By Guest: Hilary Carter (LinkedIn)
Company: The Linux Foundation
Show Name: The Source
Topic: Cloud Native
Kubernetes has crossed a critical threshold. The 2025 CNCF Annual Cloud Native Survey reveals that 98% of organizations have adopted cloud-native techniques, with 82% running Kubernetes in production—up from 66% just two years ago. But as Kubernetes becomes invisible infrastructure, much like the Linux kernel beneath it, the challenges facing organizations have fundamentally shifted from technical adoption to cultural maturity.
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From Differentiator to Default Infrastructure
Hilary Carter, Senior Vice President of Research at the Linux Foundation, describes the transformation as a sign of Kubernetes’ success and ubiquity. “It shows a lot of similar properties to the Linux kernel in being somewhat invisible,” Carter explains. “I think that is true for mature, foundational infrastructure, and that’s really where we are with the Kubernetes project.”
This shift has happened remarkably fast. The jump from 66% to 82% production adoption in just two years represents a major gain in Kubernetes proliferation. As more organizations modernize their core infrastructure and recognize the value of transitioning to cloud-native practices, Kubernetes has become the de facto industry standard for cloud-native orchestration and deployment.
The AI Infrastructure Connection
Perhaps the most striking finding from the survey is how deeply Kubernetes has become embedded in AI infrastructure. The data shows that 66% of organizations using generative AI rely on Kubernetes for some or all of their AI workloads. The primary activity isn’t building models from scratch—it’s running inference workloads.
“Kubernetes has always been exceptionally good at orchestration, and it’s been exceptionally good at resource-intensive workloads—running those workloads at scale and in decentralized environments,” Carter notes.
The project has found a natural intersection with AI use cases, becoming “the right foundational project at the right time in the innovation landscape.”
This finding also reveals an important economic shift. Organizations have learned that building foundation models from scratch is expensive—requiring significant energy, resources, time, and talent. Instead, they’re taking existing open models and training them with their own data.
“That’s a much more cost-effective way to go,” Carter observes, pointing to research by Frank Nagel at MIT that estimates approximately $25 billion in opportunity costs from not using open models.
The Four Stages of Cloud-Native Maturity
The survey went beyond simple adoption metrics to examine organizational maturity, revealing four distinct categories: explorers, adopters, practitioners, and innovators. What separates these stages isn’t just tooling—it’s culture.
“It’s much more than just adoption. It really is about maturity, and it’s about the culture of that project within an organization that really sets it up for success,” Carter explains.
Progression through the maturity stages is marked by practices such as GitOps, continuous integration, continuous delivery, automation, and platform engineering.
Carter emphasizes that this isn’t merely about technology. “It’s more than tooling. Importantly, it’s also about a culture of transformation—how we manifest successful technology adoption through practices like supporting CI/CD, enabling GitOps adoption, and fostering cross-project collaboration,” she says.
OpenTelemetry’s Rapid Growth
The survey also revealed that OpenTelemetry has become one of the fastest-growing CNCF projects, now at 50% production usage. This growth reflects the increasing complexity of cloud-native architectures and the critical need for visibility across distributed systems.
“As organizations become increasingly decentralized and need to optimize visibility—essentially a prerequisite for reliability—projects like OpenTelemetry have really come into their own,” Carter says.
The trend is directly tied to globally distributed teams, decentralized computing, and the growing need for observability across those dimensions.
The Persistent Challenge of Complexity
While adoption numbers are impressive, complexity remains a significant barrier. Carter points to the Kubernetes Turns 10 study, which found that complexity continues to be a major obstacle.
“How can we make these processes easier? How can we make this project less complex and more accessible?” she asks.
The challenge extends beyond Kubernetes to open-source projects more broadly. In qualitative interviews, Carter often finds that technology decisions hinge on a simple question:
“Is my ability to onboard team members going to be successful? Does this project have the right documentation and onboarding tools to make my job easier?”
Culture as the New Frontier
Perhaps the most important insight from the survey is that today’s challenges are primarily cultural, not technical.
“The numbers really reveal that it’s not just about technical aspects or technical challenges,” Carter observes. “What this study shows is that the current challenges relate to cultural change and managing transformation.”
Success now depends on managing the human dynamics of modernization: decision-making, strategy execution, and best-practice adoption. This includes how organizations manage the intake of open-source code, optimize projects, govern software releases, and foster collaboration both within and across ecosystems.
Carter’s conclusion is blunt and memorable: “Culture eats strategy for breakfast—and it does so in the context of cloud-native success as well.”
