International Business Machines Corporation (IBM) Earnings Call Transcript & Summary

Back day 2 of the RBC TIMT Conference, we are super excited to be joined by IBM, Ric Lewis, SVP of IBM Infrastructure. Thank you for being here.

Thanks for having me.

I guess we'll start at the beginning. Could you share a bit more about your career and then the journey leading up to your current role as the SVP of IBM's Infrastructure business?

Sure. Happy to do that. So I've been in the industry, the IT industry now for going on 40 years, not quite. I spent 32 years at Hewlett Packard Enterprise, worked there, did a lot of -- I was always on -- I grew up as a chip designer and then started to own businesses, small ones, bigger, bigger then big chunks of the business. But I was always spending my time on the entrepreneurial innovation side of things. So we did a lot of work with as-a-service delivery. We did a lot of work with different types of hyperconverged and composable infrastructure. It was a great ride. I had a great time. Then I retired at 55 on the early retirement, planning to fully retire and stay retired, and then I failed at that and ended up in a conversation with Arvind at IBM about the transformation he wanted to do with the company. And I thought I don't want to miss that. And I've been doing that now 4 years and almost half, and it's been a fantastic ride, and we've made a lot of progress in IBM. And so it's been really fun. And the industry has changed pretty significantly in that time. It's never been more fun to be in the IT industry because it's so critical to what's going on in every company.

Yes. Sorry, I think that's exactly where we want to start off is under Arvind's leadership, we have seen a transformation that you've been a part of these last few years. At Investor Day that you highlighted the turnaround in infrastructure from declining revenue to growth and profitability. And recently, I think what we've heard more and more is about this being a secular, not cyclical trend. So I think for a lot of investors, when they think hardware refresh, they think replacing boxes with boxes, right, a cyclical dynamic. Can you kind of talk about what are the key actions that you've taken on the strategy to really like lean into this opportunity?

Sure. Yes. I'm sure we'll talk later about Z and the cycles and all of that. But really, it's interesting, my job to kind of help the group go from what had been a general flat to declining business with big cycle swings. From the beginning, the goal was let's shift that to generally growing with dampened cycle swings so that it's still going to -- you're always going to have a big bump when you ship new hardware, but the idea would be less dramatic and more importantly, generally growing. And we've been able to do that in those 4.5 years. At the core of it was really getting great at where we're investing and focusing on the innovation areas and making sure that, that's tied exactly to client needs. That would be the first thing that we did. The second thing was really all about the business model and product strategy, getting really tight about what's the segment we're going after, what do they really value? Is it hardware refreshes that they value? Or is it software stack performance or is it AI, all of those things. So getting really good at that. The third thing was if you're going to invest in innovation and ramp up to be able to grow, you got to fuel and fund that. And so we completely changed our operating model, how we define, design, develop, build, ship, how -- even down to the freight that we use to ship products and again, by segment and product strategy, and we saved over $1 billion run rate out of the business that we could then mostly invest. Some went to IBM and cash flow and things like that. A lot went back into investing in things like AI capabilities in Z and software, AI capabilities for storage and all of those great things. So it's been a huge transformation project. I mentioned all those mechanical kind of things, but the biggest thing that we did was work on culture inside the group. And that was if you came at this business with a mindset of just protect, just don't lose clients and kind of stay where you're at, there's a lot of corporate research that kind of talks about you have to have a growth mindset. You have to be taking risks. You have to be collaborating. You have to be getting out of your comfort zone and trying new things. And so things like putting AI in Z before there ever was a ChatGPT moment before anyone was talking about AI was a big brilliant move by IBM. Things like shifting to software value-add and hardware, all of those were experiments. They're about collaborating, being willing to take some risk and things like that, and it's paid off. We've been really lucky about the places we've invested. And we have a lot more engaged workforce because they feel like, hey, we're part of the growth of IBM at this point rather than just providing some cash flow to do other stuff. So it's been a really fantastic journey.

Yes. And so what you're seeing now, it sounds like is a refresh cycle conversation doesn't start off with capacity. It starts off with security enablement, AI, latency. And I can see that that's just a completely different conversation for you. Absolutely.

I mean z17 is all about what are the new capabilities I'm getting, the new AI capabilities that we have built into the chip, but also the AI capabilities that we're adding in both an external chip that's on a card that you add to the system that we just started shipping and the software associated with that dramatically changes the use cases for clients about what they can use. So you're talking about new workloads for a platform that's been around for 60 years and very exciting for everybody, clients and us.

