Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got
Speaker 1: something a little bit different to share with you. It's
Speaker 1: a new season of the Smart Talks with IBM podcast series.
Speaker 2: This season on Smart Talks with IBM, Malcolm Gladwell is back,
Speaker 2: and this time he's taking the show on the road.
Speaker 2: Malcolm is stepping outside the studio to explore how IBM
Speaker 2: clients are using artificial intelligence to solve real world challenges
Speaker 2: and transform the way they do business.
Speaker 1: From accelerating scientific breakthroughs to reimagining education. It's a fresh
Speaker 1: look at innovation in action, where big ideas meet cutting
Speaker 1: edge solutions.
Speaker 2: You'll hear from industry leaders, creative thinkers, and of course
Speaker 2: Malcolm Gladwell himself as he guides you through each story.
Speaker 1: New episodes of Smart Talks with IBM drop every month
Speaker 1: on the iHeartRadio app, Apple Podcasts, or wherever you get
Speaker 1: your podcasts. Learn more at IBM dot com slash smart Talks.
Speaker 1: This is a paid advertisement for IBM pushkin.
Speaker 3: Hello.
Speaker 4: Hello, I'm Malcolm Gladwell and you're listening to Smart Talks
Speaker 4: with IBM. This season, we've been bringing you stories of
Speaker 4: how IBM works with its clients to solve complex problems
Speaker 4: like helping Lreel reimagine how scientists approach cosmetic formulation, or
Speaker 4: enabling Scuderia FERRARIHP to connect with fans in new ways.
Speaker 4: But in this episode, we're going to zoom out and
Speaker 4: look at the bigger picture. Earlier this month, I had
Speaker 4: the chance to meet the person who's shaping IBM's future.
Speaker 4: It's CEO and Chairman Arvind Krishna. We sat down in
Speaker 4: front of an intimate live audience at IBM's New York
Speaker 4: City office and talked about his uncanny ability to anticipate
Speaker 4: where technology is heading, the future of AI, and his
Speaker 4: passion for quantum computing, which he says is as revolutionary
Speaker 4: as a semiconductor.
Speaker 3: Thank you everyone, Thank you Arvin.
Speaker 4: You're a difficult man to scare for what they said,
Speaker 4: so we're we're enormously pleased that you.
Speaker 3: Could join us.
Speaker 4: Let's start with a I have all these cousins, two
Speaker 4: cousins who work for IBM their entire career.
Speaker 3: I would ask them what does IBM do?
Speaker 4: And they would always give me different, confusing, complicated answers.
Speaker 3: What's your answer? What's your simple answer to that question?
Speaker 5: IBM's role is to help our clients improve their business
Speaker 5: by deploying technology. That means you're not ever gated to
Speaker 5: one product. It is what makes sense at that time,
Speaker 5: but it is about improving their business, not just giving
Speaker 5: them a commodity. Then to go to the next layer,
Speaker 5: I would say we help them through a mixture of
Speaker 5: hybrid cloud and artificial intelligence and a taste of quantum
Speaker 5: coming down the road is kind.
Speaker 3: Of where I would take it.
Speaker 5: That's that's what IBM is.
Speaker 3: So you are technology agnostic in some sense.
Speaker 5: I'm product agnostic. Product I'm not technology agnostic.
Speaker 4: Yes, But if I twenty five years from now, IBM
Speaker 4: could be doing things that would be unrecognizable to contemporary IBM, it.
Speaker 3: Is completely possible.
Speaker 5: Yeah, it could be that in twenty five years from now,
Speaker 5: the only software IBM does his open source. It could
Speaker 5: be the only computing you do is quantum computers. And
Speaker 5: if I add those two people today, WLD say that's
Speaker 5: not the IEM of today.
Speaker 4: Is it even simpler to say you just IBM solves
Speaker 4: problems at the highest technical level.
Speaker 5: If you say highest technical level, yes, yeah, Like the
Speaker 5: guy who invented the bar code, he was solving a
Speaker 5: problem retailers wanted to scale. Many of you may not
Speaker 5: know it was an IBM or who invented the bar code,
Speaker 5: by the way, not somebody who was a PhD. Not
Speaker 5: somebody who was a deep researcher. I think it was
Speaker 5: actually a field engineer. Oh really, Yeah, and lasers were
Speaker 5: out and you could use lasers to scan things, but
Speaker 5: they could be upside down, they could be muddy, they
Speaker 5: could be partly scraped off. And he came up with
Speaker 5: the idea of the bar code. Yeah, and that changed
Speaker 5: inventory management forever. But the world needs to know that
Speaker 5: IBM invented the barcode. You guys should do a better
Speaker 5: job ombicizing that. I am sure our CMO will listen
Speaker 5: to this podcast and we'll get that idea.
Speaker 4: Tell me you started at the Thomas Watson Research Center.
Speaker 4: What were you doing when you first started it, IBM.
Speaker 5: I started in nineteen ninety and that was the era
Speaker 5: in which computers and networking but beginning to converge. And
Speaker 5: for the first five years I was actually building networks.
Speaker 5: So let's remember this was pre laptops. Laptops came in
Speaker 5: ninety two or ninety three, but it was clear to
Speaker 5: us that they were going to come supportable computing, and
Speaker 5: I spent my first five years building what today you
Speaker 5: would call Wi Fi. We used to have these debates
Speaker 5: can be builded, it's got to be small enough. I
Speaker 5: mean like, it can't be more than one hundred grams
Speaker 5: was kind of our thought, because if it's more than that,
Speaker 5: you're on a three thousand grand laptop. Why would anybody
Speaker 5: put this on? And the debate used to be, why
Speaker 5: would anybody want to walk around untethered? Won't you want
Speaker 5: to attach a big thick cable into it and sit down?
Speaker 5: Because that was the thought, that's how terminals worked. And
Speaker 5: I spent five years having a lot of fun, building
Speaker 5: many iterations of those and making progress on that.
Speaker 4: If I had a conversation with your nineteen ninety self
Speaker 4: about what the next thirty years were going to look like,
Speaker 4: is it possible to reconstruct you? What were your predictions
Speaker 4: at that age about where the company, where the industry
Speaker 4: was going.
Speaker 5: It was more about where technology was going to go,
Speaker 5: I would say, than where industry would go. I would
Speaker 5: have told you that networking and computers would fuse in
Speaker 5: nineteen ninety. That was a weird thought that some researchers
Speaker 5: held by the late nineties, that was obvious that it
Speaker 5: became the Internet. I would have told you that I
Speaker 5: believe that video streaming will be the primary way people
Speaker 5: will consume video. You would have said that in nineteen ninety. Absolutely, Now,
Speaker 5: that didn't take five years. That took twenty. But it
Speaker 5: happened because you could do it technically, except it just
Speaker 5: too expensive and too cumbersome. And if you've been in technology,
Speaker 5: like in nineteen eighty five, I would have told you
Speaker 5: the Internet is old because when I went to grad school,
Speaker 5: every one of us had a those days, an Apple,
Speaker 5: Mac or Lisa on our desks. They're all connected by
Speaker 5: a network. You're happily sending email to people all around
Speaker 5: the country. We were doing file transfers. So okay, you
Speaker 5: had to be a little bit more aware of the
Speaker 5: technology and didn't have a browser that took ten years
Speaker 5: to get the browser that took five years to be
Speaker 5: a business. But when you see the speed and the
Speaker 5: pace of technology in usually ten or fifteen years, the
Speaker 5: cost point and the consumerization is at a scale that
Speaker 5: you couldn't imagine ten years ago.
