Luis Garicano

economicsfuture-of-workknowledge-workenterprise

About Luis Garicano

Luis Garicano is a Full Professor of Public Policy at the London School of Economics, specializing in organizational economics, knowledge hierarchies, and the economics of AI. His research on how firms organize knowledge work—and how AI disrupts those structures—has become increasingly relevant as language models reshape professional services.

Before returning to LSE in 2023, Garicano served as a Member of the European Parliament (2019-2022), where he was vice president of the Renew Europe Group overseeing economic affairs. This political experience gives him unique perspective on the regulatory landscape for AI, particularly regarding the EU AI Act and GDPR's impact on European competitiveness.

Garicano's academic work spans the University of Chicago Booth School of Business (where he was Full Professor of Economics and Strategy) and the London School of Economics. His research on knowledge hierarchies and master-apprentice relationships has gained new urgency as AI threatens to eliminate the economic logic of junior professional roles.

Career Highlights

London School of Economics (2023-present): Full Professor of Public Policy
European Parliament (2019-2022): MEP, Vice President of Renew Europe for Economic Affairs
University of Chicago Booth (2004-2014): Professor of Economics and Strategy
Centre for Economic Policy Research: Research Fellow

Notable Positions

On the Training Ladder Problem

Garicano's most distinctive contribution to AI economics is identifying how AI threatens professional development pipelines. In traditional knowledge work, juniors exchange "grunt work" (contract review, basic research, PowerPoint slides) for mentorship from experts. AI devalues this currency:

"The apprentice is paying not in dollars, it's paying in menial tasks... If the AI can do the basic research at McKenzie, can do the contract review at Cravath, then how do you pay for your training?"

This creates a potential market failure where tacit knowledge—the expertise that can only be transferred through direct mentorship—stops being transmitted entirely.

On European AI Regulation

With his European Parliament experience, Garicano offers a critical view of EU regulatory approaches:

"Every single thing tells you the GDPR has been bad for EU business and now we're adding the EU AI Act. Part of the risk is you try to control the technology and you end up without technology."

He's skeptical of attempts to "direct" technology development, noting the game-theoretic impossibility of coordination between competing actors.

On Supervision Thresholds

Garicano introduces the concept of a "supervision threshold"—the point at which AI becomes autonomous enough that human oversight is no longer needed. Below this threshold, AI augments human productivity but remains bottlenecked by human attention. Above it, AI becomes a substitute:

"The moment the AI becomes autonomous, I think there you get a jump, a discrete jump."

Key Quotes

"As long as the AI needs your supervision because it makes lots of mistakes, then the bottleneck is the human." (on AI autonomy)
"You need to be smarter than AI in order to be able to correct the AI." (on supervision requirements)
"Who is 'we' here? Is it China, is the US? Is it firms? Is it workers?" (on regulatory coordination)
"A very good AI programmer with lots of AI can have enormous leverage and can reach very large market size." (on superstar effects)

AI Agents - The autonomous systems that cross Garicano's supervision threshold
Enterprise AI - Where the training ladder effects are most visible
Human-in-the-Loop - The supervision model Garicano analyzes