26 March 2023 | 9:05PM EDT

Global Economics Analyst

The Potentially Large Effects of Artiﬁcial Intelligence on
Economic Growth (Briggs/Kodnani)
The recent emergence of generative artiﬁcial intelligence (AI) raises whether weare on the brink of a rapid acceleration in task automation that will drive labor
cost savings and raise productivity. Despite signiﬁcant uncertainty around thepotential of generative AI, its ability to generate content that is indistinguishable
from human-created output and to break down communication barriers between
humans and machines reﬂects a major advancement with potentially large

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macroeconomic effects.
n

If generative AI delivers on its promised capabilities, the labor market could facesigniﬁcant disruption. Using data on occupational tasks in both the US andEurope, we ﬁnd that roughly two-thirds of current jobs are exposed to somedegree of AI automation, and that generative AI could substitute up to one-fourthof current work. Extrapolating our estimates globally suggests that generative AI
could expose the equivalent of 300mn full-time jobs to automation.

n

The good news is that worker displacement from automation has historically
been offset by creation of new jobs, and the emergence of new occupations
following technological innovations accounts for the vast majority of long-run
employment growth. The combination of signiﬁcant labor cost savings, new job
creation, and higher productivity for non-displaced workers raises the possibilityof a productivity boom that raises economic growth substantially, although thetiming of such a boom is hard to predict.

n

We estimate that generative AI could raise annual US labor productivity growthby just under 1½pp over a 10-year period following widespread adoption,although the boost to labor productivity growth could be much smaller or larger

Jan Hatzius+1(212)902-0394 | jan.hatzius@gs.com
Goldman Sachs & Co. LLC

Joseph Briggs+1(212)902-2163 |joseph.briggs@gs.com
Goldman Sachs & Co. LLC

Devesh Kodnani+1(917)343-9216 |devesh.kodnani@gs.com
Goldman Sachs & Co. LLC

Giovanni Pierdomenico+44(20)7051-6807 |giovanni.pierdomenico@gs.com
Goldman Sachs International

depending on the difﬁculty level of tasks AI will be able to perform and how
many jobs are ultimately automated.
n

The boost to global labor productivity could also be economically signiﬁcant, andwe estimate that AI could eventually increase annual global GDP by 7%.
Although the impact of AI will ultimately depend on its capability and adoption
timeline, this estimate highlights the enormous economic potential of generative
AI if it delivers on its promise.

Investors should consider this report as only a single factor in making their investment decision. For Reg AC
certiﬁcation and other important disclosures, see the Disclosure Appendix, or go to
www.gs.com/research/hedge.html.

044ede4967a843888d229f7cd36e62b1nGoldman Sachs

Global Economics Analyst

The Potentially Large Effects of Artiﬁcial Intelligence on Economic Growth
The recent emergence of generative artiﬁcial intelligence (AI) has raised questions
around whether we are at the brink of a rapid acceleration in task automation that will
signiﬁcantly save on labor costs, raise labor productivity, and increase the pace ofeconomic growth. In this Global Economics Analyst, we provide an overview of AI’s
potential macroeconomic impacts, and argue that if AI delivers on its promised
capabilities, it has the potential to signiﬁcantly disrupt labor markets and spur global
productivity growth over the coming decades.

Generative AI, Explained
We ﬁrst discuss the current state of AI development and its key capabilities. Exhibit 1
provides an overview of generative AI, in comparison to its predecessor machinelearning methods, sometimes referred to as narrow or analytical AI. In our assessment,

The ﬁrst two advances are key to expanding the set of tasks that AI can perform, while
the third is key for determining its adoption timeline. Just as the migration fromcommand line programming (e.g., MS-DOS) to graphical user interfaces (e.g., Windows)
enabled the development of programs (e.g., Ofﬁce) that brought the power of thepersonal computer to the masses, the intuitive interfaces of the current generation of AI
technologies could signiﬁcantly increase their speed of adoption. For example, ChatGPT
surpassed 1mn users in just 5 days, the fastest that any company has ever reached thisbenchmark.

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the generative AI technologies currently in focus, such as ChatGPT, DALL-E, and LaMDA,
are distinguished by three main characteristics: 1) their generalized rather thanspecialized use cases, 2) their ability to generate novel, human-like output rather thanmerely describe or interpret existing information, and 3) their approachable interfacesthat both understand and respond with natural language, images, audio, and video.

