Artificial IntelligenceIndex Report 2022

CHAPTER 4:

The Economy
and Education

Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATIONCHAPTER 4:

Chapter Preview
Overview

3

4.3 CORPORATE ACTIVITY

22

Chapter Highlights

4

Industry Adoption

22

Global Adoption of AI

22

4.1 JOBS

5

AI Adoption by Industry and Function

23

AI Hiring5

Type of AI Capabilities Adopted

24

AI Labor Demand

7

Consideration and Mitigation of Risks
From Adopting AI

25

Global AI Labor Demand

7

U.S. AI Labor Demand: By Skill Cluster

8

U.S. Labor Demand: By Sector

9

4.4 AI EDUCATION

27

U.S. Labor Demand: By State

927

AI Skill Penetration

11

CS Undergraduate Graduates inNorth America	Global Comparison

11

New CS PhDs in North America

28

Global Comparison: By Industry

11

New CS PhDs by Specialty

28

Global Comparison: By Gender

12

New CS PhDs with AI/ML
and Robotics/Vision Specialties29

4.2 INVESTMENT

13

Corporate Investment

13

Startup Activity

14

Global Trend

14

Regional Comparison by Funding Amount16

Regional Comparison by Newly Funded
AI Companies
Focus Area Analysis

18
20

New AI PhDs Employment in North America 30
Academia vs. Industry vs. Government

30

Diversity of New AI PhDs in North America

31

By Gender

31

By Race/Ethnicity

32

New International AI PhDs in North America 33
APPENDIX

34

ACCESS THE PUBLIC DATA

2

Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION

Overview
The growing use of artificial intelligence (AI) in everyday life, acrossindustries, and around the world generates numerous questions about
how AI is shaping the economy and education—and, conversely, how
the economy and education are adapting to AI. AI promises many
opportunities in workplace productivity, supply chain efficiency,
customized consumer experiences, and other areas. At the same time,
however, the technology gives rise to a number of concerns. How
do businesses adapt to recruiting and retaining AI talent? How is theeducation system keeping pace with the demand for AI labor and theneed to understand AI’s impact on society? All of these questions andmore are inextricable from AI today.
This chapter examines the economy and education, using datafrom Emsi Burning Glass, NetBase Quid, and LinkedIn to capture AI
trends in the global economy and data from the annual Computing
Research Association Taulbee Report to analyze trends in AI andcomputer science PhD graduates. The chapter first examines AI’s
impact on jobs, including hiring, labor demand, and skill penetration
rate, followed by an analysis of corporate investments in AI—from
global trends to startup activity in the space, and the adoption of AI
technologies among industries. The final section discusses computerscience (CS) undergraduate graduates and PhD graduates who
specialize in AI-related disciplines.

Chapter 4 Preview

3Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATIONCHAPTER HIGHLIGHTS• New Zealand, Hong Kong, Ireland, Luxembourg, and Sweden are the countries or regions withthe highest growth in AI hiring from 2016 to 2021.
• In 2021, California, Texas, New York, and Virginia were states with the highest number of AI job
postings in the United States, with California having over 2.35 times the number of postingsas Texas, the second greatest. Washington, D.C., had the greatest rate of AI job postingscompared to its overall number of job postings.
• The private investment in AI in 2021 totaled around $93.5 billion—more than double thetotal private investment in 2020, while the number of newly funded AI companies continues
to drop, from 1051 companies in 2019 and 762 companies in 2020 to 746 companies in 2021.
In 2020, there were 4 funding rounds worth $500 million or more; in 2021, there were 15.
• “Data management, processing, and cloud” received the greatest amount of private AI
investment in 2021—2.6 times the investment in 2020, followed by “medical and healthcare”
and “fintech.”• In 2021, the United States led the world in both total private investment in AI and the numberof newly funded AI companies, three and two times higher, respectively, than China, the next
country on the ranking.
• Efforts to address ethical concerns associated with using AI in industry remain limited,
according to a McKinsey survey. While 29% and 41% of respondents recognize “equity andfairness” and “explainability” as risks while adopting AI, only 19% and 27% are taking steps
to mitigate those risks.
• In 2020, 1 in every 5 CS students who graduated with PhD degrees specialized in artificial
intelligence/machine learning, the most popular specialty in the past decade. From 2010 to2020, the majority of AI PhDs in the United States headed to industry while a small fraction
took government jobs.

Chapter 4 Preview

4Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.1 Jobs

4.1 JOBSAI HIRING

example, an index of 1.05 in December 2021 points to ahiring rate that is 5% higher than the average month in2016. LinkedIn makes month-to-month comparisons toaccount for any potential lags in members updating theirprofiles. The index for a year is the number in Decemberof that year.

The AI hiring data draws on a dataset from LinkedIn
of skills and jobs listings on the platform. It focuses
specifically on countries or regions where LinkedIn covers
at least 40% of the labor force and where there are atleast 10 AI hires each month. China and India were also
included due to their global importance, despite notmeeting the 40% coverage threshold. Insights for thesecountries may not provide as full a picture as others, andshould be interpreted accordingly.

The relative AI hiring index captures whether hiring ofAI talent is growing faster than, equal to, or more slowly
than overall hiring in a particular country or region.
New Zealand has the highest growth in AI hiring—2.42
times greater in 2021 compared with 2016, followed byHong Kong (1.56), Ireland (1.28), Luxembourg (1.26),
and Sweden (1.24). Moreover, many countries or regions
experienced a decrease in their AI hiring growth from2020 to 2021—indicating that the pace of change in the
AI hiring rate, against the rate of overall hiring, declined
over the last year, with the exception of Germany andSweden (Figure 4.1.2).

Figure 4.1.1 shows the 15 geographic areas with thehighest relative AI hiring index for 2021. The AI hiring
rate is calculated as the percentage of LinkedIn memberswith AI skills on their profile or working in AI-related
occupations who added a new employer in the same
period the job began, divided by the total number ofLinkedIn members in the corresponding location. Thisrate is then indexed to the average month in 2016; for
RELATIVE AI HIRING INDEX by GEOGRAPHIC AREA, 2021
Source: LinkedIn, 2021 | Chart: 2022 AI Index Report

2.42

New Zealand
1.56

Hong Kong
Ireland

1.28

Luxembourg

1.26

Sweden1.24

Netherlands

1.23

United Kingdom

1.20

China

1.18

United States

1.17

Australia

1.14

Canada1.14

South Africa

1.13

Spain

1.10

Denmark

1.09

Germany

1.08

0.00

0.50

1.00

1.50
Relative AI Hiring Index

2.00

2.50
Figure 4.1.1

Chapter 4 Preview

5Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.1 Jobs

RELATIVE AI HIRING INDEX by GEOGRAPHIC AREA, 2016–21
Source: LinkedIn, 2021 | Chart: 2022 AI Index Report

Australia

Canada

1.24

China

Denmark
1.08

1.00

1.18

1.07

0.00
Relative AI Hiring Index

Germany

Hong Kong

Ireland1.15

1.00

Luxembourg
1.14
1.16

1.13

0.00
Netherlands

New Zealand
1.20

South Africa

1.40

1.00

Spain
1.18

1.14

0.00
Sweden1.24

United Kingdom

United States
1.16

1.00

1.12

0.00
2018

2020

2022

2018

2020

2022

2018

2020

2022

2018

2020

2022
Figure 4.1.2

Chapter 4 Preview

6Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.1 Jobs

AI LABOR DEMAND

Global AI Labor Demand

To analyze demand for specific AI labor skills, Emsi
Burning Glass mined millions of job postings collectedfrom over 45,000 websites since 2010 and flagged alllistings calling for AI skills.

Figure 4.1.3 shows that the percentage of AI job postings
among all job postings in 2021 was greatest in Singapore(2.33% of all job listings), followed by the United States(0.90%), Canada (0.78%), and the United Kingdom(0.74%). AI job postings increased in the United States,
Canada, Australia, and New Zealand from 2020 to2021, while they declined in Singapore and the United
Kingdom.

AI JOB POSTINGS (% of ALL JOB POSTINGS) by GEOGRAPHIC AREA, 2013–21
Source: Emsi Burning Glass, 2021 | Chart: 2022 AI Index Report

2.50%

AI Job Postings (% of All Job Postings)

2.33%, Singapore

2.00%

1.50%

1.00%

0.90%, United States
0.78%, Canada
0.74%, United Kingdom
0.58%, Australia

0.50%

0.25%, New Zealand

2013

2014

2015

2016

2017

2018

2019

2020

2021
Figure 4.1.3

Chapter 4 Preview

7Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.1 Jobs

The share of AI job postings
among all job postingsin 2021 was greatest formachine learning skills (0.6%of all job postings), followed
by artificial intelligence(0.33%), neural networks(0.16%), and natural
language processing (0.13%).