Speaker 3: Until you've seen a few of those cycles.
Speaker 4: Wait, did you make the lead to sorry, this is fascinating.
Speaker 4: I'm curious about how far did you take that. That's
Speaker 4: a really fundamental thing to have gotten right in nineteen
Speaker 4: ninety I think you're that idea.
Speaker 5: We were pretty convinced that what we used to think
Speaker 5: of as linear television or broadcast would become digitized. That
Speaker 5: was a given two with cable already the preponderance of
Speaker 5: how people got it that if you put packet television
Speaker 5: over cable, then that becomes the way it will go.
Speaker 5: I fundamentally believed, actually way back eighty seven, that on
Speaker 5: demand movies would become the way people would consume movies.
Speaker 5: So those were all things that I could have predicted.
Speaker 5: Nine didn't personally work on all those. I mean, after networking,
Speaker 5: I moved on to doing other things. But those were
Speaker 5: easy to predict.
Speaker 4: If you had a converse in those years with someone
Speaker 4: in the television industry and you gave them those predictions,
Speaker 4: did they see it?
Speaker 3: Were they convinced of this?
Speaker 5: I'm actually going to take it back to wireless networking.
Speaker 5: I think one of the reasons I do what I
Speaker 5: do today, which is at the intersection of business and technology,
Speaker 5: is because of what I saw happened with Wi Fi.
Speaker 5: So you built these wireless networks and then you say, hey,
Speaker 5: the market's going to be millions, tens of millions, billions
Speaker 5: of users. And the business looks at it and says,
Speaker 5: we think the market is confined to warehouse workers doing inventory.
Speaker 5: You can look at them and say, why not people
Speaker 5: in their homes because they could imagine outside how people
Speaker 5: bought things at that time. And so I became convinced
Speaker 5: that I can't just help invent it. I got to
Speaker 5: think about, now, how do you market it? To whom
Speaker 5: do you market it? What are their routs? How do
Speaker 5: you make it easy enough? And that was I mean
Speaker 5: not making it simple? Now, that was probably a five
Speaker 5: to ten year evolution of myself in those days.
Speaker 4: You know, this reminds me of when the telephone is
Speaker 4: invented in the eighteen seventies. It doesn't take off for
Speaker 4: forty years because the people running a telephone business and
Speaker 4: they didn't want women using it because they were worried
Speaker 4: that women would gossip with their friends. They didn't understand
Speaker 4: that that's actually what telephone is.
Speaker 3: Right, It's an.
Speaker 5: Exact parallel, Yes, it is. You see it again and again.
Speaker 3: What is the source of that blindness.
Speaker 4: So there's a gap, in other words, between the invention,
Speaker 4: the technological achievement, and the social understanding of the technology.
Speaker 3: Why is there such a gap.
Speaker 5: I think that the gap is fundamental and rooted in
Speaker 5: a lot of academic disciplines. So even channeling some of
Speaker 5: your work, though you don't intend it to be used
Speaker 5: that way, you can say a lot of things are
Speaker 5: data driven. If it is data driven, then by definition,
Speaker 5: you're looking at history. If you're looking at history, that
Speaker 5: means you're looking at existing buying patterns. If you look
Speaker 5: at existing buying patterns, you forget. All of those who
Speaker 5: have created massive value in time have all created markets,
Speaker 5: meaning they've all created new markets. And I think that
Speaker 5: is why the world is fascinated with people like Steve
Speaker 5: Jobs for example, he imagined a market that didn't exist.
Speaker 5: So I think that is the gap. And then if
Speaker 5: you can get the technology the business acumen scaler company
Speaker 5: and that imagination of making the market is how you
Speaker 5: create I think massive value.
Speaker 3: You got to get all three pieces going. It's not enough.
Speaker 4: In other words, you were thinking it's not enough to
Speaker 4: invent something new. I need to make a business case
Speaker 4: for it. Simultaneously, and that's what gets you thinking along
Speaker 4: the path that leads you to this job.
Speaker 5: Oh yeah, I'll tell you if you had met or
Speaker 5: when the nineteen ninety four and you had talked about
Speaker 5: the stock market or about a balance sheet, or looked
Speaker 5: at you like, Okay, I got those words are I
Speaker 5: can parse them. I have no idea what they are.
Speaker 5: I have no intuition on what they are. I couldn't
Speaker 5: tell you why it's relevant or why it's not. But
Speaker 5: then you began to think, Okay, why do companies get
Speaker 5: higher values? Okay, that's the stock what does that capture
Speaker 5: if I have to spend working capital and that's the
Speaker 5: balance sheet? Well, so you learn. I mean, I figure
Speaker 5: I'm willing to learn. I'm willing to read. Howether the
Speaker 5: best way I read is to go to balance sheets?
Speaker 5: Here you can read the book. It's pretty damn dry.
Speaker 5: Much easier to go talk to a financial expert who's
Speaker 5: around the corner, and people are if you're curious about
Speaker 5: what they do, they're really happy to share their expertise,
Speaker 5: and over time you learn more and more and they
Speaker 5: actually become part of your network within the company. And
Speaker 5: that's how you can both learn and evolve yourself and
Speaker 5: actually gain the extra.
Speaker 4: Skills you are to be a successful business leader. Do
Speaker 4: you have to unlearn or or deviate from some of
Speaker 4: the things that made you a successful scientist.
Speaker 5: I actually believe the exactly opposite.
Speaker 3: Yeah, but use what.
Speaker 5: You're really good at as a foundation, but don't make
Speaker 5: it the only thing you use. So then how do
Speaker 5: you add the other skills? And there's many ways. You
Speaker 5: can have people that you trust who help you out
Speaker 5: those skills. You can gain some intuition, maybe not the
Speaker 5: depth of expertise. I want to be deeper on certain
Speaker 5: areas of electrical engineering than I'm ever going to be,
Speaker 5: let's say, in finance or marketing. But I want to
Speaker 5: be curious about those. I don't want to dismiss them.
Speaker 5: So you build on your skills, and then you have
Speaker 5: to say, but I need a complete and holistic view.
Speaker 5: So I'm going to be a little deep, not very deep,
Speaker 5: in all of those. And you've also got to learn
Speaker 5: to trust your intuition a little bit.
Speaker 4: Yeah, but I forgot a question that I wanted to ask,
Speaker 4: but about the predictions of nineteen ninety arvand what did
Speaker 4: you get wrong?
