Nearly home. Isn't home where we all want to be? Reba here for Realtor.com, the Pro's number one most trusted app. A dream home isn't a dream home if it comes with a nightmare commute. That's why Realtor.com has Real Commute, so you can search by drive time. Download the Realtor.com app today, because you're nearly home. Make it real with Realtor.com. Pro's number one most trusted app, based on August 2025 proprietary survey. Hey, listeners, Tim Higgins here from Bold Names. We've got something a little different for the holiday weekend. We're re-upping our conversation with Arvind Krishna. He's the CEO of IBM. He brought the company into the AI age and is betting that quantum computing is the company's next big thing. We hope you enjoy this episode, and don't worry, Bold Names will be back with something new for you next week. Okay, on to the show. America used to run on IBM. It was the backbone of business. But after getting that jingle stuck in the heads of millions of Americans in the late 1980s, Big Blue lost its swagger. IBM is getting a lot of attention today. More now on that big miss for IBM on both earnings and revenue. It's been a rough go. There's no doubt about it. IBM spent much of the 2010s in the doldrums, but has made something of a comeback in the past five years. That's thanks to a lot of the success in its hybrid cloud business. They've also leaned heavily into consulting services, all of which has led to a surge in the company's share price. That's under the leadership of CEO Arvind Krishna. He took the reins in 2020. And now, IBM's looking at the future with quantum computing, something I saw firsthand earlier this summer. I'm standing outside the Thomas J. Watson IBM Research Center in Yorktown Heights, New York. Believe it or not, this is probably the leading lab in the world for quantum computing research. We're going to go inside today and see what all of that is. Regular computers are essentially binary. They make computations using on and off switches. The different arrangements of these switches are called bits, but quantum computers use a different kind of bit, what's known in the business as a qubit. This opens up a huge range of new computational possibilities. It's going to get technical at times, but hang on, because it's going to be worth it. you'll hear Arvind talk about something called carbon sequestration, for example, or capturing carbon from the atmosphere and storing it to help lower greenhouse gas emissions. A quantum computer can help with that. But betting on the next big thing hasn't always worked out for IBM. Just look at AI. In 2011, IBM's Watson AI famously beat Ken Jennings on Jeopardy. Usually when I play Jeopardy, it's just for the fun of it. And this time I feel like it's all part of some vast socio-technological experiment. What is Jericho? Correct. It was kind of downhill from there. It felt like IBM had lost the plot. Now we ask Arvind Krishna how IBM is going to recapture the future through quantum computing. From the Wall Street Journal, I'm Tim Higgins. And I'm Christopher Mims. This is Bold Names, where you'll hear from the leaders of the bold name companies featured in the pages of the Wall Street Journal. Today we ask, why does Big Blue think it can crack quantum? Arvind, welcome. I'm excited to have you here in part because I was just at your Thomas J. Watson Research Center in Yorktown Heights. It's very impressive. Built in the 60s. Looks like the set of a Kubrick film. And I got to spend time with your head of quantum hardware, Jerry Chow. The technology here is really complicated. I spent three hours with Jerry. And basically my summary of how your quantum computing works is that it's magic. So I want to skip over the hour we could spend describing how this works and go straight to applications. What applications of your quantum computing technology are you the most excited about? I think the reason we are so excited about quantum is its ability to solve problems that normal computers cannot, and actually, I'll make a stronger statement, will not solve. So if we want to understand how materials really work, as an example, could we design a molecule that's better for carbon sequestration? Could we come up with a way to fix nitrogen so that we can increase food production and quality in the world? Because that's fertilizers, to make it simple. Could we come up with a coating to reduce corrosion in underwater pipes so oil and gas don't ever leak into the ocean? Those are the kinds of problems that I'm super excited about. Right, because you're talking about doing simulations of quantum effects. And funny enough, you need a quantum computer to simulate the way atoms behave at that level. Richard Feynman's quote, if you want to simulate nature, you need a quantum computer. He added some more colorful language, but for the podcast, I'll stop there. And to give a bit of intuition, when you begin to look at these materials or even simple molecules, you very quickly get into this 30, 40, 100 electrons. And in order to compute them, that's impossible. If you try to simulate them on a normal computer, you begin to need the amount of memory exceeds that. That is even possible because 2 to the 200 is a number we just can't fathom in terms of the amount of state that is needed. Quantum computers will be able to do that shortly. So a bunch of other tech companies are also eyeing quantum, right? You've got Google, you've got Microsoft, which have reported some pretty exciting breakthroughs in the past year or so. Why is IBM the company that's finally going to crack this technology? So I'm going to give you a couple of statements which are somewhat dissonant. Number one, I love the fact, really, I really love the fact that a few dozen or more others are going after this technology. Why? A, it