Yes. I mean getting into the numbers of z17, we've talked about the secular growth and looking for a 1% to 3% range, really strong momentum to start off this refresh cycle. Do you still think that range is the right way to think about it as achievable? And then I guess, what are kind of the key levers to sustained growth? And if I can just make this a super long question. The secular drivers, will we see less seasonality in infrastructure over time? We came off a long tail from z16.

Yes. I mean I think you're really asking a pretty simple question, are you going to do what the whole infrastructure transformation was set to do, which is grow and dampen those cycles and...

I like this question better. Let's answer that one.

The answer is yes. And we're very confident for a few reasons. One, first and foremost, Z is the dominant product in the infrastructure. It's our biggest revenue source, and it's got fantastic momentum. z15 showed 115% program to program versus z14. z16 was 120% program to program over z15. So far in z17, we're 2 quarters in, and we're running above 120% program to program. So when your core platform is growing cycle to cycle, that gives you growth, right? But it's pretty cool because Z already had pretty good momentum before a lot of our transformation exercises, but storage didn't and power didn't and infrastructure support had been declining. And now infrastructure support, we've stabilized. It's a flat to plus/minus kind of business. Power, we've stabilized. It grew in all 3 years of its program cycle in this last time. So you're no longer seeing any decline in our UNIX and Linux business on power. And storage has been growing at roughly 1.5x the market rate in the software space and faster than the market rate in the hardware space. So it's almost like Z's momentum plus these other things that we've got momentum behind that are the distributed infrastructure space and the support space, you look at that and you go, I think the business grows, and that's really what we're doing. So we're really excited about that. To the stream -- to the question of the cycles, you say, well, you still have hardware that you ship every 3 years usually for Z and power. You're still going to have cycles. Yes, but we're working really hard on stream revenue that kind of fills in some of that gap. So an example would be Spyre that we're shipping in Z. That's our AI processor card. It's not shipping on the same exact cycle as the original Z. In fact, it just came out. So we're selling that card and subscriptions of software that go on it. So that's a new revenue source that's off the cycle. And that can be on a different cadence than the Z thing. We're also doing subscriptions on our software now for IBM i, which is one of the key operating systems that runs on power. We're doing PowerVS in our cloud, which is our power systems in a cloud offered as a service, which is a subscription, it's paid as you go as a service. So all of these revenue sources that aren't just sell new boxes every 3 years, helped you with -- now we're selling other stuff in all of the years, delivered as a service, that's making a big difference in the business and helping us achieve the second part of the goal, first grow, second, make those cycles stamp out. So that's why we're confident in our prediction of single-digit revenue growth.

I think when I try to explain to people some of the changes that IBM has gone under Arvind's leadership, I consolidate the infrastructure consulting and software strategy into digital transformation enablement is like the term that I like to use for it. assuming you somewhat agree with that term.

I love it.

How do you see the IBM platform, Z Power and Storage, bringing differentiated value to like unlocking hybrid cloud and AI workloads for your customers. And when they have options of how they're going to do that digital transformation enablement and maybe it's hyperscalers, maybe it's other enterprise hardware players, how do you differentiate yourself as IBM?

Yes. So this is a cool question because it really gets at the heart of what Arvind was trying to do. From the first day that he became the CEO, he said, our strategy is super simple and very clear, hybrid cloud and AI. And again, this was before there's ChatGPT and all these people talking about AI and this is our strategy. So we've been differentiating in that area primarily across all of IBM. And you brought it, you said consulting, software and infrastructure. That's really the first thing I would highlight about the IBM value prop because it's all 3. And that means you have full stack differentiation in the space of AI and hybrid cloud. And so we're providing hardware that helps clients do AI through inferencing and speeding up that capability. We're providing software stacks and models to be able to do the work on enterprise data. And then we have consulting services so that if you don't know how to do that, if you're a client and you say, my Board is all over me and saying, get AI in here and add some value to all this data and do what everybody else is doing, then you can -- then we can help you do that because our consulting team knows exactly, okay, here's what we would do. It may involve our stack of stuff. It may involve others. But whatever it is, we're going to know what's the most optimal thing. So full stack is the first thing I would say. The second thing I would say is just the notion of hybrid. I think what's cool about centering on hybrid cloud. So there's lots of AI players that are in clouds. There aren't very many AI players who are doing much on-prem. We do both, and we do it seamlessly together. And if you think about these enterprises that we serve, a lot of them, this data is the lifeblood of their company. They don't want it out in the cloud. They don't want it anywhere. They want it on their prem. They want it backed up. They want to secure wall around it. And so being able to do hybrid, meaning do this work wherever that data is, is a big advantage for us. And we have a lot of use cases for very specific AI applied to enterprise data on and off-prem, the key there being on, lots of people can do off. We can do both, and we do it together. It looks the same and it's seamless. I think that's what really resonates about our value proposition. The last thing I would say is a true laser focus on the enterprise. There are a lot of AI players that are doing a lot of great things and changing our lives, right? I'm using all kinds of new things, ChatGPT to help plan my vacation and that kind of stuff. But I'm a consumer person kind of thing. IBM, we're not doing that. We're doing enterprise. We happen to be close to these clients' most important data, the transactions that you all do, 45 out of 50 of the top banks run on our Z systems, 4 out of 5 of the top airlines, 9 out of 10 of the top retailers. All those transactions are going through our systems. That data is a gold mine for those clients if they can get their head around, okay, what does this tell me about my clients, their transaction patterns, all of that stuff is where the action is in IT, and we're poised focused, laser-focused on that, not laser-focused on how do I help you plan your vacation or that kind of stuff. So I think we're just in a really good spot with a really focused strategy, and it works really well.

