Red Hat Summit: Early production and themes

The customary dating of AI is to the 1956 Dartmouth Summer Research Project, a gathering of elite researchers at Dartmouth College in New Hampshire. What would come to be called Cognitive Science dated to another gathering a few months later (at MIT with some attendance overlap) but that’s a topic for another day. We could also go back to some of Alan Turing’s earlier work and probably find antecedents before that. But we’ll fast forward past the various AI winters between then and now and fast forward to the present.

I’ll skip to the recent Red Hat Summit in Atlanta, not so much because Red Hat is an especially visible (though increasingly successful) player in AI in part by fusing their own cloud and AI products with their relationship with IBM and IBM Research in particular. They’ve plumbed their open source depth to bring more open approaches around AI. A deeper dive around AI and its relationship to open source is a topic for another day. The co-evolution of AI with cloud provides some interesting parallels and insights to AI as a whole. It is worthwhile to start with some context for Red Hat’s announcements at their recent event and how they represent an evolution with parallels to how cloud has evolved, how AI is moving into practical production uses, and what may lie ahead.

The context

I worked at Red Hat until a couple years back so it’s been both interesting and informative to view the progress of Red Hat’s AI strategy over that time. (I don’t totally love the use of AI in this context for various reasons I’ll get into at some point but not here and everyone does it.)

If we view it on the timeframe of Red Hat Summits, I put it this way.

At first, AI was aspirational. There was some there there. But it was more packaging of largely existing products together with IBM watsonx developments. There were also useful product architectures applied to real customer use cases in areas like retail.

But a fair bit of handwaving. To be honest, it felt a bit like cloud when I was brought on board to help weave a story around existing architectures and visions. Which did work towards Red Hat ultimate cloud success even if that involved some necessary adjustments over time. Last year, 2025, saw more concrete AI product firming up, but what about this year?

What were some of the themes?

If Year 1ish was the vision, 2025 was starting to look more like real product, and 2026 was starting to look more like proof points and legit, if early, customer stories.

I liked a lot of the emphasis. How can you not like NASA on stage talking about Artemis II and all the telemetry data involved? And they weren’t the only one talking about real AI use even if I’d probably characterize it as a lot of cherry-picked early days deployments in general.

Of course, AI heads the list. And it pervaded announcements in general.

Certainly, Red Hat’s OpenShift Kubernetes-based application development platform was prominent. But so were AI-related features in Red Hat Enterprise Linux (RHEL) which is still an important revenue source (and still the focus of many Red Hat Summit attendees).

Recent NVIDIA announcements were one highlight. NVIDIA’s GPU work ended up perfectly dovetailing with many of the needs of the current generation of AI. Probably not the endgame for AI processing but that’s a discussion for another day—but it certainly made sense for Red Hat to partner more closely. Red Hat emphasized its own strategy throughout but it is certainly intertwined with its IBM parent perhaps particularly through the AI work in IBM Research.

There was a general theme of freedom and choice through open source.That’s to be expected from Red Hat. But the world has also changed. Ashesh Badani, Senior Vice President and Chief Product Officer at Red Hat, emphasized the AI sovereignty and other data sovereignty-related issues that make portability and flexibility of infrastructure increasingly important. This has been true since Red Hat initially started emphasizing an open hybrid cloud strategy. But more recent geopolitics has made it a far less theoretical concern. Certainly the idea that “cloud” would become this general utility like electricity, as some envisioned, is not the way things have developed.

Something CTO Chris Wright said especially resonated with me: “Sitting on the AI sidelines is a failed strategy.” You’ll probably get things wrong with various technology transitions—and many of us did over time in various ways. Also another story for another day. But Chris also argued that “No one prepared for this level of change.”

An important implication of jumping in and fast change? Build in flexibility. Own your key assets. But be ready to adapt. That’s what happened with cloud too.

Technology matters. But organization and culture do too.

With cloud, we had server huggers. And they weren’t always wrong in spite of calls that economics just made it obvious that everything needed to go into the public cloud. And we’ve seen that hasn’t played out, important as the hyperscalers are. With AI we have… we have humans in the loop and they’re certainly not always wrong. So the challenge that companies have is not to over-rotate and thoughtfully integrate new technologies where they make sense and pivot as needed.

It’s easy to focus on tech developments with blinders on. But the reality is that they happen in the context of organizational momentum and the fact that important established business processes and domain knowledge aren’t things that can be shoved aside by the new shiny thing.

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 one 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 steroids. We need better 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 (and 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.