Senior VP of Software & Chief Commercial Officer, IBM
Why IBM Bet $11 Billion on Confluent
Rob Thomas, SVP of Software at IBM, uses the Industrial Revolution railroad analogy to explain why IBM is acquiring Confluent ...
About Robert Thomas
Rob Thomas, Senior Vice President of Software and Chief Commercial Officer at IBM, has been a frequent commentator on enterprise AI adoption and value creation. In early 2026, he stated that the most incorrect idea about AI is fear, arguing that productivity growth—driven by AI—is the only path to continued GDP growth. He noted that S&P 500 operating margins have risen from roughly 13% in 2019 to about 19% in recent quarters, attributing this in part to technology. Thomas has emphasized that 2025 would be a year of "value creation" with AI, moving beyond experimentation. He has also discussed IBM's own use of AI, stating the company generated $4.5 billion in productivity by automating manual tasks and has displaced some jobs while hiring into R&D roles. Thomas has been involved in several major IBM acquisitions, including the $11 billion purchase of Confluent, which he described as "the rails of this AI moment" in a December 2025 interview. He has also discussed the importance of open source in AI, arguing it prevents vendor lock-in and reduces barriers to adoption. Thomas has advised companies to focus on cost savings before revenue when starting with AI, and to embrace iteration over large, long-term projects. He has also spoken about the cultural and change management challenges of AI adoption, noting that a Fortune 50 CIO told him that while the technology works, getting people to use it remains difficult.
Transcript (13 segments)
I
Interviewer0:00
So Rob, I want to start with you. Talk to us about the rationale here for the deal.
R
Robert Thomas0:05
We're incredibly excited about Confluent. It's an incredible team that Jay has built. And I'll give you an analogy. If you think about the industrial revolution, what drove all the growth was when the railroads connected factories to cities and to people. And that's exactly what's happening to AI right now. This is the new industrial revolution. And we need the rails that will make AI successful. Confluent and what they've built in terms of real-time data is the rails of this AI moment. So, we're incredibly excited to have this become part of IBM once we complete the closing process.
I
Interviewer0:42
Great. Well, Jay, I want to talk to you a little bit about the business that Confluent is in. If you were explaining it to someone who isn't in tech, how would you explain the data services that Confluent provides?
J
Jay0:54
The service we offer is all about the real-time flow of data between the parts of a company. So you can imagine if you're a big retailer and something is sold, what are the different software systems and databases and parts of the company that have to update or react to that? It turns out there are hundreds. And that's not just a problem for a retailer. That's a problem for a bank or an insurance company or a tech company. That's ultimately where the software was born. So this is all about harnessing and reacting to data in real time as things happen in a business, being able to build applications, AI agents that can react to that and use that data. That's what the software is for.
I
Interviewer1:36
And what is it that you feel you can do with IBM that you couldn't have otherwise done as a public company?
J
Jay1:43
I think this is an opportunity to take this to a larger stage. IBM has built a fabric for applications, infrastructure, security that works across the different cloud providers, out into on-premise and edge environments. And Confluent had done a similar thing for data. So the two things fit together. If you think about what's the future of these applications that these organizations are building, I think data is a big part of that. That's the thing each organization has to figure out how to combine with these new language models in the age of AI. And so putting those two things together was a natural fit. And of course IBM has done a great job with open source. A key aspect of the platform they've built is having these open standard layers that the world wants to build around. And that's true of Confluent as well. So there was a certain strategic fit element that I think was obvious on both sides even the first time Rob and I chatted.
I
Interviewer2:40
So Rob, I want to talk to you. A couple months ago, Jay and his team spoke on an earnings call about how AI native customers were finding ways to actually reduce their spend on Confluent by managing the data themselves. I wonder how you think about that issue within IBM. How do you think about combating that issue or how you might be able to help with that issue?
R
Robert Thomas3:04
I think these things always go in waves. We saw this actually when we did the acquisition of HashiCorp as well. Cloud consumption is going to go up and down based on the moment, the macroeconomics, the specifics of a company. That's always going to evolve. But when we acquire, we are acquiring for growth. We step back and say what does this look like over a five or a ten-year view. In our view, Confluent and the capability they provide is going to be up and to the right when it comes to AI. Yes, there may be some modulations here and there, but the need for data to support anything that a company wants to do in terms of intelligence and how they operate, that's not going to change. And if anything, the demand is only going to go up. Part of what we've done in IBM building on Red Hat and HashiCorp is we've delivered this multicloud platform partnering with AWS, with Azure, with GCP. And so as cloud consumption grows, Confluent is going to grow and the other capabilities that IBM provides will also grow.
I
Interviewer4:07
Rob, I want to stick with you for a minute here because I want to ask you about IBM's place in the AI story at large. IBM was so early to a lot of this stuff with natural language processing, with Deep Blue. Watson is decades old. And yet, we're at a moment where, be it as it may, IBM is not the first company. It's not the flashiest company that people think about when they think about AI. They think about OpenAI and about Anthropic, where Jay, I believe, is a board member as well. So I'm curious as a salesperson, as someone leading a sales team, how do you think about positioning the company in that landscape? And how do you go to market trying to increase that brand awareness?
R
Robert Thomas4:51
Most of the brand excitement right now in AI is around consumer companies. Yes, I acknowledge IBM has no consumer play. We don't intend to. We are a B2B company. But I would think of maybe three phases of AI. The first one was around machine learning. That was the original Watson, which required a lot of data labeling. And to be honest, there was as much success as there was failure. When we brought out Watson X, that was about this new era of generative AI. And as we shared on our last earnings call, our book of business is now $9 billion. So we've had incredible momentum here, but it's all in a B2B context. We're helping clients do things like optimize their technology and their operations, make their supply chain work better. So we have a lot of momentum here, but it's not necessarily in the consumer realm.
I
Interviewer5:41
Right. And Jay, I want to ask you one question about adoption. This is the age-old question everyone comes back to: how do we get adoption to be faster for these businesses that are buying AI? You talk to a ton of these companies from a different angle, which is that we're helping you get your data in order so that you can use AI. What's your view on how we can actually accelerate adoption? Is it people? Is it ROI? Is it maturity of the products?
J
Jay6:08
I think it's all of these things. First of all, there's a tendency to underestimate the amount of time for any major technological change to really make its way into businesses. I think what we're seeing is value realization at the equivalent of the speed of light. This is as quick as you could imagine big businesses rethinking how they do things. And there are a ton of valuable projects happening. It doesn't mean every project that happens is valuable. It's the same way every startup that has started doesn't succeed. But it's easy to be distracted by the failures in the midst of the larger wave of what's happening. The reality is right now AI is transforming software development, having a huge impact on things like how customers are supported, and it's finding its way into all these really interesting niches in a really big and meaningful way. I think we're still at the very early days of that. These use cases are almost the first ones people latch on to. What you would see beginning in customers is a much more interesting diversity of this attacking the business-specific problems unique to their area. I think that's ultimately what the enterprise opportunity for AI is. Rob touched on this: the thing we all see is the app that answers your questions, like Wikipedia but better, and that's obviously valuable. But if you think about the scope of value in all of these massive organizations all around the world, what can you do if you can augment the people that they have and attack the specific problems they have? I think that's by far the bigger pile of value creation. I think we're at the beginning of that. But there is a tendency to imagine that the technology will drop out of the sky and everything will be different tomorrow, and it's not overnight.
I
Interviewer7:57
Right. Great. Well, Jay and Rob, I want to thank you for coming on. Congrats on the deal, and I'm excited to see how things evolve in the months to come. We appreciate you coming on.