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