Jeff Lawson: Well, thanks, Mark. Obviously, it’s very early in this game, so it’s hard to tell exactly how things are going to play out. We set out our vision for customer AI for what we think is going to happen, and I kind of said that not only is this going to become possible, I think it’ll become inevitable. And the key to a lot, if not, all of those things I talked about, is companies having a really good handle on all the data about their customers, right? So if you’ve got your data spread across all the different systems and sitting in all these different places and not aligned, it’s very dirty. It’s been really hard to actually put AI to use solving some of the really big things that I think AI will be able to solve for companies.
And so the first order of business here is getting customer data in order so that as these AI use cases come to maturity, they have the raw information that they need to understand who they’re talking to and how you can start going about optimizing these customer interactions, customer relationships, and overall, like, the business and the front office of every company. And so that’s how we’re thinking about it today. Now, the other thing I think that’s super interesting in the world of generative AI in particular, I think that SaaS businesses that license per seat have the opportunity to be very much disrupted in this coming world, because I think companies will need fewer seats. I think that the things that AI is going to need to latch onto is essentially data sitting in systems, and that data is going to be really used in a usage type model.
And so I think our business is, generally speaking, well set up for a world where companies may need fewer seats, they may contract the number of seats they’re using. They may not grow with the same number of seats, but the data, the backend systems, the processes, the workflows that are triggered by AI, that’s what really matters in this coming world. And so I’m very happy that Twilio is not in a position to largely be monetizing our service on a per seat basis, but rather we have a usage based model based on our communications business and even the data business as well.
Mark Murphy: Yes. Thank you, Jeff. Very insightful. And I think that’s a super important point. Really appreciate it.
Jeff Lawson: Thank you, Mark.
Operator: Our next question comes from Nick Altmann with Scotiabank. Please go ahead.
Nick Altmann: Awesome. Thanks, guys. First, can you just talk about the communications usage trends you guys have sort of seen in October and November? And then just given there’s some seasonality in Q4 for messaging, can you maybe just speak to how much the guidance is sort of one-time in nature or more due to sort of seasoning factors versus sort of underlying stabilization? I just think people are trying to understand the extent in which you’re seeing stabilization on the communication side, but the seasonal trends in Q4 blur that a bit. So any way you can kind of parse out those two would be helpful. Thanks.
Aidan Viggiano: Yes. So I’ll start. This is Aidan, and talk a little bit about the guide, and then I’ll hand it over to Khozema for any comments. So we’re guiding to $1.03 billion to $1.04 billion of revenue in the quarter, which is roughly flat compared to the third quarter. And so what I’d say is overall, we’re really encouraged by the performance and the volume stabilization that we saw in both the second quarter and the third quarter in communications. And we’re optimistic that volumes will remain stable. But we know that the environment remains uncertain, with some customers really seeing variability in their revenue lines and with many cutting costs. And therefore we’re continuing to plan prudently, particularly given the usage based nature of that business, which is nearly 90% of our revenue. With that I’ll hand it to Khozema.
Khozema Shipchandler: Yes. I wouldn’t really add anything additional to what Aidan said. I mean, I think we obviously can’t comment on October, November, those being in quarter periods. But we are encouraged by what transpired in Q3. And I would just echo what Aidan said, that volumes remain stable and we’re kind of cautiously optimistic heading into Q4 and certainly into 2024.
Operator: Our next question comes from Kash Rangan with Goldman Sachs. Please go ahead.
Kash Rangan: Hi. Thank you very much for taking my question. Jeff, I’m curious to get your thoughts on the interplay of AI and data. It looks like there’s some logical conclusion that if you’re a system of record, a full blown CRM system, then it has all the data, and the AI will be able to work with the data to create actionable campaigns, and there’s a closed feedback loop. I’m curious how you think about Twilio’s assets sounds that system of record, which you don’t have, but how are you planning to add value to that? What seems to be the closed loop where you have a system of record data, AI and a whole AI loop can function within that application ecosystem, whereas you bring a slightly different perspective? I’m just curious to get your thoughts on how you take advantage of your assets in the world the way we laid it out. Thank you so much.
Jeff Lawson: Yes. Thanks, Kash. There’s a reason why we bought Segment when we did, which is I think that you’ve been looking at companies trying to solve this problem of having a single view of their customer for, give or take, 20 years. And CRM has been the thing that oftentimes customers have turned to and said, oh like, this will be the answer. This will be how we’re going to have that system of record, the single view of our customer. And if that were working, then I don’t think companies would also be turning to data warehouses to try to solve this problem as well. So I think there’s ample proof when you talk to customers that CRM is not solving this problem. It is actually a bunch of systems of record. And by the way, none of that speaks to all the event data, the streaming data of clicks and scrolls and page views and mobile app opens and all that kind of stuff that is going on in the world of especially consumer scale data and consumer scale companies.
