Olivier Pomel (Datadog): Reimagining observability for the AI-powered enterprise

**SPEAKER_1** (0:01)
Welcome to Perspectives. I am your host, Eléonore Crespo. AI has opened a new chapter for businesses. In this series, I sit down with leaders who are turning ambition into action, using AI to fundamentally change how their organizations operate and make decisions. Together, we explore what it really takes to deliver impact at scale, how they think about value, manage risk, and adapt their approach to leadership in the era of AI.
Thank you so much for joining us, Olivier. It's such a pleasure to have you today. Really appreciate your time. Thank you. Olivier, you are the co-founder and CEO of Datadog, a company that grew from zero to 50-plus billion of market cap today. So Datadog is a solution for engineering and product teams and helps them make sense of the most complex system on the planet. It's been a tremendous success, and I'm really happy that you're here today with us to share your journey. So let's start maybe with a question of observability on AI as AI becomes embedded in every layer of technology, from infrastructure to decision making. Observability in that world takes a new meaning. So I'd love to start there and ask you, how do you define observability today? Why does it matter more than ever in the age of AI? And what does that become when AI moves into mission critical workflows, and how should organizations be thinking about that in the future ahead?

**SPEAKER_2** (1:32)
Yes, so thanks for having me here. And I apologize for forcing you to say observability so many times in one hour. It's the worst word to pronounce in a fast pace like that. So what we define observability is, it's basically the idea of understanding how your applications are working, how they're working internally, how they're working from a technical perspective, how they're working for the engineers that built them, but also how they're working for the business and the customers that end up using them. So the idea is to understand everything from the very low level, the CPU and the networks and things like that, all the way up to how much revenue applications are generating and what they're doing for the business, and bringing all that together. So that's what we think of as observability. It's been around for a while. So ever since the first computer was sold, or the first computer software was sold, there was software that was sold with it to manage it. By the time it was called monitoring, not observability, but it was the same idea. I think it's becoming more and more relevant because there's been an explosion of complexity for applications in general. So it used to be that the engineers would spend years shipping small pieces of software that would run on one computer. Today, with all of the gains of productivity, in a few minutes you can build something that's going to be shipped to customers immediately and run on thousands of machines and include hundreds of services. And AI is just accelerating that. There's just way more that can be built, way faster. And also the applications we're building with AI are not as deterministic as the applications we used to have before.
It used to be that everything an application did, you'd spend weeks and months understanding them, building them, specing them. Today with AI, you can either build an application on top of an AI model, which is stochastic in nature, it's not deterministic. Or even if you write code, it's very possible that a lot of the code you won't write yourself, like the AI will write most of it, and you actually don't understand really what's happening there. So if you zoom out a little bit and if you look at the broader picture, what's happening is that we're spending a lot less time building and a lot more time running applications. And so a lot of the understanding of whether these applications are doing the right thing, they're working right, they're doing what's expected for the business, or if they're secure and performing as expected. All of that you have to understand in production environments using observability, as opposed to doing that at the design phase when you're first building your software.

**SPEAKER_1** (3:52)
Thank you so much for sharing that context. And I know that one of the things you said is that it would be so much better, I'm going to quote you here, it would be so much better if systems fix themselves and didn't wake you up at night. If you think about the future ahead, how close do you think we are to that vision?

**SPEAKER_2** (4:10)
So we're not there yet. And that gives you a little bit of a background, like if you zoom out, the work of fixing an application when it goes down at night, because usually it's at night, I don't know why, but you have to wake up people and you have to get them to fix an application. The work they're doing is actually pretty complicated, because applications are all fairly different. So if you compare, say, auto-fixing an application with self-driving, self-driving looks like it's around the corner. It's been around the corner for a while, but it looks like it's really around the corner now. It's really happening, it's arriving. Anybody can drive a car, like anybody over the age of 16 can drive a car. And it's something that most humans do pretty well. Versus solving a problem in an application usually involves a team of really skilled engineers, where you have a few PhDs in the mix, and people work really hard to understand formula hypotheses, understand what might work and not work, and how to solve the issue. So it's an inherently more complicated problem. As I said earlier, also there's an explosion of complexity, and the problem is that we're not standing still. So everyday applications are becoming more complex. So as companies such as, for example, build tooling to auto-remediate and auto-understand issues, the application themselves keep getting more complicated. So far we've been playing catch-up game with that.