By Francesco Filippucci, Peter Gal and Matthias Schief, OECD Economics Department.
Artificial Intelligence (AI) could unleash productivity gains, boost growth, and raise incomes. Indeed, many firms are looking to the technology to increase productivity, with large documented gains in workers’ performance from using Generative AI tools (e.g. Large Language Models similar to ChatGPT) in business contexts such as customer service, business consulting, or software development. Moreover, given its rapidly expanding capabilities, AI is widely heralded as a new General-Purpose Technology (GPT) that could lift macroeconomic productivity growth, as it was the case with the internet and personal computers or with previous breakthrough innovations like the steam engine and electricity (Agrawal, Gans and Goldfarb, 2019; Lipsey, Carlaw and Bekar, 2005; Filippucci et al, 2024).
But can current micro-level productivity gains really lead to large productivity gains from AI at the macroeconomic level over the next decade? To answer this question, one needs to consider what share of economic activities would experience productivity gains if AI was adopted (“exposure to AI”), and how quickly firms will adopt AI. Additionally, at the macroeconomic level, one needs to consider that productivity gains can also depend on broader economic factors, such as sectoral linkages, demand responses, or labour and capital market frictions.
A new working paper by the OECD Economics Department (Filippucci, Gal and Schief, 2024) considers these mechanisms and assesses the macroeconomic productivity gains from AI over the coming 10-years. The results suggest that AI could contribute significantly to aggregate productivity growth over the next decade, contributing between 0.25 to 0.6 percentage points to annual Total Factor Productivity (TFP) growth in the United States (or 0.4 to 0.9 percentage points to annual labour productivity growth) in our main scenarios (Figure 1). Estimates for other economies are of similar magnitude, though somewhat lower given that adoption of AI is expected to be slower. These estimates imply a substantial improvement in the context of the weak productivity growth across the OECD over the past decades, which has been in the range of 1-1.5% per year.
Figure 1: Macro-level productivity gains from AI over
Estimated impact on annual growth rates over a 10-year horizon
Note: The bars correspond to different scenarios regarding the adoption, capabilities, and micro-level gains of AI (as in Figure 1). In scenarios 1 and 2, demand is assumed to be relatively elastic, and the factors of production (labour and capital) can reallocate freely across sectors. In scenarios 3-5 with adjustment frictions, demand is assumed to be very inelastic, and factors cannot reallocate across sectors. See more details in section 3 of Filippucci, Gal and Schief (2024).
The aggregate productivity gain from AI is the sum of three effects: 1) a direct effect of increasing productivity at the sectoral level; 2) an input-output multiplier effect as productivity gains in one sector also benefit other sectors through reduced costs of intermediate inputs; and 3) a negative reallocation effect in the spirit of Baumol’s growth disease (Baumol, 1967; Nordhaus, 2008) that arises if the sectors with limited productivity growth increase as a share of GDP.
A key insight that emerges from this analysis is that the macroeconomic impact of AI will depend primarily on the adoption speed and the degree to which AI can benefit economic activities across a wide range of sectors in the economy. Current adoption varies strongly across firms and sectors, with country-level adoption rates being generally low, in the range of 5-15%, as reported by official statistics of businesses and firm-level studies (e.g. Calvino and Fontanelli, 2023). Fast and productive integration of AI in a wider range of economic activities through expanded AI capabilities (e.g. further integration with other digital tools) is necessary for the emergence of large macroeconomic gains (Scenario 2 vs 1).
However, even with high adoption rates and expanded capabilities, general equilibrium effects working through prices could reduce the overall macroeconomic gain if the productivity benefits of AI remain concentrated in a few sectors (knowledge intensive services such as ICT, finance and professional services) (Scenarios 3 and 4). Demand for these services can become saturated, and growth will thus be limited “not by what we do well but rather by what is essential and yet hard to improve” (Aghion, Jones and Jones, 2019). In contrast, macroeconomic gains would be larger if AI gains were more widespread across sectors, for instance in the case of further integration with robotics technology, which would enable not only cognitive but also manual-intensive activities to benefit from AI (Scenario 5).
Overall, AI holds significant promise to revitalise productivity growth in OECD countries and beyond. Governments can also play a role in shaping the macroeconomic gains for AI, for example by resolving legal uncertainties around accountability, which may hold back productive AI adoption by firms (OECD, 2024a). At the same time, governments can foster a competitive environment (both in the AI-using as well as the AI-producing sectors; see Aghion and Bunel, 2024; OECD, 2024b) which is conducive to innovation and experimentation, while monitoring potential labour market disruptions and supporting workers as they transition into new roles in the AI economy (e.g. Acemoglu, Autor and Johnson; Baily, Brynjolfsson and Korinek, 2023; OECD, 2023).
References
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Acemoglu, D., D. Autor and S. Johnson (2023), Can we Have Pro-Worker AI? Choosing a path of machines in service of minds, MIT Shaping the Future of Work Initiative, Policy Memo, https://shapingwork.mit.edu/wp-content/uploads/2023/09/Pro-Worker-AI-Policy-Memo.pdf
Aghion, P. and S. Bunel (2024), “AI and Growth: Where Do We Stand?”, https://www.frbsf.org/wp-content/uploads/AI-and-Growth-Aghion-Bunel.pdf
Aghion, P., B. Jones and C. Jones (2019), “Artificial Intelligence and Economic Growth”, in: The Economics of Artificial Intelligence: An Agenda, p. 237-82, University of Chicago Press, https://www.nber.org/system/files/working_papers/w23928/w23928.pdf
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