This video features an in-depth interview with Rick Lewis, a prominent technology leader currently overseeing IBM's infrastructure organization. The discussion begins with Rick reflecting on his transition from a hardware engineering background at Hewlett Packard (where he spent over three decades) into leading massive software and business innovation initiatives. He describes how the mindset difference between hardware and software shaped his career—hardware, being high-stakes and costly to iterate, creates a culture of extensive up-front planning, while software encourages rapid iteration and experimentation. Rick details his journey from retirement back to the IT industry, driven by his excitement about IBM's talent, heritage, and the profound role of IT in today’s business revolutions, namely AI and cloud technologies.

The interview highlights the major transformations within IBM's infrastructure group under Rick's leadership. He discusses how IBM’s core products—mainframes, servers, storage, and cloud—continue to shape the wider digital economy, and tackles surprises he encountered, such as the enduring success and innovation within IBM’s mainframe business. Rick delves into the underpinnings of AI's explosive recent growth, crediting both hardware advances (especially GPUs enabling massive matrix math for large language models) and cultural shifts in risk-taking and cross-unit collaboration within IBM. He gives concrete examples of how AI and data storage enable practical business improvements—such as dramatically reducing customer support response times—and emphasizes the exponential growth of data as a key industry driver. Rick further explores the challenges of implementing AI at scale, the necessity of hybrid IT models, and he dismisses popular fears of AI eliminating jobs, instead viewing AI as a force for enhancing productivity and customer experience.

Main takeaways from the video:

Please remember to turn on the CC button to view the subtitles.

Key Vocabularies and Common Phrases:

1. iterate [ˈɪtəˌreɪt] - (verb) - To repeat a process or procedure, often to achieve better or more refined results each time. - Synonyms: (repeat, cycle, redo, revise, reiterate)

And you're kind of iter. iterate. iterate, yeah.

2. insular [ˈɪnsjələr] - (adjective) - Detached, inward-looking, isolated from outside influences or competition. - Synonyms: (isolated, inward-looking, parochial, closed-off)

with that success, you can tend to get a little bit insular, like you don't keep an eye on the competition as well.

3. reinvigoration [ˌriːɪnˌvɪɡəˈreɪʃən] - (noun) - The act of giving new energy or strength to something. - Synonyms: (revitalization, renewal, refreshment, rejuvenation)

So we did a lot of reinvigoration of the innovation in that.

4. granular [ˈɡrænjələr] - (adjective) - Characterized by a high level of detail; consisting of small, distinct parts. - Synonyms: (detailed, specific, fine, intricate)

It's segmentation, it's product strategy at a granular level across a bunch of dimensions and then putting the investment behind it.

5. inflection point [ɪnˈflɛkʃən pɔɪnt] - (noun) - A moment of significant change or turning point in a process, industry, or trend. - Synonyms: (turning point, tipping point, pivotal moment, watershed)

I think the inflection point for that whole thing really, at its root was around hardware, meaning the algorithms needed to do larger language models.

6. brute force [bruːt fɔːrs] - (noun) - A method of solving problems by trying all possible solutions, usually using large amounts of resources rather than cleverness. - Synonyms: (forceful approach, exhaustive method, trial and error, sheer power)

now we can do this in a brute force way. Massive amounts of matrix math to get weights correct so that you can do the right level of predictions that enable large language models.

7. threshold [ˈθrɛʃˌhoʊld] - (noun) - The point or level at which something begins or changes significantly. - Synonyms: (limit, boundary, brink, starting point)

So, but my point is to get to that point, that threshold we got there because was it because we simply threw a lot more resources at the problem, or is it because the underlying technology got suddenly or gradually so much more efficient?

8. fit for purpose [fɪt fər ˈpɜːrpəs] - (adjective phrase) - Appropriately suited to fulfill a specific task or requirement. - Synonyms: (suitable, appropriate, tailored, custom-made)

the Z and Power product lines are well known in the industry as, as really fit for purpose computing that, that have strengths that, you know, Z runs most of the world's economic backbone

9. holistic [hoʊˈlɪstɪk] - (adjective) - Considering a system as a whole, rather than individual parts. - Synonyms: (comprehensive, integrated, all-encompassing, complete)

It's an exponential problem. It's a holistic problem that takes a lot more than just, you know, little chunks of rules, et cetera.

10. amenable [əˈmiːnəbl] - (adjective) - Willing to be influenced, responsive, or open to change or suggestions. - Synonyms: (receptive, open, responsive, compliant)

and is a lot more amenable to fit for purpose.

How Infrastructure is Powering the Age of AI

I'm here with Rick Lewis. Rick, welcome. Thank you. Here we are in the IBM's new New York City headquarters at 1 Madison Avenue. I'm going to start with, you're a hardware guy. I'm a hardware guy. I grew up doing hardware chip engineering. But like I tell a lot of people, a chip engineering project is actually a giant software project with a piece of hardware at the end of the project. I think if you have that analytical brain, you like to solve problems, you like to get things working, you can do that inside hardware. But does being someone coming from a hardware background mean that you think about problems in a different way? I think one thing that you do from a hardware background, and especially a chip background, is a chip spin costs millions of dollars. So you're a lot more likely to make sure everything has a great chance of being perfect from the get go. Where if you start kind of from a software background, your general mindset is, I don't know, try this, see if it works. I don't know, try that and see if it works. And you're kind of iter. iterate. iterate, yeah. Chip people are a little more uptight about, okay, if, if this first round of the chip breaks costs us from building another new round of the chip. Yeah, so you're a little more, you, you guys are, spend more time planning and planning, verifying, tons of time verifying. Yeah, you.

