Open source has not died in the age of AI. Instead, it has quietly moved into the background, becoming the control plane for the infrastructure that powers modern artificial intelligence. While the AI community frequently releases ambitious and often closed models, open source engagement is shifting to layers that matter most: Kubernetes, observability, platform engineering, networking, and the infrastructure required to make AI work in production.

The Cloud Native Computing Foundation (CNCF) now hosts more than 230 projects with over 300,000 contributors worldwide. Its 2025 survey found that 98% of organizations have adopted cloud-native techniques, and 82% of container users run Kubernetes in production. GitHub‘s 2025 Octoverse report reveals 1.12 billion contributions, more than 180 million developers, and a record 518.7 million merged pull requests. The Apache Software Foundation remains robust with 9,905 committers across 295 projects and 1,310 software releases in fiscal year 2025.

Control Through Code

The most telling shift is who is contributing and why. In 2025, Red Hat led CNCF contributions with 194,699 contributions, followed by Microsoft with 107,645 and Google with 91,158. Independent contributors still mattered, landing fourth at 52,404, but the center of gravity is unmistakably corporate. Serious companies are spending serious money to shape the plumbing their products depend on. The top contributors have remained constant over the past decade, signaling a long-term strategic commitment, while an influx of new contributors also appears.

This shift changes how we should interpret open source contributions. Many still view them as philanthropy. Many open source program offices try to convince engineering teams to contribute because “it’s the right thing to do,” hoping to ingratiate the company into a nebulous community. But the reality is different: open source is increasingly where vendors set defaults, normalize interfaces, and shape the operational assumptions everyone else must live with. Open source has become less about openness for its own sake and more about control — not proprietary control, but control over the layers where ecosystems harden into standards. The companies investing upstream are not discovering civic virtue; they are seeking leverage over everything built on top of the substrate they help shape.

Who Gives, and Why

Red Hat’s dominance in CNCF is easy to explain. Its OpenShift is a Kubernetes-centric application platform, so pouring effort into the Kubernetes world is product strategy, not community service. 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.

Microsoft’s position is even more revealing. Once the company most associated with open source hostility, it now sits second in overall CNCF contributions. But the more interesting signal is where companies like Microsoft are investing. OpenTelemetry has become one of the fastest-rising CNCF projects, with a 39% rise in commits in 2025 and a contributor base that grew from 1,301 to 1,756 in a single year. This is not charity — it is a land grab around observability standards. Microsoft, Splunk, and other top contributors are all helping in order to help themselves. That is the way open source has always worked.

Then there is Cilium, which sits at the intersection of networking, observability, and security — categories that become mission-critical once workloads become distributed, latency-sensitive, and expensive. Cilium‘s journey report says the number of contributing companies rose 90% after joining 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. AI may be driving headlines, but a lot of the real strategic work is happening in projects like Cilium, where the infrastructure determines whether AI workloads are governable, visible, and efficient.

Nvidia, a company with so much cash it could buy several countries, ranked 14th in Kubernetes contributions in the past two years, with 5,892 contributions. It has also open sourced KAI Scheduler, a Kubernetes-native GPU scheduler that came out of Run:ai, and described itself as a key contributor to Kubeflow. Nvidia is not just selling chips; it is investing in the scheduling, orchestration, and workflow layers that determine how effectively those chips get used in real-world AI systems. And it is doing so through developer communities rather than lump sum cash payouts.

The Nvidia work signals where open source is going in AI. CNCF says 66% of organizations hosting generative AI models now use Kubernetes for some or all inference workloads, and explicitly calls Kubernetes the de facto operating system for AI. While CNCF’s dependence on Kubernetes as a tentpole project may bias this statement, it does not diminish the reality that Kubernetes and Kubeflow are increasingly central to training and inference systems. AI is making open infrastructure more important because few organizations want to build their future on opaque, inescapable infrastructure they cannot inspect or influence.

So is open source increasing in importance? Absolutely, but not in the warm, nostalgic way some people still imagine. It is becoming less romantic and more essential. The old story about open source as a fringe alternative or a developer-led morality play was never true, and it is not even remotely credible now. 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.

Source: InfoWorld News