IBM’s $10 billion bet on what comes after AI

While many tech companies race to build ever-larger AI models, IBM CEO Arvind Krishna sees the future differently. Speaking with host Bob Safian before a live audience during New York Tech Week, Krishna explains why enterprises are overcomplicating AI adoption, what kinds of risks leaders should be taking right now, and how to weigh AI's costs against its benefits. He also shares why IBM believes quantum computing will reshape the next era of technology. Subscribe to the Masters of Scale weekly newsletter: https://mastersofscale.com/subscribe See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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• 2:11Speaker 02

  I'll say something provocative. I think foundation models are going to become commodities. I think that right now, the token price on all of these is going to go way up. It just has to to justify the capital investments.

• 2:25Speaker 03

  That's Arvind Krishna, CEO of IBM, and he has a strong metaphor for current AI systems that are just not the right size for all uses.

• 2:36Speaker 02

  I guess you could, for those in the suburbs, take your kids to school in an 18-wheeler every morning. You could go milk shopping in an 18-wheeler. Then you'd ask yourself, is it really the most effective vehicle for that? I think right now, we're using the 18-wheeler for everything.

• 2:53Speaker 03

  This is Masters of Scale. I'm Bob Safian, your host. IBM is playing a distinctive role in the AI race, not building AI models, but betting on how best to use them and on what comes after them. In this conversation, recorded in front of a live audience as part of New York Tech Week at IBM's Manhattan HQ, we dig into why Arvind thinks most enterprises are using an 18-wheeler for every task, plus what kind of risk-taking businesses need to take right now, how to think about costs versus benefits when implementing AI, IBM's big bet on quantum computing, and much, much more.

• 3:35Speaker 03

  Please welcome to the stage IBM Chairman and CEO, Arvind Krishna.

• 3:46Speaker 03

  Arvind, first of all, thank you for hosting us at your house. We're together as part of New York Tech Week. IBM is a New York institution. It is a global institution. As a company, it's had to continually reinvent itself, you know, from mainframes to PCs and consulting and cloud and now AI and on the cusp of quantum computing. How do you think about staying fresh as tech moves? And are there things about IBM's legacy that are an advantage versus what isn't an advantage?

• 4:23Speaker 02

  Yeah.

• 4:25Speaker 02

  The advantage is client intimacy, knowing your clients. The advantages are around trust. I don't think we have knowingly ever done anything wrong with a client's IP or data or people. Those are advantages. I think our people are incredibly technically adept. I would say they're experts in the areas where they have spent time and energy. So those are all the advantages. Now, technology keeps changing. And I'll be the first to acknowledge, sometimes we are really good at predicting where it's going to go. And we kind of get ahead of the wave and do that. You mentioned a few of those.

• 5:08Speaker 02

  The mainframe certainly, but this is now 60 years ago was one of them. I would say the IBM PC may have been another one of them. I think embracing Java in the internet era was another great one. And then you sometimes miss them.

• 5:24Speaker 03

  Hmm.

• 5:25Speaker 02

  It's not for actually a lack of knowledge. You miss them because the business model doesn't align. You don't quite know how to get the investments and the returns to come together. I would say public cloud is one of them candidly.

• 5:35Speaker 03

  Mm hmm.

• 5:36Speaker 02

  That we missed. I would actually turn around and say despite inventing the IBM PC, client server was another one that we missed. So I think it comes into, you could probably miss some.

• 5:48Speaker 03

  And you kind of know you're going to miss some sometimes.

• 5:50Speaker 02

  And you know you're going to, I think because if you don't take risk, you're never going to succeed. So part of it is, hey, I'm getting a lot from that one. I kind of want to keep my focus there. And kind of if you're not two, three years ahead of the wave, you're going to miss the wave.

• 6:03Speaker 03

  Mm hmm.

• 6:03Speaker 02

  But the whole point then is, can you do enough of those where you can be with the wave as opposed to way behind?

• 6:11Speaker 03

  Yeah.

• 6:12Speaker 02

  Because it moves so fast nowadays that if you're two, three years behind, you're not going to catch up. And so right now, we are very focused on hybrid cloud. That is sort of our answer to this, the cloud movement. And AI, where I think that our play is going to be much more of similar to a hybrid play. How do we help our enterprise clients take advantage of it fully?

• 6:33Speaker 03

  I mean, the first mover advantage is this sort of catchphrase in tech land. And I was thinking about, you know, AI and IBM Watson. And you were sort of ahead in some ways, but you didn't maybe make the splash that you wanted. Was that, was that a missed moment or is it more that? You know, you were too early, or the tech wasn't quite ready, or...

