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Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. This season,

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Speaker 1: non Smart Talks with IBM, Malcolm Glabwell is back, and

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Speaker 1: this time he's taking the show on the road. Malcolm

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Speaker 1: is stepping outside the studio to explore how IBM clients

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Speaker 1: are using artificial intelligence to solve real world challenges and

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Speaker 1: transform the way they do business. From accelerating scientific breakthroughs

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Speaker 1: to reimagining education. It's a fresh look at innovation in action,

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Speaker 1: where big ideas meet cutting edge solutions. You'll hear from

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Speaker 1: industry leaders, creative thinkers, and of course Malcolm Glabwell himself

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Speaker 1: as he guides you through each story. New episodes of

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Speaker 1: Smart Talks with IBM drop every month on the iHeartRadio app,

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Speaker 1: Apple Podcasts, or wherever you get your podcasts. Learn more

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Speaker 1: at IBM dot com, slash smart Talks.

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Speaker 2: Pushkin Hello.

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Speaker 3: Hello, I'm Malcolm Gladwell and you're listening to Smart Talks

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Speaker 3: with IBM. This season, we've been bringing you stories of

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Speaker 3: how IBM works with its clients to solve complex problems,

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Speaker 3: like helping Lareel reimagine how scientists approach cosmetic formulation, or

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Speaker 3: enabling Scuderia FERRARIHP to connect with fans in new ways.

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Speaker 3: But in this episode, we're going to zoom out and

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Speaker 3: look at the bigger picture. Earlier this month, I had

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Speaker 3: the chance to meet the person who's shaping IBM's future.

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Speaker 3: It's CEO and Chairman Arvind Krishna. We sat down in

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Speaker 3: front of an intimate live audience at IBM's New York

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Speaker 3: City office and talked about his uncanny ability to anticipate

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Speaker 3: where technology is heading, the future of AI, and his

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Speaker 3: passion for quantum computing, which he says is as revolutionary

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Speaker 3: as a semiconductor. Thank you everyone, Thank you to Arvin.

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Speaker 3: You're a difficult man to schedule for one of these things,

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Speaker 3: so we're enormously please you could join us. Let's start

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Speaker 3: with a I have all these cousins, two cousins who

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Speaker 3: work for IBM their entire career. I would ask them

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Speaker 3: what does IBM do? And they would always give me different, confusing,

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Speaker 3: complicated answers.

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Speaker 2: What's your answer? What's your simple answer to that question.

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Speaker 4: IBM's role is to help our clients improve their business

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Speaker 4: by deploying technology. That means you're not ever gated to

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Speaker 4: one product. It is what makes sense at that time,

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Speaker 4: but it is about improving their business, not just giving

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Speaker 4: them a commodity.

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Speaker 5: Then to go to the.

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Speaker 4: Next layer, I would say, we help them through a

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Speaker 4: mixture of hybrid cloud and artificial intelligence and a taste

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Speaker 4: of quantum coming down the road is kind of where

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Speaker 4: I would take it.

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Speaker 5: That's that's what IBM is.

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Speaker 2: So you are technology agnostic in some sense.

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Speaker 4: I'm product agnostic. Product I'm not technology agnostic.

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Speaker 3: Yes, But if I twenty five years from now, IBM

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Speaker 3: could be doing things that would be unrecognizable to contemporary IBM.

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Speaker 5: It is completely possible.

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Speaker 4: Yeah, it could be there in twenty five years from now.

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Speaker 4: The only software IBM does is open source. It could

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Speaker 4: be the only computing you do is quantum computers. And

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Speaker 4: if I add those two people today, WLD say that's

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Speaker 4: not the IEM of today.

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Speaker 3: Is it even simpler to say you just IBM solves

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Speaker 3: problems at the highest technical level.

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Speaker 5: If you say highest technical level, yes, yeah.

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Speaker 4: Like the guy who invented the bar code, he was

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Speaker 4: solving a problem retailers wanted to scale. Many of you

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Speaker 4: may not know it was an IBM or who invented

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Speaker 4: the bar code, by the way, not somebody who was

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Speaker 4: a PhD. Not somebody who was a deep researcher. I

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Speaker 4: think it was actually a field engineer. Oh really, Yeah,

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Speaker 4: And lasers were out and you could use lasers to

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Speaker 4: scan things, but they could be upside down, they could

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Speaker 4: be muddy, they could be partly scraped off. And he

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Speaker 4: came up with the idea of the bar code. Yeah,

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Speaker 4: and that changed inventory management forever. But the world needs

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Speaker 4: to know that IBM invented the Parker. You guys should

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Speaker 4: do a better job opicizing that. I am sure our

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Speaker 4: CMO will listen to this podcast and we'll get that idea.

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Speaker 3: Tell me you started at the Thomas Watson Research Center.

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Speaker 3: What were you doing when you first started it, IBM.

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Speaker 4: I started in nineteen ninety and that was the era

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Speaker 4: in which computers and networking were beginning to converge. And

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Speaker 4: for the first five years I was actually building networks.

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Speaker 4: So let's remember this was pre laptops. Laptops came in

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Speaker 4: ninety two or ninety three, but it was clear to

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Speaker 4: us that they were going to come supportable computing, and

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Speaker 4: I spent my first five years building what today you

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Speaker 4: would call Wi Fi. We used to have these debates

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Speaker 4: can be built it, it's got to be small enough.

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Speaker 4: I mean like, it can't be more than one hundred

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Speaker 4: grams was kind of our thought, because if it's more

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Speaker 4: than that, you're on a three thousand grand laptop.

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Speaker 5: Why would anybody put this on?

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Speaker 4: And the debate used to be why would anybody want

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Speaker 4: to walk around untethered?

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Speaker 5: Won't you want to attach a big thick cable into

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Speaker 5: it and sit down?

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Speaker 4: Because that was the thought, that's how terminals worked. And

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Speaker 4: I spent five years having a lot of fun building

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Speaker 4: many iterations of those and making progress on that.

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Speaker 3: If I had a conversation with your nineteen ninety self

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Speaker 3: about what the next thirty years were going to look like,

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Speaker 3: is it possible to reconstruct what your What were your

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Speaker 3: predictions at that age about where the company, where the

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Speaker 3: industry was going.

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Speaker 4: It was more about where technology was going to go,

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Speaker 4: I would say, than where industry would go. I would

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Speaker 4: have told you that networking and computers would fuse in

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Speaker 4: nineteen ninety That was a weird thought that some researchers held.

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Speaker 4: By the late nineties, that was obvious that it became

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Speaker 4: the Internet. I would have told you that I believe

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Speaker 4: that streaming will be the primary way people will consume.

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Speaker 2: Video you would have said that in nineteen ninety.

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Speaker 5: Absolutely, Now, that didn't take five years. That took twenty.

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Speaker 4: But it happened because you could do it technically, except

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Speaker 4: it just too expensive and too cumbersome. And if you've

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Speaker 4: been in technology, like in nineteen eighty five, I would

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Speaker 4: have told you the internet is old because when I

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Speaker 4: went to grad school, every one of us had a

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Speaker 4: those days, an Apple, Mac or Lisa on our desks.

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Speaker 4: They're all connected by a network. You're happily sending email

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Speaker 4: to people all around the country. We were doing file transfers,

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Speaker 4: so okay, you had to be a little bit more

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Speaker 4: aware of the technology. And it didn't have a browser.

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Speaker 4: That took ten years to get the browser. That took

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Speaker 4: five years to be a business. But when you see

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Speaker 4: the speed and the pace of technology in usually ten

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Speaker 4: or fifteen years, the cost point and the consumerization is

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Speaker 4: at a scale that you couldn't imagine ten years ago

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Speaker 4: until you've seen a few of those cycles.

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Speaker 3: Wait, did you make the lead to sorry, this is fascinating,

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Speaker 3: I'm curious, but how far did you take that? That's a

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Speaker 3: really fundamental thing to have gotten right in nineteen ninety

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Speaker 3: I think that we.

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Speaker 4: Were pretty convinced that what we used to think of

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Speaker 4: as linear television or broadcast would become digitized. That was

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Speaker 4: a given two with cable already the preponderance of how

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Speaker 4: people got it that if you put packet television over cable,

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Speaker 4: then that becomes the way it will go. I fundamentally believe,

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Speaker 4: actually way back eighty seven, that on demand movies would

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Speaker 4: become the way people would consume movies. So those were

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Speaker 4: all things that I could have predicted nine then personally

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Speaker 4: work on all those. I mean, after networking, I moved

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Speaker 4: on to doing other things, but those were easy to predict.

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Speaker 3: If you had a conversation in those years with someone

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Speaker 3: in the television industry and you gave them those predictions,

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Speaker 3: did they see it?

