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

Note: Transcripts are automatically generated from episode audio, and are not fully corrected for spelling, grammar, and formatting.

KRISHNA: 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.

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

KRISHNA: 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 are using the 18-wheeler for everything.

SAFIAN: This is Masters of Scale.

[THEME MUSIC]

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 cost versus benefits when implementing AI, IBM’s big bet on quantum computing, and much more.

Please welcome to the stage IBM Chairman and CEO Arvind Krishna. 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 from mainframes to PCs and consulting and cloud and now AI and, on the cusp, 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?

KRISHNA: Yeah. Look, 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 that sometimes we are really good at predicting where it’s going to go, and we kind of get ahead of the wave. You mentioned a few of those. The mainframe certainly, though 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 sometimes you miss them.

It’s not actually for 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 was one of them, candidly, that we missed. I would also turn around and say, despite inventing the IBM PC, client-server was another one that we missed. So I think it comes down to the fact that you probably will miss some.

SAFIAN: And you kind of know you’re going to miss some sometimes.

KRISHNA: I think so, 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 if you’re not two or three years ahead of the wave, you’re going to miss the wave. But the whole point then is, can you do enough of those where you can be with the wave as opposed to way behind?

SAFIAN: Yeah.

KRISHNA: Because it moves so fast nowadays that if you’re two or 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 the cloud movement, and AI, where I think our play is going to be much more similar to a hybrid play. How do we help our enterprise clients take advantage of it fully?

Copy LinkWhat IBM learned from catching and missing technology waves

SAFIAN: The first-mover advantage is this sort of catchphrase in tech. And I was thinking about 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 a missed moment, or is it more that you were too early, or the tech wasn’t quite ready?

KRISHNA: There’s always all those things. I think when we won Jeopardy with Watson, it woke the world up because, for the first time in a long time, AI started doing something people thought it could not do. 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 20/20 vision looking backward. 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. That, I think, is a mistake, as technology shows.

SAFIAN: You went too quickly to the application.

KRISHNA: We went too quickly, and we wanted to make a monolithic application. Mistake No. 1. Mistake No. 2, we picked a domain that is perhaps the hardest of them all, which is health care. Mistake No. 3, how many IBMers sell to doctors, and how many IBMers deal with the FDA? None. So you pick the wrong solution set in an industry you know nothing about and with a customer you know nothing about. Other than that, it was pretty good.

Copy LinkWhy IBM is betting on AI orchestration not foundation models

SAFIAN: As you think about IBM today and its role in the AI ecosystem, what is it? You’re not trying to be OpenAI or Anthropic. You’re not trying to be Google or Microsoft. Are you ahead? Are you behind? How do you think about all of that?

KRISHNA: So we are not going to be a hyperscaler, which was two of the four you 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. Go to the commodity markets. So is iron. Commodity 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 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-premises or smaller models, which are effectively one-hundredth of the cost to run. So I’ll use the analogy in a very crude way. In the end, an automobile, which I’ll include trucks in, 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 this country, it is the most effective vehicle for that. I think right now we are using the 18-wheeler for everything. 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.

SAFIAN: It’s getting more expensive to use these tools.

KRISHNA: And right now, when you’re pre-public, it’s OK to lose money because you’re gearing toward 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 are maybe a year from that point.

Copy LinkWhy business leaders should treat AI as day zero

SAFIAN: 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. You’ve got trillion-dollar AI companies. A lot of business leaders worry that they’re falling behind. Does day zero mean you’re not too far behind, you don’t have to rush too much, or what do you mean by that?

KRISHNA: Yeah. First, let me be clear, because I can sound a little 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 and take it seriously. You’re not in the experimentation phase. 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 100, and learn how to do them at scale, because that’ll teach you how to 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, then do 10, and then give yourself the confidence to do the next 20.

SAFIAN: There’s this expression that’s used to talk about the economy these days, a K-shaped economy. Some households do great and some don’t do as well. Sometimes I get the sense that when it comes to AI, we’re sort of having K-shaped businesses, that the tech companies and 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 falling behind.

KRISHNA: So unfortunately, I think corporate performance is even more differentiated. If you look at corporate performance, actually it tends to be a 20-80 rule, more of a power law than a K. K is really more of a 50-50, I think, if I follow The Economist correctly. Here it’s more of a 20-80. And so 20 percent get it, they go forward, they jump into it, they kind of are going to get their returns, and 80 percent are either not getting a return or don’t quite know what to do. If you’re in that 80, figure out what it is 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.

