Artificial IntelligenceIndex Report 2024

CHAPTER 4:

EconomyCHAPTER 4:

Artificial IntelligenceIndex Report 2024

EconomyPreview
Overview

3

4.4 Corporate Activity

46

Chapter Highlights

4

Industry Adoption

46

4.1 What’s New in 2023: A Timeline

6

Adoption of AI Capabilities

46

Adoption of Generative AI Capabilities54

Use of AI by Developers

57

4.2 Jobs

11

Preference57

AI Labor Demand

11

Workflow

58

Global AI Labor Demand11

AI’s Labor Impact

60

U.S. AI Labor Demand by Skill Cluster
and Specialized Skill

Earnings Calls

65

12

Aggregate Trends

65

U.S. AI Labor Demand by Sector

16

Specific Themes

66

U.S. AI Labor Demand by State

17

AI Hiring

20

AI Skill Penetration

22

AI Talent

24

Highlight: How Much Do
Computer Scientists Earn?

28

Highlight: Projecting AI’s Economic Impact

67

4.5 Robot Installations

71

Aggregate Trends

71

Industrial Robots:
Traditional vs. Collaborative Robots
By Geographic Area

73
74

4.3 Investment

30

Corporate Investment

30

Sectors and Application Types

80

Startup Activity

30

China vs. United States

82

Global Trends

31

Regional Comparison by Funding Amount35

Regional Comparison by Newly Funded
AI Companies

39

Focus Area Analysis

42

Country-Level Data on Service Robotics

Appendix

78

84

ACCESS THE PUBLIC DATA

2

Artificial IntelligenceIndex Report 2024

CHAPTER 4:

EconomyOverview
The integration of AI into the economy raises many compelling questions. Some predictthat AI will drive productivity improvements, but the extent of its impact remains uncertain.
A major concern is the potential for massive labor displacement—to what degree will jobs
be automated versus augmented by AI? Companies are already utilizing AI in various ways
across industries, but some regions of the world are witnessing greater investment inflows
into this transformative technology. Moreover, investor interest appears to be gravitating
toward specific AI subfields like natural language processing and data management.
This chapter examines AI-related economic trends using data from Lightcast, LinkedIn,
Quid, McKinsey, Stack Overflow, and the International Federation of Robotics (IFR). It
begins by analyzing AI-related occupations, covering labor demand, hiring trends, skill
penetration, and talent availability. The chapter then explores corporate investment in
AI, introducing a new section focused specifically on generative AI. It further examines
corporate adoption of AI, assessing current usage and how developers adopt thesetechnologies. Finally, it assesses AI’s current and projected economic impact and robot
installations across various sectors.

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Artificial IntelligenceIndex Report 2024

CHAPTER 4:

Economy

Chapter Highlights
1. Generative AI investment skyrockets. Despite a decline in overall AI private investment last year,
funding for generative AI surged, nearly octupling from 2022 to reach $25.2 billion. Major players in the generative
AI space, including OpenAI, Anthropic, Hugging Face, and Inflection, reported substantial fundraising rounds.

2. Already a leader, the United States pulls even further ahead in AI private investment.
In 2023, the United States saw AI investments reach $67.2 billion, nearly 8.7 times more than China, the next
highest investor. While private AI investment in China and the European Union, including the United Kingdom,
declined by 44.2% and 14.1%, respectively, since 2022, the United States experienced a notable increase of 22.1%
in the same time frame.

3. Fewer AI jobs, in the United States and across the globe. In 2022, AI-related positions madeup 2.0% of all job postings in America, a figure that decreased to 1.6% in 2023. This decline in AI job listings is
attributed to fewer postings from leading AI firms and a reduced proportion of tech roles within these companies.

4. AI decreases costs and increases revenues. A new McKinsey survey reveals that 42% of surveyedorganizations report cost reductions from implementing AI (including generative AI), and 59% report revenue
increases. Compared to the previous year, there was a 10 percentage point increase in respondents reporting
decreased costs, suggesting AI is driving significant business efficiency gains.

5. Total AI private investment declines again, while the number of newly funded AI
companies increases. Global private AI investment has fallen for the second year in a row, though less than
the sharp decrease from 2021 to 2022. The count of newly funded AI companies spiked to 1,812, up 40.6% fromthe previous year.

6. AI organizational adoption ticks up. A 2023 McKinsey report reveals that 55% of organizations now
use AI (including generative AI) in at least one business unit or function, up from 50% in 2022 and 20% in 2017.

7. China dominates industrial robotics. Since surpassing Japan in 2013 as the leading installer ofindustrial robots, China has significantly widened the gap with the nearest competitor nation. In 2013, China’s
installations accounted for 20.8% of the global total, a share that rose to 52.4% by 2022.

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Artificial IntelligenceIndex Report 2024

CHAPTER 4:

Economy

Chapter Highlights (cont’d)
8. Greater diversity in robotic installations. In 2017, collaborative robots represented a mere 2.8% of all
new industrial robot installations, a figure that climbed to 9.9% by 2022. Similarly, 2022 saw a rise in service robot
installations across all application categories, except for medical robotics. This trend indicates not just an overall
increase in robot installations but also a growing emphasis on deploying robots for human-facing roles.

9. The data is in: AI makes workers more productive and leads to higher quality work.
In 2023, several studies assessed AI’s impact on labor, suggesting that AI enables workers to complete tasks
more quickly and to improve the quality of their output. These studies also demonstrated AI’s potential to bridge
the skill gap between low- and high-skilled workers. Still other studies caution that using AI without proper
oversight can lead to diminished performance.

10. Fortune 500 companies start talking a lot about AI, especially generative AI.
In 2023, AI was mentioned in 394 earnings calls (nearly 80% of all Fortune 500 companies), a notable increase
from 266 mentions in 2022. Since 2018, mentions of AI in Fortune 500 earnings calls have nearly doubled. The
most frequently cited theme, appearing in 19.7% of all earnings calls, was generative AI.

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Artificial IntelligenceIndex Report 2024

Chapter 4: Economy
4.1 What’s New in 2023: A Timeline

The chapter begins with an overview of some of the most significant AI-related economic events in 2023, as selectedby the AI Index Steering Committee.

4.1 What’s New in 2023: A Timeline
Jan. 10,2023

InstaDeep acquired by BioNTech
BioNTech, known for developing the first mRNA COVID-19
vaccine in partnership with Pfizer, acquires InstaDeep for $680
million to advance AI-powered drug discovery, design, anddevelopment. InstaDeep specializes in creating AI systems forenterprises in biology, logistics, and energy sectors.

Jan. 23,
2023

Source: Reuters, 2022
Figure 4.1.1

Microsoft invests $10 billion in ChatGPT maker OpenAI
With this deal, Microsoft Azure remains the exclusive cloud provider for OpenAI, which relies
on Azure to train its models. This follows Microsoft’s initial $1 billion investment in 2019 and asubsequent investment in 2021.
Source: Microsoft, 2023
Figure 4.1.2

Feb. 14,2023

GitHub Copilot for Business becomes publicly available
Copilot for Business leverages an OpenAI Codex model toenhance code suggestion quality. At launch, GitHub Copilot
contributed to an average of 46% of developers’ code acrossvarious programming languages, with this figure rising to 61%
for Java.

March 7,2023

Source: GitHub, 2023
Figure 4.1.3

Salesforce introduces Einstein GPT
Einstein GPT, the first comprehensive AI for CRM, utilizes
OpenAI’s models. Einstein GPT aids Salesforce customers insales, marketing, and customer management.
Source: Salesforce, 2023
Figure 4.1.4

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6Chapter 4: Economy
4.1 What’s New in 2023: A Timeline

Artificial IntelligenceIndex Report 2024

March 16,2023

Microsoft announces integration of
GPT-4 into Office 365
Microsoft rolls out Copilot across Office365, offering AI assistance in Word,
PowerPoint, and Excel.

March 30,
2023

Source: Microsoft, 2023
Figure 4.1.5

Bloomberg announces LLM for financeBloomberg’s 50-billion parameter LLM is custom-built
for analyzing financial data and tailored to financeprofessionals. This model is capable of performing financial
analyses on Bloomberg’s extensive datasets.
Source: Bloomberg, 2023
Figure 4.1.6

May 23,
2023

Adobe launches generative AI tools inside
Photoshop
Adobe introduces generative AI features in
Photoshop via Adobe Firefly, its generative
image tool. Users can now add, remove, and edit
images within seconds using text prompts.

June 8,2023

Source:TechCrunch, 2023
Figure 4.1.7

Cohere raises $270 million
Cohere, focused on developing an AI model ecosystem forenterprises, raises $270 million in an oversubscribed Series
C round. Inovia Capital led the round, with participation from
Nvidia, Oracle, Salesforce Ventures, Schroders Capital, andIndex Ventures.

Chapter 4 Preview

Source: Cohere, 2023
Figure 4.1.8

7

Chapter 4: Economy
4.1 What’s New in 2023: A Timeline

Artificial IntelligenceIndex Report 2024

June 13,2023

Nvidia reaches $1 trillion valuation
Nvidia’s market capitalization consistently exceeds $1 trillion
USD, driven by rising demand for its AI-powering chips.
Nvidia becomes the fifth company to reach a valuation of $1
trillion, joining the ranks of Apple Inc. (AAPL.O), Alphabet
Inc. (GOOGL.O), Microsoft Corp. (MSFT.O), and Amazon.
com Inc. (AMZN.O).

June 26,
2023

Source: The Brand Hopper, 2023
Figure 4.1.9

Databricks buys MosaicML for $1.3 billion
Databricks, a leader in data storage and management,
announces its acquisition of MosaicML, a generative AI
orchestration startup founded in 2021, for $1.3 billion. This
move aims to enhance Databricks’ generative AI capabilities.
Source: Databricks, 2023
Figure 4.1.10

June 29,
2023

Thomson Reuters acquires Casetext for $650 million
Thomson Reuters finalizes its acquisition of Casetext, a legal startup renowned for its artificial
intelligence–powered assistant for law, for a staggering $650 million. At the time of acquisition,
Casetext boasted a substantial customer base of over 10,000 law firms and corporate legal
departments. Among its flagship offerings is CoCounsel, an AI legal assistant driven by GPT4, which enables rapid document review, legal research memos, deposition preparation, andcontract analysis within minutes.
Source:Legal.io, 2023
Figure 4.1.11

June 30,
2023

Inflection AI raises $1.3 billion from Bill Gates andNvidia, among others
Inflection AI raises $1.3 billion through a combination of cash
and cloud credits, bringing the company’s valuation to over $4
billion. Founded by Mustafa Suleyman of Google DeepMind
and Reid Hoffman of LinkedIn, Inflection AI is developing a“kind and supportive” chatbot named Pi. The funding roundattracts investments from Microsoft, Nvidia, Reid Hoffman,

Source: TechCrunch, 2023
Figure 4.1.12

Bill Gates, and Eric Schmidt, former CEO of Google.
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8Artificial IntelligenceIndex Report 2024

Aug. 24,
2023

Chapter 4: Economy
4.1 What’s New in 2023: A Timeline

Hugging Face raises $235 million from investors
Hugging Face, a platform and community dedicated to machinelearning and data science, secures an impressive $235 million
funding round, pushing its valuation to $4.5 billion. The platform
serves as a one-stop destination for building, deploying, andtraining machine learning models. Offering a GitHub-like hub
for AI code repositories, models, and datasets, Hugging Face
has attracted significant attention from industry giants.

Sep. 26,
2023

Source: TechCrunch, 2023
Figure 4.1.13

SAP introduces new generative AI assistant Joule
Joule is a ChatGPT-style digital assistant integrated
across SAP’s diverse product range. Joule will seamlessly
integrate into SAP applications spanning HR, finance,
supply chain, procurement, and customer experience.
Additionally, it will be incorporated into the SAP BusinessTechnology Platform, extending its utility across SAP’s

Source: SAP, 2023
Figure 4.1.14

extensive user base of nearly 300 million.

Oct. 27,2023

Amazon and Google make multibillion-dollar
investments in Anthropic
Amazon announces its intent to invest up to $4
billion in Anthropic, a rival of OpenAI. This significant
investment follows Google’s agreement to invest up
to $2 billion in Anthropic. The deal comprises an initial$500 million upfront, with an additional $1.5 billion to
be invested over time.

Nov. 5,2023

Source: TechCrunch, 2023
Figure 4.1.15

Kai-Fu Lee launches OpenSource LLM
Kai-Fu Lee’s LLM startup publicly unveils an open-source
model and secures funding at a $1 billion valuation, with
Alibaba leading the investment. Lee, known for his leadershiproles at Google in China and for establishing Microsoft
Research China, one of Microsoft’s key international researchhubs, spearheads this initiative.

Chapter 4 Preview

Source: TechCrunch, 2023
Figure 4.1.16

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Artificial IntelligenceIndex Report 2024

Nov. 17,2023

Chapter 4: Economy
4.1 What’s New in 2023: A Timeline

Sam Altman, OpenAI CEO, fired and then rehired
OpenAI’s board claims Altman was “not consistently
candid in his communications.” Chaos ensues at OpenAI.
Many employees resign in response to the news, and 745
sign a letter threatening resignation if the current board
members do not resign. A few days later, Altman
is reinstated.

Dec. 11,2023

Source: CoinGape, 2024
Figure 4.1.17

Mistral AI closes $415 million funding roundLess than six months after raising a $112 million seedround, Europe-based Mistral AI secures an additional$415 million. The startup, cofounded by alumni from
Google’s DeepMind and Meta, focuses on developing
foundation models with an open-source technology
approach, aiming to compete with OpenAI. Leading
the round is Andreessen Horowitz, with participation

Source: TechCrunch, 2023
Figure 4.1.18

from Lightspeed Venture Partners, Salesforce, BNP
Paribas, General Catalyst, and Elad Gil.

Chapter 4 Preview

10Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

4.2 Jobs
AI Labor DemandThis section analyzes the demand for AI-related skills
in labor markets, drawing on data from Lightcast.
Lightcast has analyzed hundreds of millions of job
postings from over 51,000 websites since 2010,
identifying those that require AI skills.

requiring AI skills, in many, the number of AI-related
job postings over the past five years has increased.1
Lightcast speculates that the 2023 decrease in AI job
postings is driven by many top AI employers (suchas Amazon, Deloitte, Capital One, Randstad, andGlobal AI Labor Demand

Elevance Health) scaling back their overall posting

Figure 4.2.1 shows the percentage of job postings

counts. Additionally, many companies shifted thedemanding AI skills. In 2023, the United States (1.6%),

occupational mix of their postings. For example,

Spain (1.4%), and Sweden (1.3%) led in this metric.

Amazon, in 2023, posted a higher share of operational

In 2022, AI-related jobs accounted for 2.0% of allroles like sales delivery driver, packager, and postal

American job postings. In 2023, that number dropped

service/mail room worker than in 2022. At the same

to 1.6%. Although most countries saw a decreasetime, there was a lower share of demand for tech roles

from 2022 to 2023 in the share of job postings

like software developers and data scientists.

AI job postings (% of all job postings) by geographic area, 2014–23
Source: Lightcast, 2023 | Chart: 2024 AI Index report

AI job postings (% of all job postings)

2.00%

1.62%, United States
1.35%, Spain
1.31%, Sweden
1.20%, Belgium
1.12%, Netherlands
1.07%, France
1.05%, Canada
1.04%, Switzerland
1.00%, Australia
0.89%, Austria
0.85%, United Kingdom
0.81%, Germany
0.76%, Italy

1.50%

1.00%

0.50%

0.00%

0.50%, New Zealand

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

Figure 4.2.1

1 In 2023, Lightcast slightly changed its methodology for determining AI-related job postings from what was used in previous versions of the AI Index report. Lightcast also updated its
taxonomy of AI-related skills. As such, some of the numbers in this chart do not completely align with those featured in last year’s report.

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4.2 Jobs

Artificial IntelligenceIndex Report 2024

U.S. AI Labor Demand by Skill Cluster andSpecialized Skillat 0.2%. Despite a recent dip, machine learning

Figure 4.2.2 highlights the most sought-after AI

continues to be the most in-demand skill. Since last

skills in the U.S. labor market since 2010. Leading theyear, every AI-related skill cluster tracked by Lightcast

demand was machine learning at 0.7%, with artificial

had a decrease in market share, with the exception of

intelligence at 0.5%, and natural language processing

generative AI, which grew by more than a factor of 10.

AI job postings (% of all job postings) in the United States by skill cluster, 2010–23
Source: Lightcast, 2023 | Chart: 2024 AI Index report

AI job postings (% of all job postings)

1.00%

0.80%
0.68%, Machine learning
0.60%

0.46%, Arti cial intelligence
0.40%

0.15%, Natural language processing
0.14%, Autonomous driving
0.10%, Neural networks
0.07%, Visual image recognition
0.05%, Robotics
0.05%, Generative AI

0.20%

0.00%

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023
Figure 4.2.2

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12Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

Figure 4.2.3 compares the top 10 specialized skills sought in AI job postings in 2023 versus those from 2011 to
2013.2 On an absolute scale, the demand for nearly every specialized skill has increased over the past decade, withPython’s notable increase in popularity highlighting its ascendance as a preferred AI programming language.