So you began your career at Hewlett Packard? Yes, correct. And you were there for how many years? I was there for 32 years, yeah. And your last job there was. I was leading the software defining cloud business. I had grown up a hardware guy. I had done all kinds of hardware projects, big complicated UNIX servers and things like that. And then came, you know, grew out of R and D and more into the business realm. And then I'm much an innovator at heart. I really like innovating new concepts, things like that. And what I learned is I enjoyed innovating business models and software projects as much as I did hardware products and projects. And so getting teams inspired toward doing that was really a deep fascination for me. So I ended up doing a fantastic variety of experiences and had a successful run and honestly retired, intending to retire and do some of my outside activities and things like that. So. And then how long did you stay retired before IBM? It was almost two years. And when, when I first got the call, I thought, no, I'm having too much fun. But I would say three things really got me thinking hard about it. One, the industry that we're in the IT industry, I think it's the golden age. And what I mean by that is for 20 years of that career, it is kind of in the back office. Hey, make sure that stuff doesn't crash and can you please reduce the cost as much as possible? Because it's not that important to the main business. It's just a back office function. You can see it right now. It is at the forefront of all business revolution. It happened with the Internet, it happened again with cloud and how that changed every ounce of business, not just IT business, but all business. And I think it's happening again with AI. So to be in that career that long and to miss the kind of this age where it's like we, this is front and center. This changes everything about all businesses, not just technology businesses. I was kind of feeling like, gosh, you trained to be in these really awesome environments. Why wouldn't you do that for a little while longer while you still can do it? That combined with IBM and IBM seeing the talent pool, the brilliant people at IBM, I worked with a ton of brilliant people before I saw a chance to work with even a larger staff of brilliant people. And then the assets that IBM had, which is, you know, they'd already been doing a lot of experimentation in AI. They're working in, in quantum, the deep rich heritage of successful projects. I thought, who wouldn't want to kind of see if they could be part of, of that next great wave of IBM? And so I kind of decided, all right, I'm going to put the outside interests on hold for a while and get back in the game.

How long between the phone call, the first phone call and you saying yes, it was a while. It was probably six months. Arvind, our CEO, teases me about that a lot. He like, I don't think six months is that long. It took a while. You're in retirement. I know, exactly. I mean, it's one thing to compare. I'm working here and doing this stuff versus working there. It's really hard to compare. I'm doing exactly as I want to do every single day when I wake up. And now I'm not going to get to do that again. It took a while for me to get over it and I thought, I can't miss this wave. And, and I'm really, really happy that I did because we're doing some amazing fun things and I'm getting challenged in ways that I never did. So it's really fun.

Talk a little bit about your job here at IBM. You oversee a Kind of massive portfolio. It's a big group. So I run the infrastructure organization. There's three main groups of products at IBM. There's the infrastructure group, which I run, the software group, and the consulting group. And infrastructure is built up of mainframes, which is called our zone portfolio, our servers, which is our power portfolio, Storage, by the way. Those businesses include the supply chain to build all of that stuff. So that's in the group. Then I have the worldwide customer support organization. It's called TLS Technology Lifecycle Services, which is a network of about 13,000 people around the globe that make sure that everything runs and works when you buy IBM products. And then also our IBM cloud, which is how we host applications, deliver as a service products for our client base. So there's a lot, it's, I think it's about 45,000 people total.

Do those components of the infrastructure group, are they aligned in their trajectory or do they, are they on different paths? I'm just curious about a little of both. It's interesting you would ask that because I think of all of the challenges coming to the new company. There were things I expected, things that I didn't expect. But getting that culture right in that group has been a big challenge. IBM has a great culture toward quality products, toward emphasizing passion for the client and making sure that the client is happy, and for delivering innovation on a scale that for more than 100 years has been extremely powerful. But with success comes some challenges, and with that success, you can tend to get a little bit insular, like you don't keep an eye on the competition as well. You can get more siloed where you know, this is my business unit, this is my business unit, I compete with the other business unit. That's not a good thing. When you, when you're a company and you can get really risk averse, meaning you, you feel like, hey, this is a successful business, I don't want to do anything to mess it up, so I don't need to try new things. Well, that's exactly the recipe to kind of be shrinking. And infrastructure had been shrinking for a little while. And so a lot of what the challenge was for me was to invigorate that risk taking and get to a growth mindset where you're trying new things and seeing what works and what doesn't work and changing some of the models, like investing a little bit less in hardware for some software differentiation that goes into the hardware. So it's been very successful so far and it's been a good journey. It's almost four years now.

