89% of Companies Adopt Open-Source AI, Cutting Costs and Fueling Growth

The Linux Foundation and Meta have published a collaborative study that offers concrete figures showing how open-source artificial intelligence (AI) initiatives are fueling both growth in innovation and broader adoption across sectors. Drawing on findings from hundreds of technology leaders and developers worldwide, the paper reveals that AI has become nearly ubiquitous: 94 percent of organizations now employ some form of AI tool, and within that group, a striking 89 percent have plugged open-source models and libraries into their core operations.

This collaboration between Meta and The Linux Foundation reflects a corporate shift toward community stewardship. By combining Meta’s engineering resources with The Linux Foundation’s open governance expertise, the initiative seeks to foster sustainability and collective oversight of core AI frameworks.

The report combines rigorous academic analysis, real-world case studies from leading enterprises, and survey responses gathered across North America, Europe, Asia, and other regions. It portrays an AI landscape grounded in community-driven development and low entry costs. By tracing contributions to open-source repositories and surveying IT decision-makers, the authors map out an ecosystem where organizations can adopt and adapt powerful AI tools without facing the steep price tags or restrictive licenses that often come with proprietary software.

Cost savings rank among the most compelling reasons to choose open-source AI. Two-thirds of surveyed firms report that launching AI projects with open models is noticeably less expensive than relying on proprietary offerings. Almost half of all participants point to these economic gains as a principal factor in their decision-making. Analysts warn that without open-source platforms, companies would face expense projections nearly three and a half times higher than current levels, making many projects financially unviable.

Beyond trimming budgets, open-source AI encourages experimentation and rapid iteration. The paper notes that deploying community-built models can slash business unit spending by over 50 percent, paving the way for expanded revenue streams. When developers and organizations can access base models free of charge or at minimal cost, they gain room to explore novel use cases, test variations, and fine-tune performance without worrying about costly license renewals or service tiers. This flexibility levels the playing field, allowing startups, research labs, and small businesses to compete with larger organizations in developing AI-driven solutions.

Smaller and midsize companies often outpace major corporations in embracing open-source AI, thanks to lighter budgets and more agile decision-making. In many cases, these entities pilot cutting-edge models to reimagine products, refine customer experiences, or streamline internal workflows. Their successes then attract attention from larger players, creating a ripple effect. By offering free or low-cost building blocks, open-source frameworks empower innovators of every scale and help sustain a dynamic marketplace for AI-driven applications. Many of these ventures attribute accelerated development cycles and lower risk profiles to the availability of community support, extensive documentation, and shared best practices that accompany open platforms.

One of the clearest examples of community-led success lies in PyTorch, Meta’s open-source deep learning framework. In 2023, governance of PyTorch shifted from the company to a nonprofit foundation guided by a technical steering committee. Researchers Yue and Nagle (2024) analyzed repository activity both before and after the change. They found that Meta’s own contributions “significantly decreased,” a deliberate step away from single-vendor control. At the same time, contributions from external companies—especially hardware vendors—rose sharply, and user engagement metrics remained “no change.”

Report authors Anna Hermansen and Cailean Osborne highlight that this model “promotes broader participation and increased contributions and decreases the dominance of any single company.” They emphasize that fostering a diverse contributor base not only mitigates risk but also unlocks creative problem-solving paths. As they argue, “engagement in open, collaborative activities is a better indicator of innovation than patents.” This perspective suggests that metrics around community activity may offer greater insight into AI progress than traditional intellectual property counts.

Manufacturing stands out as a sector ready to capture large gains from open-source AI. Because source code is freely available, companies can integrate models directly into production control systems, automate quality checks, and optimize inventory planning. A 2023 McKinsey report cited in the study estimates that advanced manufacturing could see an additional $290 billion in output value once AI-driven solutions reach full scale. Those gains come from reduced downtime, fewer production errors, and more precise demand forecasting. The open-code approach also supports customization for different manufacturing contexts, from heavy industry to consumer goods assembly, letting engineers tweak model parameters and retrain on site-specific data sets without incurring extra licensing fees or restrictions.

In clinical environments, AI applications built on open-source code can assist with imaging analysis, early disease detection, and patient monitoring. A 2024 study highlighted in the report found these freely available models matching or exceeding the performance of closed-source alternatives, allowing providers to maintain high accuracy and meet privacy requirements without straining limited budgets. McKinsey data projects that once AI tools are fully integrated across hospitals and clinics, the global healthcare sector could realize up to $260 billion in additional value.

The analysis also underscores the human capital impact of expanding open-source AI. Demand for professionals skilled in model development, data engineering, and AI operations is on the rise, pushing salaries for these roles up by as much as 20 percent, according to the report. Training programs, certification courses, and community workshops are emerging to equip men and women with the knowledge needed to design, deploy, and maintain open AI systems. That emphasis on workforce development may prove as critical as software itself.

Hilary Carter, Senior Vice President of Research at The Linux Foundation, summed up the report’s implications: “The findings in this report make it clear: open-source AI is a catalyst for economic growth and opportunity. As adoption scales across sectors, we’re seeing measurable cost savings, increased productivity and rising demand for AI-related skills that can boost wages and career prospects. Open-source AI is not only transforming how businesses operate—it’s reshaping how people work.”