Goldman Sachs

Global Economics Analyst

Exhibit 1: An Overview of Generative AI
Step 1: Training Data to Neural
Network

Step 2: Neural Network to AI
OutputStep 3: AI Output to Human
InterfacePrevious ML methods:
Models generate statisticalpredictions based on relationships intraining data.

Previous ML methods:
Data trained on specialized
databases for specific purposes (e.g.,
make statistical predictions about
election results, answer questions
about biomedical literature, etc.)

Generative AI:
Models seek to generate new
information that is indistinguishable
from human data. Achieved via theintroduction of a second"discriminative" neural net, which
evaluates the output of the primary"generative" neural net forauthenticity relative to human output.
This "adversarial neural networks"
approach forces the generative
network to revise its output and learn
to consistently "fool" thediscriminative network.

Generative AI:
Data trained on large, generalized
databases (i.e., the entire internet);thus 1) wider range of use cases and2) more easily able to spawn
complementary innovations withspecialized use cases ("deepening ofAI").

Previous ML methods:
Users must use specific code orsyntax to make narrow requests
based on the model's intended
function.
Generative AI:
Use of large language models (LLMs)
allows for advanced natural language
processing (NLP) that incorporates
context in larger swaths of text,
enabling a wider variety of requests
and a more accessible interface forhuman-AI interaction.

Step 4: Applications

Previous ML methods:- Text classification- Facial and image recognition- Statistical prediction and inference
Generative AI:- Answer complex textual questions
with human-like language andstructure- Create original images, graphics,
and video based on user queries- Generate and explain code which
can be used for other programmingand data science applications

Source: Goldman Sachs Global Investment Research

Beyond these changes, exponential increases in the raw computing power available1 hasenabled rapid advances in the complexity of tasks AI can perform and the accuracy atwhich it can perform them. For example, the latest iteration of OpenAI’s GPT model—
GPT-4, released in March 2023, roughly one year after the GPT-3.5 model currently
underlying ChatGPT ﬁnished training—scores 150 points higher on the SAT than its
predecessor, is 40% more likely to produce accurate responses, and can now accept
visual input (rather than just text).2 As Exhibit 2 shows, the algorithms underlying
generative AI had begun to surpass human benchmarks for tasks such as image
classiﬁcation and reading comprehension even before these recent advances.
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Three key changes:
1. Generalized rather than specialized - wider use cases and more complementary innovations
2. Generative rather than descriptive - can produce original results indistinguishable from human output
3. Approachable rather than technical - can interface via complex and contextual natural language

1

Sevilla et al. (2022) show that since the advent of deep learning approaches in the 2010s, the “training
compute” used to train AI models (i.e., the number of computations used to train AI models) has doubled
approximately every 6 months, less than one-third the doubling time implied by Moore’s law. See: Sevilla,
James et al., “Compute Trends Across Three Eras of Machine Learning”, 2022 International Joint Conference
on Neural Networks, 9 March 2022.
2

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OpenAI, “GPT-4 Technical Report”, 2023.
3

Goldman Sachs

Global Economics Analyst

Exhibit 2: AI Increasingly Outperforms Human Benchmarks
AI Performance vs. Human Benchmark
Percent

ImageNet: Top-5 Image Classification Accuracy

Percent

SQuAD: Reading Comprehension Accuracy

Percent

100 100

100

95

95

95

95

90

90

85

85

90

90

85

85
AI: With Extra Training Data
AI: Without Extra Training Data
Human Baseline

80

AI: Simple Questions (v1)
AI: Complex Questions (v2)
Human Baseline: Simple Questions (v1)
Human Baseline: Complex Questions (v2)80802011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

75
2015

Note: Data visually approximated from Stanford AI Index Report, 2022.

Note: Data visually approximated from Stanford AI Index Report, 2022.

