KI Forum - OECD

BDI AI Forum 2 October 2025 | Berlin, Germany

Macroeconomic productivity gains from Artificial Intelligence Peter Gal Deputy Head of Division and Senior Economist OECD Economics Department Based on joint work with Francesco Filippucci, Katharina Laengle, Matthias Schief, and building on the latest OECD Economic Survey of Germany and forthcoming work by OECD Science, Technology and Innovation Restricted Use - À usage restreint

Productivity is key for long-term rise in living standards but its growth has been slowing down Labour productivity growth Annualised growth, percentage points 3,5

2000-2010

2010-2019

2019-2023

3 2,5 2 1,5 1 0,5 0 •

OECD

G20ADV

G20EME

Notes: labour productivity of the total economy. Source: OECD ADB. Advanced economies include Australia, Canada, Germany, France, UK, Italy, Japan, Korea and the US, and emerging-market economies include Brazil, Indonesia, Turkey and South Africa. Emerging-market economies include Argentina, Brazil, Bulgaria, Chile, Colombia, Costa Rica, India, Indonesia, Mexico Romania, Russia, Saudi Arabia, South Africa and Türkiye, while all other OECD economies are covered in the ADV average.

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Productivity growth across the OECD has been disappointing…

Can AI turn this around?

• Seems very promising…

• Large documented gains in specific activities (coding, translation, writing, etc.)

• But these are only a fraction of all economic activities…

• Typically cognitive, knowledge intensive jobs

• AI adoption is still limited…

• According to official surveys by statistical agencies, which ask about core business functions and regular use

• Aggregate productivity gains also depend on demand through general equilibrium effects • Is there demand for the increased output of AI-boosted sectors, e.g. legal services?

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Can AI revert the productivity slowdown?

Predicted impact of AI on aggregate labour productivity growth Annualised gains over the next 10 years (pp.)

Gains associated with ICT boom in the US in the 90s

Source: Filippucci, Gal and Schief (2024) and Filippucci, Gal, Laengle and Schief (forthcoming) Restricted Use - À usage restreint

1. Micro-level gains: Large across a range of tasks…

Performance gains on specific tasks Estimated % increase in productivity and 95% standard errors, as reported by various studies

100% 80% 60%

30%: our baseline assumption

40%

(average of 4 most precise estimates)

20% 0%

Customer service (US+PH, 2020-21)

Coding (US, 2022)

Professional writing (US, 2022)

Business consulting (US, 2023)

General writing (UK, 2023)

Coding (US+SE Asia, 2023)

Coding (CN, 2023)

Note: The graph shows the worker-level productivity effects reported in different studies together with 95% confidence intervals.

Source: Compilation from the literature by Filippucci al. (2024).

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2. Exposure: AI strongly benefits a few sectors, whose importance varies across G7 economies Share of tasks exposed to AI by sector 60%

Average over G7 economies

GDP-share of the most AI-exposed sectors (knowledge intensive services)

50% 40% 30%

20% 10% 0%

Source: Eloundou et al. (2024, Science) aggregated from tasks and occupations to sectors (left panel).

Sectoral value-added data come from OECD Input-Output tables, 2019 (right panel) Restricted Use - À usage restreint

3. Adoption: We benchmark our scenarios to the adoption paths of previous General Purpose Technologies (electricity, ICT tools)…

Share of firms using the technology

70% Mobiles phones (+60 pp.)

60%

2007

50% 40%

Internet

Internet & PCs (+40 pp.)

Electricity (+23 pp.)

1984

AI 1900 2024 2023 0% 0

10%

1919

Computers

30% 20%

2003

1997

Electricity + 10 years

5 10 15 20 Years passed since the introduction of user-friendly breakthrough

25

Sources: For computers and internet: US Census; for electricity adoption of businesses: Woolf (1987) and Our World in Data; for mobile phone use of individuals: International Telecommunication Union; for AI: United States Census Bureau, Business Trends and Outlook Survey (BTOS).

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Reasons to be optimistic about AI adoption?

Rapidly declining cost of AI, similar to previous digital technologies Historical costs of computation vs. cost of AI

Historical costs of computer memory vs. cost of AI

Index, first year of data =100

Index, first year of data=100

100

100

10 1 10 0,1

0,01 1 0

1

2

3

4

5

6

7

8

9

10

0,001 1

2

3

4

5

6

7

8

9

Cost of computation (USD per instr./second)

Cost of computer memory (USD/Tb, range across diff. types)

Cost of accessing GenAI models (quality adjusted price)

Cost of accessing GenAI models (quality adjusted price)

Sources: Cost of computation: Kurzweil (2024); Cost of computer memory: McCallum (2023).

Cost of accessing GenAI models : Andre, Betin, Gal, Peltier (May 2025).

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10

The expected increase in AI adoption varies a lot across countries Current and future adoption (in 10 years)

Based on the S-shaped adoption paths seen in previous GPTs

Note: current adoption rates are taken from official national statistics after harmonisation steps (CAN, EU, USA) or when this is not possible, using predictions as a function of the digital infrastructure, skills and the sectoral composition (JPN, GBR).

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AI uptake in the EU manufacturing sector New OECD evidence, joint with the EU Commission OECD (forthcoming), Monitoring the implementation of the EU Coordinated Plan on AI: Uptake in Key Priority Sectors: • AI adoption in EU manufacturing: modest but rising • 2021: 7% ➔ 2024: 11% • With large variation across industries, countries and firm size • Type of tasks affected • Language-related and administrative tasks • Integration into core production processes is still limited • Key barriers: • Data fragmentation, • Insufficient infrastructure, high upfront costs, • Regulatory complexity, • Shortages of AI-skilled professionals Restricted Use - À usage restreint

AI’s macroeconomic productivity gains can be significant but adoption and sectoral specialisation are key Predicted labour productivity growth due to AI over the next 10 years (percentage points, annualised) 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

Goldman Aghion and Acemoglu Sachs Bunel (2024, USA) (2023, USA) (2024, USA) Low adoption

USA

GBR

DEU

High adoption and expanded AI capabilities

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CAN

FRA

ITA

JPN

Medium adoption and expanded AI capabilities

The role for government policies 1. Macroeconomic gains are led by speed of adoption & sector specialisation → Investing in skills & building trust in adoption → Fostering secure and interoperable data sharing (also sub-nationally) → Enabling digital infrastructure and ecosystems, → Clearer regulatory guidance → Foster business dynamism (lowering regulatory barriers and help with startup financing)

2. International accessibility of leading AI models → International trade in digital services → Lively competition among AI developers → Local capacity in AI development has likely spillovers on AI use…

Note: also building on the OECD’s latest Economic Survey of Germany as well as a forthcoming OECD review, joint with the EU Commission, on AI in manufacturing Restricted Use - À usage restreint

The role for government policies Improving digital infrastructure across regions would support greater adoption of digital technologies Average download speed for fixed network in the regions, Mbps, first quarter of 2023 300

300

Range across regions

National average

EU-27

FRA

DNK

ESP

ROU

HUN

LUX

NLD

PRT

POL

SWE

FIN

LTU

IRL

BEL

0

ITA

0

DEU

50

LVA

50

SVK

100

SVN

100

BGR

150

EST

150

CZE

200

HRV

200

AUT

250

GRC

250

Note: The range across regions indicates the variation at the level of NUTS2 regions (Regierungsbezirk for Germany). “Mbps” refers to megabits per second.

Source: OECD Economic Survey of Germany (2025), based on European Commission (2024), “European Commission Regional and Urban Policy Ninth report on economic, social and territorial cohesion”.

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Thank you!

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