Give me an example of what was a really hard problem that you've dealt with in those four years. Boy, a really hard problem. An interesting an are you. Interesting is a better word than hard. One of the first things that I kind of chewed on a little bit is I talked about how we have Z Power and storage. The Z and Power product lines are well known in the industry as, as really fit for purpose computing that, that have strengths that, you know, Z runs most of the world's economic backbone and if you use a credit card, 90% of credit card transactions for the globe go through these Z mainframes. They're in every bank. It's a big business, it's well known in the industry. Same with Power. Very tuned and optimized for smaller operations than our giant Z mainframes. But really mission critical workloads for retail, for insurance, for banking, for all of that. Our storage business not so well known. In fact when I came I thought do they have storage? Will I have storage when I come into IBM? So I got online and I thought it's still hard for me to tell did they have storage or not. Now I own a storage business. So one of the things was not just to get the market perception up but to invest in that business. Because if you look at infrastructure overall around the globe, it's growing at 5% a year. The infrastructure business had been kind of flat to declining. And so a challenge was how do we grab onto the growth. Well, one of the biggest growth areas due to the explosion of data in the world is storage. So what do you do to kind of get on that growth rate? So we did a lot of reinvigoration of the innovation in that. A lot of software value add, a lot of doubling down on the things that are working. Portfolio rationalization where you segment the market and you say, okay, we're going to do less of this and really go big in these areas. And that's been probably the most dramatic turnaround inside the group is our storage thing. When you say it's a hard problem, it's not just oh, how do we do the math? No, it's cultural, it's strategy. And how do you get the strategy set? It's segmentation, it's product strategy at a granular level across a bunch of dimensions and then putting the investment behind it. It's a big challenge. It takes a long time but it's working. So we're happy with it. Yeah.

Give me a little bit of perspective on you've been There four years. Imagine we're having this conversation four years ago. Yeah. What sorts of things have happened over the last four years that have surprised you that you didn't see come? Or at least are we having exactly the same conversation four years ago? No, because I didn't know what was in. I'll tell you some of the biggest surprises I thought from the outside and you hear from a lot of customers, especially 10 years ago, we're all going to cloud, we're all doing. So I thought, well, I wonder if the mainframe business is struggling when I get inside of there. I found the opposite to be true. The mainframe business is actually flourishing because transaction demand across the globe has done nothing but grow. And even more surprising was the level of innovation that the team was already doing in mainframes before I got here was astounding. For example, we have AI. They were building AI technology into the mainframe processors three years before ChatGPT made Everybody Talk about it in the industry. So that was really pleasantly surprising. So that was wonderful. Other surprises, I knew about the kind of the IP of IBM and the mystique in that and I used to joke with people, especially on the outside. I said, I can't wait to get in there and see what's in the big blue toolbox. Right? What, what are all the things they have going on? I way underestimated the size of the big blue toolbox and what was in there, meaning the amount of really hardcore research that we're still doing into how to build chips and how to get to things beyond 2 nanometer and, and that kind of capability. Packaging industry leading packaging technologies. And that's in my hardware kind of patch quantum. The next thing that'll come after we're done talking about AI. You know, all of, all of those things were surprising, but it wasn't just that. It was then the software innovations that are going on. Heavy investment in AI technologies before it was really popular to be talking about that. But as I saw that, I thought this is going to get really fun because I had a good feel for where the industry was going. I just didn't. And I knew, man, I know that talent is really good and there's brilliant people there, but I didn't know the level of IP frankly that that IBM had at, at its disposal. And now you're seeing that in things like Watson X and things like AI in, in mainframes, et cetera.

Building on that, since you brought up AI, can you walk me through what has to happen from your perspective, from the Infrastructure perspective to make the AI explosion work. Yeah. So everyone wants to do more of this stuff, but clearly there has to be some underpinning of it. Yeah. I would tell you, I think that people feel like where we're at right now in the AI journey had to do with one specific piece of software. I think the inflection point for that whole thing really, at its root was around hardware, meaning the algorithms needed to do larger language models. And all of that had been around, they'd been talked about in the industry. But at some point you hit a tipping point of hardware capability where it's like, oh, now we can do this in a brute force way. Massive amounts of matrix math to get weights correct so that you can do the right level of predictions that enable large language models. And once we've got to that horsepower, and that's why you hear about giant GPUs that are driving this and the sales of those, et cetera, it's because we just barely got over the hump where you can do these big hard things in terms of hardware capability to do it.

Give me a layman, give me a sense of when you say there was a kind of threshold where suddenly these things became possible. Yeah. I don't know if there's an exact number, but a more basic question that I get from a lot of people, you know, my friends and family outside, is why GPUs? What. What does a GPU, a graphics processor, have to do with AI? It's not. Well, graphics processors are really good at this thing, matrix math, because they're figuring out how do I map a pixel, and as I move an object across the screen, it's essentially matrix math to figure out, okay, what does this pixel on a screen look like and what's it doing? And as you know, we've gotten more high resolution graphics, more high resolution monitors, et cetera. It's a lot more pixels and a lot more math and a lot more matrix math about how you compute that. The first big thing that kind of started to look like that, it turns out, was crypto and crypto mining. And so you saw graphics companies starting to sell to crypto. The technology got to a certain point and there were use cases like Bitcoin and that, that, that kind of said, hey, we need to do a lot of this matrix math to be able to do that. So graphics chips were a natural fit and that kind of sustained. But meanwhile, behind the scenes, a lot of this AI, AI is about numeric calculations having to do with weights and matrices that say you know, giant consolidated things that predict what's going to kind of happen based on what other things have happened. Just like predicting where a pixel goes. But it's really about being able to do enough data ingest to be able to do and then the calculations to be able to simplify things like entire sets of language or giant chunks of the Internet to get enough weightings in there to be able to say, okay, we can predict what you would say in this language based on all of the volumes of stuff that we've seen, that when you start talking like this, the next word is likely. Oh, it's this. Yeah.

