5 MIN READ
Frank Nagle | 04 June 2025
As we approach the midpoint of 2025, the potential of AI to transform businesses, economies, and industries is not only widely anticipated and nearly universal but also well documented. In a commissioned project by Meta, LF Research set out to capture existing evidence on this topic, with the specific aim of understanding how open source is playing a role in this transformation.
In its latest publication, The Economic and Workforce Impacts of Open Source AI, LF Research describes the nuances of how and to what extent open source AI (OSAI) is impacting the global economy and workforce. By examining existing evidence from industry, academic, and open source research, the authors found important insights on OSAI’s adoption rates, cost effectiveness, innovation-boosting potential, and more. Here are the big takeaways.
First, the adoption of open source AI is already widespread. Nearly all software developers have experimented with open models, and about 63% of companies are actively using them. In fact, among organizations that have embraced AI in any form, a striking 89% incorporate open source AI somewhere in their infrastructure. It’s no longer a fringe approach—it’s becoming the standard.
Why? Cost is a huge factor. Open source tools often come with significantly lower price tags than their proprietary counterparts. My prior research with Manuel Hoffmann and Yanuo Zhou has shown that if open source didn’t exist, companies would spend 3.5 times more on software than they currently do. The new LF report shows that two-thirds of organizations say OSAI is cheaper to deploy, and nearly half cite cost savings as a primary reason for choosing open source. Combine that with studies showing AI’s ability to cut business unit costs by over 50%, while still being user friendly and maintaining high performance, and it’s clear that OSAI represents a strategic advantage for boosting margins and scaling innovation.
Innovation and entrepreneurship are other major benefits of open source. In research with Nataliya Langburd Wright and Shane Greenstein, we found that when open source contributions increase at the country level, so do new startups; at the company level, there is a positive relationship between contributing to open source and startup growth. Open source encourages collaboration, inviting contributions from a global pool of developers and researchers. This external input helps accelerate the development of high-quality models. As Daniel Yue and I found when Meta donated the machine learning library PyTorch to the Linux Foundation, there was a notable increase in corporate contributions, especially from chip manufacturers.
AI’s cost-cutting capabilities are not only linked to the increased productivity that comes from freed-up resources, but also from a re-orienting of the way people work—similar to how the full impact of the steam engine led to the industrial revolution, but only after factories re-oriented their entire work flow around it. Manuel Hoffmann, Sam Boysel, Kevin Xu, Sida Peng, and I found this to be the case with software developers. When GitHub rolled out their GenAI coding tool Copilot, developers changed the way that they worked by spending more time writing code and substantially less time doing project management. However, according to existing research identified in the LF study, this has not translated to substantial layoffs: 95% of surveyed hiring managers over the past two years said they do not plan to reduce headcount due to AI. What’s more, being able to use AI tools effectively may actually increase wages by over 20%.
Looking ahead, open source AI is likely to become foundational in areas like edge computing, where smaller, privacy-preserving models need to run efficiently on local devices. OSAI is also making big inroads in industry-specific applications. In manufacturing, for instance, open models offer the flexibility required to integrate AI into complex operational workflows. And in healthcare—a traditionally conservative and risk-averse field—open models are already matching proprietary ones in performance, giving institutions confidence to adopt without compromising on quality. OSAI is an important avenue to level the playing field, no matter your organization’s size or financial resources—as the report found, small businesses are adopting OSAI at higher rates than their larger counterparts.
OSAI is an economic force. It’s reducing costs, accelerating innovation, and empowering a wider range of players to shape the future of technology.
What’s Next for OSAI? Five Areas Ripe for Research
While the impact of OSAI is starting to take shape, the full scope of its influence is just beginning to unfold. To better understand and harness the potential of OSAI, the report outlines five key areas for future research, each crucial to shaping smart policy, business strategy, and innovation ecosystems.
- Tracking the Bigger Picture: OSAI’s Role in Market Growth
One pressing question is how open models are influencing the overall AI market. Beyond the tools themselves, OSAI may be driving complementary innovation, spurring growth in services, applications, and platforms built on top of open infrastructure. Understanding this broader ripple effect is essential for grasping the true economic footprint of open AI. - Making the Case for Investment
To help make informed decisions, researchers are encouraged to analyze the return on investment in OSAI infrastructure at both country and company levels. Quantifying the long-term value of these open components, from datasets and compute to developer tooling, can guide resource allocation and policy decisions in a fast-moving field. - Connecting Openness to Innovation
Does OSAI directly foster more startups, patents, or efficient R&D? Future studies should explore how open access to models and tools correlates with concrete innovation metrics. This could provide evidence for how openness accelerates not just adoption, but invention. - Crunching the Cost Numbers
A detailed comparison of costs between open and proprietary AI solutions across sectors, company sizes, and global regions would shed light on who benefits most from going open. These insights would be invaluable for organizations navigating tight budgets and evaluating technology strategies. - Understanding Workforce Impacts
Finally, the human side matters. As AI tools reshape work, it’s vital to measure how open models affect worker productivity, satisfaction, and work patterns. Do open tools empower workers in certain tasks or industries more than others? Do they lead to more flexible, fulfilling roles? Answers to these questions will help ensure that AI benefits not just business, but people.
By exploring these future research areas, we can unlock a deeper understanding of how open source AI is transforming the global economy and workforce. The era of open source AI is here—and it’s time to study its impact with depth and rigor.
