IBM CEO Arvind Krishna: Creating Smarter Business with AI and Quantum

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“On Christmas Eve 1995, author Miguel Angel Hernandez,”

best friend, murdered his sister, and took his own life by jumping off a clip.

No one ever knew why. The investigation was closed in the crime for gotten. 20 years later, Miguel returns home in an attempt to reconstruct that tragic night that marked the end of his adolescence. But revisiting the past will awaken personal ghosts.

Based on true events, the pain of others is a raw and moving novel that uses a police thriller with compelling reportage. Find the pain of others at pushkin.evm/audio books or at audible, Spotify, or wherever you get your audiobooks. Hello, hello, I'm Malcolm Gladwell, and you're listening to Smart Talks with IBM.

This season, we've been bringing you stories of how IBM works with its clients to solve complex problems. Like helping L'Oreal reimagine how scientists approach cosmetic formulation, or enabling Scuderia Ferrari HP to connect with fans in new ways. But in this episode, we're going to zoom out and look at the bigger picture. Earlier this month, I had the chance to meet the person who's shaping IBM's future.

It's CEO and Chairman Arvind Krishna. We sat down in front of an intimate live audience at IBM's New York City office and talked about his uncanny ability to anticipate where technology is heading. The future of AI and his passion for quantum computing, which he says, is as revolutionary as a semiconductor.

Thank you, everyone. Thank you, Arvind. You're a difficult man to schedule for one of these things. So, we're enormously pleased that you could join us. Let's start with AI of all these cousins, two cousins who work for IBM,

their entire career. Now we'll ask them, "What does IBM do?" And they will always give me different

confusing, complicated answers. What's your answer? What's your simple answer to that question? IBM's role is to help our clients improve their business by deploying technology. That means you're not ever gated to one product. It is what makes sense at that time. But it is about improving their business, not just giving them a commodity. Yeah. Then, to go to the next layer, I would say, we help them through a mixture of hybrid cloud

and artificial intelligence and a taste of quantum coming down the road is kind of where I would take it.

“That's what IBM is. So, your technology agnostic, in some sense.”

I'm product agnostic. I'm not technology agnostic. Yes. But if I 25 years from now, IBM could be doing things that would be unrecognizable to contemporary IBM. It is completely possible. It could be that in 25 years from now, the only software IBM does is open source. It could be that the only computing you do is quantum computers. And if I add those two, people today would say that's not the idea of today.

Isn't it even simpler to say you just IBM solves problems at the highest technical level? If you say a highest technical level, yes. Like the guy who invented the barcode, he was solving a problem. Retailers wanted to scale. Many of you may not know. It was an IBM who invented the barcode. By the way, not somebody who was a PhD, not somebody who was a deep researcher. I think it was actually a field engineer. Oh really? Yeah. And lasers were

out and you could use lasers to scan things. But they could be upside down. They could be muddy. They could be partly scrapped off. And he came over the idea of the barcode. Yeah. And that changed inventory management forever. I've been the world needs to know that IBM invented the barcode. You guys should do a better job. I'm excited to start. I am sure I'll see him over and listen to this barcode. And we'll get that idea. Tell me, you started at Thomas Watson Research Center.

What were you doing when you first started at IBM? I started in 1990. And that was the

era in which computers and networking were beginning to converge. And for the first five years,

“I was actually building networks. So let's remember, this was pre-lapped drops,”

laptops came in 92 or 93. But it was clear to us that they were going to come. So portable computing. And I spent my first five years building what today you would call Wi-Fi. We used to have these debates. Can be built. It's got to be small enough. I mean, like it can't be more than 100 grams. It was kind of hard thought. Because if it's more than that, you're on a 3000 gram laptop. I would anybody put this on. And the debates used to be, why would anybody want to walk around

untethered? Do you want to attach a big thick cable into it and sit down? Because that was the thought. That's how tomatoes worked. And I spent five years having a lot of fun building many iterations of

next 30 years were going to look like, is it possible to reconstruct what you are? What were your

predictions at that age about where the company with industry was going? It was more about where technology was going to go. I would say then where industry would go. I would have told you that networking and computers would fuse. In 1990 that was a weird thought that some researchers held by the late 90s that was obvious. Lot of became the internet. I would have told you that

“I believe that video streaming will be the primary way people will consume video.”

You said that in 1990? Absolutely. Now that didn't take five years that took 20, but it happened because you could do it technically except it was just too expensive and too cumbersome.

And if you've been in technology like in 1985 I would have told you the internet is old.

