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VoxEU Column Productivity and Innovation


SHOULD AI STAY OR SHOULD AI GO: THE PROMISES AND PERILS OF AI FOR PRODUCTIVITY
AND GROWTH

 * Francesco Filippucci
 * Peter Gal
 * Cecilia Jona-Lasinio
 * Alvaro Leandro
 * Giuseppe Nicoletti
 * /

2 May 2024

There is considerable disagreement about the growth potential of artificial
intelligence. Though emerging micro-level evidence shows substantial
improvements in firm productivity and worker performance, the macroeconomic
effects are uncertain. This column argues that the promise of AI-related
economic growth and social welfare hinges on the rate of adoption and its
interplay with labour markets. Policies should focus on both domestic and global
governance issues – including threats to market competition and increased
inequality – and do so rapidly to keep pace with fast-evolving AI.

AUTHORS


FRANCESCO FILIPPUCCI


PETER GAL


CECILIA JONA-LASINIO


ALVARO LEANDRO


GIUSEPPE NICOLETTI

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Income and wellbeing gains in advanced economies have been held back by weak
productivity performance. The growth rate of labour productivity declined in
OECD economies from about 2% annual growth rate between the 1970s and 1990s, to
1% in the 2000s (Goldin et al. 2024, Andre and Gal 2024). This poses a dramatic
challenge for ageing societies and makes it harder to allocate resources for the
green transition.

There is widespread enthusiasm about the growth potential of rapidly developing
artificial intelligence (AI). Some analysts argue that, under reasonable
conditions, AI could lead to large and persistent gains, on the order of adding
1–1.5 percentage points to annual growth rates over the next 10–20 years (Baily
et al. 2023, Artificial Intelligence Commission of France 2024, McKinsey 2023,
Briggs and Kodnani 2023). On the other hand, Acemoglu (2024) contends that the
available evidence combined with the economic theory of aggregation supports
only moderate total factor productivity and GDP growth impacts, on the order of
about 0.1% per year.

Recent work from the OECD provides a broad overview of AI’s impact on
productivity and discusses the conditions under which it is expected to deliver
strong benefits, with a focus on the role of policies (Filippucci et al. 2024).


AI AS A NEW GENERAL-PURPOSE TECHNOLOGY

Given its transformative potential in a wide range of economic activities, AI
can be seen as the latest general-purpose technology (Agrawal et al. 2019,
Varian 2019) – similar to previous digital technologies such as computers and
the internet or, going back further, to the steam engine and electricity. From
an economic perspective, AI can be seen as a production technology combining
intangible inputs (skills, software, and data) with tangible ones (computing
power and other hardware), to produce three broad types of outputs:

 * Content, such as texts or images (generative AI)
 * Predictions, optimisations, and other advanced analytics, which can be used
   to assist with or fully automate human decisions (non-generative AI)
 * Physical tasks when combined with robotics (including autonomous vehicles).

Additionally, AI has some peculiar features, even compared to previous digital
technologies. These include the potential for being autonomous (less dependent
on human inputs) and the capacity for self-improvement, by learning from
patterns in unstructured data or leveraging feedback data about its own
performance. Altogether, these features imply that AI can boost not only the
production of goods and services but also the generation of ideas, speeding up
research and innovation (Aghion et al. 2018).


INITIAL MICRO-LEVEL EVIDENCE SHOWS LARGE PRODUCTIVITY AND PERFORMANCE GAINS

According to our overview of the fast-growing literature, initial micro-level
evidence covering firms, workers, and researchers is indicative of several
positive effects from using AI. First, micro-econometric studies find that the
size of the gains from non-generative AI on firms’ productivity is comparable to
previous digital technologies (up to 10%; see panel a of Figure 1). Second, when
using more recent generative AI in various tasks – assisting in writing,
computer programming, or customer service requests – the estimated performance
benefits are substantially larger but vary widely (between 15 and 56%; see panel
b of Figure 1) depending on the context. In particular, Brynjolfsson et al.
(2023) found that AI has a much stronger impact on the performance of workers
with less experience in their job. These estimates focus on specific tasks and
individual-level gains. Hence, they are narrower in scope than previous
firm-level studies but tend to rely more on more causal identification in
experimental settings.