2016

2017

2018

2019

2020

2021

80

75
2022

Source: Stanford Institute for Human-Centered Artiﬁcial Intelligence, Goldman Sachs Global Investment Research

As AI has become increasingly advanced and accessible, interest and investment havefollowed. Management teams of publicly-traded corporations increasingly cite AI inearnings calls—and at a rapidly increasing rate—and these indications of interest predictlarge increases in capital investment at the company level (Exhibit 3). As of 2021, US
and global private investment in AI totaled $53bn and $94bn respectively—each up
more than ﬁvefold in real terms from ﬁve years prior3—and if investment continues toincrease at the more modest pace that software investment grew at during the 1990s,
US investment in AI alone could approach 1% of US GDP by 2030.

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Percent

100

3Zhang, Daniel et al., “The AI Index 2022 Annual Report”, Stanford Institute for Human-Centered AI, March
2022.
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4

Goldman Sachs

Global Economics Analyst

Source: GS Data Works, FactSet, Goldman Sachs Global Investment Research

While much uncertainty remains around both the capability and adoption timeline ofgenerative AI, these developments suggest that AI is well-positioned to advance rapidly
and grow in scale in the coming years.

The Future of Work: Substitute Sometimes, Complement Often
Generative AI’s ability to 1) generate new content that is indistinguishable fromhuman-created output and 2) break down communication barriers between humans andmachines reﬂects a major advancement with potentially large macroeconomic effects.
To assess the size of these effects, we consider the likely impact generative AI will haveon the labor market if it delivers on its promised capabilities. In particular, we use datafrom the O*NET database on the task content of over 900 occupations in the US (andlater extend to over 2000 occupations in the European ESCO database) to estimate theshare of total work exposed to labor-saving automation by AI by occupation and industry.
Based on our review of existing literature on the probable use cases of generative AI,
we classify 13 work activities (out of 39 in the O*NET database) as exposed to AI
automation, and in our base case assume that AI is capable of completing tasks up to a
difﬁculty of 4 on the 7-point O*NET “level” scale (see Appendix for more details). We
then take an importance- and complexity-weighted average of essential work tasks foreach occupation and estimate the share of each occupation’s total workload that AI has
the potential to replace. We further assume that occupations for which a signiﬁcantshare of workers’ time is spent outdoors or performing physical labor cannot be
automated by AI.
In Exhibit 4, we report the occupation-level distribution of the share of tasks that AI canperform. We ﬁnd that roughly two-thirds of US occupations are exposed to some

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Exhibit 3: Management Teams Are Increasingly Focused on Opportunities from AI on Company Earning Calls, and More Mentions of AI
Predict Higher Capex

Goldman Sachs

Global Economics Analyst

degree of automation by AI, and that of those occupations which are exposed, mosthave a signiﬁcant—but partial—share of their workload (25-50%) that can be replaced.

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Exhibit 4: Two-Thirds of Current Occupations Could be Partially Automated by AI

Weighting our estimates by the employment share of each occupation in the US
Occupational Employment and Wage Survey (OEWS) and aggregating to the industry
level, we estimate that one-fourth of current work tasks could be automated by AI in the
US (Exhibit 5, top panel), with particularly high exposures in administrative (46%) andlegal (44%) professions and low exposures in physically-intensive professions such as
construction (6%) and maintenance (4%). Matching our occupation-level estimates tothe European ISCO occupation classiﬁcation system and performing a similar exercise
for the Euro Area using the Eurostat Labor Force Survey (LFS) database yields estimatesof a similar magnitude, both in aggregate and across industries (Exhibit 5, bottom panel).

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Source: Goldman Sachs Global Investment ResearchGoldman Sachs

Global Economics Analyst

Exhibit 5: One-Fourth of Current Work Tasks Could Be Automated by AI in the US and Europe
Share of Industry Employment Exposed to Automation by AI: US
Percent