Because when I went to grad school, everyone of us had those days an apple, back, or Lisa on our desks. They were all connected by a network. We were happily sending email to people all around the country. We were doing file transfers. Okay, you had to be a little bit more aware of the technology and it didn't have a browser. That took 10 years to get the browser, that took five years to be a business. But when you see the speed and the pace of technology

in usually 10 or 15 years, the cost point and the consumerization is at a scale that you couldn't imagine 10 years ago until you've seen a few of those cycles. We did you make the leap too.

Sorry, this is fascinating. I'm curious about how far did you take that? That's a really

“fundamental thing you have gotten right in 1990. I think you have that idea. We were pretty convinced”

that what we used to think of as linear television or broadcast would become digitized. That was a given. Two, with cable already the preponderance of how people got it, that if you put packet television over cable, then that becomes the way it'll go. I fundamentally believe, actually way back 87, that on demand movies would become the way people would consume movies. So those were all things that I could have predicted. I didn't

personally work on all those. I mean, after networking, I moved on to doing other things. But those were easy to predict. If you had a conversation in those years with someone in the television industry and you gave them those predictions, did they see it, did they convince to put this?

“I'm actually going to take it back to wireless networking. I think one of the reasons”

I do what I do today, which is the intersection of business and technology, is because of what I saw happened with Wi-Fi. So you build these wireless networks. And then you say, hey, the market's going to be millions, tens of millions, billions of users. And the business looks at it and says, we think the market is confined to warehouse workers doing inventory. You can't look at them and say, why not people in their homes? Because they couldn't imagine outside how people bought things

at that time. And so I became convinced that I can't just help invent it. I got to think about now, how do you market it? To whom do you market it? What other routes? How do you make it easy enough? And that was, I mean, I'm making it simple now. That was probably a five to ten year evolution of myself in those days. You know what this reminds me of? When the telephone is invented in the 1870s, it doesn't take up to 40 years because the people running a telephone

business, they didn't want women using it because they were worried that women would gossip with their friends. They didn't understand that that's actually what telephone is, right? But it's an exact parallel. Yes, it is. You see, again and again, what is the source of that blindness? So there's a gap in other words, between the invention, the technological achievement and the social understanding of the technology. Why is it such a gap? I think that the gap is fundamental and rooted in a lot of

academic disciplines. So even channeling some of your work, though you don't intend it to be used that way, you can say a lot of things a data driven. If it is data driven, then by definition, you're looking at history. If you're looking at history, that means you're looking at existing buying patterns. If you look at existing buying patterns, you forget all of those who have created massive value in time, have all created markets, meaning they've all created new markets.

And I think that is why the world is fascinated with people like Steve Jobs, for example,

the technology, the business acumen to scale a company, and that imagination of making the market

is how you create, I think, massive value. You've got to get all three pieces going. It's not enough, in other words, you are thinking, it's not enough to invent something new. I need to make a business

“case for it simultaneously. And that's what gets you thinking along the path that leads you to this”

job. Oh, yeah, I would tell you. If you had met Orwin the 1994, and you talked about the stock market or about a balance sheet, I would have looked at you like, okay, I got those words or I can parse them, but I have no idea what they are. I'm no intuition on what they are. I couldn't tell you why it's relevant or why it's not. But then you began to think, okay, why do companies get

higher values? Okay, that's the stock. What does that capture? Well, if I have to spend working capital

and that's not the balance sheet. So you learn, I mean, I figure I'm willing to learn. I'm willing to read. However, the best way I read is to go to the balance sheets. Here, you can read the book. It's pretty darn dry. Much easier to go talk to a financial expert who's around the corner. And people are curious about what they do. They're really happy to share their expertise and over time you learn more and more and they actually become part of your network within the company.

“And that's how you can both learn and evolve yourself and actually gain the extra skills.”

You need to be a successful business leader. Do you have to unlearn or deviate from some of the

things that made you a successful scientist? I actually believe the exact opposite. Yeah, but use

what you're really good at as a foundation, but don't make it the only thing you use. So then how do you add the other skills? And there's many ways. You can have people that you trust who help you add those skills. You can gain some intuition. Maybe not the depth of expertise. I want to be deeper on certain areas of electrical engineering than I've ever going to be, let's say, in finance or marketing. But I want to be curious about those. I don't want to dismiss them. So you build

on your skills and then you have to say, but I need a complete and holistic view. So I'm going to be a little deep, not very deep on all of those. And you also got to learn to trust your intuition a little bit. Yeah. We'd have forgotten a question that I wanted to ask about the predictions

“of 1990, Ireland. What did you get wrong? A lot of things. I think that people were thinking that”

in those days and it started my phrase, but I'll come back to it. I think most people thought that the communication companies would turn out to be the winners of how networking got carried. If you all think through the 90s of the investments that were being done by, let's not take the names of all of the telecom carriers, did turn out to be the case. Actually, I think that's the business model case. The reason is they all had in their heads that you can charge people by the minute.

