<\/span><\/h2>\nTo derive projections for macroeconomic productivity growth, we proceed in two steps. First, inspired by Acemoglu (2024), we obtain sectoral productivity gains by combining estimates of worker-level performance gains with measures of sectoral exposure to AI (Figure 2) and projections of future adoption rates based on the historical experience with previous general-purpose technologies (Figure 3). The resulting ten-year sectoral gains in total factor productivity range from 1\u20132% in manual-intensive activities (agriculture, fishing, mining) to 15\u201320% in knowledge-intensive services (ICT, finance, professional services), depending on the specific assumptions on AI adoption and exposure.<\/p>\n
Figure 2<\/strong> Exposure to AI varies across sectors<\/p>\nFigure 3<\/strong> Different scenarios for the adoption path of AI<\/p>\nIn the second step, we derive the implied macroeconomic productivity gains using a calibrated multisector general-equilibrium model that accounts for sectoral input-output linkages and the role of demand in driving price adjustments and factor reallocation across sectors (Baqaee and Farhi 2019). Macroeconomic productivity gains are derived under different scenarios regarding the magnitude of micro-level productivity gains, sectoral exposure to AI, the speed of adoption, and structural determinants of sectoral reallocation (Figure 4). The aggregate productivity gains from AI can be decomposed into three effects: (1) a direct effect of increased 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 Baumol effect.<\/p>\n
Figure 4<\/strong> Macro-level productivity gains from AI under different scenarios<\/p>\nEstimated impact on annual growth rates of total factor productivity over a 10-year horizon<\/p>\n
<\/span>Notes<\/em>: 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, the elasticity of substitution between the output of different sectors is close to one, and the factors of production (labour and capital) can reallocate freely across sectors. In scenarios 3\u20135 with adjustment frictions, the elasticity in consumption is assumed to be very low, and factors cannot reallocate across sectors. See more details in section 3 of Filippucci et al. (2024).<\/span><\/h6>\n<\/span>AI adoption is a key driver of productivity growth, but uneven sectoral gains could limit aggregate growth through a Baumol effect<\/span><\/h2>\nA 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. Currently, adoption varies strongly across firms and sectors, with country-level adoption rates being generally low, in the range of 5%\u201315%, as reported by official statistics of businesses and firm-level studies (e.g. Calvino and Fontanelli 2023a, 2023b). A comparison of scenarios 1 (low adoption) and 2 (high adoption and expanded capabilities) shows that 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.<\/p>\n
A negative Baumol effect on aggregate productivity growth arises if the productivity benefits of AI are concentrated in a few sectors, as in scenario 3 (high adoption and expanded capabilities, plus uneven sectoral gains and adjustment frictions), where sectoral gains are more uneven because knowledge-intensive sectors such as ICT and finance are assumed to adopt AI more quickly.
\n Productivity gains in the previous technology-driven boom (during the ICT boom decade starting in the mid-90s) were concentrated in a few sectors. In this spirit, scenario 4 (very large gains, concentrated in most exposed sectors, plus adjustment frictions) considers a concentration of sectoral gains that are closer to what was observed during that period.
\n Here, the Baumol effect reduces aggregate productivity gains by a third.<\/p>\n
In contrast, no Baumol effect arises if AI gains are more widespread across sectors, for instance if AI is better integrated with robotics technology, which would mean that not only cognitive but also manual-intensive activities could benefit from AI (scenario 5, AI combined with robotics technology, plus adjustment frictions).<\/p>\n
We also explore how aggregate productivity effects might depend on the presence of frictions through their impact on changes in the sectoral composition of the economy. Specifically, we consider the possibility that factors of production (capital and labour) cannot be freely reallocated across sectors over our projection horizon. We show that such frictions could magnify the negative Baumol effect by requiring steeper declines in the relative output prices of AI-boosted sectors to create enough demand for their increased output. This would lead to a larger decline in their GDP share, especially if demand is inelastic.
\n Hence, even though such frictions would prevent the reallocation of factors from high- to low-growth sectors, a general equilibrium perspective clarifies that aggregate productivity growth would still be harmed by preventing the efficient allocation of factors towards sectors where they are most valued.<\/p>\n
Overall, AI holds promise to revitalise productivity growth in OECD countries and beyond. Governments can also play a role in shaping the macroeconomic gains from 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) that 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 et al. 2023a,b, Baily et al. 2023, OECD 2023).<\/p>\n