So, but my point is to get to that point, that threshold we got there because was it because we simply threw a lot more resources at the problem, or is it because the underlying technology got suddenly or gradually so much more efficient? It's always yes and yes. But, you know, the industry for a lot of years would talk about Moore's Law. Rick. Oh, quick. Will you define for us Moore's Law? For those who've forgotten it, yeah. So Gordon Moore at Intel coined this thing. It was basically that the horsepower. I'm going to translate it roughly of technology will double every couple of years. We're still on Moore's Law. Moore's Law changed a little bit for a while. It was always about frequency. Things would go faster, faster, faster. That kind of petered out. But what happened is rather than faster, faster, faster, we did more and more and more. So rather than one operating unit going a lot faster on its throughput, you put 10 operating units on a chip. Now you put 100 operating units on a chip, now a thousand. Some of these problems, the matrix math problem, scale parallel, extremely well. You don't have to do something really fast. You just have to do a lot of the similar things in parallel at the same time. So again, that kind of, that extension of Moore's Law, more and more hardware on a chip to be able to do more and more of those calculations in parallel and come up with that. Yeah.

Was that threshold predictable? In other words, do people in the industry like you sit down X number of years ago and say, when we get here, AI is going to become much more of a. It's funny, the horsepower that very predictable. The use cases, not always so easy to kind of figure out that's where the human spirit kind of gets involved? I think for some people, they'd say, oh, I saw that coming. But people have been predicting kind of the rise of AI for 25 years. Oh, well, then when we get to this next generation. Oh, when we get here, it kind of hadn't happened. There's always a magic point where you kind of get to where the technology and the use case and somebody does something to kind of make it catch on. And I think we're at one of those moments in AI for sure right now. And I don't think it's. You know, people have said, oh, this is just the latest wave of, you know, I used to hear. I've heard this about a lot of technologies, but AI is the technology, the future, and it always will be. I used to hear that. You're not hearing that now, right? It's like, no, it's prime time. It will change everything. Just like some of these other things changed everything.

I noticed sort of personally, when I speak somewhere or when I'm listening in an audience somewhere over the last, let's say, 12 months, there's always a whole bunch of AI questions. And if I go back two years ago, there were no AI questions. Now my question is. So there's been this explosion in popular fascination with what's going on in AI. It seems like the last year. I agree with you. In your world, when did the explosion of conversation around this start? I love this question because IBM had a fairly big effort in business called Watson before WatsonX. And this is going back kind of 10 years. I'll give you another kind of example. I knew about a lot of tablet technology before there was an iPad. A lot. For 10 years, there were a lot. But it kind of takes a magic combination of the technology, the user experience, the software and the need and the market ready for to kind of go. Now it's the thing. Now we all have either an iPad or we have the Google equivalent of. And so I think this is a little like that, meaning IBM was on the right track with Watson. Some of the hardware wasn't there. The use cases weren't exactly figured out. Some of the early use cases didn't pan out perfectly. But the good news about that is it's back to that culture of risk taking. You don't look back on that and say, oh, we shouldn't have done that. That was a bad idea. No, you look back on that and say, what did we learn? How should we try something new? How would we pivot this time? That's what we've done with WatsonX. And now that's a growing, healthy piece of our business and very important to our strategic future. So we're all in.