Because they had been doing that already, because they had been doing that for a hundred years now. And the end, the winners and networking with those are flat price. 30 bucks a month or 50 bucks a month or whatever. And that was just too much of a leap. I think it's simple. That is the most price amount of explanation for why you think they fail. No, there were a couple of other more technical things. One was written by somebody who was excited inside one of the telecom companies.

And he labeled his oracle the rise of the stupid network. So telephone people believe that the network should be really smart. The end device is dumb. If you think about the telephone, telephone is dumb. It doesn't actually do anything. It doesn't about the release. And the network is smart. It routes you. It figures out where to send it. It does that. Okay, let me show you the backwards. And the current internet is completely dumb on the inside. It just takes the bits

and shrubs them out of the other end. All the intelligence is the computer at the end. That's probably a bit more of a found explanation. But business model does didn't help them either. Yeah. Wait, did 1990 or didn't think that the network should be down my smart. I'm not sure I thought about it deeply, but everything I worked on, the network was dumb. The network moved bits. That's all it did. Yeah. Because even I, in those days, understood,

I couldn't imagine all the applications. So if all you do is voice, maybe the network can be smart. But if you're doing all those other things, how could the network possibly know all those things and be smart for it? Yeah. If you, so you've been sealed for about five years, five years. Wait, so with your five year increment, what was your most misunderstood decision? Were you ended up being right, but I wouldn't thought you were crazy.

2000 and 18, I proposed to our board that we should buy a company called Red Hat.

And today most people are turned around and say this is the most successful acquisition

that IBM has done in all time and probably the most successful software acquisition in history. So it was completely misunderstood because people didn't see that you actually did need a platform that could make your diagnostic across multiple cloud platforms across on-premise environments.

“So you've got to have a view of what it could be and we drove it to a place where I think today,”

it stands as the leader in its place. How did you come to believe this theoretical notion? So cloud was happening. You could ask yourself the question, should we spend a lot of capital and chase cloud? Okay, your five years to be generous, maybe longer, behind at that point, the two leaders. So you could spend maybe 10 billion a year and a lot of businesses tend to do that. Okay, it's so important that it's going to be half the market, I can't not.

Maybe it was, we'll always be five years behind, they're not done, but then I'll slow.

So if you're going to be there, you're going to be best case, a distant third, worst case, maybe a fourth or fifth because there's Chinese also in the mix. Why would you do that? Instead, is there a different space you can occupy? Instead of competing with them, can you become their best partner? In this case, you write their success.

“If I want to be their best partner, then what are the set of technologies that would be useful?”

So you can flip the problem, is how I thought about it? How hard was it to convince people needed convincing before that acquisition? Probably six to nine months of breaking my head with no success,

and then six months of building the momentum once a couple of people began to see it.

You're very persistent. Oh yes, very. Would you describe that as you're defining trade? I am very persistent and I'm very patient. I'm also probably very impatient, but I'm not a yellow and screamer. I don't rant and rave, but as I say, if I think we're going to do something, I can be remarkably stubborn about it. We will do it. If I got your family, put them up on stage,

and ask them this exact question, is this how they would answer as well? They would tell you, I'm very stubborn. They might not agree that I don't rant and rave. Well, you know, one of the principal observations of psychology is that our home self and our work self are uncorrelated. Once you know that, you know everything. Wait a curious, well last question about that. How long does it take for you to be vindicated with Red Hat?

Probably took five maybe four years. I think by 2023, so 2018, we announced it. We took the big short crop. It took a year to close 2019. If I count, not that I'm the counting that much, but July 9, 2019, as the day that we got all the approvals, took another few weeks to actually transfer the money. But from there, probably 2023, the World Walkup and said, hey, you guys deserve credit for this. This was actually a great move, not a bad move. But this is, it's interesting,

because this is a real gamble. If it doesn't work, you're not sitting in the chair right now, right? Oh, for sure. There were two steps. One, if it was obviously not going to work, I wouldn't have

“been selected. And do that and work after that. That's why CEOs can be short-lived.”

Can I ask you a personal question? How much sleep did you lose over this? Once we had made the decision, none. Can you give me pointers on how you do this? Because I wake up at two a.m. every morning. It's over much more trivial things in this. Once a week, I'll probably wake up at two or three in the morning. I acknowledge it, because I wake up and my brain is running. And once it's running, I don't even try to go back to sleep.