Yes. And I think we've heard before on -- maybe from your consulting arm that some of the early days of these Gen AI projects are helping customers get their data in order, right, set the foundation to be able to build upon that. As enterprise data grows, the volume and complexity, it's going to need different infrastructure strategies. Could you just talk about what role this plays in your product road map and how you're -- again, maybe going back to the idea of enablement, how are you enabling companies with your infrastructure to be ready for Gen AI?

So I like where you went because I think that a lot of people who aren't doing this every day don't think about it this way. But AI, I will tell you, is mostly about the data, right? Whatever you feed it is kind of going to help with the value that you get out of it. So for us, being able to -- yes, 10 years ago, if someone was trying to do an AI project, you had to structure and label your data, and it was a massive data science exercise to even get things to where you could ingest that data. We have products now, our Fusion product in storage that you can just feed it random stuff. You can just give it a bunch of disks, and we have this thing called content aware storage technology that will look on all those disks and say, here's generally what's on those disks, here's what it's associated with, here's what it's used for all of that, completely unstructured random data. So that's a good example of in this direction, the prework necessary to kind of get things in order to get value out of your data has changed exponentially, which is a really good thing. Our job is to make that -- continue on that journey, make it easier to ingest that data, get value out of it, tune models, be able to do inferencing on your key enterprise data. And I think the sky is the limit. So we have products in storage that are doing that capability. We have products in Z that I talked about already. Power is being used in -- one example is Julich in Europe, the exascale computer, a 21 petabyte ingest of data and a 700 petabyte backup needed so that when you do your model training and inferencing, you can go back and say, well, what data did I use and how did I use that? All of these are examples of us making it easier for you to be able to do AI in a hybrid environment, and it's really resonating with clients.

And then if we're looking at z17 cycle compared to z16, how would you compare like the current cadence in terms of how those started off? And then if we're looking at maybe sources for upside, how viable is AI inferencing on z17 today? And are you seeing people day 1 using it for this capability? Or is it more future-proofing saying, well, I want to have it, but I don't actually need it currently.

So we are off to a great start with z17. And typically, you would expect me to be saying, that's a tough compare because we were off to a fantastic start with z16. But comparing the starts, z17 is as good or better than z16. So tremendous momentum. To your second question, I think that's part of why is, yes, clients are using the AI. It's not a, hey, I'll just buy this and hopefully, I'll figure out a way to use it kind of thing. We have 250 use cases that clients are either in production or developing or exploring with us in great detail, but a lot of them are in production. The primary use cases, I would say, in z17 for AI, they're kind of in 3 buckets. One is the obvious and one that we've talked about a lot because we did it on z16. That's fraud detection. And that's a huge value for our clients in this space being able to detect that. But even fraud detection has improved in this latest generation. Old fraud detection looked kind of mechanical in nature. The transaction pattern looked like this. It was structured data, and we could kind of say that one looks weird, stop. Now we can combine structured data and unstructured data, large language models. So you can look and say, okay, this transaction pattern happened and what's the company rating that's associated with this transaction? And are there bad reviews on websites about this company? Or do they have a better business bureau. So you can kind of take the 2 worlds of mechanical fraud detection and unstructured gut feel fraud detection and make that mathematical so that you can do that. So that's a use case that's kind of a bucket of a lot of what people are doing. People are also doing code modernization, which means commenting and kind of reorganizing and supporting their code to help with the skills on Z, and then the third thing that they're doing is just managing the Z. We have something called the Z Assistant that's based on AI capabilities. And what we've learned from clients is they just wanted to run the fastest. They'd like to be able to chat with the box and say, configure yourself, this is my typical workloads, configure yourself to do that rather than you have to be a geek who's been doing this for 20 years to figure out, okay, I need to turn on this option, turn on this buffering, turn on this queuing, all that kind of thing. So that's the third big use case. All of those are within the same, we call it the RAS domain, Reliability, Availability, Serviceability. It means basically you don't have to take any of this off your Z to kind of optimize it. It all happens in there. The inferencing is done in the Z ecosystem. So it's secure. You know it's rock solid. You know it's backed up. You know it's quantum safe. You know all the great things about Z, except even in how you operate it and what you're doing with it. So it's a fantastic platform for the future and resonating more than ever, fantastic momentum.

