Automation, AI, and the Intergenerational Transmission of Knowledge

Motivated by concerns that AI-driven entry-level automation may disrupt early-career learning, this paper examines how technological change affects the intergenerational transmission of tacit knowledge—practical, hard-to-codify skills acquired through workplace interaction. I develop a task-based overlapping-generations model in which novices acquire tacit knowledge by working alongside experts. Knowledge-transfer contracts are incomplete because tacit knowledge is embodied and non-verifiable. In equilibrium, endogenous growth arises because only the most knowledgeable experts manage production and transmit their expertise to multiple novices, diffusing best practices. I show that improvements in entry-level automation increase output upon adoption but can reduce growth and welfare, even without reducing entry-level employment. This occurs when such improvements reallocate novices away from the most productive experts, slowing the diffusion of best practices. By contrast, technological improvements that increase the number of novices learning from the most productive experts strengthen knowledge transmission and raise growth.

Introduction. Recent advances in Artificial Intelligence (AI) have fueled optimistic forecasts of dramatic increases in productivity and economic growth. Unlike earlier technological innovations—such as robots or conventional computers—AI systems can learn and adapt from examples, enabling them to perform work once considered inherently human (Autor, 2024; Brynjolfsson et al., 2025b; Ide and Talam`as, 2025). As a result, many technologists foresee an economic transformation that could rival or even surpass that of the Industrial Revolution (Amodei, 2024; Levy, 2025; Altman, 2025).

However, the widespread adoption of AI may carry unintended long-term costs. By allowing senior workers to accomplish more tasks independently, AI-driven automation may reduce entry-level opportunities and threaten the implicit contract in which younger workers exchange their labor for training and the prospect of future promotion (Roose, The New York Times, 2025; The Journal Podcast, The Wall Street Journal, 2025; BloombergTV, Bloomberg.com, 2025). Such arrangements have historically been crucial for transmitting tacit knowledge—practical insights that cannot be fully articulated yet remain indispensable for carrying out complex work—raising concerns about how future generations will acquire essential expertise (Beane, 2024a,b; Garicano, 2025).

Motivated by these concerns, this paper develops a framework for analyzing how different forms of technological change—automation, task creation, and labor- and capital-augmenting technologies— reshape the intergenerational transmission of tacit knowledge and, through that channel, long-run growth. The central premise is that technological change affects current and future productivity not only by changing how tasks are performed, but also by changing who works with whom and, therefore, who learns from whom.

Existing research on automation and AI has mainly focused on how new technologies directly affect productivity, labor-market outcomes, and the distribution of income and wealth (e.g. Autor et al., 2003; Acemoglu and Autor, 2011; Moll et al., 2022; Acemoglu et al., 2024; Autor and Thompson, 2025; Ide and Talam`as, 2025). However, much less attention has been paid to how these innovations reshape the interpersonal interactions through which valuable workplace skills are transmitted. This paper addresses that gap by embedding technological change within a growth model of knowledge diffusion and learning (e.g., Lucas, 2009; Lucas and Moll, 2014; Perla and Tonetti, 2014; De la Croix et al., 2018). In doing so, it provides a unified framework for analyzing how new technologies jointly affect current output, knowledge transmission, and long-run growth.

I then use the framework to study several technological shocks, beginning with automation. I first show that expanding the set of automatable tasks raises output upon adoption, but can also reduce subsequent growth. The immediate output gain reflects two margins: routine tasks are reallocated from novices to more productive machines, and the novices released by high-skill experts allow a new While applicable to many technologies, these results are especially relevant to the recent advent of generative AI. Early evidence comparing employment changes across age groups and occupations differentially exposed to AI suggests that junior workers have experienced weaker relative employment outcomes in more AI-exposed occupations (Berger et al., 2024; Hosseini and Lichtinger, 2025; Brynjolfsson et al., 2025a; Klein Teeselink, 2025; Atkinson and Yamco, 2026). This pattern is consistent with some displacement of junior labor occurring in such environments. Although the available evidence does not yet track affected workers’ subsequent placements, anecdotal reports suggest that some graduates may be taking positions at smaller, mid-tier firms that previously struggled to attract such talent (Ellis, 2026; Lahart, 2026).

In this sense, the evidence so far suggests that current iterations of generative AI may be a form of “seniority-biased technological change” (Hosseini and Lichtinger, 2025): a technology that substitutes for entry-level execution while complementing expert judgment. Holding expertise fixed, such a change is unambiguously beneficial. But expertise is endogenous, and its intergenerational transmission is governed by incomplete contracts. As a result, to the extent that such technologies reduce junior workers’ access to the most productive mentors and firms, seniority-biased technological change can erode the future supply of the very expertise it complements.

Related work. This paper bridges three strands of literature. It connects the task-based literature on technology and labor demand with the literature on idea flows and economic growth by examining how new technologies reshape the workplace interactions through which tacit skills are transmitted across generations. It also contributes to the literature on the economics of apprenticeships by showing how early-career learning links technology adoption to aggregate productivity over time.