• 6:57Speaker 02

  There's always all those things. I think when we won Jeopardy with Watson, I think it woke the world up. Because I think for the first time in a long time, AI started doing something people thought it could not do. Okay? Unfortunately, it woke the world up completely. In the sense that a number of other companies started investing very heavily. Now, we had an advantage. We might have been able to succeed, but I'm not putting on 2020 vision, looking backwards. The mistakes we made were actually much more of a strategic nature. As opposed to creating building blocks, we wanted to create solutions in verticals.

• 7:41Speaker 02

  That, I think, is a mistake, as technology shows. In the beginning, it's much more...

• 7:45Speaker 03

  You went too quickly to the application.

• 7:47Speaker 02

  We went too quickly, and we wanted to make a monolithic application. Mistake number one. Mistake number two, we picked a domain, which is perhaps the hardest of them all, which is health. Mistake number three, how many IBMers sell to doctors? And how many IBMers deal with the FDA? Like, none. So you picked the wrong solution set in an industry you know nothing about,

• 8:16Speaker 02

  and with a customer you know nothing about. Other than that, it was pretty good.

• 8:21Speaker 03

  So as you, I mean, as you think about IBM today and its role in the AI ecosystem, like, what is it? I mean, you're not trying to be OpenAI or Anthropic. You're not trying to be Google or Microsoft. Like, are you ahead, are you behind? You know, how do you think about all that?

• 8:40Speaker 02

  So we are not going to be a hyperscaler, which was two of the four we mentioned, and we are not a foundation model provider. I'll say something provocative. I think foundation models are going to become commodities. By the way, not that far out. Is it a year, is it two years, is it three years? Commodities doesn't mean that they don't have value. Gold is a commodity. So is iron.

• 9:07Speaker 02

  So commodities means that there is very little switching cost to go from one to the other. Second, I think that right now, the token price on all of these is going to go way up. It just has to to justify the capital investments. You put those two together and there's going to be a huge motivation then from everybody who's using them to say, I need to optimize, I need to use each one, but in the most economic way possible. Our role is to A, let our enterprise clients do that. Two, do it in a way that is safe. And right now, there is very little demand for on-premise or smaller models, which are effectively then 100 of the cost to run.

• 9:50Speaker 02

  So I'll use the analogy in a very crude way. In the end, a automobile, which I'll include trucks into it, is an automobile at some gross level if you step back. I guess you could, for those in the suburbs, take your kids to school in an 18-wheeler every morning. You could go milk shopping in an 18-wheeler. Then you'd ask yourself, is it really the most effective vehicle for that? But if you're moving homes, which you do every seven years on average in the country, it is the most effective vehicle for that. I think right now, we're using the 18-wheeler for everything.

• 10:28Speaker 02

  And this is the transition that I'll predict will happen within 24 months. I'm not sure it'll happen within 12 months. Underlying GPU pricing, which is what all of these things run on, has doubled in the last six months on a per-hour basis.

• 10:42Speaker 03

  It's getting more expensive to use these tools.

• 10:45Speaker 02

  And right now, when you're pre-public, it's okay to lose money because you're gearing towards a number of customers. I think you've seen this before, right? It used to be called eyeballs, and then it suddenly became the economics are important. So I think we're right maybe a year from that point.

• 11:01Speaker 03

  I mean, you said something at the IBM Think event a few weeks ago. You said it's day zero of the AI revolution. And I think for a lot of folks, it feels further along than that. I mean, you've got, you know, trillion-dollar AI companies. A lot of business leaders worry that they're falling behind. Does day zero mean like you're not too far behind? You don't have to rush too much? Or what do you mean by that?

• 11:27Speaker 02

  So first, Let me be clear, because I can sound a little bit cynical about the economics, and I actually am. That said, I think AI is an incredible productivity tool. I think those who don't take advantage of it will be perpetually disadvantaged compared to those who do. So let me begin by saying that. It's going to optimize how you market, it's going to optimize how you write code, it's going to optimize enterprise operations, it's going to optimize how you sell, it's going to optimize how you get your daily work done. So there is no question about it. It's going to make a profound and deep impact on all of those things. By day zero, I mean, it's time to sit down, take it seriously.

• 12:11Speaker 02

  You're not in the experimentation phase, so this is not like you're in high school. Day zero, the race is about to start. Put yourself in the blocks and start sprinting. But by that, I mean, take three, four, five things, not a hundred, and learn how to do them at scale. Because that'll teach you how do you get all your change management done? How do you get your data organized? How do you really get people motivated to change a process? So do a few things at scale, learn how to do that really well. Now do ten, and then give yourself the confidence to do the next twenty.