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Speaker 2: Were they convinced of this?

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Speaker 4: I'm actually going to take it back to wireless networking.

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Speaker 4: I think one of the reasons I do what I

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Speaker 4: do today, which is at the intersection of business and technology,

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Speaker 4: is because of what I saw happened with Wi Fi.

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Speaker 4: So you built these wireless networks and then you say, hey,

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Speaker 4: the market's going to be millions, tens of millions, billions

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Speaker 4: of users, and the business looks at it and says

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Speaker 4: we think the market is confined to warehouse workers doing inventory.

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Speaker 4: You can look at them and say, why not people

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Speaker 4: in their homes because they could imagine outside how people

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Speaker 4: bought things at that time. And so I became convinced

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Speaker 4: that I can't just help invent it. I got to

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Speaker 4: think about, now, how do you market it? To whom

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Speaker 4: do you market it? What are their routs? How do

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Speaker 4: you make it easy enough? And that was probably I mean,

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Speaker 4: I'm making it simple now. That is probably a five

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Speaker 4: to ten year evolution of myself in those days.

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Speaker 3: You know what this reminds me of when the telephone

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Speaker 3: is invented in the eighteen seventies, it doesn't take off

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Speaker 3: for forty years because the people running a telephone business

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Speaker 3: they didn't want women using it because they were worried

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Speaker 3: that women would gossip with their friends. They didn't understand

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Speaker 3: that that's actually what telephone is, right, it's an exact parallel,

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Speaker 3: Yes it is.

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Speaker 5: You see it again and again.

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Speaker 2: What is the source of that blindness?

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Speaker 3: So there's a gap, in other words, between the invention,

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Speaker 3: the technological achievement, and the social understanding of the technology.

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Speaker 2: Why is there such a gap?

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Speaker 4: I think that the gap is fundamental and rooted in

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Speaker 4: a lot of academic disciplines. So even channeling some of

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Speaker 4: your work, though you don't intend it to be used

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Speaker 4: that way, you can say a lot of things that

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Speaker 4: data driven. If it is data driven, then by definition,

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Speaker 4: you're looking at history. If you're looking at history, that

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Speaker 4: means you're looking at existing buying patterns. If you look

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Speaker 4: at existing buying patterns, you forget. All of those who

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Speaker 4: have created massive value in time have all created markets,

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Speaker 4: meaning they've all created new markets.

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Speaker 5: And I think that is why.

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Speaker 4: The world is fascinated with people like Steve Jobs. For example,

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Speaker 4: he imagined a market that didn't exist. So I think

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Speaker 4: that is the gap. And then if you can get

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Speaker 4: the technology the business acumen scaler company, and that imagination

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Speaker 4: of making a market is how you create I think

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Speaker 4: massive value.

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Speaker 5: You got to get all three pieces going.

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Speaker 2: It's not enough.

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Speaker 3: In other words, you were thinking it's not enough to

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Speaker 3: invent something new. I need to make a business case

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Speaker 3: for it simultaneously, and that that's what gets you thinking

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Speaker 3: along the path that leads you to this job.

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Speaker 4: Oh yeah, I'll tell you if you had met or

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Speaker 4: when the nineteen ninety four and you had talked about

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Speaker 4: the stock market or about a balance sheet, or looked

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Speaker 4: at you like, Okay, I got the those words are,

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Speaker 4: I can parse them.

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Speaker 5: I have no idea what they are. I have no

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Speaker 5: intuition on what they are.

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Speaker 4: I couldn't tell you why it's relevant or why it's not.

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Speaker 4: But then you began to think, Okay, why do companies

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Speaker 4: get higher values? Okay, that's the stock what does that

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Speaker 4: capture if I have to spend working capital and that's

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Speaker 4: the balance sheet? Well, so you learn. I mean, I figure,

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Speaker 4: I'm willing to learn. I'm willing to read. However, the

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Speaker 4: best way I read is to go to balance sheets.

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Speaker 4: Here you can read the book. It's pretty damn dry.

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Speaker 4: Much easier to go talk to a financial expert who's

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Speaker 4: around the corner, and people are if you're curious about

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Speaker 4: what they do, they're really happy to share their expertise,

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Speaker 4: and over time you learn more and more and they

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Speaker 4: actually become part of your network within the company. And

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Speaker 4: that's how you can both learn and evolve yourself and

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Speaker 4: actually gain the extra.

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Speaker 2: Skills you are to be a successful business leader.

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Speaker 3: Do you have to unlearn or deviate from some of

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Speaker 3: the things that made you as successful scientist?

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Speaker 5: I actually believe the exactly opposite.

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Speaker 4: Yeah, but use what you're really good at as a foundation,

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Speaker 4: but don't make it the only thing you use. So

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Speaker 4: then how do you add the other skills? And there's

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Speaker 4: many ways you can have people that you trust who

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Speaker 4: help you out those skills. You can gain some intuition,

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Speaker 4: maybe not the depth of expertise. I want to be

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Speaker 4: deeper on certain areas of electrical engineering than I'm ever

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Speaker 4: going to be, let's say, in finance or marketing. But

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Speaker 4: I want to be curious about those. I don't want

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Speaker 4: to dismiss them. So you build on your skills, and

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Speaker 4: then you have to say, but I need a complete

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Speaker 4: and holistic view. So I'm going to be a little deep,

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Speaker 4: not very deep, in all of those. And you've also

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Speaker 4: got to learn to trust your intuition a little bit.

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Speaker 3: Yeah, but I forgot a question that I wanted to ask,

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Speaker 3: But about the predictions of nineteen ninety arvand what did

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Speaker 3: you get wrong?

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Speaker 4: All lots of things. I think that people were thinking

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Speaker 4: that in those days, and it started my phrase, but

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Speaker 4: I'll come back to it. I think most people thought

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Speaker 4: that the communication companies would turn out to be the

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Speaker 4: winners of how networking got carried. If you all think

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Speaker 4: through the nineties of the investments that were being done

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Speaker 4: by let's not take the names of all of the

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Speaker 4: telecom carriers, didn't turn out to be the case. Actually,

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Speaker 4: I think that's the business model case. The reason is

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Speaker 4: they all had in their heads that you can charge

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Speaker 4: people by the minute.

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Speaker 2: Because they had been doing that already.

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Speaker 5: Because they'd been doing that for one hundred years.

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Speaker 4: Yeah, and in the end, the winners the networking were

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Speaker 4: those who sat flat price thirty bucks a month or

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Speaker 4: fifty bucks a month or whatever, and that was just

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Speaker 4: too much of.

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Speaker 5: A leap for them.

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Speaker 3: You think it's as simples, That is the most parsimonious

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Speaker 3: explanation for why you think they failed.

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Speaker 4: No, there were a couple of other more technical things.

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Speaker 4: The one was written by somebody who was inside one

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Speaker 4: of these telecom companies, and he labeled his article the

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Speaker 4: Rise of the Stupid Network. So telephone people believe that

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Speaker 4: the network should be really smart. The end device is dumb.

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Speaker 4: If you think about the telephone, telephone is dumb. It

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Speaker 4: doesn't actually do anything. It's just about the relays. And

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Speaker 4: the network is smart. It routes you, it figures out

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Speaker 4: where to send it. It does echocaculation backwards, and the

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Speaker 4: current Internet is completely dumb with the inside. It just

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Speaker 4: takes the bits and shoves them out the other end.

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Speaker 4: All the intelligence is the computer at the end. That's

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Speaker 4: probably a bit more of a found explanation. But business

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Speaker 4: model didn't help them either.

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Speaker 2: Yeah.

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Speaker 3: Wait, did nineteen ninety r end think that the network

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Speaker 3: should be dumb or smart?

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Speaker 4: I'm not sure I thought about it deeply, but everything

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Speaker 4: I worked on the network was dumb.

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Speaker 5: The network movements, That's all I did.

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Speaker 4: Yeah, because even I in those days understood I can't

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Speaker 4: imagine all the applications.

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Speaker 5: So if all you do is voice me, maybe the

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Speaker 5: network can be smart.

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Speaker 4: But if you're doing all those other things, how could

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Speaker 4: the network possibly know all those things and be smart

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Speaker 4: for it?

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Speaker 2: Yeah, so you've been COO for five years.

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Speaker 5: Five years?

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Speaker 3: Wait, so in your five year increment, what was your

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Speaker 3: most misunderstood decision?

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Speaker 2: Well, you ended up being right, but everyone thought you

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Speaker 2: were crazy.