Top 10 specialized skills in 2023 AI job postings in the United States, 2011–13 vs. 2023

Source: Lightcast, 2023 | Chart: 2024 AI Index report

152,201

Python (programming language)

13,503133,066

Computer science

43,74893,541

SQL (programming language)25,19491,883

Data analysis
Data science
Agile methodology
Amazon web services
Automation
Software engineering
Project management
0

20,77085,4803,892
73,0698,602
68,4591,712
67,772
12,32764,557
18,7042023
62,1802011–1325,95350,000100,000

150,000
200,000
Number of AI job postings

250,000

300,000
Figure 4.2.3

2 The decision to select 2011–2013 as the point of comparison was because some data at the jobs/skills level from earlier years is quite sparse. Lightcast therefore used 2011–2013 to have alarger sample size for a benchmark from 10 years ago with which to compare. Figure 4.2.3 juxtaposes the total number of job postings requiring certain skills from 2011 to 2013 with the total
amount in 2023.

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4.2 Jobs

Artificial IntelligenceIndex Report 2024

In 2023, Lightcast saw great increases in the number of U.S. job postings citing generative AI skills. That year,
15,410 job postings specifically cited generative AI as a desired skill, large language modeling was mentioned in
4,669 postings, and ChatGPT appeared in 2,841 job listings (Figure 4.2.4).

Generative AI skills in AI job postings in the United States, 2023
Source: Lightcast, 2023 | Chart: 2024 AI Index report

Generative arti cial intelligence

15,410Large language modeling

4,669

ChatGPT

2,841

Prompt engineering

1,299

Generative adversarial networks

1,102

Variational autoencoders

372
0

2,000

4,000

6,000

8,000
10,000
Number of AI job postings

12,00014,00016,000Figure 4.2.4

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14Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

Figure 4.2.5 illustrates what proportion of all generative AI job postings released in 2023 referenced particular
generative AI skills. The most cited skill was generative AI (60.0%), followed by large language modeling (18.2%)
and ChatGPT (11.1%).

Share of generative AI skills in AI job postings in the United States, 2023
Source: Lightcast, 2023 | Chart: 2024 AI Index report

Generative arti cial intelligence

59.98%

Large language modeling18.17%

ChatGPT

11.06%

Prompt engineering

Generative adversarial networks

Variational autoencoders
0%

5.06%

4.29%

1.45%
10%

20%

30%
40%
Skill share in AI job postings (%)

50%

60%
Figure 4.2.5

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15Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

U.S. AI Labor Demand by Sector

and educational services. The leading sectors were

Figure 4.2.6 shows the percentage of U.S. job

information (4.6%); professional, scientific, andpostings requiring AI skills by industry sector fromtechnical services (3.3%); and finance and insurance

2022 to 2023. Nearly every sector experienced a(2.9%). As noted earlier, the decrease in AI job postings

decrease in the proportion of AI job postings in 2023

was related to changes in the hiring patterns of several

compared to 2022, except for public administration

major U.S. employers.

AI job postings (% of all job postings) in the United States by sector, 2022 vs. 2023
Source: Lightcast, 2023 | Chart: 2024 AI Index report

4.63% (-3.67%)
4.81%

Information
3.33% (-9.52%)
3.67%
2.94% (-11.71%)
3.33%
2.48% (-18.81%)
3.06%

Professional, scienti c, and technical services
Finance and insurance
Manufacturing
Public administration
Educational services
Management of companies and enterprises
Utilities
Agriculture, forestry, shing and hunting
Mining, quarrying, and oil and gas extraction
Wholesale trade
Real estate and rental and leasing
Transportation and warehousing
Retail trade
Waste management and administrative support services0%

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0.79%

1.49% (+88.81%)

1.41% (+5.98%)
1.33%
1.33% (-21.15%)
1.69%
1.19% (-1.53%)
1.21%
0.85% (-44.98%)
1.54%
0.82% (-33.26%)
1.23%
0.70% (-20.52%)
0.89%
0.55% (-25.92%)
0.74%
0.48% (-25.92%)
0.65%
0.48% (-54.68%)
1.06%
0.40% (-22.71%)
0.51%

1%

2%
3%
4%
AI job postings (% of all job postings)

2023
2022

5%
Figure 4.2.6

16

Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

U.S. AI Labor Demand by State
Figure 4.2.7 highlights the numberof AI job postings in the UnitedStates by state. The top three states

Number of AI job postings in the United States by state, 2023
Source: Lightcast, 2023 | Chart: 2024 AI Index report

AK

ME

1,309

2,125

VT

were California (70,630), followed
by Texas (36,413) and VirginiaWA

14,725(24,417).

OR

CA

MT

ND

892

1,293

SD

1,351

5,478

3,357

ID

WY
729

2,288

NE

NV

UT

CO

KS

AZ

NM

70,630 3,404 3,679 10,292 5,431

OK

9,022 2,952

4,125

TX

LA

36,413 2,924

NH

MA

1,003

2,077 23,017

NY

CT

OH

PA

NJ

DC

MD

DE

MN

WI

MI

IA

IL

IN

KY

WV
533

6,861 16,312 2,462

TN

VA

NC

AL

GA

SC

5,824 5,055 13,734

24,397 5,415

RI

2,992

3,531 20,178 5,267 10,409 13,294 14,705

MO

7,390

AR

3,783

MS

1,736

HI

2,315

5,318 24,417 12,976
6,214 14,325 3,305

FL

2,283

17,678Figure 4.2.7

Figure 4.2.8 demonstrates whatpercentage of a state’s total job
postings were AI-related. The top
states according to this metric werePercentage of US states job postings in AI, 2023
Source: Lightcast, 2023 | Chart: 2024 AI Index report

AK
1.06%

ME
1.55%
NH MA
VT
1.14% 0.87% 1.88%

the District of Columbia (2.7%),
followed by Delaware (2.4%) andMI
WA MT NDSD MN WI
1.65% 0.75% 1.03% 1.01% 0.80% 0.69% 1.20%

Maryland (2.1%).

CT
NY
RI
1.43% 1.09% 1.74%

ID
IA
IL
IN
OH
NJ
OR
WY NE
PA
0.91% 1.43% 0.95% 0.81% 0.75% 1.26% 0.62% 0.75% 1.06% 1.63%
CA
NV
CO
KS MO KYDEUT
WV DC MD
1.60% 0.79% 0.94% 1.03% 1.19% 0.92% 0.52% 0.46% 2.66% 2.10% 2.38%
AZ NM OK
AR
NC
TN
VA
0.94% 1.14% 0.89% 1.34% 0.64% 2.09% 0.98%
TX
LA
MS
AL
GA
SC
1.11% 0.59% 0.79% 1.05% 1.14% 0.58%
HI
1.25%

FL
0.71%
Figure 4.2.8

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17Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

Percentage of US AI job postings by state, 2023

Figure 4.2.9 examines which U.S.

Source: Lightcast, 2023 | Chart: 2024 AI Index reportstates accounted for the largest
proportion of AI job postings

AK
0.28%

nationwide. California was first: In

ME
0.46%
NH MA
VT
0.22% 0.45% 4.99%

2023, 15.3% of all AI job postings
in the United States were for jobs

SD MN WIMIWA MT ND
3.19% 0.19% 0.28% 0.29% 1.26% 1.10% 2.98%

based in California, followed by

NY
CT
RI
5.29% 1.17% 0.65%

ID
IA
IL
IN
OR
WY NE
OH
PA
NJ
1.19% 0.73% 0.16% 0.50% 0.77% 4.37% 1.14% 2.26% 2.88% 3.19%

Texas (7.9%) and Virginia (5.3%).

CA
NV
CO
KS MO KYUTWV DC MD DE
15.31% 0.74% 0.80% 2.23% 1.18% 1.60% 0.50% 0.12% 1.49% 3.54% 0.53%
AZ NM OK
AR
TN
NC
VA
1.96% 0.64% 0.89% 0.82% 1.15% 5.29% 2.81%
LA
MS
AL
GA
TX
SC
7.89% 0.63% 0.38% 1.35% 3.11% 0.72%
HI
0.49%

FL
3.83%
Figure 4.2.9

Figure 4.2.10 illustrates the trends in the four states with highest AI job postings: Washington, California, New York,and Texas. Each experienced a notable decline in the share of total AI-related job postings from 2022 to 2023.

Percentage of US states’ job postings in AI by select US state, 2010–23
Source: Lightcast, 2023 | Chart: 2024 AI Index report

Percentage of U.S. states’ job postings in AI

2.50%

2.00%

1.65%, Washington
1.60%, California

1.50%

1.43%, New York

1.11%, Texas
1.00%

0.50%

0.00%

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023
Figure 4.2.10

Chapter 4 Preview

18Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

Figure 4.2.11 shows how AI-related job postings have been distributed across the top four states over time. Since
2019, California’s proportion of AI job postings has steadily declined, while Texas has seen a slight increase.

Percentage of US AI job postings by select US state, 2010–23
Source: Lightcast, 2023 | Chart: 2024 AI Index report

Percentage of United States AI job postings

25%

20%

15.31%, California

15%

10%
7.89%, Texas
5.29%, New York

5%

3.19%, Washington

0%

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023
Figure 4.2.11

Chapter 4 Preview

19Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

AI Hiring

added a new employer in the same period the job began,

The hiring data presented in the AI Index is based ona LinkedIn dataset of skills and jobs that appear on
their platform. The geographic areas included in thesample make at least 10 AI hires each month and have
LinkedIn covering a substantial portion of the labor
force. LinkedIn’s coverage of India’s and South Korea’s
sizable labor forces fall below this threshold, so insights
drawn about these countries should be interpreted withparticular caution.

divided by the total number of LinkedIn members in
the corresponding location. Conversely, the relative AI
talent hiring rate is the year-over-year change in AI hiring
relative to overall hiring rate in the same geographic
area.3 Therefore, figure 4.2.12 illustrates which specificregions have experienced the most significant rise in AI
talent recruitment compared to the overall hiring rate,serving as an indicator of AI hiring vibrancy. In 2023, theregions with the greatest relative AI hiring rates year overyear were Hong Kong (28.8%), followed by Singapore

Figure 4.2.12 reports the relative AI hiring rate year-over-(18.9%) and Luxembourg (18.9%). This means, foryear ratio by geographic area. The overall hiring rate is

example, that in 2023 in Hong Kong, the ratio of AI

computed as the percentage of LinkedIn members whotalent hiring relative to overall hiring grew 28.8%.

Relative AI hiring rate year-over-year ratio by geographic area, 2023
Source: LinkedIn, 2023 | Chart: 2024 AI Index report

Hong Kong

28.83%

Singapore

18.93%

Luxembourg

18.85%

India

16.83%

Portugal

14.84%

United Arab Emirates

13.40%

Denmark

13.23%

Norway13.08%

Belgium

10.41%

Spain

9.63%

United Kingdom

8.57%

Sweden7.37%

Finland

7.27%

South Africa

7.17%

Chile
0%

6.65%
5%

10%

15%
20%
Relative AI hiring rate year-over-year ratio

25%

30%
Figure 4.2.124

Figure 4.2.13 showcases the year-over-year ratio of AI hiring by geographic areas over the past five years. Startingfrom the beginning of 2023, countries including Australia, Canada, Singapore, and India have experienced anoticeable uptick in AI hiring.
3 For each month, LinkedIn calculates the AI hiring rate in the geographic area, divides the AI hiring rate by overall hiring rate in that geographic area, calculates the year-over-year change ofthis ratio, and then takes the 12-month moving average using the last 12 months.
4 For brevity, the visualization only includes the top 15 countries for this metric.

Chapter 4 Preview

20Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

RelativeAI
AIhiring
hiringrate
rate
year-over-year
ratio
by geographic
2018–23
Relativeyear-over-year
ratio
by geographic
area,area,
2018–23
Source:LinkedIn,
LinkedIn,2023
2023| Chart:2024
AI Index
reportSource:| Chart:2024
AI Index
reportAustralia
Australia

Belgium
Belgium

100%
100%
50%
50%

50%50%

0%
−50%

−50%

−50% 2019 2021 2023
2019
2021 2023
Chile

0%

−50%

2023

Relative
hiringrate year-over-year
Relative AI
hiringAI
rate
year-over-year
ratio ratio

2021

2019

Israel

2021

−50%

2023

100%

2021

−50%

New Zealand

2019

50%

2021

2019

2021

2021

South Africa−50%

50% 2019

0%

2021

2021

2021

2021

2019

2021

−50%

2021

0%
−50%

2023

0%United Arab Emirates

5.81%

−50%

50%

Switzerland -0.24%

0%

100%

100%

2019

2021

2023

0%

100%

2019

2021

2023

0%
2021
Chapter2019
4 Preview−50%-0.24%
2023

2019

50%

2021

−50%

13.40%

2019

2021

2019

2021

2023

2023

France2019
India 2021

0%

2023

18.85%

50%

−50%

2021

50%
0%

2021

2021

2023

2023

100%
5.21%

0%

50%

−50%

2023

2019

5.21%

2021

50%

2023

Singapore
18.93%

100%

0%

50%

2019

2021

2023

18.93%

0%

100%

2021

0%

2023 50%

2019

2021

2023

7.37%

201950%2021

2023

7.37%

United States
0%

−50%

2019

2021

2023

5.68%
UnitedStates

100%
2019

2021

50%

8.57%

2023

Sweden
100%

0%
−50%

2023

2021

−50%

9.63%

0%
2019

-0.48%

2023

Netherlands

50%

−50%

2023

8.57%
United Kingdom
2021

2021

−50%
Netherlands
2019
2021

100%

100%

2019

0%

50%

9.63%

2021

-0.48%

Sweden

100%

−50%

2019

14.84%

United Kingdom
0%

2019

50%

−50%

Spain

−50%

0%

2023 100%

2023

100%
50%
2019

100%

Spain

2019

2023

Singapore

14.84%

2021

2021
Ireland50%

Portugal

2019

2023

2019
Ireland−50%
18.85%
2019 0%2021

2023

Portugal

2019

−50%

−50%

Luxembourg
100%-0.43%

2021

100%

2023

−50% Luxembourg
2019
2021

2019
0%

2019

16.83%

2021-0.43%

0%2023

16.83%

50%
2019

50%

0%
−50%

2023

India

50%

0%

2021

100%

7.27%

0%

0%
−50%

2023

2023

2019

4.44%

50%

50%

13.40%
United Arab Emirates−50%2021
2019

100%

7.27%

50%

5.81%

4.44%

FranceFinland50%

100% −50%

2023

South Korea

100%
50%
2019

2023

2023

100%

0% 100%13.08%

100%

2019

2021

50%

2023

0%

7.17%

100%

South Korea

−50%

2023

2023

13.08%

50%

Switzerland

0%

2019

0%

7.17%

−50%

6.03%

Norway

−50%
100%

100%

−50%

100%
50%

2023

100%
0%

6.15%

2023

0%

50%

2023

Norway−50%50%
2019

South Africa

2019

2021

50%

2023

100%

−50%

2021

6.03%

2019
0%

100%

6.15%

50%

50%

Italy

Italy

−50%

2023

New Zealand

0%

−50%

2019

−50%

2023

0%

6.22%

2023

100%
0%

−50%
100%

2021

50%

0% 2019

50%
−50%

0%

-0.47%−50%2023

50% 100%

28.83%

100%

6.22%

0%

2021

2019

100%

28.83%

50%

50%
−50%

−50%
2019

−50%

2023

Hong Kong

2019

Israel

50%

−50%

2019
2021
Hong Kong

0.16%−50% 0%

2023

0%

−50%

2023

0%50%

0.16%

100%

100%

2021

100%
50%

0%
50%

−50%

2019

100%

Germany

2019

0%13.23%

0%−50%

2019 Germany
2021 2023

100%
50%

100%

13.23%

0%

6.65%

0%

−50%
0%

0%

-0.47%

0%

50%

50%

6.65%

100%

50%

100%

50%

50%

−50%

Denmark

100%

50%

2021

50%

50%

Finland

100%

2019

50%

Denmark

100%

−50%

100%

−50%

−50%
2019
2021 2023
2019
2021 2023

Chile
100%

100%

0%
10.41%

6.53% 0% 0%0%CanadaCanada

100% 100%

10.41%

6.53%

BrazilBrazil

100%
100%

2023

Figure4.2.13
5.68%

0%
−50%

2019

2021

2023

21

Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

AI Skill Penetration

which they appear in LinkedIn members’ profiles. If,
for instance, four of the skills that engineers possess

Figures 4.2.14 and 4.2.15 highlight relative AI skill

belong to the AI skill group, the penetration of AI skills

penetration. The aim of this indicator is to measure theamong engineers is estimated to be 8% (4/50).

intensity of AI skills in an entity (such as a particularcountry, industry, or gender). The AI skill penetration

For the period from 2015 to 2023, the countries with

rate signals the prevalence of AI skills acrossthe highest AI skill penetration rates were India (2.8),

occupations or the intensity with which LinkedIn

the United States (2.2), and Germany (1.9). In themembers utilize AI skills in their jobs. For example,

United States, therefore, the relative penetration of

the top 50 skills for the occupation of engineer are

AI skills was 2.2 times greater than the global averagecalculated based on the weighted frequency withacross the same set of occupations.

Relative AI skill penetration rate by geographic area, 2015–23
Source: LinkedIn, 2023 | Chart: 2024 AI Index report

India

2.75

United States

2.22

Germany

1.90

Canada1.67

Israel

1.63

United Kingdom

1.63

Singapore

1.50

France1.48

South Korea

1.35

Spain

1.29

Brazil1.21

Netherlands

1.13

Italy

1.08

Switzerland

1.06

United Arab Emirates
0.00

Chapter 4 Preview

0.98
0.50

1.00

1.50
Relative AI skill penetration rate

2.00

2.50
Figure 4.2.14

22

Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

Figure 4.2.15 disaggregates AI skill penetration rates

the country. For all countries in the sample, the relative

by gender across different countries or regions.