The task-based approach to technology and labor demand was developed by Zeira (1998), Autor et al. (2003), and Acemoglu and Autor (2011). In this framework, production requires the completion of a range of tasks allocated across factors of production according to comparative advantage. Technological change therefore affects labor demand, wages, inequality, and productivity by altering who performs which tasks and at what cost (Acemoglu and Restrepo, 2018, 2019, 2022; Acemoglu et al., 2024). Building on this approach, Autor and Thompson (2025) model occupations as bundles of tasks and show that automation can be expertise-leveling, reducing occupational expertise requirements.4 More recently, Ide and Talam`as (2024, 2025, 2026) build on the knowledge-hierarchies literature of Garicano (2000) and Garicano and Rossi-Hansberg (2004, 2006) to study AI’s distinctive implications. Knowledge hierarchies are a specialized version of the task-based framework in which task linkages emerge endogenously from organizational choices aimed at using tacit knowledge effi- The literature on idea flows and economic growth emphasizes how knowledge diffuses through interactions among individuals and firms, shaping productivity and growth (Kortum, 1997; Eaton and Kortum, 1999, 2002; Lucas, 2009; Lucas and Moll, 2014).5 My paper contributes to this literature by studying a margin largely absent from existing models: how technological change reshapes who learns from whom in production, with consequences for the diffusion of best practices.

In the context of idea flows and economic growth, the papers closest to mine are Perla and Tonetti (2014), De la Croix et al. (2018), and Caicedo et al. (2019). In both my paper and Perla and Tonetti (2014), long-run growth is tied to the endogenous evolution of a fat-tailed productivity distribution as better practices diffuse through the economy. In Perla and Tonetti (2014), this evolution is driven by firm imitation: low-productivity firms upgrade by drawing a new productivity level from the distribution of producing firms. In my model, by contrast, diffusion is intergenerational and workplacebased: the most productive experts replicate their expertise by transmitting it to the novices who work with them and later become experts themselves.

Method. 2 Conceptual Foundations This section outlines the conceptual foundations of the analysis: the tacit dimension of expertise, the inherent constraints on its transfer, and its transmission across generations.

The Tacit Dimension of Expertise.— Codifiable knowledge refers to explicit rules and procedures that can be easily transmitted via manuals, books, and databases. Tacit knowledge, in contrast, encompasses intuitive skills and insights that are difficult to articulate precisely (Polanyi, 1966; Foray, 2004). These two forms of knowledge are interdependent: codified instructions usually require tacit knowledge to be properly interpreted and adapted to real-world complexities. As Mokyr (2002, pp. 14-15) explains:

Tacit knowledge is needed to obtain inexpensive, reliable access to codified instructions [. . . ] no set of instructions [...] can ever be complete. It would be too expensive to write a complete set of instructions for every technique. Judgement, dexterity, experience, and other forms of tacit knowledge inevitably come into play when technique is executed.

While professional capability relies on both forms of knowledge, codifiable elements can be standardized and scaled. This paper therefore focuses on the tacit dimension. Hereafter, I use “expertise” and “tacit knowledge” interchangeably to refer to the unwritten insights required to execute and orchestrate well-defined tasks. In investment banking, for instance, populating financial models and verifying due diligence documents are largely standardized procedures. However, transforming their completion into financial advice requires the judgment to select appropriate models, identify anomalies that standard procedures might overlook, and adjust methods when unforeseen contingencies arise.

Because tacit knowledge is tied to personal experience, it is embodied in the individual (Polanyi, 1966; Garicano, 2000). Thus, while tacit insights can be transferred, their transmission requires the expert’s direct involvement. This makes the expert’s time a key bottleneck to the use and diffusion of this knowledge (Foray, 2004; Garicano and Rossi-Hansberg, 2015; Ide and Talam`as, 2025).

Labor as a Mechanism and Currency for Acquiring Tacit Knowledge.— Lacking a codifiable structure, tacit knowledge must be acquired experientially through active execution and collaboration with more experienced individuals. As Polanyi (1962, p. 55) observes:

An art which cannot be specified in detail cannot be transmitted by prescription [...] It can be passed on only by example from master to apprentice [...] By watching the master and emulating his efforts, the apprentice unconsciously picks up the rules of the art, including those which are not explicitly known to the master himself.

Thus, labor supplied alongside an expert serves a dual role: it is both a productive input and a mechanism for assimilating tacit knowledge (Lave and Wenger, 1991; Brown and Duguid, 1991; Beane, 2019). This apprenticeship model is pervasive in expertise-intensive professions (e.g., medicine, law, finance), where novices perform routine tasks under expert supervision, observing how experts apply judgment to integrate these inputs into a cohesive whole (Garicano and Rayo, 2017; Beane, 2024b). accept demanding hours and low effective pay to work alongside top practitioners.6,7 The contracts governing the transfer of tacit knowledge, however, are inherently incomplete (Mokyr, 2019). Because such expertise resists precise articulation, it is non-verifiable; because it is embodied in the individual, it cannot be seized or repossessed, and therefore cannot be pledged as collateral. These features make it difficult to finance training through claims on the future returns to acquired expertise (Becker, 1964; Garicano and Rayo, 2017). Concurrent labor thus becomes the natural means of payment in this environment.