U.S. AI Labor Demand: By Skill Cluster
Figure 4.1.4 shows the U.S. labor demand from 2010
to 2021 by skill cluster. Each skill cluster consists of alist of AI-related skills; for example, the neural network
skill cluster includes skills like deep learning andconvolutional neural networks.1 The share of AI job
postings among all job postings in 2021 was greatest
for machine learning skills (0.6% of all job postings),
followed by artificial intelligence (0.33%), neuralnetworks (0.16%), and natural language processing(0.13%). Postings for AI jobs in machine learning andartificial intelligence have significantly increased in
the past couple of years, despite small declines inboth categories from 2019–2020. Machine learningjobs are at nearly three times the level, and artificial
intelligence jobs are at around 1.5 times the level theyeach reached, respectively, in 2018.

AI JOB POSTINGS (% of ALL JOB POSTINGS) in the UNITED STATES by SKILL CLUSTER, 2010–21
Source: Emsi Burning Glass, 2021 | Chart: 2022 AI Index Report

0.57%, Machine Learning

AI Job Postings (% of All Job Postings)

0.50%

0.40%
0.33%, Arti cial Intelligence

0.30%

0.20%
0.15%, Neural Networks
0.13%, Natural Language Processing (NLP)
0.11%, Robotics
0.10%, Visual Image Recognition
0.06%, Autonomous Driving

0.10%

0.00%
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021
Figure 4.1.4

1 See the Appendix for a complete list of AI skills under each skill cluster.

Chapter 4 Preview

8Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.1 Jobs

U.S. Labor Demand: By Sector

followed by professional, scientific, and technicalservices (2.59% of all listings), manufacturing (2.02%),
and finance and insurance (1.81%).

Figure 4.1.5 shows that 3.30% of all job postings in theinformation sector in the United States were AI-related,

AI JOB POSTINGS (% of ALL JOB POSTINGS) in the UNITED STATES by SECTOR, 2021
Source: Emsi Burning Glass, 2021 | Chart: 2022 AI Index Report

3.30%

Information
2.59%

Professional, Scienti!c, and Technical Services
2.02%

Manufacturing
1.81%

Finance and Insurance
0.95%

Agriculture, Forestry, Fishing and Hunting

0.95%

Public Administration

0.84%

Educational Services

0.78%

Utilities

0.69%

Mining, Quarrying, and Oil and Gas Extraction
Management of Companies and Enterprises

0.67%

Wholesale Trade

0.63%
0.54%

Retail Trade

0.40%

Real Estate and Rental and Leasing

0.33%

Transportation and Warehousing

0.28%

Waste Management and Remediation Services
0.00%

1.00%

2.00%
AI Job Postings (% of All Job Postings)

3.00%
Figure 4.1.5

U.S. Labor Demand: By State
Figure 4.1.6 breaks down the U.S.
AI labor demand by state. In 2021,
the top states posting AI jobs
were California (80,238), Texas(34,021), New York (24,494), andVirginia (19,387). California, in first,
had over 2.35 times the numberof postings as Texas, the second
greatest. Proportionally, however,Washington, D.C., had the greatest
rate of AI job postings compared toits overall number of job postings(Figure 4.1.7). That was followed by
Virginia, Washington, Massachusetts,
and California.

NUMBER of AI JOB POSTINGS in the UNITED STATES by STATE, 2021
Source: Emsi Burning Glass, 2021 | Chart: 2022 AI Index Report

ME
693

AK
481

HI
866

VT
552

NH
1,211
MA
18,430WA
19,253

ID
2,309

MT
629

ND
726

MN
7,453

IL
15,775

WI
5,142

MI
9,935

NY
24,494

RI
1,090

OR
4,393

NV
2,253

WY
340

SD
693

IA
2,388

IN
3,899

OH
9,593

PA
10,806NJ
10,503

CT
4,291

CA
80,238

UT
3,631

CO
11,189

NE
1,385

MO
5,314

KY
2,107

WV
706

VA
19,387

MD
7,745

DE
1,764

AZ
9,606

NM
1,719

KS
2,058

AR
1,539

TN
5,219

NC
12,863

SC
2,259

DC
6,381

OK
1,462

LA
1,437

MS
986

AL
3,605

GA
14,283TX
34,021

FL
14,972

Figure 4.1.6

Chapter 4 Preview

9Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.1 Jobs

AI JOB POSTINGS (TOTAL and % of ALL JOB POSTINGS) by U.S. STATE and DISTRICT, 2021
Source: Emsi Burning Glass, 2021 | Chart: 2022 AI Index Report

District of Columbia

AI Job Postings (% of All Job Postings)

2.00%

Virginia

1.50%

Washington
Massachusetts
Delaware1.00%

MarylandNew Mexico
Vermont

Utah

Maine

Wyoming

HawaiiArkansas

Mississippi

Kansas0.50%
AlaskaCalifornia

New York
Georgia
Illinois

Minnesota

Texas

Missouri Arizona

Alabama

Ohio
FloridaIndianaLouisiana

0.00%
200

500

1,000

2,000

5,000

10,00020,00050,000100,000

Number of AI Job Postings (Log Scale)Figure 4.1.7

Chapter 4 Preview

10Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.1 Jobs

A I S K I L L P E N E T R AT I O N

AI skills is measured as the sum of the penetration ofeach AI skill across occupations in a given country or
region, divided by the global average across the same
occupations. For example, a relative penetration rate of
2 means that the average penetration of AI skills in thatcountry or region is 2 times the global average across thesame set of occupations. Figure 4.1.8 shows that India
led the world in the rate of AI skill penetration—3.09
times the global average from 2015 to 2021—followedby the United States (2.24) and Germany (1.7). After thatcame China (1.56), Israel (1.52), and Canada (1.41).2

The AI skill penetration rate shows the prevalenceof AI skills across occupations, or the intensity withwhich LinkedIn members use AI skills in their jobs. It is
calculated by computing the frequencies of LinkedIn
users’ self-added skills in a given area from 2015–2021,
then reweighting those figures by using a statisticalmodel to get the top 50 representative skills in thatoccupation.

Global Comparison
For global comparison, the relative penetration rate of

RELATIVE AI SKILL PENETRATION RATE by GEOGRAPHIC AREA, 2015–21
Source: LinkedIn, 2021 | Chart: 2022 AI Index Report

3.09

India
2.24

United States
1.70

Germany
1.56

China

1.52

Israel
1.41

Canada1.40

United Kingdom
1.28South Korea

1.22

Singapore
1.11

France
0.84

Spain

0.77

Australia
Switzerland

0.76

Italy

0.74
0.60

Sweden
0.00

0.50

1.00

1.50

2.00

2.50

3.00

Relative AI Skill Penetration Rate
Figure 4.1.8

Global Comparison: By Industry
India and the United States had the highest relative
AI skill penetration across the board—leading theother countries or regions in skill penetration rates insoftware and IT services, hardware and networking,manufacturing, education, and finance (Figure 4.1.9).
Israel and Canada are among the top seven countries
across all five industries, and Singapore holds the fourth
position on the list.

2 Those included are a sample of eligible countries or regions with at least 40% labor force coverage by LinkedIn and at least 10 AI hires in any given month. China and India were also included in thissample because of their increasing importance in the global economy, but LinkedIn coverage in these countries does not reach 40% of the workforce. Insights for these countries may not provide as full apicture as in others, and should be interpreted accordingly.