helps to make a market because if it's just us, you'll begin to question, why should I believe you? Well, if others aren't trying it, is it really of any value? So actually, it's incredibly useful from a client, government, as well as, I'll say, from a media perspective that there's many others. Because I think that defines a market as opposed to not. The race for the future. Right. Now it comes to, so why would we be a winner of the race? And you know, Tim, I'm saying a winner, not the only winner. I got it. I want my technology teams to be super excited to be the only winner. But as long as we are a winner, I'm good. Okay, so now why would we do that? I think a lot of people are working on incredibly exciting, and we haven't yet used the term qubit, but let's think of that as the fundamental building block of a quantum computer. Not the only, but a fundamental building block. Lots of people are working on really exciting qubit technologies. I think that's great because it opens up many options and science usually advances by people standing on each other's shoulders so actually I think that's wonderful. But now you need to connect thousands of qubits together because to compute you need to have let call it signals flow from one to the other then it not enough to have those connected you need to be able to read and write to it Then you need to be able to have it function all day all week and all month without needing a team of PhDs to come and tune it between every single run because if that's the case, that's not really a computer. And that's really the game, though, ultimately, reliability. A couple of different answers to what you're describing. Number one, we need the computer to function. And then there is the whole question of, because you're trying to operate in very, very tiny energy states, that's the point of the cooling. Cool it down so that the normal thermal effects can be taken out of the picture because they're not going to happen at these super cold temperatures. Christopher, to be candid, that's a pretty straightforward problem. Yeah, it sounds interesting. No big deal. It's colder than deep space. It sounds like one of those, you know, really, you said Kubrick, I'll say Arthur C. Clarke, same difference there. I read you. Because I'm thinking in channeling 2001, a space of DC. Open the pod bay doors, Hal. I'm sorry, Dave. I'm afraid I can't do that. And it begins to sound like some kind of industrial or maybe post-apocalyptic scenario. but that is actually pretty standard technology to me. It's arcane. It's not ultra cheap, but neither is it ultra expensive. So let's put aside the cooling and all of the things. The question becomes, can you take what is inherently going to be unreliable? I make that very strong statement at a single qubit level and stitch it together using, I'll use the word coding techniques, so that what is there is a lot more resilient to errors. All that said, it's still going to be resilient to errors for maybe 100 milliseconds to a second. And then you have to start a game. So how much competition can you get done in that amount of time is, I think, the singular measurement for quantum computers. Wow. So a lot of math there, a lot of science, A lot of science fiction references. We got a lot there. How or more maybe when do you realistically expect to see a return on IBM's massive investment in this area? When do you expect this to be commercial? When do you expect this to start happening? I would tell you that we are looking at 2029, 2030. So that is four to five years out for that return that you're talking about. Now, that's the beginning of the return. And then you'll begin to see people beginning to use these. Then I think a year or two after, there'll be even more. And then a year or two after, there'll be even more. We have seen what happens when technology begins to work into scale. The first few years are remarkably exciting about adoption. Coming up, IBM has built up billions in generative AI contracts, mostly for consulting. But as AI continues to change work, how could it disrupt that business? Do I fully believe that work will be replaced by AI and the collection, reasoning, AI agents, all of that? I'm actually firmly convinced of that. We are running hard towards making that happen. Stay with us. For anybody listening, in case they haven't guessed, you are an engineer. You are the first engineer to run IBM ever. How is that helping you navigate this time? Because this is a time of, I would say this is a time of more rapid change for IBM than at any point in the entire history of me watching your company. I kind of call it, I'm an unabashed technologist and geek at heart. I love trying to figure out and hear explanations of how these things work, because that, I think, Christopher, answers the question you're asking. So how does it help me? When you have these really intrinsic and arcane technologies that kind of seem like science fiction, having a bit of the background, it helps me understand kind of two things. One, it is going to take time, so we're still four years out. But two, when we get there, I now know how to scale really, really quickly to go get it into lots of people's hands. So it helps make those decisions. It also makes the conversation much, much faster with the teams that work on it. Because I don't have to go through a layer of intermediaries or trying to translate it into a different language. I mean, they can talk to me how they want to. I may understand maybe half of it, but that's better than understanding only 1% of it. And I just want to flag what you've already mentioned, which is that 2029 is when you're going to really start scaling your quantum computing efforts with Starling, right? Correct, Christopher, but for the following reasons. We've got two steps to do between now and then. Number one, you've got to get these systems much bigger. Number two, you've got to prove that the error correction works. Clearly, you've got a lot of momentum. You were early to quantum. You're kind of the bell of the ball. But how do you stay ahead? How do you maintain this momentum? Look, quantum is pretty straightforward. To maintain the momentum, we have to grow a very large ecosystem on quantum. So the use cases multiply and we might perhaps collect 10% of the value. 