This paper defines pro-worker technologies, including Artificial Intelligence, as technologies that make human skills and expertise more valuable by expanding worker capabilities. Our conceptual framework distinguishes among five categories of technological change: labor-augmenting, capital-augmenting, automating, expertise-leveling, and new task-creating. Only the last category is unambiguously pro-worker, generating demand for novel human expertise rather than commodifying it. We illustrate these distinctions through hypothetical and real-world examples spanning aviation maintenance, electrical services, custodial work, education, patent examination, and gig delivery. While AI’s capacity to automate work is substantial, we argue that its potential to serve as a collaborator, by extending human judgment, enabling new tasks, and accelerating skill acquisition, is equally transformative and currently underexploited. We identify market failures, including misaligned firm and developer incentives, path dependence, and a pervasive pro-automation ideology, that may lead to underinvestment in pro-worker AI. We consider nine policy directions that would change incentives, including targeted investments in health care and education, tax code reform, antitrust enforcement, and intellectual property protections for worker expertise. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Support for populist and authoritarian regimes is rising worldwide, despite evidence that they tend to underperform economically. We examine the role of (mis)perceptions of regime performance as drivers of political attitudes, leveraging two survey experiments with 11,377 respondents during Argentina’s 2023 presidential elections. Optimistic beliefs about the performance of populist and non-democratic regimes were widespread, and displayed a strong correlation with support for these regimes. When exposed to randomly assigned informational treatments challenging optimistic views about these political regimes, individuals significantly adjusted their beliefs, and reduced their support for candidates they associate with populist and authoritarian leanings. Exploring the impact of different information sources, we find that academic sources and newspapers were more influential than social media. Although individuals adjusted their beliefs and attitudes in response to information on regime performance, contradicting their prior beliefs reduced their demand for additional information, consistent with an important role for motivated reasoning. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

The secular decline in birth rates across the globe over the past seven decades has slowed population growth, raised average ages, and reshaped labor markets and the macroeconomy. Contrary to the widespread expectation that these trends hamper economic growth, we find lower birth rates are associated with higher growth in GDP per working-age adult across countries and higher wage growth across US commuting zones, with no negative impact on aggregate GDP or earnings. These patterns are not explained by educational upgrading, rising female labor force participation, the declining importance of agriculture, or neoclassical-Solow mechanisms. We argue that they reflect the endogenous, labor-saving response of technology to the scarcity of younger workers. Consistent with this interpretation, countries and regions with lower birth rates exhibit more labor-saving patents and growing high-tech activity. There is also higher TFP growth across countries and industries. Exploiting cross-country variation in WWII military and civilian deaths, we find that declines in younger population, rather than population size per se, drive our results. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

In turbulent times, political labels become increasingly uninformative about politicians’ true policy preferences or their ability to withstand the influence of special interest groups. We offer a model in which politicians use campaign rhetoric to signal their political preferences in multiple dimensions. In equilibrium, the less popular types try to pool with the more popular ones, whereas the more popular types seek to separate themselves. The ability of voters to process information shapes politicians’ campaign rhetoric. If the signals on the cultural dimension are more precise, politicians signal more there, even if the economy is more important to voters. The unpopular type benefits from increased conformity, which bridges the candidates’ rhetoric and makes it more difficult for voters to make an informed decision. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We develop an assignment model of automation. Each of a continuum of tasks of variable complexity is assigned to either capital or one of a continuum of labor skills. We characterize conditions for interior automation, whereby tasks of intermediate complexity are performed by capital. Interior automation arises when low-skill wages are low and effective cost of capital in low-complexity tasks is high. Minimum wages make interior automation less likely. Higher capital productivity causes employment and wage polarization, changes the skill premium nonmonotonically, and reduces the real wage of workers with comparative advantage profiles close to that of capital.

Artificial intelligence (AI) changes social learning when aggregated outputs become training data for future predictions. To study this, we extend the DeGroot model by introducing an AI aggregator that trains on population beliefs and feeds synthesized signals back to agents. We define the learning gap as the deviation of long-run beliefs from the efficient benchmark, allowing us to capture how AI aggregation affects learning. Our main result identifies a threshold in the speed of updating: when the aggregator updates too quickly, there is no positive-measure set of training weights that robustly improves learning across a broad class of environments, whereas such weights exist when updating is sufficiently slow. We then compare global and local architectures. Local aggregators trained on proximate or topic-specific data robustly improve learning in all environments. Consequently, replacing specialized local aggregators with a single global aggregator worsens learning in at least one dimension of the state.