I've always been fascinated by the gap between Insider sense of what is happening and outsider sense. It absolutely is that in this case, we've all been talking about and thinking about AI and is it time for that and what does this mean, Et cetera. And yet none of us really predicted that actual moment, which was kind of early 2022, where it was like, oh, now you have a simple human interface of software innovation combined with large language models. There's a moment there where you're like, oh, unlike. I think all of us are frustrated if we ask our phone, hey, tell me about this. And it says, I found this on the web page that does you no good. But you know, all of a sudden with, with chat, GPT and some of these other things, you could ask a question and would give you a clear answer. Sometimes it's wrong, but at least it was like, I'm getting an answer rather than, hey, I don't know, here's some references. Good luck to you. Yeah, and that's really changing. Talk about the kind of macro trends that are going to shape your infrastructure battle. Yeah, we've talked about a few already, but I'm actually going to go a little different direction. So macro trends first. And this one has been before even this AI conversation that we've had. Explosion of data. As humans, we don't think exponentially very well. We really struggle with exponential thinking. We think linearly. Oh, there'll be more, there'll be more, there'll be more. But we don't think. Well, when it's like, no, there'll be more and there'll be ten times more, and then there'll ten times that more. That's what's going on with data right now in our industry. It's one of the reasons that that storage business is doing so well. Is there just more and more and more data? You know, you'd say, well, how can there be more data? It's just life and that thing, the things that we care about, video capture, video images, you know, the, the you. I don't know, for my parents, you needed a drawer with all your family photos. Now we need gigabytes and gigabytes. You knew how many pictures my wife is taken of our children. You would. Exactly, exactly. So that, so that's your case. Now think of companies who used to just think about their transaction data. What's the ledger say that now have video assets of all of their campaigns and their marketing. They're trying to figure out, you know, what campaigns are working the best. It's just an explosion of data and that's not going to stop dealing with that. And more importantly, getting value from that data is a massive trend in the industry. Second trend, AI. And this is the AI, not like we were just talking about, about how it changes how I search for things or how I learn about things. But I would argue, dealing with that data, how do I figure out what's in all those video streams? How do I figure out, okay, I want all of the chunks of my corporate video that have to do with client buying some specific product or something. That's a. It's a different problem. It's not just, okay, we'll look it up in a spreadsheet. And here's the math associated with that. That is a huge trend in the industry. You're seeing it play out in this regard. It's a little different bent on AI. Fraud detection is the one that we cite in our mainframes. It's a similar problem where it was kind of a traditional AI problem. Look up a rule, you know, if somebody does two small transactions, then a massive one, it might be fraud. Right. Because they were seeing whether it worked. Yeah. Now, to detect fraud, you might be saying, okay, two transactions, then a huge one. Plus, does this entity have a real address? Second, is there any web traffic on, you know, Better Business Bureau kind of things that says, this is a bad business, that can help you with fraud? So it's a lot more of a. It's an exponential problem. It's a holistic problem that takes a lot more than just, you know, little chunks of rules, et cetera. And then the third one, you know, after AI, is the nature of hybrid IT or hybrid computing. For a while, 10 years ago, when cloud was on the rise, I think the notion of hybrid computing basically having to do with things in the cloud versus things that people still have on the premises inside a business was almost a religious argument. Now it's. No, it's the reality. And the reason is because that data that I talked about is the lifeblood of These companies, particularly IBM's companies, our client set. Usually that data has to be secure. They have to be able to get value from it. It is the lifeblood of the company. If you go to an ATM and you can't get your money out, you know, to our financial transactions, if that lasts a day, you're probably going to change banks immediately. So it's like life or death for these companies. So having that hybrid infrastructure so that they can still hold their data yet still interact with clouds and still get value from it, from AI, that's kind of the magic where we play. And it's a huge business opportunity. It is a true inflection point for the industry.

I'm going to go back. I interrupted you when you were in the middle of a really. We were talking about what has to happen for. For AI to scale from the infrastructure standpoint. You gave one example that I got you off on a tangent. Can you go back and talk it very, sort of practically like, so I'm, you know, I'm a big company. I have all these dreams of AI, of how I'm going to use this strategically. So give me a very granular sense of the works you have to do. Yeah. To make that dream possible. So let me first say what the company has to do and then maybe I'll say then how do I help them? If that makes sense. So if I'm a company and I want to do that, so it turns out I am a company, meaning I want to use AI in my processes. I mentioned that I have a global network of 13,000 employees that support our infrastructure around the world. That challenge is a great challenge for AI. That means I have data for every customer situation for 13,000 employees globally around the world. On what was their problem, how did we fix it? What next steps did they have to do? How did they remediate that? That data is extremely valuable to me because if I can get better at doing that than anybody else in the world, that brings my cost down. I sell more products, I sell more service, I sell more anything. So what I have to do to get there is I have to figure out, okay, what's my objective? I have a couple objectives. One, I want customers to be able to support themselves without even calling me first off. And I don't want they call for the first answer to come back to be. Did you try rebooting? Because I think that irritates every single one of us. Did you try. Of course I tried rebooting. I've had a laptop my entire life. Of course I. Well, okay, well then tell me. Okay, what firmware version? All that other stuff. Okay. We know this interaction. So that's kind of the problem set. Do I want that to be customers solving their own problems? Well, even for my support agents, I want something in their pocket, on their phone where they say I'm seeing these symptoms and says, oh, this happened around the globe. Here's, here's kind of specific. So there's my problem set. What does it mean for infrastructure on the back end? So first I got to get all that data together right? All of those customer law, all that customer support around the globe, et cetera, that needs to be stored. That's a big set of data. And some of it's not just fix and, and that kind of thing. Some of it is okay, you know, what was the firmware version? Who was the tech? Because it can matter. Is this their first time fixing this problem? Is it their 150th time? What's their level? It's a very complicated problem. Ingesting all that data takes an architecture. We have a product called Scale, which is one of our storage projects that actually makes it easy to ingest all that data, get it organized, et cetera, and then have a model. It's a whole different process to kind of say did we train our model? We can train our own models. Inside of IBM we have a granite set of models. Those models we fine tune and then we inference based on those models. So we can do that inferencing in our cloud. I have a cloud set of infrastructure or in my power servers we can do inferencing with our capabilities and say okay, based on what I'm seeing, here's what the remediation that you should do for that customer. We already are doing that today. We've seen over a third of our support calls have had significant reduction in the amount of time that it takes to resolve that support call. Just by what I said right there does that.

