWDI, we divided their FWDI’s by
the average worldwide FWDI. 2019 AI Index and Elsevier (FWCI and FWDI,
FWDI data), accessed August 4, 2019,https://docs.google.com/spreadsheets/d/1PPKTUAc0DF5_F1gXkWKSqPCq7BBy7Q-HGk9eBkL-NE/edit#gid=0.

67.

Ella Hollowood and Alex Clark, “Part Five: the New World Order,” TortoiseMedia, December 2019,https://members.tortoisemedia.com/2019/12/05/the-global-ai-index-part-5the-new-world-order/content.html.

68.

To calculate an EU-28 and EU-27 re-based FWDI, we divided their FWDI’s by
the average worldwide FWDI. 2019 AI Index and Elsevier (FWCI and FWDI,
FWDI data), accessed August 4, 2019,https://docs.google.com/spreadsheets/d/1PPKTUAc0DF5_F1gXkWKSqPCq7BBy7Q-HGk9eBkL-NE/edit#gid=0.

69.

European Commission, EU Industrial R&D Investment Scoreboard 2019
(World 2500), accessed January 6, 2021, https://iri.jrc.ec.europa.eu/data;
World Bank, World Bank Open Data (labor force, total), accessed July 2020,
https://data.worldbank.org/indicator/SL.TLF.TOTL.IN.

70.

European Commission, EU Industrial R&D Investment Scoreboard 2019
(World 2500, all data world 2500, SB 2016), accessed January 6, 2021,
https://iri.jrc.ec.europa.eu/data.

71.

European Commission, EU Industrial R&D Investment Scoreboard 2019
(World 2500), accessed January 5, 2021, https://iri.jrc.ec.europa.eu/data;
World Bank, World Bank Open Data (labor force, total), accessed January
2021, https://data.worldbank.org/indicator/SL.TLF.TOTL.IN.

72.

European Commission, EU Industrial R&D Investment Scoreboard 2019
(World 2500, all data world 2500, SB2018, SB2020), accessed January 6,
2021, https://iri.jrc.ec.europa.eu/data.

73.

European Commission, EU Industrial R&D Investment Scoreboard 2019
(World 2500), accessed January 5, 2021, https://iri.jrc.ec.europa.eu/data.

74.

European Commission, EU Industrial R&D Investment Scoreboard 2019
(World 2500, all data world 2500, SB2018, SB2020), accessed January 6,
2021, https://iri.jrc.ec.europa.eu/data.

75.

European Commission, EU Industrial R&D Investment Scoreboard 2019
(World 2500), accessed January 5, 2021, https://iri.jrc.ec.europa.eu/data.

76.

Raymond Perrault et al., 2019 Artificial Intelligence Index.

77.

Asa Fitch and Kate O’Keeffe, “Lawmakers Propose Spending Billions toStrengthen U.S. Chip Industry,” The Wall Street Journal, June 10, 2020,

CENTER FOR DATA INNOVATION

42http://wsj.com/articles/lawmakers-propose-spending-billions-to-strengthen-us-chip-industry-11591825784.
78.

“Global Semiconductors Industry” (Global Industry Analysts, industry report,
September 2020), https://www.globenewswire.com/newsrelease/2020/09/04/2089215/0/en/Global-Semiconductors-Industry.html.

79.

“2020F Top 15 Semiconductor Sales Leaders Including Foundries,”
AnySilicon, November 24, 2020, https://anysilicon.com/2020f-top-15semiconductor-sales-leaders-including-foundries.

80.

Daniel Castro, Michael McLaughlin, and Eline Chivot, “Who is Winning the AI
Race: China, the EU, or the United States?”

81.

“2020F Top 15 Semiconductor Sales Leaders Including Foundries,”
AnySilicon, November 24, 2020, https://anysilicon.com/2020f-top-15semiconductor-sales-leaders-including-foundries.

82.

Ibid.

83.

Devi Keller, Jimmy Goodrich, and Zhi Su, “The U.S. Should be Concerned with
its Declining Share of Chip Manufacturing, Not the Tiny Fraction of U.S. Chips
Made in China,” Semiconductor Industry Association, July 10, 2020,
https://www.semiconductors.org/the-largest-share-of-u-s-industry-fabcapacity-is-in-the-united-states-not-china-lets-keep-it-that-way.

84.

Antonio Varas and Raj Varadarajan, “How Restrictions to Trade with China
Could End U.S. Leadership in Semiconductors” (Boston Consulting Group,
March 2020), https://media-publications.bcg.com/flash/2020-03-07-HowRestrictions-to-Trade-with-China-Could-End-US-Semiconductor-Leadership.pdf.

85.

Ibid.

86.

