Open source is no longer the fringe movement it once was. Over the past few years, the conversation around open source has shifted dramatically. Instead of debates about licensing or community governance, the focus is now on how open source projects underpin the infrastructure that powers artificial intelligence. The narrative of open source as a purely altruistic endeavor has faded, replaced by a pragmatic understanding that strategic contributions to key projects yield significant competitive advantages. This transformation is most visible in the cloud-native ecosystem, where Kubernetes has become the de facto operating system for AI workloads, and observability and networking projects are attracting massive investment from the industry's largest players.

The quiet rise of open infrastructure

While the media is saturated with news of the latest AI models and their capabilities, the infrastructure that makes those models run efficiently often goes unnoticed. Yet it is in these layers—Kubernetes, container orchestration, service meshes, and observability tools—that open source is experiencing its most profound impact. The Cloud Native Computing Foundation (CNCF) now hosts more than 230 projects, with over 300,000 contributors worldwide. According to the CNCF’s 2025 survey, 98% of organizations have adopted cloud-native techniques, and 82% of container users now run Kubernetes in production. These numbers are not just statistics; they represent a fundamental shift in how enterprises build and deploy software. Kubernetes has become the standard control plane for managing containerized applications, including AI inference and training workloads. The rise of generative AI has only accelerated this trend, as organizations seek scalable, portable, and cost-effective ways to run machine learning models in production.

GitHub’s 2025 Octoverse report paints a similar picture on a broader scale. With 1.12 billion contributions, more than 180 million developers, and a record 518.7 million merged pull requests, the open source ecosystem is more active than ever. However, the nature of these contributions has changed. They are increasingly concentrated in infrastructure projects that solve real-world operational challenges, rather than in niche or experimental repositories. The Apache Software Foundation also continues to be a significant player, with 9,905 committers working across 295 projects and issuing 1,310 software releases in fiscal year 2025. These data points confirm that open source is not dying; it is maturing and becoming deeply embedded in the enterprise stack.

Strategic contributions: Control through code

The notion that open source contributions are primarily acts of charity is no longer tenable. Today, companies invest in open source to exert influence and establish defaults. This is particularly evident in the CNCF contribution statistics for 2025. Red Hat led with 194,699 contributions, followed by Microsoft with 107,645, Google with 91,158, and independent contributors with 52,404. While independent developers still matter, the center of gravity has unmistakably shifted toward large corporations. These companies are not contributing out of altruism; they are shaping the plumbing that their products depend on. For example, Red Hat’s OpenShift is a Kubernetes-centric application platform, so it is natural that Red Hat pours resources into improving Kubernetes and related projects. Microsoft, once known for its hostility toward open source, now sits second in overall CNCF contributions, reflecting its strategic pivot to cloud and developer tools. Google, the creator of Kubernetes, continues to invest heavily to ensure the platform remains aligned with its vision.

This pattern extends beyond traditional infrastructure. OpenTelemetry, a project for observability and telemetry data, has become one of the fastest-growing CNCF projects, with a 39% increase in commits in 2025 and a contributor base that grew from 1,301 to 1,756 individuals. The leading contributors—Microsoft, Splunk, and others—are all companies with products that integrate with OpenTelemetry. Their contributions are a form of land grab, aimed at defining how observability data is collected and processed. Similarly, Cilium, a project for networking, security, and observability based on eBPF, has seen remarkable growth. After joining CNCF, the number of contributing companies rose 90%, from 533 to 1,011, while individual contributors jumped from 1,269 to 4,464. Google, Datadog, and Cloudflare all expanded their contributions as Cilium matured. This is no coincidence: Cilium sits at the intersection of networking, observability, and security, all of which become critical when workloads are distributed, latency-sensitive, and expensive—exactly the conditions created by AI.

Nvidia and the AI infrastructure play

Perhaps the most telling indicator of where open source is headed is Nvidia’s involvement. With a market capitalization that dwarfs most technology companies, Nvidia could easily afford to build its own proprietary infrastructure. Instead, it ranks 14th in Kubernetes contributions over the past two years, with 5,892 contributions. Nvidia has also open sourced KAI Scheduler, a Kubernetes-native GPU scheduler developed by Run:ai, and describes itself as a key contributor to Kubeflow, the machine learning platform for Kubernetes. This investment reveals Nvidia’s understanding that success in AI depends not only on selling chips but also on ensuring those chips are used efficiently in real-world systems. By contributing to open source orchestration and scheduling layers, Nvidia helps make its hardware the default choice for AI workloads. This is a long-term strategy that aligns with the company’s goal of dominating the AI infrastructure stack.

The CNCF’s 2025 survey highlighted that 66% of organizations hosting generative AI models now use Kubernetes for some or all inference workloads. While the foundation has a vested interest in promoting Kubernetes, the data is corroborated by other sources. Kubernetes and Kubeflow are increasingly central to training and inference systems, because they provide the portability, scalability, and resource management that AI demands. Open source infrastructure is becoming more essential as organizations realize they cannot afford to build their future on opaque, vendor-locked platforms they cannot inspect or influence. This is especially true in the rapidly evolving AI landscape, where flexibility and ability to adapt quickly are paramount.

The dull revolution

Open source has become less romantic and more essential. The old stories of lone developers building world-changing software in their garages have given way to a reality where teams of engineers, employed by the largest companies in the world, collaborate on projects that standardize the cloud-native stack. This shift is not a sign of decline but of maturity. Open source now underpins the critical infrastructure that powers modern enterprises, including AI. The projects that are thriving—Kubernetes, OpenTelemetry, Cilium, Kubeflow—are those that solve dull but necessary problems: scheduling, networking, observability, and security. They are the unglamorous layers that ensure AI workloads are governable, visible, and efficient. As AI continues to permeate every industry, the importance of these open source foundations will only grow. The companies that understand this are the ones investing today, not out of charity, but because they know that control over the substrate yields leverage over everything built on top of it.

In summary, open source has not died; it has become the control plane for AI. The strategic contributions from major vendors are reshaping the landscape, making open infrastructure more robust and more essential. The future of AI will be built on open source, even if that part of the story rarely makes headlines.

Source: InfoWorld News