I've really been curious about this. If I introduce something like AI into that equation as you just did and you said we've already seen a 30% say. Did you say 30% reduction? 30, 30% of our interactions have seen significant reduction in the time. Was that your primary goal to reduce the time of the interaction? In other words, if everything else was the same, but what you were doing is was shrinking the amount of time that would you one of the primary goals. So to us in that business net promoter score, kind of the satisfaction of a client is the supreme goal. What makes them satisfied. It doesn't cost me a fortune. Happens really quickly and if I can do it myself, I'd be thrilled. It affects all of those, right? It kind of says it got resolved faster. It didn't cost me an arm and a leg because the deck was barely here because it's a common problem or I solved it myself without even calling one. So all of those objectives we kind of hit across all so that now you see it. So that's a little microcosm. That's just me and my customer support business. Now think of how many problems for businesses around the world there are like that. It's not a, it's not like a new AI application that changes the entire user experience. That's. Those will come. But right now it's kind of practical, which is I just want to do what I'm doing better and faster and I can get immediate economic return from those things.

How long, how long did it take you to just stick with that example of the customer interaction reducing 30% of the time? How long? From the very beginning of that project. Yeah. To that 30% reduction was how long? Less than a year. And yeah, one of the challenges, and this is interesting, with a very large organization, as you can imagine, just like you're seeing in the industry, we don't have a problem of generating ideas for how AI could help us. We actually have a problem filtering the thousands of ideas from our employees and from everywhere. It's like, hey, we could use AI to solve and filtering down and saying, okay, which of these will have a return on investment quickly and at a level that sustains, that's worth kind of going and investing in the infrastructure and the software and kind of making that happen.

Is that unusual? If I Talked to you 25 years ago and said, do you have a problem of too many good ideas or too few, what would you have said in this specific area? Probably too few, because at some point you reach diminishing returns. So, for example, let's use this exact same example. Can those 13,000 technicians go faster? Can they spend less time driving to the side? I mean, there's only so much you can kind of do on those things. But if you can get them an answer to the problem and maybe even avoid them having to visit at all because the client helped themselves, that's a step function. So this, that's why people are kind of talking about there's a business revolution coming with AI where there are some step function changes that can be there. And notice I didn't say I'm going to have less of those agents. That's not my objective. My objective, and I think that's the fear in the industry about AI is going to eliminate all the jobs. No, I just created 13,000 superpowered agents that can do more. Right. And so I'm not just going to support IBM products, I'm going to go out and support other people's products because I know how to do that really well. And once I have the data on how to fix their problems, I may just have a customer support business that's independent of my Boxes. So, you know, I think that's where people sometimes get it wrong. And the AI thing is, it's like, you know, did word processing eliminate the need for writers? No, it enabled writing. Instead of mucking around with mimeograph machines and clickety click typewriters, it may have enabled too much writing. Yeah, maybe.

Can I give you a hypothetical? And I asked this because I was at some conference and I ran into some guy from the IRS who was really, really, really, really excited about AI. So let's suppose they call you up and they say, you're going to ask me with the IRS issue of the irs. I call you up and I say, Rick, clearly there's something that we could do for the IRS if we work together. Yeah. What would your answer be? Of course, no, I think we sell to a lot of government agencies. Yeah, you can imagine in, in the business that we're in, we enable a lot of Social Security transactions and things like that through our mainframes. And I think, you know, we're in the business of, of helping whatever client get the most out of their data and be able to secure it and, and be able to do analytics with it. And IRS has a heck of a lot of data, I was going to say. So yes, we would help them. Do you know how the amount of data they have compares to some of the corporate clients you have? I don't know specifically for the IRS how much data they have, but I would assume it's a whole lot. It's mountains, but. But that's our business. I mean, it's interesting sometimes people have asked, what's the most, you know, what is it that IBM has that's of great value? Is it a server, is it a storage array? Is it, you know, software and all that? What we have is the most important entities in the world have their data on our stuff. The most important data in the world. It's not, you know, pictures of your grandkids and things like that. Generally for us, it's all of the financial transactions that happen globally. Right. It's all of the, it's.

The world's economy is kind of running through our systems. And so we take that really seriously. You know, you would be distraught if you lost one photo on your laptop, whatever. But you know, if we lose a transaction, like somebody moves a big amount of money and it's like, oh, don't know what happened there. It is a massive deal. Right. So that doesn't happen. But you want to go back to my IRS example for yes. So one is it reasonable to assume that you could that somebody, IBM or somebody else could in a short period of time put together not just the AI capability to audit returns, but also this, the infrastructure support for that in a reasonable amount of time, for a reasonable amount of cost. Or is it a over, Is it going to the moon or is definitely. I mean, so we're already doing that kind of thing right across a network of banks and others. Essentially all credit card transactions for all of the world go through our systems. So that in some ways is more volume than the tax returns of the US people and their W2s and all that stuff. And we do that stuff too. I try not to describe it too much in detail, but we definitely do a lot of that. In fact, I think most. If you think, okay, what is super critical data? Who would be doing the business transaction processing? It is most likely us in almost all cases, whether it's government things or private or banks or that kind of thing. That's what we do.