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Speaker 4: Twenty and eighteen, I proposed to our boat that we

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Speaker 4: should buy a company called rad Hat. IBM does proprietary

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Speaker 4: but that was open source. The stock twelve fifteen percent

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Speaker 4: of the day we announced it, and today most people

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Speaker 4: will turn around and say, this is the most successful

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Speaker 4: acquisition that IBM has done in all time, and probably

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Speaker 4: the most successful software acquisition in history. So it was

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Speaker 4: completely misunderstood because people didn't see that you actually did

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Speaker 4: need a platform that could make it agnostic across multiple

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Speaker 4: cloud platforms, across on premise environments. So you've got to

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Speaker 4: have a view of what it could be, and we

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Speaker 4: drove it to a place where I think today it

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Speaker 4: stands as the leader in its space.

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Speaker 3: So how did you come to believe this heretical notion?

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Speaker 4: So Cloud was happening, you could ask yourself the question,

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Speaker 4: should we spend a lot of capital and chase Cloud? Okay,

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Speaker 4: you're five years to be generous, maybe longer behind at

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Speaker 4: that point the two leaders, So you could spend maybe

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Speaker 4: ten billion a year, and a lot of businesses tend

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Speaker 4: to do that. Okay, it's so important, it's going to

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Speaker 4: be half the market. I can't not. My view was

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Speaker 4: we'll always be five years behind. They're not dumb and

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Speaker 4: they're not slow. So if you're going to be there,

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Speaker 4: you're going to be best case, a distant third, worst

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Speaker 4: case maybe a fourth or a fifth. Because there's Chinese

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Speaker 4: also in the mix. Why would you do that instead?

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Speaker 4: Is there a different space you can occupy instead of

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Speaker 4: competing with them? Can you become their best partner? In

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Speaker 4: which case you right there success. If I want to

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Speaker 4: be the best partner, then what are the set of

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Speaker 4: technologies that would be useful? So you can flip The

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Speaker 4: problem is how I thought about it.

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Speaker 3: How hard was it to convince people needed convincing before

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Speaker 3: that acquisition.

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Speaker 5: Probably six to nine months of.

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Speaker 4: Breaking my head with no success, and then six months

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Speaker 4: of building the momentum once a couple of people began

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Speaker 4: to see it.

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Speaker 2: Yeah, you're very persistent.

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Speaker 3: Oh yes, very Would you describe that as you defining trade?

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Speaker 5: I am very persistent and I'm very patient.

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Speaker 4: I'm also probably very impatient, But I'm not a yeller

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Speaker 4: and screamer.

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Speaker 5: I don't rant and rave.

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Speaker 4: But as I say, if I think we're going to

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Speaker 4: do something, I can be remarkably stop about it.

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Speaker 5: We will do it.

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Speaker 3: If I got your family, put them up on stage

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Speaker 3: and ask them this exact question, is this how they

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Speaker 3: would answer?

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Speaker 2: As well?

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Speaker 4: They will tell you I'm very stubborn. They might not

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Speaker 4: agree that I don't rant and drave.

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Speaker 3: Well, you know, one of the principal observations of psychology

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Speaker 3: is that our home self and our work self are uncorrelated.

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Speaker 2: Once you know that, you know everything.

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Speaker 3: Wait, I'm curious one last question about that. How long

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Speaker 3: does it take for you to be vindicated with red Hat?

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Speaker 4: Probably took five maybe four years, I think by twenty

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Speaker 4: twenty three, So twenty eighteen we announced it, We took

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Speaker 4: the big shot croft. It took a year to close

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Speaker 4: twenty nineteen, so if I count, not that I'm counting

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Speaker 4: that much, but July ninth, twenty nineteen as the day

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Speaker 4: that we got all the approvals. Took another few weeks

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Speaker 4: to actually transfer the money. But from there, probably twenty

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Speaker 4: twenty three, the world woke up and said, hey, you

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Speaker 4: guys deserve credit for this was actually.

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Speaker 5: A great move, not a bad move.

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Speaker 3: Yeah, but this is it's interesting because this is a

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Speaker 3: real gamble. If it doesn't work, you're not sitting in

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Speaker 3: this chair right now.

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Speaker 2: Right.

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Speaker 5: Oh, for sure. There were two steps.

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Speaker 4: One if it was obviously not going to work, I

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Speaker 4: wouldn't have been selected, and two if it hadn't worked

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Speaker 4: after that, that's why CEOs can be short lived.

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Speaker 2: Can I ask you a sort of a personal question.

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Speaker 2: How much sleep did you lose over this?

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Speaker 5: Once we had made the decision, none.

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Speaker 3: Can you give me pointers on how you do this?

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Speaker 3: Because I wake up at two am every morning and

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Speaker 3: I over much more trivial things than this.

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Speaker 5: Once a week, I'll probably wake up at two or

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Speaker 5: three in the morning.

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Speaker 4: I acknowledge it because I wake up and my brain

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Speaker 4: is running, and once it's running, I don't even.

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Speaker 5: Try to go back to sleep.

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Speaker 4: I mean, go get up and do work and make

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Speaker 4: yourself productive. You're gonna be tired before in the afternoon,

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Speaker 4: that's fine, you'll sleep well that night. I have actually

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Speaker 4: learned a long time back. You can't do it across.

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Speaker 4: You can't do it early morning, through the day and

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Speaker 4: late at night. So an hour before I think I

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Speaker 4: want to go to bed, I will actually change what

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Speaker 4: I'm doing, meaning I will start reading something interesting to

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Speaker 4: me but completely outside the scope of work. I may

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Speaker 4: read a biography, I might read somebody who's spontefecating on

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Speaker 4: demographics and population. But I won't read it on leadership,

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Speaker 4: because that's too close now. Twenty years ago I might

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Speaker 4: have that would have been different. I won't read it

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Speaker 4: on deep signs because that's too close to what we

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Speaker 4: do for a living. So it's got to be outside

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Speaker 4: the things that will make my brain churn about work.

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Speaker 4: But it's got to be something that is dense enough

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Speaker 4: to occupy your brain, so it shift gears.

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Speaker 2: Sorry, I want to dwell on this just for a moment.

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Speaker 2: The red hat thing.

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Speaker 3: Was there someone or is there someone who you went

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Speaker 3: to and explained the logic of this and they saw

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Speaker 3: the logic of this, and that made a big difference

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Speaker 3: to you.

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Speaker 4: Getting their support made a big difference. You'd be surprised.

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Speaker 4: I'm remarkably open inside. I mean when I have are

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Speaker 4: there probably a half dozen to a dozen people inside

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Speaker 4: that I'll talk to and I'll be completely open about Hey,

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Speaker 4: this is what I'm thinking. I don't know. Here are

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Speaker 4: the risks. I'm open about those. Also, it's not just

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Speaker 4: the benefits. I think the other risks, but I think

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Speaker 4: the benefits outweigh the risks. I talk about that to

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Speaker 4: people all the time. So whether for example, I mean

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Speaker 4: i'll take names. I think our current chro Nicol who

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Speaker 4: introduced us, she has been in that loop since at

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Speaker 4: least twenty fifteen. For me, if I look at our CFO,

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Speaker 4: Jim Cavanaugh.

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Speaker 5: He's been in that.

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Speaker 4: Loop probably since twenty thirteen, and the IBM's will probably wonder,

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Speaker 4: what the hell intersection did you guys have? It didn't

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Speaker 4: when I talked about learning finance. I will go to

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Speaker 4: him and say, hey, explain this to me. I don't

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Speaker 4: understand why it's like this, and to me it's okay.

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Speaker 5: A patient you go learn.

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Speaker 4: If I think about many of the people in the

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Speaker 4: software business, they've been having these discussions with me for always.

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Speaker 4: I mean, now I'll acknowledge I can get probably impatient

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Speaker 4: and a serbic, but it's meant to be a discussion.

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Speaker 4: I mean, like, let's have the discussion. If you have

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Speaker 4: a strong point of view, I got it. Nobody has

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Speaker 4: a going to be perfectly correct, but I always look

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Speaker 4: for if you have a strong point of view, that

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Speaker 4: means it's from a different perspective than mine. So what

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Speaker 4: do I learn from that is the question which helps

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Speaker 4: to improve my point of view?

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Speaker 5: That makes sense.

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Speaker 4: I actually think that each person should try to build

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Speaker 4: a community of a one hundred people inside your enterprise

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Speaker 4: and a hundred outside that you can call up. I

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Speaker 4: have no hesitation. Somebody introduced me to a long time

439
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Speaker 4: back to a CEO on the outside. I called them

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Speaker 4: up all the time and say, hey, do you have

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Speaker 4: five minutes. I'm just thinking about something this way, the

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Speaker 4: CEO of red At who left IBM in twenty twenty one,

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00:23:15,080 --> 00:23:17,800
Speaker 4: we probably talk every two or three months on a

444
00:23:17,880 --> 00:23:18,600
Speaker 4: random topic.