Chapter 4 Preview

11Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.1 Jobs

RELATIVE AI SKILL PENETRATION RATE by INDUSTRY across GEOGRAPHIC AREA, 2015–21
Source: LinkedIn, 2021 | Chart: 2022 AI Index Report

Education

Finance
3.96

India
United States
IsraelGermany
CanadaSouth Korea
United Kingdom
France

2.20
1.35
1.27
1.23
1.18
0.91
0.80

1.00

2.00

3.00

Hardware

India
United States
Germany
CanadaSingapore
IsraelNetherlands
United Kingdom

4.00

3.08
1.89
1.48
1.31
1.28
1.15
1.12
1.00

0.00 1.00

Manufacturing

2.00

3.00

India
United States
IsraelChina
Germany
CanadaSingapore
South Korea

2.92
1.98
1.37
1.34
1.22
1.12
1.12
0.88
0.00

1.00

2.00

3.00

Software
2.39

India
United States
Germany
IsraelSwedenSingapore
CanadaChina

India
United States
China
Germany
IsraelCanadaSingapore
South Korea

2.21
1.66
1.31
1.16
1.04
0.99
0.93

0.00

1.00

3.02
2.18
1.58
1.38
1.22
1.20
1.16
1.13

0.00 1.00
2.00
3.00
Relative AI Skill Penetration Rate

2.00

Figure 4.1.9

Global Comparison: By Gender
Figure 4.1.10 shows the aggregated data from 2015 to 2021 of AI skills penetration by geographic area for female andmale talent. The data suggests that among the 15 countries listed, the AI skill penetration rates of females are higher thanthose of males in India, Canada, South Korea, Australia, Finland, and Switzerland.
RELATIVE AI SKILL PENETRATION RATE by GENDER, 2015–21
Source: LinkedIn, 2021 | Chart: 2022 AI Index Report

India
United States
CanadaSouth Korea
China
Singapore
Germany
IsraelAustralia
FinlandUnited Kingdom
Netherlands

FemaleMaleFranceSwitzerland
Brazil
0.00

1.00

2.00

3.00

4.00

Relative AI Skill Penetration Rate
Figure 4.1.10

Chapter 4 Preview

12Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.2 Investment

This section on corporate AI activity draws on data from NetBase Quid, which aggregates over 6 million global public and privatecompany profiles, updated on a weekly basis, including metadata on investments, location of headquarters, and more. NetBase Quidalso applies natural language processing technology to search, analyze, and identify patterns in large, unstructured datasets, likeaggregated blogs, company and patent databases.

4.2 INVESTMENTC O R P O R AT E I N V E S T M E N T
Corporate investment in artificial intelligence, frommergers and acquisitions to public offerings, is a key
contributor to AI research and development. It also
contributes to AI’s impact on the economy. Figure 4.2.1
highlights overall global corporate investment in AI from2013–2021. In 2021, companies made the greatest AI

investment through private investment (totaling around$93.5 billion), followed by mergers and acquisitions(around $72 billion), public offerings (around $9.5
billion), and minority stake (around $1.3 billion). In
2021, investments from mergers and acquisitions grewby 3.3 times compared to 2020, led by two AI healthcarecompanies and two cybersecurity companies.3

GLOBAL CORPORATE INVESTMENT in AI by INVESTMENT ACTIVITY, 2013–21
Source: NetBase Quid, 2021 | Chart: 2022 AI Index Report

Total Investment (in billions of U.S. Dollars)

176.47Merger/Acquisition
Minority Stake
Private Investment
Public O"ering

150

72.03

119.54
21.53

100

66.98
47.33

50

14.99
0

22.78

2013

2014

20.42

22.49

29.17

5.23

45.58

19.83

8.98

15.73

2015

2016

44.06
93.54

46.00
35.26

42.57

2018

2019

23.09

2017

7.94

9.58

2020

2021
Figure 4.2.1

3 Nuance Communications (by Microsoft, $19.8 billion), Varian Medical Systems (Siemens, $17.2 billion), and Proofpoint (Thoma Bravo, $12.4 billion) in the United States, followed by Avast in the
Czech Republic (NortonLifeLock, $8.0 billion).

Chapter 4 Preview

13Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.2 Investment

S TA R T U P ACT I V I T Y

However, Figure 4.2.3 shows that the number of newly
funded AI companies continues to drop, from 762
companies in 2020 to 746 companies in 2021—thethird year of a decline that started in 2018. The largest
private investments in 2021 have been led by two data
management companies and two robotics/autonomous
driving companies.4

The following section analyzes artificial intelligence andmachine learning companies globally that have received
more than $1.5 million in investment from 2013 to 2021.

Global Trend
In 2021, global private investment in AI totaled around$93.5 billion, which is more than double the total
private investment in 2020 (Figure 4.2.2). That marks
the greatest year-over-year increase since 2014 (wheninvestment from 2013 to 2014 more than doubled).
Among companies that disclosed the amount of funding,the number of AI funding rounds that ranged from$100 million to $500 million more than doubled in 2021
compared to 2020, while funding rounds that were
between $50 million and $100 million more than doubled
as well (Table 4.2.1). In 2020, there were only four funding
rounds worth $500 million or more; in 2021, that number
grew to 15. Companies attracted significantly higher
investment in 2021, as the average private investment
deal size in 2021 was 81.1% higher than in 2020.

In 2021, global private
investment in AI totaled
around $93.5 billion,
which is more thandouble the total private
investment in 2020.

PRIVATE INVESTMENT in AI, 2013–21
Source: NetBase Quid, 2021 | Chart: 2022 AI Index Report

Total Investment (in bIllions of U.S. Dollars)

100

93.54

80

60

40

20

0
2013

2014

2015

2016

2017

2018

2019

2020

2021
Figure 4.2.2

4 The largest private investments have been Databricks (United States), Beijing Horizon Robotics Technology (China), Oxbotica Limited (United Kingdom), and Celonis (Germany).

Chapter 4 Preview

14Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.2 Investment

NUMBER of NEWLY FUNDED AI COMPANIES in the WORLD, 2013–21
Source: NetBase Quid, 2021 | Chart: 2022 AI Index Report

1200

Number of Companies

1000

800

746

600

400

200

0
2013

2014

2015

2016

2017

2018

2019

2020

2021
Figure 4.2.3

Funding Size

2020

2021

Total

Over $1 billion

3

5

8

$500 million – $1 billion

1

10

11

$100 million – $500 million

93

235

328

$ 50 million – $100 million

85

194

279

Under $50 million

2,102

2,120

4,222

Undisclosed

354

395

749

Total

2,638

2,959

5,597
Table 4.2.1

Chapter 4 Preview

15Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.2 Investment

Regional Comparison by Funding AmountIn 2021, as captured in Figure 4.2.4, the United States
led the world in overall private investment in funded AI
companies—at approximately $52.9 billion—over threetimes the next country on the list, China ( $17.2 billion).
In third place was the United Kingdom ($4.65 billion),
followed by Israel ($2.4 billion) and Germany ($1.98
billion). Figure 4.2.5 shows that when combining total
private investment from 2013 to 2021, the same ranking
applies: U.S. investment totaled $149 billion and Chinese
investment totaled $61.9 billion, followed by the United

Kingdom ($10.8 billion), India ($10.77 billion), and Israel($6.1 billion). Notably, U.S. private investment in AI
companies from 2013–2021 was more than double thetotal in China, which itself was about six times the total
investment from the United Kingdom in the same period.
Broken out by geographic area, as shown in Figure
4.2.6, the United States, China, and the European Union
all grew their investments from 2020 to 2021, with theUnited States leading China and the European Union by
3.1 and 8.2 times the investment amount, respectively.

PRIVATE INVESTMENT in AI by GEOGRAPHIC AREA, 2021
Source: NetBase Quid, 2021 | Chart: 2022 AI Index Report

52.88

United States
17.21

China
4.65

United Kingdom
2.41

IsraelGermany

1.98

Canada

1.87

France

1.55

India

1.35

Australia

1.25South Korea

1.10

Singapore

0.93

Spain

0.89

United Arab Emirates

0.82

Hong Kong

0.63

Portugal 0.52
0

10

20

30

40

50

Total Investment (in billions of U.S. Dollars)
Figure 4.2.4

Chapter 4 Preview

16Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.2 Investment

PRIVATE INVESTMENT in AI by GEOGRAPHIC AREA, 2013–21
Source: NetBase Quid, 2021 | Chart: 2022 AI Index Report

149.0

United States
61.9

China
10.8

United Kingdom

10.8

India
Israel6.1

Canada5.7

Germany

4.4

France3.9
2.3

Singapore
Japan

2.2

Australia

1.8South Korea

1.8

Spain 1.3
Hong Kong 1.2
Netherlands 1.2
0

20

40

60

80

100

120

140

160

Total Investment (in billions of U.S. Dollars)
Figure 4.2.5

PRIVATE INVESTMENT in AI by GEOGRAPHIC AREA, 2013–21
Source: NetBase Quid, 2021 | Chart: 2022 AI Index Report

52.87, United States

Total Investment (in billions of U.S. Dollars)

50

40

30

20

17.21, China

106.42, European Union

0
2013

2014

2015

2016

2017

2018

2019

2020

2021
Figure 4.2.6

Chapter 4 Preview

17Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.2 Investment

Regional Comparison by Newly Funded
AI Companies

shows a similar trend (Figure 4.2.8).