Percent

46

50

44
37

40

36

35

33

32

40
31

30

29

28

28

28

27

26

26

30

25
19

20

12

20
11

10

4

1
Building and Grounds Cleaning
and MaintenanceInstallation, Maintenance, andRepairConstruction and Extraction

Transportation and Material
MovingFood Preparation and Serving
Related

Personal Care and Service

All Industries

Arts, Design, Entertainment,
Sports, and Media

Healthcare Support

Educational Instruction andLibrary

Healthcare Practitioners andTechnical

0
Protective Service

Computer and Mathematical

Sales and Related

Management

Community and Social Service

Business and Financial
Operations

Life, Physical, and Social
ScienceArchitecture and Engineering

6

Share of Industry Employment Exposed to Automation by AI: Euro Area
Percent

Percent

Share of Industry Employment Exposed to Automation by AI: Euro Area

50

45

40

40

34

31

30

29
24

30
22

21

20

20

15
8

10

7

4
Craft and Related Trades
Workers

Plant and Machine Operators,
and Assemblers

Elementary Occupations

Service and Sales Workers

Skilled Agricultural, Forestry and
Fishery Workers

Armed Forces Occupations

Total

Managers

Technicians and Associate
Professionals

0Professionals

010

Source: Goldman Sachs Global Investment Research

We next extend our US and European estimates globally, adjusting for differences inindustry composition across countries and further assuming that AI does not impact theagricultural sector in EM economies due to signiﬁcant differences in the compositionand production approaches in that industry between EM and DM economies.4 Our
estimates intuitively suggest that fewer jobs in EMs are exposed to automation than inDMs, but that 18% of work globally could be automated by AI on an
employment-weighted basis (Exhibit 6).

4This assumption is conservative, and we could imagine a scenario in which AI improves the efﬁciency ofagricultural logistics and production, particularly if AI and robotics interact to automate physical tasks.
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50

Clerical Support Workers

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Office and Administrative
SupportLegal

0

Farming, Fishing, and Forestry

10

9

Production

50

Goldman Sachs

Global Economics Analyst

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Exhibit 6: Globally, 18% of Work Could be Automated by AI, with Larger Effects in DMs than EMs

Collectively, our estimates suggest that a large share of employment and work is atleast partially exposed to automation by AI, raising the prospect of signiﬁcant laborsavings. To assess the robustness of our estimates, we compare our baseline US
estimate to a wider range of scenarios, including those in which AI can perform more orless difﬁcult tasks than we assume in our baseline, and in which we relax ourassumption that AI cannot assist with jobs which are primarily outdoors or physical (i.e.,a scenario in which AI is complementary with robotics and existing machinery). Our
scenario analysis suggests that the ultimate share of work exposed to automation could
range from 15-35% (Exhibit 7, left panel), a range which is consistent with—but on theconservative side of—existing estimates in the literature (Exhibit 7, right panel). Our
relatively conservative baseline primarily reﬂects our narrower focus on the impact ofgenerative AI, in contrast to other studies which sometimes consider a wider range ofrelated technologies (including robotics) that increase the scope for automation.

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Source: Goldman Sachs Global Investment ResearchGoldman Sachs

Global Economics Analyst

Exhibit 7: Our Estimates Conﬁrm that a Signiﬁcant Share of Employment Is at Least Partially Exposed to Automation by AI, but Larger Effects
Frequently Cited Include the Automation of Physical Tasks That Seem Less Likely in the Near-Term
Percent

Share of Full-Time Equivalent US Employment
Exposed to Automation by AI, Scenario Analysis

60

Percent

Share of Full-Time Equivalent US Employment
Exposed to Automation by AI, External Estimates*
46

50 50

50
40

35

30

26

25

27

40 40
30 30

20

34
23

Percent
60

47

38

50
40
30

25

20 20

20

10

10 10

10

0

0

0

Frey andOsborne (2013)

Muro, Maxim,
and Whiton (2019)

Hawksworth andBerriman (2018)

Agrawal, Gans,
and Goldfarb (2020)

GS

0
Manyika et al. (2017)

AI/Robotics Interact toAutomate Physical Tasks

Much More Powerful AI

Slightly More Powerful AI

Baseline

Slightly Less Powerful AI

14

Much Less Powerful AI

20*When a range of estimates are given, we report the central case/estimate from the report.

