Open source has entered a new phase of maturity. While the narrative often focuses on the latest closed AI models and flashy releases, the real transformation is happening in the layers that make AI work in production. Kubernetes, observability tools, platform engineering, and networking have become the critical infrastructure upon which AI workloads depend.
The era of open source as a fringe alternative or a developer-led morality play is over. Instead, open source has become the control plane for AI—a place where companies vie to set standards, normalize interfaces, and shape the operational assumptions that everyone else must adopt.
Control through code
The Cloud Native Computing Foundation (CNCF) now hosts more than 230 projects with over 300,000 contributors worldwide. Its 2025 survey revealed that 98% of organizations have adopted cloud-native techniques, and 82% of container users run Kubernetes in production. GitHub’s Octoverse report for 2025 tells a similar story: 1.12 billion contributions, more than 180 million developers, and 518.7 million merged pull requests. The Apache Software Foundation also reported robust activity, with 9,905 committers working across 295 projects and 1,310 software releases in fiscal year 2025.
Who is driving this activity? The top contributors are large corporations with clear strategic interests. Red Hat leads CNCF contributions with 194,699 contributions in 2025, followed by Microsoft with 107,645, and Google with 91,158. Independent contributors still matter—landing fourth with 52,404—but the center of gravity is unmistakable. These companies are not contributing out of altruism; they are investing to shape the plumbing their products depend on.
Who gives, and why?
Red Hat’s dominance makes sense given that OpenShift is a Kubernetes-centric application platform. Pouring effort into Kubernetes is product strategy, not community service. Similarly, Microsoft’s rise to second place is striking for a company once hostile to open source. The real signal, however, lies in where Microsoft and others are focusing: OpenTelemetry, which saw a 39% rise in commits in 2025 and a contributor base growing from 1,301 to 1,756. This is a land grab around observability standards, with Microsoft, Splunk, and others helping themselves by helping the project.
Cilium is another example. Its journey report shows contributing companies rose 90% after joining the CNCF, from 533 to 1,011, and individual contributors jumped from 1,269 to 4,464. Google, Datadog, and Cloudflare all expanded their contributions as the project matured. Cilium sits at the intersection of networking, observability, and security—precisely the categories that become mission-critical when workloads are distributed, latency-sensitive, and expensive.
Nvidia’s involvement is particularly telling. Despite having vast resources, Nvidia ranked 14th in Kubernetes contributions with 5,892 contributions over the past two years. The company has open-sourced the KAI Scheduler, a Kubernetes-native GPU scheduler, and is a key contributor to Kubeflow. Nvidia is investing in the scheduling, orchestration, and workflow layers that determine how effectively its chips are used in real-world AI systems—and it’s doing so through developer communities rather than cash payouts.
This work underscores where open source is headed in AI. The CNCF reports that 66% of organizations hosting generative AI models now use Kubernetes for some or all inference workloads, calling Kubernetes the de facto operating system for AI. While this self-serving claim has some truth, it reflects the reality that Kubernetes and Kubeflow are increasingly central to training and inference systems. AI is making open infrastructure more important because organizations do not want to build their future on opaque, inescapable infrastructure they cannot inspect or influence.
The depth of corporate involvement changes how we interpret open source contributions. Too many people still talk about them as philanthropy. Too many open source program offices try to convince engineers to contribute because “it’s the right thing to do,” hoping developers’ efforts will ingratiate the company into some nebulous community. That view is outdated. Open source is now where vendors set defaults, normalize interfaces, and shape the operational assumptions everyone else must live with. It is about control—not proprietary control, but control over the layers where ecosystems harden into standards.
An essential supporting actor
Open source is becoming less romantic and more essential. It 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 days of viewing open source as a fringe movement are over. It has grown up, become dull, and that is exactly what the industry needs. The strategic investments by major companies ensure that the infrastructure powering AI remains open, inspectable, and evolvable—even if the motivations are far from altruistic.
Source: InfoWorld News