Speaker 5: All lots of things. I think that people were thinking
Speaker 5: that in those days, and it started my phrase, but
Speaker 5: I'll come back to it. I think most people thought
Speaker 5: that the communication companies would turn out to be the
Speaker 5: winners of how networking got carried. If you all think
Speaker 5: through the nineties of the investments that were being done
Speaker 5: by let's not take the names of all of the
Speaker 5: telecom carriers.
Speaker 3: Didn't turn out to.
Speaker 5: Be the case. Actually, I think that's the business model case.
Speaker 5: The reason is they all had in their heads that
Speaker 5: you can charge people by the.
Speaker 3: Minute, because they had been doing that already, because they
Speaker 3: had been doing that for one hundred years.
Speaker 5: Yeah, And in the end, the winners the networking were
Speaker 5: those who said flat price thirty bucks a month or
Speaker 5: fifty bucks a month or whatever, and that was just
Speaker 5: too much of a.
Speaker 3: Leap for them.
Speaker 4: You think it's as simple. That is the most parsimonious
Speaker 4: explanation for why you think they failed.
Speaker 5: No, there were a couple of hours the more technical things.
Speaker 5: The one was written by somebody who was actually inside
Speaker 5: one of these telecom companies, and he labeled his article
Speaker 5: the rise of the Stupid Network. So telephone people believe
Speaker 5: that the network should be really smart, the end device
Speaker 5: is dumb. If you think about the telephone, telephone is dumb.
Speaker 5: It doesn't actually do anything. It's just about the relays.
Speaker 5: And the network is smart. It routes you, it figures
Speaker 5: out where to send it, it does echocacul backwards. And
Speaker 5: the current Internet is completely dumb with the inside. It
Speaker 5: just takes the bits and shoves them out the other end.
Speaker 5: All the intelligence is the computer at the end. That's
Speaker 5: probably a bit more of a found explanation. But business
Speaker 5: model didn't help them either.
Speaker 3: Yeah.
Speaker 4: Wait, did nineteen ninety r of end think that the
Speaker 4: network should be dumb or smart?
Speaker 3: I'm not sure.
Speaker 5: I thought about it deeply, but everything I worked on
Speaker 5: the network was dumb.
Speaker 3: The netwuck movements. That's all I did.
Speaker 5: Yeah, because even I in those days understood I can't
Speaker 5: imagine all the opp loss. So if all you do
Speaker 5: is voice, maybe the network can be smart. But if
Speaker 5: you're doing all those other things, how could the network
Speaker 5: possibly know all those things that be smart for it?
Speaker 3: Yeah, so you've been COO for five years. Five years.
Speaker 4: Wait, so in your five year increment, what was your
Speaker 4: most misunderstood decision? Well, you ended up being right, but
Speaker 4: everyone thought you were crazy.
Speaker 5: Twenty and eighteen, I proposed to our boat that we
Speaker 5: should buy a company called rad Hat. IBM does proprietary
Speaker 5: but that was open source. The stock twelve fifteen percent
Speaker 5: of the day we announced it, and today most people
Speaker 5: will turn around and say, this is the most successful
Speaker 5: acquisition that IBM has done in all time, and probably
Speaker 5: the most successful software acquisition in history. So it was
Speaker 5: completely misunderstood because people didn't see that you actually did
Speaker 5: need a platform that could make you agnostic across multiple
Speaker 5: cloud platforms, across on premise environments.
Speaker 3: So you've got to have a view of what.
Speaker 5: It could be, and we drove it to a place
Speaker 5: where I think today it stands as the leader in
Speaker 5: its space.
Speaker 4: So how did you come to believe this heretical notion?
Speaker 5: So Cloud was happening, you could ask yourself the question,
Speaker 5: should we spend a lot of capital and chase Cloud? Okay,
Speaker 5: you're five years to be generous, maybe longer behind at
Speaker 5: that point the two leaders, So you could spend maybe
Speaker 5: ten billion a year, and a lot of businesses tend
Speaker 5: to do that. Okay, it's so important, it's going to
Speaker 5: be half the market. I can't not My view was
Speaker 5: we'll always be five years behind. They're not dumb and
Speaker 5: they're not slow. So if you're going to be there,
Speaker 5: you're going to be best case, a distant third, worst
Speaker 5: case maybe a fourth or a fifth. Because there's Chinese
Speaker 5: also in the mix, why would you do that instead?
Speaker 3: Is there a.
Speaker 5: Different space you can occupy instead of competing with them?
Speaker 5: Can you become their best partner, in which case you
Speaker 5: write their success. If I want to be the best partner,
Speaker 5: then what are the set of technologies that would be
Speaker 5: useful so you can flip The problem is how I
Speaker 5: thought about it.
Speaker 4: How hard was it to convince people needed convincing before
Speaker 4: that acquisition.
Speaker 5: Probably six to nine months of breaking my head with
Speaker 5: no success, and then six months of building the momentum
Speaker 5: once a couple of people began to see it.
Speaker 3: Yeah, you're very persistent.
Speaker 4: Oh yes, very Would you describe that as you defining trade?
Speaker 5: I am very persistent and I am very patient. I'm
Speaker 5: also probably very impatient, but I'm not a yeller and screamer.
Speaker 5: I don't rant and rave. But as I say, if
Speaker 5: I think we're going to do something, I can be
Speaker 5: remarkably stubborn about it.
Speaker 3: We will do it.
Speaker 4: If I got your family, put them up on stage
Speaker 4: and ask them this exact question, is this how they
Speaker 4: would answer?
Speaker 5: As well, they will tell you I'm very stubborn. They
Speaker 5: might not agree that I don't rant and drave.
Speaker 4: Well, you know, one of the principal observations of psychology
Speaker 4: is that our home self and our work self are uncorrelated.
Speaker 3: Once you know that, you know everything.
Speaker 4: Wait, I'm curious one last question about that. How long
Speaker 4: does it take for you to be vindicated with red Hat?
Speaker 5: Probably took five maybe four years, I think by twenty
Speaker 5: twenty three, So twenty eighteen we announced it, We took
Speaker 5: the big shot crop. It took a year to close
Speaker 5: twenty nineteen, so if O count not that I'm counting
Speaker 5: that much, but July ninth, twenty nineteen as the day
Speaker 5: that we got all the approvals. Took another few weeks
Speaker 5: to actually transfer the money. But from there, probably twenty
Speaker 5: twenty three, the world woke up and said, hey, you
Speaker 5: guys deserve credit for this was actually.
Speaker 3: A great move, not a bad move.
Speaker 4: Yeah, but this is it's interesting because this is a
Speaker 4: real gamble. If it doesn't work, you're not sitting in
Speaker 4: the chair right now.
Speaker 3: Right, Oh, for sure. There were two steps.
Speaker 5: One if it was obviously not going to work, I
Speaker 5: wouldn't have been selected, and two if it hadn't worked
Speaker 5: after that, That's why CEOs can be short lived.
Speaker 4: Can I ask you, sif a personal question, how much
Speaker 4: sleep did you lose over this.
Speaker 5: Once we had made the decision?
Speaker 3: None?