In my case, it's thing. Go get up and do work and make yourself productive. We're going to be tired before the afternoon. That's fine. You'll sleep well that night. I have actually learned a long-term back. You can't do it across. You can't do it early morning through the day and late at night. So, an hour before I think I want to go to bed, I will actually change what I'm doing. Meaning, I will start reading something interesting to me, but completely outside

modifying on demographics and population, but I won't read it on leadership, because that's too close

now. 20 years ago, I might have that would have been different. I won't read it on deep signs, because that's too close to what we do for the living. So, it's got to be outside. The things that will make my brain turn about work, but it's got to be something that is dense enough to occupy your brain, so it shifts gears. Sorry, I want to dwell on this just for a moment. The red hat thing. Was there someone or is there someone who you went to and explained the logic of this? And

they saw the logic of this and that meant a big difference to you. Getting their support made a big difference, you'd be surprised. I'm remarkably open inside. I mean, when I have

other probably a half dozen to a dozen people inside that I'll talk to and I'll be completely

open about, hey, this is what I'm thinking. I don't know here are the risks. I'm open about those

“also. It's not just the benefits. I think these are the risks, but I think the benefits are”

the risks. I talk about that to people all the time. So, whether for example, I mean, I'll take names. I think our currency HRO, Nickel, who introduced us, she has been in that loop since at least 2015 for me. If I look at our CFO, Jim Cavana, he's been in that loop probably since 2013. And IBM was a really wonder, what the hell intersection did you guys have? It didn't. When I talked about learning finance, I'll go to him and say, hey, explain this to me. I don't understand

why it's like this. And to me, it's okay, you're patient, you go learn. If I think about many of the

people in the software business, they've been having these discussions with me for always.

I mean, now I'll acknowledge I can get probably impatient and it's so weak, but it's meant to be a discussion. I mean, like, let's have the discussion. If you have a strong point of view, I got it. Nobody has a going to be perfectly correct. But I always look for, if you have a strong point of view, that means it's from a different perspective than mine. So, what do I learn from that is the question, which helps to improve my point of view,

that makes sense. Yeah. I actually think that each person should try to build a community of 100 people inside your enterprise and 100 outside that you can call up. I have no hesitation. Somebody introduced me to a long time back to a CEO of the outside. I call them up all the time and say, hey, do you have five minutes? I'm just thinking about something this way. The CEO of Red Hat, who left IBM in 2021, we probably talked every two or three months on a random

drop egg, but it becomes mutual. He asked me my opinion on some things. Now, by the way, three or four times you might do something different, but he wants me to happen here.

“The other way around, if I gave you my phone number, can I be on that list?”

I would just be fascinating. I don't know if I can help you, but I would be really fun to get the call. Sure, you can. You think that we can ever succeed, unless people who influence opinions, say things about us? So, you may not think deeply about maybe the physics of quantum computing. But would you think deeply about why and what movement may make it much more attractive to a large audience? Sure, you would. You'd be far better as a thinker of that topic and probably

most of the people? I was thinking, you know, when you were making your comments about your 1990s self and streaming that the rational thing would have been for there have been a reserved board seat for every television network from someone from the World of Technology, which I 100% sure they did not have that in 1990. But they, their board was probably composed of people like them. Let's talk a little bit about technology now.

There's so much, so much of the changes going on right now are accompanied by a great deal of hype. What are we overestimating? What are we underestimating? Now, let's go back to 1995, the internet because I think that the current moment is very much like the internet moment. Actually, all the moments in the middle of a much smaller,

“I think mobile streaming or much smaller, internet was the major moment. If you remember back”

to 99 and 2000, people claimed there was a lot of hype. Would we say that the internet of today has more than fulfilled all those expectations and more? Yes. Along the way, did 8 out of 10 of the companies that were invested in heavily go bankrupt? Yes. I actually think of that as being the huge positive of the United States capital system. That investment happened 8 out of 10 went broke. Anyway, the assets didn't go away. They got consumed at 10 cents in the dollar by somebody else.

for all the capital. If we just take Amazon and Alphabet, A.K. Google, just those two have probably

“paid for all the capital of that time. So that's what's going to happen this time. There will”

be a lot of tears, but in aggregate, there will be a lot of success. And I think that's a fundamental difference between the US model and almost all of the countries. All of the countries that desperate to keep all the companies alive. So that means you're diluting. But that's a horrible thing. So to me, let the system work is worked really effectively. Well, they're not just now. I mean, all the way back to the railways and electrification and you mentioned telephone system,

you can keep going on. Oil, consumer goods, it goes on and on. I think the system is very effective. It deploys capital. It senses this is a big market. It's completely willing to over deploy capital in the short term, not the long term. That results in more competition. So it actually improves the rate of innovation. That means what might have taken 20 years takes five and the winners emerge. Exactly the same is going to happen this time. Yeah. I saw that. I grew

up in Waterloo. And Blackberry, of course, is from Waterloo. Yep. Everyone used to work for Blackberry.