This section breaks down the investment data by the
number of newly funded AI companies in each region. For
2021, Figure 4.2.7 shows that the United States led with299 companies, followed by China with 119, the United
Kingdom with 49, and Israel with 28. The gaps between
each are significant. Aggregated data from 2013 to 2021

However, the number of newly funded AI companies has
declined in both the United States and China since 2018and 2019 (Figure 4.2.9). Despite that downward trend,
the United States still leads in the number of newly
funded companies, with 299 funded in 2021, followed by
China (119) and the European Union (96).

NUMBER of NEWLY FUNDED AI COMPANIES by GEOGRAPHIC AREA, 2021
Source: NetBase Quid, 2021 | Chart: 2022 AI Index Report

299

United States
119

China
49

United Kingdom
Israel28

France27

Germany

25

India

23

Canada22

South Korea

19

Netherlands

16

Australia

12

Japan

12

Switzerland

12
10Singapore
0

50

100

150

200

250

300

Number of Companies
Figure 4.2.7

Chapter 4 Preview

18Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.2 Investment

NUMBER of NEWLY FUNDED AI COMPANIES by GEOGRAPHIC AREA, 2013–21 (SUM)
Source: NetBase Quid, 2021 | Chart: 2022 AI Index Report

3,234

United States
940

China
427

United Kingdom
264

IsraelFrance242

Canada

239
172

Japan
India

169

Germany

154

Singapore

99

Australia

77

South Korea

74

Switzerland

65

Spain

56

0

500

1,000

1,500

2,000

2,500

3,000

3,500

Number of Companies
Figure 4.2.8

NUMBER of NEWLY FUNDED AI COMPANIES by GEOGRAPHIC AREA, 2013–21
Source: NetBase Quid, 2021 | Chart: 2022 AI Index Report

500

Number of Companies

400

300

299, United States

200

119, China
106, European Union

100

0
2013

2014

2015

2016

2017

2018

2019

2020

2021
Figure 4.2.9

Chapter 4 Preview

19Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.2 Investment

Focus Area Analysis
Private AI investment also varies by focus area. According
to Figure 4.2.10, the greatest private investment in AI in
2021 was in data management, processing, and cloud(around $12.2 billion). Notably, this was 2.6 times theinvestment in 2020 (around $4.69 billion) as two of thefour largest private investments made in 2021 are data
management companies. In second place was private
investment in medical and healthcare ($11.29 billion),
followed by fintech ($10.26 billion), AV ($8.09 billion), andsemiconductors ($6.0 billion).

Aggregated data in Figure 4.2.11 shows that in the last
five years, the medical and healthcare category received
the largest private investment globally ($28.9 billion);
followed by data management, processing, and cloud($26.9 billion); fintech ($24.9 billion); and retail ($21.95
billion). Moreover, Figure 4.2.12 shows the overall trend
in private investment by industries from 2017–2021 andreveals a steady increase in AV, cybersecurity and dataprotection, fitness and wellness, medical and healthcare,and semiconductor industries.

PRIVATE INVESTMENT in AI by FOCUS AREA, 2017–21 (SUM)

PRIVATE INVESTMENT in AI by FOCUS AREA, 2020 vs. 2021

Source: NetBase Quid, 2021 | Chart: 2022 AI Index Report

Source: NetBase Quid, 2021 | Chart: 2022 AI Index Report

Data Management, Processing, Cloud

Medical and Healthcare

Medical and Healthcare

Data Management, Processing, Cloud

FintechFintechAV

RetailSemiconductor

2021
2020

Industrial Automation, Network

28.92
26.91
24.92
21.95

Industrial Automation, Network

19.03

AV

18.60

RetailSemiconductor

11.91

Fitness and Wellness

Fitness and Wellness

10.75

NLP, Customer Support

NLP, Customer Support

8.45

Energy, Oil, and Gas

Ed Tech

8.16

Cybersecurity, Data Protection

Energy, Oil, and Gas

7.82

DronesFacial Recognition

7.49

Marketing, Digital Ads

Cybersecurity, Data Protection

7.30

HR TechMarketing, Digital Ads

7.20

Facial Recognition

AR/VR

4.46

Insurtech

HR Tech4.27

AgritechDrones

3.91

Sales Enablement

Sales Enablement

3.54

AR/VR

Insurtech

3.29

Ed TechAgritech

3.13

Geospatial

Legal Tech

2.62

Legal Tech

Geospatial

1.82
1.33

Entertainment

Music, Video Content

Music, Video Content

Entertainment

1.31

VC

VC

1.20

0

2

4

6

8

10

12

Total Investment (in billions of U.S. Dollars)

Figure 4.2.10

Chapter 4 Preview

0

5

10

15

20

25

30

Total Investment (in billions of U.S. Dollars)
Figure 4.2.11

20

Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.2 Investment

PRIVATE INVESTMENT in AI by FOCUS AREA, 2017–21
Source: NetBase Quid, 2021 | Chart: 2022 AI Index Report
Agritech

AR/VR

AV

Cybersecurity, Data Protection

1.43

Total Investment (in billions of U.S. Dollars)

Data Management, Processing, Cloud

8.09

10

12.17

2.65

1.26

0
Ed TechDronesEnergy, Oil, and Gas

Entertainment

Facial Recognition

10
2.78

2.48

Fintech10.26

10Fitness and Wellness

1.87

0.71

1.24

0

Geospatial

HR TechIndustrial Automation, Network
5.85

5.56
2.18

0.93
0
Insurtech

Legal Tech

Marketing, Digital Ads

Medical and Healthcare

11.29

Music, Video Content

10
1.77

2.31

0.91

0
NLP, Customer Support

RetailSales Enablement

10

0.47
Semiconductor

5.74

4.01

VC
6.00

1.28

0.29

0
2018

2020

2018

2020

2018

2020

2018

2020

2018

2020

Figure 4.2.12

Chapter 4 Preview

21Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.3 Corporate Activity

4.3 CORPORATE ACTIVITYI N D U S T RY A D O P T I O NGlobal Adoption of AI

This section on corporate AI activity draws on McKinsey’s“The State of AI in 2021” report from December 2021. The
report based its conclusions on a global online surveyof 1,843 participants conducted earlier in 2021. Survey
respondents came from a range of industries, companies,functional specialties, tenures, and regions of the world—and each provided answers to questions about the stateof artificial intelligence today.

Figure 4.3.1 shows AI adoption by organizations globally,broken out by geographic area. In 2021, India led with
65% adoption, followed by “Developed Asia-Pacific”(64%), “Developing markets (incl. China, MENA)” (57%),
and North America (55%). The average adoption rate
across all geographies was 56%, up 6% from 2020.
Notably, “Developing markets (incl. China, MENA)”
registered a 21% increase from 2020, and India had an
8% increase from 2020.

AI ADOPTION by ORGANIZATIONS in the WORLD, 2020-21
Source: McKinsey & Company, 2021 | Chart: 2022 AI Index Report56%

All geographies
50%

64%

Developed
Asia-Paci!c

61%
57%

Developing markets(incl. China, MENA)

45%
51%

Europe

2021
2020

47%

65%

India
57%
47%

Latin America
41%

55%

North America
51%

0%

10%

20%

30%

40%

50%

60%

70%

% of Respondents
Figure 4.3.1

Chapter 4 Preview

22Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.3 Corporate Activity

AI Adoption by Industry and Function
Figure 4.3.2 shows AI adoption by industry and function
in 2021. The greatest adoption was in product and/or
service development for high tech/telecommunications(45%), followed by service operations for financial
services (40%), service operations for high tech/telecommunications (34%), and risk function for
financial services (32%).

