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Title page 1

Contents 7

Foreword 3

Acknowledgements 5

Abstract/Résumé 6

1. Introduction 9

2. The macroeconomic gains from AI in an international context: from tasks to sectors and countries 14

3. Determinants of sector-level productivity gains 16

3.1. Micro-level performance gains from AI 16

3.2. The exposure of different sectors to AI 17

3.3. Estimating current and future AI adoption among firms 18

3.3.1. Current AI adoption in core business functions: aiming for cross-country comparability 19

3.3.2. Future AI adoption: following the path of previous General Purpose Technologies 20

3.4. Predicted sector level productivity gains 22

4. How to capture the role of international linkages in shaping macroeconomic AI gains? 23

4.1. A global country-sector general equilibrium model 25

4.1.1. Production 25

4.1.2. Consumption 27

4.1.3. Market clearing conditions, equilibrium and solution 27

4.1.4. Data and calibration 28

4.2. Learning from suppliers: Knowledge spillovers affecting AI adoption across countries 28

5. Results 29

5.1. Estimates on the macroeconomic gains from AI across the OECD and the G20 30

5.1.1. Dissecting aggregate effects: price changes induced by the AI shock and the role of trade specialisation 32

5.1.2. Welfare gains from AI adoption with international trade: a decomposition 35

5.1.3. Counting on foreign productivity gains? Assessing the scope for real income gains without domestic AI adoption through model experiments 39

5.2. The role of international knowledge spillovers through boosting AI adoption 42

6. Conclusions 45

References 47

Annex A. Supplementary material 53

A.1. Additional Tables and Figures 53

A.2. Calculating sector-level exposure to AI in different countries 59

A.3. AI adoption calculations 60

Harmonisation of existing AI adoption measures and out-of-sample predictions 60

Cross validation of our preferred AI adoption estimate 67

Current AI adoption extrapolated over 10 years 68

A.4. Model Details 70

Set-up 70

Production 71

Consumption 73

Market Clearing and Solution Methodology 74

First-order Linear Approximations 75

Elasticities 76

A.5. Accounting for under-reported services trade 76

Adjusting AI-related knowledge spillover weights for under-reporting in international services trade 79

Tables 7

Table 1. AI and international trade: the main channels 24

Table 2. The assumptions underlying the scenarios presented in the paper 30

Table 3. Summary of results: trade channels are important and conditional on AI domestic adoption 46

Figures 8

Figure 1. The macroeconomic gains from AI in an international context: a micro-to-macro framework 15

Figure 2. The share of the 5 most AI-exposed sectors in GDP in the lowest and the highest AI-exposed countries 18

Figure 3. Mapping AI's future adoption path with that of previous General Purpose Technologies 21

Figure 4. The expected increase in AI adoption varies a lot across countries 22

Figure 5. The projected AI-driven productivity gains vary across sectors 23

Figure 6. Production Structure - an example focused on Turkish motor vehicle manufacturing 26

Figure 7. Consumption structure - an example focused on Turkish households 27

Figure 8. AI's macroeconomic gains will vary widely across countries 31

Figure 9. Large TFP gains lead to large declines in output prices 33

Figure 10. The most impacted countries by the AI shock in terms of exports and imports 34

Figure 11. Trade price and quantity changes reflect import and export specialisation patterns of countries 35

Figure 12. The role of trade in driving the welfare gains from AI 38

Figure 13. Foreign contributions and allocation effects due to AI correlate positively with trade openness and adoption rates 39

Figure 14. Relying only on foreign generated AI gains leads to moderate gains from AI due to a loss in competitiveness 41

Figure 15. Missing out on AI adoption undermines competitiveness in global markets for countries specialised in the most impacted sectors... 42

Figure 16. Technology spillovers from leading AI-adopter trading partners can boost gains significantly in lagging AI adopter countries 44

Boxes 8

Box 1. Decomposing AI induced welfare gains with international trade 36

Annex Tables 8

Table A.1. Overview of variables considered as potential regressors 62

Table A.2. The econometric link between high-intensity AI adoption and its structural drivers 63

Table A.3. Parameter values 76

Annex Figures 8

Figure A.1. Micro-level gains from Generative AI are found to be large in a range of tasks 54

Figure A.2. AI exposure in OECD and G20 countries 54

Figure A.3. AI exposure in the construction sector, OECD countries and key partners 55

Figure A.4. Employment share of specific occupations, in lowest vs. highest AI-exposed countries 55

Figure A.5. The AI cost decline seems comparable to what happened with other digital technologies in the past 56

Figure A.6. High price declines in sectors with large TFP gains and high elasticity of substitution 56

Figure A.7. AI-induced labour productivity growth across countries 57

Figure A.8. Fostering AI adoption would close a large part of the gap vis-à-vis the United States in terms of predicted labour productivity gains 57

Figure A.9. The projected AI-driven per capita income gains are larger for initially more developed countries 58

Figure A.10. Top 5 Oil and Mining Exporters by Share 58

Figure A.11. Overview over most important steps in the calculation of harmonised AI adoption in core business functions 60

Figure A.12. AI adoption predictor variables: Descriptives statistics of basic predictor variables 64

Figure A.13. AI adoption predictor variables: Descriptives statistics of additional predictor variables 65

Figure A.14. Harmonised estimates for current AI use in core business functions, 2024 66

Figure A.15. Cross-survey comparison of AI adoption in China and India relative to the US 67

Figure A.16. AI adoption relative to the US 68

Figure A.17. Diffusion path of past technologies 69

Figure A.18. S-shaped adoption path of past technologies: the example of the internet 69

Figure A.19. Current adoption rate differences across countries are likely to drive adoption 10 years from now: United States versus Chile 70

Figure A.20. Correcting for unreported service trade introduces foreign generated gains from AI in intangible intensive services and... 77

Figure A.21. Correcting for unreported service trade lifts gains from foreign generated AI only moderately in a few countries 78

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