Speaker 4: Can you give me pointers on how you do this?
Speaker 4: Because I wake up at two am every morning and
Speaker 4: I over much more trivial things than this.
Speaker 5: Once a week, I'll probably wake up at two or
Speaker 5: three in the morning. I acknowledge it because I wake
Speaker 5: up and my brain is runrunning, and once it's running,
Speaker 5: I don't even.
Speaker 3: Try to go back to sleep.
Speaker 5: I mean, okay, go get up and do work and
Speaker 5: make yourself productive. You're going to be tired before in
Speaker 5: the afternoon, that's fine, you'll sleep well that night. I
Speaker 5: have actually learned a long time back. You can't do
Speaker 5: it across. You can't do it early morning, through the
Speaker 5: day and late at night. So an hour before I
Speaker 5: think I want to go to bed, I will actually
Speaker 5: change what I'm doing, meaning I will start reading something
Speaker 5: interesting to me but completely outside the scope of work.
Speaker 5: I may read a biography, I might read somebody who's
Speaker 5: spontefecating on demographics and population. But I won't read it
Speaker 5: on leadership because that's too close. Now, twenty years ago
Speaker 5: I might have that would have been different. I won't
Speaker 5: read it on deep signs because that's too close to.
Speaker 3: What we do for a living.
Speaker 5: So it's got to be outside the things that will
Speaker 5: make my brain churn about work. But it's got to
Speaker 5: be something that is dense enough to occupy your brain.
Speaker 3: So shifted gears. Sorry, I want to dwell on this
Speaker 3: just for a moment. The red hat thing.
Speaker 4: Was there someone or is there someone who you went
Speaker 4: to and explained the logic of this and they saw
Speaker 4: the logic of this, and that made a big difference
Speaker 4: to you.
Speaker 5: Getting their support made a big difference. You'd be surprised.
Speaker 5: I'm remarkably open inside. I mean, when I have are
Speaker 5: there probably a half dozen to a dozen people inside
Speaker 5: that I'll talk to and I'll be completely open about, Hey,
Speaker 5: this is what I'm thinking. I don't know. Here are
Speaker 5: the risks. I'm open about those. Also, it's not just
Speaker 5: the benefits. I think the other risks, but I think
Speaker 5: the benefits outweigh the risks. I talk about that to
Speaker 5: people all the time. So whether for example, I mean
Speaker 5: i'll take names. I think our current chro O Nicol
Speaker 5: who introduced us, she has been in that loop since
Speaker 5: at least twenty fifteen. For me, if I look at
Speaker 5: our CFO Jim Cavanaugh, he's been in that loop probably
Speaker 5: since twenty thirteen. And the IBM's will probably wonder, what
Speaker 5: the hell intersection do you guys have? It didn't when
Speaker 5: I talked about learning finance. I will go to him
Speaker 5: and say, hey, explain this to me. I don't understand
Speaker 5: why it's like this, And to me it's okay. You're
Speaker 5: a patient, you go learn. If I think about many
Speaker 5: of the people in the software business, they've been having
Speaker 5: these discussions with me for always. I mean, now I'll
Speaker 5: acknowledge I can get probably impatient and a SERVIIK, but
Speaker 5: it's meant to be a discussion. I mean, like, let's
Speaker 5: have the discussion. If you have a strong point of view,
Speaker 5: I got it. Nobody has a going to be perfectly correct,
Speaker 5: but I always look for if you have a strong
Speaker 5: point of view, that means it's from a different perspective
Speaker 5: than mine. So what do I learn from that? Is
Speaker 5: the question which helps to improve my point of view.
Speaker 5: That makes sense. I actually think that each person should
Speaker 5: try to build a community of one hundred people inside
Speaker 5: your enterprise and outside that you can call up.
Speaker 3: I have no hesitation.
Speaker 5: Somebody introduced me to a long time back, to a
Speaker 5: CEO of the outside. I call them up all the
Speaker 5: time and say, hey, do you have five minutes. I'm
Speaker 5: just thinking about something. This way, the CEO of red
Speaker 5: At who left IBM in twenty twenty one, we probably
Speaker 5: talk every two or three months on a random topic.
Speaker 5: One way it becomes mutual. He asked me my opinion
Speaker 5: on some things. Now, by the way, three or four
Speaker 5: times he might do something different, but he wants my opinion.
Speaker 5: The tour the other way around.
Speaker 4: If I gave you my phone number, can I be
Speaker 4: on that list? I would just be fascinating. I don't
Speaker 4: know if I can help you, but it would be
Speaker 4: really fine to get the call.
Speaker 5: Sure you can. Do you think that we can ever
Speaker 5: succeed unless people who influence opinions say things about us.
Speaker 5: So you may not think deeply about maybe the physics
Speaker 5: of one of computing, but would you think deeply about
Speaker 5: why and what moment may make it much more attractive
Speaker 5: to a large audience. Sure you would, you'd be all
Speaker 5: better as a thinker of that topic, and probably most
Speaker 5: of the people.
Speaker 4: I was thinking, you know, when you were making your
Speaker 4: comments about your nineteen ninety self and streaming, that the
Speaker 4: rational thing would have been for there have been a
Speaker 4: reserved board seat for every television network from someone from
Speaker 4: the world with technology, which I one hundred percent sure
Speaker 4: they did not have that in nineteen ninety, but they
Speaker 4: their board was probably composed of people like them. Let's
Speaker 4: talk a little bit about technology now. There's so much,
Speaker 4: so much of the changes going on right now are
Speaker 4: accompanied by a great deal of hype. What are we overestimating?
Speaker 3: What are we underestimating?
Speaker 5: Okay, let's go back to ninety ninety five the Internet,
Speaker 5: because I think that the current moment is very much
Speaker 5: like the Internet moment. Actually, all the moments in the
Speaker 5: middle were much smaller. I think mobile streaming were much smaller.
Speaker 5: Internet was the major moment. If you remember back to
Speaker 5: ninety nine and two thousand people claimed there was a
Speaker 5: lot of hype. Would we say that the Internet of
Speaker 5: today has more than fulfilled all those expectations and more?
Speaker 3: Yes?
Speaker 5: Along the way, did eight out of ten of the
Speaker 5: companies that were invested in heavily go bankrupt.
Speaker 3: Yes, I actually think of that as.
Speaker 5: Being the huge positive of the United States capital system,
Speaker 5: that that investment happened. Eight out of ten went broke.
Speaker 5: By the way, those acids didn't go away. They got
Speaker 5: consumed at ten cents in the dollar by somebody else
Speaker 5: who could then make a lot of money. But the
Speaker 5: two out of ten, just take two, it probably has
Speaker 5: paid for all the capitals. If you just take Amazon
Speaker 5: and Alphabet, ak, Google, just those two have probably paid
Speaker 5: for all the capital of that time. So that's what's
Speaker 5: going to happen this time. There will be a lot
Speaker 5: of tears, but in aggregate, there will be a lot
Speaker 5: of success. And I think that's the fundamental difference between
Speaker 5: the US model and almost all other countries. In all
Speaker 5: other countries, they're desperate to keep all the companies alive.