445
00:23:18,920 --> 00:23:21,320
Speaker 5: One way, it becomes mutual. He'll asks me my opinion

446
00:23:21,400 --> 00:23:21,960
Speaker 5: on some things.

447
00:23:22,520 --> 00:23:24,400
Speaker 4: Now by the way, three or four times he might

448
00:23:24,440 --> 00:23:28,399
Speaker 4: do something different, but he wants my opinion to the

449
00:23:28,400 --> 00:23:29,040
Speaker 4: other way around.

450
00:23:29,240 --> 00:23:30,960
Speaker 3: If I gave you my phone number, can I be

451
00:23:31,040 --> 00:23:33,840
Speaker 3: on that list? I would just be fascinating. I don't

452
00:23:33,840 --> 00:23:35,000
Speaker 3: know if I can help you, but it would be

453
00:23:35,000 --> 00:23:35,880
Speaker 3: really fun to get the call.

454
00:23:36,000 --> 00:23:36,439
Speaker 5: Sure you can.

455
00:23:36,560 --> 00:23:39,320
Speaker 4: Do you think that we can ever succeed unless people

456
00:23:39,359 --> 00:23:44,720
Speaker 4: who influence opinions say things about us. So you may

457
00:23:44,720 --> 00:23:48,120
Speaker 4: not think deeply about maybe the physics of one of computing,

458
00:23:48,600 --> 00:23:52,040
Speaker 4: But would you think deeply about why.

459
00:23:51,720 --> 00:23:54,040
Speaker 5: And what moment may make it much more.

460
00:23:53,920 --> 00:23:57,119
Speaker 4: Attractive to a large audience. Sure you would. You'd be

461
00:23:57,160 --> 00:24:00,520
Speaker 4: far better as a thinker on that topic. Probably most

462
00:24:00,520 --> 00:24:01,360
Speaker 4: of the people.

463
00:24:02,080 --> 00:24:03,479
Speaker 3: I was thinking, you know, when you were making your

464
00:24:03,480 --> 00:24:09,320
Speaker 3: comments about your nineteen ninety self and streaming that the

465
00:24:10,040 --> 00:24:13,359
Speaker 3: rational thing would have been for there have been a

466
00:24:13,480 --> 00:24:19,000
Speaker 3: reserved board seat for every television network from someone from

467
00:24:19,000 --> 00:24:22,320
Speaker 3: the world of technology, which I one hundred percent sure

468
00:24:22,320 --> 00:24:24,600
Speaker 3: they did not have that in nineteen ninety but they

469
00:24:25,040 --> 00:24:28,120
Speaker 3: they're board was probably composed of people like them. Let's

470
00:24:28,119 --> 00:24:32,480
Speaker 3: talk a little bit about technology now. There's so much,

471
00:24:32,560 --> 00:24:34,879
Speaker 3: so much of the changes going on right now are

472
00:24:35,560 --> 00:24:40,000
Speaker 3: accompanied by a great deal of hype. What are we overestimating?

473
00:24:40,000 --> 00:24:41,200
Speaker 2: What are we underestimating?

474
00:24:41,600 --> 00:24:44,400
Speaker 4: Okay, let's go back to ninety ninety five the Internet,

475
00:24:44,440 --> 00:24:46,320
Speaker 4: because I think that the current moment is very much

476
00:24:46,400 --> 00:24:48,680
Speaker 4: like the Internet moment. Actually, all the moments in the

477
00:24:48,720 --> 00:24:51,679
Speaker 4: middle were much smaller. I think mobile streaming were much smaller.

478
00:24:51,720 --> 00:24:54,440
Speaker 4: Internet was the major moment. If you remember back to

479
00:24:54,520 --> 00:24:56,520
Speaker 4: ninety nine and two thousand, people claimed there was a

480
00:24:56,520 --> 00:24:59,359
Speaker 4: lot of hype. Would we say that the Internet of

481
00:24:59,359 --> 00:25:02,800
Speaker 4: today has more than fulfilled all those expectations and more?

482
00:25:03,359 --> 00:25:03,639
Speaker 5: Yes.

483
00:25:04,760 --> 00:25:08,280
Speaker 4: Along the way, they'd eight out of ten of the

484
00:25:08,320 --> 00:25:11,440
Speaker 4: companies that were invested in heavily go bankrupt.

485
00:25:11,520 --> 00:25:11,800
Speaker 2: Yes.

486
00:25:12,800 --> 00:25:15,639
Speaker 4: I actually think of that as being the huge positive

487
00:25:16,400 --> 00:25:20,400
Speaker 4: of the United States capital system that that investment happened.

488
00:25:21,400 --> 00:25:23,640
Speaker 4: Eight out of ten went broke. By the way, those

489
00:25:23,680 --> 00:25:26,600
Speaker 4: acids didn't go away. They got consumed at ten cents

490
00:25:26,600 --> 00:25:28,679
Speaker 4: in the dollar by somebody else who could then make

491
00:25:28,680 --> 00:25:30,840
Speaker 4: a lot of money. But the two out of ten,

492
00:25:31,640 --> 00:25:34,639
Speaker 4: just take two, it probably has paid for all the capitals.

493
00:25:34,800 --> 00:25:35,440
Speaker 5: If you just.

494
00:25:35,480 --> 00:25:40,280
Speaker 4: Take Amazon and Alphabet aka Google, just those two have

495
00:25:40,320 --> 00:25:43,679
Speaker 4: probably paid for all the capital of that time. So

496
00:25:44,320 --> 00:25:47,240
Speaker 4: that's what's going to happen this time. There will be

497
00:25:47,280 --> 00:25:50,560
Speaker 4: a lot of tears, but in aggregate, there will be

498
00:25:50,560 --> 00:25:53,000
Speaker 4: a lot of success. And I think that's the fundamental

499
00:25:53,000 --> 00:25:55,840
Speaker 4: difference between the US model and almost.

500
00:25:55,600 --> 00:25:56,520
Speaker 5: All other countries.

501
00:25:57,080 --> 00:25:59,439
Speaker 4: On all other countries, they're desperate to keep all the

502
00:25:59,440 --> 00:26:00,000
Speaker 4: companies alone.

503
00:26:00,520 --> 00:26:03,960
Speaker 5: So that means you're dialuting. But that's a horrible thing.

504
00:26:04,440 --> 00:26:08,199
Speaker 4: So to me let the system works worked really effectively,

505
00:26:08,400 --> 00:26:10,119
Speaker 4: by the way, not just now, I mean all the

506
00:26:10,160 --> 00:26:14,840
Speaker 4: way back to railways and electrification, and you mentioned telephone system.

507
00:26:15,320 --> 00:26:19,919
Speaker 4: You can keep going on oil, I mean consumer goods.

508
00:26:20,280 --> 00:26:22,240
Speaker 4: It goes on and on. I think this system is

509
00:26:22,359 --> 00:26:26,000
Speaker 4: very effective. It deploys capital. Its census is a big

510
00:26:26,040 --> 00:26:29,880
Speaker 4: market is completely willing to over deploy capital in the short.

511
00:26:29,720 --> 00:26:31,240
Speaker 5: Term, not the long term.

512
00:26:31,600 --> 00:26:35,440
Speaker 4: That results in more competition, so it actually improves a

513
00:26:35,520 --> 00:26:38,840
Speaker 4: rate of innovation. That means what might have taken twenty

514
00:26:38,880 --> 00:26:42,920
Speaker 4: years takes five and the winners emerge exactly the same

515
00:26:43,000 --> 00:26:44,240
Speaker 4: is going to happen this time.

516
00:26:44,400 --> 00:26:45,800
Speaker 2: Yeah, I saw that.

517
00:26:46,040 --> 00:26:50,120
Speaker 3: I grew up in Waterloo and BlackBerry closest from Waterloo.

518
00:26:50,200 --> 00:26:52,119
Speaker 2: Yep, everyone used to work for BlackBerry.

519
00:26:52,240 --> 00:26:52,320
Speaker 1: Ye.

520
00:26:52,440 --> 00:26:55,679
Speaker 3: BlackBerry goes into its dive and that's the best thing

521
00:26:55,720 --> 00:26:57,760
Speaker 3: that happened to Waterloo because it was not just capital

522
00:26:57,800 --> 00:26:58,280
Speaker 3: but talent.

523
00:26:58,600 --> 00:27:00,800
Speaker 5: Yep. Talent is way any other companies.

