The Simple Macroeconomics of AI

Source: NBER Working Paper 32487 (Daron Acemoglu) · May 2024

ABSTRACT This paper evaluates claims about large macroeconomic implications of new advances in AI. It starts from a task-based model of AI’s effects, working through automation and task complementarities. So long as AI’s microeconomic effects are driven by cost savings/ productivity improvements at the task level, its macroeconomic consequences will be given by a version of Hulten’s theorem: GDP and aggregate productivity gains can be estimated by what fraction of tasks are impacted and average task-level cost savings. Using existing estimates on exposure to AI and productivity improvements at the task level, these macroeconomic effects appear nontrivial but modest—no more than a 0.66%increase in total factor productivity (TFP) over 10 years. The paper then argues that even these estimates could be exaggerated, because early evidence is from easy-to-learn tasks, whereas some of the future effects will come from hard-to-learn tasks, where there are many context-dependent factors affecting decision-making and no objective outcome measures from which to learn successful performance. Consequently, predicted TFP gains over the next 10 years are even more modest and are predicted to be less than 0.53%. I also explore AI’s wage and inequality effects. I show theoretically that even when AI improves the productivity of low-skill workers in certain tasks (without creating new tasks for them), this may increase rather than reduce inequality. Empirically, I find that AI advances are unlikely to increase inequality as much as previous automation technologies because their impact is more equally distributed across demographic groups, but there is also no evidence that AI will reduce labor income inequality. Instead, AI is predicted to widen the gap between capital and labor income. Finally, some of the new tasks created by AI may have negative social value (such as design of algorithms for online manipulation), and I discuss how to incorporate the macroeconomic effects of new tasks that may have negative social value.

Introduction. Artificial intelligence (AI) has captured imaginations. Promises of rapid, even unparalleled, productivity growth as well as new pathways for complementing humans have become commonplace. There is no doubt that recent developments in generative AI and large language models that produce text, information and images—and Shakespearean sonnets—in response to simple user prompts are impressive and even spellbinding. ChatGPT, originally released on November 30, 2022, soon became the fastest spreading tech platform in history, with an estimated 100 million monthly users only two months after launch.

AI will have implications for the macroeconomy, productivity, wages and inequality, but all of them are very hard to predict. This has not stopped a series of forecasts over the last year, often centering on the productivity gains that AI will trigger. Some experts believe that truly transformative implications, including artificial general intelligence (AGI) enabling AI to perform essentially all human tasks, could be around the corner.1 Other forecasters are more grounded, but still predict big effects on output. Goldman Sachs (2023) predicts a 7% increase in global GDP, equivalent to $7 trillion, and a 1.5% per annum increase in US productivity growth over a 10-year period. Recent McKinsey Global Institute (2023) forecasts suggest that generative AI could offer a boost as large as $17.1 to $25.6 trillion to the global economy, on top of the earlier estimates of economic growth from increased work automation. They reckon that the overall impact of AI and other automation technologies could produce up to a 1.5 −3.4 percentage point rise in average annual GDP growth in advanced economies over the coming decade.2 Are such large effects plausible? And if there are going to be productivity gains, who will be their beneficiary? With previous automation technologies, such as robotics, most gains accrued to firm owners and managers, while workers in impacted occupations experienced negative outcomes (e.g., Acemoglu and Restrepo, 2020a). Could it be different this time?

Some experts and commentators are more optimistic. A few “proof-of-concept” experimental studies document nontrivial productivity gains from generative AI, largely driven by improvements for less productive or lower-performing workers (e.g., Peng et al., 2023; Noy and Zhang, 2023; Brynjolfsson et al., 2023), and this has prompted some experts to be cautiously optimistic (Autor, 2024), while others are forecasting a “blue-collar bonanza” (The Economist, 2023).

This paper uses the framework from Acemoglu and Restrepo (2018, 2019b, 2022) to provide some insights for these debates, especially relevant for the medium-term (about 10-year) macroeconomic effects of AI. I build a task-based model, where the production of a unique final good requires a series of tasks to be performed, and these tasks can be allocated to either capital or labor, which have different comparative advantages. Automation corresponds to the expansion of the set of tasks that are produced by capital (including digital tools and algorithms). In this framework, AI-based productivity gains—measured either as growth of average output per worker or as total factor productivity (TFP) growth—can come from a number of distinct channels (see Acemoglu and Restrepo, 2019a):

ˆ Automation (or more precisely extensive-margin automation) involves AI models taking over and reducing costs in certain tasks. In the case of generative AI, various mid-level clerical functions, text summary, data classification, advanced pattern recognition, and computer vision tasks are among those that can be profitably automated.

