Open source has become less of a romantic ideal in the past few years and more of a practical, strategic layer for controlling infrastructure. The AI boom has accelerated this shift, transforming open source from a community-driven alternative into the operational backbone for AI workloads. Far from fading, open source engagement is now concentrated in the layers that matter most for AI production: Kubernetes, observability, platform engineering, networking, and the infrastructure needed to make AI work reliably at scale.
Infrastructure as the new frontier
The Cloud Native Computing Foundation (CNCF) reports it 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 a staggering 82% of container users run Kubernetes in production. GitHub's Octoverse data for 2025 tells a similar story: 1.12 billion contributions from more than 180 million developers, with a record 518.7 million merged pull requests. The Apache Software Foundation, while less flashy, remains robust with 9,905 committers working across 295 projects and issuing 1,310 software releases in fiscal year 2025. These numbers underscore a fundamental transformation: open source has become the operational substrate for the modern digital economy, and AI is the primary driver of this transformation.
Historically, open source was often associated with volunteer-driven projects and ideological commitments to freedom and transparency. But today, the bulk of contributions come from large corporations that invest strategically to shape the tools and standards their businesses depend on. The top CNCF contributors in 2025 illustrate this clearly: Red Hat leads with 194,699 contributions, followed by Microsoft with 107,645, Google with 91,158, and independent contributors in fourth place with 52,404. This concentration of corporate investment reflects a recognition that whoever controls the infrastructure layers gains leverage over everything built on top of them.
Strategic contributions, not charity
Red Hat's dominance in CNCF contributions is no accident. Red Hat's OpenShift is a Kubernetes-centric application platform, so the company pours effort into maintaining and evolving the Kubernetes ecosystem. This is product strategy, not community service. Kubernetes won the container orchestration war because it became too important for any serious infrastructure company to ignore. Red Hat contributes heavily because its business depends on that continued relevance. Similarly, Microsoft, once the poster child for open source hostility, now sits second in overall CNCF contributions. A more telling signal is where Microsoft invests: OpenTelemetry, one of the fastest-rising CNCF projects, saw a 39% rise in commits in 2025 and a contributor base that grew from 1,301 to 1,756 in a single year. Companies like Microsoft, Splunk, and others are investing in observability standards to ensure their tools and platforms remain integral to how organizations monitor and manage complex systems.
The same pattern plays out with Cilium, a project that sits at the intersection of networking, observability, and security. After joining CNCF, Cilium's 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 the project matured. This is not random; Cilium is precisely the kind of project that becomes mission-critical when workloads become distributed, latency-sensitive, and expensive. AI workloads, in particular, require efficient networking and security, driving further investment in such infrastructure projects.
Nvidia's play for AI infrastructure
Nvidia, the dominant AI chipmaker, provides a telling example of how open source is being leveraged for AI. Despite having enormous cash reserves, Nvidia chooses to invest in open source rather than simply buying its way into the ecosystem. It ranked 14th in Kubernetes contributions over the past two years, with 5,892 contributions. It also open-sourced KAI Scheduler, a Kubernetes-native GPU scheduler that came out of its acquisition of Run:ai, and is a key contributor to Kubeflow. Nvidia is not just selling chips; it is shaping the scheduling, orchestration, and workflow layers that determine how effectively its hardware gets used in real-world AI systems. By contributing to developer communities rather than relying solely on proprietary tools, Nvidia ensures that its technology is deeply embedded in the open source stack that many organizations depend on.
CNCF reports that 66% of organizations hosting generative AI models now use Kubernetes for some or all inference workloads. The foundation explicitly calls Kubernetes the de facto operating system for AI. While CNCF has a vested interest in promoting Kubernetes, the data supports the claim: 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. Open source provides the transparency and customizability that AI workloads demand, particularly as companies seek to avoid vendor lock-in and maintain control over their data and models.
The dulling of open source
This transformation means open source is becoming less romantic and more essential. The old story of open source as a fringe alternative or a developer-led morality play was never entirely accurate, but it is no longer credible. 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 shift is not about abundance of contributions but about strategic alignment. Companies invest upstream not because they have discovered civic virtue, but because shaping the substrate gives them leverage over everything built on top of it. The data from CNCF, GitHub, and Apache all point in the same direction: open source engagement is growing, but it is growing in the layers that matter for AI and cloud-native operations.
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. The same logic applies to OpenTelemetry, Cilium, and Kubeflow. These projects are not just tools; they are control points in the infrastructure landscape. As AI workloads become more distributed and complex, the importance of these control points will only increase. Open source is not dying; it is maturing into the operational backbone of the AI era.
Source: InfoWorld News