AI: Research and Policy

From Knowledge Hierarchies to the Age of AI: My Research Trajectory

The central pursuit of my academic career has been understanding how knowledge is acquired, organized, and utilized within the economy. My research demonstrates that the structure of our organizations, the distribution of wages, and ultimately, economic growth, are fundamentally determined by the costs of accessing and communicating information. Historically, specialized knowledge has been embodied in human experts with finite time; organizations emerged as mechanisms to leverage this scarce resource.

My early work introduced the concept of "knowledge hierarchies" (JPE, 2000) as the natural organizational response to these constraints. When acquiring expertise is costly and communication is imperfect, firms structure themselves to employ "management by exception." Production workers manage routine tasks, while specialized experts focus only on the most complex and unusual problems. This division of cognitive labor allows expert knowledge to scale efficiently.

Building on this foundation, my extensive collaboration with Esteban Rossi-Hansberg (2006; 2012; 2015) and also Pol Antras (2006), and with Willie Fuchs and Luis Rayo (2015) embedded these hierarchies into general equilibrium models. We showed how the ability to organize knowledge determines critical macroeconomic outcomes. The way firms leverage expertise dictates not only their internal productivity but also patterns of wage inequality, the dynamics of international trade, and the structure of global supply chains. With Tom Hubbard I tested empirically predictions using US Census data of US law firms (2009, 2016, 2018)

The Information and Communication Technology (ICT) changed the costs of knowledge transfer. Crucially, my research with colleagues (Nick Bloom, Raffaella Sadun, and John Van Reenen, 2014) disaggregated empirically the effects of these technologies. We found that, consistently with the theory,  tools reducing the cost of accessing information (like databases or enterprise resource planning) tend to empower lower-level workers, allowing them to solve more problems autonomously and leading to decentralized decision-making. Conversely, tools reducing the cost of communication (like intranets or email) facilitate the application of centralized expertise, allowing managers to direct more subordinates and often leading to greater centralization. The evolution of the modern knowledge economy has been shaped by the tension between these two forces.

My current focus on Artificial Intelligence is the direct and necessary continuation of this research agenda. AI, particularly generative AI, represents a leap because it for the first time allows for the accumulation of tacit knowledge by machines. AI collapses the costs of processing, deploying, and even generating specialized knowledge, moving them toward zero.

This fundamentally challenges the structure of the traditional knowledge hierarchy—based on the premise that specialized knowledge is a scarce resource inextricably tied to human time. AI is a fundamental shock to the organization of knowledge itself. The theoretical tools developed over decades to study human knowledge organization are indispensable for understanding how AI will reshape firms, the future of expertise, and the structure of the economy.

Current research

• I just finished, with Luis Rayo (NWU) a first draft of a working paper on the impact of GenAI on the career ladder: "Training in the Age of AI: A Theory of Apprenticeship Viability." Here is the abstract:  Apprenticeships let juniors pay for training by doing menial work. AI now performs an increasing share of that work, putting the bargain at risk. We introduce AI into a dynamic apprenticeship model with an automation threshold and possible complementarity for experts. A single statistic—the expertise leverage ratio, measuring the AI-augmented value of a graduate relative to AI’s standalone output—governs the impact of AI. Our central result is that apprenticeships are guaranteed viable, in the sense that they are at least as profitable as they were before the arrival of AI, when this ratio is above a critical threshold, specifically Euler’s number e; in this case, training has a fixed duration and the apprenticeship is not at risk. Below the threshold, training compresses as the master’s saleable knowledge shrinks; in this case, advances in AI threaten wholesale apprenticeship collapse.

• This paper is forthcoming as a chapter for an NBER volume a paper on Economics of Transformative AI, edited by Ajay K. Agrawal, Anton Korinek and Erik Brynjolfsson and published by University of Chicago Press: "The Economics of Superabundant AI: Autonomy, Scarcity and the Future of Work".  The paper analyzes how "superabundant" AI can simultaneously augment and displace workers. The outcome depends on what remains scarce. When compute is scarce, or the AI is non Autonomous, AI is a "co-pilot," and human time retains value. If compute is abundant and AI is autonomous, "opportunities" or "slots" become the bottleneck, displacing low-skill humans. If compute is abundant but AI is non-autonomous, human input is the bottleneck, and all humans work, but wages compress.  Hence the paper argues displacement is avoidable. If firms can create new "addressable opportunities" at a cost lower than the value AI provides, they will. This keeps compute scarce and sustains human employment.

• I recently submitted for publication in a business journal "Trust as a Scaling Strategy: How Internal Entrepreneurs Drive Corporate AI Adoption", with Elena Alfaro, Antonio Cabrales, José Elías Durán Roa, Luis Garicano, Isabel Pérez del Caño, Toni Roldán Monés, Guillermo Vieira de Santiago.  We argue most corporate GenAI programs disappoint. Since value is generated to a large extent from the bottom‑up it is a good idea to use your employees’ technical and entrepreneurial talent. The job is to achieve this in a safe, visible, and scalable manner—fast—then build the organization around it. BBVA’s human-centered, bottom-up strategy demonstrates how to do that effectively at scale, in a heavily regulated industry.

• I also recently submitted for publication a paper with Adam Brzezinski that, while not on AI, heavily uses Large Language Models as a tool: Narrative Entanglement in Climate Policy. Our starting point is that political narratives on climate policy have turned more skeptical despite evidence of climate urgency. We explain this shift with a theory of narrative entanglement: to appeal to voters, politicians intertwine economic and environmental narratives rather than treating them separately. Hence, shocks unrelated to climate change can impact environmental narratives. We test our theory in the context of Russia’s invasion of Ukraine, which affected the economic costs of the European Green Deal without changing its impact on emissions. We use large language models to identify climate narratives across all speeches in the 9th European Parliament (2019-2024).  Exploiting only variation within each parliamentarian, we show that after the invasion, narratives become both more negative in the cost assessments of climate policies and more skeptical about their environmental impact

I am currently writing a book with Jin Li and Yanhui Wu (HKU) on AI in organizations. I will keep you updated on this. 

Policy Writing on AI

In our substack Sillicon Continent (with Pieter Garicano) we have  focused  a lot of our attention on AI. Here are some of my writings.

•  R without G: Good news on AI could be bad news for Euro,
  An AI driven growth acceleration could paradoxically create debt sustainability problems. 

• The AI Becker Problem: Who will train the next generation?  
  The initial version of the training problem above. 

• The smart second mover with Jesús Saa-Requejo.
  A policy proposal for developing AI in Europe. 

• Can AI solve Europe’s problems?: Baumol's disease, regulatory resistance, and the O-ring problem.
  Obstacles to AI driving large productivity gains. 

• The Compliance Doom Loop: Why the rules keep growing
  Not only about AI, but about Europe creating a huge compliance eocnomy but wait till you see the AI agency 

• How to think about the economic impact of AI: The scarce factor is humans’ cognitive ability.
  A discussion of the Knowledge-Hierarchies view of Ai. 

• Is GDPR undermining innovation in Europe?: 
  The General Data Protection Regulation (GDPR) was supposed to be Europe's big move to protect consumer privacy and reassert its technological relevance.