Source: Goldman Sachs Global Investment Research

Although the impact of AI on the labor market is likely to be signiﬁcant, most jobs andindustries are only partially exposed to automation and are thus more likely to becomplemented rather than substituted by AI. In Exhibit 8, we assume for illustration thatjobs for which at least 50% of importance- and complexity-weighted tasks are exposed
to automation are likely to be substituted by AI, while jobs with an exposure of 10-49%
are more likely to be complemented, and jobs with a 0-9% exposure are unlikely to be
impacted. In our baseline, these assumptions would be consistent with 7% of current
US employment being substituted by AI, 63% being complemented, and 30% being
unaffected, though the ultimate effects will depend on how labor demand andoccupational workloads evolve in response to partial labor savings in the majority ofoccupations.
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Percent

60 60

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9Goldman Sachs

Global Economics Analyst

Exhibit 8: Replacement in Legal and Administrative Fields, Little Effect in Manual and Outdoor Jobs, and Productivity-Enhancement
Everywhere Else
Percent

Percent

Computer and Mathematical

Educational Instruction andLibrary

Business and Financial
Operations

Community and Social Service

Legal

Sales and Related

Architecture and Engineering

Management

Life, Physical, and Social
Science

Office and Administrative
SupportProtective Service0
Farming, Fishing, and Forestry0
Healthcare Practitioners andTechnical

20

Arts, Design, Entertainment,
Sports, and Media20

All Industries

40

Healthcare Support

40

Personal Care and Service

60

Food Preparation and Serving
Related60

Transportation and Material
Moving80

Production

80

Construction and Extraction

100

Installation, Maintenance, andRepair100

Building and Grounds
Cleaning and MaintenanceSource: Goldman Sachs Global Investment ResearchSizing the Boost to Productivity and Growth
The large share of employment exposed to automation from generative AI raises thepotential for a boom in labor productivity that signiﬁcantly increases global output. There
are two main channels through which AI-driven automation could raise global GDP.
First, most workers are employed in occupations that are partially-exposed to AI
automation and, following AI adoption, will likely apply at least some of their freed-up
capacity toward productive activities that increase output.
Academic studies conﬁrm that workers at early-adopting ﬁrms experience higher laborproductivity growth following AI adoption, with estimates generally implying a
2-3pp/year boost (Exhibit 9). While differences in the capability of generative AI relative
to earlier vintages make it hard to extrapolate these results forward, they clearlysuggest that generative AI can drive an economically signiﬁcant increase in productivity.

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Share of Industry Employment by Relative Exposure to Automation by AI
0-10%: No Automation
10-49%: AI Complement
50%+: Likely Replacement

Goldman Sachs

Global Economics Analyst

Exhibit 9: Academic Studies Generally Find That AI Adoption Increases Within-Firm Annual Worker
Productivity Growth by 2-3pp
Percentage
points8
6.8
7Effect of AI Adoption on Annual Worker
Productivity Growth, Within Firm
Average: 3.1%, Median: 2.6%

Percentage
points
8
7

6

6

5

5

4

4
2.7

3

2.6
1.9

2

3
1.7

1

1

0

0
Alederucci et.al(2022)For the exclusive use of GIULIA.LORIA@COMMUNITY.IT

2

Behrens andCzarnitzki,
Fernandez, and Trunschke (2020)
Rammer (2022)

Acemoglu etal.(2022)

Bessen and Righi(2019)

Source: Goldman Sachs Global Investment Research

To demonstrate how technological innovation that initially displaces workers drives
employment growth over a long horizon, in Exhibit 10 we show results from a recent
study by economist David Autor and coauthors.5 Using Census data, they ﬁnd that 60%
of workers today are employed in occupations that did not exist in 1940, implying thatover 85% of employment growth over the last 80 years is explained by thetechnology-driven creation of new positions.

5

Autor, David, Caroline Chin, Anna M. Salomons, and Bryan Seegmiller. New Frontiers: The Origins andContent of New Work, 1940–2018. No. w30389. National Bureau of Economic Research, 2022.
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Second, we anticipate that many workers that are displaced by AI automation will
eventually become reemployed—and therefore boost total output—in new occupations
that emerge either directly from AI adoption or in response to the higher level ofaggregate and labor demand generated by the productivity boost from non-displaced
workers. Both of these channels have plenty of historical precedent. For example,
information technology innovations introduced new occupations like webpage
designers, software developers, and digital marketing professionals, but also increased
aggregate income and indirectly drove demand for service sector workers in industries
like health care, education, and food services.