AI skill penetration rate is greater for men than women.

A country’s rate of 1.5 for women means female

India (1.7), the United States (1.2), and Israel (0.9) havemembers in that country are 1.5 times more likely tothe highest reported relative AI skill penetration rates

list AI skills than the average member in all countries

for women.

pooled together across the same set of occupations in

Relative AI skill penetration rate across gender, 2015–23
Source: LinkedIn, 2023 | Chart: 2024 AI Index report

India
United States

2.21

1.23

Israel0.86

Canada0.85

Germany

1.92
1.70
2.00

0.82

United Kingdom

0.71

Singapore

0.70

France0.67

Netherlands

0.55

Spain

0.55

Italy

0.46

Brazil0.44

United Arab Emirates

0.43

Switzerland

0.39

Australia

0.37

0.00

2.78

1.65

0.50

1.64
1.51
1.46
1.20
1.41
1.10
1.26
0.89

Male

1.14

Female

0.92

1.00

1.50
2.00
Relative AI skill penetration rate

2.50

3.00
Figure 4.2.15

Chapter 4 Preview

23Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

AI Talent

AI talent concentration by geographic area, 2023
Source: LinkedIn, 2023 | Chart: 2024 AI Index report

IsraelFigures 4.2.16 to 4.2.18 examine AI talent by

Singaporecountry. A LinkedIn member is considered AI

South Korea

talent if they have explicitly added AI skills to

Luxembourg

their profile or work in AI. Counts of AI talent

1.13%
0.88%
0.79%
0.74%

Finland0.71%

are used to calculate talent concentration, or

Germany

0.69%

Switzerland

0.68%

the portion of members who are AI talent. Note

Netherlands

that concentration metrics may be influenced by

SwedenLinkedIn coverage in these countries and should

Irelandbe used with caution.
Figure 4.2.16 shows AI talent concentration invarious countries. In 2023, the countries with thehighest concentrations of AI talent included Israel

0.61%
0.56%
0.55%

France0.49%

Canada0.45%

Denmark

0.42%

India

0.42%

Cyprus

0.40%

0.00%

0.20%

0.40%
0.60%
0.80%
AI talent concentration(1.1%), Singapore (0.9%), and South Korea (0.8%).

1.00%

1.20%
Figure 4.2.16

Figure 4.2.17 looks at the percent change in AI
talent concentration for a selection of countries
since 2016. During that time period, several major
economies registered substantial increases in their
AI talent pools. The countries showing the greatest
increases are India (263%), Cyprus (229%), andDenmark (213%).

Percentage change in AI talent concentration bygeographic area, 2016 vs. 2023
Source: LinkedIn, 2023 | Chart: 2024 AI Index report

India

263%

Cyprus229%

Denmark

213%

Canada188%

Singapore

172%

Germany

169%

Ireland161%

Switzerland

142%

Israel116%

Luxembourg

107%

Netherlands

75%

Finland70%

Sweden51%

South Korea

50%

France
0%

41%
40%

80%
120%
160%
200% 240%% change in AI talent concentration

280%

Figure 4.2.17

Chapter 4 Preview

24Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

AI talent concentration by gender, 2016–23

Source: LinkedIn, 2023 | Chart: 2024 AI Index report

Australia
0.43%

0.40%

0.55%

0.40%

0.20%
0.00%

Belgium

0.60%

0.19%
2017

2020

2023

0.20%
2017

Chile

0.10%
0.00%

0.06%
2017

2020

2023

0.50%

0.45%
2020

0.20%

2023

AI talent concentration

2017

2020

0.10%
0.00%

0.09%
2017

2023

0.40%

0.34%

0.20%
0.00%

2017

2020

2023

0.33%
2017

0.15%
2017

2020

2023

Sweden
0.50%
0.00%

0.50%

2017

2020

0.40%
0.20%

2017

2020

2023

0.41%
2017

2020

2023

0.00%

0.77%

1.00%

0.35%

0.50%

2023

0.67%

0.50%

0.22%
2017

0.00%

2020

2023

2020

0.50%

0.37%
2017

2020

2023

1.00%

0.75%

0.50%
0.00%

0.40%

2017

2020

0.21%

0.20%
0.00%

2017

2023

0.49%

0.20%
0.00%

0.19%
2017

2023

0.30%

0.20%

0.14%

0.10%
0.00%

2017

0.20%
2017

2020

2023

2023

0.53%
0.24%

0.20%
0.00%

2017

0.09%
2017

2020

2020

2023

Spain
0.17%

0.15%

2023

0.51%

0.40%
0.20%
0.00%

0.16%
2017

2020

2023

United Kingdom
0.26%
0.21%

0.47%

0.40%

0.23%

0.20%

0.10%
0.00%

2020

0.40%

0.20%

2023

2023

0.30%

United Arab Emirates

0.38%

2020

Norway

0.10%
0.00%

2023

0.37%

0.20%
0.00%

2020

0.40%

New Zealand

0.92%

2020

0.59%

Italy
1.73%

1.50%

Switzerland

2017

0.60%

Hong Kong
0.89%

0.00%

2023

0.40%

0.20%
0.00%

2020
Denmark

0.59%

0.05%

2017

2017

South Africa
1.03%

1.00%

United States

0.00%

2020

0.00%

Israel

0.79%

0.00%

2023

0.60%

Singapore
0.44%

0.20%

2023

0.72%

Portugal
0.40%

0.00%

2020

0.60%
0.40%
0.20%
0.00%

2020

0.26%

0.20%

Germany

Netherlands

0.46%
2017

2023

0.65%

0.99%

0.50%
0.00%

2020

0.59%

Cyprus0.60%

Luxembourg
1.00%

2017

Ireland
0.45%
0.38%

0.20%
0.00%

0.00%

0.03%

0.40%

India
0.40%

0.05%

France
1.09%

2017

2023

0.25%

Finland
1.00%

0.00%

2020

0.60%
0.40%

Costa Rica
0.22%

0.20%

Canada
0.14%

0.10%

0.20%
0.00%

Brazil

0.15%

2017

2020

2023

0.00%

2017

2020

2023

Uruguay
0.48%

0.20%

0.21%

0.10%

2023

0.00%

0.22%
0.07%
2017

2020

Male
Female2023

Figure 4.2.18

Chapter 4 Preview

25Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

LinkedIn data provides insights on the AI talent gained

per 10,000 LinkedIn members by geographic area.

or lost due to migration trends. Net flows are defined

The countries that report the greatest incoming

as total arrivals minus departures within the given time

migration of AI talent are Luxembourg, Switzerland,

period. Figure 4.2.19 examines net AI talent migrationand the United Arab Emirates.

5

Net AI talent migration per 10,000 LinkedIn members by geographic area, 2023
Source: LinkedIn, 2023; World Bank Group, 2023 | Chart: 2024 AI Index report

Luxembourg

3.67

Switzerland

1.60

United Arab Emirates

1.48

Cyprus1.24

Germany

1.04

Canada0.96

Finland0.90

Netherlands

0.76

Australia

0.67

IrelandSingapore

0.60
0.50

Denmark

0.44

United Kingdom

0.41

Norway0.41

United States

0.40

0.00

0.50

1.00

1.50
2.00
2.50
Net AI talent migration (per 10,000 LinkedIn members)

3.00

3.50
Figure 4.2.19

Figure 4.2.20 documents AI talent migration data over time. In the last few years, Israel, India, and South Korea
have seen declining net AI talent migration figures, suggesting that AI talent has been increasingly flowing out ofthese countries.

5 LinkedIn membership varies considerably between countries, which makes interpreting absolute movements of members from one country to another difficult. To compare migration
flows between countries fairly, migration flows are normalized for the country of interest. For example, if country A is the country of interest, all absolute net flows into and out of country
A (regardless of origin and destination countries) are normalized based on LinkedIn membership in country A at the end of each year and multiplied by 10,000. Hence, this metric indicates
relative talent migration of all other countries to and from country A.

Chapter 4 Preview

26Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024Net
per10,000LinkedIn
members
by geographic
area, area,
2019–23
Net AI
AItalent
talentmigration
migrationper
10,000LinkedIn
members
by geographic
2019–23
Source:World
BankGroup,
2023| Chart:20242024
AI Index
reportreport
Source:LinkedIn,
LinkedIn,2023;
2023;
World
Bank
Group,
2023| Chart:AI Index

Australia
Australia

Belgium
Belgium

11

0.67
0.67

0

0

−1
−1
2019

2019

2021

2021

2023

2023

1
0

0.28

0

−1
−1
2019

2019

Chile

1

0

-0.07

0

1

-0.07
2023

2021

Finland

0.90

Net AI
talent migration(per 10,000
LinkedIn
members)
Net AI talentmigration(per 10,000
LinkedIn
members)

01

0.90
2021

−1
2019

India
2021

1

−1
62019

5
4
3
26
15
04
−13
2019
2

1
0
−1
2019
1

0
−11
2019

0
−1
1
2019
0
−11
2019

2023

0

2021

2023

Luxembourg*

3.67

3.67
2023

2021

2023

Portugal

Ireland
2021

0.23

2023

0.23

2021

2023
0.25

Spain

−1
1
2019

2021

−1 1
2019

0

United Kingdom−10.50 2019
3

2023

2021

2023

2021

2023

−1
2019

2
0
−1
2019

0.17

0
2023

0.40

1

−1
2019

2023

−1
2019

2021

2023

−1
2019

-0.12
2023 1

Switzerland*

0

2023

−1
1.602019

Uruguay

−1
2019

2021

2023

2021

2023

0.12
2021

2023

−1
2019 Italy

2021

2023

Italy
-0.18
2023

-0.18

−1 Norway
2019
2021

2023

Norway0.41
10 2021

0.41

2023

−1
2019

2021

2023

South Korea
-0.30

1
2021

2023

0

United Arab Emirates
1.48
−1

2019

1 2021

2021

-0.30
2023

United Arab Emirates
1.48
2023

−1
2019

2021

2023

-0.13

Uruguay
1

2021

2023

Figure 4.2.206

0.40

−1
2019

0

0

0 indicate that a country’s y-axis label is scaled
0 differently than the y-axis label for the other countries.
0
6 Asterisks

Chapter 4 Preview

1

1.60

2021

2023

0.12

0

2023

2021

0

United States

2023

Switzerland*1

1

0 2021−1
0.192019

1

2

2023

2021

Hong Kong

−1
2019

0

-0.12

3

2021

−1
2019

1

South Africa

−1
2019

0.44

0

South Korea

2021

2021

0.44

1

2023

1
0

1

0

-0.57

0.19

2023

Denmark

1

New Zealand

1

0.17

United States

0.41

2023

-0.57

0

2023

2021

Hong Kong

−1
2019

2023

1

0

2023

2021

1

−1 20212019

1

Israel

0

2023

2021

2023

Israel2021

0.50

2021

2019

−1
20191

2023

2023

Sweden1 −12023

South Africa

2021

Sweden

0

−1
2019

−1
2019

0.96

0

0

1.24

2021

1

2023

2021

1.24

−1 New Zealand
2019
2021

Singapore

0

0

−1
2019

2023

0.76

2021

1

2021

0.96

Denmark

1.040

−1
2019

0

−1
1 2019

0.25

2023
0.41

2021

Singapore

0

−1 1
2019

2023

2023

2023

−1
2019

Cyprus*

1

0.60

-0.05

Germany
1.04

0

2019

1

−1
2019

4
3
2
1
0
2021
−1
2019

0.06 −1
2023

Netherlands

2023

2021

Germany

0

2021

2021

0

Canada1

Cyprus*

1

0.60
2021

0

0

Netherlands0.76

−1
20190

1

2023

1

1

Spain

2021

2021

-0.05

1
0.06

Ireland−1
2019

Portugal

2021

France

1

2019

2023

CanadaBrazil1−1
2019 −1

4
3
2
1
0.01
0.01 0
−1
2023
2019

1

−1
20190

0

2021

2023

France

−1
2019

-0.76

Luxembourg*

2021

2023

2021

1

0
-0.76
2021

2021

1

India

United Kingdom

−1
2019
1

−1
2019

2023

0

0

0

−1
2019

−1 0
2019

2023

01
−1
0
2019

1

0

2023

Finland

−10
2019

2021

0.28 0

Costa Rica

1

2021

2021

Costa Rica

1

−1
2019

1

1

Chile

−1
2019

Brazil

−1
2019

-0.13
2021

2023

27

Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

Highlight:

How Much Do Computer Scientists Earn?
Every year, Stack Overflow conducts a survey ofSalaries vary by position and geography. For

its community of professional developers who

instance, the average global salary for a cloud

use their tools. The latest iteration of the survey

infrastructure engineer is $105,000. In the United

profiled over 90,000 developers.

States, the average salary for such a position

Through this survey, respondents were asked
about their income. It is important to note thatthese respondents do not work exclusively with
AI. However, examining developer salaries canserve as a means to approximate the compensation

is $185,000. Both globally and in the UnitedStates, the highest compensated roles are senior
executives, followed by engineering managers.
For all surveyed positions, salaries are significantly
higher in the United States than in other countries.

of talent in AI-adjacent industries. Figure 4.2.21
examines the salaries of professional developers
disaggregated by position.

Chapter 4 Preview

28Chapter 4: Economy
4.2 Jobs

Artificial IntelligenceIndex Report 2024

Highlight:

How Much Do Computer Scientists Earn? (cont’d)Median yearly salary by professional developer type, 2023

Source: Stack Over ow Developer Survey, 2023 | Chart: 2024 AI Index report

Senior executive (C-suite, VP, etc.)

124.75Engineering manager

124.14Marketing or sales professional

116.00Engineer, site reliability

115.66Developer experience

107.09Cloud infrastructure engineer

105.00Blockchain

103.74

195

180
210
185

100.31Developer advocate

99.31

Security professional

173

92.32

Scientist

132.5088.93

Product manager

Developer type220

Hardware engineer

85.67

Research and development role

85.67

Engineer, data
Data scientist or
machine learning specialist
DevOps specialist

83.52

160
160

80.16

160

78.69

120

77.10

140

76.03

Developer, full-stack

71.14

Developer, game or graphics
Developer, desktop or
enterprise applications
Developer, mobile

71.01

68.19

Educator

65.27

Developer, QA or test

63.93

Project manager

63.18

Data or business analyst

61.55

Developer, front-end

59.97

Designer

59.81

165
140
158

70.76

System administrator

55.76

Academic researcher

53.55

0

160

80.32

Database administrator
Developer, embedded
applications or devices
Developer, back-end

Student

198.50
140

130
163
100
124
125
105
140
151
87.50

All respondents
United States

90

15.42
50

100
150
Median yearly salary (in thousands of U.S. dollars)

200

Figure 4.2.21

Chapter 4 Preview

29Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

This section monitors AI investment trends, leveraging data from Quid, which analyzes investment data from more than
8 million companies worldwide, both public and private. Employing natural language processing, Quid sifts throughvast unstructured datasets—including news aggregations, blogs, company records, and patent databases—to detect
patterns and insights. Additionally, Quid is constantly expanding its database to include more companies, sometimes
resulting in higher reported investment volumes for specific years. For the first time, this year’s investment section in the
AI Index includes data on generative AI investments.

4.3 Investment
Corporate Investment

In 2023, the total investment dropped to $189.2

Figure 4.3.1 illustrates the trend in global corporate AI

Despite a slight reduction in private investment, theinvestment from 2013 to 2023, including mergers andmost significant downturn occurred in mergers andacquisitions, minority stakes, private investments, andacquisitions, which fell by 31.2% from the previous

public offerings. For the second consecutive year,

year. However, over the past decade, AI-related

global corporate investment in AI has seen a decline.

investments have increased thirteenfold.

billion, a decrease of approximately 20% from 2022.

Global corporate investment in AI by investment activity, 2013–23
Source: Quid, 2023 | Chart: 2024 AI Index report

Merger/acquisition

350

337.4

Minority stake
Private investment
Public o ering

Total investment (in billions of U.S. dollars)

300

173.42250

234.95202.49200

189.1639.04

117.16150

80.61

103.27

100
79.62
53.72

50

0

14.57

19.04

2013

2014

25.43

33.82

24.68

86.42
132.36

36.43

21.89
43.1

103.4
58.18

64.02

29.51

25.72

2015

2016

2017

95.99

2018

2019

2020

2021

2022

2023
Figure 4.3.1

Chapter 4 Preview

30Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

Global Trends

Startup Activity

Global private AI investment has declined for theThis section analyzes private investment trends insecond consecutive year (Figure 4.3.2). However, theartificial intelligence startups that have received overdecrease from 2022 was small (-7.2%) and smaller than

$1.5 million in investment since 2013.

the drop observed from 2021 to 2022. Despite recent
declines, private AI investment globally has grown
substantially in the last decade.

Private investment in AI, 2013–23
Source: Quid, 2023 | Chart: 2024 AI Index report

Total investment (in billions of U.S. dollars)

120

100

95.99

80

60

40

20

0

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023
Figure 4.3.2

Chapter 4 Preview

31Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

While overall AI private investment decreased last year, funding for generative AI sharply increased (Figure 4.3.3).
In 2023, the sector attracted $25.2 billion, nearly nine times the investment of 2022 and about 30 times the amountfrom 2019. Furthermore, generative AI accounted for over a quarter of all AI-related private investment in 2023.