• 12:48Speaker 03

  I mean, there's this expression that's used to talk about the economy these days, that's a K-shaped economy, you know? Some households do great and some don't as well. Sometimes I get the sense that when it comes to AI, we're sort of having K-shaped businesses, that like, the tech companies, folks like you are super excited, and then there are a bunch of other companies, and these may be clients of yours, I don't know, that are like falling behind.

• 13:12Speaker 02

  Yeah. So unfortunately, I think corporate performance is even more of a differentiated K.

• 13:21Speaker 02

  If you look at corporate performance, actually, it tends to be a 2080 rule, more of a power law. Then the K is really more of a 5050, I think, if I follow the economists correctly. Here is more of a 2080. And so 20% get it, they go forward, they're jumping into it. They kind of are going to get their returns. And 80% are either not getting a return or don't quite know what to do. If you're in that 80, figure out what is it that you should do. And it probably doesn't matter where you start, as long as you're starting to try to do it at scale, to make a real difference to your bottom line.

• 13:57Speaker 03

  So what you're saying, though, is like, you don't have to know what to do? Like, it's better to pick something and go than to just be like, I'm not sure what to do?

• 14:07Speaker 02

  Like, I have a client walk up to me and say, "Look, I get it that I need to do it, but I don't have the right people on my team. Can you give me a deep AI expert, somebody who's kind of done their PhD in AI?" And I looked at them and I said, "Actually, I recommend we give you somebody from a domain who doesn't really know the depth of AI, but who understands the difference AI could make to your domain. So you don't need to know what to do, because those domain experts exist in every company. Find that 20 or 30% of them who are motivated to say, 'I want to learn a new way to do things.'" So I think curiosity, willingness to adapt is more important. I think we're getting hung up on, "I need to know AI like a PhD in computer science." I think that's the wrong thing.

• 14:51Speaker 02

  Because that's for the inventors of AI. That's not needed for the deployers of AI.

• 14:55Speaker 03

  There's also, you know, this idea that AI is going to save me a lot of money. It's going to be very efficient for me. And I think for a lot of businesses, when they start implementing, those results don't necessarily come. Now, you guys have talked about that you've, you know, unlocked four, four and a half billion dollars of efficiency from AI. So you're doing something. But there are also these, like, hidden costs, as you mentioned, tokens. Like, how do you balance what are the costs versus the efficiency? And what you should be expecting?

• 15:28Speaker 02

  Yeah. So this was my point of scaling. I would probably turn around and say that for our first six months to a year, we were probably spending more than we were saving. Because if you think about you're putting a couple of hundred engineers to work at it, that's an incremental cost. The underlying infrastructure, aka the tokens, if you're doing it on public, is an added cost. There's opportunity costs also of not doing other things with these people that could have resulted in revenue. That's a cost. Now, once we learn a rinse and repeat method that you're not doing it across two or three, but across ten or twenty, when you're saving a billion dollars a year, well, that's a lot more than the cost of a couple of hundred people.

• 16:11Speaker 02

  After year two, we were definitely getting a return that was 10x compared to what we were spending. And now at year four, we'll be, I think, over five billion from a baseline of 22 spent. So that's not an incremental five over last year. That's an incremental saving compared to our year-end 22 spending. So that is tremendous. That is more than enough to offset any extra expense.

• 16:37Speaker 03

  There was a CEO I was talking to about some of these issues. We were talking about the inexact nature of some of the outputs you get from AI, right? I don't want to call them hallucinations, but you know, the things that don't go the way you want.

• 16:50Speaker 02

  So unlike humans, right?

• 16:52Speaker 03

  So unlike humans. What he was saying was that the money that he was saving by having his engineers use AI, that on the few cases where it was wrong, he had to spend so much trying to find what was wrong and fix it, that he wasn't actually coming out ahead.

• 17:11Speaker 02

  Yeah. So I actually think that that is an edge case of how you use AI that I think is actually wrong. I think that you should try to use AI in a case where you're not going to have to go undo six months of work or undo having spent hundreds of millions. Take customer service. If it gives a wrong answer, you got to undo one customer service answer. Then you can put all kind of evaluations and checks. So AI can check itself to make sure that you're not like way off in the wild. Okay, it may be slightly off, but you're not way off. So you can put checks and balances, and this is the sophistication of how you use it.

• 17:53Speaker 02

  So when we use it, for example, for our software developers to help them code, I don't think they realize it. We actually have checks built in to make sure that what it's suggesting is not absolutely crazy. Right?