CrunchBase (companies, operating status, headquarters location, categories),
accessed July 2020, https://www.crunchbase.com/searchhome; Shan Tang,“AI Chip (ICs and IPs)”; World Bank, World Bank Open Data (labor force, total),
accessed July 2020, https://data.worldbank.org/indicator/SL.TLF.TOTL.IN.

87.

CrunchBase (companies, operating status, headquarters location, categories),
accessed July 2020, https://www.crunchbase.com/searchhome; Shan Tang,“AI Chip (ICs and IPs)”; Daniel Castro, Michael McLaughlin, and Eline Chivot,“Who is Winning the AI Race: China, the EU, or the United States?”

88.

“Graphcore,” Crunchbase, accessed January 8, 2020,
https://www.crunchbase.com/organization/graphcore/company_financials.

89.

TOP500, (Lists, top500 list (excel)), accessed January 2021,
https://www.top500.org/lists/top500/2020/11/; World Bank, World Bank
Open Data (labor force, total), accessed January 2021,
https://data.worldbank.org/indicator/SL.TLF.TOTL.IN.

90.

Top 500, Statistics (Development Over Time), accessed January 8, 2020,
https://www.top500.org/statistics/overtime.

91.

TOP500, (Lists, top500 list (excel)), accessed January 2021,
https://www.top500.org/lists/top500/2020/11/; World Bank, World Bank
Open Data (labor force, total), accessed January 2021,
https://data.worldbank.org/indicator/SL.TLF.TOTL.IN.

92.

TOP500, (Lists, top500 list (excel)), accessed January 2021,
https://www.top500.org/lists/top500/2020/11.

93.

Ibid.

94.

“An understanding of AI’s limitations is starting to sink in,” The Economist,
June 13, 2020, https://www.economist.com/technology-

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quarterly/2020/06/11/an-understanding-of-ais-limitations-is-starting-to-sinkin.
95.

Steve Lohr, “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights,”
New York Times, August 17, 2014,
https://www.nytimes.com/2014/08/18/technology/for-big-data-scientistshurdle-to-insights-is-janitor-work.html.

96.

Mike Butcher, “As the pandemic creates supply chain chaos, Craft raises$10M to apply some intelligence,” TechCrunch, August 21, 2020,
https://techcrunch.com/2020/08/21/as-the-pandemic-creates-supply-chainchaos-craft-raises-10m-to-apply-some-intelligence/.

97.

“Craft: Source of Truth on Companies,” CIO Review, n.d., https://supplychain.cioreview.com/vendor/2020/craft.

98.

“A £2 billion European investment fund has stopped giving money to UK techstartups because of Brexit,” Business Insider, May 19, 2017,https://www.businessinsider.com/the-eif-has-frozen-funding-to-new-uk-vcsand-cancelled-an-angel-investment-scheme-2017-5.

99.

“European startups are moving to the US later. Why?” Sifted, September 23,
2020, https://sifted.eu/articles/european-startups-us-expansion/.

100. “Expanding to the U.S.: The guide for European Entrepreneurs,” Index
Ventures, 2020, https://www.indexventures.com/us-expansion/handbook.
101. “European startups are moving to the US later. Why?” Sifted, September 23,
2020, https://sifted.eu/articles/european-startups-us-expansion/.
102. Angus Loten, "Financial Firms Cite Dearth of Talent, Data as Hurdles to AI
Expansion,” The Wall Street Journal, February 19, 2020,
https://www.wsj.com/articles/financial-firms-cite-dearth-of-talent-data-ashurdles-to-ai-expansion-11582108202.
103. World Bank, World Bank Open Data (fixed broadband subscriptions),
accessed January 6, 2021,
https://data.worldbank.org/indicator/IT.NET.BBND.
104. The World Bank does not include the United Kingdom in its definition of theEuropean Union. As such, we calculated an EU-28 figure for broadband
subscriptions per 100 people by weighing both the EU-27’s and UK’s
broadband subscriptions per 100 people figures by the percentage they makeup of the EU-28 population. The EU-27 had 34.4 subscriptions per 100
people while the UK had 39.6. In addition, the EU-27 accounted for 87
percent of the EU-28 population and the UK accounted for nearly 13 percent.
Thus, we calculated our estimate by doing (0.87*34.4) + (0.13*39.6) to get
35.1 broadband subscriptions per 100 people for the EU-28. It is also of notethat the EU-27 subscription data is from 2018 while the UK data is from2019. World Bank, World Bank Open Data (fixed broadband subscriptions(per 100 people)), accessed January 6, 2021,
https://data.worldbank.org/indicator/IT.NET.BBND.P2.
105. World Bank, World Bank Open Data (fixed broadband subscriptions (per 100
people)), accessed January 6, 2021,
https://data.worldbank.org/indicator/IT.NET.BBND.P2.
106. Population Reference Bureau, International Data (Average Household Size),
accessed January 7, 2021, https://www.prb.org/international/indicator/hhsize-av/map/country/; Eurostat, Average Household Size (EU-SILC Survey),
accessed January 7, 2021,
https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=ilc_lvph01&lang=en; National Bureau of Statistics of China, Average Number of People Living