Rick, we're going to end with the way we always end with a couple of quick fire questions. Okay, here we go. What single piece of advice would you give to businesses trying to use AI in an effective way? The simple version is get started. By get started, I mean think of what is something that I want to improve. The things that we have traction on right now in the market are around business process automation, digital labor, those kind of things. But my other little piece of advice there is keep it simple to begin with. You're going to learn a lot, but getting started means you'll start that learning curve. I even advise, you know, my friends like, hey, should I be playing around with some of this AI stuff? And I say, yeah, because I think it will help you start to be more comfortable and you may find a use case personally for that. I think the same is true for businesses. The first step in that journey is always with what data. Notice when I talked about our customer support people, I thought about, okay, what's the data? The data is all of those logs of all of those service engagements around the world. And what could I do with that? Well, I could use that to get to a knowledge base that really helps and hopefully that I can do it in multiple languages because it's global and I can, you know, all of those things. That was kind of my data center. That one's not super simple, but we've had a lot of experience in AI. For other people it might just be how do I automate filling out travel expense reports for my company? We can Help people with that. We have consulting, we have Watson X Tools. We can do that like this. And we're doing it globally for people around the world. Pick that thing. What's the data you have in that case? It's data of expense reports. And it's like, okay, we can help you automate that for people where they could do it just by, you know, a verbal interface. What did you spend? Where did you go? Who you were with? Okay, we filled out your travel expense report for you, and you don't have to mess around with it.

So we were playing with this idea where we would pick a business and go in there and do a. It would be AI makeover. Yeah, I love that idea. Okay, what is the ideal business to do? We only have a couple months. We don't want to spend a kajillion dollars. We want to be able to show tangibly and quickly what AI can do. What's an ideal business to do that in? It can be a small business, but we're not talking. This isn't a grand corporate thing here. Ah, boy. Small business. That we could do an AI makeover. Customer support is one of my favorites because I have it on the business side where I provide customer support, but I have it on the consumer side where it drives me nuts when I have to go through 30 layers of phone menus, speak to an agent, speak to an agent, speak to an agent. That for any business, I think is just ripe to be able to say, why do I have to click through these messages? I just need to tell you in human language, here's the issue. And I'll be really good about telling you details about, you know, I tried to set up this thing for my bank, and I did. They can go through all the menus, automate that process. I think it would change everything because all that frustration as a consumer would go down dramatically. Yeah. And it's all, you know, why are you making me. The beep booth? Press one. Exactly. Well, don't offload to me. Offload to AI. We can help you with that. Here's my version of that. Drives me crazy. Every morning I go to the same coffee shop and I get a cup of tea and a croissant. And here's what happens. The person has their screen and they go, I go, cup of tea, croissant, sparkling water. Like, at least 20 keystrokes, right? And then, like, then the screen is turned around. Like, at this point, we're like 45 seconds in, and I'm like, why is this? First of all, it's not for me. All those keystrokes. It's for their internal. Correct. So they're burdening me in order to service their back end. You should be able to walk in, go up, and they go, hi, Malcolm. Same thing. Yeah. And you just go. Yes. And then boom, we're done. Can we do AI makeover of my coffee shop? You notice I quickly jumped more to banks than your coffee shop because I think I'm a business person. I'm not trying to kind of do a deal on one coffee shop. No, no, no. But this is interesting because it takes me back to something you said that I thought was really important. When you were talking about when you were using AI in your customer service thing, it was clear that your goal, you could have any number of goals going in. It could be to cut costs. Yeah. It could be to dramatically improve profits. But your goal quite specifically was to improve the experience of your customer. Right. So you were using it to. That all the other things come from that. Come from that. That is actually one of the, the beautiful pillars of the IBM culture is delighting clients is actually where all of the good stuff comes from. So my coffee shop thing is the same principle. Right now they're making my customer experience worse. And they don't want to. Yeah. But their eyes are glued to the screen at a moment when I walk in and I want to say, hi, how are you doing? We could have a conversation. They're busy beeping and pooping. They're too busy. Busy beeping and pooping. So, like, this is the same thing. If they had. Oh, we, this is if they understood they had an opportunity to improve the experience, the customer experience. I, I would not be surprised if a chain comes along where that is their value proposition. I would not be surprised at all. Yeah. Yeah, right. So, I mean, and, and when those things kind of catch hold, it becomes a revolution, you know, when the guy comes to do, like to redo your roof and they put a sign out front like, you know, Joe's roofing. Yes. You guys could do the same with my coffee shop. Like IBM infrastructure was here. Exactly, exactly.

In five years, the mainframe will be dot, dot dot, going strong. The mainframe going strong and with new capabilities, continuous new capabilities. I think when we announced the last version, Z16, the latest version, I should say, and we said, hey, there's AI processing built into it. This was before everybody was talking about that. I think a lot of people thought, what's that for? And we did it specifically for traditional AI fraud detection et CETERA this next version, not only do we have the traditional AI built in, but we have optional cards that you can plug into it to allow you to do large language models for the enhanced fraud detection cases that we talked about, where it's more than just what transactions were happening. So if you take that and say, okay, the next generations, we have more transaction volume than we've ever had in mainframes today. The business is growing, it's strong, we keep innovating. In five years it'll be going strong. But we're people were. You're saying this in the context of for years people were predicting, weren't they, that the mainframe was going to go away or there were pundits in the market that said everything will go away there. No one will ever have a box, it'll all be online. I think this is something I've learned big time in my long career, you know, in the IT industry is don't believe everything you hear. So I went back for my master's degree at Stanford after I had worked a while in in as a hardware designer. And everybody told me, be sure to do your master's in software. Hardware is dead. I went on to work for 30 plus years in hardware in infrastructure. Now software became important and I'm glad I had that extra training in software because it helped me in hardware. But hardware wasn't dead. Then I heard all infrastructure will go into the cloud. There won't be any. That hasn't happened. It's not happening. Then I heard there will only be one cloud because one of the players will dominate. There's not one cloud. So I think it's as humans we like to oversimplify and go, oh, it's all going to be this. And kind of what I've learned is fit for purpose matters in everything. It matters in size of infrastructure, it matters in the stack that goes along with solving a specific use case. If you're willing to design something that's the best, best at that use case. If you're willing to design the coffee shop that is the best at greeting me, there's a spot for you. And there may be a big business in doing that. So oversimplifying is really dangerous.

