The Simple Macroeconomics of AI* Daron Acemoglu Massachusetts Institute of Technology May 12, 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. JEL Classification: E24, J24, O30, O33. Keywords: Artificial Intelligence, automation, ChatGPT, inequality, productivity, technology adoption, wage. *Paper prepared for Economic Policy. I am grateful to Can Yeşildere for phenomenal research assistance, and to Leonardo Bursztyn, Mert Demirer, Lauren Fahey, Roberto Galbiati, Isabelle Méjean, Shaked Noy, Sida Peng, Julia Regier, and Whitney Zhang as well as participants in the MIT Solow Memorial conference and the Economic Policy conference for useful comments. I am especially grateful to my discussants, David Hémous and Benoît Coeuré, for insightful comments and suggestions. I thank Pamela Mishkin and Daniel Rock for generously sharing their data on AI exposure. I am also heavily indebted to my collaborators on several projects related to these topics, David Autor, Simon Johnson and Pascual Restrepo, from whom I learned a great deal and who have also given me very useful comments on the current draft. Financial support from the Hewlett Foundation is gratefully acknowledged. All remaining errors are mine. --- 1 # 1 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 ¹ Korinek and Suh (2024) predict a “baseline” GDP growth of 100% over the next 10 years, and also entertain the possibility of much higher “aggressive” AGI growth rates, such as a 300% increase in GDP. Many others are seeing recent developments as a confirmation of the forecasts in Kurzweil (2005) about the impending arrival of “singularity” and “explosive” economic growth (Davidson, 2021). ² Three caveats are in order. First, although most recent advances are in generative artificial intelligence, the economic forces explored here apply to other types of AI, and estimates of exposed tasks I use come on the basis of anticipated improvements in a range of AI-related technologies, including computer vision and software building on large language models. Hence, I consider the numbers here to apply to all of artificial intelligence and thus typically refer to “AI”, unless there is a reason to emphasize generative AI. Second, I focus on the US economy because much of the existing evidence on microeconomic effects of AI and prevalence of exposed tasks is from the United States. The impact on other industrialized nations should be similar, whereas the consequences for the developing world are harder to ascertain and require much more in-depth research. Third, some commentators use “productivity” to refer to output per worker (or average labor productivity), while others mean total factor productivity (TFP). Throughout, I distinguish between aggregate TFP and GDP (per capita/worker) effects, and I use productivity improvement at the micro/task level as synonymous to cost savings. --- 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)*: - *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. - *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. - *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. - *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. I do not dwell on deepening of automation, because the tasks impacted by (generative) AI are different than those automated by the previous wave of digital technologies, such as robotics, advanced manufacturing equipment and software systems.⁴ I also do not discuss how AI can have revolutionary effects by changing the process of science (a possibility illustrated by neural network-enabled advances in protein folding and new crystal structures discovered by the Google subsidiary DeepMind), because large-scale advances of this sort do not seem likely within the 10-year time frame and many current discussions focus on automation and task complementarities. I show that when AI’s microeconomic effects are driven by cost savings (equivalently, productivity improvements) at the task level—due to either automation or task complementarities—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. This equation disciplines any GDP and productivity effects from AI. Despite its simplicity, applying this equation is far from trivial, because there is huge uncertainty about which tasks will be automated or complemented, and what the cost savings will be. Nevertheless, as an illustrative exercise, I use data from a number of recent studies, in particular, Eloundou et al. (2023) and Svanberg et al. (2024), as well as the experimental studies mentioned above, to obtain some back-of-the-envelope numbers. Eloundou et al. (2023) provide the first systematic estimates of what tasks will be impacted by generative AI and computer vision technologies. Their methodology does not fully distinguish whether the impact will take the form of automation or task complementarities, and does not provide information on when we should expect these impacts to be realized and how large their cost savings will be.