ˆ Task complementarity can increase the productivity in tasks that are not fully automated and may even raise the marginal product of labor. For example, workers performing certain tasks may have better information or access to other complementary inputs. Alternately, AI may automate some subtasks, while at the same time enabling workers to specialize and raise their productivity in other aspects of their job.

ˆ Deepening of automation can take place, increasing the productivity of capital in tasks that have already been automated. For example, an already-automated IT security task may be performed more successfully by generative AI.

ˆ New tasks may be created thanks to AI and these tasks may impact the productivity of the whole production process.3 In this paper, I focus on the first two channels, though I also discuss how new tasks enabled by AI can have positive or negative effects.

Method. 2.2 How AI Could Affect Production Before completing the characterization of equilibrium, I discuss how AI could affect production in this economy.

1. AI enables further (extensive-margin) automation, increasing I. Such automation could be triggered either because AI reduces the cost of capital for some marginal tasks (i.e., tasks slightly above I) or increases the effectiveness of machinery or algorithms performing some marginal tasks, thus raising γK(z) for some z above I. Obvious examples of this type of automation include generative AI tools such as large language models taking over simple writing, translation and classification tasks as well as somewhat more complex tasks related to customer service and information provision, or computer vision technologies taking over image recognition and classification tasks.

2. AI can generate new task complementarities, raising the productivity of labor in tasks it is performing.9 For example, AI could provide better information to workers, directly increasing their productivity. This possibility could be modeled as AI reducing the cost of complementary capital kC(z) in some tasks z > I in the more general formulation in footnote 8. Alternatively, AI could automate some subtasks (such as providing readymade subroutines to computer programmers) and simultaneously enable humans to specialize in other subtasks, where their performance improves. This channel would require the explicit modeling of the range of subtasks making up each task. In this case, new AI technologies would perform some of these subtasks and do so with sufficiently high productivity, so that the subtask-level displacement would be weaker than the productivity gains, expanding the demand for labor and the marginal productivity of labor in these tasks. The possibility that the productivity effect could be larger than the displacement effect and expand labor demand is the same as in the basic models of automation, as exposited in Acemoglu and Restrepo (2018, 2019b). Even more interestingly, AI may enable workers to specialize in the non-automated subtasks and raise their expertise in these activities (e.g., when humans spend less time in writing standard subroutines, they can become better at other parts of programming).

I represent task complementarities by an increase in γL(z) in some tasks z ≤I, or when they happen in all tasks, by an increase in AL.

3. AI could induce deepening of automation—meaning improving performance, γK(z), or reducing costs, ρ(z), in some previously capital-intensive tasks (tasks z ≤I). Examples include IT security, automated control of inventories, and better automated quality control (see Acemoglu and Restrepo, 2019a).

4. AI can generate new labor-intensive products or tasks, which corresponds to an increase in N. As argued in Acemoglu and Restrepo (2020b), Acemoglu (2021) and Acemoglu et al. (2023), there are many pathways for such new tasks. Later I also discuss the case where some of these new products and tasks can be manipulative and have negative social value.

The effects of new AI tools will depend on the extent of each one of these effects, and I will try to provide more specificity on these possibilities later. In the rest of this section, I derive the consequences of different effects of AI.

2.5 Easy Tasks and Hard Tasks As noted above, the proof-of-concept studies Peng et al. (2023), Noy and Zhang (2023), and Brynjolfsson et al. (2023) have focused on tasks where AI could have clear benefits and where there were already sophisticated applications (such as GitHub Copilot for the first study) or where some businesses were already making an effort to use generative AI (as in the customer service application of the third study). I now argue that estimates from tasks that are relatively easy for current AI technologies, even if reliable within their chosen context, cannot be directly extrapolated to the rest of the economy. More generally, it is useful to distinguish between “easy-to-learn tasks” where outside learning (and thus learning by AI models) is easy from those where gaining expertise on the basis of outside observation is hard.