524
00:27:00,840 --> 00:27:03,359
Speaker 3: So all these smart people went on the next really

525
00:27:03,440 --> 00:27:07,240
Speaker 3: more interesting thing. And yeah, the wait, you haven't answered,

526
00:27:08,240 --> 00:27:11,120
Speaker 3: So what is you an idea that we are underestimating

527
00:27:11,119 --> 00:27:13,800
Speaker 3: at the moment that's in the current kind of suite

528
00:27:13,880 --> 00:27:14,919
Speaker 3: of innovations.

529
00:27:15,119 --> 00:27:17,800
Speaker 4: So I don't think AI is being underestimated because when

530
00:27:17,800 --> 00:27:19,320
Speaker 4: you look at the amount of capital and the amount

531
00:27:19,359 --> 00:27:23,000
Speaker 4: of things chasing it, I think it's incredible. I do

532
00:27:23,040 --> 00:27:25,520
Speaker 4: think that a lot of enterprises are deploying it in

533
00:27:25,560 --> 00:27:29,119
Speaker 4: the wrong place. They're running after shiny experiments. There's a

534
00:27:29,200 --> 00:27:32,359
Speaker 4: lot of basic things you can do to use AI

535
00:27:32,440 --> 00:27:33,800
Speaker 4: to improve the business today.

536
00:27:34,200 --> 00:27:36,280
Speaker 5: So that's really just my one advice to them.

537
00:27:36,600 --> 00:27:40,160
Speaker 4: Pick areas you can scale, don't pick the shiny little

538
00:27:40,400 --> 00:27:41,399
Speaker 4: toys on the side.

539
00:27:42,240 --> 00:27:44,480
Speaker 2: Then I think, for example, that.

540
00:27:46,320 --> 00:27:51,679
Speaker 4: If anybody has more than ten percent of what they

541
00:27:51,720 --> 00:27:56,439
Speaker 4: had for customer service ten years ago, they're already five

542
00:27:56,520 --> 00:28:02,480
Speaker 4: years behind. If anybody is not using AI to make

543
00:28:02,560 --> 00:28:07,199
Speaker 4: their developers who write software thirty percent more productive today

544
00:28:07,560 --> 00:28:11,280
Speaker 4: with the goal of being seventy percent more productive, that's

545
00:28:11,280 --> 00:28:13,240
Speaker 4: not to say you will need less, you'll just get

546
00:28:13,280 --> 00:28:14,160
Speaker 4: more software done.

547
00:28:15,560 --> 00:28:16,760
Speaker 5: Then they're not.

548
00:28:17,080 --> 00:28:18,560
Speaker 4: And I would turn around and tell you I think

549
00:28:18,600 --> 00:28:22,560
Speaker 4: only maybe five percent of the enterprises on both.

550
00:28:22,400 --> 00:28:23,320
Speaker 5: Those metrics today.

551
00:28:23,560 --> 00:28:28,280
Speaker 4: Yeah, yeah, and the one that is completely underestimated. I

552
00:28:28,359 --> 00:28:32,440
Speaker 4: kind of put it like this, Quantum today is where

553
00:28:32,600 --> 00:28:36,200
Speaker 4: GPUs a AI war in twenty fifteen, and I bet

554
00:28:36,320 --> 00:28:39,960
Speaker 4: you every AI person is thinking and hoping. I wish

555
00:28:40,000 --> 00:28:43,400
Speaker 4: I had started doing more in twenty fifteen as opposed

556
00:28:43,400 --> 00:28:46,600
Speaker 4: to wait until twenty twenty two. Quantum today is there,

557
00:28:46,880 --> 00:28:49,240
Speaker 4: So it's not good enough that you can get a

558
00:28:49,320 --> 00:28:51,960
Speaker 4: big advantage, But if you learn how to use it,

559
00:28:52,400 --> 00:28:55,840
Speaker 4: then in five years you'll be ready to exploit what comes.

560
00:28:55,960 --> 00:28:58,160
Speaker 3: Yeah, we're gonna get to quantum in a moment. But

561
00:28:58,200 --> 00:29:01,400
Speaker 3: I have a couple other AI questions you know I

562
00:29:01,520 --> 00:29:04,160
Speaker 3: as you know where This conversation is part of this

563
00:29:04,200 --> 00:29:07,400
Speaker 3: thing that we do with IBM Smart Talks, and I've

564
00:29:07,400 --> 00:29:11,640
Speaker 3: been The last episode I did was on Kenya, which

565
00:29:11,640 --> 00:29:15,360
Speaker 3: has a massive deforestation problem, and they got together IBM

566
00:29:15,600 --> 00:29:19,600
Speaker 3: took all the NASA satellite data, ran it through an LLM,

567
00:29:19,880 --> 00:29:23,600
Speaker 3: and gave them this incredibly precise ten meter by ten

568
00:29:23,680 --> 00:29:27,000
Speaker 3: meter analysis of what trees to plant, where to plant

569
00:29:27,000 --> 00:29:30,400
Speaker 3: them exactly where the you know, an astonishing kind of

570
00:29:30,400 --> 00:29:33,880
Speaker 3: blueprint about how to fix their country ecologically. And it

571
00:29:33,920 --> 00:29:38,360
Speaker 3: made me think, when we analyze the potential of AI,

572
00:29:39,160 --> 00:29:41,400
Speaker 3: are we making a mistake by spending too much thinking

573
00:29:41,400 --> 00:29:44,320
Speaker 3: about the developed world when it's actually the developing world

574
00:29:44,640 --> 00:29:47,040
Speaker 3: where the greatest ROI for this is to me.

575
00:29:47,160 --> 00:29:50,680
Speaker 4: Look, software technologies are wonderful and the sense they can

576
00:29:50,680 --> 00:29:52,880
Speaker 4: scale and they can be an ad so you don't

577
00:29:52,920 --> 00:29:56,520
Speaker 4: have to do one or the other. You use deforestation.

578
00:29:57,080 --> 00:30:01,200
Speaker 4: How about the use of pesticides and fertilizers, We overuse it.

579
00:30:01,640 --> 00:30:04,840
Speaker 4: We tend to for irrigation, we tend to just flood everything,

580
00:30:04,920 --> 00:30:07,280
Speaker 4: as opposed to say, okay, only that one needs it.

581
00:30:07,680 --> 00:30:09,240
Speaker 4: You could do all those things to get a ten

582
00:30:09,280 --> 00:30:13,000
Speaker 4: times effectiveness and that all would apply to the developing world.

583
00:30:13,480 --> 00:30:16,560
Speaker 4: How about remote healthcare or telehealth using an AI agent.

584
00:30:16,880 --> 00:30:21,560
Speaker 4: So the examples are numerous. In the developed world, I

585
00:30:21,720 --> 00:30:25,360
Speaker 4: believe we are running out of people. I know that

586
00:30:25,600 --> 00:30:29,160
Speaker 4: nobody likes to hear it. Most of the Far East

587
00:30:29,520 --> 00:30:31,400
Speaker 4: is going to have half the number of people by

588
00:30:31,480 --> 00:30:33,960
Speaker 4: twenty seventy compared to today. That's not that far away.

589
00:30:34,680 --> 00:30:38,000
Speaker 4: If I look at Europe, birth rates are far under

590
00:30:38,440 --> 00:30:43,040
Speaker 4: sustaining or keeping population flat. How the US, also, depending

591
00:30:43,040 --> 00:30:44,600
Speaker 4: on which number you want to look at, is either

592
00:30:44,640 --> 00:30:46,560
Speaker 4: one point six births per women.

593
00:30:46,960 --> 00:30:50,480
Speaker 5: Or two or two point one. Why are the three numbers?

594
00:30:50,480 --> 00:30:52,240
Speaker 4: One point six is to women who were born in

595
00:30:52,240 --> 00:30:55,440
Speaker 4: the United States, It becomes two point two if you

596
00:30:55,520 --> 00:30:59,000
Speaker 4: include immigrant women. It becomes two point one if you

597
00:30:59,000 --> 00:31:00,520
Speaker 4: include children who to make getting in.

598
00:31:01,120 --> 00:31:03,560
Speaker 5: So you got to decide where the trend is obvious.

599
00:31:04,000 --> 00:31:04,760
Speaker 5: This is going down.

600
00:31:05,120 --> 00:31:08,120
Speaker 4: So AI in the developed world is going to be

601
00:31:08,320 --> 00:31:11,800
Speaker 4: essential because to keep our current quality of life, you

602
00:31:11,880 --> 00:31:14,600
Speaker 4: need more work done or what's going to do the

603
00:31:14,640 --> 00:31:16,320
Speaker 4: work if they're people to do the work.

604
00:31:16,560 --> 00:31:19,200
Speaker 5: So the problems are different in the places.