Private investment in generative AI, 2019–23
Source: Quid, 2023 | Chart: 2024 AI Index report

25.23

Total investment (in billions of U.S. dollars)

25

20

15

10

5

0

2019

2020

2021

2022

2023
Figure 4.3.3

Chapter 4 Preview

32Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

Interestingly, the number of newly funded AI companies jumped to 1,812, a 40.6% increase over the previous year
(Figure 4.3.4). Figure 4.3.5 visualizes the average size of AI private investment events, calculated by dividing the total
yearly AI private investment by the total number of AI private investment events. From 2022 to 2023, the averageincreased marginally, growing from $31.3 million to $32.4 million.
Number of newly funded AI companies in the world, 2013–23
Source: Quid, 2023 | Chart: 2024 AI Index report

1,812

1,800
1,600

Number of companies

1,400
1,200
1,000
800
600
400
200
0

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Average size of AI private investment events, 2013–23

2023
Figure 4.3.4

Source: Quid, 2023 | Chart: 2024 AI Index report

35

Average investment (in millions of U.S. dollars)

32.44
30

25

20

15

10

5

0

2013

Chapter 4 Preview

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023
Figure 4.3.5

33

Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

2023 marked a significant increase in the number of newly funded generative AI companies, with 99 new startups
receiving funding, compared to 56 in 2022, and 31 in 2019 (Figure 4.3.6).

Number of newly funded generative AI companies in the world, 2019–23
Source: Quid, 2023 | Chart: 2024 AI Index report

99

100

Number of companies

80

60

40

20

0

2019

2020

2021

2022

2023
Figure 4.3.6

Figure 4.3.7 reports AI funding events disaggregatedby size. In 2023, AI private investment events

AI private investment events by funding size,2022 vs. 2023
Source: Quid, 2023 | Table: 2024 AI Index report

decreased across nearly all funding size categories,

Funding Size

except for those exceeding $500 million.

Over $1 billion

7$500 million – $1 billion

67$100 million – $500 million

187

120

$50 million – $100 million

260

182

2,840

2,641

Under $50 million

Undisclosed
Total

2022

2023
9

694

680

3,994

3,639

Figure 4.3.7

Chapter 4 Preview

34Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

Regional Comparison by Funding Amounttimes greater than the amount invested in the next

The United States once again led the world in terms

highest country, China ($7.8 billion), and 17.8 times

of total AI private investment. In 2023, the $67.2

the amount invested in the United Kingdom ($3.8

billion invested in the United States was roughly 8.7

billion) (Figure 4.3.8).

Private investment in AI by geographic area, 2023
Source: Quid, 2023 | Chart: 2024 AI Index report

United States

67.22

China

7.76

United Kingdom

3.78

Germany

1.91

Sweden1.89

France1.69

Canada1.61

Israel1.52

South Korea

1.39

India

1.39

Singapore

1.14

Japan

0.68

United Arab Emirates 0.41
Australia 0.37
Spain 0.36
0

5

10

15

20

25
30
35
40
45
Total investment (in billions of U.S. dollars)

50

55

60

65

70

Figure 4.3.8

Chapter 4 Preview

35Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

When aggregating private AI investments since 2013, the country rankings remain the same: The United Statesleads with $335.2 billion invested, followed by China with $103.7 billion, and the United Kingdom at $22.3 billion(Figure 4.3.9).

Private investment in AI by geographic area, 2013–23 (sum)Source: Quid, 2023 | Chart: 2024 AI Index report

United States

335.24

China

103.65

United Kingdom

22.25

Israel

12.83

Canada10.56

Germany

10.35

India

9.85

France8.31

South Korea

7.25

Singapore

6.25

Japan

4.81

Australia

3.40

Switzerland

3.28

Hong Kong

3.15

Sweden

2.88
0

20

40

60

80

100

120
140
160
180
200
220
Total investment (in billions of U.S. dollars)

240

260

280

300

320

340
Figure 4.3.9

Chapter 4 Preview

36Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

Figure 4.3.10, which looks at AI private investment over time by geographic area, suggests that the gap in private
investments between the United States and other regions is widening over time. While AI private investments havedecreased in China (-44.2%) and the European Union plus the United Kingdom (-14.1%) since 2022, the UnitedStates has seen a significant increase (22.1%) during the same period.

Private investment in AI by geographic area, 2013–23
Source: Quid, 2023 | Chart: 2024 AI Index report

80

Total investment (in billions of U.S. dollars)

70

67.22, United States

60

50

40

30

20
11.00, European Union and United Kingdom
7.76, China

10

0
2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023
Figure 4.3.10

Chapter 4 Preview

37Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

The disparity in regional AI private investment becomes particularly pronounced when examining generative AIrelated investments. For instance, in 2022, the United States outpaced the combined investments of the European
Union plus United Kingdom in generative AI by approximately $1.9 billion (Figure 4.3.11). By 2023, this gap widened
to $21.1 billion.

Private investment in generative AI by geographic area, 2019–23
Source: Quid, 2023 | Chart: 2024 AI Index report

22.46, United States

Total investment (in billions of U.S. dollars)

20

15

10

5

0.74, European Union and United Kingdom
0.65, China

0
2019

2020

2021

2022

2023
Figure 4.3.11

Chapter 4 Preview

38Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

Regional Comparison by Newly Funded
AI Companies

Consistent with trends in private investment, theThis section examines the number of newly funded

companies, followed by China with 122, and the

AI companies across different geographic regions.

United Kingdom with 104 (Figure 4.3.12).

United States leads all regions with 897 new AI

Number of newly funded AI companies by geographic area, 2023
Source: Quid, 2023 | Chart: 2024 AI Index report

United States

897

China

122

United Kingdom

104

Germany76

Canada59

France

58

India

45

South Korea

44

Israel

43

Japan

42

Singapore

29

Australia

24

Spain

21

Switzerland

17

Brazil

15

0

100

200

300

400
500
Number of companies

600

700

800

900
Figure 4.3.12

Chapter 4 Preview

39Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

A similar trend is evident in the aggregate data since 2013. In the last decade, the number of newly funded AI
companies in the United States is around 3.8 times the amount in China, and 7.6 times the amount in the United
Kingdom (Figure 4.3.13).

Number of newly funded AI companies by geographic area, 2013–23 (sum)Source: Quid, 2023 | Chart: 2024 AI Index report

United States

5,509

China

1,446

United Kingdom

727

Israel442

Canada397

France

391

India

338

Japan

333

Germany

319

Singapore

193

South Korea

189

Australia

147

Switzerland

123

Spain

94

Sweden

94
0

500

1,000

1,500

2,000

2,500
3,000
3,500
Number of companies

4,000

4,500

5,000

5,500
Figure 4.3.13

Chapter 4 Preview

40Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

Figure 4.3.14 presents data on newly funded AI

United States, along with the European Union and thecompanies in specific geographic regions, highlighting

United Kingdom, have seen significant increases in thea decade-long trend where the United States

number of new AI companies, in contrast to China,

consistently surpasses both the European Union andwhich experienced a slight year-over-year decrease.

the United Kingdom, as well as China. Since 2022, theNumber of newly funded AI companies by geographic area, 2013–23
Source: Quid, 2023 | Chart: 2024 AI Index report

900

897, United States

800

Number of companies

700
600
500
400

368, European Union and United Kingdom

300
200
122, China

100
0
2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023
Figure 4.3.14

Chapter 4 Preview

41

Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

Focus Area Analysis
Quid also disaggregates private AI investment by

Figure 4.3.16 presents trends over time in AI focus

focus area. Figure 4.3.15 compares global private AI

area investments. As noted earlier, most focus areas

investment by focus area in 2023 versus 2022. The

saw declining investments in the last year. Conversely,

focus areas that attracted the most investment insome of the areas that saw growth since 2022

2023 were AI infrastructure/research/governanceinclude AI infrastructure/research/governance and($18.3 billion); NLP and customer support ($8.1 billion);

data management, processing. Although now stilland data management and processing ($5.5 billion).

substantial, investments in medical and healthcare as

The prominence of AI infrastructure, research, andwell as NLP, customer support peaked in 2021 andgovernance reflects large investments in companies

have since then declined.

specifically building AI applications, such as OpenAI,
Anthropic, and Inflection AI.
Private investment in AI by focus area, 2022 vs. 2023
Source: Quid, 2023 | Chart: 2024 AI Index report

AI infrastructure/research/governance
NLP, customer support
Data management, processing
Medical and healthcare
AV
FintechQuantum computing
Semiconductor
Energy, oil, and gas
Creative, music, video content
Ed techMarketing, digital ads
DronesManufacturing
Cybersecurity, data protection
AR/VR
RetailInsurtechEntertainment
VC
Agritech
Legal tech
Facial recognition

2023

Fitness and wellness2022

Geospatial
0

2

4

6

8
10
12
Total investment (in billions of U.S. dollars)

14

16

18

Figure 4.3.15

Chapter 4 Preview

42Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

Privateinvestment
investmentinin
focus
area,
2017–23
Private
AIAI
byby
focus
area,
2017–23
Source:Quid,
Quid,2023
2023| Chart:| Chart:2024
AI Index
reportSource:2024
AI Index
reportAI
AI infrastructure/research/governanceinfrastructure/research/governance
18.27
18.27
15
15 15
15
10

10

10

5

5

5

0
0
2017

2017

2020

2020

2023

2023

2017

15

10

10

10

10

2020

2020

2023

10
5

5
1.34
2020

2023 1.34

2020
Ed tech0.67 0

2017

2023

15
10
0.89
2020

2023

0
2017Energy, oil,2020
and gas050.89 2017
2023

Energy, oil, and gas

5

5

0
2017
15
10

1.24
Fintech

2020

5
2.13

2017

2020

2023

0
2017

Legal tech

10

Legal tech

10
5

0.42
2020

2023

5

NLP, customer support

2020

2023

8.06
NLP, customer support

2017

2017

15

0
2017

10

2020

2023

8.06

VC

2020

0
2017
10

2020

2023

2020

2023

0.52

5

2020

2023

2017

0
2017

0.31

2020

2023

15

2023

5 2020

15
10
5

2023

1.06
2023152020
Retail
0.67
2020

0
2017

2023

2017

2020

0.60
2023

Medical and healthcare
4.20
2020

2023

5

Retail

0
2017

0.60
2020

Medical0 and healthcare

10

2020

0.28
2023

Insurtech

15

1.06

0.28
2023

0
Insurtech
2017
2020

0
2017

15

2023

0
2017

2020

2023

Semiconductor

5
0
2017

4.20

Semiconductor

10

5
2020

10

5

15

1.97

15

15

5
0.31

0.96
2023

Facial recognition

10

15
10

0
2017 10

2023

0
2017

2023

0
2017recognition
2020
FacialMarketing, digital ads 10

15

0.50
2023

0.96
2020

15

2023

Hardware

5

5
0
2017

0.52

10

1.97

5

2017

5

0.89

Quantum computing

5

5.50
0

5

10
5

10

10

15

0
10
2017

2023

2020

15

5

2023

Entertainment

5

2020

2023

Drones102023

Quantum computing

15

15
10 2020

1.66
2023

5
0.67
2020

2023

0
2017

1.66
2020

2023

VC

5
0
15

15

2020

0
2017
2020
Entertainment

Marketing,digital ads
0

0.89

0
10
2017

0.42

150
10

2023

Manufacturing

5

15

0
2017
10

0
2017

0.22

2020

2023

15

2023

Manufacturing

2020

10

5 15

0

15
2017

0
2017

10

5
15

5

2020

0.50

0 20202017
Drones5.50

0Hardware
2017
2020

2023

5

5

15

1.47

0.22

2017

2023

0
2017

15

0 10

2.13

2020

2023

Fitness and wellness

10

5

0
10
2017

2020

15

Fintech

15

15

1.47

0
Fitness and wellness
2017
2020

2023

10
0
5

10

2023

10

15

5

0
5
2017

15
10

10

2023

2020

2017

5

15

5

10 15

2020

2023

Agritech

Data management, processing

10

15

1.24

0 2020
2017

5

2.66
0

Data management, processing

10
15

0
5
2017

2.66

15

0 5
2017

2023

Ed tech5

Cybersecurity, data protection
10

10
Total investment(in ofbillionsof U.S. dollars)
Total investment(in billions
U.S. dollars)

15

15

15

15

15

Cybersecurity, data protection

10

0
2017

0.67

Agritech

AV

15

5

5

Creative, music, video content

05
2017

AV

10

0
20170

Creative, music, video content
15

AR/VR
AR/VR

0.51
2020

2023

Figure 4.3.16

10
5Chapter 4 Preview
0
2017

2020

0.51
2023

43

Artificial IntelligenceIndex Report 2024

Chapter 4: Economy
4.3 Investment

Finally, 4.3.17 shows private investment in AI by focus

and the European Union and United Kingdom inarea over time within select geographic regions,investment in almost all focus area categories.

highlighting how private investment priorities in AI

A notable exception is facial recognition, where

differ across geographies. The significant increases

2023 investment totals were $90 million in theobserved in AI infrastructure/research/governance

United States and $130 million in China. Likewise, inwere mostly driven by investment in the United

semiconductor investments, China ($630 million) isStates. The United States significantly outpaces China

not far behind the United States ($790 million).

Chapter 4 Preview

44Chapter 4: Economy
4.3 Investment

Artificial IntelligenceIndex Report 2024

Private investment in AI by focus area and geographic area, 2017–23

Source: Quid, 2023 | Chart: 2024 AI Index report

AI infrastructure/research/governance

AR/VR

Agritech

15

15

15

15

10

10

10

10

5
0
2017

2020

US, 18.22
CN, 0.03
5
EU/UK, 0.01
0
2023
2017

2020

US, 0.60
CN, 0.01
5
EU/UK, 0.04
0
2023
2017

2020

US, 1.92
CN, 0.28
5
EU/UK, 0.02
0
2023
2017

Creative, music, video content

Cybersecurity, data protection

Data management, processing

15

15

15

10
5
0
2017

2020

10
US, 0.76
CN, 0.02
5
EU/UK, 0.39
0
2023
2017

Ed tech2020

10
US, 0.28
CN, 0.00
5
EU/UK, 0.54
0
2023
2017

2020

10
US, 4.72
CN, 0.37
5
EU/UK, 0.18
0
2023
2017

Entertainment

15

15

15

10

10

10

2020
Fintech2020

US, 0.68
CN, 0.36
5
EU/UK, 0.27
0
2023
2017

Fitness and wellness

15

15

10
5
0
2017

2020

2020

US, 0.28
CN, 0.02
5
EU/UK, 0.18
0
2023
2017

Hardware

Legal tech

2020

2020

10
US, 0.31
CN, 0.00
5
EU/UK, 0.00
0
2023
2017

Marketing, digital ads

15

15

15

15

10

10

10

0
2017

2020

US, 0.27
CN, 0.01
5
EU/UK, 0.03
0
2023
2017

NLP, customer support
15

5
2020

US, 0.20
CN, 0.53
5
EU/UK, 0.09
0
2023
2017

Quantum computing
15

10

0
2017

2020

10
US, 5.42
CN, 0.60
5EU/UK, 1.19
0
2023
2017

2020

US, 0.41
CN, 0.41
5
EU/UK, 0.08
0
2023
2017

Retail
15

2020

2023

US, 0.09
CN, 0.13
EU/UK, 0.01
2020

2023

10
US, 1.84
CN, 0.00
5
EU/UK, 0.08
0
2023
2017

US, 0.42
CN, 0.00
EU/UK, 0.10
2020

2023

Medical and healthcare

10
5

2020

15

10
US, 0.20
CN, 0.00
5
EU/UK, 0.02
0
2023
2017

Manufacturing

US, 0.87
CN, 0.05
EU/UK, 0.02

Insurtech

15

10
US, 0.96
CN, 0.04
5
EU/UK, 0.67
0
2023
2017

2023

Facial recognition

15

0
2017

2020

15

Energy, oil, and gas

US, 0.87
CN, 0.09
5
EU/UK, 0.15
0
2023
2017

US, 0.29
CN, 0.01
EU/UK, 0.13

Drones105Total investment (in billions of U.S. dollars)AVUS, 2.74
CN, 0.48
EU/UK, 0.53
2020

2023

Semiconductor
15

2020

10
US, 0.35
CN, 0.12
5
EU/UK, 0.08
0
2023
2017

US, 0.79
CN, 0.63
EU/UK, 0.14
2020

2023

VC
15
10

US, 0.23
CN, 0.10
EU/UK, 0.00

5
0
2017

2020

Chapter 4 Preview

2023

Figure 4.3.17

45

Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

This section examines the practical application of AI by corporations, highlighting industry adoption trends, howbusinesses are integrating AI, the specific AI technologies deemed most beneficial, and the impact of AI adoption on
financial performance.

4.4 Corporate Activity
Industry Adoption

Adoption of AI Capabilities

This section incorporates insights from McKinsey’s

The latest McKinsey report reveals that in 2023, 55%“The State of AI in 2023: Generative AI’s Breakoutof organizations surveyed have implemented AI in at

Year,” alongside data from prior editions. The 2023

least one business unit or function, marking a slight

McKinsey analysis is based on a survey of 1,684

increase from 50% in 2022 and a significant jump

respondents across various regions, industries,

from 20% in 2017 (Figure 4.4.1). AI adoption has spiked

company sizes, functional areas, and tenures. For theover the past five years, and in the future, McKinsey

first time, this year’s version of the McKinsey survey

expects to see even greater changes happening at

included detailed questions about generative AI

higher frequencies, given the rate of both AI technicaladoption and hiring trends for AI-related positions.

advancement and adoption.

