Fresh data has us asking, does AI demand Kubernetes?
Kubernetes is rapidly emerging as the de facto operating system for AI, with two-thirds of organizations using it for generative AI inference and 82% adopting it in production. Its ecosystem — including tools like Kubeflow — enables organizations to build, scale, and retain control of AI systems through open, community-driven infrastructure. Bob Killen of CNCF and Liam Bollmann-Dodd of SlashData shared insights from recent reports showing that AI success still hinges on strong engineering fundamentals—especially internal developer platforms and overall developer experience.
While AI-generated code accelerates development, it shifts bottlenecks to DevOps, reliability, and security, increasing operational complexity. As a result, operator experience and well-defined guardrails have become critical to safely scaling AI. These controls help constrain both human and AI developers, reducing risk while enabling speed. At the same time, organizations are evolving team structures, expanding platform engineering groups to support internal users more effectively. Despite growing complexity, the core lesson remains consistent: open source innovation thrives on people, processes, and collaboration as much as on technology itself.
Learn more from The New Stack around the latest in Kubernetes and its emergence as an operating system for AI:
Kubernetes and AI: Are They a Fit?
How AI Is Pushing Kubernetes Storage Beyond Its Limits
Kubernetes and AI Are Shaping the Next Generation of Platforms
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Research, Steering and Honking, with Bob Killen
Bob Killen is co-chair of Kubernetes' SIG Contributor Experience and was last week elected to the project's Steering Committee. He worked in academia for 15 years, latterly working on research projects using Kubernetes, with a focus on computer security. He's now made the leap to working on Cloud Native full time at Google. Bob joins us to explain why Kubernetes twitter is occasionally full of cartoon geese.
Do you have something cool to share? Some questions? Let us know:
web: kubernetespodcast.com
mail: kubernetespodcast@google.com
twitter: @kubernetespod
Chatter of the week Relive New Zealand's General Election coverage - 57% of the electorate voted early! tl:dr; Jacinda won by a lot
One NZ electorate had a 421 vote lead on the night
Ballot box in Washington State
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News of the week VMware Tanzu Kubernetes Grid 1.2 is GA
Red Hat integrates Ansible and OpenShift
Changes to the KubeCon EU Episode 107, with Priyanka Sharma
Cloud Native in China survey results
Introducing HA MicroK8s Episode 60, with Mark Shuttleworth
Helm turns 5 Episode 102, with Mark Butcher
Google Cloud Code adds support for 400+ CRDs
A holiday gift from AKS
Links from the interview University of Michigan
Little Bobby Tables Another Bobby Tables!
2600 Beige boxes
Red boxes
Steve Jobs, Steve Wozniak and the Blue Box
Jeff Sica
ARC-TS: Advanced Research Computing — Technology Services Great Lakes, the UMich HPC cluster
Kubernetes the New Research Platform - Lindsey Tulloch, Brock University & Bob Killen, University of Michigan
kube-batch
Volcano
Orchestructure meet-up and Mario Loria
SIG Contributor Experience Episode 74, with Jorge Castro
Episode 100 with Paris Pittman
Kubernetes Steering Committee 2020 Election
Election results
Travel support program
HONK Untitled Goose Game
/honk
Ian Coldwater's goose-themed talk from KubeCon NA 2019
honk.ci Announcement
GitHub repo
Challenges
Walkthrough
KubeCon NA events: SIG Honk AMA: Ian Coldwater, Duffie Cooley, Brad Geesaman, Rory McCune
Having Cloud Native Fun with HonkCTL: Jeff Sica
SIG Beard: see episode 46, with Aaron Crickenberger
Bob Killen on Twitter
Managing Research Needs at the University of Michigan using Kubernetes w/ Bob Killen - #344
Today we’re joined by Bob Killen, Research Cloud Administrator at the University of Michigan. In our conversation, we explore how Bob and his group at UM are deploying Kubernetes, the user experience, and how those users are taking advantage of distributed computing. We also discuss if ML/AI focused Kubernetes users should fear that the larger non-ML/AI user base will negatively impact their feature needs, where gaps currently exist in trying to support these ML/AI users’ workloads, and more!