AI and Productivity: Is This Time Different?
Paper Session
Saturday, Jan. 3, 2026 2:30 PM - 4:30 PM (EST)
This session will be streamed live.
- Chair: Karen Dynan, Harvard University
Generative AI at the Crossroads: Lightbulb, Dynamo, or Microscope?
Abstract
With the advent of generative AI (genAI), the potential scope of artificial intelligence has increased dramatically, but the future effect of genAI on productivity remains uncertain. GenAI-induced productivity gains will depend on the range of tasks enhanced by the technology, the extent of adoption for those tasks, and the success of firms in integrating the adopted technology; it is too early to draw confident conclusions on those questions. Moreover, the effects of the technology on the innovation process will play a crucial role. Some labor-saving innovations, such as the light bulb, temporarily raise productivity growth as adoption spreads, but the effect fades when the market is saturated; that is, the level of output per hour is permanently higher but the growth rate is not. In contrast, two types of technologies stand out as having longer-lived effects on productivity growth. First, there are technologies known as general-purpose technologies (GPTs). GPTs are (1) widely adopted, (2) spur abundant knock-on innovations (new goods and services, process efficiencies, and business reorganization), and (3) improve continuously, refreshing this innovation cycle; the electric dynamo is an example. Second, there are inventions of methods of invention (IMIs). IMIs increase the efficiency of the research and development process, generating new ideas more quickly and cheaply; the compound microscope is an example. We show that GenAI has the characteristics of both a GPT and an IMI---an encouraging sign. Even so, for genAI to boost productivity growth, its contribution will have to outpace past IT innovations which are baked into the trend, including machine learning.Miracle or Myth? Assessing the Macroeconomic Productivity Gains from Artificial Intelligence
Abstract
Artificial Intelligence delivers large productivity gains in specific tasks, but its impact on aggregate productivity remains debated. This paper discusses the drivers of micro-level AI gains and develops a micro-to-macro framework to predict aggregate productivity gains, arguing that AI could contribute between 0.3-0.9 percentage points to annual TFP growth over the next decade. Our micro-to-macro approach proceeds in two steps. In a first step, we follow Acemoglu (2025) and combine estimates of task-level productivity gains with measures of exposure of different tasks to AI and with projections for AI adoption rates to predict sectoral productivity gains from AI. This step reveals that AI-driven productivity gains will likely differ strongly across sectors; we predict that cumulative total factor productivity gains over the next decade could range from a low 0.1 pp. of additional growth in manual-intensive activities (agriculture, fishing, mining) and up to 3 pp. growth effect in knowledge-intensive services (Information and Communication Technologies, finance, professional services), driven by sectoral heterogeneity in exposure and also depending on the projections for future AI adoption rates. The pronounced sectoral heterogeneity in expected productivity gains form AI will likely have implications for the sectoral composition of the economy. Aggregate productivity growth could be limited by a Baumol effect if the sectors with the lowest productivity gains grow as a share of GDP. In a second step, we therefore account for the role of AI-driven structural change by aggregating sectoral productivity gains using a multi-sector general equilibrium model, building on Baqaee and Farhi (2019), that allows for sectoral reallocation of factors and adjustments in sectoral relative output prices. We find that sectoral differences in AI-driven productivity growth could diminish aggregate productivity gains through the Baumol effect, especially if elasticities of substitution in consumption across sectors are low and factor reallocation is limited.AI as an Innovation in the Method of Innovation: Implications for Productivity Growth
Abstract
This paper estimates the possible contribution of AI to future labor productivity growth assuming that AI is both a “general purpose technology” and an “innovation in the method of innovation.” The framework used in this paper separates an upstream innovation sector from a downstream production sector. The impact of AI is modelled as (a) boosting upstream total factor productivity (a “production effect”) and (b) enhancing intangible capital use downstream (a “use effect”). The framework can be used to show how AI’s boost to upstream total factor productivity growth can drive long-term labor productivity growth. We relate this framework to the “task accounting” framework commonly used. We have two main findings. First, we argue that AI can already be seen in productivity statistics for the United States. The production and use effects of software and software R&D (alone) contributed (a) 50 percent of the 2 percent average rate of growth in US nonfarm business labor productivity from 2017 to 2024 and (a) 50 percent of its 1.2 percentage point acceleration relative to the pace from 2012 to 2017. Second, taking additional intangibles and data assets into account, we calculate a long-run contribution of AI to labor productivity growth based on assumptions that follow from the recent trajectories of investments in software, software R&D, other intangibles, and productivity growth in both US and Europe. Our central estimates are that AI will boost annual labor productivity growth by as much as 1 percentage point in the United States and about .3 percentage point in Europe.Discussant(s)
Timothy DeStefano
,
Georgetown University
Anton Korinek
,
University of Virginia
Chad Syverson
,
University of Chicago
Bart van Ark
,
University of Manchester
JEL Classifications
- O4 - Economic Growth and Aggregate Productivity
- D2 - Production and Organizations