Goldman Sachs

Global Economics Analyst

Exhibit 10: Technological Innovation Leads to the Creation of New Occupations That Account for the Bulk
of Employment GrowthMillions
200
175
150
125
100
75
50
25
0

Farming

HealthCleaning Services

Sales

Technicians

Transportation

Personal Services

Construction

Production

Managers

Occupations That Existed in 1940
Occupations That Did Not Exist in 1940

Total

Employment, by New and Pre-Existing Occupations

Clerical & Admin

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Professionals

Millions
200
175
150
125
100
75
50
25
0

Source: Autor et al. (2022), Goldman Sachs Global Investment Research

Exhibit 11 leverages a different academic study by economists Daren Acemoglu andPascual Restrepo that decomposes changes in labor demand into contributions fromproductivity growth and technology-driven worker displacement and reemployment(among other factors) to show how the drivers of labor demand change over time.6
Technological change displaced workers and created new employment opportunities atroughly the same rate for the ﬁrst half of the post-war period, but has displaced workers
at a faster pace than it has created new opportunities since the 1980s. These results

044ede4967a843888d229f7cd36e62b1

suggest that the direct effects of generative AI on labor demand could be negative in
the near-term if AI affects the labor market in a manner similar to earlier advances in
information technology, although the effects on labor productivity growth would still bepositive.

6

Acemoglu, Daron and Pascual Restrepo. “Automation and new tasks: How technology displaces andreinstates labor.” Journal of Economic Perspectives 33, no. 2 (2019): 3-30.
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Goldman Sachs

Global Economics Analyst

Exhibit 11: Historically, Worker Displacement from Automation Was Roughly Offset by Creation of New
Roles/Tasks Prior to 1980, but Displacement Has Created a Net Drag on Labor Demand More Recently
Percentage points
1.5

Remployment of Workers in New Roles
Displacement of Workers From Old Roles

1

1

0.5

0.5

0

0

-0.5

-0.5

-1
-1.5

*Lines indicate 10-year moving averages ofreinstatement (green) and displacement (red)effects

1950

1960

1970

-1

Green shading =IT automation boom

-1.5

1980

1990

2000

2010

Source: Acemoglu and Restropo (2019), Goldman Sachs Global Investment Research

The combination of signiﬁcant labor cost savings, new job creation, and a productivity
boost for non-displaced workers raises the possibility of a labor productivity boom likethose that followed the emergence of earlier general-purpose technologies like theelectric motor and personal computer. These past experiences offer two key lessons.
First, the timing of a labor productivity boom is hard to predict, but in both cases started
about 20 years after the technological breakthrough, at a point when roughly half of US
businesses had adopted the technology (Exhibit 12, left panel). Second, in both of these
instances, labor productivity growth rose by around 1.5pp/year in the 10 years after the
productivity boom started, suggesting that the labor productivity gains can be quite
substantial (Exhibit 12, right panel).
Exhibit 12: Previous Milestone Technologies Have Led to Labor Productivity Booms, but the Timing Is Hard to Predict
Percent

1981
100

Adoption of Previous Technologies Over Time

1986

1991

1996

2001

2006

2011

Percent

2016

4.0

Productivity
boom begins

80

60

60
50% Adoption Threshold

40

40

20

0

⌄Development of electric motor

1899

1908

1917

US Labor Productivity

Percent change,
10-year annual rate

100 4.5^Personal computer invented

80

Percent change,
10-year annual rate

Electricity: Manufacturing
Electricity: Household
Personal Computing: Workplace
Personal Computing: Household
1926

1935

1944

1953

20

3.5

4.5
Developmentof electricmotor:
~1890

4

3.5

3.0

3

2.5

2.5

2.0

2

1.5

1.5

1.0

1

0.5
0

Personalcomputerinvented:
1981

0.0
1885

0.5

Green = Resulting productivity boom

0
1905

1925

1945

1965

1985

2005

2025

Source: US Bureau of Labor Statistics, Census Bureau, Our World in Data, Woolf (1987), Haver Analytics, Goldman Sachs Global Investment Research