• 18:06Speaker 03

  Right. But don't you, I mean, as with a human worker, you have to expect that sometimes.

• 18:11Speaker 02

  It will go wrong.

• 18:12Speaker 03

  It will go wrong.

• 18:13Speaker 02

  Why can't it turn around? If I look at customer service, I think the stat which would be a good one is eighty-five percent of the time the human people get it right. And fifteen percent of the time. And I say, of course, humans get angry, humans get pissed off, humans will not like the tone of the person on the other end. If it's a call, humans are sometimes overconfident. I'm sure we all remember things perfectly, right? I mean, a perfect.

• 18:34Speaker 03

  You and I do.

• 18:35Speaker 02

  I'm not sure. Customers have never told us that we are completely in the wrong and remember it completely. So humans have all those too. I think AI is at least if you keep it constrained to some extent, is probably ninety-five percent correct. So the evals I'm talking about is more like saying, when you think you're way off, punt it to a human. Don't try to venture into the underconfident range.

• 19:01Speaker 03

  But I mean, but the AI is always confident, right?

• 19:03Speaker 02

  After that, one, no, it's not actually. You'd be surprised. If you tell it, don't pretend to be confident, you will get from it that, hey, I'm not quite sure that this is as this thing. And you can put something to check it. Who's rewarded on actually pointing out the others' mistakes? So now you have.

• 19:20Speaker 03

  So this is the, this is the advantage of having multiple agents, multiple.

• 19:25Speaker 02

  Multiple models.

• 19:26Speaker 03

  Multiple models working, working at the same time.

• 19:29Speaker 02

  Just like humans, you put four eyes. We call it four eyes in software development. You put two people. One is coding, one is checking their work. Just like the models, one is doing something, the other is checking its work.

• 19:39Speaker 03

  And, and when, but when you have two models instead of two people, does that mean that you need fewer people? I mean, that, that is one of the, you know, you, you got some grief a few years back for saying, you know, your, your back office was going to get smaller, which is, seems like it's. Like small change compared to the things that some other CEOs are saying right now. Do you think there's going to be a lot of?

• 20:03Speaker 02

  I think this is the part, so I'll address both parts of the question because it's an and it's not a. So our software developers are probably forty percent more productive today than they were two years ago. So I'm not saying it's a one month over two years, they're forty percent more productive. So you could turn around and say, that means you need forty percent fewer developers. We actually tripled our college level entry hiring this year, tripled compared to last year.

• 20:32Speaker 02

  So you say, wait, that seems off. No, because this is what people are missing. If my cost of software development is going down, that means we can make products that were not economically affordable three years ago. If we can do those, we can get more revenue at an appropriate margin. So why wouldn't I get more people? Because these are value creating. Then there is the twenty percent, I'll call it, is what you need to run the operation. So is it compliance? Is it accounts payable? Is it procurement? Is it all those things? It's not going to go down to zero, but I would not be surprised if about thirty percent of the total headcount in those areas is not needed within a few years.

• 21:16Speaker 02

  That's the statement I had made and I'm actually still consistent. Note, I just said we tripled our entry level hiring. So while there is some decrease on this side, in about twenty percent of the enterprise, there is a big increase in the remaining eighty percent of the enterprise. So I actually think net will have increased demand for jobs, but there is some displacement, which is always a little bit painful.

• 21:38Speaker 03

  And always happens with new technology.

• 21:40Speaker 02

  And always has happened with new technology.

• 21:43Speaker 03

  What kind of responsibility do you feel like you have? Do you think other CEOs have helping to ameliorate the displacement? It's inevitable. You know, tech folks are very excited about the future because they're beneficiaries of it, but not everyone is a beneficiary in that way.

• 21:59Speaker 02

  Well, actually, I think that if we can get five to ten points of productivity in every enterprise around the planet, everybody's a beneficiary. Tech may be the early, early beneficiary, but I will note, everybody in tech right now is losing money in it. So we can claim, is it going to be a long-term beneficiary or not? I think there's open questions in that. I think everybody is going to be a beneficiary. I think that in our societies, at least in the West, the responsibility of business leaders is to provide an opportunity. So we want to help our people get upskilled. We want to help our people get reskilled. We want to open up that there are other opportunities or jobs. We can't force them to do any of that. But then it's on them, do they want to take advantage of those opportunities? And step up to do it?

• 22:44Speaker 02

  And I would say that answer has always been about 50 50. Some do, but a lot of people say, I don't want to get retrained. I want my old job. Okay, I'm sorry, that's not going to happen.