SAFIAN: So what you’re saying, though, is you don’t have to know what to do. It’s better to pick something and go than to just be like, “I’m not sure what to do.”

KRISHNA: I had 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 has 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 percent who are motivated to say, “I want to learn a new way to do things.” So I think curiosity and willingness to adapt are more important. I think we are getting hung up on, I need to know AI like a PhD in computer science. I think that’s the wrong thing, because that’s for the inventors of AI. That’s not needed for the deployers of AI.

Copy LinkHow to measure real AI returns beyond the hype

SAFIAN: There’s also 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 it, those results don’t necessarily come. Now, you guys have talked about how you’ve unlocked four, four and a half billion dollars of efficiency from AI. So you’re doing something. But there are also these hidden costs, as you mentioned, tokens. How do you balance the costs versus the efficiency and what you should be expecting?

KRISHNA: Yeah. So this was my point about scaling. I would probably tone it down 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 it, you’re putting a couple hundred engineers to work on it. That’s an incremental cost. The underlying infrastructure, a.k.a. the tokens, if you’re doing it on public infrastructure, is an added cost.

There’s opportunity cost also in not doing other things with these people that could have resulted in revenue. That’s a cost. Now, once we learned a rinse-and-repeat method, you’re not doing it across two or three use cases, but across 10 or 20. When you’re saving a billion dollars a year, well, that’s a lot more than the cost of a couple hundred people. 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 spend. So that’s not an incremental five over last year. That’s an incremental savings compared to our year-end ’22 spending. So that is tremendous. That is more than enough to offset any extra expense.

SAFIAN: 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 the things that don’t go the way you want.

KRISHNA: Unlike humans, right?

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

KRISHNA: Yeah. So I actually think that is an edge case of how you use AI that is wrong. I think you should try to use AI in cases where you’re not going to have to undo six months of work or undo having spent hundreds of millions. Take customer service. If it gives a wrong answer, you’ve got to undo a customer service answer. Then you can put all kinds of evaluations and checks in place. So AI can check itself to make sure you’re not way off in the wild. It may be slightly off, but you’re not way off. So you can put checks and balances in place, and this is the sophistication of how you use it. So when we use it, for example, for our software developers to help them code, I don’t think they realize it, but we actually have checks built in to make sure that what it’s suggesting is not absolutely crazy, right?

SAFIAN: I mean, as with a human worker, you have to expect that sometimes it will go wrong.

KRISHNA: I kind of turn it around. If I look at customer service, I think the stat that would be a good one is that 85% of the time, the human people get it right, and 15% of the time they don’t. Of course, humans get angry, humans get thrown 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, we have perfect records.

SAFIAN: You and I do. I’m not sure everyone else does.

KRISHNA: Our spouses have never told us that we are completely in the wrong and remember it completely differently. So humans have all those issues, too. I think AI, at least if you keep it constrained to some extent, is probably 95% correct. So the evals I’m talking about are more like saying, when you think you’re way off, punt it to a human. Don’t try to venture into the underconfident range.

SAFIAN: But the AI is always confident, right?

KRISHNA: No, it’s not, actually. You’d be surprised. If you tell it, “Don’t pretend to be confident,” you will get from it, “Hey, I’m not quite sure that this is the right thing.” And you can put something in place to check it that’s rewarded for actually pointing out the other model’s mistakes. So now you have multiple models.

SAFIAN: So this is the advantage of having multiple models working at the same time.

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

Copy LinkWhat AI productivity means for hiring and job displacement

SAFIAN: But when you have two models instead of two people, does that mean that you need fewer people? I mean, that is one of the… You got some grief a few years back for saying your back office was going to get smaller, which seems like small change compared to the things some other CEOs are saying right now. Do you think there’s going to be a lot of job displacement?

KRISHNA: So I’ll address both parts of the question because it’s a and it’s not. Our software developers are probably 40% more productive today than they were two years ago. I’m not saying in one month. Over two years, they’re 40% more productive. So you could tone it down and say, “That means you need 40% fewer developers.” We actually tripled our college-level entry hiring this year, tripled compared to last year.

So we 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 20%, I’ll call it, that 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 30% of the total headcount in those areas is not needed within a few years. 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 20% of the enterprise, there is a big increase in the remaining 80% of the enterprise.

So I actually think, net, we’ll have increased demand for jobs, but there is some displacement, which is always a little painful.

SAFIAN: And always happens with new technologies.

KRISHNA: And always has happened with new technology.