AI ADOPTION by INDUSTRY and FUNCTION, 2021

Industry

Source: McKinsey & Company, 2021 | Chart: 2022 AI Index Report
Human Resources

Manufacturing

Marketing and Sales

Product and/or
Service Development

Risk

Service Operations

Strategy andCorporate FinanceSupply-chain
Management

All Industries

9%

12%

20%

23%

13%

25%

9%

13%

Automotive andAssembly

11%

26%

20%

15%

4%

18%

6%

17%

Business, Legal, andProfessional Services

14%

8%

28%

15%

13%

26%

8%

13%

ConsumerGoods/Retail

2%

18%

22%

17%

1%

15%

4%

18%

Financial Services

10%

4%

24%

20%

32%

40%

13%

8%

Healthcare
Systems/Pharma andMedical Products

9%

11%

14%

29%

13%

17%

12%

9%

High Tech/Telecom

12%

11%

28%

45%

16%

34%

10%

16%

% of Respondents (Function)
Figure 4.3.2

Chapter 4 Preview

23Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.3 Corporate Activity

Type of AI Capabilities Adopted

telecommunications industry (34%), followed by robotic
process automation for both the financial services andautomotive and assembly industry (33%) and natural
language text understanding for financial services (32%).

With respect to the type of AI capabilities embedded
in standard business processes in 2021, as indicatedby Figure 4.3.3, the highest rate of embedding was innatural language text understanding for the high tech/

AI CAPABILITIES EMBEDDED in STANDARD BUSINESS PROCESSES, 2021

Industry

Source: McKinsey & Company, 2021 | Chart: 2022 AI Index Report
PhysicalRobotics

Recommender
Systems

24%

12%

17%

16%

26%

11%

12%

24%

12%

5%

23%

18%

19%

13%

22%

17%

11%

13%

14%

4%

11%

16%

12%

18%

32%

25%

12%

19%

10%

8%

22%

6%

17%

17%

18%

FacialKnowledge
NL
Regonition
GraphsGeneration

NL Speech
NL Text UnUnderstanding
derstanding

Computer
VisionDeep
Learning

All Industries

23%

19%

11%

17%

12%

14%

Automotive andAssembly

15%

14%

9%

16%

3%

Business, Legal, andProfessional Services

29%

24%

15%

20%

ConsumerGoods/Retail

23%

12%

14%

Financial Services

17%

16%

Healthcare
Systems/Pharma andMedical Products

30%High Tech/Telecom

28%

ReinforceRobotic
ment
Process Simulations
Learning Automation

Transfer
Learning

VirtualAgents

17%

12%

23%

33%

27%

6%

12%

27%

31%

18%

21%

19%

8%

8%

16%

9%

1%

15%

4%

13%

16%

33%

12%

12%

28%

26%

28%

22%

13%

28%

22%

19%

31%

34%

5%

19%

15%

23%

14%

11%

25%

% of Respondents (AI Capability)
Figure 4.3.3

Chapter 4 Preview

24Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.3 Corporate Activity

Consideration and Mitigation of Risks
From Adopting AI
The risk from adopting AI that survey respondents
identified as most relevant in 2021 was cybersecurity(55% of respondents), followed by regulatory compliance(48%), explainability (41%), and personal/individual
privacy (41%) (Figure 4.3.4). Fewer organizations foundAI risks from cybersecurity to be relevant in 2021 thanin 2020, declining from just over 60% of respondents
expressing concern in 2020 to 55% in 2021. Concern over
AI regulatory compliance risks, meanwhile, remained
virtually unchanged from 2020.

Figure 4.3.5 shows risks from AI that organizations aretaking steps to mitigate. Cybersecurity was the mostfrequent response (47% of respondents), followed byregulatory compliance (36%), personal/individual privacy(28%), and explainability (27%). It is worth noting thegaps between risks that organizations find relevant andrisks that organizations take steps to mitigate—a gap of 10
percentage points with equity and fairness (29% to 19%),
12 percentage points with regulatory compliance (48%
to 36%), 13 percentage points with personal/individual
privacy (41% to 28%), and 14 percentage points with
explainability (41% to 27%).

RISKS from ADOPTING AI that ORGANIZATIONS CONSIDER RELEVANT, 2019–21
Source: McKinsey & Company, 2021 | Chart: 2022 AI Index Report

60%
55%, Cybersecurity

50%48%, Regulatory Compliance% of Respondents

41%, Explainability

40%

41%, Personal/Individual Privacy
35%, Organizational Reputation

30%

29%, Equity and Fairness
26%, Workforce/Labor Displacement
21%, Physical Safety

20%

14%, National Security

10%9%, Political Stability

0%
2019

2020

2021
Figure 4.3.4

Chapter 4 Preview

25Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.3 Corporate Activity

RISKS from ADOPTING AI that ORGANIZATIONS TAKE STEPS to MITIGATE, 2019–21
Source: McKinsey & Company, 2021 | Chart: 2022 AI Index Report

50%
47%, Cybersecurity40%% of Respondents

36%, Regulatory Compliance30%

28%, Personal/Individual Privacy
27%, Explainability
22%, Organizational Reputation

20%

19%, Equity and Fairness
16%, Physical Safety
15%, Workforce/Labor Displacement

10%8%, National Security
4%, Political Stability

0%
2019

2020

2021
Figure 4.3.5

Chapter 4 Preview

26Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.4 AI Education

The following section draws on data from the annual Computing Research Association (CRA) Taulbee Survey. For the latest survey
featured in this section, CRA collected data in Fall 2020 by reaching out to over 200 PhD-granting departments in the United States andCanada. Results are published in May 2021. The CRA survey documents trends in student enrollment, degree production, employmentof graduates, and faculty salaries in academic units in the United States and Canada that grant doctoral degrees in computer science(CS), computer engineering (CE), or information (I). Academic units include departments of CS and CE or, in some cases, colleges orschools of information or computing.

4.4 AI EDUCATIONC S U N D E R G R A D UAT E G R A D UAT E S
I N N O R T H A M E R I CA
In North America, most AI-related courses are offeredas part of the CS curriculum at the undergraduate level.

The number of new CS undergraduate graduates at
doctoral institutions in North America has grown 3.5
times from 2010 to 2020 (Figure 4.4.1). More than 31,000
undergraduates completed CS degrees in 2020—an
11.60% increase from the number in 2019.

NUMBER of NEW CS UNDERGRADUATE GRADUATES at DOCTORAL INSTITUTIONS in NORTH AMERICA, 2010–20
Number of New CS Undergraduate Graduates (in thousands)

Source: CRA Taulbee Survey, 2021 | Chart: 2022 AI Index Report

31.84

30

25

20

15

10

5

0
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020
Figure 4.4.1

Chapter 4 Preview

27Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.4 AI Education

N E W C S P H D S I N N O R T H A M E R I CA

In 2020, 1 in every 5
CS students who
graduated with PhD
degrees specialized
in AI/ML, the mostpopular specialty
in the past decade.

The following sections show the trend of CS PhD graduates
in North America with a focus on those with AI-related
specialties.5 The CRA survey includes 20 specialties in total,
two of which are directly related to the field of AI: artificial
intelligence/machine learning (AI/ML) and robotics/vision.

New CS PhDs by Specialty
In 2020, 1 in every 5 CS students who graduated with
PhD degrees specialized in AI/ML, the most popularspecialty in the past decade (Figure 4.4.2). It is also thespeciality that exhibits the most significant growth from2010 to 2021, relative to 18 other specializations (Figure
4.4.3). Robotics/vision is also among the most popular
CS specialties of PhD graduates in 2020, registering a
1.4 percentage point change in the share of total new CS
PhDs in the past 11 years.

NEW CS PHDS (% of TOTAL) in the UNITED STATES by SPECIALITY, 2020
Source: CRA Taulbee Survey, 2021 | Chart: 2022 AI Index Report

21.00%

Arti!cial Intelligence/Machine Learning
7.30%

Software Engineering
Security/Information Assurance

7.10%

Theory and Algorithms

7.00%

Databases/Information Retrieval

6.50%

Robotics/Vision

6.30%

Networks

6.10%

Other

5.80%
5.30%

Human-Computer Interaction

4.90%

Graphics/Visualization

4.30%

Operating Systems

3.40%

Programming Languages/Compilers

3.30%

Informatics: Biomedical/Other Science

2.90%

High Performance Computing
Information Systems

2.70%

Hardware/Architecture

2.40%
2.00%

Social Computing/Social Informatics/CSCW
Computing Education

0.70%

Information Science

0.50%

Scienti!c/Numerical Computing

0.50%

0%

5%

10%

15%

20%

% of New CS PhDs
Figure 4.4.2

5 New CS PhDs in this section include PhD graduates from academic units (departments, colleges, or schools within universities) of computer science in the United States.