90% of the value should go to the people and the consumers of the technology, not to us. So educating people, teaching them how to use quantum, unlocking their imagination is a big part of what we have to do. And I'm honest about the 1090 tradeoff. I think that that is where it has to go. Otherwise, the technology doesn't really scale. In the today, though, I think investors are very excited about the potential for AI and where they see IBM playing in that space. That's the hot technology of the moment in the valley and in a lot of investors' minds. Since you became CEO in early 2020, this IBM stock has just been on a tear, right? I think your stock in a lot of ways is trading with the kind of premium that you see with the AI heavyweights out there. And I wonder, I want to talk a little bit about how you kind of see AI in the business and how you're balancing kind of that with your traditional role. Because I think one of the things that IBM has received a lot of praise for under your tenure is really clarifying its consulting business, right? You've worked to make IBM really a facilitator for your clients, making this tech transformation, not necessarily steering them into IBM products per se, but growing a consulting business. And I wonder, with AI, doesn't AI potentially disrupt your consulting business as it changes work in general? Do you go to bed afraid at night or excited at night? Yeah, I'm actually excited. I lose no sleep over what AI will do to our consulting teams. Why do I say that? Do I fully believe that, and we can debate, is it a third, is it half, is it two-thirds of what people do in consulting that work will be replaced by AI and the collection, reasoning, AI agents, all of that? I'm actually firmly convinced of that. We are running hard towards making that happen. So you then say, wait, if half the work happens, don't you lose half your consulting business? and I go to know the exact opposite happens if we run towards it that means we are more productive in giving people consulting help if you are more productive history has shown that the more productive company gains market share because if you offer higher quality things at a lower unit cost you tend to take market share And if you take market share you actually win more customers So that is why I believe that that is the route we need to go and go at it hard. And that is how we are going to go with. For AI and for big company adoption or just company adoption in general, it seems like we're still in this area, this era of mostly experimental, trying to figure out how this might be useful. We're seeing some early things. But I'm curious what you're hearing from customers and how that conversation is changing on what they want from AI in the now and in the next few years and the tangible. So let's acknowledge the vast majority, as in over 80% of the clients we talked to, will acknowledge they're still in the era of AI experimentation. You know, typical conversation goes like, how many AI POCs are you doing? I'm using proof of concepts as the jargon for experiments. Oh, I'm doing 100. I'm so proud of it. I got every function doing a little bit. Okay, how many of them do you think are going to scale? And they kind of look at their shoes like one of them told me, well, I think maybe three. I said, like, so is the flaw in your design of how you're using AI? And then they begin to acknowledge, and that is the push now on the ROI point. ROI is only achieved when a technology begins to give value back to the business. Doing the experiment has no value in itself except to prove feasibility. So they've all begun to ask the question now, how do I scale and how do I get that value? That means I need common tool sets across the different experiments. I need to think about some of the questions up front, like what is the life cycle of a model? What is the governance? How do I make sure that I'm correct about privacy? And all the other aspects that we always need when we are dealing with data and with critical functions. And so if I was to use a sports analogy, I would tell you AI is in the first innings. So players are on the field, you know who's going to play, but it's still early, early to see how the game works out and how it goes along. After the break, how is IBM reinventing itself for a new era? If you're overly wedded to your past and you find it really hard to let go, then effectively you're forcing your engineers and your sales teams to be very, very defensive in front of clients, and that is not usually a good practice. That's next. You know, listening to you talk about quantum, I mean, clearly you're excited about it, right? But I also feel like you're tempering your enthusiasm a little bit. The old IBM, perhaps, and I think of kind of the AI IBM talking about Watson. Watson? Who is Agatha Christie? The appearance on Jeopardy, there was all those ads. Yet Watson, I think, ultimately was a little bit disappointing to a lot of folks. It was maybe ahead of its time. And this time around with quantum, it feels like you're trying to walk a line between being excited and enthusiastic, but also not overpromising. And I wonder what that is like for you, especially in an era where nobody is tempering any enthusiasm, especially when it comes to the next technology, right? I