• 22:54Speaker 03

  And and those folks, that ends up being the responsibility of government and society, not necessarily of the business.

• 23:02Speaker 02

  Correct. We try to be compassionate. We don't force people out like in a day. But if over six months or nine months, they are not willing to learn the appropriate skills where they're needed, that's actually bad for the other 90% who are around them.

• 23:16Speaker 03

  Still ahead, why Arvind thinks the math behind today's AI bubble just doesn't add up? The cybersecurity question keeping his team up at night? And IBM's $10 billion bet on quantum? Stay with us.

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• 25:21Speaker 03

  Welcome back to Masters of Scale. You can find this conversation and much more on our YouTube channel, and be sure to check out the link in the show notes to subscribe to our newsletter.

• 25:35Speaker 03

  Are there signs of a bubble that you see in different places?

• 25:38Speaker 02

  If I take all the verbal promises, and we say there's 125 gigawatts of AI data centers that are going to come online in the next two to three years, that's the eight to 12 trillion of CapEx in total. Not in one year, in total. That is where I come to. I don't see the economics of that at all, because that would imply close to a trillion dollars of profit, which is, in the best case, that means four trillion of more revenue. Okay, where exactly is that going to come from?

• 26:11Speaker 03

  The math doesn't add up for you.

• 26:13Speaker 02

  Now, will it really, really work out well for at least half of them? Yes. Some are going to thrive, but some will disappoint. So, and I don't think, in a commodity world, there probably isn't space for a dozen foundation models. Is there space for three or four, probably? But since there's a dozen being run after globally, it tells you that... They're not all going to work out.

• 26:40Speaker 03

  So I'm going to ask you a super basic technology question, which will maybe reveal something about me, but may help those in the room. What is the difference between a data center and a mainframe? I mean, aren't they both buildings with a lot of boxes in them?

• 26:58Speaker 02

  So first, for the few geeks in the room, a mainframe is actually one box.

• 27:07Speaker 02

  But a mainframe is designed differently. When we say data centers nowadays, what people are intuitively implying is there is a collection of similar boxes, hundreds, thousands, tens of thousands, maybe hundreds of thousands of them in a single data center. And the work is such that you can divide it up amongst all of these, and they can talk to each other if they need to collaborate. That's the network or the optics that does all that. That's a data center. A mainframe, while it could be used in that context, rarely is. A mainframe is really useful when you have one piece of work that has an incredible volume. Example, airline reservations.

• 27:52Speaker 02

  If we sell you the seat, you probably don't want to sell the same exact seat on the same flight to somebody else. That would be inconvenient. That would be. So that is a different kind of workload than you asking an AI model a question, and Joe asking it a question, and me asking it a question. That can be divided up because it doesn't need to know all the three answers. So the work is inherently can be divided or parallelized.

• 28:17Speaker 03

  Part of the reason I ask is because, you know, IBM is sort of the mainframe shop, the OG mainframe shop, right? And there was a time where mainframes were sort of, seemed like they were, I don't know, passe. Everything was going to the cloud. And that has shifted. Like, suddenly, they're back. I'm sure the mainframe people don't like the idea of saying they're back, but... But.

• 28:38Speaker 02

  So I remember in 1993, there was, I think it was Time Magazine, where they were showing a mainframe dressed up as a dinosaur. And it was called the death of the mainframe. That was only 34 years ago. And then every 10 years, people talk about the death of it. I think you've got to be a bit more astute. What is the workload that is great for a mainframe? What is the workload that's not good for a mainframe? I really am a believer in fit for purpose. The same way as a GPU is probably not ideal for running your smartphone, because you kind of want your battery to last all day, not be over in three minutes. So there is a fit for purpose that is underneath these things. Where do you do AI training? That's one kind. Where do you do inferencing? That's the second kind.

• 29:20Speaker 03

  Right.

• 29:20Speaker 02

  Where do you do web serving or streaming?

• 29:23Speaker 03

  And well, and if you want to keep things secure, you want to have them on your own premises.

• 29:27Speaker 02

  Sovereignty also comes into play, because especially if you're outside the US, people care deeply about which government has control over the tech stack. All of those things come into play for where do you want to run things?

• 29:40Speaker 03

  IBM recently announced a $5 billion initiative called Project Lightwell to identify and fix AI vulnerabilities in the open source world. And that was reportedly triggered by Anthropic's release of Mythos. What did you see that sparked this at that time? And what do you think people sort of misunderstand about cybersecurity overall?