“Yeah. Blackberry goes into its dive. And that's the best thing that happened to Waterloo.”

Because it was not just capital of a talent. Yep. Talent is many other companies. It's all these smart people went on the next really more interesting thing. Yeah. But wait, that you haven't answered. So what is your idea that we are underestimating at the moment that's in the current kind of suite of innovations? So I don't think AI is being underestimated. Because when you look at the

amount of capital and the amount of things chasing it, I think it's incredible. I do think that

a lot of enterprises are deploying it in the wrong place. They're running off the shiny experiments. There's a lot of basic things you can do to use AI to improve the business today. So that's what we just might want advice to them. Big areas you can scale. Don't pick the shiny little

“toys on the side. Mm-hmm. Then I think what for example then? If anybody has”

more than 10% of what they had for customer service 10 years ago, they're already five years behind. If anybody is not using AI to make their developers who write software, 30% more productive today, with a goal of being 70% more productive, that's not to say you will need less. You can just get more software done. Then you're not. And I would turn on and tell you, I think only maybe 5% of the enterprises are on both those metrics today. Yeah. Yeah. Yeah. And the one that is completely

underestimated, I kind of put it like this. One thing today is where GPUs and AI board in 2015. And I bet you every AI person is thinking and hoping I wish I had started doing more in 2015 as opposed to weight until 2022. Wanted today is there. So it's not good enough that you can get a big advantage. But if you learn how to use it, then in five years you'll be ready to exploit what comes. Yeah. We're going to get to quantum in a moment, but I have a couple other AI

questions. You know, I, as you know, where this conversation is part of this thing that we do with IBM Smart Talks. And I've been the last episode I did was on Kenya, which has a massive deforestation problem. And they got together. IBM took all of the NASA satellite data ran it through an LLM and gave them this incredibly precise 10 meter by 10 meter analysis of what trees to plant, where to plant them exactly with the, you know, astonishing kind of blueprint

about how to fix their country ecologically. And it maybe think when we analyzed the potential of AI, are we making a mistake by spending too much thinking about the developed world when it's actually the developing world, where the greatest ROI for this is? To me, look, software technology is a wonderful and the sense they can scale and they can be in and. So you don't have to do one or the

other. You use deforestation, how about the use of pesticides and fertilizers? We always use it.

We tend to, for irrigation, we tend to just flood everything as opposed to say, okay, only that one needs it. You could do all those things to get a 10-time defective analysis, that all would apply to the developing world. How about remote health care, utterly health using an AI agent? So the examples are numerous. In the developed world, I believe we are running out of people. I know that, no, you'd like to hear it. Most of the far east is going to have half the number of

far under sustaining or keeping population flat. The US, also depending on which number you want to look at is either 1.6 births per women or 2 or 2.1. Why are the three numbers? 1.6 is to women who are born in the United States. It becomes 2.0, if you include immigrant women, it becomes 2.1, if you include children who are migrating in. So you've got to decide where the trend is obvious.

“This is going down. So AI in the developed world is going to be essential because to keep our”

current quality of life, you need more work done. Well, what's going to do the work? If there aren't people to do the work. So the problems are different. Oh, I see. It gives you in the

developing world, you get access to a suite of technologies and things at a price you could never

have been able to afford. Correct. That was my, in talking to the Kenyan thing. It was like the whole, it's maybe one of the largest ecological projects of its kind at 15 billion trees they want to plan. And that is one country that might get it done because they do take a lot of pride in their ecology and in sort of returning to the land and giving back. Yeah. What's different about IBM's version of AI versus some of your, so we are not a consumer company. So we have no

focus on a B2C chatbot. And the reason I say that is, if you're making a B2C chatbot,

“does it help you to make it even bigger and more computationally inefficient in the short”

answer as yes? Because you have a certain number of users and you kind of say, I kind of say this

jokingly, if I add finish to French capabilities, I can probably add five million users.

