Key facts about AI and open source

• CNCF now hosts more than 230 projects with over 300,000 contributors worldwide.
• 98% of organizations have adopted cloud-native techniques, and 82% of container users run Kubernetes in production (2025 CNCF survey).
• GitHub's 2025 Octoverse report recorded 1.12 billion contributions, 180 million developers, and 518.7 million merged pull requests.
• Apache Software Foundation had 9,905 committers across 295 projects, issuing 1,310 software releases in fiscal year 2025.
• Red Hat led CNCF contributions with 194,699, followed by Microsoft (107,645) and Google (91,158). Independent contributors came fourth (52,404).
• OpenTelemetry saw a 39% rise in commits in 2025; contributor base grew from 1,301 to 1,756.
• Cilium's contributing companies rose 90% after joining CNCF (533 to 1,011); individual contributors jumped from 1,269 to 4,464.
• Nvidia ranked 14th in Kubernetes contributions (5,892 in two years), open-sourced KAI Scheduler, and contributes to Kubeflow.
• 66% of organizations hosting generative AI models use Kubernetes for some or all inference workloads.

Open source has become less of a romantic ideal and more of a practical necessity. In the last few years, the conversation has moved from 'open source is always better' to a quiet recognition that the most critical layers of modern infrastructure are being built and standardized through open source projects. This is not about charity or developer-led morality plays; it is about strategic control over the plumbing that powers everything from cloud-native applications to AI workloads.

The numbers tell a clear story. The Cloud Native Computing Foundation (CNCF) now hosts more than 230 projects with more than 300,000 contributors worldwide. Its 2025 survey found that 98% of organizations have adopted cloud-native techniques, and 82% of container users now run Kubernetes in production. GitHub's 2025 Octoverse report reinforces the trend: 1.12 billion contributions, more than 180 million developers, and a record 518.7 million merged pull requests. The Apache Software Foundation, though less flashy, reported 9,905 committers working across 295 projects and issued 1,310 software releases in fiscal year 2025. These are not isolated metrics; they reflect a systemic shift in how technology companies build and maintain their core infrastructure.

The motivation behind this massive investment is control. By contributing to open source projects, vendors set defaults, normalize interfaces, and shape the operational assumptions that everyone else must live with. Red Hat, for example, remains the heavyweight in CNCF contributions with 194,699 contributions in 2025. This is not community service; it is product strategy. Red Hat's OpenShift is a Kubernetes-centric application platform, so pouring effort into the Kubernetes ecosystem directly benefits its business. Microsoft, once the symbol of open source hostility, now sits second with 107,645 contributions. Its investment in OpenTelemetry, which saw a 39% rise in commits in 2025 and a contributor base growing from 1,301 to 1,756, is a land grab around observability standards. Google, with 91,158 contributions, continues to shape the infrastructure landscape through projects like Kubernetes and Kubeflow.

The rise of AI has accelerated this trend. Nvidia, a company with enormous financial resources, has chosen to invest in open source rather than buy its way in. It ranked 14th in Kubernetes contributions over the past two years, with 5,892 contributions, and has open-sourced KAI Scheduler, a Kubernetes-native GPU scheduler. Nvidia also describes itself as a key contributor to Kubeflow. This is not about charity; it is about ensuring that the scheduling, orchestration, and workflow layers that determine how effectively AI chips are used are shaped in ways that favor Nvidia's hardware. The CNCF reports that 66% of organizations hosting generative AI models now use Kubernetes for some or all inference workloads, and it calls Kubernetes the de facto operating system for AI. This claim may be self-serving, but it reflects reality: Kubernetes and Kubeflow are becoming central to training and inference systems.

Open source is also evolving in areas like Cilium, which sits at the intersection of networking, observability, and security. Cilium's journey report shows that the number of contributing companies rose 90% after it joined CNCF, from 533 to 1,011, while individual contributors jumped from 1,269 to 4,464. Google, Datadog, and Cloudflare all expanded their contributions as the project matured. This is not random; Cilium addresses precisely the categories that become mission-critical once workloads become distributed, latency-sensitive, and expensive. AI may drive headlines, but the real strategic work is happening in projects like Cilium, where the infrastructure determines whether those AI workloads are governable, visible, and efficient.

The involvement of independent contributors remains significant—they landed fourth in CNCF contribution rankings with 52,404 contributions. This is a useful reminder that open source has not become purely corporate. However, the center of gravity is unmistakably shifting toward companies that understand that open source is where the cloud-native stack gets standardized, where observability gets normalized, where platform engineering gets productized, and where AI infrastructure is increasingly being built. The old story of open source as a fringe alternative is no longer credible. Open source has become the control plane for the future of technology.

The implications are profound. Companies that once treated open source as a marketing expense are now treating it as a core engineering investment. The top contributors have remained constant over the past decade, indicating a willingness to play the long game. But during that same time, an influx of new contributors has expanded the base. This is not about openness for its own sake; it is about control over the layers where ecosystems harden into standards. Kubernetes won because it became too important for any serious infrastructure company to ignore, and Red Hat contributes heavily because its business depends on that remaining true. Similarly, Microsoft's investment in OpenTelemetry is not about altruism; it is about ensuring that observability standards align with Azure's ecosystem.

For AI, the shift is even more pronounced. Organizations do not want to build their future on opaque, inescapable infrastructure they cannot inspect or influence. Open source provides a path to transparency and customization. Nvidia's open-source contributions are a tell: the company is not just selling chips; it is shaping the software layer that determines how effectively those chips are used. This is a classic open source strategy—invest in the substrate to gain leverage over everything built on top of it. As AI workloads become more distributed and complex, the need for standardized, open infrastructure will only grow. The projects that matter most—Kubernetes, OpenTelemetry, Cilium, Kubeflow—are those that provide the plumbing for AI in production.

Open source did not die. It became the control plane for AI. And that is far more important than any romantic notion of a developer-led revolution.

Source: InfoWorld News