IBM CEO Talks Confluent Acquisition and AI Impact
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What is the main topic discussed in this episode?
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How is that important in this age of AI? Yeah, so Karan, 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 this space. Oh, watch this 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 hold 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 software.
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 WatsonX a little bit. 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.
What is the significance of IBM's acquisition of Confluent?
Again, this is about faster data analysis, but cheaper, more effective. How does that help you seal more deals? In that one, actually, the work we were doing together with NVIDIA was a five-time speedup. 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 WatsonX.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 presto data engine uh nvidia and the example at nestle but then we're very excited we're going to 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 a 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.
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Chapters
5 chapters
1
What is the main topic discussed in this episode?
0:00–2:50
2
What is the significance of IBM's acquisition of Confluent?
2:50–4:21
3
How does real-time data movement impact AI capabilities?
4:21–6:45
4
What role does the regulatory environment play in M&A activities?
6:45–9:36
5
What areas is IBM focusing on for future acquisitions?
9:36–12:16
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