These observations motivate the structure of the baseline model. The model represents earlycareer workers as novices and more experienced practitioners as experts. Expertise is embodied in experts and transmitted through joint production.

Discussion. This section examines the implications of key assumptions and introduces additional margins that shape how technological change affects the intergenerational transmission of tacit knowledge. The Online Appendix provides further extensions and robustness checks.

6.1 Scale Effects and Other Technological Shocks Scale Effects.— The baseline model assumes that production is non-scalable. Due to limited time and attention, each expert oversees only the fixed measure N of tasks required for a single project. To relax this assumption, let the expert choose a continuous scale of operation, μ, representing the measure of concurrent projects she oversees. The total output of an expert with skill q is therefore:

Consider then the automation shock studied in Section 5.1. Although output continues to increase on impact, the effect on the span of control—and therefore on long-run growth—is now ambiguous. Differentiating ln lwith respect to I isolates the competing forces:

When the displacement effect dominates, the central implication of Section 5.1 remains: an improvement in automation lowers the scale-adjusted span of control and, by slowing knowledge diffusion, reduces growth. This occurs in two different cases. First, displacement dominates when automation is “so-so” (Acemoglu and Restrepo, 2019): machines are adopted but are only marginally more productive than novices (m ≈h). Here, replacing a novice with a machine yields minimal time savings. Indeed, as m →h, the scale effect vanishes (∂M/∂I →0), and displacement entirely drives the impact on the scale-adjusted span of control.

Second, the displacement effect dominates when management time is additive or more bottlenecklike in task-level supervision requirements (η ≥1). The limiting case illustrates the logic: as η →∞, the CES aggregator converges to a maximum, or bottleneck, aggregator, so project supervision time is pinned down by the most supervision-intensive tasks—here, those performed by novices. In that limit, even if machines are substantially more productive than novices (m ≫h), automation frees no effective expert time while novice tasks remain. The same logic extends throughout η ≥1: the time savings from an automation improvement are too small to offset the reduction in novices per project.

By contrast, when η < 1 and machines are sufficiently productive relative to novices, the scale effect can dominate. In this case, the expert’s expanded scale of operation outweighs the displacement of novices per project. Top experts hire a larger aggregate measure of novices, accelerating the diffusion of best practices. The macroeconomic effects of the shock therefore resemble those of a task-creating technology, even without introducing new labor-intensive tasks. As the evidence reviewed in Section 5.1 suggests, however, displacement rather than expert scaling appears to be the more salient margin so far in the case of generative AI.

The Reallocation Margin.— As the previous discussion shows, new technologies can set multiple, often competing, forces in motion: they can automate tasks, create new labor-intensive ones, and augment For future empirical and policy work on how technological change shapes expertise formation across generations, this suggests the need to look beyond aggregate entry-level employment and examine how new technologies reallocate junior workers across firms and mentors.

6.2 Further Considerations Relaxing the Strict Division of Labor.— The baseline framework imposes a strict division of labor: experts exclusively apply tacit knowledge as orchestrators, while novices execute routine tasks. This assumption forces the least-skilled experts in any cohort into inactivity, yielding zero second-period income.

Conclusion. Policy Implications.— The incompleteness of knowledge-transfer contracts creates scope for policy intervention. At the same time, the analysis cautions against treating new technologies as a single force. This is especially important for AI as a general-purpose technology, since it is likely to take the form of a wide range of tools: some may automate entry-level tasks, while others may create new labor-intensive tasks or augment labor or capital in existing tasks. The policy challenge is therefore to harness the gains from these tools without undermining the formation of expertise in future generations.

The model offers three insights for addressing this challenge. First, policy assessment should look beyond aggregate entry-level employment: the deployment of new technologies may reduce welfare even if the total number of entry-level jobs remains unchanged, provided it reallocates novices away from the most skilled experts. Second, tools that automate entry-level tasks deserve particular scrutiny, since they are especially likely to reduce novices’ access to high-quality mentors. Third, whether this risk materializes depends on the strength of countervailing margins, such as scale effects, that may offset the decline in demand for novices among the most skilled experts.

When evidence indicates that a particular tool or technology weakens knowledge transmission enough to make laissez-faire adoption welfare-reducing, policy should aim to preserve novices’ access to high-quality learning environments. The appropriate response will depend on the institutional context, but possible instruments include taxes or limits on uses of entry-level automation tools, subsidies for junior positions that provide access to high-quality mentorship, and public support for apprenticeship- or residency-style training.