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in Households in China from 1990 to 2019 (sourced from Statista), accessed
January 7, 2021, https://www.statista.com/statistics/278697/average-sizeof-households-in-china.
107. World Bank, World Bank Open Data (fixed broadband subscriptions),
accessed January 6, 2021,
https://data.worldbank.org/indicator/IT.NET.BBND; Daniel Castro, Michael
McLaughlin, and Eline Chivot, “Who is Winning the AI Race: China, the EU, or
the United States?”
108. Stilgherrian, “Apple Pay Isn’t Magic, and It Isn’t Private,” ZDNet, October 27,
2014, https://www.zdnet.com/article/apple-pay-isnt-magic-and-it-isnt-private.
109. For the European Union, we only had data for France, Germany, and theUnited Kingdom. These nations accounted for 42 percent of the EU
population ages 15 and above. We summed each nation's number of users(21.6 million) and then divided it by 42 percent to estimate that roughly 51
million individuals in the EU use mobile payments. eMarketer only listed
percentage of population using mobile payments data for China. As such, we
calculated a percentage using World Bank population data. Our percentage
for China differs slightly from the percentage eMarketer presents because it
considers the population of relevant users to be ages 14 and above. The
World Bank provides total population data and data for individuals 14 andbelow, and we divided the number of users in each region by the number ofindividuals 15 and above in the region. As such, our percentage estimates
may slightly overestimate the percent of the population using mobile
payments in each region. Man-Chung Cheung, "China Mobile Payment Users
2019," October 24, 2019, https://www.emarketer.com/content/chinamobile-payment-users-2019; Jasmine Enberg, "France Mobile Payment Users
2019," October 24, 2019, https://www.emarketer.com/content/francemobile-payment-users-2019; Alina Brentnall, "Germany Mobile Payment
Users 2019," October 24, 2019,https://www.emarketer.com/content/germany-mobile-payment-users-2019;
Yoram Wurmser, "US Mobile Payment Users 2019," October 24, 2019,https://www.emarketer.com/content/us-mobile-payment-users-2019; Bill
Fisher, "UK Mobile Payment Users 2019," October 24, 2019,https://www.emarketer.com/content/uk-mobile-payment-users-2019; World
Bank, World Bank Open Data (population ages 0–14, total), accessed July 11,2020, https://data.worldbank.org/indicator/SP.POP.0014.TO.
110. Daniel Castro, Michael McLaughlin, and Eline Chivot, “Who is Winning the AI
Race: China, the EU, or the United States?”
111. Ibid
112. While individuals often use the terms interchangeably, an EMR is typically adigital version of a patient’s chart from one practice. In contrast, an EHR can
include data from multiple doctors, such as radiology reports, x-ray images,
and lab reports. Practice Fusion, “EHR (Electronic Health Record) vs. EMR(Electronic Medical Record),” January 1, 2019,https://www.practicefusion.com/blog/ehr-vs-emr/; NextGen Healthcare,“Understanding EMR vs. EHR,” accessed August 5, 2020,
https://www.nextgen.com/insights/emr-vs-ehr/emr-vs-ehr.
113. The Commonwealth Fund, “Primary Care Physicians’ Use of Electronic
Medical Records, 2019,” accessed August 5, 2020,
https://www.commonwealthfund.org/international-health-policycenter/system-stats/electronic-medical-records.

