So we think that overall, these increases in productivity are going to favor observability. In terms of the future growth of AI, look, I think, like everyone, we’re trying to guess how transformative it’s going to be. Looks like it’s going to be pretty transformative if you adjust from just internally how much of that technology we are adopting and how much of a productivity impact it seems to be having. So, again, today we’re only seeing a tiny, tiny bit of it, which is early adoption by mobile providers and a lot of companies that are trying to scale up and experiment and figure out how it applies to their businesses and what they can ship to use the technology. But we think it’s going to drive a lot of growth in the years to come.
Kash Rangan: Okay. Thank you guys.
Operator: Thank you. One moment for our next question. And our next question comes from Jake Roberge of William Blair.
Jacob Roberge: Hey, thanks for taking the questions and I’ll echo my congrats on the great results. Olivier, you called out the 2.5 points from AI native customers a few times, but you’ve also said that the broader customer base should start adding AI workloads to your platform over time. When do you think that actually takes place in the broader customer base starts to impact that AI growth in more earnest?
Olivier Pomel: We don’t know. And I think it’s too early to tell. So for one part, there’s some uncertainty in terms of these customers having to figure out what it is they’re going to ship to their own customers. I think everybody is trying to learn that right now and experiment it. But the other part is also that right now the innovation is largely concentrated among the mobile providers. And so, it’s rational right now for most customers to rely on those instead of deploying their own infrastructure. Again, we think it’s likely going to change. We see a lot of demand and interest in other ways to host models and run models and custom models and things like that. But today that’s the other trends of the market today basically.
Jacob Roberge: Okay, helpful. And then just to follow up on the optimization front, it sounds like the early optimizers took about a year to complete those initiatives. But what type of timelines are you seeing from kind of the second or third layer of customers that started their optimizations later in the game? Have those also started to stabilize given they weren’t as large as the early optimizers, just trying to kind of parse out the customer base there.
Olivier Pomel: Yeah, so we don’t really know for sure. That’s also why we’re careful not to go and enter this forever. I would say for customers that are not part of this initial cohort, there’s less of an overhang. So the customers that were the early optimizers and that had the most acute optimizations tended to be cloud-native, so all in on the cloud, very heavy on IT spending in general, and substantially all of their IT being in the cloud. These tended to be also companies that were fairly high growth but low profitability that needed to pivot their financials over a fairly short amount of time. I think if you look at the rest of the customer base they’re mostly not in that situation. So we expect the behavior to be different.
Operator: Thank you. This concludes the question and answer session. I would now like to turn it back to CEO, Olivier Pomel for closing remarks.
Olivier Pomel: Thank you very much. I want to thank everyone for attending the call today. I also want to take a minute to thank our customers for their trust. And we know these are trying times with all the macro uncertainty and we thank them for their trust. I also want to thank our employees, all the Datadog’s for a quarter of hard work and great successes. And on these good words, we’ll all get back to work and get busy for the end of the year. So thank you very much.
Operator: This concludes today’s conference call. Thank you for participating and you may now disconnect.