SAFIAN: What kind of responsibility do you feel like you have, and do you think other CEOs have, in helping to ameliorate the displacement that’s inevitable? Tech folks are very excited about the future because they’re beneficiaries of it, but not everyone is a beneficiary in that way.

KRISHNA: Well, actually, I think that if we can get five to 10 points of productivity in every enterprise around the planet, everybody’s a beneficiary. Tech may be the early beneficiary, but I will note everybody in tech right now is losing money on it. So we can claim, is it going to be a long-term beneficiary or not? I think there are open questions in that. I think everybody’s 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 re-skilled. We want to open up other opportunities or jobs. We can’t force them to do any of that. So then it’s on them: Do they want to take advantage of those opportunities and step up to do it?

And I would say that answer has always been about fifty-fifty. Some do, but a lot of people say, “I don’t want to get retrained. I want my old job.” OK, I’m sorry. That’s not going to happen.

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

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

Copy LinkWhy the economics of the AI buildout may not hold

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

KRISHNA: 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 $8 trillion to $12 trillion of capital expenditures in total, not in one year, in total. That’s 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, in the best case, means $4 trillion more of revenue. Where exactly is that going to come from?

SAFIAN: The math doesn’t add up for you.

KRISHNA: It doesn’t add up. Now, will it work out well for at least half of them? Yes. Some are going to thrive, but some will disappoint. And I don’t think, in a commodity world, there is space for a dozen foundation models. Is there space for three or four? Probably. But since there’s a dozen being chased globally, it tells you that they’re not all going to work out.

SAFIAN: So I’m going to ask you a super basic technology question, which will maybe reveal something about me and 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?

KRISHNA: First, for the few geeks in the room, a mainframe is actually one box. But a mainframe is designed differently. When we say data centers nowadays, what people are intuitively implying 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 among 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, is really useful when you have one piece of work that has incredible volume. For example, airline reservations. If we sell you the seat, you probably don’t want to sell the same exact seat on the same flight to somebody else.

SAFIAN: That would be inconvenient.

KRISHNA: 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 three answers. So the work inherently can be divided, or parallelized.

SAFIAN: Part of the reason I ask is because IBM is sort of the mainframe shop, the OG mainframe shop, right? And there was a time when mainframes seemed, I don’t know, passé. Everything was going to the cloud, and that has shifted. Suddenly they’re back. I’m sure the mainframe people don’t like the idea of saying they’re back.

KRISHNA: I remember in 1993, I think it was Time magazine, where they showed a mainframe dressed up as a dinosaur, and it was called “The Death of the Mainframe.” It 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. In the same way, a GPU is probably not ideal for running your smartphone, because you want your battery to last all day, not be over in three minutes. So there is a fit for purpose underneath these things. Where do you do AI training? That’s one kind. Where do you do inferencing? That’s a second kind. Where do you do web serving or streaming?

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

KRISHNA: Sovereignty also comes into play because, especially if you’re outside the U.S., people care deeply about which government has control over the tech stack. So all of those things come into play for where you want to run things.

Copy LinkHow AI is changing the cybersecurity arms race

SAFIAN: 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 misunderstand about cybersecurity overall?

KRISHNA: First, 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. So for the expert, they could already do all this using other models. We will completely acknowledge that Mythos is way easier to use than in the past. So what it did do was open up the attack 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 something to be worried about.

When Mythos came along, it wasn’t just Mythos. The ability of these foundation models to help you write code and understand code is there at the same time. So the same thing that 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 a very quick, “We can.” So we said, as opposed to only worrying about, “Oh my God, I got all these things, and I have my list of 10,000 of them,” 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 usurious price, for it. Can we 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 with others who are inside the closed set also that, hey, your friend here found your wallet. 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. But you can actually get the same fix if you want.

And yes, we are throwing a lot of people at it, but despite throwing that many people at it, without using the current AI tools, it would have been impossible for us to say 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 or fix against that vulnerability.

SAFIAN: It seems like in this AI world, cybersecurity is sort of like, my AI’s got to be better than the AI the attackers are using.

KRISHNA: It’s the old Willie Sutton line, 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 brand new. The bad news is simply that 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.

SAFIAN: And I guess it means organizations that might otherwise not have been targets — smaller, midsize — the bar is going to-

KRISHNA: The potential threshold has come down, so it’s easier to target more.

SAFIAN: Right. So we all have to be a little bit more prepared even before we reach the scale of-

KRISHNA: I would turn around and say, “If you don’t think you’re protecting yourself, it is only a matter of time.”