• 30:02Speaker 02

  So first, for the good news. At least in our case, from the things that we've been running for the last few months, Mythos didn't find anything that other models didn't and couldn't find. I'll call that the good news. Here's the bad news. We have a lot of people, tens of thousands, who are experts in using these models to try to find vulnerabilities and then go fix them. Okay, so for the expert, they could already do all this using other models.

• 30:35Speaker 03

  Hmm.

• 30:36Speaker 02

  We will completely acknowledge that Mythos is way easier to use than the past. So what it did do was it opens up the attack's surface to where I don't need one of those hundred experts to go do it. I can now do it with somebody with average skills. So that is definitely uh something to be worried about. So when Mythos came along, it's not just Mythos. The ability of these foundation models to actually help you write code, to understand code, is also there at the same time. So the same thing which could be used to exploit, we could turn around and say, can I use it to fix at least all open source? And that answer became very quick, we can.

• 31:21Speaker 02

  So we said, as opposed to only worrying about, oh my god, I got all these things and like, okay, I have my list of 10,000 of them. We said, can we do something? By the way, it's not altruistic. It is good for society, but we do intend to charge people a fair price, not a not a serious price for it. To say, can we instead turn this into a utility where people can come to us? We can be a clearinghouse so that they can get their fix after giving us the vulnerability, but we can share to others who are inside the closed set also that, hey, your friend here found a vulnerability, we're not going to tell you which friend, and we're not going to tell you where they're using it, so that that information is anonymized and protected.

• 32:07Speaker 02

  But you can actually get the same fix if you want.

• 32:10Speaker 03

  Hmm.

• 32:11Speaker 02

  And yes, we are throwing a lot of people at it, but despite throwing that many people, without using the current AI tools, it would have been impossible for us to go about saying that if you give us a piece of open source, we can actually give you what is in our belief a very well-constructed patch of fix against that vulnerability.

• 32:32Speaker 03

  I mean, it's it seems like in this AI world, sort of cybersecurity is like, my AI has got to be better than your AI, right? Like than the attackers are using.

• 32:43Speaker 02

  It's the old, I think it was really sudden, right? Why do you rob banks? Well, that's where the money is. Why are you attacking cyber infrastructure? Well, today, that's where the data is, which is where the money is. That's why I began by saying the good news is that it's not really a brand new. The bad news is simply it'll be done faster. So nation states have been doing this for decades. But you would say three or four nation states were capable of doing it. Maybe that opens up to a couple of dozen. That means more.

• 33:11Speaker 03

  And I guess it means organizations that might otherwise not have been targets. Smaller, midsize, it's the bar.

• 33:20Speaker 02

  Potential function has come down, so it's easier to target more.

• 33:24Speaker 03

  Right, so we all have to be a little bit more prepared, even if, even before we reach the scale of.

• 33:30Speaker 02

  I would turn down and say, if you don't think you're protecting yourself, it is only a matter of time.

• 33:37Speaker 03

  It will come.

• 33:40Speaker 03

  Earlier this year, the IBM Institute for Business Value released a provocative report called Enterprise in 2030, and citing the big bets that CEOs needed to be making. And one of those bets was about quantum computing. Now, we've talked here, most business leaders are struggling to adapt to AI. You've partnered with the US government on a new quantum foundry, investing $10 billion in a large-scale commercial quantum computer. Why go all in on something even harder to understand and control than AI?

• 34:15Speaker 02

  So let's go back to your very first question. If you can get ahead of the curve, and if what you're doing is hard enough that you actually have a couple of years' advantage. Our industry, the tech industry, has shown that you can create outsized returns for yourself and outsized returns for your clients in doing that. We felt that quantum is going to be one of those. We actually came to that recognition many years ago. Then the question became, can we do the hard science it takes to be able to make progress? I would say earlier this year, we convinced ourselves of that. The evidence of that is both in our $10 billion investment, because that means we expect to see a real return on it,

• 35:00Speaker 02

  as well as in the government agreeing to invest, because that is a sign that they did their homework and agreed it is now time to scale this as an industry. So I think you should think of quantum as doing the following. CPUs, we've had them for 60 or 70 years, do a lot of great problems, right? GPUs came around and did a different kind of problem. They did matrix math that allowed AI and other things to happen. But in some sense, it's not that CPUs couldn't do it. They were 10,000 times slower to do it. So last summer, summer of '25, they could simulate a five-atom molecule. I'll be honest, a five-atom molecule, a really good computational chemist, if it's a simple molecule,

• 35:45Speaker 02

  could probably solve by hand. Maybe an expert, but they could do it by hand. So you'd say, okay, your quantum can do it, but who cares? Last winter, November, December, they could do a 300-atom molecule. That's good progress. Now you're getting beyond what you can do by hand, but you could do that on a normal supercomputer pretty easily. So you say, okay, you still haven't told me that this is interesting. In April, they did 12,000 atoms. They're now getting into the protein realm. When you're in the 10,000 to 30,000 to 40,000 atom range, you're in the protein realm. So this was a piece of a protein called tryptine.

