Welcome back, everyone, to theCUBE's coverage here in Las Vegas for AWS re:Invent 2024. I'm John Furrier, host of theCUBE. It's our 12th year covering AWS re:Invent. 2013 was the first year. Watching the progression, watching the companies grow in the ecosystem, watching Amazon grow, it's been fun to watch, and we have a great CEO here. Jennifer Tejada is the CEO of PagerDuty, which was a revolution in its time, but in the early days when it solved a big problem, and then back on stage here, it was great to see you on stage in the morning program with Andy Jassy, Matt Garman. Great presentation. Thanks for coming on theCUBE.
Well, thank you so much for having me. It was fun today. We sort of feel like we've grown up alongside of AWS and the leadership team here has been so good to us. PagerDuty was built on AWS from the very early days, and in fact, our three co-founders started as Amazon software engineers and that's where they found this big problem of needing to automate on-call and better address incidents and issues when they arrive.
I love you got the origination story on the stage, but I have to ask you, when you're up on stage and see how big Amazon has gotten, when you're looking out on the fans out there in the audience, could you see everyone? Was the lights too bright and you're like—
The lights are bright, but you can see that there's people there. And so I did sort of walk out and was like, "How are you doing?" because I hadn't really checked in on them. I'm really inspired by Amazon as a company. There are very few companies on the planet that have excelled in so many different and diverse businesses, and one of the ways they have been successful is by leading with technology. And when we first built our original app, it was built in the Amazon cloud and we leveraged a lot of Amazon's services to make it work, and they've continued to be a great technology partner, offering lots of different solutions that have enabled us to scale quickly, but also reliably and securely.
Yeah, it was nice to see you guys on stage. And what's notable too is about a decade and a half into the business that was started from Amazon engineers, as you pointed out, PagerDuty has grown and changed. So take us through, I was saying before we came on camera that you go back to 15 years ago, someone who's 25 was in middle school, they didn't know the PagerDuty problem. So while we're here, just lay out the problem statement that made the company become that initial escape velocity that PagerDuty hit because you solved the core problem in one of the most important computing errors of our time, which was the cloud movement with DevOps and cloud scale, the hype, now called hyperscalers. You guys were a key player in that movement.
Yeah. I mean, part of the transition to the cloud included the development of a cultural movement called DevOps. And the idea behind DevOps, the central idea is that if you build something and you ship it, then you actually own its quality in production, where historically, developers had passed over that production responsibility to an ops team. And by asking developers to take ownership and be accountable for their code in production, it led to higher quality code, people not shipping things at strange times of day or night on the weekends, not shipping things during major business activities or periods. But it also meant that developers were now put in a position that when something went wrong in a service that they built, they were the first on-call, so to speak. And being on-call is just like you could imagine if you were a physician working with an emergency room. Something bad happens and you get a call and you've got to try and figure out what is the issue, how important is it, how do we address it, what's the root cause, what impact is it having on our customers?
And then stopping what you're doing and it could be at a dinner and a kid's soccer game or whatever.
It's what we call interrupt work, right?
It's like you could be in flow or getting married or doing something really important.
And things generally don't break in convenient moments. And big, major incidents are now on the rise. They're up 43% year over year, and they cost nearly $1 million every time they happen because we all rely on technology for almost everything.
Yeah, I'm old enough to say that I had a pager, many pagers, they've gotten better, then phones came out. But again, the pager, incident response, you guys solved that problem. And I think what I liked about the presentation, I'm going to get into some of the news you guys are doing, is that we're now at another inflection point that's even bigger than that one and then maybe other ones combined, but the same kind of thing's happening with the generative AI and now the boom with automating with software. How software is going to be rewritten is going to change. So what DevOps did, as you just pointed out, changed the control point of production and operations versus waterfalling and the QA department, all that. So software changed and then the infrastructure changed. So now we're in a similar position, which it's a whole other level. It's getting real, let's go.
I love these technology transformations. I love these disruptions, these kind of breakthroughs. And I've been at this for a long time, but when the smartphone really became smart, it changed a lot of things because suddenly you had a supercomputer in your pocket. And that opened up just a whole new world of possibilities for software engineers to develop applications that could change the way we live, the way we learn, and the way we work. The cloud then accelerated that, right? The cloud made it less costly and much easier to start a startup. We heard Bret Taylor talking about how Amazon makes it much easier to start a startup these days than even 10 years or 20 years ago. And now with the advent of machine learning and artificial intelligence and generative AI, you're not limited by your access to bleeding-edge technology. And I think that's something that Amazon's done well for many years is democratizing new technology and making it not only accessible, but affordable for companies small and really large. You heard from Lori Beer from JPMC talking about their transformation at the bank. But what I love about this era we're in with generative AI is that there's so much untapped power and potential in everything from our data to our creativity. And to your point, solving problems now means you leverage automation. You don't have to solve them yourself. You can solve very, very complex problems at a magnitude faster than you could before, 10 times, 20 times, 100 times faster than we were able to before. You can sift through complexity in a way that we couldn't in the past. And for developers and for our large enterprise customers, that means that you can also ship a lot more code a lot more quickly, and that code is imperfect. Agentic AI or agents will be imperfect. They're going to cause incidents themselves. And so incident response and operations is becoming more important, but we're going to solve more of those problems with machines and agents ourselves, not just software.
So your business started with solving that problem but has had that same trajectory of value proposition, but the environment's changed. How are you guys looking at the business today? Obviously, you had some news on stage. There's more incidents coming and they're happening all the time. You're just taking care of them for the customer, is that—
Well, we really help automate the kind of work that is unpredictable, unstructured, but mission-critical and incredibly valuable. And sometimes that's a major incident. Sometimes it could be taking advantage of a major opportunity the moment it strikes. And more and more of the work that we see across an enterprise is unstructured, time-sensitive, and mission-critical. So it's giving us an opportunity to leverage all that our platform has learned by collecting that information and having a machine learning background and having AI at the backbone of what we do, to now surface new ways to help people manage those tasks or manage that kind of work. And so for instance, just having PagerDuty recommend the best next step as opposed to trying to figure it out yourself, trying to find the experts who might know, trying to orchestrate work to the right teams. PagerDuty will do all that for you. So much like generative AI removes a lot of complexity across multiple use cases, we're taking a really complex operating environment, simplifying it, and just driving better outcomes for the end customer experience.
