Yes, the way I think about it, Pinjalim, thanks for the question, is fundamentally that AIOps is core to how you ensure that you can enable observability users to do their job in the most efficient way possible. At the end of the day, AIOps is all about ensuring that you can, first and foremost, bring in all relevant data, whether it's logs, metrics, traces, profiles, real user monitoring information, et cetera, all into one place and then apply the right kinds of machine learning to it to ensure that you can quickly surface those issues that might be most relevant. So you're reducing the level of fatigue that a typical site reliability operator has to go through to get to root cause analysis to ensure that they are meeting their SLOs and SLIs. I think that's very, very critical, and that's how we look at the AIOps use cases. And it's great to see that all the work that we've been putting into machine learning, into incorporating that in core into our platform, making sure that we always make it possible for users to bring in all of their TRACE data, metrics data, log data in one place and apply the kinds of algorithms that we provide, but also algorithms that users might bring, all of that work, it's actually being recognized. We're seeing great -- the customer -- I talked about some of the customer examples that I gave, included customers using AI Ops, but it's also the analyst recognition, which really makes me feel good about the fact that the work that we are doing is being seen, it's being recognized, and we feel very confident about how this is going to help continue to make us a stronger and stronger player in observability.