This chapter presents a tractable framework for the study of technology adoption and diffusion in the context of economic development. Firms in countries behind the world technology frontier can rapidly adopt new techniques from the world frontier. Lower absorptive capacity (because of weak education systems, poor management practices, or barriers to technology adoption), institutional distortions, mismatch between frontier technologies and the needs of firms in the country (i.e., “inappropriate technology”), and credit market frictions slow down technology adoption and cause the economy in question to have a greater distance to the frontier and thus lower income per capita—although the long-run growth rate of the country still remains equal to that of the frontier. This framework is extended to study the choice between innovation and imitation, as well as the role of selection for higher-productivity and higher-absorptive capacity firms during the process of economic development. We illustrate the main comparative statics of our framework with a number of correlations based on cross-country and firm-level data. The tractability of the framework makes it amenable to a range of additional extensions. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This article studies the effects of automation in a task-based economy in which some jobs pay workers rents-wages above workers' outside options. We show that automation targets high-rent tasks, dissipating rents, amplifying wage losses, and reducing within-group wage dispersion in exposed groups. This form of rent dissipation is inefficient and offsets the productivity gains from automation. Using U.S. data from 1980 to 2016, we find evidence of sizable rent dissipation and reduced within-group wage dispersion due to automation. Automation accounts for 52% of the increase in between-group inequality since 1980, with rent dissipation explaining one-fifth of this total. Our estimates imply that inefficient rent dissipation has offset 60%-90% of the productivity gains from automation over this period.

We study how generative AI, and in particular agentic AI, shapes human learning incentives and the long-run evolution of society’s information ecosystem. We build a dynamic model of learning and decision-making in which successful decisions require combining shared, community-level general knowledge with individual-level, context-specific knowledge; these two inputs are complements. Learning exhibits economies of scope: costly human effort jointly produces a private signal about their own context and a “thin” public signal that accumulates into the community’s stock of general knowledge, generating a learning externality. Agentic AI delivers context-specific recommendations that substitute for human effort. By contrast, a richer stock of general knowledge complements human effort by raising its marginal return. The model highlights a sharp dynamic tension: while agentic AI can improve contemporaneous decision quality, it can also erode learning incentives that sustain long-run collective knowledge. When human effort is sufficiently elastic and agentic recommendations exceed an accuracy threshold, the economy can tip into a knowledge-collapse steady state in which general knowledge vanishes ultimately, despite high-quality personalized advice. Welfare is generally non-monotone in agentic accuracy, implying an interior, welfare-maximizing level of agentic precision and motivating information-design regulations. In contrast, greater aggregation capacity for general knowledge—meaning more effective sharing and pooling of human-generated general knowledge—unambiguously raises welfare and increases resilience to knowledge collapse. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

How has Wikipedia activity changed for articles with content similar to ChatGPT following its introduction? We estimate the impact using differences-in-differences models, with dissimilar Wikipedia articles as a baseline for comparison, to examine how changes in voluntary knowledge contributions and information-seeking behavior differ by article content. Our analysis reveals that newly created, popular articles whose content overlaps with ChatGPT 3.5 saw a greater decline in editing and viewership after the November 2022 launch of ChatGPT than dissimilar articles did. These findings indicate heterogeneous substitution effects, where users selectively engage less with existing platforms when AI provides comparable content. This points to potential uneven impacts on the future of human-driven online knowledge contributions.