Figure 1 The positive relationship between AI use and productivity or worker
performance: Selected estimates from the literature

a) Non-generative AI, firm-level studies on labour productivity



b) Generative AI, worker-level studies on performance in specific tasks



NOTE: IN PANEL A, ‘AI USE’ IS A 0-1 DUMMY OBTAINED BY FIRM SURVEYS, WHILE ‘AI
PATENTS’ REFERS EITHER TO A 0-1 DUMMY FOR HAVING AT LEAST ONE PATENT (US STUDY)
OR TO THE NUMBER OF PATENTS IN FIRMS. THE SAMPLE OF COUNTRIES UNDERLYING THE
STUDIES ARE SHOWN IN PARENTHESES. THE YEAR(S) OF MEASUREMENT IS ALSO INDICATED.
*CONTROLLING FOR OTHER ICT TECHNOLOGIES. FOR MORE DETAILS, SEE FILIPPUCCI ET AL.
(2024).

Third, researchers believe that AI allows for faster processing of data –
speeding up computations and decreasing the cost of research – and may also make
new data sources and methods available, as documented by a recent survey in
Nature (Van Noorden and Perkel 2023). Fourth, AI-related inventions are cited in
a broader set of technological domains than non-AI inventions (Calvino et al.
2023). Finally, there are promising individual cases from specific industries:
AI-predicted protein-folding gives new insights in biomedical applications;
AI-assisted discoveries of new drugs help with pharmaceutical R&D; and research
on designing new materials can be broadly used in manufacturing (OECD 2023).


LONG-RUN AGGREGATE GAINS ARE UNCERTAIN

As generative AI’s technological advances and its use are very recent, findings
at the micro or industry level mainly capture the impacts on early adopters and
very specific tasks, and likely indicate short-term effects. The long-run impact
of AI on macro-level productivity growth will depend on the extent of its use
and successful integration into business processes.

According to official representative data, the adoption of AI is still very low,
with less than 5% of firms reporting the use of this technology in the US
(Census Bureau 2024; see Figure 2). When put in perspective with the adoption
path of previous general-purpose technologies (e.g. computers and electricity),
AI has a long way to go before reaching the high adoption rates that are
necessary to detect macroeconomic gains. While user-friendly AI may spread
faster through the economy, the successful integration of AI systems and
exploiting their full potential may still require significant complementary
investments (in data, skills, reorganisations) which take time and necessitate
managerial talent. Moreover, future advances in AI development – and its
successful integration within business processes – will require specialised
technical skills that are often concentrated within a few firms (Borgonovi et
al. 2023).

Figure 2 AI adoption is still limited compared to the spread of previous
general-purpose technologies

The evolution of technology adoption in the US (as % of firms)



NOTE: THE 2024 VALUE FOR AI IS THE EXPECTATION (EXP.) AS REPORTED BY FIRMS IN
THE US CENSUS BUREAU SURVEY. FOR MORE DETAILS, SEE THE SOURCES.
SOURCE: FOR PC AND ELECTRICITY, BRIGGS AND KODNANI (2023); FOR AI, US CENSUS
BUREAU, BUSINESS TRENDS AND OUTLOOK SURVEY, UPDATED 28 MARCH 2024.

It is also an open question whether AI-driven automation will displace
(reallocate) workers from heavily impacted sectors to less AI-affected
activities or the human-augmenting capabilities of AI will prevail, underpinning
labour demand. Currently, AI exposure varies greatly across sectors:
knowledge-intensive, high-productivity activities are generally much more
affected (Figure 2), with significant potential for automation in some cases
(Cazzaniga et al. 2024, WEF 2023). Hence, an eventual fall in the employment
shares of these sectors would act as a drag on aggregate productivity growth,
resembling a new form of ‘Baumol disease’ (Aghion et al. 2019).

