Arvind Krishna joins us. It is a joy to have you on our network. And look, speed is the name of the game. This deal has completed swiftly, and indeed Confluent is all about speed and data analysis. How is that important in this age of AI?
Yeah. So, Caroline, it's great to be here with you and on Bloomberg. So just look at what Confluent does, moving data in real time so that it gets available both for the enterprise, for analytics, but more importantly for AI agents. And doing that in a way that is the most capable product in the world is why it is so exciting to get it done. And your point on speed, I think the regulatory environment is definitely friendlier where we got this done in just under four months, whereas it used to take a lot longer a few years back.
If regulatory environment is friendlier, should you be doing more of it? Should there be more M&A, particularly with some beaten up overall valuations of software companies at the moment?
I'll just say watch the space.
Oh, watch the space. Okay. But where would you want to add on in this moment? I mean, what would make sense to be adding to your portfolio?
So we are very focused. Hybrid cloud and AI and the intersection. So if you look at Confluent, some of the data is in cloud. Some of the data is in SaaS properties. Some of the data is on premise. An AI agent needs to get ahold of it wherever. So that's the hybrid piece combined with the AI piece. Our sweet spot is going to be hybrid cloud, AI, automation, other areas where we are very, very focused on M&A activities as well as organic development. If you look at what we have done around our Watson X products, what we have done around mainframe modernization, these are all things that we have built organically. But then we supplement it with targeted acquisitions where the multiple makes sense, where it fits our strategy, and where we can normally increase the growth rate of the target property, which we certainly hope to do in the Confluent case.
Let's talk about Watson X a little bit. Let's look more at partnerships because, of course, that's what was announced a little bit more of a deeper partnership with NVIDIA yesterday. Helped your stock. We're seeing expanded collaboration. Again, this is about faster data analysis, but cheaper, more effective. How does that help you seal more deals?
Yeah. So in that one, actually, the work we're doing together with NVIDIA was a five times speed up. So five times, not 5%, not a little amount, but five times. So there, we began to leverage the NVIDIA GPUs together with some other CUDF software, combining it with our Watson X dot data. And the example we used was our client Nestle, where together we managed to get that speed up across their massive amounts of data, and that really is important. In that case, combining some of the technologies we work on also in open source with the Presto Data Engine, NVIDIA, and the example at Nestle, but then we are very excited. We're gonna do more work on that and then take it into the market and take it out to hundreds of clients from there.
You're offering these tools, but you're also helping companies like Nestle just embed AI, make sure they're using it in the most effective manner possible. When your consultants go in, how much does Nestle want to use your offerings? But those of Anthropic, of OpenAI, of others, how much do you see that as a competitive force or a competitive threat?
Look. Our goal has always been that we want to help our clients integrate the best capabilities from where they come. And the word integration comes in here because, to some extent, we are model agnostic. We believe that our clients are gonna use the frontier models from all three or four of the main providers. They're gonna use open source models, and they're also gonna use models from us, though we tend to make much smaller models. But helping them really get value, and I really believe 2026 is the year when enterprises are gonna be focused and obsessive on ROI or what they're doing on AI. And so if you take Nestle, how do you take cost out of procurement? How do you take cost out of HR, the finance function, procurement? All of these things. And that's where our consultants and our technologies come together to help them do that using both technologies from IBM, but also in the case of Nestle, from many other partners and the software vendors that they use.
So do you think the investor base and just more broadly, your customers as well, see that AI isn't a double-edged sword for you, but, actually, that competitive threat that sunk the shares so much on February 23 was a misunderstanding of what Anthropic could do with COBOL?