Chapter 4 Preview

28Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.4 AI Education

PERCENTAGE POINT CHANGE in NEW CS PHDS in the UNITED STATES by SPECIALTY, 2010–20
Source: CRA Taulbee Survey, 2021 | Chart: 2022 AI Index Report

6.80%

Arti!cial Intelligence/Machine Learning
2.50%

Human-Computer Interaction

2.10%

Security/Information Assurance

1.50%

Information Systems

1.40%

Robotics/Vision
0.60%

High Performance Computing
Databases/Information Retrieval

0.10%

Information Science

0.00%-0.10%

Social Computing/Social Informatics/CSCW
Other-0.50%

Theory and Algorithms-0.60%-0.60%

Operating Systems-1.20%

Software Engineering
Graphics/Visualization-1.50%

Informatics: Biomedical/Other Science-1.60%-1.70%

Hardware/Architecture-1.90%

Scienti!c/Numerical Computing-2.00%

Programming Languages/Compilers-4.10%

Networks

-4%

-2%

0%

2%

4%

6%

8%

Percentage Point Change in New CS PhDs

Figure 4.4.3

New CS PhDs with AI/ML and Robotics/Vision Specialties
Between 2010 and 2020, the number of CS PhD graduates with AI/ML and robotics/vision specialities grew by 72.05%
and 50.91%, respectively. The slight decrease in the total number for both specialties from 2019 to 2020 may be due tothe impact of the COVID-19 pandemic.
NEW CS PHDS with AI/ML and ROBOTICS/VISION SPECIALTY
in the UNITED STATES, 2010–20

NEW CS PHDS (% of TOTAL) with AI/ML and
ROBOTICS/VISION SPECIALTY in the UNITED STATES, 2010–20

Source: CRA Taulbee Survey, 2021 | Chart: 2022 AI Index Report

Source: CRA Taulbee Survey, 2021 | Chart: 2022 AI Index Report350

21.00%

286

200
171
138

159

144

266
218

138

100

15%

10%

6.30%

5%
68

65

74

76

72

75

66

91

83

Arti!cial Intelligence/Machine Learning
Robotics/Vision

2018

2017

2016

2015

2014

2013

2012

2011

Arti!cial Intelligence/Machine Learning
Robotics/Vision
Figure 4.4.4a

Chapter 4 Preview

2010

2020

2019

2018

2017

2016

2015

2014

0%
2013

46

2011

55

0

2012

50

2019

161

277

2009

150

233

2010

Number of Graduates

250

New CS PhDs (% of Total)

20%
300

Figure 4.4.4b

29

Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.4 AI Education

N E W A I P H D S E M P LOY M E N T I N
N O R T H A M E R I CA
Where do new AI PhDs choose to work following
graduation? This section analyzes the employment
trends of new AI PhDs across North America in academia,
industry, and government.6

Academia vs. Industry vs. Government
In 2020, the share of new AI PhD graduates in North

America who chose to work in the industry dippedslightly, with its share dropping from 65.7% in 2019 to60.2% in 2020, whereas the share of new AI PhDs whowent into academia and government changed little(Figure 4.4.5a and Figure 4.4.5b). Note that the 2020 data
may be impacted by the increasing number of new AI
PhDs who went abroad upon graduation, a number thatgrew from 19 in 2019 to 32 in 2020.

EMPLOYMENT of NEW AI PHDS to ACADEMIA, GOVERNMENT, EMPLOYMENT of NEW AI PHDS (% of TOTAL) to ACADEMIA,or INDUSTRY in NORTH AMERICA, 2010–20
GOVERNMENT, or INDUSTRY in NORTH AMERICA, 2010–20
Source: CRA Taulbee Survey, 2021 | Chart: 2022 AI Index Report

Source: CRA Taulbee Survey, 2021 | Chart: 2022 AI Index Report

250
60%

65
61
63

150

60
47
51

63

100

43
42
162
134

50

101
76

74

64

85

180
153

50%

40%

30%
24.02%

20%

116

10%

77

1.97%

Government

Industry

Academia
Figure 4.4.5a

Government

2019

2018

2017

2016

2015

2014

2013

2012

2011

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

Academia

2010

0%

0

2020

72

New AI PhDs (% of Total)

Number of New AI PhDs

60.24%

73

200

Industry
Figure 4.4.5b

6 New AI PhDs in this section include PhD graduates who specialize in artificial intelligence from academic units (departments, colleges, or schools within universities) of computer science,computer engineering, and information in the United States and Canada.

Chapter 4 Preview

30Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.4 AI Education

D I V E R S I T Y O F N E W A I P H D S I N N O R T H A M E R I CA
By Gender
Figure 4.4.6 shows that the share of new female AI and CS PhDs in North America remains low and has changed little
from 2010 to 2020.
FEMALE NEW AI and CS PHDS (% of TOTAL NEW AI and CS PHDS) in NORTH AMERICA, 2010–20
Source: CRA Taulbee Survey, 2021 | Chart: 2022 AI Index Report

Female New AI PhDs (% of All New AI PhDs)

20.20%, AI

20%
19.90%, CS

15%

10%

5%

0%
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020
Figure 4.4.6

Chapter 4 Preview

31Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.4 AI Education

By Race/Ethnicity
According to Figure 4.4.7, among the new AI PhDs from 2010 to2020 who are U.S. residents, the largest percentage has been
non-Hispanic white and Asian—65.2% and 18.8% on average.
By comparison, around 1.5% were Black or African American(non-Hispanic) and 2.9% were Hispanic on average over the

past 11 years. Figure 4.4.8 shows all PhDs awarded in the
United States to U.S. residents across departments of CS, CE,
and information between 2010 and 2020. In the past 11 years,
the share of new white (non-Hispanic) PhDs has changed little,
while the percentage of new Black or African American (nonHispanic) and Hispanic computing PhDs is significantly lower.

NEW U.S. AI RESIDENT PHDS (% of TOTAL) by RACE/ETHNICITY, 2010–20
Source: CRA Taulbee Survey, 2021 | Chart: 2022 AI Index Report80%

New U.S. AI Resident PhDs (% of Total)

70%60%
50.86%, White (Non-Hispanic)

50%
40%

30.17%, Asian

30%
20%

8.62%, Unknown
6.90%, Hispanic
1.72%, Black or African American (Non-Hispanic)
0.86%, Native Hawaiian or Paci c Islander
0.86%, Multiracial (Non-Hispanic)
0.00%, Native American or Alaskan Native

10%
0%
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020
Figure 4.4.7

NEW COMPUTING PHDS, U.S. RESIDENT (% of TOTAL) by RACE/ETHNICITY, 2010–20
Source: CRA Taulbee Survey, 2021 | Chart: 2022 AI Index Report

New Computing PhDs, U.S. Resident (% of Total)

70%

60%

57.50%, White (Non-Hispanic)

50%

40%

30%
24.80%, Asian20%
7.80%, Unknown
4.20%, Hispanic
3.60%, Black or African American (Non-Hispanic)
1.80%, Multiracial (Non-Hispanic)
0.10%, Native American or Alaskan Native

10%

0%

0.10%, Native Hawaiian or Paci c Islander

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020
Figure 4.4.8

Chapter 4 Preview

32Artificial IntelligenceIndex Report 2022

CHAPTER 4: THE ECONOMY AND EDUCATION
4.4 AI Education

N E W I N T E R N AT I O N A L A I P H D S I N
N O R T H A M E R I CA
The share of international students among new AI PhDs
in North America in 2020 decreased slightly from 64.3%
in 2019 to 60.5% in 2020 (Figure 4.4.9). For comparison,of all computing PhDs graduating in 2022, 65.1% ofthem were international students. In addition, moreinternational students—14.0% of all new AI PhDs—took
jobs outside the United States in 2020, compared to 8.6%
in 2019 (Figure 4.4.10).

NEW INTERNATIONAL AI PHDS (% of TOTAL NEW AI PHDS) in NORTH AMERICA, 2010–20

New International AI PhDs (% of Total New AI PhDs)

Source: CRA Taulbee Survey, 2021 | Chart: 2022 AI Index Report

60%

60.50%

50%

40%

30%

20%

10%

0%
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020
Figure 4.4.9

INTERNATIONAL NEW AI PHDS (% of TOTAL) in the UNITEDSTATES by LOCATION of EMPLOYMENT, 2020
Source: CRA Taulbee Survey, 2021 | Chart: 2022 AI Index Report

11.80%,
Unknown

14.00%,
Outside United States

74.20%,
United States

Figure 4.4.10

Chapter 4 Preview

33Artificial IntelligenceIndex Report 2022

APPENDIX
Chapter 4: The Economy and Education

APPENDIXEMSI BURNING GLASS
Prepared by Julia Nitschke, Summer Jasinski, Bledi Taska
and Rucha Vankudre
Emsi Burning Glass delivers job market analytics
that empower employers, workers, and educators tomake data-driven decisions. The company’s artificial
intelligence technology analyzes hundreds of millions
of job postings and real-life career transitions to provide
insight into labor market patterns. This real-time strategic
intelligence offers crucial insights, such as what jobs aremost in demand, the specific skills employers need, andthe career directions that offer the highest potential for
workers. For more information, visit burning-glass.com.