Speaker 3: But that means you're dialuting. That's a horrible thing.
Speaker 5: So to me, let the system works worked really effectively,
Speaker 5: by the way, not just now, I mean all the
Speaker 5: way back to railways and electrification, and you mentioned telephone system.
Speaker 5: You can keep going on oil, I mean consumer goods.
Speaker 5: It goes on and on. I think this system is
Speaker 5: very effective. It deploys capital. Its census is a big
Speaker 5: market is completely willing to over deploy capital in the short.
Speaker 3: Term, not the long term.
Speaker 5: That results in more competition, so it actually improves a
Speaker 5: rate of innovation. That means what might have taken twenty
Speaker 5: years takes five and the winners emerge exactly the same
Speaker 5: is going to happen this time.
Speaker 3: Yeah, I saw that.
Speaker 4: I grew up in Waterloo and BlackBerry closs from Waterloo. Yep,
Speaker 4: everyone used to work for BlackBerry. Ye, BlackBerry goes into
Speaker 4: its die and that's the best thing that happened to
Speaker 4: Waterloo because it was just capital with.
Speaker 5: Talents, yep, talents many other companies. So all these smart.
Speaker 4: People went on the next really more interesting thing. And yeah,
Speaker 4: the wait, you haven't answered. So what is your an
Speaker 4: idea that we are underestimating at the moment that's in
Speaker 4: the current kind of suite of innovations.
Speaker 5: So I don't think AI is being underestimated because when
Speaker 5: you look at the amount of capital and the amount
Speaker 5: of things chasing it, I think it's incredible. I do
Speaker 5: think that a lot of enterprises are deploying it in
Speaker 5: the wrong place they're running after shiny experiments. There's a
Speaker 5: lot of basic things you can do to use AI
Speaker 5: to improve the business today. So that's really just my
Speaker 5: one advice to them. Pick areas you can scale, don't
Speaker 5: pick the shiny little toys on the side.
Speaker 3: Then I think, for example, that.
Speaker 5: If anybody has more than ten percent of what they
Speaker 5: had for customer service ten years ago, they're already five
Speaker 5: years behind.
Speaker 3: If anybody is not using.
Speaker 5: AI to make their developers who write software thirty percent
Speaker 5: more productive today with the goal of being seventy percent
Speaker 5: more productive, that's not to say you will need less,
Speaker 5: you'll just get more software done.
Speaker 3: Then they're not. And I would turn.
Speaker 5: Around and tell you I think only maybe five percent
Speaker 5: of the enterprises on both.
Speaker 3: Those metrics today.
Speaker 5: Yeah, yeah, and the one that is completely underestimated. I
Speaker 5: kind of put it like this, Quantum today is where
Speaker 5: GPUs and AI war in twenty fifteen, and I bet
Speaker 5: you every AI person is thinking and hoping I wish
Speaker 5: I had started doing more in twenty fifteen as opposed
Speaker 5: to wait until twenty twenty two. Quantum today is there.
Speaker 5: So it's not good enough that you can get a
Speaker 5: big advantage. But if you learn how to use it,
Speaker 5: then in five years you'll be ready to exploit what comes.
Speaker 3: We're going to get to contum in a moment.
Speaker 4: But I had a couple other AI questions, you know,
Speaker 4: I as you know where this conversation is part of
Speaker 4: this thing that we do with IBM Smart Talks, and
Speaker 4: I've been The last episode I did was on Kenya,
Speaker 4: which has a massive deforestation problem, and they got together.
Speaker 4: IBM took all the NASA satellite data, ran it through
Speaker 4: an LLM, and gave them this incredibly precise ten meter
Speaker 4: by ten meter analysis of what trees to plant, where
Speaker 4: to plant them exactly where the you know, an astonishing
Speaker 4: kind of blueprint about how to fix their country ecologically.
Speaker 4: And it made me think, when we analyze the potential
Speaker 4: of AI, are we making a mistake by spending too
Speaker 4: much thinking about the developed world when it's actually the
Speaker 4: developing world where the greatest ROI for this.
Speaker 3: Is to me.
Speaker 5: Look, software technology is are wonderful and the sense they
Speaker 5: can scare and they can be an ad so you
Speaker 5: don't have to do one or the other. You use defrautestation,
Speaker 5: how about the use of pesticides and fertilizers, We overuse it.
Speaker 3: We tend to for.
Speaker 5: Irrigation, we tend to just flood everything, as opposed to say, okay,
Speaker 5: only that one needs it. You could do all those
Speaker 5: things to get a ten times effectiveness, and that all
Speaker 5: would apply to the developing world. How about remote healthcare
Speaker 5: or telehealth using an AI agent. So the examples are numerous.
Speaker 5: In the developed world, I believe we are running out
Speaker 5: of people. I know that nobody likes to hear it.
Speaker 5: Most of the Far East is going to have half
Speaker 5: the number of people by twenty seventy competed today. That's
Speaker 5: not that far away. If I look at Europe, birth
Speaker 5: rates are far under sustaining or keeping population flat. How
Speaker 5: the US, also, depending on which number you want to
Speaker 5: look at, is either one point six births per women
Speaker 5: or two or two point one.
Speaker 3: Why are the three numbers?
Speaker 5: One point six is to women who were born in
Speaker 5: the United States, It becomes two point two. If you
Speaker 5: include immigrant women, it becomes two point one if you
Speaker 5: include children or remigrating in So you got to decide
Speaker 5: where the trend is obvious, This is going down. So
Speaker 5: AI in the developed world is going to be essential
Speaker 5: because to keep our current quality of life you need
Speaker 5: more work done, or what's going to do the work
Speaker 5: if there aren't people to do the work. So the
Speaker 5: problems are different in the places.
Speaker 4: Yeah, it gives you in the developing world, you get
Speaker 4: access to a suite of technologies and things at a
Speaker 4: price you could never been able to afford.
Speaker 3: Correct. That was my in talking to the Kenyon thing.
Speaker 4: It was like the whole it's it's maybe one of
Speaker 4: the largest ecological projects of its kind at fifteen billion
Speaker 4: trees they want to plan, and.
Speaker 5: That is one country that might get it done because
Speaker 5: they do take a lot of pride in their ecology
Speaker 5: and in sort of returning to the land and giving back.
Speaker 3: Yeah.
Speaker 4: Yeah, what's different about IBM's version of AI versus some
Speaker 4: of your.
Speaker 5: So we are not a consumer company, so we have
Speaker 5: no focus on a B two C chatbot. And the
Speaker 5: reason I say that is, if you're making a B
Speaker 5: two C chat bot, does it help you to make
Speaker 5: it even bigger and more competitionally inefficient? And the short
Speaker 5: answer is yes, because you have a certain number of
Speaker 5: users and you kind of say, I kind of say
Speaker 5: this jokingly. If I add finished to French capabilities, I
Speaker 5: can probably add five million users.
Speaker 3: If I add.