605
00:31:19,280 --> 00:31:22,360
Speaker 3: Yeah, it gives you In the developing world, you get

606
00:31:22,400 --> 00:31:26,479
Speaker 3: access to a suite of technologies and things at a

607
00:31:26,600 --> 00:31:28,880
Speaker 3: price that you could never been able to afford.

608
00:31:29,080 --> 00:31:29,440
Speaker 5: Correct.

609
00:31:29,720 --> 00:31:31,600
Speaker 2: That was my in talking to the Kenyon thing.

610
00:31:31,600 --> 00:31:34,320
Speaker 3: It was like the whole it's it's maybe one of

611
00:31:34,320 --> 00:31:38,520
Speaker 3: the largest ecological projects of its kind at fifteen billion

612
00:31:38,560 --> 00:31:39,600
Speaker 3: trees they want to plan.

613
00:31:39,520 --> 00:31:41,240
Speaker 4: And that is one country that might get it done

614
00:31:41,280 --> 00:31:43,520
Speaker 4: because they do take a lot of pride in their

615
00:31:43,920 --> 00:31:47,680
Speaker 4: ecology and the sort of returning to the land and

616
00:31:47,720 --> 00:31:48,280
Speaker 4: giving back.

617
00:31:48,520 --> 00:31:48,760
Speaker 2: Yeah.

618
00:31:48,960 --> 00:31:54,200
Speaker 3: Yeah, what's different about IBM's version of AI versus some of.

619
00:31:54,160 --> 00:31:56,760
Speaker 4: Your So we are not a consumer company, so we

620
00:31:56,800 --> 00:31:59,960
Speaker 4: have no focus on a B two C chat pot.

621
00:32:00,880 --> 00:32:03,440
Speaker 4: And the reason I say that is, if you're making

622
00:32:03,520 --> 00:32:06,280
Speaker 4: a B two C chat bot, does it help you

623
00:32:06,360 --> 00:32:09,920
Speaker 4: to make it even bigger and more computationally inefficient? And

624
00:32:09,960 --> 00:32:12,200
Speaker 4: the short answer is yes, because you have a certain

625
00:32:12,240 --> 00:32:14,920
Speaker 4: number of users, and you kind of say, I kind

626
00:32:14,920 --> 00:32:18,880
Speaker 4: of say this jokingly. If I add finished to French capabilities,

627
00:32:19,120 --> 00:32:22,360
Speaker 4: I can probably add five million users. If I add

628
00:32:23,040 --> 00:32:25,000
Speaker 4: writing a high coup I might be able to add

629
00:32:25,000 --> 00:32:29,000
Speaker 4: another five million users. If I add writing an email

630
00:32:29,080 --> 00:32:31,160
Speaker 4: in the voice of Steinbeck, I can probably add another

631
00:32:31,160 --> 00:32:34,800
Speaker 4: five million users. Do all those things if my goal

632
00:32:35,040 --> 00:32:38,440
Speaker 4: is to get help a company summarize the legal documents

633
00:32:38,480 --> 00:32:39,720
Speaker 4: in English.

634
00:32:40,040 --> 00:32:41,280
Speaker 5: That can be a model.

635
00:32:41,000 --> 00:32:45,920
Speaker 4: That's one hundred size as effective, probably higher quality, but

636
00:32:46,040 --> 00:32:49,360
Speaker 4: I don't need to go wide. So if you're focusing

637
00:32:49,360 --> 00:32:52,840
Speaker 4: on the enterprise, that actually takes away the focus of

638
00:32:52,920 --> 00:32:56,920
Speaker 4: having to go to extremely large models, which but definition

639
00:32:56,960 --> 00:33:01,320
Speaker 4: are going to be computationally expensive, power hungry, and demand

640
00:33:01,360 --> 00:33:02,480
Speaker 4: lots and lots of data.

641
00:33:02,920 --> 00:33:04,480
Speaker 5: So I can turn ontell the enterprise.

642
00:33:04,560 --> 00:33:06,719
Speaker 4: You don't need to worry about copyright issues, about all

643
00:33:06,720 --> 00:33:09,240
Speaker 4: those because you can train on a much smaller amount

644
00:33:09,240 --> 00:33:09,680
Speaker 4: of data.

645
00:33:10,080 --> 00:33:11,240
Speaker 5: And now, by.

646
00:33:11,080 --> 00:33:14,160
Speaker 4: The way, turning it for you yourself is a weekend exercise,

647
00:33:14,400 --> 00:33:17,720
Speaker 4: it's not a six month on a big super computer

648
00:33:17,800 --> 00:33:19,360
Speaker 4: cluster somewhere out there.

649
00:33:19,520 --> 00:33:21,200
Speaker 5: That's one big difference of what we do.

650
00:33:21,920 --> 00:33:27,400
Speaker 4: Second, we are very focused on helping those problems that

651
00:33:27,440 --> 00:33:29,680
Speaker 4: can give people immediate benefit.

652
00:33:29,320 --> 00:33:30,720
Speaker 5: Where we have domain knowledge.

653
00:33:30,880 --> 00:33:36,560
Speaker 4: So our domain knowledge is around operations, is around programming, encoding,

654
00:33:37,280 --> 00:33:43,400
Speaker 4: is around customer service, is around customer experience, logistics, procurement.

655
00:33:43,880 --> 00:33:47,200
Speaker 4: Let's change the areas where we have a lot of expertise.

656
00:33:47,880 --> 00:33:51,120
Speaker 4: And then three we kind of apply it to ourselves

657
00:33:51,800 --> 00:33:54,200
Speaker 4: and so we are not asking our clients to be

658
00:33:54,320 --> 00:33:55,680
Speaker 4: the first experiment on it.

659
00:33:55,760 --> 00:33:59,040
Speaker 5: We say you can leverage what we did. We're happy to.

660
00:33:59,000 --> 00:34:01,840
Speaker 4: Bring out all our learnings, including what needs to change

661
00:34:02,320 --> 00:34:04,719
Speaker 4: in the process, because the biggest change is not technology,

662
00:34:05,120 --> 00:34:08,000
Speaker 4: is getting people to accept that there's a different way

663
00:34:08,040 --> 00:34:08,720
Speaker 4: to do things.

664
00:34:09,640 --> 00:34:14,080
Speaker 3: Other challenges to explaining what makes you different to potential customers.

665
00:34:14,080 --> 00:34:17,040
Speaker 4: For sure, the shining object is always attractive. Oh, I

666
00:34:17,120 --> 00:34:20,480
Speaker 4: can go and try chat GPT. Why don't you have

667
00:34:20,520 --> 00:34:21,560
Speaker 4: your GPT version?

668
00:34:23,080 --> 00:34:24,240
Speaker 2: Do you use chat gipt?

669
00:34:25,200 --> 00:34:26,040
Speaker 5: I have used it.

670
00:34:27,440 --> 00:34:29,440
Speaker 3: I asked it a question recently which I thought was

671
00:34:29,480 --> 00:34:32,800
Speaker 3: really simple, and it made up about ten people.

672
00:34:34,239 --> 00:34:35,440
Speaker 2: Anyway, I had a bad experience.

673
00:34:35,480 --> 00:34:38,719
Speaker 4: I should think that that's the fundamental issue with all

674
00:34:39,040 --> 00:34:41,480
Speaker 4: lms as they get larger. Yeah, because you had to

675
00:34:41,520 --> 00:34:45,560
Speaker 4: ask what was the original insight that led to these

676
00:34:46,719 --> 00:34:50,960
Speaker 4: It was a reward function with intent. So if it

677
00:34:51,040 --> 00:34:55,000
Speaker 4: has learned by using a reward function, it's reward function

678
00:34:55,200 --> 00:34:59,160
Speaker 4: comes from giving an answer that satisfies you. So if

679
00:34:59,160 --> 00:35:01,279
Speaker 4: it thinks that if it makes up an answer that

680
00:35:01,320 --> 00:35:04,600
Speaker 4: will satisfy you, how will you stop it? Why do

681
00:35:04,680 --> 00:35:08,520
Speaker 4: we think this is different than the clever college kid

682
00:35:08,520 --> 00:35:09,439
Speaker 4: who doesn't know an answer.

683
00:35:09,480 --> 00:35:12,840
Speaker 5: What bullshits the way to an answer. Well, it's exactly

684
00:35:12,920 --> 00:35:13,239
Speaker 5: the same.

685
00:35:13,280 --> 00:35:16,240
Speaker 3: It's like the example of clever hands in that story

686
00:35:16,120 --> 00:35:18,920
Speaker 3: the horse that they thought could speak, then all it.

687
00:35:19,000 --> 00:35:21,880
Speaker 2: Was doing was pleasing it. It's master. Yes, it is

688
00:35:21,880 --> 00:35:22,959
Speaker 2: a little bit of clever hands.