Job Posting Data
To support these analyses, Emsi Burning Glass mined
its dataset of millions of job postings collected since2010. Emsi Burning Glass collects postings from over
45,000 online job sites to develop a comprehensive,
real-time portrait of labor market demand. It aggregates
job postings, removes duplicates, and extracts datafrom job postings text. This includes information on job
title, employer, industry, and region, as well as required
experience, education, and skills.
Job postings are useful for understanding trends in thelabor market because they allow for a detailed, realtime look at the skills employers seek. To assess therepresentativeness of job postings data, Emsi Burning
Glass conducts a number of analyses to compare the
distribution of job postings to the distribution of official
government and other third-party sources in the UnitedStates. The primary source of government data on U.S.
job postings is the Job Openings and Labor Turnover
Survey (JOLTS) program, conducted by the Bureau ofLabor Statistics. Based on comparisons between JOLTS

Chapter 4 Preview

and Emsi Burning Glass, the labor market demand
captured by Emsi Burning Glass data represents over 95%
of the total labor demand. Jobs not posted online areusually in small businesses (the classic example being the“Help Wanted” sign in the restaurant window) and union
hiring halls.

Measuring Demand for AI
In order to measure employers’ demand for AI skills, Emsi
Burning Glass uses its skills taxonomy of over 17,000 skills.
The list of AI skills from Emsi Burning Glass data is shown
below, with associated skill clusters. While some skills
are considered to be in the AI cluster specifically, for the
purposes of this report, all skills below were considered AI
skills. A job posting was considered an AI job if it requested
one or more of these skills.
Artificial Intelligence: Expert System, IBM Watson, IPSoft
Amelia, Ithink, Virtual Agents, Autonomous Systems, Lidar,
OpenCV, Path Planning, Remote Sensing
Natural Language Processing (NLP): ANTLR, Automatic
Speech Recognition (ASR), Chatbot, Computational
Linguistics, Distinguo, Latent Dirichlet Allocation, Latent
Semantic Analysis, Lexalytics, Lexical Acquisition, Lexical
Semantics, Machine Translation (MT), Modular Audio
Recognition Framework (MARF), MoSes, Natural Language
Processing, Natural Language Toolkit (NLTK), Nearest
Neighbor Algorithm, OpenNLP, Sentiment Analysis/
Opinion Mining, Speech Recognition, Text Mining, Text to
Speech (TTS), Tokenization, Word2Vec
Neural Networks: Caffe Deep Learning Framework,
Convolutional Neural Network (CNN), Deep Learning,
Deeplearning4j, Keras, Long Short-Term Memory (LSTM),
MXNet, Neural Networks, Pybrain, Recurrent Neural
Network (RNN), TensorFlow

34

Artificial IntelligenceIndex Report 2022

APPENDIX
Chapter 4: The Economy and Education

Machine Learning: AdaBoost algorithm, Boosting(Machine Learning), Chi Square Automatic Interaction
Detection (CHAID), Classification Algorithms, Clustering
Algorithms, Decision Trees, Dimensionality Reduction,
Google Cloud Machine Learning Platform, Gradientboosting, H2O (software), Libsvm, Machine Learning,
Madlib, Mahout, Microsoft Cognitive Toolkit, MLPACK (C++library), Mlpy, Random Forests, Recommender Systems,
Scikit-learn, Semi-Supervised Learning, Supervised
Learning (Machine Learning), Support Vector Machines(SVM), Semantic Driven Subtractive Clustering Method(SDSCM), Torch (Machine Learning), Unsupervised
Learning, Vowpal, Xgboost

and classified by taxonomists at LinkedIn into 249 skill
groupings, which are the skill groups represented in thedataset. The top skills that make up the AI skill grouping
are machine learning, natural language processing, data
structures, artificial intelligence, computer vision, image
processing, deep learning, TensorFlow, Pandas (software),and OpenCV, among others.

Robotics: Blue Prism, Electromechanical Systems,
Motion Planning, Motoman Robot Programming, Robot
Framework, Robotic Systems, Robot Operating System(ROS), Robot Programming, Servo Drives / Motors,
Simultaneous Localization and Mapping (SLAM)

Skills Genome

Visual Image Recognition: Computer Vision, Image
Processing, Image Recognition, Machine Vision, Object
Recognition

Skill groupings are derived by expert taxonomists
through a similarity-index methodology that measures
skill composition at the industry level. Industries areclassified according to the ISIC 4 industry classification(Zhu et al., 2018).

For any entity (occupation or job, country, sector, etc.), theskill genome is an ordered list (a vector) of the 50 “mostcharacteristic skills” of that entity. These most characteristic
skills are identified using a TF-IDF algorithm to identify themost representative skills of the target entity, while downranking ubiquitous skills that add little information aboutthat specific entity (e.g., Microsoft Word).

Included countries represent a select sample of eligible
countries with at least 40% labor force coverage byLinkedIn and at least 10 AI hires in any given month.
China and India were included in this sample because of
their increasing importance in the global economy, but
LinkedIn coverage in these countries does not reach 40%of the workforce. Insights for these countries may notprovide as full a picture as other countries, and should beinterpreted accordingly.

TF-IDF is a statistical measure that evaluates how
representative a word (in this case a skill) is to a selected
entity. This is done by multiplying two metrics:
1. The term frequency of a skill in an entity (TF).
2. The logarithmic inverse entity frequency of the skill
across a set of entities (IDF). This indicates how
common or rare a word is in the entire entity set.
The closer IDF is to 0, the more common a word is.
So, if the skill is very common across LinkedIn entities,
and appears in many job or member descriptions, the IDF
will approach 0. If, on the other hand, the skill is unique tospecific entities, the IDF will approach 1. Details available
at LinkedIn’s Skills Genome and LinkedIn-World Bank
Methodology note.

Skills (and AI Skills)

AI Skills Penetration

LINKEDIN
Prepared by Akash Kaura and Murat Erer

Country Sample

LinkedIn members self-report their skills on their LinkedIn
profiles. Currently, more than 38,000 distinct, standardized
skills are identified by LinkedIn. These have been coded

Chapter 4 Preview

The aim of this indicator is to measure the intensity of AI
skills in an entity (in a particular country, industry, gender,etc.) through the following methodology:

35

Artificial IntelligenceIndex Report 2022

• Compute frequencies for all self-added skills by
LinkedIn members in a given entity (occupation,industry, etc.) in 2015–2021.
• Re-weight skill frequencies using a TF-IDF model
to get the top 50 most representative skills in that
entity. These 50 skills compose the “skill genome” ofthat entity.
• Compute the share of skills that belong to the AI skill
group out of the top skills in the selected entity.
Interpretation: The AI skill penetration rate signals theprevalence of AI skills across occupations, or the intensitywith which LinkedIn members utilize AI skills in theirjobs. For example, the top 50 skills for the occupation ofengineer are calculated based on the weighted frequency
with which they appear in LinkedIn members’ profiles. If
four of the skills that engineers possess belong to the AI
skill group, this measure indicates that the penetration of
AI skills is estimated to be 8% among engineers (e.g., 4/50).

Jobs or Occupations
LinkedIn member titles are standardized and grouped
into approximately 15,000 occupations. These are notsector or country specific. These occupations are further
standardized into approximately 3,600 occupation
representatives. Occupation representatives groupoccupations with a common role and specialty, regardlessof seniority.

AI Jobs or Occupations
An AI job (technically, occupation representative) is
an occupation representative that requires AI skills toperform the job. Skills penetration is used as a signal
for whether AI skills are prevalent in an occupation
representative, in any sector where the occupation
representative may exist. Examples of such occupations
include (but are not limited to): machine learning
engineer, artificial intelligence specialist, data scientist,
computer vision engineer, etc.