Copy LinkWhy IBM sees quantum as the next hard advantage

SAFIAN: It will come. Earlier this year, the IBM Institute for Business Value released a provocative report called Enterprise in 2030, citing the big bets that CEOs needed to be making. And one of those bets was about quantum computing. Now, we’ve talked here about how most business leaders are struggling to adapt to AI. You partnered with the U.S. 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?

KRISHNA: 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 outsize returns for yourself and outsize returns for your clients by doing that. We felt that quantum was 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, 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 have, for 60 or 70 years, solved 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 2025, 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, could probably solve by hand. Maybe an expert, but they could do it by hand. So you’d say, OK, your quantum can do it, but who cares?

Last winter, in November and 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, OK, you still haven’t told me that this is interesting. In April, they did 12,000 atoms. Now you’re 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 trypsin. We are pretty sure that in another month or two we’ll be at double that range, which means you can solve trypsin. If you can understand the properties of a protein using a few minutes of computation, you can now understand which molecule, your drug, may bind to it to stop its bad behavior. We’ve now possibly opened up a new pathway for health that didn’t even exist.

SAFIAN: When you give that example, part of the amazing thing about AI has been the exponential pace at which it keeps improving. And as you give that example about quantum, you’re implying that that is moving at a similar kind of pace. We’re getting that much closer to it not being science fiction.

KRISHNA: Correct. I think we are solving problems now, whether it’s in biology and molecules — because I think most people intuitively get that that’s a hard problem — but there are also problems like fluid dynamics and aerodynamics. All of these are problems that are now, right about now, coming within the range of quantum computers to solve. Understand where quantum may be in two or three years and 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 people do today. Then it becomes an easy choice. It’s not a dilemma to say which of the two you do.

SAFIAN: And this report from the IBV, The Enterprise in 2030 — do you know what IBM will look like in 2030?

KRISHNA: We want to be known not just for being technologically innovative. I think we’ve had that reputation. We weren’t always great at making that innovation easily accessible to clients. We want to be in a position where we are bringing all of our innovation to clients in a way they can easily consume. That’s one big piece.

Second, we were about 20% software in 2019. We are now about 45%. I think that number will keep going up by 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, but to help improve our clients’ business.

Copy LinkWhy the biggest business risk is avoiding risk

SAFIAN: I wanted to ask you 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, what advice do you have on how to make that adjustment?

KRISHNA: I would tell everybody the riskiest route is taking zero risk. What happens in any business that takes no risk? It means you’re 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, to innovate from the bottom, and pick off the most profitable parts of your business. So now you have a declining profit pool.

Once you begin to have declining profit pools, if you’re conservative by nature, you’re going 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 chews you up and spits you out for parts, or you just fall over into oblivion. To me, that’s the most risky path.

So how do you maintain enough innovation that you’re actually growing? Not all innovation pays off. Innovation is risky by nature. So you have to ask, how do I manage it when 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? That’s the nature of it.

SAFIAN: People say, “Yeah, I’m fine with taking a risk as long as I don’t lose anything.”

KRISHNA: Humans are incredibly loss-averse. If you’re given the option that you can gain $10 but lose $1, nobody wants to take that bet because they’d rather not lose the $1, even though they can gain $10. We recognize that that’s human nature, so the point of leadership, at least in corporations, is how do you counteract that basic human nature? Because people are not going to take risks if they think their jobs are on the line or if you chastise them in public.

What I always go to is, I want to hear 50% probability wins. 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 out of two times you won’t meet the deadline.” You start building a bit of a buffer in to say, “Okay, I get it that they won’t meet the deadline, but if you’re doing six things and four work out, fine.”

SAFIAN: And I guess you have to own up to the things that maybe didn’t work out the way you wanted, as you were talking about Watson maybe not working out the way IBM might have ideally wanted.

KRISHNA: Plenty of things. I can think about our digital sales channel, something we’re still working on. We’re probably on our fourth try in my tenure, but we’re not going to give up. If it didn’t work out, that means you have to not just say, go harder at it. Not working out means think deeply about what was structurally inside it that didn’t make it work, or whether the market is different from what we presumed. You have to pressure-test all those things and keep trying.

SAFIAN: Well, Arvind, this has been great. Thank you so much for doing it.

KRISHNA: It’s my pleasure. Talking on these topics, I could probably go on all day. Thank you, Bob.

SAFIAN: Thank you.

KRISHNA: Thank you.

SAFIAN: 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: 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.