mean, we're seeing sky high valuations and huge amounts of money pouring into technology on the hope, on the hope that it'll happen. Look, I think maybe being a technologist and understanding these things helps a little bit here. When you're still three to four years away, and I'm acknowledging that on quantum, why would I want to hype it up today? Because that means some client might misread it and come to me and say, hey, can I use it tomorrow? And that's not there. I really want to take an approach. Look, we're not afraid of marketing and sales. I think IBM is reasonably well known for being willing to do that. But I want to do it through the lens of my client and the lens of the people who use it. When I have that lens and they're talking about the value they derive from it, you'll find us not at all shy or abashed about going there. But right now, it would be us describing what's possible, not the end user describing how excited they are by what they could do with it. That's kind of the change that I want to bring to IBM in terms of how we talk about things. Absolutely, we will amplify it. And we are not shy about where we are really, really good. But I want it through the lens of the people who are getting value. Yeah, enthusiasm, of course, that's super important for your stock price to make sure your employees feel like they're working for someplace that's continuing to grow. But that's all of tech, right? I mean, everybody's growing their revenues right now, if not their profits. However, a lot of that money is going into talent. You've seen Mark Zuckerberg credibly offer $100 million pay packages to potential AI researchers. Google, of course, they spent billions on a code completion tool. How do you compete where there's this overheated environment where everyone wants this talent? I jokingly asked Jerry, have you had a $100 million offer yet? He said no. But seriously, how are you going to hold on to talent? Maybe a bit of experience helps here. This is not the first time we've gone through these. This happens every 10 or 20 years in the technology industry. The internet era was identical. I remember it deeply back in 97 to 99 and 2000. All these same things happened. When mobile came around, the same thing was talked about very early. Now with AI, the same thing is happening. No doubt it'll happen on quantum also. The way you do it and the way you avoid, sort of, I'll call it with irrational reactions, because some of these I think are actually irrational reactions, is that you have to have a deep enough bench and you've got to ask the question, so why do these incredible people want to come work with you? And on Quantum, they come and work with us because they can work with an incredible team that has justifiably made more progress than anywhere else, So they'd rather be part of a winning team than part of a team that has a much higher chance of losing. I actually think that that is the same thing that happened in AI. The people who are in these collections were part of the winning teams. Well, now that they have won in some sense, I would say maybe tongue-in-cheek, those who are on the losing side think they can buy victory. History shows that usually does not turn out to be the case. So yeah, so I noticed that your research center, there's a distinct lack of nap pods, or ping pong tables, there's no slides between floors. It really felt like you're attracting serious people who want to work on serious, difficult problems. And how do you do that, as opposed to, you know, attracting people with free kombucha on tap? Well, we want people who want to work quantum, as you pointed out, has been a long journey. So let's talk about it as 10, because 10 years ago is when we began to scale and go forward. But we knew it would be a 10-year journey. So we want to attract the people who have that grit in mind. That is something they can do at very few places, because we are committed to solve these really hard problems. Because if you do, and I'm saying if, then the prize is massive. Okay. In a lot of places, their horizon is one, two, three years. That does not work. And so I have a huge focus on saying, what are the one or two things? Because you can't do it across all things that have that kind of hardness. And then the price is significant. And that brings value back to our shareholders, to all our employees at that point, because quantum begins to scale. Our consulting teams can take quantum into clients People are going to build quantum applications and a lot more will benefit But we need to do that and actually to some sense buffer the team that working on it for those years until it reaches commercial viability. We're in this interesting moment, I think. Clearly, we have these big names, the big upstarts, OpenAI and whatnot. But we also have some big names who are making comebacks, right? I think if we go back 10 years, 15 years ago, Microsoft may have seemed like its best days were behind it, but it's really reinvented itself in a very dramatic way with cloud computing being its adoption of AI. And I think IBM also a generation ago may have seemed like its best days were behind it. You are in the process of reinventing it for a new era. Do you see any parallels between kind of how IBM is doing it and what Microsoft has done? Is there a way to successfully pivot to become a new behemoth? Well, there are a few common principles, just to make it straightforward. Number one, if you're overly wedded to your past and you find it really hard to let go, then effectively you're forcing your engineers and your sales teams to be very, very defensive in front of clients. And that is not usually a good practice. So if you think about Microsoft with Windows or if you think about IBM with some of our legacy software from the 70s and 