Professor Simon Gervais explains how active managers can grow the economy, even when they don’t beat the market

Many investors are told that active funds rarely outperform passive index funds after fees. So why bother paying for them?

In this episode of Duke Fuqua Insights podcast, Professor Simon Gervais, a financial economist at Duke University’s Fuqua School of Business, challenges the narrow way we evaluate active money management. Drawing on his paper, “Money Management and Real Investment”, Gervais argues that focusing only on fund returns misses a critical role active managers play in the economy: improving how capital is allocated across firms and industries. Rather than acting as passive observers, active managers influence real corporate investment decisions through the information embedded in stock prices.

The central insight of Gervais’s research is that active money managers can create economic value even if their funds generate negative net returns after fees. By trading on information about industries, competition, and macroeconomic trends, active managers help direct capital toward more productive uses. The result is a “bigger economic pie,” even if investors receive a slightly smaller slice of it.

Active managers trade on information that corporate executives may not fully have, pushing stock prices up or down. Firms then learn from these price movements and adjust investment decisions accordingly. As Gervais puts it, if a firm announces a merger and its stock went down, maybe that is the signal that the merger was a bad idea.

For MBA students and business leaders, the takeaway is a broader view of value creation. Passive investors may benefit indirectly from better capital allocation, but unlike active managers, they don’t automatically adjust as the economy changes. The research calls into question how regulators, investors, and institutions evaluate asset managers. If active management improves productivity and risk allocation, then traditional performance metrics like alpha may be incomplete. As Gervais suggests, the real challenge is learning how to measure not just who captures value, but who creates it.

Duke Fuqua Insights features digestible conversations with our faculty about the most impactful research from their careers, including studies they teach in Fuqua classes. New episodes every other week in season.

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Artificial intelligence promises massive productivity gains, but not everyone can immediately jump in to reap its benefits. While some firms and countries race ahead with new tools, others deliberately stick with older technologies, a choice may be entirely rational.

In this episode of the Fuqua Insights podcast, Professor John Coleman of Duke University’s Fuqua School of Business joins host Sarah Kern to discuss how economies and businesses choose technologies based on the skills of their workforce. Drawing on his paper The World Technology Frontier, co-authored with Francesco Caselli of the London School of Economics, Coleman explores why countries with access to the same global technologies nonetheless adopt very different production methods and experience sharply different income levels.

Coleman’s central finding is that technology adoption is often an optimal choice, not a failure. Coleman distinguishes between skilled and unskilled labor, economic terms based primarily on formal education levels rather than the actual difficulty or value of work. In this framework, "skilled" workers have training that allows them to adapt to and leverage new technologies, while "unskilled" refers to workers with less formal education who may be highly capable but less equipped to use tools like AI.

Advanced economies tend to develop and use technologies that favor skilled labor, while less-developed economies rely on older, less skill-intensive tools because those technologies better match their labor force. As Coleman explains, a poorer country may “optimally choose to adopt what seems to an advanced economy to be a backward technology”, because using more advanced tools without the right skills can actually reduce productivity.

While technologies developed a long time ago are most suitable for labor that is mostly unskilled, newer technologies are designed for highly educated workers, Coleman notes. This framework applies directly to AI: “AI was developed by advanced economies that have the skilled labor that would benefit most from AI,” he says. Without that skill base, adopting AI may not pay off.

Business leaders should remember to align innovations with workforce capabilities. “Adopting AI without a well-developed plan to integrate it into your workforce would likely be a disaster,” Coleman warns. Globally, AI may widen gaps in the short run, but Coleman emphasizes that technology is not a zero-sum game. Over time, moving up the technology frontier can create opportunities for growth across economies.

Editor's Note: This episode was recorded prior to the extradition of Nicolás Maduro on January 3, 2026.

Duke Fuqua Insights features digestible conversations with our faculty about the most impactful research from their careers, including studies they teach in Fuqua classes. New episodes every other week in season.

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Professor David Brown explains how simple strategies can guide better decisions even when information is incomplete

New job postings appear daily. Real estate markets update constantly with fresh listings. In an environment where alternatives continuously multiply and options can seem endless, the hardest decision is knowing when to stop searching and commit.

In this episode, David Brown, the Snow Family Business Professor of Decision Sciences at Duke University’s Fuqua School of Business, discusses how people and organizations can make better decisions when information is scarce or costly.

Building on economist Martin Weitzman’s classic “Pandora’s Box Problem,” Brown and his co-author, Fuqua Ph.D. student Cagin Uru, found that straightforward search rules perform nearly as well as complex algorithms. Their research shows a surprisingly simple solution: commit upfront to search a specific number of alternatives based on search costs, then simply rank what you've seen and choose the best.

What makes their approach practical and appealing is its simplicity: it requires only the ability to rank alternatives you've seen and the discipline to stop searching at the right point, not probability calculations or complex data analysis. This applies broadly, from navigating job searches to booking flights to hiring contractors.

The conversation also explores when sophisticated algorithms are truly necessary. Their research shows that, across several search settings, their simple, transparent rules perform nearly as well as those based on more complex approaches (e.g., AI), raising questions about when algorithmic solutions are worth the investment.

Duke Fuqua Insights features digestible conversations with our faculty about the most impactful research from their careers, including studies they teach in Fuqua classes. New episodes every other week in season.

For more from Duke Fuqua, visit us on LinkedIn, Instagram, Facebook, Bluesky, and the Duke Fuqua Insights newsletter.