Figure 3 High-productivity and knowledge-intensive services are most affected by
AI

AI exposure of workers by sector, 2019



NOTE: THE INDEX MEASURES THE EXTENT TO WHICH WORKER ABILITIES ARE RELATED TO
IMPORTANT AI APPLICATIONS. THE MEASURE IS STANDARDISED WITH MEAN ZERO AND
STANDARD DEVIATION ONE AT THE OCCUPATION LEVEL AND THEN MATCHED TO SECTORS.
FIGURE DOES NOT YET INCLUDE RECENT GENERATIVE AI MODELS. *INCLUDING NON-MARKET
SERVICES, MANUFACTURING, UTILITIES, ETC.
SOURCE: FILIPPUCCI ET AL. (2024) AND OECD (2024) BASED ON FELTEN ET AL. (2021).

Historically, the automation of high-productivity activities, combined with
saturating demand for their output, has pushed employment from manufacturing to
services (Bessen 2018). This structural change also played a role – though a
moderate one – in the ongoing slowdown in aggregate productivity growth (Sorbe
et al. 2018). Similarly, if AI enhances productivity only in selected
activities, aggregate growth will be limited by the slower productivity growth
and higher employment share in sectors that are less exposed to AI (such as
labour-intensive personal services like leisure and health care). This may occur
more quickly with AI compared to past technologies given the rapid and
wide-ranging advances in its capabilities. However, in the extreme case of AI
impacting (nearly) all tasks and boosting productivity in (nearly) all economic
activities, this negative effect may be muted (Trammel and Korinek 2023).


AI POSES POLICY CHALLENGES RELATED TO COMPETITION, INEQUALITY, AND BROADER
SOCIETAL RISKS

AI poses significant threats to market competition and inequality that may weigh
on its potential benefits, either directly or indirectly, by prompting
preventive policy measures to limit its development and adoption.

First, the high fixed costs and returns to scale related to data and computing
power may lead to excessive concentration of AI development. Second, AI use in
downstream applications may lead to market distortions, especially if it allows
first movers to build up a substantial lead in market share and market power.
Moreover, AI-powered pricing algorithms have a tendency to charge
supra-competitive prices (Calvano et al. 2020) and could eventually enhance
harmful price discrimination (OECD 2018).          

The impact of AI on inequality remains ambiguous. The technology can potentially
substitute for high-skilled labour and narrow wage gaps with low-skilled
workers, thereby reducing inequalities (Autor 2024) at least within occupations
(Georgieff 2024). Though there are indications that AI can be associated with
higher unemployment (OECD 2024), AI could also lead to more inclusion and
stronger economic mobility by improving education quality and access, expanding
credit availability, and lowering skill barriers (e.g. foreign languages).

Further uncertainties surrounding AI include broader societal concerns. More
immediate concerns relate to privacy, misinformation, and bias (possibly leading
to exclusion in areas such as labour and financial markets), while longer-term
concerns include mass unemployment or even existential risks (Nordhaus 2021,
Jones 2023).

A comprehensive policy approach is needed to effectively manage these risks and
harness AI's full potential. Immediate priorities include promoting market
competition and widespread access to AI technologies while preserving innovation
incentives (e.g. via adapting intellectual property rights protection) and
addressing issues of reliability and bias, which require adequate auditing and
accountability mechanisms. Job displacement, reallocation and inequality impacts
might emerge over longer periods, but they require preventive policy action
through training, education, and redistribution measures to ensure human skills
remain complementary to AI. Policymakers should also devise national and
international governance mechanisms to cope with rapid and unpredictable
developments in AI. 