AI Talent
A LinkedIn member is considered AI talent if they have

Chapter 4 Preview

APPENDIX
Chapter 4: The Economy and Education

explicitly added AI skills to their profile and/or they areoccupied in an AI occupation representative. The counts of
AI talent are used to calculate talent concentration metrics(e.g. to calculate the country-level AI talent concentration,
we use the counts of AI talent at the country level visa-vis the counts of LinkedIn members in the respective
countries).

Relative AI Skills Penetration
To allow for skills penetration comparisons acrosscountries, the skills genomes are calculated and a relevantbenchmark is selected (e.g., global average). A ratio is thenconstructed between a country’s and the benchmark’s AI
skills penetrations, controlling for occupations.
Interpretation: A country’s relative AI skills penetration of
1.5 indicates that AI skills are 1.5 times as frequent as in
the benchmark, for an overlapping set of occupations.

Global Comparison
For cross-country comparison, we present the relative
penetration rate of AI skills, measured as the sum of thepenetration of each AI skill across occupations in a given
country, divided by the average global penetration of AI
skills across the overlapping occupations in a sample ofcountries.
Interpretation: A relative penetration rate of 2 meansthat the average penetration of AI skills in that country
is two times the global average across the same set ofoccupations.

Global Comparison: By Industry
The relative AI skills penetration by country for industry
provides an in-depth sectoral decomposition of AI skill
penetration across industries and sample countries.
Interpretation: A country’s relative AI skill penetration
rate of 2 in the education sector means that the average
penetration of AI skills in that country is two times theglobal average across the same set of occupations in that
sector.

36

Artificial IntelligenceIndex Report 2022

Relative AI Hiring Index• LinkedIn Hiring Rate or Overall Hiring Rate isa measure of hires normalized by LinkedIn
membership. It is computed as the percentage of
LinkedIn members who added a new employer inthe same period the job began, divided by the total
number of LinkedIn members in the corresponding
location.
• AI Hiring Rate is computed following the overall
hiring rate methodology, but only considering
members classified as AI talent.
•R elative AI Hiring Index is the pace of change in AI
Hiring Rate normalized by the pace of change inOverall Hiring Rate, providing a picture of whether
hiring of AI talent is growing at a higher, equal,
or lower rate than overall hiring in a market. The
relative AI Hiring Index is equal to 1.0 when AI hiringand overall hiring are growing at the same rate year
on year.
Interpretation: Relative AI Hiring Index shows how fasteach country is experiencing growth in AI talent hiring
relative to growth in overall hiring in the country. A ratioof 1.2 means the growth in AI talent hiring has outpaced
the growth in overall hiring by 20%.

N E T BA S E Q U I D
Prepared by Julie Kim and Tejas Sirohi
NetBase Quid delivers AI-powered consumer and market
intelligence to enable business reinvention in a noisy
and unpredictable world. The software applies artificial
intelligence to reveal patterns in large, unstructured
datasets and to generate visualizations that enable users
to make smart, data-driven decisions accurately, quickly,
and efficiently. NetBase Quid uses Boolean query to
search for focus areas, topics, and keywords within social,news, forums and blogs, companies, and patents data
sources, as well as other custom datasets. NetBase Quidthen visualizes these data points based on the semantic
similarity.

Chapter 4 Preview

APPENDIX
Chapter 4: The Economy and Education

Search, Data Sources, and Scope
Over 6 million global public and private company profiles
from multiple data sources are indexed in order tosearch across company descriptions, while filtering andincluding metadata ranging from investment information
to firmographic information, such as founded year, HQ
location, and more. Company information is updated on a
weekly basis. NetBase Quid algorithm reads a big amountof text data from each document to make links between
different documents based on their similar language. This
process is repeated at an immense scale, which produces
a network with different clusters identifying distinct topics
or focus areas. Trends are identified based on keywords,
phrases, people, companies, institutions that NetBase
Quid identifies, and the other metadata that is put into thesoftware.

Data
Organization data is embedded from Capital IQ and
Crunchbase. These companies include all types ofcompanies (private, public, operating, operating as a
subsidiary, out of business) throughout the world. The
investment data includes private investments, M&A, public
offerings, minority stakes made by PE/VCs, corporate
venture arms, governments, and institutions both within
and outside the United States. Some data is simply
unreachable—for instance, when the investors or thefunding amounts by investors are undisclosed. NetBase
Quid also embeds firmographic information such as
founding year and HQ location.
NetBase Quid embeds Capital IQ data as a default andadds in data from Crunchbase for the ones that are notcaptured in Capital IQ. This not only yields comprehensive
and accurate data on all global organizations, but it also
captures early-stage startups and funding events data.
Company information is uploaded on a weekly basis.

Search Parameters
Boolean query is used to search for focus areas, topics,
and keywords within the archived company database,within their business descriptions and websites. We canfilter out the search results by HQ regions, investment
37

Artificial IntelligenceIndex Report 2022

amount, operating status, organization type (private/public), and founding year. NetBase Quid then visualizes
these companies by the semantic similarity. If there
are more than 7,000 companies from the search result,
NetBase Quid selects the 7,000 most relevant companies forvisualization based on the language algorithm.
Boolean Search: “artificial intelligence” or “AI” or “machinelearning” OR “deep learning”
Companies:• Chart 4.2.1: Global AI & ML companies that havebeen invested (private, IPO, M&A) from 01/01/2012 to
12/31/2021.
• Chart 4.2.2–4.2.12: Global AI & ML companies that haveinvested over $1.5M for the last 10 years (January 1,
2012 to December 31, 2021)—7,000 companies out of
7,500 companies have been selected through Quid’s
relevance algorithm.

Target Event Definitions• Private investments: A private placement is a private
sale of newly issued securities (equity or debt) by acompany to a selected investor or a selected groupof investors. The stakes that buyers take in private
placements are often minority stakes (under 50%),although it is possible to take control of a company
through a private placement as well, in which casethe private placement would be a majority stake
investment.
• Minority investment: These refer to minority stakeacquisitions in Quid, which take place when the buyer
acquires less than 50% of the existing ownership stakein entities, asset product, and business divisions.
• M&A: This refers to a buyer acquiring more than 50%of the existing ownership stake in entities, asset
product, and business divisions.

Chapter 4 Preview

APPENDIX
Chapter 4: The Economy and Education

COMPUTING RESEARCHA S S O C I AT I O N (C R A TAU L B E E
SURVEY)
Prepared by Betsy Bizot (CRA senior research associate)

SourceComputing Research Association (CRA) members
are 200-plus North American organizations active incomputing research: academic departments of computerscience and computer engineering; laboratories andcenters in industry, government, and academia; andaffiliated professional societies (AAAI, ACM, CACS/AIC,IEEE Computer Society, SIAM USENIX). CRA’s mission
is to enhance innovation by joining with industry,
government, and academia to strengthen research andadvanced education in computing. Learn more about
CRA here.

Methodology
CRA Taulbee Survey gathers survey data during the fallof each academic year by reaching out to over 200 PhDgranting departments. Details about the Taulbee Survey
can be found here. Taulbee doesn’t directly survey thestudents. The department identifies each new PhD’s areaof specialization as well as their type of employment.
Data is collected from September to January of eachacademic year for PhDs awarded in the previous
academic year. Results are published in May after datacollection closes. So the 2020 data points were newly
available last spring, and the numbers provided for 2021
will be available in May 2020.
The CRA Taulbee Survey is sent only to doctoral
departments of computer science, computerengineering, and information science/systems.
Historically, (a) Taulbee covers 1/4 to 1/3 of total BS CS
recipients in the United States; (b) the percent of women
earning bachelor’s degrees is lower in the Taulbee
schools than overall; and (c) Taulbee tracks the trends inoverall CS production.

38

Artificial IntelligenceIndex Report 2022

APPENDIX
Chapter 4: The Economy and Education

Nuances• Of particular interest in PhD job market trends are
the metrics on the AI PhD area of specialization. The
categorization of specialty areas changed in 2008 andwas clarified in 2016. From 2004-2007, AI and robotics
were grouped; from 2008-present, AI is separate; 2016clarified to respondents that AI includes ML.
• Notes about the trends in new tenure-track hires(overall and particularly at AAU schools): In the 2018
Taulbee Survey, for the first time we asked how many
new hires had come from the following sources: new
PhD, postdoc, industry, and other academic. Results
indicated that 29% of new assistant professors camefrom another academic institution.
• Some may have been teaching or research faculty
rather than tenure-track, but there is probably somemovement between institutions, meaning the total
number hired overstates the total who are actuallynew.

Chapter 4 Preview

39