• 36:27Speaker 02

  We're pretty sure that in another month or two, we'll be at double that range, which means you can solve tryptine. If you can understand the properties of a protein using a few minutes of computation, you can now understand which molecule, AKA your drug, may bind to it to stop its bad behavior. You've now opened up a new pathway. Possibly for health, which didn't even exist.

• 36:55Speaker 03

  I mean, when you when you give that example, like, part of the amazing part of AI has been the exponential pace that it keeps improving. And as you give that example about quantum, you're implying that that is moving at that similar kind of pace. It's we're getting that much closer to not being science fiction?

• 37:15Speaker 02

  Correct. So I think we're solving problems now, whether it's in. I talked about biology and molecules because I think most people intuitively get that that's a hard problem. But there are problems, things like fluid dynamics, aerodynamics. All of these are problems that are now coming right about now within the range of quantum computers to solve. Understand where quantum may be in two or three years, what kind of algorithms you may need to develop. So when it is there, you don't then spend two years doing all that. That's what I would recommend to people to do today. But that makes it an easy and that's not a a choice then.

• 37:58Speaker 02

  It's not a dilemma to say which of the two do you do?

• 38:01Speaker 03

  And this this this report from the IBV, the enterprise in 2030. Do you know what IBM will look like in 2030?

• 38:10Speaker 02

  We want to be known for not just being technologically innovative. I think that we had that reputation. We weren't always great at making that easily accessible to clients. So I think we want to be in the position where we are bringing all of our innovation to clients in a way that they can easily consume. That's one big piece. Two, we were about 20% software in 2019. We are now about 45%. I think that number will keep going up at a few percent a year. So we'll be much more in that space than anything else. I think we'll become known as one of the exemplars of how we are deploying AI and agents to not just improve our own business,

• 38:55Speaker 02

  but to help improve our clients' business.

• 38:58Speaker 03

  I wanted to ask you one one last thing here. A big focus for you is making IBM's culture more willing to take risks. For the business leaders who are here and who are listening or watching at home, like, what advice do you have on how you make that adjustment?

• 39:18Speaker 02

  So I would tell everybody, the most risky route is taking zero risk. What happens in any business that takes no risk? That means that you're kind of trying to extract profit, or what an economist would call rent, from what you already have. But that means you're giving everybody else the opportunity to clone you or copy you, be innovative from the bottom, so they will pick off the most profitable parts of your business. So now you have a declining profit pool. Now you begin to have declining profit pools, and if you're conservative by nature, you go to invest even less because you're getting smaller.

• 40:02Speaker 02

  So you're going to accelerate your decline, and you'll be approaching a cliff without realizing it. Go read history and see how many companies behave like that when you sit and watch them. Right? It takes about five years and you begin to hit a decline, and then you're going along, and then five or 10 years later, somebody comes along and either bites you up and chews you up and spits you out for parts, or you actually just go and fall over into oblivion. So I call that, that's the most risky. So how do you maintain enough innovation that you're actually growing? All innovation does not pay off. Innovation is risky by nature.

• 40:42Speaker 02

  So you got to say, how do I manage it where I'm generating enough profit to pay for innovation, recognizing that not all of it will have a long-term return, but enough of it should. They will pick off the most profitable parts of your business. So now you have a declining profit pool. And if you're conservative by nature, you go to invest even less because you're getting smaller. So you're going to accelerate your decline, and you'll be approaching a cliff without realizing it. Go read history and see how many companies behave like that. It takes about five years and you begin to hit a decline, and then five or 10 years later, somebody comes along and either bites you up and chews you up and spits you out for parts,

• 41:27Speaker 02

  or you actually just go and fall over into oblivion. So I call that, that's the most risky. So how do you maintain enough innovation that you're actually growing? All innovation does not pay off. Innovation is risky by nature. So you got to say, how do I manage it where I'm generating enough profit to pay for innovation, recognizing that not all of it will have a long-term return, but enough of it should. Enough, that's the nature of risk.

• 41:58Speaker 03

  I mean, people say like, yeah, I'm fine with taking a risk as long as I don't lose anything.

