Open source has entered a new phase of evolution, driven largely by the rise of artificial intelligence. The days of romanticizing open source as a purely philanthropic endeavor are fading. Today, the most significant contributions come from corporations seeking strategic advantage. AI is not killing open source; it is reshaping it into the foundational control plane for modern computing.
Open source becomes the backbone of AI infrastructure
The most visible AI developments—large language models, generative AI tools, and proprietary platforms—often overshadow the quiet but critical work happening in open source. While companies like OpenAI and Google release closed models, the underlying infrastructure that makes AI viable in production is increasingly open source. Kubernetes, the container orchestration platform, has become the de facto operating system for AI workloads. According to the Cloud Native Computing Foundation (CNCF), 66% of organizations hosting generative AI models now use Kubernetes for inference workloads. This shift underscores how open source projects are becoming essential for managing the complexity of AI.
The CNCF now hosts over 230 projects with more than 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 mirrors this growth: 1.12 billion contributions, over 180 million developers, and 518.7 million merged pull requests in 2025. The Apache Software Foundation also reported steady activity with 9,905 committers across 295 projects and 1,310 software releases in fiscal year 2025. These numbers indicate that open source engagement is not declining; it is concentrating on the layers that matter most for AI and cloud-native computing.
Strategic investments by major tech companies
Who is driving this surge in contributions? The list of top contributors reveals a strategic shift. Red Hat leads the CNFC with 194,699 contributions in 2025, followed by Microsoft with 107,645 and Google with 91,158. Independent contributors still rank fourth at 52,404, showing that community participation remains relevant but no longer dominates. The key takeaway is that serious companies are spending serious money to shape the plumbing their products depend on. This is not charity; it is product strategy. Red Hat invests heavily because OpenShift, its Kubernetes-centric platform, relies on a healthy Kubernetes ecosystem. Microsoft, once hostile to open source, now sits second, recognizing that controlling observability and networking standards gives it leverage in the cloud market.
Google's strong presence is expected given its role in creating Kubernetes and supporting projects like Istio and Kubeflow. But perhaps the most telling example is Nvidia. Despite its tremendous wealth, Nvidia ranks 14th in Kubernetes contributions with 5,892 commits over the past two years. The company has also open sourced KAI Scheduler, a GPU scheduler for Kubernetes, and is 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 are used in AI systems. By doing so through developer communities rather than cash payouts, Nvidia ensures its hardware is optimized in the environment where most AI workloads will run.
OpenTelemetry and Cilium: Rising stars in observability and networking
Another area of rapid growth is observability. OpenTelemetry has become one of the fastest-rising CNCF projects, with a 39% increase in commits in 2025 and a contributor base growing from 1,301 to 1,756. Companies like Microsoft and Splunk heavily contribute to OpenTelemetry because they want to set the standard for how data is collected and monitored in cloud-native environments. This is a land grab for observability standards—whoever defines the default tracking mechanism has a competitive advantage in selling tools and services around it.
Cilium, a project at the intersection of networking, observability, and security, has seen even more dramatic growth. After joining the CNCF, the number of contributing companies rose 90%, from 533 to 1,011, while individual contributors jumped from 1,269 to 4,464. Major contributors include Google, Datadog, and Cloudflare. Cilium's importance stems from its role in managing the performance, governance, and visibility of distributed workloads. As AI models become more latency-sensitive and expensive to run, infrastructure projects like Cilium become mission-critical. They enable organizations to govern, monitor, and optimize their AI workloads.
Control through code: How open source becomes a leverage point
The underlying motivation for these investments is not altruism but control. Companies contribute to open source not just to give back but to shape the defaults, normalize interfaces, and influence the operational assumptions that everyone else must adopt. Open source has become less about openness for its own sake and more about setting the standards that make proprietary offerings viable. This is especially true in AI infrastructure, where the choice of scheduler, networking plugin, or observability tool can determine the cost, performance, and security of AI deployments.
For example, Kubernetes won because it became too important for any serious infrastructure company to ignore. Red Hat contributes heavily because its business depends on Kubernetes remaining the standard. Similarly, Nvidia's investment in KAI Scheduler ensures that GPU allocation in Kubernetes clusters is optimized for AI workloads, locking customers into Nvidia hardware. The pattern is clear: open source is where vendors vie to set the defaults in the layers where ecosystems harden into standards.
The quiet growth of open source in AI
While news cycles focus on flashy new AI models, the real strategic work happens in the infrastructure layers that make AI production-ready. The CNCF's data shows that 66% of organizations use Kubernetes for inference, and the foundation explicitly calls Kubernetes the de facto operating system for AI. This might be self-serving, but the numbers back it up. The shift to cloud-native and AI workloads has increased demand for open source tools that provide portability, scalability, and vendor independence. No organization wants to build its future on opaque, inescapable infrastructure they cannot inspect or influence. Open source offers a way to maintain agency in a rapidly commoditizing market.
GitHub's Octoverse data further confirms that developer engagement is at an all-time high, with contributions rising across project categories. The most active areas are those related to AI and cloud-native computing: Kubernetes, OpenTelemetry, Cilium, and project like Kubeflow. This is a direct consequence of AI's compute and orchestration demands. Without open source infrastructure, managing AI at scale would be prohibitively expensive and complex.
The narrative that open source is dying is therefore misguided. What is happening is a maturation and a shift in focus. Open source has become less of a fringe movement and more of a utility layer—dull, reliable, and essential. The excitement has moved from the code itself to the impact it enables. And the impact is enormous: AI is being democratized not by open models alone but by the open infrastructure that runs them. Kubernetes, Cilium, OpenTelemetry, and hundreds of other projects are the unsung heroes of the AI revolution. They are not glamorous, but they are indispensable. And they are built by companies that see open source as the most effective lever for shaping the future of computing.
Source: InfoWorld News