⁵ For computer vision technologies, Svanberg et al. (2024) provide estimates ³New tasks in this framework also capture the possibility of productivity-enhancing reorganizing production. The role of AI in enabling such reorganization is emphasized by, among others, Bresnahan (2019) and Agrawal et al. (2023). ⁴Eloundou et al. (2023) report negative statistical associations between their measure of exposure to AI, which I use below, and measures of exposure to robots and manual routine tasks. ⁵More specifically, I use the most granular information that Eloundou et al. (2023) present, which is their “automation index”, coded with help from GPT-4. This index provides information on how much of the activities involved in a task/occupation can be performed by AI. Although this index has somewhat greater emphasis on automation, it does not systematically distinguish between automation and task complementarities. As I discuss further below and Eloundou et al. (2023) themselves note, their exposure measure often captures the possibility that generative AI and related digital technologies can perform some of the 3 --- of what fraction of tasks that are potentially exposed to AI can be feasibly automated in different time frames. I take Eloundou et al.’s estimates of tasks that are exposed to AI (without distinguishing automation vs. task complementarities). I then aggregate this to the occupational level and weight the importance of each occupation by its wage bill share in the US economy. This calculation implies that 20% of US labor tasks are exposed to AI. I then use Svanberg et al.’s estimate for computer vision tasks that, among all exposed tasks, 23% can be profitably performed by AI (for the rest, the authors estimate that the costs would exceed the benefits). I take the average labor cost savings to be 27%—the average of the estimates in Noy and Zhang (2023) and Brynjolfsson et al. (2023)—and turn this into overall cost savings using industry labor shares, which imply average overall cost savings of 14.4%. This calculation implies that total factor productivity (TFP) effects within the next 10 years should be no more than 0.66% in total—or approximately a 0.064% increase in TFP growth annually. If we add bigger productivity gains from Peng et al. (2023), which are less likely to be broadly applicable, or incorporate further declines in GPU costs, this number still remains around 0.9%. To turn these numbers into GDP estimates, we need to know how much the capital stock will increase due to AI. I start with the benchmark of a rise in the capital stock proportional to the increase in TFP. This benchmark is consistent with the fact that generative AI does not seem to require huge investments by users (beyond those made by designers and trainers of the models). With these investment effects incorporated, GDP is also estimated to grow by $0.93\%-1.16\%$ over the next 10 years. When I assume that the investment response will be similar to those for earlier automation technologies and use the full framework from Acemoglu and Restrepo (2022) to estimate the increase in the capital stock, the upper bound on GDP effects rises to around $1.4\%-1.56\%$. Nevertheless, my framework also clarifies that if the capital-output ratio increases in response to the TFP rise, this may increase GDP by more than TFP, but does not additionally contribute to welfare, because the extra investment comes out of consumption. I then argue that the numbers above may be overestimates of the aggregate productivity benefits from AI, because existing estimates of productivity gains and cost savings are in tasks that are “easy-to-learn”, which then makes them easy for AI. In contrast, some of the --- future effects will come from “hard-to-learn” tasks, where there are many context-dependent factors affecting decision-making, and most learning is based on the behavior of human agents performing similar tasks (rather than objective outcome measures). Productivity gains from AI in these hard tasks will be less—though, of course, it is challenging to determine exactly how much less. Using a range of (speculative) assumptions, I estimate an upper bound of 73% easy tasks among Eloundou et al.’s exposed tasks. I suppose that productivity gains in hard tasks will be approximately one quarter of the easy ones. This leads to an updated, more modest increase in TFP and GDP in the next 10 years that can be upper bounded by 0.53% and 0.90%, respectively. New tasks created with AI can more significantly boost productivity. However, some of the new AI-generated tasks are manipulative and may have negative social value, such as deepfakes, misleading digital advertisements, addictive social media or AI-powered malicious computer attacks. While it is difficult to put numbers on good and bad new tasks, based on recent research I suggest that the negative effects from new bad tasks could be sizable. I make a very speculative attempt using numbers on the negative welfare effects of social media from a recent paper by *Bursztyn et al. (2023)*. These authors find that consumers have positive willingness to pay for using social media (in particular Instagram and TikTok) when others are using it, but they would prefer that neither themselves nor others use it. Roughly speaking, their estimates imply that revenue can increase by about $53 per user-month, but this has a negative impact on total GDP/welfare equivalent to $19 per user-month. Combining these numbers with an estimate of the fraction of activities that may generate negative social value (in practice, revenues from social media and spending on attack-defense