• 42:02Speaker 02

  Well, this is, humans are incredibly loss averse. If you're given the option of you can gain $10 but lose one, nobody wants to take that bet because they'd rather not lose one, even though you can gain 10. Okay, so we recognize that that's human nature. That means the point of leadership in at least corporations is, how do you counteract that base human nature? Because people are not going to take risk if they think their jobs are on the line or if you chastise them in public. So what I always go to is, I want your 50% probability win. I don't want the 90%, because if you tell people it needs to be 90% certain, that's no risk. So you have to tell people, hey, it's okay that one or two times you won't meet the deadline. So you start building a bit of a buffer in to say,

• 42:47Speaker 02

  okay, I got it that they won't meet the deadline, but if you're doing six things and four work out, fine.

• 42:52Speaker 03

  And I guess you have to yourself own up to the things that maybe didn't work out the way you want. As you were talking about, you know, Watson maybe not working out the way IBM might have ideally wanted.

• 43:02Speaker 02

  Plenty of things. I mean, I can think about our digital sales channel, something we're still working on. We're probably on our fourth trial in my tenure. But you're not going to give up. It did work out, means you got to not just say, go harder at it. Not working out means think deeply about what is there structurally inside that didn't make it work? Or is it that the market is different than what you presumed? You got to sort of pressure test all those things and keep trying.

• 43:26Speaker 03

  Well, Aravind, this has been great. Thank you so much for doing it.

• 43:30Speaker 02

  It's my pleasure. Look, talking on these topics, I could probably go on all day. Thank you, Bob.

• 43:39Speaker 02

  Thank you.

• 43:39Speaker 03

  Thank you.

• 43:46Speaker 03

  I found Arvind unexpectedly candid from acknowledging IBM's strategic lapses with Watson to a looming AI data center bubble. And I appreciated that he could explain quantum computing's impact without getting too deep into the complex science of it all. What sticks with me most is his emphasis on risk, as he puts it, that the biggest risk is taking no risk. That's particularly true in times of tech transition like right now. Thanks again to Arvind for joining us. I'm Bob Safian. Thanks for listening.

• 44:29Speaker 00

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• 45:10Speaker 00

  Before we get to the rest of our conversation today, I want to spend a moment sharing with you about Masters of Scale Summit, which returns to San Francisco this October 20th to 22nd. So many of us feel overwhelmed right now. It feels like so much of the world is careening and we don't have power. We don't have agency. That's the thing about the Masters of Scale Summit. It's really designed to remind us that we do have agency, agency to lead, agency to design, to build the relationships and the partnerships that empower us and to make the change that we want to see. We program a stage, we curate an audience, we set a schedule that builds in time for these relationships to be made

• 45:52Speaker 00

  and to grow, so that by the time you leave Summit, you are fired out of a cannon, ready to run through walls to do better in the world with the agency that you actually have. I hope to see you there. Visit mastersofscale.com/summit.

• 46:13Speaker 04

  Humans will never be more intelligent than AI.

• 46:16Speaker 05

  There's going to be two types of companies. Those are great at AI and those that went out of business because they weren't.

• 46:21Speaker 06

  How do we build a future that is human centered? I'm Rana Elkhayubi and on my podcast, Pioneers of AI, we answer that question and so many more. As an AI scientist, entrepreneur and investor, I know what it takes to build AI that works for everyone. Every week I sit down with the pioneers shaping our future and we take you behind the scenes of the AI that's transforming our lives.

• 46:51Speaker 06

  Find Pioneers of AI wherever you tune in.

• 46:57Speaker 03

  Masters of Scale is a Wait What original. Our executive producer is Eve Trow. Senior supervising producer is Trishu Boveda. Associate producer is Masha Makutonina. Video editor is Noah Wolstein. Senior talent executive is Stephanie Stern. Mixing and mastering by Aaron Bastinelli and Brian Pugh. Original music by Ryan Holiday. Our head of podcasts is Lital Malad. Special thanks to Jodin Dorsay, VP of Live Events. Visit mastersofscale.com to find the transcript for this episode and to subscribe to our newsletter. And be sure to check out our YouTube channel.

• 47:48Speaker 03

  Hey listeners, Bob here. If you listen to Rapid Response on Masters of Scale, you may be missing half the show. Because every Friday, we release a second Rapid Response exclusively in the Rapid Response feed. The guests and topics are just as compelling and timely. From Ford CEO to NASA's administrator, to the lessons from the devil wears Prada. It takes about 10 seconds to find. Just search Rapid Response wherever you listen to podcasts and hit follow to make sure you never miss an episode. I hope to see you there.