Authors’ note: The main paper underlying this column (Filippucci et al. 2024)
was developed within the Joint OECD-Italy’s Department of Treasury Project for
Multilateral Policy Support.


REFERENCES

Acemoglu, D (2024), “The Simple Macroeconomics of AI”, MIT, 5 April.

Acemoglu, D and T Lensman (2023), “Regulating Transformative Technologies”, NBER
Working Paper 31461, July.

Aghion, P, B Jones and C Jones (2018), “Artificial Intelligence and Economic
Growth”, NBER Working Paper 23928, October.

Agrawal, A, J Gans and A Goldfarb (2019), “Economic Policy for Artificial
Intelligence”, Innovation Policy and the Economy, vol. 19.

Andre, C and P Gal (2024), “Reviving productivity growth: A review of policies”,
OECD Economic Policy Papers, forthcoming.

Artificial Intelligence Commission of France (2024), “AI, Our Ambition for
France” (“IA : Notre Ambition pour la France”), March.

Autor, D (2024), “Applying AI to Rebuild Middle Class Jobs”, NBER Woking Paper
32140, February.

Baily, M, E Brynjolfsson and A Korinek (2023), “Machines of mind: The case for
an AI-powered productivity boom”, The Economics and Regulation of Artificial
Intelligence and Emerging Technologies, Brookings, 10 May.

Bessen, J (2018), “AI and Jobs: The Role of Demand”, NBER Working Paper 24235,
January.

Borgonovi, F et al. (2023), “Emerging trends in AI skill demand across 14 OECD
countries”, OECD Artificial Intelligence Papers, no. 2, Paris: OECD Publishing.

Briggs, J and D Kodnani (2023), “The Potentially Large Effects of Artificial
Intelligence on Economic Growth”, Global Economics Analyst, New York: Goldman
Sachs.

Brynjolfsson, E, D Li and L Raymond (2023), “Generative AI at Work”, NBER
Working Paper 31161, November.

Calo, R (2013), “Digital market manipulation”, The George Washington Law
Review 82/995.

Calvano, E et al. (2020), “Artificial intelligence, algorithmic pricing, and
collusion”, American Economic Review 110(10): 3267–97.

Calvino, F and L Fontanelli (2023), “A portrait of AI adopters across countries:
Firm characteristics, assets’ complementarities and productivity”, OECD Science,
Technology and Industry Working Papers, no. 2, Paris: OECD Publishing.

Cazzaniga, M et al. (2024), “Gen-AI: Artificial Intelligence and the Future of
Work”, IMF Staff Discussion Notes, International Monetary Fund, 14 January.

Census Bureau (2024), Business Trends and Outlook Survey, 28 March.

Felten, E, M Raj and R Seamans (2021), “Occupational, industry, and geographic
exposure to artificial intelligence: A novel dataset and its potential uses”,
Strategic Management Journal 42(12): 2195–217.

Filippucci, F, P Gal, A Leandro, C Jona-Lasinio and G Nicoletti (2024), “The
impact of Artificial Intelligence on productivity, distribution and growth: Key
mechanisms, initial evidence and policy challenges”, OECD Artificial
Intelligence Papers, no. 15, Paris: OECD Publishing.

Georgieff, A (2024), “Artificial intelligence and wage inequality”, OECD
Artificial Intelligence Papers, no. 13, Paris: OECD Publishing.

Goldin, I, P Koutroumpis, F Lafond and J Winkler (2024), “Why Is Productivity
Slowing Down?”, Journal of Economic Literature 62(1): 196–268.

Haslberger, M, J Gingrich and J Bhatia (2023), “No great equalizer: experimental
evidence on AI in the UK labor market”, SSRN, 2 November.

Jones, C (2023), “The A.I. Dilemma: Growth versus Existential Risk”, NBER
Working Paper 31837, November.

McKinsey (2023), “The economic potential of generative AI”, McKinsey Digital, 14
June.