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Percentage points
1.5

Contributions to Total Wage Bill Growth*Goldman Sachs

Global Economics Analyst

To estimate the boost to labor productivity in the US from widespread adoption ofgenerative AI, in Exhibit 13 we sum up the implied labor productivity effects from direct
labor cost savings, a productivity boost for non-displaced workers, and a composition
effect from the reemployment of displaced workers in new positions.
Our baseline analysis incorporates our key ﬁndings from above, including that about 7%
of workers are fully displaced but that most are able to ﬁnd new employment in only
slightly less productive positions, that partially exposed workers experience a boost in
productivity consistent with existing estimates (Exhibit 9), and that effects are realized
over a 10-year period that starts around the time when roughly half of businesses haveadopted generative AI. Under these assumptions we estimate that widespread adoptionof generative AI could raise overall labor productivity growth by around 1.5pp/year (vs. a
recent 1.5% average growth pace), roughly the same-sized boost that followed theemergence of prior transformative technologies like the electric motor and personal
computer.

nFirst, we vary the O*NET difﬁculty level of the tasks that AI is capable ofcompleting. In a much less powerful AI scenario where, for example, generative AI
is only ultimately able to “skim a short article to gather the main point” (difﬁcultyscore 2) rather than “determine the interest cost to ﬁnance a new building”(difﬁculty score 4), the implied labor productivity growth boost would fall to
0.3pp/year. If AI is instead more powerful and is able to, again for example, “analyze
the cost of medical care services for all US hospitals” (difﬁculty score 6), the impliedlabor productivity growth boost would rise to 2.9pp/year.

n

Second, we vary the amount of labor that is fully displaced by generative AI.
Assuming no labor displacement implies only a moderately smaller productivity
growth boost of 1.2pp/year because non-displaced workers would still experiencesigniﬁcant productivity gains, while assuming that a much larger share of workers
are displaced would raise the boost to productivity growth to 2.4pp/year.

n

Third, we vary the timeline of adoption. The productivity growth boost would only beroughly half as large if the gains are realized over a 20-year period and one-third aslarge if realized over a 30-year period.

Our key takeaway from these analyses is that the ultimate boost to labor productivity is
uncertain, but in most scenarios would remain economically signiﬁcant.

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This estimated boost to labor productivity growth is both admittedly quite large andhighly uncertain. Exhibit 13 therefore also considers other plausible scenarios andshows that the boost to US productivity growth could easily range from 0.3-3.0pp
depending on the difﬁculty level of tasks generative AI can perform, how many jobs areultimately automated, and the speed of adoption:

Goldman Sachs

Global Economics Analyst

Exhibit 13: We Estimate That Generative AI Could Boost Aggregate Labor Productivity Growth by 1.5pp in
the US, Although the Size of the Boost Will Depend on AI’s Capability and Adoption Timeline

Much More Powerful AI

Percentage points
6
5
4
2.9
2.4
3
2
1
0
-1
More Labor Displacement

Slightly More Powerful AI

Baseline

No Labor Displacement

Increased Productivity of Non-Displaced Workers
Labor Displacement
Reemployment of Displaced Workers
2.4
1.5
1.3
0.8
0.7

Slower Adoption (20Years)

Slower Adoption (30Years)

Effect of AI Adoption on Annual Labor
Productivity Growth, 10-Year Adoption Period

Slighlty Less Powerful AI

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Much Less Powerful AI

Percentage points
6
5
4
3
2
0.5
1
0.3
0
-1

Source: Goldman Sachs Global Investment Research

In Exhibit 14 we extrapolate our analysis for the US to other countries under theassumption that differences in the industry-composition of labor can account for most of
the differences in labor productivity growth. Our estimates imply that AI adoption could
boost global annual productivity growth for countries in our coverage by 1.4pp(FX-weighted average) over a 10-year period, although we would likely expect a more
delayed impact in EM economies.

044ede4967a843888d229f7cd36e62b1

Exhibit 14: Productivity Growth Boosts Could Be Sizable in Other Countries As Well; We Estimate
Widespread AI Adoption Could Boost Global Annual Productivity Growth by 1.4pp Over a 10-Year Period

Source: Goldman Sachs Global Investment Research

Applying our estimated global labor productivity boost to countries in our coverage
implies that widespread AI adoption could eventually drive a 7% or almost $7tn increase

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Goldman Sachs

Global Economics Analyst

in annual global GDP over a 10-year period. Although the size of AI’s impact will
ultimately depend on its capability and adoption timeline—and uncertainty around bothof these factors is sufﬁciently high that we are not incorporating our ﬁndings into ourbaseline economic forecasts at this time—our estimates highlight the enormous
economic potential of generative AI if it delivers on its promise.