If I add writing a high coup, I might be able to add another five million users. If I add writing an email in the voice of Steinbeck, I can probably add another five million users. Do all those things. If my goal is to help a company, summarize the legal documents in English, that can be a model that's 100 the size. As effective, probably higher quality, but I don't need to go wide. So if you're focusing on the enterprise,

that actually takes away the focus of having to go to extremely large models, which by definition are going to be computationally expensive, power hungry, and demand lots and lots of data. So I can turn it out the enterprise. Do you don't need to worry about copyright issues, about all those, because you can create a much smaller amount of data. And now, by the way, tuning it for yourself is a weakened exercise. It's not a six month on a big supercomputer cluster,

somewhere out there. That's one big difference of what we do. Yeah. Second, we are very focused on helping those problems that can give people immediate benefit, where we have domain knowledge. So out of my knowledge is around operations, is around programming, encoding, is around customer service, is around customer experience, logistics, procurement. Let's stay in the areas where we have a lot of expertise. And then three, we kind of apply it to ourselves. And so we are not asking our

clients to be the first experiment on it. We say, you can leverage what we did. We're happy to bring out all our learnings, including what needs to change in the process, because the biggest change is not technology. It's getting people to accept that there's a different way to do things.

“Other challenges to explaining what makes you different, to potential customers?”

For sure, the shiny object is always a tractor. Well, I can go and try to charge

GPT. Why don't you have your GPT version? Juniors, chat, GPT. I have used it. I asked you the question recently, which I thought was really simple. And it made up about 10 people. Anyway, I had a bad experience. I actually think that's a fundamental issue with all our limbs as they get larger. Yeah. Because you had to ask, what was the original insight that meant to these? It was a reward function with intent. So, if it has learned

by using a reward function, it's reward function comes giving an answer that satisfies you. So, if it thinks that if it makes up an answer that satisfies you, how will you stop it? Why do we think this is different than the clever college kid who doesn't know an answer, what a bullshit to the way to answer. Yeah. Well, it's exactly the same. It's like the example of clever hands. Juniors, that story, the horse that they thought could speak, and all it was doing was

pleasing. It's master. Yes. It is a little bit of clever hands. Yeah, it's like dogs are kind of meditating and looking. What would you identify as the most significant bottleneck in the development of AI?

where we'll get incremental improvements, but I for one don't believe that LLMs are going to get us to super-intelligence or AGI. So, I'll park that on the side and simply say, we have to find a way to fuse knowledge and how do you represent knowledge as opposed to have to statistically rediscover it each time I ask a question. And how do we fuse knowledge with LLMs? Maybe then we'll

“get to leave some balance beyond today. Yeah. On LLMs alone, my view is I think we can get a”

thousand X efficiency in power and cost and compute from today. So, if you make something a thousand times cheaper, would people use a lot more of it? Yes. And I think those answers lie as is usually in compute. So, advances in semiconductors, advances in software, and advances in agroscopic techniques. All three, but how come we're not working in any of those three? We're just taking the current semiconductor and going board. We're taking the current agrophic techniques and not

really trying to invent new ones. So, I think those are all happened, less than five years. But why? You see, there is a, we're in a moment where people are not pursuing the, the optimal strategy for exploiting this technology. Why? Because when you see a few people running really hard and they're willing to invest any amount of money. So, efficiency is not the focus. People feel, if we don't do the same, we'll get left behind. So,

this is the case with as too much money. Humans have never had four more, right? Ever.

But is this a consequence of over-investment in the field? Going back to my internet analogy, if two out of ten are going to succeed, how do you guarantee or improve the odds that you are one of those two? So, if you pause to say, I want to make a more efficient, that's not the way to win. So, first you win, then you become efficient. Yeah. Yeah. Let's talk about what is, I was told your favorite topic. It's quantum. It is. What? Boy even go any further. Why is quantum your favorite

topic? They only had two kinds of compute in the history. So, 1945, I was to use that year for any act, all the way to 20, 20, we had one kind of compute, classical, what today you would call a classical computer. Then GPUs and AI came around. So, you would say the intuition there as you went from sort of bits, which is algebra or high school algebra, including neurons, which is captured in linear algebra, but that gives you a different kind. But it can do problems that are really hard to do.

“I don't say impossible. Just hard to do on normal computers. Want to add a third kind of math?”

Yes, the physics properties, which really get people energized and their imagination going and we use all these words about entangled mind and so on. But maybe because I'm a bit of a math guy, the real thing is it does a third kind of math to make it really simple. A third kind of math that comes from the field of abstract algebra. It does the math, you can use Hamiltonians for those like physics or you can use the word "lay algebra" for those who like abstract mathematics.

If you can do a third kind of math, which algorithms are suited to that third kind of math.