When you heard all those predictions, did you believe them at the time? They looked like they were trending in that direction. I'll tell you some right now which might be useful. There will only be one GPU company and they're going to end up taking over the world. It's a pretty obvious answer whose economic values risen Dramatically, I don't think that's going to be the case. In fact, I think that 90% of processing for AI actually happens at inferencing. And inferencing is not as GPU and hardware intensive as the other things and is a lot more amenable to fit for purpose. So the model size will matter, the tuning matters a lot. As we're learning, we have a product around Instruct Lab that's really focused on tuning. So that was one thing is there'll be one gpu. The other thing is that the biggest model will win. I think is another thing that's kind of people are saying right now. I don't believe that. I believe they'll be fit for purpose models. It takes a lot of money to run a. To create a huge model and then to run a huge model or to even infer off of a huge model. I don't need a massive training GPU set thing to solve my 13,000 people customer support issue. So why would I feel like I got to go farm that out for a big expensive thing? I can do that on a small box. In some cases, I might even be able to do that on a laptop. The other thing I'll say in this, we are so early innings in AI. A lot of things are going to change. So anybody kind of saying it will all be X, Y or Z. I just think you have no idea how this is going to play out and it's up to us to go figure out how it plays out. Yeah. Yeah.

All right. In five years, AI will be still new. It will have moved a bunch in five years. But the potential for the disruption in the world will still be very early innings in that process. And I think that's super important to realize. That's why I say get started. Start thinking about how that could change. Because it'll be some little things first, but it will continue to snowball. This is a common observation that we. The invention of the capability massively predates the understanding of the capability. Right. Like, I love that. Yeah. Like, yes. Recorded recording shows on television is invented in the 60s. Probably the VCR. Yeah. We don't really understand what it's used for until the odds. What's really good for is being able to tell a story sequentially. Yes. Over time because you know that the person will all have seen the episode before. So you get the Sopranos and. Yes. Yes. Hollywood wanted to ban the VCR in the beginning. Yeah. Because they thought it was good. They thought the point of it was they thought they didn't understand. No, no. It's storytelling. It's actually. Your business is getting better. Yes. Yes. Took them 20 years to figure it out. Which is to your point, why would we know what AI was for in five years? Well, that's why you hear people kind of say, oh, my gosh, AI. That will just eliminate jobs. No, it'll make jobs better. That's how I view it.

What's the number one thing that people misunderstand about AI is that it. That it'll. I think that's. That that would be the human kind of understanding part of it. The. The technology part of it, I think would be what I was talking about, fit for purpose. Meaning that it isn't just going to be a GPU arms race. All of AI. I don't believe that at all. It will change everything, but it's not just going to be a GPU arm.

Next question. What advice would you give yourself 10 years ago to better prepare you for today? I'm changing this question. Okay, I want to say let's imagine that. What was your. What. What college did you go to? I went to three of them. My undergrad was Utah State University, my MBA was Santa Clara University, and my master's and double E was Stanford. Okay. Any one of those three calls you up and says, we want you to give the commencement address. And imagine that. It's. It's. It's just. Let's just say, for the sake of argument, it's just to the STEM people, because those are the relevant parties here. What do you tell them, Boy, what do I tell them? Let's see. I think I would start with. Life is a marathon, not a sprint would be the first one. The second thing I would say that in that spirit is be sure to set yourself some big, hairy, audacious goals and don't be overly disappointed if you don't hit them all. Going after those big, hairy, audacious goals will get you on a path where you will learn so much. You will achieve more than you ever could imagine you would have achieved. That's what the advice I give to my kids is. Set some big goals, get after it. You may or may not achieve them, but you'll be better for the whole process when you're done.

By the way, as someone whose kids are a lot younger than yours, is it actually useful to give your. Give advice to your kids, or is that just a pointless exercise? Tbd. We're still on the journey, and I think we will be for a long time. I don't know how are you already using AI in your day to day life today? Personally, I would say it's replacing a good chunk of my search. I'm less likely to go blindly stumbling through a bunch of web pages looking for stuff. I'm more likely to ask a question from a few AI engines, get me in the right direction, then I'll go bumble through a few things at work I can tell you. Code development Right now we are seeing massive improvements in code development and support products. We have like Watson Code Assistant that is really showing immediate return for our code developers and I think that will again be a tool that increases productivity for code developers immediately across the globe. Yeah, last question. What's the one skill that every technology leader needs that has nothing to do with technology? Being able to inspire a set of people toward a common goal and collaborate to achieve it. That's at the core of everything. Yeah, everything. What's a lovely way to end? Thank you so much Rick. Thank you.

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