This may be an impossible question, but it's one that we've all been asking for the last couple of years about when the application layer of Gen AI starts to come in, you have a really unique visibility, right, into infrastructure spend and these consulting projects. Do you have an opinion on the enterprise timeline to see broader production applications?

It's interesting. Your question, I think we're still waiting a little bit in consumer land, meaning AI is kind of this separate thing. You go look, okay, tell me the best place to buy, whatever it is. But then you still kind of end up then jumping over to a web search after -- or give me a link, that link, now I go and get on that link and I do stuff kind of the old way. Usually, consumer leads enterprise, like the for example, the advent of mobile -- we all had mobile phones before work was kind of enabling our mobile phones and things like that. I would say in what you just talked about, the application space, enterprise might be leading consumer because -- and I think the reason is because the space is smaller. You're not trying to do everything in every language, including with pirate slang and all these kind of things that you're doing in your consumer environment. What you're really trying to do is take, I have this set of transactions, and I want to know what's the customer behavior or we have insurance companies. I have this set of clients that my actuary tables say there's this much risk, but I want to combine that with data about weather and what the season looks like. And I want to do that real time. If I can get that right, my margin goes way up as an insurance company. So those things are their direct huge value to these enterprises. And so they're motivated to get a competitive advantage with that. And so they're investing to be able to do that. So in terms of simple apps, no, but in terms of deep rich analysis, enterprises are going crazy because they know, they absolutely know disrupt or be disrupted. And I would say more than the cloud era, more than client server, more than any other era in IT, there's the potential for disruption right now more than ever. And so you're either on it or you're going to get it from somebody else. So anyway.

Speaking of disruption, Quantum, everyone's favorite topic of 2025. I guess you've taken what I would consider compared to some of the peers in the space, like a more measured content-driven approach to Quantum Computing. And I think emphasizing maybe like the real-world utility over hype a little bit. 75 systems already deployed, I think 13 production. Is that the most updated number?

Yes, I think we might be a little above that, yes.

Close. 13-ish. Talk about the evolving commercial opportunity for IBM. And then what -- how this kind of fits into, you've said, those 2-pillar strategy, right, of hybrid cloud and AI. And then we were talking about this in the back. For any of us who are looking at maybe competitive press releases or anything else, like what is -- what are the things we should look for, for what's real versus hype?