So, it excites me because we can now approach algorithms that it just could never do on the other

tool. It's impossible. Now, it's different than AI. It's not data intensive. It's computer intensive. So, we kind of had a computer, and so for computers, then we went to data, which is the AI. And now, if you say there's another class of problems, there's a lot of compute. That's quantum. Mm-hmm. A couple months ago, we said that to Watson Research Center, and they have, you know, on the ground floor, they have those behind the glass. There's incredibly exciting-looking machines.

But where are we in the timeline of this? Three to five years away from shocking people,

“with the shocking people mean. Do something that nobody thought was possible in that timeline?”

Does an example come to mind? I was actually pleasantly surprised. So, one of our clients, HSBC, last week, published a result that using a quantum computer,

bond trading was 34 percent more accurate than their prior technique.

34 percent? 34 percent. This is an industry that's used to 1 percent. Correct. For 0.5 percent. Yes. That's astonishing. Now, that was not at a scale, but they could turn it into

Now, can you imagine when we'll somebody, so you were correct, you talk about an industry where one basis point. If I remember, I may be wrong, like 30 trillion dollars a money kind of moves around

in the financial industry each day, right? So, basis point would be 13 billion, something like that,

one over 10,000. So, we need to think about the kind of profit that people can make. If you can tell somebody that you can come up with a better price than your competition, by just one basis point, they would actually get in the darn market share. So, I think something around there, or something in the world of materials, can we make a better battery? Could we make a solid state battery? Which means your risk of fires, heating, decrease,

dramatically. And the reason, sorry, ask a really nice question. Why is it that a quantum computer would be better at solving a battery problem than our existing methods of computing?

So, the equations of quantum mechanics and chemistry and how things interact are well known.

To solve them, there are no known techniques, so these are not like closed form, you know,

“it's not like the square root of a quarter of a equation. So, the only way to solve them is to”

explore the state space. So, if you have a few hundred electrons, you need two to the hundred states. Well, I'm sorry, you don't have that much memory, it's impossible. So, it takes a really, really long time on a normal computer to solve those problems. Right? But that's simple a problem. If a quantum computer operates in the equation domain, it doesn't need to explore the state space. It can actually solve it. That's what I call it, a different kind of math. That's the kind of math

that it does. So, in a couple of seconds, it can tell you, this is how that material will be hit. Oh, I see. So, you've taken what could take years to a few seconds. Yeah. That's a pretty big change. Yeah. Yeah. It's speaking a different language. Speaking a different language. So, any kind of problem that comes along that's specific to that language. Correct. Which is not all problems. Yeah. Just, I call it, it's one more kind of math.

Yeah. With an example, so, so many questions like, give me another example of a, of a kind of problem that a quantum computer would love. This one is a bit more speculated, but and I'm going to use a little bit of poetic license. So, let's take a post office in a mid-sized

country. They've probably burned a billion gallons of fuel per year delivering packages and

letters because most post in advanced country says every house, every address, each day. The way to optimize this is we can formulate the problem. It's called the problem sales and problem. Solving it is really hard. Yeah. So, people have heuristics. Let's suppose today our heuristics get us to within 20 percent of the optimal answer. Let's suppose a quantum computer can get you the next 10 percent. Well, if I can get 10 percent of a billion gallons,

“that I think is 100 billion gallons of a math is right. And in the country I'm thinking about”

that could be 800 million pounds of saving to one entity in one year. And the associated carbon footprint climate change went into less mileage on vague. I'm not even counting all that. These are pretty attractive problems to go after. So, if I look at the interest recently, New York has started a whole program in some places Illinois stood up a quantum algorithm center, which means a number of the universities, the governor there was heavily behind it, etc.

So, I wouldn't say that this is widespread. This is why I'm saying three to four years for that moment. But there's enough people who are deeply cognizant who are saying wait a moment. We kind of get it. This is the new kind of math. What are the new problems we can solve? And the fact that we have about roughly 200 clients who work with us very early stage small experiments is because the intuition is I can do something here that I couldn't do in other places.

Three to four years is not a long time. No. But if I'm in a battery business,

“and I don't have a line out to a quantum computing experiment, I have a problem. Do I have a problem?”

Yeah, you probably be out of business in 10 years. Maybe you can write a big check in by the technology from somebody else. We're just quantum rank in the kind of great inventions of the last 150 years. Equal to semiconductor. And I think that it's a meconduct as vanished modern life at stop, but just stop.

No streaming. You can imagine the yells from all the kids who I were here that no streaming. The, um, and is that, it's funny because don't, as someone who's outside this world,

I feel like quantum is under discussed relative to its potential first transforming society.