Share of respondents who say their organizations have adopted AI in at least one function, 2017–23
Source: McKinsey & Company Survey, 2023 | Chart: 2024 AI Index report

60%
55%

% of respondents

50%

40%

30%

20%

10%

0%

2017

2018

2019

2020

2021

2022

2023
Figure 4.4.1

Chapter 4 Preview

46

Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Figure 4.4.2 shows the proportion of surveyedcase by function among surveyed businesses in 2023

companies that use AI for specific functions.

was contact-center automation (26%), followed byCompanies may report employing AI in multiple

personalization (23%), customer acquisition (22%), andcapacities. The most commonly adopted AI use

AI-based enhancements of products (22%).7

Most commonly adopted AI use cases by function, 2023
Source: McKinsey & Company Survey, 2023 | Chart: 2024 AI Index report

Contact-center automation

26%

Personalization

23%

Customer acquisition

22%

AI-based enhancements of products

22%

Creation of new AI-based products

19%

Product feature optimization

19%

Service operations optimization

19%

Sales

17%

Customer-service analytics

17%

Service operations
R&D/product development

Predictive service and intervention
0%Marketing and sales

16%
4%

8%

12%
16%
% of respondents

20%

24%

Figure 4.4.2

7 Personalization is the practice of tailoring products, services, content, recommendations, and marketing to the individual preferences of customers or users. For example, personalization
can include sending tailored email messages to clients or customers to improve engagement.

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Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

With respect to the type of AI capabilities embeddedof embedding was for virtual agents, also in thein at least one function or business unit, as indicatedfinancial services industry. Across all industries,

by Figure 4.4.3, robotic process automation had

the most embedded AI technologies were NL text

the highest rate of embedding within the financial

understanding (30%), robotic process automation

services industry (46%). The next highest rate(30%), and virtual agents (30%).

AI capabilities embedded in at least one function or business unit, 2023

9%

18%

10%

30%

5%

30%

Business, legal, andprofessional services

15%

23%

11%

25%

22%

18%

16%

26%

8%

14%

13%

24%

4%

19%

Consumer goods/
retail

31%

24%

7%

18%

17%

13%

7%

27%

13%

26%

2%

24%

5%

32%

Financial services

26%

23%

15%

20%

20%

15%

14%

37%

7%

11%

16%

46%

8%

42%

Healthcare systems/
pharma andmed. products

14%

15%

12%

20%

10%

12%

19%

20%

8%

11%

6%

28%

4%

21%

Tech, media,
and telecom

26%

33%

17%

31%

23%

23%

25%

39%

7%

29%

14%

26%

4%

35%

D

G

ow
Kn

i
Re

b
Ro

nt

ge
s

ng

ni

ar

le
n

io

at

g

in

rn

ea

s

om
ut
sa

es
oc
pr

la

r
fe

ua

ns

ic
ot
tl

en

s

g

in

m
te
ys
rs

de

en

ic
ot

g

in

nd

ta

nd

rs

ta

de

rs

un

de

b
ro

un

m

xt
te

n

hs

ap

gr

tio

ra

e

ch

ne

ee

sp

ge

rt

L

L

L

s

in

w

dg

lt

ng

n

sio

vi

ni

ar

le

le

ita

p

r
te
pu

AN

ig

ee

Vi

30%

a
Tr

16%

m
ce
or
nf

16%

m
co
Re

18%

al
ic
ys
Ph

23%

N

13%

N

24%

N

22%

D

All industriesomC

Industry

Source: McKinsey & Company Survey, 2023 | Chart: 2024 AI Index report% of respondents (AI capability)
Figure 4.4.3

Chapter 4 Preview

48Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Figure 4.4.4 shows AI adoption by industry and AI function in 2023. The greatest adoption was in product and/or service development for tech, media, and telecom (44%); followed by service operations for tech, media, andtelecom (36%) and marketing and sales for tech, media, and telecom (36%).

AI adoption by industry and function, 2023

Industry

Source: McKinsey & Company Survey, 2023 | Chart: 2024 AI Index report

All industries

9%

6%

25%

26%

12%

24%

8%

9%

Business, legal, andprofessional services

9%

5%

28%

24%

10%

19%

13%

6%

Consumer goods/
retail

7%

9%

31%

15%

6%

22%

2%

14%

Financial services

9%

1%

22%

20%

28%

31%

14%

4%

Healthcare systems/
pharma andmed. products

5%

7%

8%

26%

7%

15%

6%

11%

Tech, media,
and telecom

14%

6%

36%

44%

7%

36%

6%

9%

Hu

ma

nr
eso
urc
es

Ma
nu
fa

ctu

rin

Ma
rke
ti
g

ng

Pro
Ris
k
ser ductvicanane d d/o
ds
ev
ale
elo r
s
pm
en
t

Se
rv

ice

Su
Str
p
ma plyco ateg
rpo y ana chageera
rat nd
me in
e
tio
nt
ns
na
nc
e

op

% of respondents (function)
Figure 4.4.4

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49Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Figure 4.4.5 illustrates the changes in AI adoption

operations (4). Conversely, across all industries, therates by industry and function from 2022 to 2023.

functions experiencing the most significant declines in

The areas with the largest annual gains across all

adoption include strategy and corporate finance (-12industries include marketing and sales (18 percentage

percentage points), risk (-9), and human resources (-2).

points), product/service development (14), and service

Percentage point change in responses of AI adoption by industry and function, 2022 vs. 2023

Industry

Source: McKinsey & Company Survey, 2023 | Chart: 2024 AI Index report

All industries

-2%

0%

18%

14%

-9%

4%

-12%

0%

Business, legal, andprofessional services

-2%

-5%

19%

16%

-6%

-1%

-6%

-6%

Consumer goods/
retail

-7%

5%

28%

11%

-9%

-9%

-27%

3%

Financial services

8%

-7%

15%

-11%

11%

7%

-9%

2%

Healthcare systems/
pharma andmed. products

-10%

0%

6%

22%

-15%

3%

-2%

3%

Tech, media,
and telecom

8%

0%

32%

37%

-31%

15%

-19%

1%

Ris
Pro
k
ser ductvicanane d d/o
ds
ev
ale
elo r
s
pm
en
t

Se
rv

Hu

ma

nr
eso
urc
es

Ma
nu
fa

ctu

rin

Ma
rke
ti
g

ng

ice

Su
Str
p
ma plyco ateg
rpo y ana chageera
rat nd
me in
e
tio
nt
ns
na
nc
e

op

Percentage point change in responses (function)
Figure 4.4.5

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50Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Figure 4.4.6 shows the percentage of surveyed

greatest degree. Notably, a significant portion of

respondents across industries who reported hiring for

respondents within the financial services (44%) andvarious AI positions. Across all industries, respondents

the tech, media, and telecom sectors (44%) reported areported hiring data engineers (36%), AI data scientists

high rate of hiring machine-learning engineers.

(31%), and machine-learning engineers (31%) to the

AI-related roles that organizations hired in the last year by industry, 2023

Industry

Source: McKinsey & Company Survey, 2023 | Chart: 2024 AI Index report

All industries

36%

31%

31%

28%

25%

24%

22%

21%

11%

7%

Business, legal, andprofessional services

33%

28%

25%

24%

20%

21%

22%

21%

8%

4%

Consumer goods/
retail

27%

14%

15%

20%

14%

20%

21%

13%

11%

3%

Healthcare systems/
pharma andmed. products

19%

32%

16%

23%

19%

25%

13%

19%

3%

9%

Financial services

41%

37%

44%

24%

29%

19%

26%

26%

17%

10%

Tech, media,
and telecom

39%

41%

44%

38%

36%

27%

25%

27%

17%

13%

Da

ta

AI

en

gin

ee

rs

da

ta

Ma
c

hin

sci
e

nti

sts

So
ft

el

ea

rni

ng

wa
re

en

gin

Da

ta

en

ee

gin

ee

rs

Da

arc

ta

hit
ec

ts

De

sig

vis
ua

liza

rs

tio

AI

ns

ns

pe

pe

cia

cia

list

list

s

pro

du
ct

s

Tra
n

Pro
m

ow
n

sla

ers

pt
e

tor
s

/m
a

na

ng

ine

ers

ge

rs

% of respondents (AI-related role)
Figure 4.4.6

Chapter 4 Preview

51

Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Organizations have experienced both cost reductions

number of respondents reporting cost decreases

and revenue increases due to AI adoption (Figure(42%) and revenue gains (59%) as a result of using AI,

4.4.7). The areas where respondents most frequently

suggesting that AI tangibly helps businesses improvereported cost savings were manufacturing (55%),

their bottom line. Comparing this and last year’s

service operations (54%), and risk (44%). For revenue

averages reveals a 10 percentage point increase forgains, the functions benefiting the most from AI

cost decreases and a four percentage point decrease

included manufacturing (66%), marketing and sales

for revenue increases across all activities.

(65%), and risk (64%). Figure 4.4.7 shows a substantial

Cost decrease and revenue increase from AI adoption by function, 2022
Source: McKinsey & Company Survey, 2023 | Chart: 2024 AI Index report

Function

Decrease by <10%

Decrease by 10–19%

Decrease by ≥20%

Service operations

54%

34%

Manufacturing

55%

41%

Increase by >10%
12%

8%
14%

Increase by 6–10%
10%
16%

Human resources

40%

26%

10%

9%

Marketing and sales

41%

26%

11%

8%

Risk

44%

26%

33%

Supply chain management

13%

24%

R&D/product and/or service development

31%

18%

Strategy and corporate finance

31%

19%

Average across all activities

42%

28%

14%

9%

19%

7%
10%

16%

23%

10%
6%

17%

18%

60%

38%

65%

35%

64%
56%

30%
25%

16%

66%

34%

34%

12%

57%

33%
16%

13%

9%

Increase by ≤5%

24%

61%

32%

58%

35%

59%

% of respondents
Figure 4.4.7

Chapter 4 Preview

52Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Figure 4.4.8 presents global AI adoption by

adoption grew by 9 percentage points. North America

organizations, segmented by world regions. In 2023,

remains the leader in AI adoption. Greater China also

every surveyed region reported higher AI adoption

experienced a significant increase in AI adoption rates,

rates than in 2022. The most significant year-over-growing by 7 percentage points over the previous year.

year growth was seen in Europe, where organization

AI adoption by organizations in the world, 2022 vs. 2023
Source: McKinsey & Company Survey, 2023 | Chart: 2024 AI Index report55%

All geographies

50%
58%

Asia-Paci c

55%
57%

Europe48%
61%

North America

59%

Greater China(incl. Hong Kong,
Taiwan)48%
41%

Developing Markets(incl. India,
Latin America,
MENA)
0%

2023

49%

2022

44%
10%

20%

30%
% of respondents

40%

50%

60%
Figure 4.4.8

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53Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Adoption of Generative AI Capabilities

The most frequent application is generating initial

How are organizations deploying generative AI?

8drafts of text documents (9%), followed closely

Figure 4.4.9 highlights the proportion of total

by personalized marketing (8%), summarizing text

surveyed respondents that report using generative AI

documents (8%), and creating images and/or videos

for a particular function. It is possible for respondents(8%). Most of the reported leading use cases are within

to indicate that they deploy AI for multiple purposes.

the marketing and sales function.

Most commonly adopted generative AI use cases by function, 2023
Source: McKinsey & Company Survey, 2023 | Chart: 2024 AI Index reportCreating rst draft of text documents (e.g.,
advertising copy, technical sales content)

9%

Personalized marketing

8%

Summarization of text documents

8%

Creating images and/or videos

8%

Identifying trends in customer needs
and/or preferences

7%

Use of chatbots (e.g., for customer service)6%

Use of chatbots (e.g., inside sales)

6%

Identifying and/or forecasting service trends,
insights, and/or anomalies

5%

Creating rst draft of documents(e.g., customer service call scripts)

R&D/product development

Drafting of technical documents
0%Service operations

5%

Marketing and sales

5%
2%

4%

6%
% of respondents

8%

10%

Figure 4.4.9

8 The adoption of generative AI capabilities is presented separately from the charts on the adoption of general AI capabilities earlier in the chapter, as it was a separate question in the survey.

Chapter 4 Preview

54Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Figure 4.4.10 compares the proportion of respondents

AI within organizations shows similar patterns of

who report using AI versus specifically generative

distribution. Overall, general AI still dominates. The

AI for a given function. Figure 4.4.10 illustrates themost common functional applications of generative AI

degree to which generative AI has permeated general

are in marketing and sales (14%), product and/or service

AI usage patterns among businesses. When analyzed

development (13%), and service operations (10%).

9at the functional level, the use of AI and generative

AI vs. generative AI adoption by function, 2023
Source: McKinsey & Company Survey, 2023 | Chart: 2024 AI Index report

24%

Product and/or service development

13%
23%

Marketing and sales

14%
23%

Function

Service operations

10%
10%

Risk

4%
9%

Strategy and corporate nance

4%9%

Human resources

Supply-chain management

Manufacturing
0%

3%
9%
3%

AI
GenAI

8%
2%
5%

10%

15%
% of respondents

20%

25%
Figure 4.4.10

9 While all generative AI use cases are considered general AI use cases, not all general AI use cases qualify as generative AI use cases.

Chapter 4 Preview

55

Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Figure 4.4.11 depicts the variation in generative AIof businesses across all geographies (55%) thatusage among businesses across different regions ofreported using AI, which was documented earlier inthe world. Across all regions, the adoption rate of

Figure 4.4.8. North America leads in adoption at 40%,

generative AI by organizations stands at 33%. This

followed closely by developing markets (including

amount is meaningfully lower than the percentage

India, Latin America, and the MENA region).

Generative AI adoption by organizations in the world, 2023
Source: McKinsey & Company Survey, 2023 | Chart: 2024 AI Index reportAll geographies

33%

Asia-Paci c

30%Europe31%

North America

40%

Greater China(incl. Hong Kong,
Taiwan, Macau)

31%

Developing Markets(incl. India,
Latin America,
MENA)
0%

33%
5%

10%

15%

20%
% of respondents

25%

30%

35%

40%
Figure 4.4.11

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56Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Use of AI by Developers

Most popular AI developer tools among professional
developers, 2023
Source: Stack Over ow Developer Survey, 2023 | Chart: 2024 AI Index report

Computer developers are among the most likely

GitHub Copilot

individuals to use AI in professional settings. As

TabnineAI becomes more integrated into the economy,AWS CodeWhisperer

becoming increasingly important.
Stack Overflow, a question-and-answer website
for computer programmers, conducts an annual
survey of computer developers. The 2023 survey,

AI developer tool

tracking how developers utilize and perceive AI is56.04%
11.74%
4.91%

Other

1.83%

Synk Code1.33%

Codeium 1.07%
Whispr AI 0.90%
Replit Ghostwriter 0.47%

with responses from over 90,000 developers,

Mintlify 0.44%

included, for the first time, questions on AI tool

Adrenaline 0.30%

usage—detailing how developers use these tools,

Rubber Duck.AI 0.25%

which tools are favored, and their perceptions of

0%

10%

the tools used.

10

Preference
Figure 4.4.12 highlights the proportion ofsurveyed respondents who report using a specificSource: Stack Over ow Developer Survey, 2023 | Chart: 2024 AI Index report

56.0% of respondents report using GitHub’s

Bing AI

83.25%
18.80%

WolframAlpha11.15%

CodeWhisperer (4.9%).

Google Bard AI

9.13%

AI search tool

Copilot, followed by Tabnine (11.7%) and AWS

search functionality, are most favored by AI

Phind

3.11%

You.com

2.17%

Other 1.21%
Perplexity AI 0.97%

developers. The most popular AI search tools

Quora Poe 0.77%

according to professional developers wereNeeva AI 0.36%

ChatGPT (83.3%), followed by Bing AI (18.8%) and
WolframAlpha (11.2%).

60%

Most popular AI search tools among professional
developers, 2023
ChatGPT

software applications that use AI to enhance

50%

Figure 4.4.12

AI developer tool. According to the survey,

Figure 4.4.13 highlights which AI search tools,

20%
30%
40%
% of respondents

Andi 0.20%
Metaphor 0.13%
0%10% 20% 30% 40% 50% 60% 70% 80% 90%% of respondents
Figure 4.4.13

10 The survey was conducted in May 2023 and, therefore, may not account for the launch of more recently released AI tools such as Gemini and Claude 3.

Chapter 4 Preview

57Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Top 10 most popular cloud platforms amongprofessional developers, 2023

Cloud platforms are crucial elements of the AI
ecosystem, providing cloud computing services

Source: Stack Over ow Developer Survey, 2023 | Chart: 2024 AI Index reportthat allow developers to perform computationallyAmazon Web Services

intensive AI work. Figure 4.4.14 reports the

53.08%

Microsoft Azure

proportion of respondents that have reported

27.80%

Google Cloud

extensively using a specific cloud platform.
Cloud platform

According to the Stack Overflow survey, Amazon
Web Services (AWS) is the most commonly usedcloud platform among professional developers, with53.1% reporting regular use. Microsoft Azure follows

23.95%

Firebase

15.39%

Cloud are14.99%

Digital Ocean

14.11%

Heroku11.77%

at 27.8%, with Google Cloud at 24.0%.