Speaker 5: Writing a high coup I might be able to add
Speaker 5: another five million users. If I add writing an email
Speaker 5: in the voice of Steinbeck, I can probably add another
Speaker 5: five million users.
Speaker 3: Do all those things.
Speaker 5: If my goal is to get help a company summarize
Speaker 5: the legal documents in English, that can be a model
Speaker 5: that's one hundred size as effective, probably higher quality.
Speaker 3: But I don't need to go wide.
Speaker 5: So if you're focusing on the enterprise, that actually takes
Speaker 5: away the focus of having to go to extremely large models,
Speaker 5: which but definition going to be computationally expensive, power hungry,
Speaker 5: and demand lots and lots of data. So I can
Speaker 5: turn onto the enterprise. You don't need to worry about
Speaker 5: copyright issues, about all those because you can train on
Speaker 5: a much smaller amount of data.
Speaker 3: And now, by the.
Speaker 5: Way, tuning it for you yourself is a weekend exercise,
Speaker 5: it's not a six month on a big super computer
Speaker 5: cluster somewhere out there. That's one big difference of what
Speaker 5: we do. Second, we are very focused on helping those
Speaker 5: problems that can give people immediate benefit where we have
Speaker 5: domain knowledge. So our domain knowledge is around operations, is
Speaker 5: around programming, encoding, is around customer service, is around customer experience, logistics, procurement,
Speaker 5: let's change the areas where we have a lot of expertise,
Speaker 5: and then three we kind of apply it to ourselves,
Speaker 5: and so we are not asking our clients to be
Speaker 5: the first experiment on it. We say you can have
Speaker 5: what we did. We're happy to bring out all our learnings,
Speaker 5: including what needs to change in the process, because the
Speaker 5: biggest change is not technology, is getting people to accept
Speaker 5: that there's a different way to do things.
Speaker 4: Other challenges to explaining what makes you different to potential customers.
Speaker 5: For sure, the shining object is always attractive. Oh, I
Speaker 5: can go and try chat GPT. Why don't you have
Speaker 5: your GPT version?
Speaker 3: Do you use chat ChiPT?
Speaker 5: I have used it.
Speaker 4: I asked it a question recently which I thought was
Speaker 4: really simple, and it made up about ten people.
Speaker 3: Anyway, I had a bad experience.
Speaker 5: I actually think that that's the fundamental issue with all
Speaker 5: lms as they get larger, because you had to ask
Speaker 5: what was the original insight that led to these It
Speaker 5: was a reward function with intent. So if it has
Speaker 5: learned by using a reward function, it's reward function comes
Speaker 5: from giving an answer that satisfies you. So if it
Speaker 5: thinks that if it makes up an answer that will
Speaker 5: satisfy you, how will you stop it? Why do we
Speaker 5: think this is different than the clever college kid who
Speaker 5: doesn't know an answer? What bullships the way to an answer? Well,
Speaker 5: it's exactly the same.
Speaker 4: It's like the example of clever Hands during that story
Speaker 4: the horse that they thought could speak, then all it.
Speaker 3: Was doing was pleasing it. It's master. Yes, it is
Speaker 3: a little bit of clever hands. Yeah, it's like dogs
Speaker 3: kind of imitating and looking.
Speaker 4: What would you identify as the most significant bottleneck in
Speaker 4: the development of AI?
Speaker 3: What's slowing us down right now?
Speaker 5: I am not convinced that LLMS is the way to
Speaker 5: get much beyond where we'll get incremental improvements, But I,
Speaker 5: for one, don't believe that LMS are going to get
Speaker 5: us to super intelligence or AGI. So I'll park that
Speaker 5: on the side and simply say, we have to find
Speaker 5: a way to fuse knowledge and how do you represent
Speaker 5: knowledge as opposed to have to statistically rediscover it each
Speaker 5: time I ask a question? And how do we fuse
Speaker 5: knowledge with LLM? Maybe then we'll get to leaves and beyond.
Speaker 5: Today on LLMS alone, my view is I think we
Speaker 5: can get a thousand x efficiency in power and cost
Speaker 5: and compute from today. So if you make something a
Speaker 5: thousand times cheaper, would people use a lot more of it?
Speaker 3: Yes?
Speaker 5: And I think those answers lie as is usually in
Speaker 5: compute through advances in semiconductors, advances in software, and advances
Speaker 5: in algorithmic techniques, all three. But how come we're not
Speaker 5: working in any of those three? Were just taking the
Speaker 5: current semic conductor and going more. We're taking the current
Speaker 5: algorithmic techniques and not really trying to invent new ones.
Speaker 5: So I think those are all happen less than five years.
Speaker 3: Mm hmm.
Speaker 4: But why you say there is a we're in a
Speaker 4: moment where people are not pursuing the the optimal strategy
Speaker 4: for exploiting this technology.
Speaker 5: Why because when you see a few people running really
Speaker 5: hard and they're willing to invest any amount of money,
Speaker 5: so efficiency is not the focus. People feel if we
Speaker 5: don't do the same, you'll get left behind.
Speaker 4: So is this a case where there's too much money
Speaker 4: humans have never had for more?
Speaker 2: Right?
Speaker 3: Ever?
Speaker 4: Yeah, but this is this a consequence of overinvestment in
Speaker 4: the in the field.
Speaker 5: Going back to my internet allology, if two out of
Speaker 5: ten are going to succeed. How do you guarantee or
Speaker 5: how do you improve the orders that you are one
Speaker 5: of those two. So if you pause to say I
Speaker 5: want to become more efficient, that's not the way to win.
Speaker 3: So first you win, then you become efficient.
Speaker 4: Yeah, let's talk about what is I was told your
Speaker 4: favorite topic it's quantum?
Speaker 3: Is what? Boy even go any further? Why is quantum
Speaker 3: your favorite topic?
Speaker 5: We've only had two kinds of compute in the history,
Speaker 5: so nineteen forty five was to use that year for
Speaker 5: any act. All the way till twenty twenty, we had
Speaker 5: one kind of computerlassical what today you would call a
Speaker 5: classical computer. Then GPUs and AI came around, so you
Speaker 5: would say the intuition there is you went from sort
Speaker 5: of bits, which is algebra or high school algebra, to
Speaker 5: including neurons, which is captured in linear algebra. But that
Speaker 5: gives you a different kind But it can do problems
Speaker 5: that are really hard to do. I don't say impossible,
Speaker 5: just hard to do on normal computers. Quantum adds a
Speaker 5: third kind of math. Yes, the physics properties which really
Speaker 5: get people energized and the imagination going. And we use
Speaker 5: all these words about entanglement and silver position, but maybe
Speaker 5: because I'm a bit of a math guy. The real
Speaker 5: thing is that does a third kind of math to
Speaker 5: make it really simple, a third kind of math that
Speaker 5: comes from the field of abstract algebra. It does the
Speaker 5: math you can use Hamiltonians for those who like physics,
Speaker 5: or you can use the word Lee algebras for those
Speaker 5: who like abstract mathematics. If you can do a third
Speaker 5: kind of math, which algorithms are suited to that third
Speaker 5: kind of math. So it excites me because we can
Speaker 5: now approach algorithms that you just could never do on
Speaker 5: the other two it's impossible. Now it's different than AI.
