Dynatrace, Inc. Technology Leadership Forum 2026
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All right. Top of the hour. Welcome everyone. Kicking off day one of the KeyBank Technology Leadership Forum. Very pleased to have Rick. I do own those same socks as you, and I should have worn them, but I didn't.
Shame on me again. We'll send you another pair.
Yeah. All right. Well, Rick, thank you so much for being here. Noel, you as well. Enjoying Park City, Deer Valley here. I wanted to talk about earnings, and it was a great earnings to lead into this week. I wanted to talk on the technology side a little bit, and I want to understand better how the observability market is changing as a result of AI. We used to have deterministic applications, and it was very predictable in the way these applications change, but the application ecosystem's changing in terms of the UI. We have headless applications, non-deterministic applications, and the way they behave. Big picture question, how is this altering the observability market, the ecosystem? How is this changing the way Dynatrace operates and the strategy that you embark on?
Great place to start, Eric, and thanks for having us. The observability market we see is going through a new era, an evolution.
Yeah that is occurring in a major way right now.
I think what seems to be clear, six months ago, everybody's worrying about SaaSpocalypse, that it was going to impact every software company in a consistent way. I think over the last six months, it's proven itself out that you have to get more granular than that. You have to understand what areas and sectors of software are actually AI winners versus AI losers.
We would submit that observability is clearly an AI winner from a market space point of view. The reason is because AI workloads require more observability, not less. You have to oversee these workloads in a fundamental way, and in so doing, observability expertise is more required.
what is evolving was a market that has existed for a couple of decades in observability oriented around what I would think of as business resilience. You need to make sure that your software is always running, that it is always optimized, that it is effectively delivering against its expected requirements. In an AI observability world, it's evolving to answer a couple of incremental questions. If the first question around business resilience is it running or is it working? Then in an AI observability land, is it right?
Or is it accurate? Is the information that an AI workload is going to an LLM to extract actually the right information that would be given to an end user of that particular customer? AI observability is extending the requirements of observability overall. Then in a world that extends even further, what's fascinating is that we foresee a world happening in the very near future where humans just aren't writing that much code.
That agents are writing code. As another use case, you elements than we've seen before.
Yeah. Now I've had the view, and I'm going to lead the witness here on this one, but look, the nature of these AI applications, the non-determinism, the reasoning that goes behind it, to me, I understand that to mean that the value happens more up the stack, which is where APM is. Because infrastructure doesn't really give you the insights, and logs doesn't really give you the insights to understand the activity happening at the application layer, the reasoning, et cetera. My way of saying, does this put more emphasis, more onus on the APM side of the stack to understand that logic that the AI is undertaking?
I would clearly say you look at AI observability areas like LLM eval.
LLM experimentation. This is an area where traces become particularly critical, and traces are the bread and butter of APM.
Right. If you will. This is why we became known as Dynatrace.
It was because it was really all about APM, and it was about dynamic tracing.
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