Vercel10.31%

Workflow

Netlify

8.48%

Figure 4.4.15 explores the current and future

VMwareintegration of AI in developers’ workflows. A significant

6.45%

0%

10%

20%
30%
40%
% of respondents

majority of respondents, 82.6%, regularly use AI for

50%

60%

Figure 4.4.14

code writing, followed by 48.9% for debugging andassistance, and 34.4% for documentation. While only
23.9% currently use AI for code testing, 55.2% express
interest in adopting AI for this purpose.

Adoption of AI tools in development tasks, 2023
Source: Stack Over ow Developer Survey, 2023 | Chart: 2024 AI Index report

Writing code
Debugging and getting help

Development task

50.24%

8.07%
30.10%

48.97%

13.09%
23.87%

Testing code

55.17%

11.44%
13.52%

Project planning29.77%

38.54%

10.09%

Committing and reviewing code0%48.89%

34.37%

Learning about a codebase

Collaborating with teammates

40.66%

6.37%

Documenting code

Deployment and monitoring

82.55%

23.72%

4.48%

49.51%

22.95%
4.74%
28.33%
3.65%

20%

30%

41.38%
40%

50%% of respondents

Chapter 4 Preview

Interested in using
Not interested in using

29.98%
10%

Currently using

45.44%

60%

70%

80%
Figure 4.4.15

58

Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

When asked about the primary advantages of AI tools
in professional development, developers responded

Primary bene ts of AI tools for professional
developers, 2023
Source: Stack Over ow Developer Survey, 2023 | Chart: 2024 AI Index reportwith increased productivity (32.8%), accelerated

Increaseproductivity

learning (25.2%), and enhanced efficiency (25.0%)

32.81%(Figure 4.4.16).
Primary bene�t

Figure 4.4.17 displays the sentiments professional
developers have toward AI tools. A significant
majority of developers hold a positive view of AI
tools, with 27.7% feeling very favorably and 48.4%
favorably inclined toward them. Only 3.2% express
unfavorable opinions about AI development tools.

Speed uplearning

25.17%

Greatere�ciency

24.96%

Improve accuracy
in coding

13.31%

Improvecollaboration

Figure 4.4.18 highlights the reported level of trustdevelopers have in AI tools. More developers trust AI

3.75%

0%

10%

tools than distrust them, with 42.2% reporting high20%
% of respondents

30%

40%
Figure 4.4.16

or moderate trust in these technologies. In contrast,a smaller proportion, 27.2%, express some level of
distrust or high distrust in AI tools.

Sentiment toward AI tools in development amongprofessional developers, 2023

Trust level in AI tool output accuracy, 2023
Source: Stack Over ow Developer Survey, 2023 | Chart: 2024 AI Index report

Source: Stack Over ow Developer Survey, 2023 | Chart: 2024 AI Index report

Very favorable

Favorable

48.41%

Indi erent

16.80%

Unsure3.88%

Unfavorable

2.78%

5.46%

Somewhat distrust
Trust level

Sentiment

Highly distrust

27.71%

21.71%Neither trust
nor distrust

30.68%

Somewhat trust

Highly trust

Very unfavorable 0.42%
0%

10%

20%
30%
40%
% of respondents

50%
Figure 4.4.17

Chapter 4 Preview

0%39.30%

2.85%
10%

20%
30%
% of respondents

40%
Figure 4.4.18

59

Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

AI’s Labor Impact

First, AI has been shown to enable workers toOver the last five years, the growing integration

complete tasks more quickly and produce higher

of AI into the economy has sparked hopes ofquality work. A meta-review by Microsoft, which

boosted productivity. However, finding reliable data

aggregated studies comparing the performance ofconfirming AI’s impact on productivity has been

workers using Microsoft Copilot or GitHub’s Copilot—

difficult because AI integration has historically been

LLM-based productivity-enhancing tools—with thoselow. In 2023, numerous studies rigorously examined

who did not, found that Copilot users completed

AI’s productivity impacts, offering more conclusive

tasks in 26% to 73% less time than their counterparts

evidence on the topic

without AI access (Figure 4.4.19).11

Cross-study comparison of task completion speed of Copilot users
Source: Cambon et al., 2023 | Chart: 2024 AI Index report

Copilot information
retrieval study

73% 73%

Task

Copilot common
task study

71% 71%

LLM-based
search study

47% 47%

GitHub Copilot
study

44% 44%

Copilot in teams
meeting study
0%26% 26%

10%

20%

30%
40%
50%
60%
70%
Copilot users’ task completion speed relative to baseline (100%)

80%

90%

100%

Figure 4.4.19

11 This meta-review analyzed separate surveys of workers using Microsoft’s Copilot and GitHub’s Copilot tools. These are separate tools. Microsoft Copilot is a broader LLM-based
productivity improvement tool, while GitHub’s Copilot is a code-writing assistant.

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4.4 Corporate Activity

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that consultants with access to GPT-4 increased
their productivity on a selection of consulting tasks
by 12.2%, speed by 25.1%, and quality by 40.0%,
compared to a control group without AI access(Figure 4.4.20). Likewise, National Bureau of
Economic Research research reported that callcenter agents using AI handled 14.2% more calls perhour than those not using AI (Figure 4.4.21).

E ect of GPT-4 use on a group of consultants
Source: Dell’Acqua et al., 2023 | Chart: 2024 AI Index report

Average improvement compared to control group (%)

Similarly, a Harvard Business School study revealed

40.00%

40%
35%
30%
25.10%

25%
20%
15%

12.20%

10%
5%0%Productivity

Speed

Quality

Performance metrics on consulting tasks
Figure 4.4.20

Impact of AI on customer support agents
Source: Brynjolfsson et al., 2023 | Chart: 2024 AI Index report

Hourly chats per customer support agent

3.00

2.97
2.60

2.50

2.00

1.50

1.00

0.50

0.00

Used AIDid not use AI
Figure 4.4.21

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A study on the impact of AI in legal analysis showedcompared to the control group, in terms of both workthat teams with GPT-4 access significantly improved inquality and time efficiency across a range of tasks.

efficiency and achieved notable quality improvements

Although AI can assist with legal tasks, there arein various legal tasks, especially contract drafting.

also widespread reports of LLM hallucinations being

Figure 4.4.22 illustrates the improvements observed

especially pervasive in legal tasks.

in the group of law students who utilized GPT-4,

E ect of GPT-4 use on legal analysis by task
Source: Choi et al., 2023 | Chart: 2024 AI Index report

Contract drafting

0.24

Complaint drafting

32.10%

24.10%

Task

0.17

EE handbook

0.07

21.10%

Client memo -0.07−0.10

11.80%

0.00

0.10

0.20

Improvement compared to control group (on a 4.0 scale)

0%

10%

20%

30%

Time saved (%) compared to control group
Figure 4.4.22

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Second, AI access appears to narrow the performance

Lower-skilled (bottom half) participants exhibitedgap between low- and high-skilled workers. According

a 43.0% improvement, while higher-skilled (topto the aforementioned Harvard Business Schoolhalf) participants showed a 16.5% increase. While

study, both groups of consultants experienced

higher-skilled workers using AI still performed better

performance boosts after adopting AI, with notably

than their lower-skilled, AI-using counterparts, thelarger gains for lower-skilled consultants using AI

disparity in performance between low- and high-compared to higher-skilled consultants. Figure 4.4.23

skilled workers was markedly lower when AI was

highlights the performance improvement across autilized compared to when it was not.

set of tasks for participants of varying skill levels:

Comparison of AI work performance e ect by worker skill category
Source: Dell’Acqua et al., 2023 | Chart: 2024 AI Index report

Bottom-half skilled participants

6

Top-half skilled participants
6.06

6

5.79

5.20

4

5
Score (average on a 1–10 scale)

Score (average on a 1–10 scale)

5
4.05

3

2

1

0

4

3

2

1

Baseline task

Experimental task

0Baseline task

Experimental task
Figure 4.4.23

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4.4 Corporate Activity

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Finally, while AI tends to enhance quality andperformed worse than those who received “bad AI,”

productivity, overreliance on the technology canwhich was capable but known to make errors (Figure

impair worker performance. A study focused on

4.4.24). The performance difference between theprofessional recruiters reviewing résumés found thatlatter groups was -1.08 points. The study theorizes

receiving any AI assistance improved task accuracy

that recruiters using “good AI” became complacent,by 0.6 points compared to not receiving AI assistance.

overly trusting the AI’s results, unlike those using “badHowever, recruiters who were provided with “goodAI,” who were more vigilant in scrutinizing AI output.

AI”—believed to be high-performing—actually

E ects on job performance of receiving di erent types of AI advice
Source: Dell’Acqua, 2023 | Chart: 2024 AI Index report

Any AI

Type of AI advice

0.60

Good vs.
bad AI

-1.08

−1.20

−1.00

−0.80

−0.60

−0.40
−0.20
Change in accuracy score

0.00

0.20

0.40

0.60

Figure 4.4.24

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Earnings Calls

Aggregate Trends

The following section presents data from Quid,

The past year has seen a significant rise in the mention

which uses natural language processing tools toof AI in Fortune 500 company earnings calls. In 2023,

analyze trends in corporate earnings calls. Quid

AI was mentioned in 394 earnings calls (nearly 80%

analyzed all 2023 earnings calls from Fortune 500

of all Fortune 500 companies), up from 266 mentions

companies, identifying all mentions of “artificial

in 2022 (Figure 4.4.25). Since 2018, mentions of AI in

intelligence,” “AI,” “machine learning,” “ML,” andFortune 500 earnings calls have nearly doubled.

“deep learning.”

Number of Fortune 500 earnings calls mentioning AI, 2018–23
Source: Quid, 2023 | Chart: 2024 AI Index report

Number of earnings calls

400

394

300

200

100

0

2018

2019

2020

2021

2022

2023
Figure 4.4.25

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4.4 Corporate Activity

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Specific Themes
Mentions of AI in Fortune 500 earnings calls wereMentions of generative AI grew from 0.31% in 2022.

associated with a wide range of themes in 2023. The

The next most mentioned theme was investments in AI,

most frequently cited theme, appearing in 19.7% ofexpansion of AI capabilities, and AI growth initiatives

all earnings calls, was generative AI (Figure 4.4.26).

(15.2%), followed by company/brand AIs (7.6%).

Themes of AI mentions in Fortune 500 earnings calls, 2018 vs. 2023
Source: Quid, 2023 | Chart: 2024 AI Index report

Generative AI

19.73% (+6351%)
0.31%

Investments in AI, expansion ofcapabilities, and growth initiatives

15.21% (+5%)
14.53%
7.64% (-2%)
7.80%

Company/brand AIs

7.36% (-25%)

Data storage and management

9.79%
5.68% (+49%)

Advertising and marketing

3.82%

AI and machine learning for enhanced
customer experience and growth

4.96% (-32%)
7.34%
4.76% (-8%)
5.20%

Process automation

4.36% (-51%)

Data center GPU

8.87%
3.84%

Large language models

3.56% (-31%)
5.20%

Cloud platforms

2.76% (-52%)

Deep learning

5.81%
2.76% (-40%)
4.59%

Healthcare and medical practices
Business integration

2.36% (-9%)
2.60%

Revenue growth

2.28% (-21%)
2.91%

Data processing

2.24% (+33%)
1.68%
2.20% (-65%)

Data analytics

6.27%

Edge intelligence

1.88% (-35%)
2.91%

Adobe experience

1.80% (-9%)
1.99%

Customer support

1.76% (+28%)
1.38%

Autonomous vehicles
Digital transformation
0%

2023

1.48% (-70%)

2018

4.89%
1.36% (-36%)
2.14%

2%

4%

6%

8%

10%

12%

14%

Theme mentioned (% of total)

Chapter 4 Preview

16%

18%

20%

22%

Figure 4.4.26

66

Chapter 4: Economy
4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Highlight:

Projecting AI’s Economic Impact
In 2023, some newly published analyses aimed todollar amounts. The report projects that the high-project and better understand the future economic

tech industry could see its revenue increase by

impact of AI. A recent McKinsey report examined

4.8% to 9.3%, corresponding to an additional $240

the degree to which generative AI might impact

billion to $460 billion, as a result of generative AI.

revenues across industries. Figure 4.4.27 features

Banking, pharmaceuticals and medical products,

the projected impact range per industry, both asand education are other industries estimated toa percentage of total industry revenue and in total

grow due to the adoption of generative AI.

Anticipated impact of generative AI on revenue by industry, 2023
Source: McKinsey & Company, 2023 | Chart: 2024 AI Index report

4.80%

High tech
2.80%

4.70%

2.60%

4.50%

BankingPharmaceuticals andmedical products
Education
Telecommunications
Healthcare
Insurance
Media and entertainment
Advanced manufacturing
Consumer packaged goods
Advanced electronics andsemiconductors
0%

2.20%

240

60

3.70%

60

230

100

3.20%

150

260

50 70

3.10%

80

130

1.40% 2.40%

170

1.40%2.30%

160

1.30% 2.30%

2%

340

110
120

1.80% 2.80%
1.80%

460

200

4.00%

2.30%
1.80%

9.30%

100

4%

6%

% of total industry revenue

8%

10%

0

100

290
270

170

200

300

400

Total industry revenue (in billions U.S. dollars)

Figure 4.4.27

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4.4 Corporate Activity

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Highlight:

Projecting AI’s Economic Impact (cont’d)
The McKinsey survey cited above, the “State ofsize would decrease (Figure 4.4.28). Only 15% felt

AI in 2023,” asked business professionals aboutthat generative AI would lead to increases in thetheir expectations of AI’s impact on organizational

number of employees. There were also widespread

workforces in the next three years. Although a

predictions that AI would lead to significant

large proportion (30%) expected little to no change

employee reskilling.

in the number of employees, 43% felt that staff
Expectations about the impact of AI on organizations’ workforces in the next 3 years, 2023
Source: McKinsey & Company Survey, 2023 | Chart: 2024 AI Index report

12%

Increase by >20%

3%

Increase by 11–20%

4%

Increase by 3–10%

Don’t know

8%

Little or no change

30%

Decrease by 3–10%

Decrease by 11–20%

Decrease by >20%
0%

Share of employees expected to be reskilled

Change in the number of employees

Don’t know

25%

10%

>20%

38%

11–20%

18%

6–10%

17%

≤5%

8%
10%

8%

20%

30%

% of respondents

40%

0%

20%

10%

20%

30%

40%

% of respondents

Figure 4.4.28

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4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Highlight:

Projecting AI’s Economic Impact (cont’d)
Perspectives differ on the anticipated effect of

supply chain management (45%), and HR (41%),

generative AI on employment per business function.

are especially likely, according to respondents, toCertain functions, like service operations (54%),

experience decreasing employment (Figure 4.4.29).

Anticipated e ect of generative AI on number of employees in the next 3 years by business function, 2023
Source: McKinsey & Company Survey, 2023 | Chart: 2024 AI Index report

Decrease

Little or no change

Product and/or service development

30%

Risk

31%

Increase

Don’t know

35%

20%

37%

15%

20%

12%

25%

10%

Strategy and corporate nance37%

Marketing and sales

39%

33%

Manufacturing

40%

33%

12%

15%

HR

41%

30%

17%

11%

Supply chain management

28%

45%

Service operations
0%

17%

32%
54%

20%

23%
40%

60%
% of respondents

12%

14%

9%

12%

10%

80%

100%

Figure 4.4.29

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4.4 Corporate Activity

Artificial IntelligenceIndex Report 2024

Highlight:

Projecting AI’s Economic Impact (cont’d)Finally, a Goldman Sachs investment report

Although the report projects that many countries will

released in 2023 projects that, globally, AI could

benefit from AI-driven productivity growth, certain

lead to productivity growth over 10-year periods

geographic areas, like Hong Kong, Israel, and Japan,

ranging between 1.0% and 1.5% (Figure 4.4.30).

are especially well-positioned.

Estimated impact of AI adoption on annual productivity growth over a ten-year period
Source: Goldman Sachs Global Investment Research, 2023 | Chart: 2024 AI Index report

Percentage points

1.50

Global average

1.00

0.50

0.00

Ho

ng

Isr

ae

Ko
n

g

l

Ja

pa

n

Un
ite
d

Sw
ed

Kin

gd

en

om

Un
ite
d

Sin
Ch
Ar
ge
ile
ga
nti
po
Sta
na
re
tes

So
uth

Bra
z

il

Ko
re

a

Ma
lay
sia

Ta
iw
an

So
uth

Af

Me
xic
o

ric
a

Figure 4.4.30

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70Chapter 4: Economy
4.5 Robot Installations

Artificial IntelligenceIndex Report 2024

The deployment of robots equipped with AI-based software technologies offers a window into the real-world
application of AI-ready infrastructure. This section draws on data from the International Federation of Robotics (IFR),a nonprofit organization dedicated to advancing the robotics industry. Annually, the IFR publishes the World Robotics
Reports, which track global robot installation trends.12

4.5 Robot Installations
Aggregate Trends
The following section includes data on the installation

Figure 4.5.1 reports the total number of industrial

and operation of industrial robots, which are definedrobots installed worldwide by year. In 2022, industrial

as an “automatically controlled, reprogrammable,

robot installations increased slightly, with 553,000

multipurpose manipulator, programmable in threeunits marking a 5.1% increase from 2021. This growth

or more axes, which can be either fixed in place orreflects more than a threefold rise in installations

mobile for use in industrial automation applications.”

since 2012.