Yes. So first, let's talk about market opportunity because I think we just got a study or we just saw a study from BCG that estimates that the Quantum opportunity in kind of the early 2030 time frame is $500 billion, which is a big old TAM. Not all of that's for hardware. Probably somewhere kind of IT vendors might expect to see 20% to 40% of that, still a big old TAM. For a technology that's not really mainstream today at all, we wouldn't -- we don't think it's mainstream. But I think the cool thing that we love about IBM, we are taking a very practical, rational approach to it. And I think Arvind likes to say we're not expecting some scientific breakthrough at this point. It's a matter of engineering and execution to get to where we need to go. And that's a big milestone in and of itself. So when I watch other Quantum people and what they're saying, which, by the way, I know we're ahead in this industry, we're the lead player at this point. I try to watch for a few key things. One, do they have a believable road map, not just a road map, not just an aspiration, but like, okay, you've shown kind of through your progress through the last 5 years that you're on a certain trajectory. And do you have a believable road map for the next several steps? And our next steps, we're driving in our system development, et cetera, to be able to deliver Quantum Advantage here in the next year. We've got error correction and all of those capabilities in the pipe for coming later in this decade, so kind of '28, '29, and we actually believe we'll be transacting in that timeframe by the end of the decade on a system kind of level. We're already transacting. We already have clients that are buying cycles of Quantum and exploring and using that capability now. So it's a predictable road map. The other thing I would say is do you have an ecosystem of partners that are helping you develop, helping you figure out a fair value. And there have been some key industry announcements that you can look up, one around HSBC and there are a couple of others that kind of ship the industry a little bit of, no, there's a use case, and this could radically change how we do things like Monte Carlo analysis or Chemical analysis or those kind of things. We have an ecosystem. We have a large number of partners that we've talked about that are working with us on developing this thing. The third thing I would look for in those announcements is do you really have a software stack that kind of goes with it because you saw that same development in AI land. Before there was kind of the ChatGPT moment, we'll call it, there were stacks and capability that kind of grew out of the graphics space, but that we were helping do AI, mostly in self-driving cars and things like that. We look at our Qiskit, which is kind of our base software platform with that is showing great traction in the industry, both in academia and in business and clients adopting it. And we kind of look at that and say, hey, we're in a really good place here for Quantum when it comes. And then the final thing is Quantum is -- for lack of a better way of saying it, it's difficult to get right. It's more fragile than our digital. I grew up as a digital designer and things like that. And it's either 1 or 0, and we figured out ways to make it rock solid. Quantum is a little more fragile. There were things we had to do in digital when things got fragile in memories and things like that like check sums and error correction codes and those capabilities. In Quantum, they're even harder to do those things, but do the announcements kind of reference, hey, we know how to do error correction in the Quantum space. And that's something that IBM, I think, is very far ahead of is being able to combine classic and quantum techniques to do error correction in the Quantum space, and you're going to need it in order to be able to do it at the scale where it's really practical for the kind of problems that you're going to want to solve. So I look for those things. I also look for a philosophy that says Quantum is not a replacement to classical. And I don't -- I believe it's not. I don't believe AI is a replacement to classical computing. It's just another category. When you combine them together, you end up with something very strong. And since we play strong in classical, we play strong in AI, and I think we're the leader in Quantum, we're really well positioned for as the industry gets to this kind of 2030 time frame and all that TAM. So we're pretty bullish and excited about it, though cautious and practical, like just keep executing the road map, we make our steps, we're going to be in a really good spot. So that's what we're doing.

This time has flown by. Are there any questions from the audience here? All right. So one thing that Jim references a lot is the integrated value prop with -- cycles. Sorry, I didn't realize I was scratching my microphone. Can we talk about the IBM Z stack multiplier and how $1 of Z hardware gets that 3 to 4x of related revenue?

It definitely does. It's a huge value to our company. And if you just kind of look at our stack and the associated things around it, things you would think of, okay, we obviously sell mainframe storage when we sell a mainframe. We sell mainframe transaction processing per second, all the software that goes with it. The things you might not think about is all the infrastructure support, my TLS business is associated with it. You might not think about financing. We do financing on Z. We do leasing on Z. A big chunk of that is Z, and that's revenue and margin for us as we do the financing and leasing for those platforms. So it contributes in a whole lot of way. And then just associated, right? If you're helping the client with their most important data, you can probably help them with other things. So it drives a big bunch of consulting revenue on getting value from that data, modernizing your mainframe platform, all of those capabilities. In our software space, it gives you revenue on putting containers on there through Red Hat on optimizing the workload distribution through things like Apptio and Turbonomics and all those capabilities are incremental to the Z value proposition, but are associated with it because you're doing something extremely important to that client set.

Typically, our last question is what you're most excited for the next 3 to 5 years, and we've got 3 seconds.

Data for AI is a big one. I've mentioned some of our storage offerings. And focusing on that data and really AI in general and how it will transform how you interact with our systems, what our systems are used for and the value that clients are getting from it. It's never been a more fun time to be in the IT industry. And it's because what 15 years ago was a conversation of just make sure the thing always runs and lower the cost as much as possible. That would be the definition of the job. Now the definition of the job is a meeting with the Board in a week and they want a story on AI, and they want to know how we're going to disrupt rather than be disrupted. And you guys are the key to that in IT. That's a lot more fun conversation than cost reduction. So I think that's what I'm excited about is it's just a really fun time to be doing this.

Yes. And this is also a very fun conversation. Ric, thank you so much for joining us this morning.

Thanks, Matt. Appreciate it.