80s, that's a problem. But if I have incumbency, can I deeply understand what my clients want and give them what they will need for the future, even if they haven't fully realized it. Let's lean in there in addition to some of what we give them and then don't be defensive. Be willing to give up some of what is not important. The not being defensive, I think, is the biggest thing. I mean, Microsoft's unlock was when Windows was not the only thing they did. That turned out to be an incredible unlock. Yeah, Satya Nadella, head of cloud, became CEO and then they were off to the races. So if we take a big step back here, what is your long game what's the bigger strategy here are you going to grow ibm until you're part of the magnificent seven the mag a word yeah yeah mag 8 is ibm going to be in there of course there's still time for tesla to drop out so it can still be seven shots fired but really i mean are you gonna just maintain by occupying your niche and quantum how in other words is ibm going to survive another 114 years. Our goal is to grow. Grow means grow for our clients and grow for our employees. And that does mean revenue growth. It also is important to grow in terms of shareholder value because that is the commitment. We are a public company. We have to grow for our shareholders. on the S&P 500. We are now, I think, number 28. It depends on the date. 27, 28, 29, 30. So let's say we've come up 30 spots over five years. I don't know. You can begin to extrapolate from there and say that was not maybe an accident. That was a strategy. And so where does that begin to go? Let me acknowledge that as you head up towards the, instead of calling it the MAG-7, let's just call it the top 10. Maybe it becomes tougher and tougher. You're reaching ratified air over there. And it's not that those people are sitting still and not doing their own upward climb. Probably gives you enough to get a sense of our own ambition. Absolutely, Arvind. I appreciate you being so generous with your time and so generous with your insight. Christopher, Tim, pleasure talking with the both of you. Thank you. We reached out to Microsoft, which declined to comment. Stick around. Tim and I break down what we just heard from IBM CEO Arvind Krishna. Mims, I think we should tell the viewers, the listeners at home that you were especially excited for this episode. Why was that? Well, technically, you booked this episode, Tim. Oh, did I? No, I was excited for this episode. And the simple reason was, as I started to dig in, I was surprised that a company that, frankly, I had kind of written off as just slowly riding off as a consultancy, you know, they turned out to be ahead in quantum computing, which was something that was like cold fusion, right? We had always been told it was just around the corner. Suddenly, it was real. and IBM, of all companies, was doing it? That was a real mystery. It's super interesting. We had a former technological heavyweight. I mean, this was computers, right? But it's been struggling for decades, really, in a lot of ways until this hybrid cloud strategy, consulting, and his appointment in 2020 was a really big moment of change. but I think that you brought to this your usual and necessary skepticism right so it was interesting to hear you say why should we believe that quantum will be any different I'm just curious where that skepticism came from I mean beyond just your childhood right just kind of how I was uh brought up your personality your DNA right well I mean IBM has been on the vanguard of the next big thing before, right? With AI, they did such a good job selling us on Watson. I mean, you thought Watson was going to be your best buddy, right? Solving all the world's problems. IBM's made a lot of promises. Watson has been a decades-long egg-on-its-face moment for the company. It was one reason that I had kind of written it off, because I knew pretty intimately how early they were to AI and also how they had kind of made the wrong bet. I mean, I think at a fundamental level, they bet on a set of technologies that just got superseded by things that were happening at places like Google. You know, so we ended the conversation asking whether IBM can be part of the Mag 7. You know, I don't know, kick out NVIDIA or Tesla, you know, you decide, but is making this big bet now on quantum? Will that help Big Blue do things differently this time compared to its Watson experiment. I think that IBM has the potential to grow considerably. In the short term, not because of quantum, right? If we're talking about the year 2100 and we've fundamentally changed the way we do all of computing, yeah, maybe. Whatever it's called then, they could see a lot of growth at that point. But right now that growth is because of hybrid cloud and consulting as we talked. And since we recorded this interview in late July, IBM is ranked somewhere between 33rd and 40th largest company by market cap in the S&P 500. So still quite a bit of ways to go if it's ever going to catch up to the MAG 7. Yeah, but if and when that happens, you can say you heard it here first on Bold Names. Bold Names where bold things happen. I don't know about that kicker, Tim. Let's workshop that a little bit more. And that's bold names for this week. Our producer is Ariana Asperu. Our video producer is Kasha Broussalyan. And our fact checker is Aparna Nathan. Michael LaValle and Jessica Fenton are our sound designers. Jessica also wrote our theme music. Our supervising producer is Catherine Millsop. Our development producer is Aisha Al-Muslim. Chris Inslee is the deputy editor. And Falana Patterson is the Wall Street Journal's head of news audio. For even more, check out our columns on wsj.com. We've linked them in the show notes. I'm Tim Higgins. And I'm Christopher Mims. Thanks for listening.