Because I use man to not example 95 was a moment with net scale that internet came on people's consciousness. I said when 85, I consider it to be this is a solved problem. Because it needs something that makes it accessible, easy, that was the browser. The net scale browser is what brought it easy to understand.

“We have probably, as I said, about four to five years from that moment. That's why it's”

under discussed because the more I say a new kind of math, I probably lost 99% of the audience. If I go to quantum mechanics, I probably lost 99% of the audience. So you, as, as CEO for the last five years, have been really the birth mother for a lot of the quantum computing work. And curious, so you come in when you started a CEO, was this

your first priority? I had already started investing in it back in 2015 when I was leading IBM

research. So let me acknowledge and like nobody should try to copy it. I had a color of weird career. I was a researcher at some point if it asked me, I had to say, I'm one of those people, you know, throw a pizza under a door and like leave me alone. I don't want to talk to people. Then I decided I was interested in the business. Then I went and started acquiring companies

“and doing that. Then somebody told me, hey, why did you start doing some business strategy?”

Then I went back to research and let our research division for a couple of years. And when the people described it to me, I asked some questions. So I wasn't a big investment of that. I was, hey, can we make a computer not just a science experiment? Can it run by itself all light? Can you think about software so that even people are not deeper quantum mechanics can begin to use it? And they began to do those things. So over three, four years, did I get enough confidence?

Yeah, okay, this is something that can really work. And then you got to nurture it to where it gets bigger and bigger, until you get the confidence that, okay, now it's a big bet. What was the moment when you realized now it's a big bet? Probably two or three years ago. And how do you decide, as a head of a company like this? How much money, how many resources and how many people and how what kind of problems to give to an idea like that? So three layers,

the set of people who actually have the knowledge and the intensity to fundamentally advance the technology, I'd have to find more or higher them. So I'm constrained on people on that one, because now only there's so many people who can do these things. Two, you got to be careful. If you push too hard on timing, you'll get people to take so much risk that actually the thing will fail. So that's the art of between the leadership on the project and

me to say, okay, how hard can you push, but not so hard, that you cause it to fail, because then they can compel to commit timelines that are just impossible. Yeah. How do you, this is

fascinating. So it's ultimately a question of judgment of trying to figure out what's the

sweet spot between enough pressure to keep them ahead of the pack, but not too much pressure so that they start taking risks. How do you calibrate whether you're hitting that sweet spot? I mean, do you reassess every few months and say, I think I'm overcorrecting or undercorrecting at this moment? So one, you got to have what I call and this is channeling a word from one of my favorite books to Geekway. How open can you be? So I want to press hard,

but the team knows that they're allowed to push back and really argue back hard. That means you're get to probably that correct Goldilocks pressure. Do the people themselves should want to go as hardest possible, but not harder than possible. So that is the then personality of leadership.

“Yeah. That makes sense. But you have to be someone who people feel comfortable being honest with. Yes.”

Absolutely. And people feel comfortable being honest with you. I believe so. Yeah. When has it been a moment in this in this path with quantum, when you did think you were pushing too hard? No. Because I think that the leadership there will argue back with me any day of the week. I don't think that they feel that they have to fold. Do you drop by at sort of Saturday night at

something and say, hey, are these people doing this? And if they can answer me in reasonable terms,

“I actually don't say great. They're only watching the competition. They're watching the literature.”

They're watching the science. I don't need to push hard. If they're already ahead of it,

then me a can answer my question. I'll say, hopefully not always completely accurately. You're thinking

about it on their own. I don't need to push. Yeah. One last question I wanted to ask you. Do you

“have the most interesting job in America? I believe that it's the most interesting job, which I won't”

give up for anything. Yeah. It also sounds like you're enjoying yourself. I enjoyed as long as

look my role and goal should be to make the enterprise thrive. As long as I'm making the enterprise thrive and our clients delighted, I love it. Yeah. If I don't, somebody else should do it. Yeah.

“Marvin, this has been so much fun. Thank you so much. Taking a time and a fascinating”

completely fascinating conversation. I wish I was one of those people could help you out with quantum, but I'm afraid. I'm not. In a few years. Good. Thank you so much. Smart talks with IBM is produced by Matt Romano, Amy Gaines of a quade, Trina Manino, and Jay Carber. Mastering by Sarah Bregar, music by Grammyscope, strategy by Tatyana Lieberman, Cassidy Meyer, and Sophia Dirlon. Smart talks with IBM

is a production of push-in industries and Ruby Studio at I-Heart Media. To find more push-in podcasts, listen on the I-Heart Radio app, Apple Podcasts, or wherever you listen to podcasts. I'm Malcolm Gable. This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies, or opinions.