The zeitgeist of Kubecon in Amsterdam 2026

For various reasons, the past year has seen me devoting way too much time and mental energy to house and related matters (middle-of-the-night kitchen fire--no one hurt but very disruptive--MAKE SURE YOUR SMOKE DETECTOR BATTERIES ARE CHANGED!) which are pretty much resolved at this point. In any case, that's water under the bridge, and am jumping back into post-Red Hat analyst work again.

In addition to my personal BitMasons LLC, I'll be working with IronSpark Analysis along with my former manager (Lis Strenger) and others with a focus on open source, container ecosystem, and hardware generally. I just got back from Europe with Monkigras in London and Kubecon in Amsterdam (plus a bit of actual vacation in Paris). Tough life, I know.

Kubecon has grown into a sizable event. I caught an early one in Seattle with maybe a few hundred attendees. There were about 13,500 folks in attendance at this one--the largest yet. I don't necessarily love events getting up to this size. Given my druthers, I generally prefer more intimate gatherings such as Monkigras in London. On the other hand, it's a great opportunity to either run into or deliberately schedule time with lots of folks and to get an overall feel for the landscape--the zeitgeist to use of those delightful German loan words.

So, what grabbed me?

1. Is Kubernetes really the story?

One of the things I've made a minor nuisance of myself about over the past couple of years (a general core competency) is asking "what comes after Kuberenetes?"

My thought is that very little lasts forever in this biz. That said, certain patterns at least, endure. The basic Unix (including Linux) process model and structure go back decades. But virtual machines (VMs) have come to be increasingly folded into the Kubernetes management plane.

My observation from Amsterdam was that Kubernetes, taken by itself, is increasingly part of the base-level infrastrucure. That doesn't mean a lot of work isn't still happening. But, like Linux, it seems as if it's evolved into a substrate that needs to be maintained and updated but not necessarily the layer where a lot of obvious changes will happen routinely. As Kelsey Hightower puts it: "Kubernetes has become sort of boring. People expect it to be stable and just works." Not a new idea, Kelsey was preaching this idea at a long-ago show--maybe a Container Camp.

For a show that most people refer to as Kubecon, there was relatively little explicit mention of Kubernetes on the show floor.

2. What about AI?

Oh. That.

I found there was (thankfully) a relatively minor amount of in-your-face AI-washing on the show floor but, unsurprisingly, AI was omnipresent to greater or lesser degrees.

And the landscape is changing rapidly.

I've probably been somewhat in the skeptic camp. Not dismissive exactly. But not ready to bet the whole pot. But a lot of rapid change is happening. One line from a press and analyst luncheon stood out: "AI slop has changed to stuff we can't ignore." There are downsides as well. Software supply chain attacks are now on seroids. We need bettwe trust mechanisms. One question among many is how do we patch faster than the attacks?

3. Observability

Joined at the hip with AI is observability. If you don't know what's going on the system, AI can't help you much

. Observability has been a complicated issue over time. There's often been a tendency to capture lots and lots of data, put it in a big tub, and hope good things come out. Think data warehousing in the 1990s. Doesn't work so well. The basic challenge is to get from observability/data to outcomes. This is often not primarily a technical challenge. People at a complany need to think about the results they get back and take meaningful action based on those results.

AI can find insights from large volumes of data but constructing the right questions and gathering the most appropriate data matters a lot. Observability is also ultimately reactive. You need to understand the problem you're trying to solve going in.

4. Where does AI fit?

The nub.

There's a lot of hype. But my sense is that there's also a there there in ways that may not be the case with everything and certainly wasn't necessarily the case in years past.

Will be still be calling it AI in the coming years? Or will it just be part of how we deal with computing? (So many things are just part of what we take for granted, whether navigation, or search, or identifying a song or a bird.)

I remain deeply unconvinced that Large Language Models (LLMs) are any sort of an ultimate end-state. The collective we have punted again and again on related to AI fields such as cognitive science while shiny (an very successful) tools like LLMs and deep learning have popped into view.

I suspect that deeper explorations into cognitive science and other AI-related tasks will be needed beyond current LLM obsessions.