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