689
00:35:23,040 --> 00:35:25,240
Speaker 5: Yeah, it's like dogs kind of imitating and looking.

690
00:35:26,520 --> 00:35:31,640
Speaker 3: What would you identify as the most significant bottleneck in

691
00:35:31,719 --> 00:35:34,200
Speaker 3: the development of AI? What's slowing us down right now?

692
00:35:36,080 --> 00:35:40,600
Speaker 4: I am not convinced that LLMS is the way to

693
00:35:40,719 --> 00:35:45,880
Speaker 4: get much beyond where we'll get incremental improvements, But I,

694
00:35:46,360 --> 00:35:49,319
Speaker 4: for one, don't believe that LMS are going to get

695
00:35:49,400 --> 00:35:55,480
Speaker 4: us to super intelligence or AGI. So I'll park that

696
00:35:55,600 --> 00:35:58,120
Speaker 4: on the side and simply say, we have to find

697
00:35:58,120 --> 00:36:02,680
Speaker 4: a way to fuse and how do you represent knowledge

698
00:36:03,239 --> 00:36:06,000
Speaker 4: as opposed to have to statistically rediscover it each time

699
00:36:06,000 --> 00:36:09,240
Speaker 4: I ask a question? And how do we fuse knowledge

700
00:36:09,320 --> 00:36:15,760
Speaker 4: with LLM? Maybe then we'll get to leaves and beyond

701
00:36:15,800 --> 00:36:20,360
Speaker 4: today on LMS alone, my view is, I think we

702
00:36:20,440 --> 00:36:24,520
Speaker 4: can get a thousand x efficiency in power and cost

703
00:36:24,640 --> 00:36:28,040
Speaker 4: and compute from today. So if you make something a

704
00:36:28,120 --> 00:36:31,359
Speaker 4: thousand times cheaper, would people use a lot more of it?

705
00:36:32,520 --> 00:36:32,880
Speaker 2: Yes?

706
00:36:33,719 --> 00:36:37,040
Speaker 4: And I think those answers lie as is usually in compute.

707
00:36:37,239 --> 00:36:42,080
Speaker 4: So advances in semiconductors, advances in software, and advances in

708
00:36:42,120 --> 00:36:45,160
Speaker 4: agorthic techniques all three. But how come we're not working

709
00:36:45,160 --> 00:36:46,600
Speaker 4: in any of those three. We're just taking the current

710
00:36:46,600 --> 00:36:51,280
Speaker 4: sevic conductor and going more. We're taking the current algorithmic

711
00:36:51,360 --> 00:36:53,319
Speaker 4: techniques and not really trying to invent new ones.

712
00:36:53,440 --> 00:36:56,440
Speaker 5: So I think those are all happen less than five years.

713
00:36:58,200 --> 00:37:01,640
Speaker 3: But why say there is a we're in a moment

714
00:37:01,640 --> 00:37:06,719
Speaker 3: where people are not pursuing the the optimal strategy for

715
00:37:06,800 --> 00:37:08,040
Speaker 3: exploiting this technology.

716
00:37:08,360 --> 00:37:13,120
Speaker 4: Why because when you see a few people running really

717
00:37:13,160 --> 00:37:16,200
Speaker 4: hard and they're willing to invest any amount of money,

718
00:37:16,200 --> 00:37:20,239
Speaker 4: so efficiency is not the focus. People feel if you

719
00:37:20,280 --> 00:37:21,880
Speaker 4: don't do the same, you'll get left behind.

720
00:37:23,120 --> 00:37:25,879
Speaker 2: So is this a case where there's too much money

721
00:37:26,040 --> 00:37:27,160
Speaker 2: ones have never had for more?

722
00:37:27,239 --> 00:37:27,640
Speaker 5: Right? Ever?

723
00:37:28,200 --> 00:37:33,480
Speaker 3: Yeah, but this is this a consequence of overinvestment in

724
00:37:33,520 --> 00:37:34,560
Speaker 3: the in the field.

725
00:37:34,680 --> 00:37:36,960
Speaker 4: Going back to my internet allology, if two out of

726
00:37:37,040 --> 00:37:40,440
Speaker 4: ten are going to succeed, how do you guarantee or

727
00:37:40,440 --> 00:37:42,120
Speaker 4: how do you improve the orders that you are one

728
00:37:42,120 --> 00:37:45,080
Speaker 4: of those two? So if you pause to say I

729
00:37:45,120 --> 00:37:47,200
Speaker 4: want to become more efficient, that's not the way to win.

730
00:37:47,520 --> 00:37:49,640
Speaker 5: So first you win, then you become efficient.

731
00:37:50,719 --> 00:37:54,560
Speaker 3: Yeah, let's talk about what is I was told your

732
00:37:54,560 --> 00:37:59,759
Speaker 3: favorite topic, it's quantum. It is what boy even go

733
00:37:59,800 --> 00:38:00,279
Speaker 3: to further?

734
00:38:00,400 --> 00:38:01,920
Speaker 2: Why is quantum your favorite topic?

735
00:38:03,160 --> 00:38:06,080
Speaker 4: We've only had two kinds of compute in the history,

736
00:38:06,960 --> 00:38:10,319
Speaker 4: so nineteen forty five was to use that year for

737
00:38:10,400 --> 00:38:13,160
Speaker 4: anyac all the way till twenty twenty, we had one

738
00:38:13,200 --> 00:38:16,040
Speaker 4: kind of computerlassical what today you would call a classical computer.

739
00:38:17,600 --> 00:38:22,080
Speaker 4: Then GPUs and AI came around, so you would say

740
00:38:21,880 --> 00:38:25,080
Speaker 4: the intuition there is you went from sort of bits,

741
00:38:25,120 --> 00:38:29,200
Speaker 4: which is algebra or high school algebra, to including neurons,

742
00:38:29,320 --> 00:38:32,719
Speaker 4: which is captured in linear algebra. But that gives you

743
00:38:32,719 --> 00:38:36,200
Speaker 4: a different kind But it can do problems that are

744
00:38:36,280 --> 00:38:38,359
Speaker 4: really hard to do. I don't say impossible, just hard

745
00:38:38,400 --> 00:38:43,000
Speaker 4: to do on normal computers. Quantum adds a third kind

746
00:38:43,000 --> 00:38:49,560
Speaker 4: of math. Yes, the physics properties which really get people

747
00:38:49,719 --> 00:38:52,640
Speaker 4: energized and the imagination going. And we use all these

748
00:38:52,640 --> 00:38:57,160
Speaker 4: words about entanglement and silver position. But maybe because I'm

749
00:38:57,160 --> 00:38:59,800
Speaker 4: a bit of a math guy. The real thing is

750
00:39:00,200 --> 00:39:03,120
Speaker 4: a third kind of math to make it really simple,

751
00:39:03,600 --> 00:39:06,919
Speaker 4: a third kind of math that comes from the field

752
00:39:06,960 --> 00:39:08,120
Speaker 4: of abstract algebra.

753
00:39:08,560 --> 00:39:09,360
Speaker 5: It does the math.

754
00:39:10,320 --> 00:39:13,440
Speaker 4: You can use Hamiltonians for those who like physics, or

755
00:39:13,480 --> 00:39:15,640
Speaker 4: you can use the word Lee algebras for those who

756
00:39:15,800 --> 00:39:19,759
Speaker 4: like abstract mathematics. If you can do a third kind

757
00:39:19,760 --> 00:39:22,759
Speaker 4: of math, which algorithms are suited to that third kind

758
00:39:22,760 --> 00:39:26,120
Speaker 4: of math? So it excites me because we can now

759
00:39:26,400 --> 00:39:29,360
Speaker 4: approach algorithms that you just could never do on the

760
00:39:29,400 --> 00:39:32,319
Speaker 4: other two it's impossible. Now it's different than AI. It's

761
00:39:32,320 --> 00:39:35,600
Speaker 4: not data intensive, it's compute intensive. So we kind of

762
00:39:35,640 --> 00:39:38,560
Speaker 4: had compute and supercomputers. Then we went to data which

763
00:39:38,600 --> 00:39:40,840
Speaker 4: is AI. And now if you say there's another class

764
00:39:40,840 --> 00:39:42,520
Speaker 4: of problems, it require lots of compute.

765
00:39:42,680 --> 00:39:44,440
Speaker 5: That's quantum.

766
00:39:44,480 --> 00:39:47,120
Speaker 3: A couple months ago was at the to watch some

767
00:39:47,160 --> 00:39:49,240
Speaker 3: research center and they have you know, on the ground floor,

768
00:39:49,239 --> 00:39:50,680
Speaker 3: they have those behind the glass.