Joseph Briggs

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Devesh Kodnani

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Goldman Sachs

Global Economics Analyst

Appendix
AI-Exposed Work Activity

Examples of Automation

Getting Information

Web scrape data from online sources and consolidate into a clean
dataset; conduct and summarize a review of prior research based ona textual query and answer follow-up questions

2: Follow a standard blueprint
4: Review a budget
6: Study international tax laws

Monitoring Processes,
Materials, or Surroundings

Monitor sensor input and system logs for manufacturing and utilities
system anomalies; monitor internet activity for changes in sentimentor trending themes

2: Check to see if baking bread is done
4: Test electrical circuits
6: Check the status of a patient in critical medical care

Identifying Objects, Actions,and Events

Identify objects, music, terminology, and people when provided with
textual/visual/auditory input; provide context on identified subject

2: Test an automobile transmission
4: Judge the suitability of food products for an event
6: Determine the reaction of a virus to a new drug

Estimating the Quantifiable
Characteristics of Products,
Events, or Information

Produce market size estimates based on assumptions grounded inexisting research; estimate parameters using statistical modeling on
input data and select optimal model

2: Estimate the size of household furniture to be shipped
4: Estimate transportation delays from inclement weather
6: Estimate the size of resource deposits beneath the world's oceans

Processing Information

Process raw data from documents, sensors, and humans into clean
datafiles that are easily subscriptable for analysis; provide summaries
of data relevant to user needs

2: Calculate the costs for shipping packages
4: Calculate the adjustments for insurance claims
6: Compile data for a complex scientific report

Evaluating Information to
Determine Compliance withStandards

Review documents and proposed actions for compliance with legal,
regulatory, and corporate standards; provide arguments andscenarios for and against compliance in unclear cases

1: Review forms for completeness
4: Evaluate a complicated insurance claim for policy compliance
6: Make a ruling in court on a complicated motion

Analyzing Data or
Information

Perform statistical analysis of and identify trends within large
datasets; forecast future data based on optimal combination ofvariables and model with best out-of-sample predictive power

1: Skim a short article to gather the main point
4: Determine the interest cost to finance a new building
6: Analyze the cost of medical care services for all US hospitals

Updating and Using Relevant
Knowledge

Draft and update reports in corporate knowledge base; updatestatistical and financial models based on new data which challenges
prior scenarios/assumptions

Scheduling Work andActivities

Automatically schedule meetings and work activities using
availabilities and emails; assign tasks and estimate time tocompletion based on past experience

Organizing, Planning, andPrioritizing Work

Delegate and prioritize tasks based on time to completion andimportance; identify gaps or bottlenecks in work plans and target
resources or managerial attention

2: Organize a work schedule that is repetitive and easy to plan
4: Plan and adjust a to-do list according to changing demands
6: Prioritize and plan multiple tasks several months ahead

Documenting/Recording
Information

Transcribe and summarize the content of in-person meetings; write
system reports based on sensor and human data input

2: Record the weight of a patient during a routine health exam
4: Document the results of a crime scene investigation
6: Maintain information about satellite use for industry communications

Interpreting the Meaning ofInformation for Others

Explain the structure and function of code or statistical results in easyto-understand language; translate code and text between languages;
summarize and contextualize text with technical jargon

1: Interpret a blood pressure reading
4: Interpret how foreign tax laws apply to U.S. exports
6: Interpret a complex experiment in physics for general audiences

Performing Administrative
Activities

Draft automated email responses; schedule and manage meetings
and work calendars; file and organize paperwork; book reservations

2: Complete routine paperwork
4: Complete tax forms for a small business
6: Serve as the benefits director for a large computer sales firm

Examples of Tasks by Difficulty (O*NET 1-7 Scale)

2: Track price changes in a small retail store
4: Track changes in maintenance procedures for repairing SUVs
6: Learn information about a complex and rapidly-changing technology
2: Make appointments for patients using a predetermined schedule
4: Prepare the work schedule for salesclerks in a large retail store
6: Schedule a complex conference program with parallel sessions

Source: O*NET, Goldman Sachs Global Investment Research

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Our Baseline Assumes Tasks in 13 Categories Up to a Difﬁculty Level of 4 Could Be Automated17Goldman Sachs

Global Economics Analyst

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