arms races in IT security), I suggest that with more intensive use of AI, it is possible to have nontrivial increases in GDP. For example, AI may appear to increase GDP by 2%, while in reality reducing welfare by $-0.72\%$ (in consumption equivalent units). Finally, I explore AI’s wage and inequality effects. My framework implies that productivity gains from AI are unlikely to lead to sizable wage rises. Moreover, even if AI improves the productivity of low- and middle-performing workers (or workers with limited expertise in complex tasks), I argue that, theoretically, this may not translate into lower inequality. In fact, I show by means of a simple example how an increase in the productivity of low-skill workers in certain tasks can lead to *higher* rather than lower inequality. Adapting the general equilibrium estimates from *Acemoglu and Restrepo (2022)* to the setting of AI, I find that the more intensive use of AI is unlikely to lead to substantial wage declines for --- affected groups, because AI-exposed tasks are more evenly distributed across demographic groups than were the tasks exposed to earlier waves of automation. Nevertheless, I estimate that AI will not reduce inequality and is likely to have a negative effect on the real earnings of low-education women (especially white, native-born low-education women). My findings also suggest that AI will further expand the gap between capital and labor income as a whole. In the conclusion, I argue that as originally suggested in *Acemoglu and Restrepo (2018)*, more favorable wage and inequality effects, as well as more sizable productivity benefits, will likely depend on the creation of new tasks for workers in general and for middle- and low-pay workers in particular. While this is feasible in theory and I have argued elsewhere how it could be achieved *(Acemoglu, 2021b; Acemoglu et al., 2023)*, I also discuss why this does not seem to be the focus of artificial intelligence research at the moment. In sum, it should be clear that forecasting AI’s effects on the macroeconomy is extremely difficult and will have to be based on a number of speculative assumptions. Nevertheless, the gist of this paper is that a simple framework can discipline our thinking and forecasts, and if we take this framework and existing estimates seriously, it is difficult to arrive at very large macroeconomic gains. The rest of the paper is organized as follows. The next section outlines the conceptual framework I use throughout the paper and derives a number of theoretical insights on aggregate productivity gains, investment responses, and wage and inequality effects. It also discusses the crucial distinction between easy-to-learn and hard-to-learn tasks and their productivity implications, and introduces the contrast between good and bad new tasks. Section 3 provides a preliminary quantitative analysis of new AI breakthroughs within this framework. It first presents a baseline (upper bound) estimate on the basis of the fraction of existing tasks that are likely to be impacted by AI within the next 10 years and existing estimates of cost savings (productivity improvements) from AI. It then refines this estimate by introducing the distinction between easy-to-learn and hard-to-learn tasks and undertakes a preliminary classification of AI-exposed tasks into the easy and hard categories. I also make an even more speculative attempt at incorporating the macroeconomic implications of bad new tasks into this framework. Finally, I report estimates on the wage and inequality implications of recent AI advances. Section 4 concludes with a general discussion, while the Appendix includes additional information on how tasks are classified into exposed and non-exposed and easy-to-learn and hard-to-learn categories. ## --- 7 # 2 Conceptual Framework The model here builds on Acemoglu and Autor (2011) and Acemoglu and Restrepo (2018, 2019b, 2022), and I focus on the main elements of the framework, referring the reader to these papers for further details and refinements. The economy is static and involves the production of a unique final good, and all markets are competitive. $^7$ The production of a unique final good takes place by combining a set of tasks, with measure $N$, using the following production function $$ Y = B(N) \left(\int_{0}^{N} y(z)^{\frac{\sigma - 1}{\sigma}} dz\right)^{\frac{\sigma}{\sigma - 1}}, \tag{1} $$ where $Y(z)$ denotes the output of task $z$ for $z \in [0, N]$, $\sigma \geq 0$ is the elasticity of substitution between tasks and the parameter $B(N)$ depends on $N$ to capture the possible system-wide effects of new tasks, though in what follows I will suppress this dependence to simplify the notation. For now, the elasticity $\sigma$ can take any value, but it is reasonable to presume $\sigma \leq 1$, so that tasks are gross complements. I later set the elasticity of substitution between tasks to $\sigma \simeq 0.5$, as estimated by Humlum (2021) and also imposed in Acemoglu and Restrepo (2022). Tasks can be produced using capital or labor according to the production function $$ y(z) = A_{L} \gamma_{L}(z) l(z) + A_{K} \gamma_{K}(z) k(z) \text{ for any } z \in [0, N], $$ where $A_{L}$ and $A_{K}$ are labor-augmenting and capital-augmenting productivity terms, $\gamma_{L}(z)$ and $\gamma_{K}(z)$ are labor's and capital's task-specific productivity schedules, and $l(z)$ and $k(z)$ denote labor and capital allocated to performing task $z$. This task production function implies that capital and labor have different productivities in different tasks, but within a task they are perfect substitutes. $^{8}$ $^7$Acemoglu and Restrepo (2018) provide a dynamic version of this economy with capital accumulation and endogenous technological choices, while Acemoglu and Restrepo (2022) provide a generalization with multiple types of labor and multiple sectors, and Acemoglu and Restrepo (2023) consider a non-competitive version of this economy. Extending the framework in any of these directions does not materially affect the results I discuss here. $^8$One important simplification is to assume that tasks assigned to labor do not require any capital or tools, which is clearly unrealistic. The online Appendix of Acemoglu and Restrepo (2018) shows that the results are very similar if the task production function is modified such that: $$ y(z) = A_{L} \gamma_{L}(z) \left[ l(z)^{1 - \kappa} k_{C}(z)^{\kappa} \right] + A_{K} \gamma_{K}(z) k(z), $$ where $\kappa \in (0,1)$ and $k_{C}(z)$ is labor-complementary capital in task $z$ (while $k(z)$ denotes capital used for automating task $z$). Because $\kappa < 1$, tasks assigned to labor are still less intensive in capital than are fully-automated tasks. --- Throughout, I assume that $\gamma_{L}(z)/\gamma_{K}(z)$ is increasing in $z$, so that labor has a comparative advantage in higher-indexed tasks. This implies that there exists a threshold $I$ such that tasks $z\leq I$ are produced with capital and those above this threshold are produced with labor. I normalize the total population to 1 and assume that different workers have different units of effective labor. To simplify the discussion, I assume that there are two types of labor, high-skill and low-skill, and there is no comparative advantage difference between these two types of labor (I generalize this later). The only difference is that high-skill workers, which make up a fraction $\phi^{H}$ of the population, have $\lambda^{H}$ units of effective labor, while the remaining $\phi^{U}=1-\phi^{H}$ low-skill workers have only $\lambda^{U}<\lambda^{H}$ units of effective labor. This specification ensures that both high-skill and low-skill workers could be performing some of the same tasks. It also implies that wage inequality is pinned down by $\lambda^{H}/\lambda^{U}$—a feature I relax later. I also assume that all labor is supplied inelastically, so I write the total supply of labor as $\phi^{U}\lambda^{U}+\phi^{H}\lambda^{H}=L.$ The labor market-clearing condition is $L=\int_{0}^{N}l(z)dz,$ (2) and I denote the wage rate by $w$. Capital is specialized for the tasks in which it is used, and I assume that capital of type $z$ is produced linearly from the final good with unit cost $R(z)=R(K)\rho(z),$ (3) where $K=\int_{0}^{N}k(z)dz$ is the total capital stock of the economy. All firms take the cost of capital for task $z$, $R(z)$, as given. The first term in (3) implies that the required rate of return on capital can increase when the capital stock of the economy is larger and the second term is task-specific, representing the possibility that different types of capital could have different costs. For tasks that are not yet technologically automated—meaning that they cannot be produced by capital—we can either set $\gamma_{K}(z)=0$ or take $\rho(z)$ to be very large. Finally, I assume that there exists a (non-satiated) representative household that consumes the final good (net of capital expenditures) and I denote the consumption of this household by $C$. --- 2.1 Equilibrium I focus on a competitive equilibrium, which satisfies the following usual conditions: - The allocation of tasks $z\in[0,N]$ is cost-minimizing. That is, task $z\in[0,N]$ is produced by labor if and only if $\frac{w}{A_{L}\gamma_{L}(z)}<\frac{R(z)}{A_{K}\gamma_{K}(z)}.$ - The amount of capital $k(z)$ is chosen to maximize $Y-R(z)k(z)$, where $Y$ is given as in (1). - The labor market clears. That is, (2) holds. Notice that the first condition imposes an innocuous tie-breaking rule that when indifferent, firms use capital for performing a task. Given this tie-breaking rule, all tasks $z>I$ will be performed by labor (i.e., $l(z)=0$ for all $z\leq I$ and $k(z)=0$ for all $z>I$). Whether this is high- or low-skill labor is indeterminate in the baseline model, so I focus on the overall amount of effective labor units. In a competitive equilibrium, all tasks performed by labor must have $B^{\frac{\sigma-1}{\sigma}}A_{L}^{\frac{\sigma-1}{\sigma}}\gamma_{L}(z)^{\frac{\sigma-1}{\sigma}}l(z)^{-\frac{1}{\sigma}}Y^{\frac{1}{\sigma}}=w.$ (4) This implies that for any two tasks $z>I$ and $z^{\prime}>I$, $\frac{l(z)}{l(z^{\prime})}=\frac{\gamma_{L}(z)^{\sigma-1}}{\gamma_{L}(z^{\prime})^{\sigma-1}}.$ (5) Notice that when $\sigma<1$, less labor is allocated to tasks in which labor’s productivity is higher—a feature whose implications I will emphasize later. Equation (5), combined with the labor market-clearing condition (2), implies $l(z)=\frac{\gamma_{L}(z)^{\sigma-1}}{\int_{I}^{N}\gamma_{L}(z)^{\sigma-1}dz}L.$ (6) Moreover, with a similar reasoning for any task $z