As the Advising Chief Economist at The Linux Foundation, Frank Nagle is on a mission to answer a deceptively complex question: What is the economic value of open source? In his recent conversation with TFiR at Open Source Summit, Nagle explains how this role—once rare in tech organizations—is becoming essential as open source becomes the foundation of modern infrastructure and innovation.

His core task? Quantifying the impact that open source projects have on society and businesses, and helping organizations understand the return on investment when contributing back. “Why should companies give back when they can just use OSS for free?” Nagle asks. “Part of my job is to arm developers and contributors with data that helps them make the case to their business leaders.”

Going Beyond Lines of Code in AI

One area where the complexity of this work becomes clear is in artificial intelligence. Traditional OSS value has been measured by code commits and community contributions. But AI brings a multi-dimensional problem: code, data, models, and inference layers all play a role. Nagle points out that open source AI doesn’t follow a one-size-fits-all definition. Some companies release only models, others open data or infrastructure.

“I think I’m in the ‘big tent’ view of open source AI—meaning that if you’re a company working on AI, or already an open project, and you’re open sourcing even a part of it, that’s better than keeping everything closed,” he says.

PyTorch: A Case Study in Governance and Participation

Nagle highlights PyTorch as an example of how open governance fuels ecosystem growth. Originally developed at Meta, PyTorch gained broader community support only after it was transferred to the Linux Foundation. “Chipmakers were hesitant to contribute while Meta had control,” he explains. “Once it moved to a neutral foundation, contributions surged.” It’s a powerful example of how open governance can attract strategic investment from other major players in the AI stack.

Measuring Value for Society—and the Bottom Line

As an academic and economist, Nagle’s approach is data-driven. He’s working with the LFX Insights team to build tools that assess individual and organizational contributions across Linux Foundation projects. But he emphasizes that many of the answers require private data—information about how companies are actually using AI and open source.

ChatGPT is trained on public data, but the economic insights live behind company firewalls, Nagle notes. He invites organizations to share anonymized usage data to help advance the ecosystem’s understanding of real-world impact.

Security and the Economics of Prevention

Security is another area where Nagle believes economic insight can drive better outcomes. Collaborating with groups like OpenSSF, he’s looking at how to model the long-term benefits of building security into open source projects from the start. “It may take longer up front, but it saves massive costs down the line,” he says. His goal is to help organizations see secure-by-design not as an extra cost, but as a high-return investment.

A Changing Job Market—and the Role of Education

On the topic of AI and employment, Nagle is optimistic but pragmatic. GenAI is shifting how teams work and may displace some roles—but it’s also creating new ones. “We’ve always seen job churn when new technologies emerge,” he says. The challenge is to help workers transition, ideally through internal upskilling. “It’s better to train existing employees than to lay them off and hire externally,” he argues. “The companies that invest in their people will be better off.”