Nordhaus, W (2021), “Are We Approaching an Economic Singularity? Information
Technology and the Future of Economic Growth”, American Economic Journal:
Macroeconomics 13(1): 299–332.

OECD (2018), “Personalised Pricing in the Digital Era”, Background Note by the
OECD Secretariat for the joint meeting between the Competition Committee and the
Committee on Consumer Policy, 28 November.

OECD (2024), “Labour Market Shortages, Mismatches and Megatrends”, OECD Global
Forum on Productivity, forthcoming.

OECD (2023), Artificial Intelligence in Science: Challenges, Opportunities and
the Future of Research, Paris: OECD Publishing, June.

Sorbe, S, P Gal and V Millot (2018), “Can productivity still grow in
service-based economies? Literature overview and preliminary evidence from OECD
countries”, OECD Economics Department Working Papers, no. 1531, Paris: OECD
Publishing.

Trammell, P and A Korinek (2023), “Economic Growth under Transformative AI”,
NBER Working Paper 31815, October.

Van Noorden, R and J Perkel (2023), “AI and science: what 1,600 researchers
think”, Nature 621(7980): 672–75.

WEF (2023), “Jobs of Tomorrow: Large Language Models and Jobs”, World Economic
Forum White Papers, September.



AUTHORS


FRANCESCO FILIPPUCCI

Economist Organisation for Economic Co-Operation and Development (OECD)


PETER GAL

Deputy Head of Division and Senior Economist Organisation for Economic
Co-Operation and Development (OECD)


CECILIA JONA-LASINIO

Professor of Applied Economics Luiss Business School


ALVARO LEANDRO

Economist Caixa Bank; Economist Oecd


GIUSEPPE NICOLETTI

Senior Fellow, LUISS Lab of European Economics Luiss University

THEMES

 * Productivity and Innovation

KEYWORDS

 * Artificial intelligence
 * Ai
 * Growth

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VoxEU Column


RE-EVALUATING THE SOURCES OF THE RECENT PRODUCTIVITY SLOWDOWN

 * Julian Winkler
 * Pantelis Koutroumpis
 * François Lafond
 * Ian Goldin

31 May 2021
   ![](../../../../../../../../../../var/folders/34/zq18d8kx7kbgby0j06p_j6t40000gn/T/TemporaryItems/NSIRD_screencaptureui_EM2XPo/Screenshot
   2022-01-04 at 17.01.16.png)
 * Productivity and Innovation

VoxEU Column


THE PRODUCTIVITY EFFECTS OF GENERATIVE ARTIFICIAL INTELLIGENCE

 * Shakked Noy
 * Whitney Zhang

7 Jun 2023
   ![](../../../../../../../../../../var/folders/34/zq18d8kx7kbgby0j06p_j6t40000gn/T/TemporaryItems/NSIRD_screencaptureui_EM2XPo/Screenshot
   2022-01-04 at 17.01.16.png)
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   ![](../../../../../../../../../../var/folders/34/zq18d8kx7kbgby0j06p_j6t40000gn/T/TemporaryItems/NSIRD_screencaptureui_EM2XPo/Screenshot
   2022-01-04 at 17.01.16.png)
 * Productivity and Innovation

VoxEU Column


HOW AI CAN BECOME PRO-WORKER

 * Daron Acemoglu
 * David Autor
 * Simon Johnson

4 Oct 2023
   ![](../../../../../../../../../../var/folders/34/zq18d8kx7kbgby0j06p_j6t40000gn/T/TemporaryItems/NSIRD_screencaptureui_EM2XPo/Screenshot
   2022-01-04 at 17.01.16.png)
 * Labour Markets
   ![](../../../../../../../../../../var/folders/34/zq18d8kx7kbgby0j06p_j6t40000gn/T/TemporaryItems/NSIRD_screencaptureui_EM2XPo/Screenshot
   2022-01-04 at 17.01.16.png)
 * Productivity and Innovation


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