Speaker 5: It's not data intensive. It's compute intensive. So we kind
Speaker 5: of had compute and supercomputers. Then we went to data
Speaker 5: which is AI. And now if you say there's another
Speaker 5: class of problems, it require lots of compute.
Speaker 3: That's quantum.
Speaker 4: A couple months ago was at the to watch some
Speaker 4: research center and they have you know, on the ground floor,
Speaker 4: they have those behind the glass. There's incredibly exciting looking machines.
Speaker 4: But where are we in the timeline of this.
Speaker 5: Three to five years away from shocking people? What does
Speaker 5: shocking people mean do something that nobody thought was possible
Speaker 5: in that timeline.
Speaker 3: Does an example come to mind?
Speaker 5: I was actually pleasantly surprised. So one of our clients, HSBC,
Speaker 5: last week published a result that using a quantum computer
Speaker 5: bond trading was thirty four percent more accurate than their
Speaker 5: prior technique.
Speaker 3: Thirty four percent. Thirty four percent.
Speaker 4: This is an industry that's used to one percent correct,
Speaker 4: zero point five percent.
Speaker 3: Yes, that's astonishing.
Speaker 5: Now that was not at a scale when they could
Speaker 5: turn it into production today, but that was sort of
Speaker 5: their original thought experiment, and that's what they did. Now
Speaker 5: can you imagine when will somebody so you were correct.
Speaker 5: You talk about an industry where one basis point, if
Speaker 5: I remember I may be wrong, like thirteen trillion dollars
Speaker 5: of money kind of moves around in the financial industry
Speaker 5: each day, right, so basis point would be thirteen billion
Speaker 5: something like that, right, one over ten thousand. So when
Speaker 5: you think about the kind of profit that people can make,
Speaker 5: if you can tell somebody that you can come up
Speaker 5: with a better price than your competition by just one
Speaker 5: basis point, they would actually gain the market share. Yeah,
Speaker 5: So I think something around there or something in the
Speaker 5: world of materials. Can we make a better battery? Could
Speaker 5: we make a solid state battery? Which means your risk
Speaker 5: of fires heating decrease dramatically.
Speaker 4: And the reason, sorry to ask a really nive question,
Speaker 4: why is it that a quantum computer would be better
Speaker 4: at solving a battery problem than our existing methods of computing?
Speaker 5: So the equations of quantum mechanics and chemistry and how
Speaker 5: things interact or well known to solve them, that are
Speaker 5: no known techniques. So these are not like closed form,
Speaker 5: you know, it's not like the square root of a
Speaker 5: quarter equation. So the only way to solve them is
Speaker 5: to explore the state space. So if you have a
Speaker 5: few hundred electrons, you need two to the one hundred states.
Speaker 3: Well, I'm sorry you don't have that much memory. It's impossible.
Speaker 5: So it takes a really really long time on a
Speaker 5: normal computer to solve those problems, right, that's simple a problem.
Speaker 5: If a quantum computer operates in the equation domain, it
Speaker 5: doesn't need to explore the state space. It can actually
Speaker 5: solve it. That's why I call it a different kind
Speaker 5: of math. That's the kind of math it does. So
Speaker 5: in a couple of seconds it can tell you this
Speaker 5: is how that material will be here.
Speaker 3: Oh, I see, so you've.
Speaker 5: Taken what could take years to a few seconds. Yeah,
Speaker 5: that's a pretty big change.
Speaker 4: Yeah. Yeah, it's speaking a different language, different line. So
Speaker 4: any kind of problem that comes along that's specific to
Speaker 4: that language correctly.
Speaker 5: Which is not all problems. Yeah, just as I call it,
Speaker 5: it's one more kind of math.
Speaker 4: Yeah, what's an example? So so many questions. L give
Speaker 4: me another example of a of a of a kind
Speaker 4: of problem that a quantum computer would love.
Speaker 5: This one is a bit more speculative, and I'm going
Speaker 5: to use a little bit of poetic license. So let's
Speaker 5: take a post office in a mid sized country. They
Speaker 5: probably burn a billion gallons of fuel per year delivering
Speaker 5: packages and letters because most posts and advanced country says
Speaker 5: every house, every address, each day. The way to optimize
Speaker 5: this is we can formulate the problem. It's called the
Speaker 5: proveling sales and problem solving. It is really hard, so
Speaker 5: people have heuristics. Let's suppose today our heuristics get us
Speaker 5: to within twenty percent of the optimal answer. Let's suppose
Speaker 5: a quantum computer can get you the next ten percent. Well,
Speaker 5: if I can get ten percent of a billion gallons
Speaker 5: that I think is one hundred million gallans of my
Speaker 5: math is right, and in the country I'm thinking about,
Speaker 5: that could be eight hundred million pounds of saving to
Speaker 5: one entity in one year. And the associated carbon footprint
Speaker 5: climate change were in't less mileage on vague. I'm not
Speaker 5: even counting all that. These are pretty attractive problems to
Speaker 5: go after. So if I look at the interest recently,
Speaker 5: New York has started a whole program in some places.
Speaker 5: Illinois stood up a quantum algorithm center between a number
Speaker 5: of the universities. The governor there was heavily behind it, etc.
Speaker 5: So I wouldn't say that this is widespread. This is
Speaker 5: why I'm saying three to four years for that moment.
Speaker 5: But there's enough people who are deeply cognizant who are saying,
Speaker 5: wait a moment, we kind of get it.
Speaker 3: This is a new kind of man. What are the
Speaker 3: new problems we can solve?
Speaker 5: And the fact that we have bought roughly two hundred
Speaker 5: clients who worked with us early stage small experiments is
Speaker 5: because the intuition is I can do something here that
Speaker 5: I couldn't do in other places.
Speaker 4: Three to four years is not a long time, no,
Speaker 4: But if I'm in the battery business, and I don't
Speaker 4: have a line out to a quantum computing experiment.
Speaker 3: I have a problem, don't have a problem.
Speaker 5: Yeah, you'll probably be out of business in ten years.
Speaker 5: Well maybe you could write a big check and buy
Speaker 5: the technology from somebody else.
Speaker 4: You had to What is quantum rank in the kind
Speaker 4: of great inventions of the last one hundred and fifty years.
Speaker 5: Equal to semic conductor? And I think that if semiconductor's vanished,
Speaker 5: modern life.
Speaker 3: Would stop, like just stop.
Speaker 5: Yeah, no electricity, no automobile, no streaming. You can imagine
Speaker 5: the yells from all the kids. Who ever hear that
Speaker 5: no streaming?
Speaker 4: And is that it's funny because don't. As someone who's
Speaker 4: outside this world, I feel like quantum is underdiscussed relative
Speaker 4: to its potential for transforming society.
