Open source has transformed into the backbone of AI infrastructure, though its evolution often goes unnoticed amid the buzz around new AI models. The landscape has shifted from a community-driven movement to a strategic arena where corporations invest heavily to control the layers that power modern computing. This rewrite highlights the key facts and trends that define how open source is adapting to the AI era.
Open source becomes the control plane for AI
Contrary to fears that open source is dying, engagement has actually deepened in critical areas. The Cloud Native Computing Foundation (CNCF) now hosts over 230 projects with more than 300,000 contributors worldwide. A 2025 CNCF survey found that 98% of organizations have adopted cloud-native techniques, and 82% of container users run Kubernetes in production. Similarly, GitHub’s 2025 Octoverse report recorded 1.12 billion contributions, over 180 million developers, and 518.7 million merged pull requests. The Apache Software Foundation reported 9,905 committers across 295 projects, issuing 1,310 software releases in fiscal year 2025. These numbers confirm that open source is thriving, but the motivation behind contributions has evolved.
Corporate entities now dominate contribution charts. In 2025, CNCF Devstats showed Red Hat leading with 194,699 contributions, followed by Microsoft (107,645) and Google (91,158). Independent contributors ranked fourth with 52,404 contributions, proving that while the community still matters, the center of gravity has shifted. Companies are no longer contributing purely for altruistic reasons; they invest in open source to set defaults, normalize interfaces, and shape operational standards. Open source has become a means of control — not proprietary control, but influence over the ecosystems that harden into industry standards.
Who gives, and why: strategic investments in open source
Red Hat’s dominance in CNCF reflects its product strategy around OpenShift, a Kubernetes-centric platform. Its contributions are not charity but a direct investment in the Kubernetes ecosystem on which its business depends. Microsoft, once hostile to open source, now ranks second, with particular focus on projects like OpenTelemetry. OpenTelemetry saw a 39% rise in commits in 2025, with contributors growing from 1,301 to 1,756 in one year. Companies like Microsoft and Splunk are investing in observability standards to gain leverage over how data is collected and analyzed across cloud-native environments.
Cilium, a project at the intersection of networking, observability, and security, has seen explosive growth. After joining CNCF, contributing companies rose 90% from 533 to 1,011, while individual contributors jumped from 1,269 to 4,464. Major contributors like Google, Datadog, and Cloudflare expanded their roles as the project matured. Cilium’s importance stems from its ability to manage distributed, latency-sensitive AI workloads, making it a critical piece of infrastructure.
Nvidia, despite its immense cash reserves, has chosen to invest in open source rather than build everything in-house. It ranked 14th in Kubernetes contributions over the past two years, with 5,892 contributions. The company open-sourced KAI Scheduler, a Kubernetes-native GPU scheduler, and positions itself as a key contributor to Kubeflow. This strategy allows Nvidia to influence the scheduling, orchestration, and workflow layers that determine how effectively its chips are used in AI systems. The move signals a broader trend: even the most profitable hardware vendors see open source as essential to shaping AI infrastructure.
Open source as the essential supporting actor for AI
CNCF reports that 66% of organizations hosting generative AI models now use Kubernetes for some or all inference workloads, calling it the de facto operating system for AI. While this claim reflects the foundation’s reliance on Kubernetes, the reality is 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 platforms they cannot inspect or influence.
The old narrative of open source as a fringe alternative or a developer morality play no longer applies. Open source is where the cloud-native stack is standardized, where observability is normalized, where platform engineering is productized, and where AI infrastructure is built. The romance has faded, but the essentiality has grown. As AI continues to demand robust, scalable, and governable infrastructure, open source projects provide the foundational layers that enable innovation without vendor lock-in. The companies that contribute heavily are not acting out of civic virtue but out of self-interest: whoever shapes the substrate gains leverage over everything built on top of it.
Source: InfoWorld News