Number of industrial robots installed in the world, 2012–22
Source: International Federation of Robotics (IFR), 2023 | Chart: 2024 AI Index report

Number of industrial robots installed (in thousands)

553

500

400

300

200

100

0

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022
Figure 4.5.1

12 Due to the timing of the IFR report, the most recent data is from 2022. Every year, the IFR revisits data collected for previous years and will occasionally update the data if more accurate
figures become available. Therefore, some of the data reported in this year’s report might differ slightly from data reported in previous years.

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The global operational stock of industrial robots reached 3,904,000 in 2022, up from 3,479,000 in 2021 (Figure
4.5.2). Over the past decade, both the installation and utilization of industrial robots have steadily increased.

Operational stock of industrial robots in the world, 2012–22
Source: International Federation of Robotics (IFR), 2023 | Chart: 2024 AI Index report

3,904

4,000

Number of industrial robots (in thousands)

3,500

3,000

2,500

2,000

1,500

1,000

500

0

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022
Figure 4.5.2

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Artificial IntelligenceIndex Report 2024

Industrial Robots: Traditional vs.
Collaborative Robots

collaborative robots due to their safety, flexibility,
scalability, and ability to learn iteratively.

There is a distinction between traditional robots,
which operate for humans, and collaborative robots,
designed to work alongside them. The robotics

Figure 4.5.3 reports the number of industrial robots
installed in the world by type. In 2017, collaborative
robots accounted for just 2.8% of all new industrial

community is increasingly enthusiastic about

robot installations. By 2022, the number rose to 9.9%.

Number of industrial robots installed in the world by type, 2017–22
Source: International Federation of Robotics (IFR), 2023 | Chart: 2024 AI Index report553

Traditional
526

Collaborative
Number of industrial robots installed (in thousands)

55

42

500

424
400

400

387

389

300

200

389

405

2017

2018

366

363

2019

2020

484

498

2021

2022

100

0

Figure 4.5.3

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4.5 Robot Installations

Artificial IntelligenceIndex Report 2024

By Geographic Area
Country-level data on robot installations cantimes more than Japan’s 50,400 and 7.4 times moresuggest which nations prioritize robot integration

than the United States’ 39,500 (Figure 4.5.4). South

into their economies. In 2022, China led the world

Korea and Germany followed with 31,170 and 25,600

with 290,300 industrial robot installations, 5.8

installations, respectively.

Number of industrial robots installed by country, 2022
Source: International Federation of Robotics (IFR), 2023 | Chart: 2024 AI Index reportChina

290.30

Japan

50.40

United States

39.50

South Korea

31.70

Germany

25.60

Italy

11.50

Taiwan7.80

France7.40

Mexico6.00

Singapore

5.90

India

5.40

Spain

3.80

Turkey3.70

Thailand

3.30

Canada

3.20

0

30

60

90

120
150
180
210
Number of industrial robots installed (in thousands)

240

270

300
Figure 4.5.4

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4.5 Robot Installations

Artificial IntelligenceIndex Report 2024

Since surpassing Japan in 2013 as the leading installer of industrial robots, China has significantly widened thegap with the nearest country. In 2013, China’s installations accounted for 20.8% of the global total, a share thatrose to 52.4% by 2022 (Figure 4.5.5).

Number of new industrial robots installed in top 5 countries, 2012–22
Source: International Federation of Robotics (IFR), 2023 | Chart: 2024 AI Index report

Number of industrial robots installed (in thousands)

300

290, China

250

200

150

100

50, Japan
40, United States
32, South Korea
26, Germany

50

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022
Figure 4.5.5

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75Chapter 4: Economy
4.5 Robot Installations

Artificial IntelligenceIndex Report 2024

Since 2021, China has installed more industrial robots than the rest of the world combined, with the gap widening
further in the last year (Figure 4.5.6). This increasing gap underscores China’s growing dominance in industrial
robot installations.

Number of industrial robots installed (China vs. rest of the world), 2016–22
Source: International Federation of Robotics (IFR), 2023 | Chart: 2024 AI Index report

Number of industrial robots installed (in thousands)

300

290, China
263, Rest of the world

250

200

150

100

50

0

2016

2017

2018

2019

2020

2021

2022
Figure 4.5.6

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76Chapter 4: Economy
4.5 Robot Installations

Artificial IntelligenceIndex Report 2024

According to the IFR report, most countries reported an annual increase in industrial robot installations from 2021
to 2022 (Figure 4.5.7). The countries with the highest growth rates include Singapore (68%), Turkey (22%), andMexico (13%). Canada (-24%), Taiwan (-21%), Thailand (-18%), and Germany (-1%) reported installing fewer robots
in 2022 than in 2021.

Annual growth rate of industrial robots installed by country, 2021 vs. 2022
Source: International Federation of Robotics (IFR), 2023 | Chart: 2024 AI Index reportSingapore

68%

Turkey22%

Mexico13%

France

13%

Spain

10%

United States

10%

Japan

9%

Italy

8%

China

5%

India

4%

South Korea

1%

Germany-1%

Thailand-18%

TaiwanCanada

-21%
-24%

−20%

−10%

0%

10%
20%
30%
40%
Annual growth rate of industrial robots installed

50%

60%

70%
Figure 4.5.7

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4.5 Robot Installations

Artificial IntelligenceIndex Report 2024

Country-Level Data on Service Robotics

cleaning. In 2022, more service robots were installed

Another important class of robots are service robots,for every application category than in 2021, with thewhich the ISO defines as a robot “that performs

exception of medical robotics (Figure 4.5.8). Moreuseful tasks for humans or equipment excluding

specifically, the number of service robots installed inindustrial automation applications.” Such robots can,hospitality and in transportation and logistics increased

for example, be used in medicine and professional

2.3 and 1.4 times, respectively.

13

Number of professional service robots installed in the world by application area, 2021 vs. 2022
Source: International Federation of Robotics (IFR), 2023 | Chart: 2024 AI Index report

8

Agriculture

725

Hospitality

11

9

Medical robotics

10

7

Professional cleaning

2022

6

2021
86

Transportation and logistics

60
0

10

20

30
40
50
60
Number of professional service robots installed (in thousands)

70

80

90

Figure 4.5.8

13 A more detailed definition can be accessed here.

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4.5 Robot Installations

Artificial IntelligenceIndex Report 2024

As of 2022, the United States leads in professional

and France also have significant numbers of robot

service robot manufacturing, with approximately

manufacturers, with 85,000, 72,000, and 53,000,

2.06 times more manufacturers than China, therespectively. In most surveyed countries, the majority

next leading nation (Figure 4.5.9). Germany, Japan,of these manufacturers are established incumbents.

Number of professional service robot manufacturers in top countries by type of company, 2022
Source: International Federation of Robotics (IFR), 2023 | Chart: 2024 AI Index report

Number of professional service robot manufacturers

218
200

150

100

204

106
Startups
85

Incumbents
Unknown

72

50

53

99
74

38

35

33

36

32

28

CanadaSwitzerland

Russia

67
48

50

27
26

1

0

52

United States

China

Germany

Japan

FranceSouth Korea

United Kingdom
Figure 4.5.9

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4.5 Robot Installations

Artificial IntelligenceIndex Report 2024

Sectors and Application Types

installations with 157,000 units, closely followed by

Figure 4.5.10 shows the number of industrial robots

the automotive sector with 136,000. Both sectors haveinstalled in the world by sector from 2020 to 2022.

seen continuous growth in industrial robot installations

Globally, the electrical/electronics sector led in robot

since 2020.

Number of industrial robots installed in the world by sector, 2020–22
Source: International Federation of Robotics (IFR), 2023 | Chart: 2024 AI Index report61

All others

55
37

136

Automotive

117
83
157

Electrical/electronics

143
112
15
15

Food
12

66
68

Metal and machinery
41
24
25

Plastic and chemical products
19

2022
2021

94

Unspeci ed

102

2020

85

0

20

40

60
80
100
120
Number of industrial robots installed (in thousands)

140

160
Figure 4.5.10

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4.5 Robot Installations

Artificial IntelligenceIndex Report 2024

Figure 4.5.11 shows the number of industrial robots

welding (87,000) and 4.4 times more than for assembly

installed in the world by application from 2020 to(61,000). Except for processing, every application

2022. Data suggests that handling is the predominantcategory witnessed an increase in robot installations inapplication. In 2022, 266,000 industrial robots were2022 compared to 2020.

installed for handling tasks, 3.1 times more than for

Number of industrial robots installed in the world by application, 2020–22
Source: International Federation of Robotics (IFR), 2023 | Chart: 2024 AI Index report70

All others/unspeci ed

78
60

61
63

Assembling
47
35
32
32

Clean room

28

Dispensing

11
8
266

Handling

241
169
5
7
5

Processing

2022
2021
2020

87

Welding

94
68

0

20

40

60

80

100
120
140
160
180
200
Number of industrial robots installed (in thousands)

220

240

260

280

Figure 4.5.11

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China vs. United States
Figure 4.5.12 illustrates the number of industrial robots installed across various sectors in China over the past three
years. In 2022, the leading sectors for industrial robot installations in China were electrical/electronics (100,000),
automotive (73,000), and metal and machinery (31,000).

Number of industrial robots installed in China by sector, 2020–22
Source: International Federation of Robotics (IFR), 2023 | Chart: 2024 AI Index report73

All others/unspeci ed

75
51

73

Automotive

58
30
100

Electrical/electronics

93
66
5

Food

4
3
31

Metal and machinery

37
19
1
1
1

Pharma/cosmetics

2022
2021

6
6

Rubber and plastics

2020

5

0

10

20

30

40
50
60
70
Number of industrial robots installed (in thousands)

80

90

100
Figure 4.5.12

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In 2022, the U.S. automotive industry led in industrial robot installations with 14,500 units, significantly exceedingits 2021 figure (Figure 4.5.13). Except for the electronics sector, every other sector saw fewer robot installations in
2022 than in 2021.

Number of industrial robots installed in the United States by sector, 2020–22
Source: International Federation of Robotics (IFR), 2023 | Chart: 2024 AI Index report

5.80

All others

5.30
2.60
14.50

Automotive

9.90
10.50
3.70

Electrical/electronics

3.00
3.70
2.40

Food

3.40

2.70
3.90
4.20

Metal and machinery
2.30
3.10

Plastic and chemical products

3.60
2.70

Unspeci ed
0

2022
2021

6.20
6.60
6.30

2

3

5
6
8
9
11
Number of industrial robots installed (in thousands)

2020
12

14

15
Figure 4.5.13

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AppendixAcknowledgments
The AI Index would like to acknowledge James da Costa
for his work collecting information on significant AIrelated economic events, and Emma Williamson for her
work collecting data from the Stack Overflow survey.

International Federation of
Robotics (IFR)

removes duplicates, and extracts data from 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 inthe labor market because they allow for a detailed,real-time look at the skills employers seek. To assess
the representativeness of job postings data, Lightcast
conducts a number of analyses to compare theData presented in the Robot Installations section was

distribution of job postings to the distribution ofsourced from the “World Robotics 2023” report.

official government and other third-party sources inLightcast

the United States. The primary source of government
data on U.S. job postings is the Job Openings

Prepared by Cal McKeever, Julia Nitschke, andand Labor Turnover Survey (JOLTS) program,

Layla O’Kane

conducted by the Bureau of Labor Statistics. BasedLightcast delivers job market analytics that empower

on comparisons between JOLTS and Lightcast, theemployers, workers, and educators to make datadriven 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, suchas what jobs are most in demand, the specific skills
employers need, and the career directions that offer
the highest potential for workers. For more information,
visit www.lightcast.io.

Job Postings Data
To support these analyses, Lightcast mined its datasetof millions of job postings collected since 2010.
Lightcast collects postings from over 51,000 online job
sites to develop a comprehensive, real-time portrait

labor market demand captured by Lightcast data
represents over 99% of the total labor demand. Jobs
not posted online are usually in small businesses (theclassic example being the “Help Wanted” sign in therestaurant window) and union hiring halls.

Measuring Demand for AI
In order to measure the demand by employers of
AI skills, Lightcast uses its skills taxonomy of over
30,000 skills. The list of AI skills from Lightcast data
are shown below, with associated skill clusters. Whilesome 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 wasconsidered an AI job if it mentioned any of these skillsin the job text.

of labor market demand. It aggregates job postings,

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Artificial Intelligence: AI/ML Inference, AIOps

Natural Language Understanding, Optical Character(Artificial Intelligence for IT Operations), Applications

Recognition (OCR), Screen Reader, Semanticof Artificial Intelligence, Artificial General Intelligence,

Analysis, Semantic Parsing, Semantic Search,

Artificial Intelligence, Artificial Intelligence

Sentiment Analysis, Seq2Seq, Speech Recognition,

Development, Artificial Intelligence Markup Language

Speech Recognition Software, Speech Synthesis,(AIML), Artificial Intelligence Systems, Azure Cognitive

Statistical Language Acquisition, Text Mining, Text-

Services, Baidu, Cognitive Automation, Cognitive

to-Speech, Tokenization, Voice Assistant Technology,

Computing, Computational Intelligence, Cortana,

Voice Interaction, Voice User Interface, Word

Ethical AI, Expert Systems, Explainable AI (XAI),

Embedding, Word2Vec Models, fastText

IPSoft Amelia, Intelligent Control, Intelligent Systems,
Interactive Kiosk, Knowledge Engineering, KnowledgeBased Configuration, Knowledge-Based Systems,
Multi-agent Systems, Open Neural Network Exchange(ONNX), OpenAI Gym, Operationalizing AI, Reasoning
Systems, Watson Conversation, Watson Studio, Weka

Neural Networks: Apache MXNet, Artificial
Neural Networks, Autoencoders, Caffe2, Chainer(Deep Learning Framework), Convolutional Neural
Networks, Cudnn, Deep Learning, Deep Learning
Methods, Deeplearning4j, Evolutionary Acquisitionof Neural Topologies, Fast.ai, Keras (Neural Network

Autonomous Driving: Advanced Driver Assistance

Library), Long Short-Term Memory (LSTM),

Systems, Autonomous Cruise Control Systems,

OpenVINO, PaddlePaddle, Recurrent Neural Network

Autonomous System, Autonomous Vehicles, Guidance(RNN), TensorFlow

Navigation and Control Systems, Light Detection andRanging (LiDAR), OpenCV, Path Analysis, Path Finding,
Remote Sensing, Unmanned Aerial Systems (UAS)

Machine Learning: AdaBoost (Adaptive Boosting),
Adversarial Machine Learning, Apache MADlib,
Apache Mahout, Apache SINGA, Apache Spark,

Generative Artificial Intelligence: ChatGPT,

Association Rule Learning, Automated Machine

Generative Adversarial Networks, Generative Artificial

Learning, Autonomic Computing, AWS SageMaker,

Intelligence, Large Language Modeling, Prompt

Azure Machine Learning, Boosting, CHi-Squared

Engineering, Variational Autoencoders

Automatic Interaction Detection (CHAID),

Natural Language Processing (NLP): AI Copywriting,
ANTLR, Amazon Textract, Apache OpenNLP, BERT(NLP Model), Chatbot, Computational Linguistics,
Conversational AI, Dialog Systems, Fuzzy Logic,
Handwriting Recognition, Hugging Face (NLP
Framework), Hugging Face Transformers, Intelligent
Agent, Intelligent Virtual Assistant, Kaldi, Language
Model, Latent Dirichlet Allocation, Lexalytics, MachineTranslation, Microsoft LUIS, Natural Language
Generation, Natural Language Processing, Natural
Language Programming, Natural Language Toolkits,

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Classification and Regression Tree (CART), Cluster
Analysis, Collaborative Filtering, Confusion Matrix,
Cyber-Physical Systems, Dask (Software), DataClassification, Dbscan, Decision Models, Decision
Tree Learning, Dimensionality Reduction, Dlib (C++
Library), Ensemble Methods, Feature Engineering,
Feature Extraction, Feature Learning, Feature
Selection, Gaussian Process, Genetic Algorithm,
Google AutoML, Gradient Boosting, H2O.ai, Hidden
Markov Model, Hyperparameter Optimization,
Inference Engine, K-Means Clustering, Kernel

85Chapter 4: Economy
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Methods, Kubeflow, Loss Functions, Machine Learning,
Machine Learning Algorithms, Machine Learning
Methods, Machine Learning Model Monitoring And

LinkedIn
Prepared by Murat Erer, Carl Shan, and Akash Kaura

Evaluation, Machine Learning Model Training, Markov

Country Sample

Chain, Matrix Factorization, Meta Learning, Microsoft

Included countries represent a select sample of

Cognitive Toolkit (CNTK), MLflow, MLOps (Machine

eligible countries with at least 40% labor force

Learning Operations), mlpack (C++ Library), ModelOps,

coverage by LinkedIn and at least 10 AI hires in

Naive Bayes Classifier, Perceptron, Predictive

any given month. India, despite not reaching 40%

Modeling, PyTorch (Machine Learning Library), PyTorchcoverage, was included in this sample because of its

Lightning, Random Forest Algorithm, Recommenderincreasing importance in the global economy.