Speaker 5: Because I use my Internet example. Ninety five was the
Speaker 5: moment with Netscape that Internet came on people's consciousness. I
Speaker 5: said in eighty five I considered it to be this
Speaker 5: is a solved problem because it needs something that makes
Speaker 5: it accessible easy. That was the browser. The Netscape browser
Speaker 5: is what brought it easy to understand. We have probably,
Speaker 5: as I said, about four to five years from that moment.
Speaker 5: That's why it's under discussed because the moment I say
Speaker 5: you got a math, I've probably lost ninety nine percent
Speaker 5: of the audience. If I go to quantum mechanics, I've
Speaker 5: probably lost nine percent of the audience.
Speaker 4: So you, as c over the last five years, have
Speaker 4: been really the birth mother for a lot of the
Speaker 4: quantum computing work. I'm curious, so you come in. When
Speaker 4: you started a CEO, was this your first priority.
Speaker 5: I had already started investing in it back in twenty
Speaker 5: fifteen when I was leading IBM research. So let me
Speaker 5: acknowledge and like nobody should try to copy. And I've
Speaker 5: had I'll call it a weird career. I was a
Speaker 5: researcher at some point. If it had asked me out,
Speaker 5: I said, I'm one of those people, you know, throw
Speaker 5: a pizza under.
Speaker 3: Our door and like leave me alone. I don't want
Speaker 3: to talk to people.
Speaker 5: Then I decided I was interested in the business. Then
Speaker 5: I went and started acquiring companies and doing that. Then
Speaker 5: somebody told me, hey, why did you start doing some
Speaker 5: business strategy. Then I went back to research and led
Speaker 5: our research division for a couple of years. And when
Speaker 5: the people described it to me. I asked some questions.
Speaker 5: So it wasn't a big investment at that time. It
Speaker 5: was hey, can we make a computer not just a
Speaker 5: science experiment? Can it run by itself all night? Can
Speaker 5: you think about software so that even people who are
Speaker 5: not deep of quantum mechanic can begin to use it?
Speaker 5: And they begin to do those things. So over three
Speaker 5: four years, did they get enough confidence Yeah, Okay, this
Speaker 5: is something that can really work. And then you've got
Speaker 5: to nurture it to where it gets bigger and bigger
Speaker 5: until you get the confidence that okay, now it's a
Speaker 5: big bet.
Speaker 3: And what was the moment when you when you realize
Speaker 3: now it's a big.
Speaker 5: Bet, probably two or three years ago.
Speaker 4: And how do you decide, as the head of a
Speaker 4: company like this, how much money, how many resources, and
Speaker 4: how many people? And how what kind of promise to
Speaker 4: give to an idea like that?
Speaker 5: So three layers the set of people who actually have
Speaker 5: the knowledge and the intensity to fundamentally advance the technology.
Speaker 5: If I could find more out higher then So I'm
Speaker 5: constrained of people on that one, because normally there's only
Speaker 5: so many people who can do these things. Two, you
Speaker 5: got to be careful if you push too hard on timing,
Speaker 5: you'll get people to take so much risk that actually
Speaker 5: the thing will fail. So that's the art of between
Speaker 5: the leadership on the project and me to say, Okay,
Speaker 5: how hard can you push? But not so hard that
Speaker 5: you cause it to fail because then they get compelled
Speaker 5: to commit timelines that are just impossible.
Speaker 3: Yeah, how do you This is fascinating.
Speaker 4: So it's ultimately a question of judgment trying to figure
Speaker 4: out what's the sweet spot between enough pressure to keep
Speaker 4: them ahead of the pack, but not too much pressure
Speaker 4: so that they start taking risks. How do you calibrate
Speaker 4: whether you're hitting that sweet spot? I mean, do you
Speaker 4: reassess every few months and say I think I'm over
Speaker 4: correcting or undercorrecting at this moment.
Speaker 5: So one you got to have what I call and
Speaker 5: this is channeling a word from one of my favorite books,
Speaker 5: The Geek Away, How open can you be?
Speaker 3: So I want to press hard?
Speaker 5: But the team knows that they're allowed to push back
Speaker 5: and really argue back hard. That means you'd get do
Speaker 5: probably that correct goldilocks pressure. Do the people themselves should
Speaker 5: want to go as hard as possible, but not harder
Speaker 5: than possible. So that is then personality of leadership that
Speaker 5: makes sense.
Speaker 4: But you have to be someone who people feel comfortable
Speaker 4: being honest with.
Speaker 3: Yes, absolutely, and people feel comfortable being honest with you,
Speaker 3: I believe so. Yeah.
Speaker 4: When has there been a moment in this path with
Speaker 4: quantum where you did think you were pushing too hard?
Speaker 3: No?
Speaker 5: Because I think that the leadership there will argue back
Speaker 5: with me any day of the week. I don't think
Speaker 5: that they feel that they have to ford.
Speaker 4: Do you drop by at sort of Saturday night at
Speaker 4: ten pm to see if people are working.
Speaker 5: I tend to text people and ask questions and like
Speaker 5: I'll read something and say, hey, are these people doing this?
Speaker 5: And if they can answer me in reasonable terms, I
Speaker 5: actually then say great, they're already watching the competition, They're
Speaker 5: watching the literature, they're watching the science. I don't need
Speaker 5: to push hard. If they are already ahead of it,
Speaker 5: then me I can answer my question. I'll say thoughtfully,
Speaker 5: not always completely accurately, you're thinking about it on their own.
Speaker 3: I don't need to push. One last question I wanted
Speaker 3: to ask you, do you have the most interesting job
Speaker 3: in America?
Speaker 5: I believe that it's the most interesting job, which I
Speaker 5: won't give up for anything.
Speaker 3: It also sounds like you're enjoying yourself.
Speaker 5: I enjoy it as long as look my role and
Speaker 5: goal should be to make the enterprise thrive. As long
Speaker 5: as than making the enterprise thrive, and are clients delighted?
Speaker 3: I love it.
Speaker 5: If I don't, somebody else.
Speaker 3: Should do it. Harvin, this has been so much fun.
Speaker 4: Thank you so much taking the time and a fascinating,
Speaker 4: completely fascinating conversation. I wish I was one of those
Speaker 4: people who could help you out with quantum, but I'm
Speaker 4: afraid I'm not.
Speaker 3: In a few years. Good Thank you so much.
Speaker 4: Smart Talks with IBM is produced by Matt Ramano, Amy Gains, McQuaid,
Speaker 4: Trina Menino, and Jake Harper. Mastering by Sarah Bugerer, Music
Speaker 4: by Gramoscope, Strategy by Tatiana Lieberman, Cassidy Meyer and Sophia Derlong.
Speaker 4: Smart Talks with IBM is a production of Pushkin Industries
Speaker 4: and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,
Speaker 4: listen on the iHeartRadio app, Apple Podcasts, or wherever you
Speaker 4: listen to podcasts. I'm Malcolm Glawe. This is a paid
Speaker 4: advertisement from IBM. The conversations on this podcast don't necessarily
Speaker 4: represent IBM's positions, strategies, or opinions.
Speaker 2: The take UNT