We consider the political consequences of the use of artificial intelligence (AI) by online platforms engaged in social media content dissemination, entertainment, or electronic commerce. We identify two distinct but complementary mechanisms, the social media channel and the digital ads channel, which together and separately contribute to the polarization of voters and consequently the polarization of parties. First, AI-driven recommendations aimed at maximizing user engagement on platforms create echo chambers (or “filter bubbles”) that increase the likelihood that individuals are not confronted with counter-attitudinal content. Consequently, social media engagement makes voters more polarized, and then parties respond by becoming more polarized themselves. Second, we show that party competition can encourage platforms to rely more on targeted digital ads for monetization (as opposed to a subscription-based business model), and such ads in turn make the electorate more polarized, further contributing to the polarization of parties. These effects do not arise when one party is dominant, in which case the profit-maximizing business model of the platform is subscription-based. We discuss the impact regulations can have on the polarizing effects of AI-powered online platforms. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We build a model of online behavioral manipulation driven by AI advances. A platform dynamically offers one of n products to a user who slowly learns product quality. User learning depends on a product’s “glossiness,” which captures attributes that make products appear more attractive than they are. AI tools enable platforms to learn glossiness and engage in behavioral manipulation. We establish that AI benefits consumers when glossiness is short-lived. In contrast, when glossiness is long-lived, behavioral manipulation reduces user welfare. Finally, as the number of products increases, the platform can intensify behavioral manipulation by presenting more low-quality, glossy products. (JEL C55, D83, D91, L86)

Does capital accumulation increase labor demand and wages? Neoclassical production functions, where capital and labor are q-complements, ensure that the answer is yes, so long as labor markets are competitive. This result critically depends on the assumption that capital accumulation does not change the technologies being developed and used. I adapt the theory of endogenous technological change to investigate this question when technology also responds to capital accumulation. I show that there are strong parallels between the relationship between capital and wages and existing results on the conditions under which equilibrium factor demands are upward-sloping (e.g., Acemoglu, Econometrica 75(5) (2007), 1371–410). Extending this framework, I provide intuitive conditions and simple examples where a greater capital stock leads to lower wages, because it triggers more automation. I then offer an endogenous growth model with a menu of technologies where equilibrium involves choices over both the extent of automation and the rate of growth of labor-augmenting productivity. In this framework, capital accumulation and technological change in the long run are associated with wage growth, but an increase in the saving rate increases the extent of automation, and initially reduces the wage rate and can subsequently depress its long-run growth rate.

This paper reviews the main motivations and arguments of my work on comparative development, colonialism, and institutional change, which was often carried out jointly with James Robinson and Simon Johnson. I then provide a simple framework to organize these ideas and connect them with my research on innovation and technology. The framework is centered around a utility-technology possibilities frontier, which delineates the possible distributions of resources in a society both for given technology and working via different technological choices. It highlights how various types of institutions, market structures, norms, and ideologies influence moves along the frontier and shifts of the frontier, and it provides a simple formalization of the social forces that lead to institutional persistence and those that can trigger institutional change. The framework also enables us to conceptualize how, during periods of disruption, existing—and sometimes quite small—differences can have amplified effects on prosperity and institutional trajectories. In this way, it suggests some parallels between different disruptive periods, including the onset of European colonialism, the spread (or lack thereof) of industrial technologies in the nineteenth century, and decisions related to the use, adoption, and development of AI today. (JEL D02, D72, E23, F54, O43)

This paper proposes a new framework for studying the interplay between culture and institutions. We follow the recent sociology literature and interpret culture as a \repertoire", which allows rich cultural responses to changes in the environment and shifts in political power. Specifically, we start with a culture set, which consists of attributes and the feasible connections between them. Combinations of attributes produce cultural configurations, which provide meaning, interpretation and justification for individual and group actions. Cultural figurations also legitimize and support different institutional arrangements. Culture matters as it shapes the set of feasible cultural figurations and via this channel institutions. Yet, changes in politics and institutions can cause a rewiring of existing attributes, generating very different cultural configurations. Cultural persistence may result from the dynamics of political and economic factors - rather than being a consequence of an unchanging culture. We distinguish cultures by how fluid they are - whereby more fluid cultures allow a richer set of cultural configurations. Fluidity in turn depends on how specific (vs. abstract) and entangled (vs. free-standing) attributes in a culture set are. We illustrate these ideas using examples from African, England, China, the Islamic world, the Indian caste system and the Crow. In all cases, our interpretation highlights that culture becomes more of a constraint when it is less fluid (more hardwired), for example because its attributes are more specific or entangled. We also emphasize that less fluid cultures are not necessarily "bad cultures", and may create a range of benefits, though they may reduce the responsiveness of culture to changing circumstances. In many instances, including in the African, Chinese and English cases, we show that there is a lot of fluidity and very different, almost diametrically-opposed, cultural configurations are feasible, often compete with each other for acceptance and can gain the upper hand depending on political factors.