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114. Roosa Thikkanen et al, “China,” The Commonwealth Fund, June 5, 2020,
https://www.commonwealthfund.org/international-health-policycenter/countries/china.
115. Ibid.
116. Ibid.
117. Zhong Wang, “Data Integration of Electronic Medical Record Under
Administrative Decentralization of Medical Insurance and Healthcare in
China: a Case Study” (Israel Journal of Health Policy Research, April 1, 2019),
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6442402/; The StateCouncil, The People’s Republic of China, “China to Boost Big Data Applicationin Health and Medical Sectors,” June 24, 2016,
http://english.www.gov.cn/policies/latest_releases/2016/06/24/content_2
81475379018156.htm.
118. Michelle M. Doty et al., “2019 Commonwealth Fund International Health
Policy Survey of Primary Care Physicians,” The Commonwealth Fund, 2019,https://www.commonwealthfund.org/sites/default/files/201912/PDF_2019_intl_hlt_policy_survey_primary_care_phys_CHARTPACK_1210-2019.pdf .
119. We weighted each nation’s percentage by the percentage of the population it
made up of the group. Michelle M. Doty et al., “2019 Commonwealth FundInternational Health Policy Survey of Primary Care Physicians.”
120. Ibid.
121. European Commission, “Exchange of Electronic Health Records Across theEU,” last updated October 29, 2020, https://ec.europa.eu/digital-singlemarket/en/exchange-electronic-health-records-across-eu.
122. Hodan Omaar, “Department of Health and Human Service’s Finalized Rules
Support Interoperability,” Center for Data Innovation, May 5, 2020,
https://www.datainnovation.org/2020/05/department-of-health-and-humanservices-finalized-rules-support-interoperability.
123. John Lester and Jacek Warda, “Enhanced Tax Incentives for R&D Would Make
Americans Richer” (Information Technology and Innovation Foundation,
September 2020), http://www2.itif.org/2020-enhanced-tax-incentives-rd.pdf.
124. Albert Link, Cody Morris, and Martijn van Hasselt, “The Impact of the Third
Sector of R&D on the Innovative Performance of Entrepreneurial Firms,”
UNCG Economics Working Papers No 20-2, April 2020, University of North
Carolina at Greensboro.
125. “MGHPCC Fact Sheet,” The Massachusetts Green High Performance
Computing Center, http://www.mghpcc.org/about/mghpcc-fact-sheet.
126. “White Paper on Artificial Intelligence: A European approach to excellence andtrust,” European Commission, February 2020,
https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificialintelligence-feb2020_en.pdf.
127. Eline Chivot, “Data Governance Act Sets EU on a Path to Protectionism,”Center for Data Innovation, November 25, 2020,
https://datainnovation.org/2020/11/data-governance-act-sets-eu-on-a-pathto-protectionism.
128. Raymond Perrault et al., 2019 Artificial Intelligence Index.
129. AI Scholarship-for-Service Act, S. 3901, 116th Cong. (2019–2020).
130. Tina Huang and Zachory Arnold, “Immigration Policy and the Global
Competition for AI Talent” (Center for Security and Emerging Technology, June

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2020), https://cset.georgetown.edu/research/immigration-policy-and-theglobal-competition-for-ai-talent.
131. John Lester and Jacek Warda, “Enhanced Tax Incentives for R&D Would Make
Americans Richer” (Information Technology and Innovation Foundation,
September 2020), http://www2.itif.org/2020-enhanced-tax-incentives-rd.pdf.
132. David Freeman Engstrom, “Government by Algorithm: Artificial Intelligence inFederal Administrative Agencies” (report submitted to The Administrative
Conference of the United States, Stanford University and New York University,
February 2020), https://www-cdn.law.stanford.edu/wpcontent/uploads/2020/02/ACUS-AI-Report.pdf.
133. Hodan Omaar, “U.S. Global Leadership on AI a Welcome Development,”
Center for Data Innovation, July 17, 2020,
https://www.datainnovation.org/2020/07/u-s-global-leadership-on-ai-awelcome-development.
134. “Prize Challenges,” Defense Advanced Research Projects Agency,
https://www.darpa.mil/work-with-us/public/prizes; “AI to Solve the World’s
Grand Challenges,” XPRIZE Foundation,
https://www.xprize.org/prizes/artificial-intelligence.

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ACKNOWLEDGEMENTS
The authors wish to thank Rob Atkinson for providing editorial guidanceon this report, and the Center for Global Enterprise for providing
financial support. Any errors or omissions are the authors’
responsibility alone.

ABOUT THE AUTHORS
Daniel Castro is the director of the Center for Data Innovation and vicepresident of the Information Technology and Innovation Foundation.
He has a B.S. in foreign service from Georgetown University and an M.S.
in information security technology and management from Carnegie
Mellon University.
Michael McLaughlin was a research analyst at the Information
Technology and Innovation Foundation. Michael graduated from Wake
Forest University, where he majored in Communication with Minors in
Politics and International Affairs and Journalism. He received his Master’sin Communication at Stanford University, specializing in Data Journalism.
He is currently pursuing a Juris Doctor degree at Georgetown University
Law Center.

ABOUT THE CENTER FOR DATA INNOVATIONThe Center for Data Innovation is the leading global think tank studying
the intersection of data, technology, and public policy. With staff inWashington, D.C. and Brussels, the Center formulates and promotes
pragmatic public policies designed to maximize the benefits of datadriven innovation in the public and private sectors. It educatespolicymakers and the public about the opportunities and challenges
associated with data, as well as technology trends such as open data,
artificial intelligence, and the Internet of Things. The Center is a part of
the nonprofit, nonpartisan Information Technology and Innovation
Foundation.

contact: info@datainnovation.org
datainnovation.org

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