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.