The landscape of open source has undergone a profound transformation in recent years, driven largely by the rise of artificial intelligence. While the rhetoric of 'open source is always better' persists in some circles, the reality is that open source has become less a philosophical stance and more a strategic tool for controlling the infrastructure upon which AI and other modern technologies depend. This evolution is not a death knell for open source but rather its maturation into a critical, albeit less romanticized, component of the tech ecosystem.
The data supports this shift. The Cloud Native Computing Foundation (CNCF) now hosts over 230 projects with more than 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 Octoverse report for 2025 tells a similar story: 1.12 billion contributions, over 180 million developers, and a record 518.7 million merged pull requests. The Apache Software Foundation also reports robust activity, with 9,905 committers across 295 projects and 1,310 software releases in its fiscal year 2025.
Control through code
Beneath the headlines about the latest AI models lies a quieter but more consequential story: open source is where the fight for control over infrastructure is being waged. Companies like Red Hat, Microsoft, and Google are not contributing to open source out of altruism; they are investing heavily to shape the defaults, interfaces, and operational models that others will rely on. Red Hat, for instance, leads CNCF contribution activity with 194,699 contributions in 2025, driven by its Kubernetes-centric OpenShift platform. Microsoft follows with 107,645 contributions, and Google with 91,158. Independent contributors rank fourth, a reminder that the community still matters, but the center of gravity has shifted toward corporate interests.
This shift changes how we should interpret open source contributions. They are not merely philanthropy or community service; they are strategic investments. Companies contribute because controlling the substrate gives leverage over everything built on top of it. For example, Red Hat's deep involvement in Kubernetes is a product strategy, not charity. Similarly, Microsoft's contributions to OpenTelemetry—one of the fastest-rising CNCF projects with a 39% increase in commits in 2025—reflect a land grab around observability standards. Splunk and other major players join in, helping themselves while helping the project.
Who gives, and why?
Nvidia is a telling case. Despite having enormous financial resources, Nvidia chooses to engage with open source communities rather than simply buy its way into influence. The company ranks 14th in Kubernetes contributions over the past two years, with 5,892 contributions. It has also open-sourced the KAI Scheduler, a Kubernetes-native GPU scheduler from its Run:ai acquisition, and is 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 real-world AI systems. By participating in open source, Nvidia ensures that its hardware remains central to the AI infrastructure stack.
The growing importance of projects like Cilium further illustrates this trend. Cilium sits at the intersection of networking, observability, and security—categories that become mission-critical when workloads are distributed, latency-sensitive, and expensive. Since joining the CNCF, Cilium's contributing companies rose 90%, from 533 to 1,011, and 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; it reflects a strategic focus on the infrastructure that underpins AI and other advanced workloads.
Kubernetes itself has become the de facto operating system for AI. According to the CNCF, 66% of organizations hosting generative AI models now use Kubernetes for some or all inference workloads. This statistic underscores how open source infrastructure is increasingly central to training and inference systems. Organizations do not want to build their future on opaque, inescapable infrastructure they cannot inspect or influence. Open source provides the transparency and control they need, even if the motivations behind it are less idealistic than in the past.
The historical context is important. Open source began as a movement driven by ideals of freedom and collaboration. The GNU General Public License, the Linux kernel, and the Apache Web Server were all products of a community that valued openness for its own sake. Over time, however, commercial entities realized the value of open source and began to participate. The rise of companies like Red Hat, MySQL, and others showed that open source could be a viable business model. But the current era is different. Now, the largest technology companies in the world—Microsoft, Google, Nvidia, Amazon—are all deeply involved in open source, not as an afterthought but as a core part of their strategy.
This involvement has led to a professionalization of open source. Projects like Kubernetes, OpenTelemetry, and Cilium are no longer hobbyist efforts; they are critical infrastructure managed by full-time engineers from multiple companies. The CNCF, the Apache Software Foundation, and the Linux Foundation provide governance and support, ensuring that these projects remain stable and secure. The result is that open source has become dull in the best possible way—reliable, predictable, and foundational. It no longer makes headlines, but it powers the technologies that do.
The implications for AI are significant. As AI models become more powerful and more prevalent, the infrastructure that supports them must be robust, scalable, and manageable. Open source projects like Kubernetes, Kubeflow, and KAI Scheduler provide the building blocks for this infrastructure. They allow organizations to build customized AI platforms that meet their specific needs, without being locked into proprietary solutions. At the same time, the corporate contributions to these projects ensure that they are well-funded and continuously improved.
But there is a tension here. The very control that companies seek through open source can also lead to fragmentation and conflict. When multiple companies contribute to the same project, they may have competing interests. The governance structures of open source foundations are designed to manage these conflicts, but they are not always successful. The recent controversies around licensing changes in projects like Elasticsearch and MongoDB highlight the challenges of balancing corporate interests with community values. However, the overall trend is toward greater collaboration, as the benefits of shared infrastructure outweigh the risks of fragmentation.
In the context of AI, the role of open source is evolving rapidly. The release of models like LLaMA, Mistral, and others under open licenses has sparked a wave of innovation, but it has also raised questions about safety, misuse, and regulation. Open source AI models allow researchers and developers to experiment and build applications without waiting for permission from large companies. But they also make it easier for bad actors to deploy harmful technologies. The open source community is grappling with these issues, exploring ways to ensure that openness does not come at the cost of security or ethics.
Despite these challenges, the importance of open source in AI is likely to grow. The infrastructure needed to train and deploy large-scale AI models is expensive and complex. Open source reduces barriers to entry, enabling smaller organizations and startups to compete with tech giants. It also fosters transparency, which is crucial for building trust in AI systems. As AI becomes more integrated into everyday life, the ability to inspect and understand the underlying infrastructure will become increasingly important.
The journey of open source from a fringe movement to a central pillar of the tech industry is a testament to its resilience and adaptability. It has weathered criticism, legal battles, and commercial pressures, and has emerged stronger each time. Today, it is the control plane for AI, shaping how models are trained, deployed, and managed. The companies that invest in open source are not doing it out of charity; they are doing it because it gives them a competitive advantage. And that is a sign of health, not a betrayal of ideals.
The data from CNCF and GitHub confirms that open source engagement is shifting to the layers that matter most: Kubernetes, observability, platform engineering, networking, and AI infrastructure. These are the areas where standards are set and ecosystems harden. The companies that shape these layers will have leverage over everything built on top of them. Open source has grown up and become dull, but it has also become essential. The AI revolution is being built on open source foundations, and that is a good thing for innovation, transparency, and control.
Source: InfoWorld News