The next breakthrough drug or industry-disrupting innovation may come from a university lab funded by a federal grant. But a policy shift around how the government supports overhead costs could change which projects are pursued, and which institutions can afford to compete.

When the federal government funds scientific research, universities negotiate overhead rates of 50 to 70% over the direct costs of research to cover expenses like lab space and utilities. In reality, they receive far less due to complex accounting rules. For more than 60 years, this complicated system of "indirect cost recovery” has funded America’s research infrastructure.

Now that system faces a major disruption. Professor Daniel P. Gross of Duke's Fuqua School of Business analyzed data from 350 U.S. research institutions over 60 years and found that while negotiated rates keep rising, the actual overhead rates paid to research institutions have in practice have been flat for decades. Recent policy proposals to cap indirect cost recovery rates at 15% would have major impacts on research operations. Focusing on NIH funding alone, Gross's analysis shows these changes would reduce universities’ NIH research funding by 15 to 20% annually, with some institutions losing over $100 million a year. Strikingly, the hardest-hit institutions would be those whose work most frequently leads to private sector patents and FDA-approved drugs.

In the interview, Gross walks through how seemingly technical policy changes could reshape American innovation and explores potential reforms that could address concerns about the current system. Drawing on historical examples from antibiotics to AI, Gross grounds these changes in a broader context, revealing how funding mechanisms shape what kinds of research gets done–and what’s at stake for the future of scientific discovery.

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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Before you mention that AI tool that's been helping you at work, consider how your colleagues might respond. In this podcast, Professors Richard Larrick and Jack Soll, along with PhD candidate Jessica Reif from Duke University’s Fuqua School of Business, discuss their research into how artificial intelligence tools are reshaping workplace dynamics. Their findings highlight a key paradox: while generative AI can boost performance, using it often makes workers appear less competent, less diligent, and even lazier in the eyes of others.

Drawing on a series of experiments, the researchers show that people who use AI for work are judged more harshly than those who seek help from a colleague or even use other software tools. This “social evaluation penalty” can lead to lower hiring prospects, negative perceptions of job fit, and cause employees to hide their AI use altogether.

But the penalty isn’t universal. The bias disappears when evaluators are frequent AI users. And when a task clearly benefits from AI (e.g., writing or coding), users face fewer reputational risks. The research also reveals that this judgment bias persists across gender, age, and job types — suggesting its effects are widespread.

The research offers insights for both managers and MBA students: creating a culture that normalizes and openly discusses AI use may be critical to unlocking its benefits. Managers must lead by example, openly using generative AI tools to signal that their organization is a safe space for AI innovation and experimentation.

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.