Systems, Reinforcement Learning, Scikit-Learn (Python

Skills (AI Engineering and AI Literacy skills)

Package), Semi-supervised Learning, Soft Computing,Sorting Algorithm, Supervised Learning, Support
Vector Machine, Test Datasets, Theano (Software),Torch (Machine Learning), Training Datasets, Transfer
Learning, Transformer (Machine Learning Model),Unsupervised Learning, Vowpal Wabbit, Xgboost
Robotics: Advanced Robotics, Bot Framework,
Cognitive Robotics, Motion Planning, Nvidia Jetson,
Robot Framework, Robot Operating Systems, Robotic
Automation Software, Robotic Liquid Handling
Systems, Robotic Programming, Robotic Systems,
Servomotor, SLAM Algorithms (Simultaneous
Localization and Mapping)
Visual Image Recognition: 3D Reconstruction,
Activity Recognition, Computer Vision, Contextual
Image Classification, Digital Image Processing, Eye

LinkedIn members self-report their skills on theirLinkedIn profiles. Currently, more than 41,000
distinct, standardized skills are identified by LinkedIn.
These have been coded and classified by taxonomists
at LinkedIn into 249 skill groupings, which are the skill
groups represented in the dataset.
Skill groupings are derived by expert taxonomists
through a similarity-index methodology thatmeasures skill composition at the industry level.
LinkedIn’s industry taxonomy and their corresponding
NAICS codes can be found here.
This year LinkedIn made updates to the AI skills list
and categorized them into “AI Engineering” and “AI
Literacy” skills. See “AI Skills List Update Comparedto Last Year” section for more details.

Tracking, Face Detection, Facial Recognition, Gesture

Skills Genome

Recognition, Image Analysis, Image Matching, Image

For any entity (occupation or job, country, sector,

Recognition, Image Segmentation, Image Sensor,etc.), the skill genome is an ordered list (a vector) of

Imagenet, Machine Vision, Motion Analysis, Object

the 50 “most characteristic skills” of that entity. These

Recognition, OmniPage, Pose Estimation

most characteristic skills are identified using a TF-IDF
algorithm to identify the most representative skills of
the target entity, while down-ranking ubiquitous skills
that add little information about that specific entity(e.g., Microsoft Word).

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TF-IDF is a statistical measure that evaluates how

members’ profiles. If four of the skills that engineers

representative a word (in this case, a skill) is to a selected

possess belong to the AI skill group, this measureentity). This is done by multiplying two metrics:

indicates that the penetration of AI skills is estimated

1. The term frequency of a skill in an entity (“TF”).
2. The logarithmic inverse entity frequency of theskill 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 aword is.

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
not sector- or country-specific. These occupations
are further standardized into approximately

So, if the skill is very common across LinkedIn entities,

3,600 occupation representatives. Occupation

and appears in many job or member descriptions, therepresentatives group occupations with a common

IDF will approach 0. If, on the other hand, the skill isrole and specialty, regardless of seniority.

unique to specific entities, the IDF will approach 1.

AI Jobs or Occupations

Details are available at LinkedIn’s Skills Genome andLinkedIn–World Bank Methodology note.

AI Skills Penetration
The aim of this indicator is to measure the intensity ofAI skills in an entity (in a particular country, industry,
gender, etc.) through the following methodology:• Compute frequencies for all self-added skills by
LinkedIn members in a given entity (occupation,industry, etc.) in 2015–2023.
• Re-weight skill frequencies using a TF-IDF model
to get the top 50 most representative skills inthat entity. These 50 skills compose the “skill
genome” of that entity.
• Compute the share of skills that belong to
the AI skill group out of the top skills in theselected entity.
Interpretation: The AI skill penetration rate signals
the prevalence of AI skills across occupations, or theintensity with which LinkedIn members utilize AI skillsin their jobs. For example, the top 50 skills for theoccupation of engineer are calculated based on the

An “AI” job is an occupation representativethat requires AI skills to perform the job. Skills
penetrations are 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, and Computer
Vision Engineer.

AI Talent
A LinkedIn member is considered AI talent if they
have explicitly added AI skills to their profile
and/or they are occupied in an AI occupation
representative. The counts of AI talent are used to
calculate talent concentration metrics. For example,
to calculate the country-level AI talent concentration,
we use the counts of AI talent at the country levelvis-a-vis the counts of LinkedIn members in therespective countries. Note that concentration metrics
may be influenced by LinkedIn coverage in thesecountries and should be utilized with caution.

weighted frequency with which they appear in LinkedIn

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Relative AI Skills Penetration

countries’ male/female AI skill penetrations to the

To allow for skills penetration comparisons acrossglobal average of the same gender. Since the global

countries, the skills genomes are calculated and aaverages are distinct for each gender, this metricrelevant benchmark is selected (e.g., global average).

should only be used to compare country rankings

A ratio is then constructed between a country’s andwithin each gender, and not for cross-gender

the benchmark’s AI skills penetrations, controllingcomparisons within countries.

for occupations.

Interpretation: A country’s AI skills penetration forInterpretation: A country’s relative AI skills penetration

women of 1.5 means that female members in thatof 1.5 indicates that AI skills are 1.5 times as frequent

country are 1.5 times more likely to list AI skills thanas in the benchmark, for an overlapping set ofthe average female member in all countries pooled

occupations.

together across the same set of occupations that

Global Comparison

exist in the country/gender combination.

For cross-country comparison, we present the relative

Global Comparison: Across Genders

penetration rate of AI skills, measured as the sum of

The “Relative AI Skills Penetration Across Genders”

the penetration of each AI skill across occupations

metric allows for cross-gender comparisons withinin a given country, divided by the average global

and across countries globally, since we compare thepenetration of AI skills across the overlapping

countries’ male/female AI skill penetrations to theoccupations in a sample of countries.

same global average regardless of gender.

Interpretation: A relative penetration rate of 2 means

Relative AI Talent Hiring Rate YoY Ratio

that the average penetration of AI skills in that country• L inkedIn Hiring Rate or Overall Hiring

is two times the global average across the same set

Rate is a measure of hires normalized byof occupations.

LinkedIn membership. It is computed as the

Global Comparison: By Industry

percentage of LinkedIn members who added

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

a new employer in the same period the job
began, divided by the total number of LinkedIn
members in the corresponding location.
• A
 I Hiring Rate is computed following theoverall hiring rate methodology, but only
considering members classified as AI talent.
• R elative AI Talent Hiring Rate YoY Ratio isthat sector.

the year-over-year change in AI Hiring Rate

Global Comparison: By Gender

relative to Overall Hiring Rate in the same

The “Relative AI Skills Penetration by Gender”
metric provides a cross-country comparison of AI
skill penetrations within each gender, comparing
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country. For each month, we first calculate AI
Hiring rate in the country, then divide AI Hiring
Rate by Overall Hiring Rate in that country,

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then calculate YoY change of this ratio, and thenand destination countries, are normalized based ontake the 12-month moving average using the last

LinkedIn membership in country A at the end of each

12 months.

year and multiplied by 10,000. Hence, this metricInterpretation: In 2023 in India, the ratio of AI talenthiring relative to overall hiring has grown 16.8% year
over year.

AI Talent Migration
Data on migration comes from the World Bank Group–
LinkedIn “Digital Data for Development” partnership(please see Zhu et al. [2018]).
LinkedIn migration rates are derived from the selfidentified locations of LinkedIn member profiles. For
example, when a LinkedIn member updates his or
her location from Paris to London, this is counted asa migration. Migration data is available from 2019
onward. LinkedIn data provides insights into countries
on the AI Talent gained or lost due to migration trends.
AI Talent migration is considered for all memberswith AI Skills/holding AI jobs at time t for country A
as the country of interest and country B as the sourceof inflows and destination for outflows. Thus, net AI
Talent migration between country A and country B—for
country A—is calculated as follows:

indicates relative talent migration from all countries toand from country A.

AI Skills List Update Compared to Last Year
1. LinkedIn introduced “AI Literacy” skills.
a. The following skills were added to the list andcategorized as “AI Literacy” skills: ChatGPT,DALL-E, GPT-3, GPT-4, Generative Art, Github
Copilot, Google Bard, Midjourney, Prompt
Engineering, and Stable Diffusion.
2. LinkedIn updated the former AI skills list andcategorized them as “AI Engineering” skills:
a. The following skills were excluded fromthe list: Alexa, Common Lisp, Data
Structures, Gaussian 03, Graph Theory, IBM
Watson, Information Retrieval, Jena, Julia(Programming Language), Linked Data, Lisp,
Pandas (Software), Parallel Algorithms, Perl
Automation, Resource Description Framework,
Smalltalk, and dSPACE.
b. The following skills were added to thelist: Apache Spark ML, Applied Machine
Learning, Audio Synthesis, Autoencoders,
Automated Clustering, Automated Feature

Net flows are defined as total arrivals minus departures
within the given time period. LinkedIn membershipvaries considerably between countries, which makes
interpreting absolute movements of members fromone country to another difficult. To compare migration
flows between countries fairly, migration flows arenormalized for the country of interest. For example,
if country A is the country of interest, all absolute net
flows into and out of country A, regardless of origin

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Engineering, Automated Machine Learning(AutoML), Autoregressive Models, Chatbot
Development, Chatbots, Concept Drift
Adaptation, Conditional Generation,
Conditional Image Generation, Decision
Trees, Deep Convolutional Generative
Adversarial Networks (DCGAN), Deep Neural
Networks (DNN), Generative AI, Generative
Adversarial Imitation Learning, Generative
Adversarial Networks (GANs), Generative89

Chapter 4: Economy
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Design Optimization, Generative Flow Models,

PostgreSQL database delivery, and so on.

Generative Modeling, Generative Neural

Quid applies best-in-class AI and NLP to reveal

Networks, Generative Optimization, Generative

hidden patterns in large datasets, enabling users

Pre-training, Generative Query Networks

to make data-driven decisions accurately, quickly,(GQNs), Generative Replay Memory, Generativeand efficiently.

Synthesis, Google Cloud AutoML, Graph
Embeddings, Graph Networks, Hyperparameter
Optimization, Hyperparameter Tuning, Image
Generation, Image Inpainting, Image Synthesis,
Image-to-Image Translation, Large Language
Models (LLM), MLOps, Machine Learning
Algorithms, Machine Translation, Meta-learning,
Model Compression, Model Interpretation,
Model Training, Music Generation, Neural
Network Architecture Design, Predictive
Modeling, Probabilistic Generative Models,
Probabilistic Programming, Random Forest,
Recurrent Neural Networks (RNN), Responsible
AI, Style Transfer, StyleGAN, Synthetic
Data Generation, Text Generation, Text-toImage Generation, Time Series Forecasting,Transformer Models, Variational Autoencoders,
Variational Autoencoders (VAEs), Video
Generation, and k-means clustering.

Search, Data Sources, and Scope
Over 8 million global public and private company
profiles from multiple data sources are indexed tosearch across company descriptions, while filteringand including metadata ranging from investment
information to firmographic information, such as
founded year, HQ location, and more. Company
information is updated on a weekly basis. The Quidalgorithm reads a large amount of text data fromeach 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,
and institutions that Quid identifies, and the othermetadata that is put into the software.

Data
Companies

Quid

Organization data is embedded from Capital IQ and

Quid Insights prepared by Bill Valle and Heather English

organizations (private, public, operating, operating as

Quid uses its own in-house LLM and other smart
search features, as well as traditional Boolean query,to search for focus areas, topics, and keywords withinmany datasets: social media, news, forums and blogs,
companies, patents, as well as other custom feeds ofdata (e.g., survey data). Quid has many visualization
options and data delivery endpoints, including network

Crunchbase. These companies include all types ofa 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, andinstitutions both within and outside the United States.
Some data is simply unreachable—for instance, wheninvestors’ names or funding amounts are undisclosed.

graphs based on semantic similarity, in-platform

Quid embeds Capital IQ data as a default anddashboarding capabilities, as well as programmatic

adds in data from Crunchbase for the data points

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that are not captured in Capital IQ. This not only

AI” OR “generative artificial intelligence”) for

yields comprehensive and accurate data on all global

companies that have received over $1.5M fororganizations, but it also captures early-stage startups

the last 10 years (January 1, 2013, to December

and funding events data. Company information is31, 2023).

updated on a weekly basis.
Earnings Calls

Target Event Definitions• P rivate investments: A private placement is a

Quid leverages earnings call transcript data embedded

private sale of newly issued securities (equityfrom Seeking Alpha. For this report, Quid has analyzed

or debt) by a company to a selected investor or

mentions of AI-related keywords across all earnings calla selected group of investors. The stakes thattranscripts from Fortune 500 companies from Januarybuyers take in private placements are often

2018 through December 2023. New earnings callminority stakes (under 50%), although it istranscript data is updated in Quid on the 1st and 15th ofpossible to take control of a company throughevery month.

a private placement as well, in which case the

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
amount, operating status, organization type (private/public), and founding year. Quid then visualizes thesecompanies by semantic similarity. If there are more than
7,000 companies from the search result, Quid selects
the 7,000 most relevant companies for visualization
based on the language algorithm.
Boolean search: “artificial intelligence” or “AI” or“machine learning” or “deep learning”

Companies• Global AI and ML companies that have received
investments (private, IPO, M&A) from January 1,
2013, to December 31, 2023.

private placement would be a majority stake
investment.
• M inority investment: These refer to minority
stake acquisitions in Quid, which take placewhen the buyer acquires less than 50% of
the existing ownership stake in entities, asset
products, and business divisions.
• M
 &A: This refers to a buyer acquiring morethan 50% of the existing ownership stake inentities, asset products, and business divisions.

McKinsey & Company
Data used in the Corporate Activity–Industry
Adoption section was sourced from the McKinsey
Global Survey “The State of AI in 2023: Generative
AI’s Breakout Year.”
The online survey was in the field April 11, 2023, to• Global AI and ML companies that have received

April 21, 2023, and garnered responses from 1,684

over $1.5M for the last 10 years (January 1, 2013,

participants representing the full range of regions,

to December 31, 2023).

industries, company sizes, functional specialties,• Global data was also pulled for a Generative AI
query (Boolean search: “generative AI” OR “genChapter 4 Preview

and tenures. Of those respondents, 913 said theirorganizations had adopted AI in at least one function

91Artificial IntelligenceIndex Report 2024

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Appendix

and were asked questions about their organization’s AI
use. To adjust for differences in response rates, the data
is weighted by the contribution of each respondent’s
nation to global GDP.
The AI Index also considered data from previous
iterations of the survey. More specifically, the AI index
made use of data from:The State of AI in 2022—and a Half Decade in Review
The State of AI in 2021
The State of AI in 2020
AI Proves Its Worth, But Few Scale Impact (2019)
AI Adoption Advances, But Foundational Barriers
Remain (2018)

Stack Overflow
Data on the use of AI by developers was sourced fromthe 2023 Developer Survey. The survey was conductedfrom May 8, 2023, to May 19, 2023, and incorporates
the insights of 89,184 software developers from 185
countries around the world.

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Appendix

Works Cited
Brynjolfsson, E., Li, D. & Raymond, L. R. (2023). Generative AI at Work (Working Paper 31161). National Bureau of Economic
Research. https://doi.org/10.3386/w31161.
Cambon, A., Hecht, B., Edelman, B., Ngwe, D., Jaffe, S., Heger, A., Peng, S., Hofman, J., Farach, A., Bermejo-Cano, M.,
Knudsen, E., Sanghavi, H., Spatharioti, S., Rothschild, D., Goldstein, D. G., Kalliamvakou, E., Cihon, P., Demirer, M., Schwarz, M.
& Teevan, J. (2023). Early LLM-Based Tools for Enterprise Information Workers Likely Provide Meaningful Boosts to Productivity.
https://www.microsoft.com/en-us/research/uploads/prod/2023/12/AI-and-Productivity-Report-First-Edition.pdf.
Choi, J. H., Monahan, A. & Schwarcz, D. (2023). “Lawyering in the Age of Artificial Intelligence.” Minnesota Law Review(forthcoming). https://doi.org/10.2139/ssrn.4626276.
Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K., Sukharevsky, A., Yee, L. & Zemmel, R. (2023). The Economic Potentialof Generative AI: The Next Productivity Frontier. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinseydigital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier.
Chui, M., Yee, L., Hall, B., Singla, A. & Sukharevsky, A. (2023). The State of AI in 2023: Generative AI’s Breakout Year.
McKinsey & Company. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023generative-ais-breakout-year#widespreadhttp://ceros.mckinsey.com/commentary-ai-2023-lareina-ye-desktop.
Dell’Acqua, F. (2022). “Falling Asleep at the Wheel: Human/AI Collaboration in a Field Experiment on HR Recruiters.”
Laboratory for Innovation Science, Harvard Business School, working paper. https://static1.squarespace.com/
static/604b23e38c22a96e9c78879e/t/62d5d9448d061f7327e8a7e7/1658181956291/Falling+Asleep+at+the+Wheel++Fabrizio+DellAcqua.pdf.
Dell’Acqua, F., McFowland, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F. & Lakhani,
K. R. (2023). “Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge
Worker Productivity and Quality.” Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 24-013.
https://doi.org/10.2139/ssrn.4573321.
Hatzius, J., Briggs, J., Kodnani, D. & Pierdomenico, G. (2023). The Potentially Large Effects of Artificial Intelligence on Economic
Growth. Goldman Sachs. https://www.gspublishing.com/content/research/en/reports/2023/03/27/d64e052b-0f6e-45d7967b-d7be35fabd16.html.

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