769
00:39:51,239 --> 00:39:55,279
Speaker 2: There's incredibly exciting looking machines. But where are we in

770
00:39:55,320 --> 00:39:56,399
Speaker 2: the timeline of this.

771
00:39:57,960 --> 00:40:01,560
Speaker 4: Three to five years away from shocking people?

772
00:40:02,520 --> 00:40:03,839
Speaker 2: What does shocking people mean?

773
00:40:04,200 --> 00:40:07,000
Speaker 5: Do something that nobody thought was possible in that timeline?

774
00:40:07,280 --> 00:40:08,720
Speaker 2: Does an example come to mind?

775
00:40:09,560 --> 00:40:14,080
Speaker 4: I was actually pleasantly surprised. So one of our clients, HSBC,

776
00:40:15,160 --> 00:40:19,280
Speaker 4: last week published a result that using a quantum computer.

777
00:40:20,520 --> 00:40:24,680
Speaker 4: Bond trading was thirty four percent more accurate than their

778
00:40:24,719 --> 00:40:25,520
Speaker 4: prior technique.

779
00:40:26,440 --> 00:40:27,640
Speaker 2: Thirty four percent.

780
00:40:27,760 --> 00:40:28,560
Speaker 5: Thirty four percent.

781
00:40:29,120 --> 00:40:32,480
Speaker 3: This is an industry that's used to one percent correct,

782
00:40:32,760 --> 00:40:34,000
Speaker 3: zero point five percent.

783
00:40:34,480 --> 00:40:36,880
Speaker 2: Yes, that's astonishing.

784
00:40:37,160 --> 00:40:39,960
Speaker 4: Now that was not at a scale when they could

785
00:40:40,000 --> 00:40:42,600
Speaker 4: turn it into production today, but that was sort of

786
00:40:43,040 --> 00:40:45,520
Speaker 4: their original thought experiment, and that's what they did.

787
00:40:46,040 --> 00:40:51,239
Speaker 5: Now. Can you imagine when will somebody so you.

788
00:40:50,960 --> 00:40:53,920
Speaker 4: Were correct, you talk about an industry where one basis point,

789
00:40:54,239 --> 00:40:57,360
Speaker 4: if I remember I may be wrong, like thirteen trillion

790
00:40:57,440 --> 00:40:59,720
Speaker 4: dollars of money kind of moves around in the financial

791
00:40:59,719 --> 00:41:07,080
Speaker 4: industr each day, right, So basis point would be thirteen

792
00:41:07,120 --> 00:41:11,600
Speaker 4: billion something like that, right, one over ten thousand. So

793
00:41:11,920 --> 00:41:14,280
Speaker 4: when you think about the kind of profit that people

794
00:41:14,280 --> 00:41:16,799
Speaker 4: can make, if you can tell somebody that you can

795
00:41:16,840 --> 00:41:21,680
Speaker 4: come up with a better price than your competition by

796
00:41:21,800 --> 00:41:24,759
Speaker 4: just one basis point, they would actually gain the dire

797
00:41:24,840 --> 00:41:30,040
Speaker 4: market share. Yeah, so I think something around there or

798
00:41:30,080 --> 00:41:31,680
Speaker 4: something in the world of materials.

799
00:41:31,960 --> 00:41:35,160
Speaker 5: Can we make a better battery? Could we make a

800
00:41:35,200 --> 00:41:36,200
Speaker 5: solid state battery?

801
00:41:37,520 --> 00:41:42,239
Speaker 4: Which means your risk of fires heating decrease dramatically.

802
00:41:42,280 --> 00:41:44,560
Speaker 3: And the reason, sorry to ask a really nice question.

803
00:41:45,920 --> 00:41:48,279
Speaker 3: Why is it that a quantum computer would be better

804
00:41:48,320 --> 00:41:52,840
Speaker 3: at solving a battery problem than our existing methods of computing.

805
00:41:52,920 --> 00:41:57,080
Speaker 4: So the equations of quantum mechanics and chemistry and how

806
00:41:57,640 --> 00:42:02,000
Speaker 4: things interact are well known. To solve them, that are

807
00:42:02,000 --> 00:42:04,279
Speaker 4: no known techniques. So these are not like closed form,

808
00:42:04,320 --> 00:42:06,160
Speaker 4: you know, it's not like the square root of a

809
00:42:06,239 --> 00:42:09,480
Speaker 4: qualtertic equation. So the only way to solve them is

810
00:42:09,520 --> 00:42:12,480
Speaker 4: to explore the state space. So if you have a

811
00:42:12,480 --> 00:42:17,399
Speaker 4: few hundred electrons, you need two to the one hundred states. Well,

812
00:42:17,480 --> 00:42:19,719
Speaker 4: I'm sorry, you don't have that much memory. It's impossible.

813
00:42:20,160 --> 00:42:23,000
Speaker 4: So it takes a really really long time on a

814
00:42:23,040 --> 00:42:26,160
Speaker 4: normal computer to solve those problems. Right, But that's simple

815
00:42:26,200 --> 00:42:31,280
Speaker 4: a problem. If a quantum computer operates in the equation domain,

816
00:42:31,760 --> 00:42:33,759
Speaker 4: it doesn't need to explore the state space. It can

817
00:42:33,800 --> 00:42:36,080
Speaker 4: actually solve it. That's why I call it a different

818
00:42:36,200 --> 00:42:38,600
Speaker 4: kind of math. That's the kind of math it does.

819
00:42:39,239 --> 00:42:42,000
Speaker 4: So in a couple of seconds, it can tell you

820
00:42:42,400 --> 00:42:44,080
Speaker 4: this is how that material will be here.

821
00:42:45,560 --> 00:42:46,960
Speaker 2: Oh, I see, so you've.

822
00:42:46,760 --> 00:42:50,000
Speaker 4: Taken what could take years to a few seconds. Yeah,

823
00:42:50,080 --> 00:42:51,640
Speaker 4: that's a pretty big change.

824
00:42:51,760 --> 00:42:54,080
Speaker 2: Yeah. Yeah, it's speaking a different language.

825
00:42:54,360 --> 00:42:54,640
Speaker 5: Different.

826
00:42:54,880 --> 00:42:57,120
Speaker 2: So any kind of problem that comes along.

827
00:42:56,960 --> 00:43:00,000
Speaker 3: That's specific to that language correctly, which is not all problems.

828
00:43:00,080 --> 00:43:02,040
Speaker 2: Yeah, just as I call it.

829
00:43:02,040 --> 00:43:03,120
Speaker 5: It's one more kind of math.

830
00:43:03,440 --> 00:43:07,719
Speaker 3: Yeah, what's an example? So so many questions. L give

831
00:43:07,719 --> 00:43:11,359
Speaker 3: me another example of a of a of a kind

832
00:43:11,400 --> 00:43:13,439
Speaker 3: of problem that a quantum computer would love.

833
00:43:15,239 --> 00:43:18,560
Speaker 4: This one is a bit more speculative, and I'm going

834
00:43:18,600 --> 00:43:21,839
Speaker 4: to use a little bit of poetic license. So let's

835
00:43:21,840 --> 00:43:25,319
Speaker 4: take a post office in a mid size country. They

836
00:43:25,400 --> 00:43:28,120
Speaker 4: probably burn a billion gallons of fuel per year delivering

837
00:43:28,120 --> 00:43:31,640
Speaker 4: packages and letters. Because most posts and advanced country says

838
00:43:31,960 --> 00:43:37,440
Speaker 4: every house, every address, each day. The way to optimize

839
00:43:37,480 --> 00:43:39,840
Speaker 4: this is we can formulate the problem. It's called the

840
00:43:39,880 --> 00:43:44,040
Speaker 4: proveling sales and problem solving. It is really hard, so

841
00:43:44,040 --> 00:43:48,239
Speaker 4: people have heuristics. Let's suppose today our heuristics get us

842
00:43:48,280 --> 00:43:51,880
Speaker 4: to within twenty percent of the optimal answer. Let's suppose

843
00:43:51,880 --> 00:43:55,879
Speaker 4: a quantum computer can get you the next ten percent. Well,

844
00:43:55,920 --> 00:43:58,160
Speaker 4: if I can get ten percent of a billion gallons

845
00:43:58,160 --> 00:43:59,800
Speaker 4: that I think is one hundred million gallons of my

846
00:43:59,880 --> 00:44:02,840
Speaker 4: man others right, and in the country I'm thinking about,

847
00:44:03,080 --> 00:44:06,360
Speaker 4: that could be eight hundred million pounds of saving to

848
00:44:06,440 --> 00:44:12,040
Speaker 4: one entity in one year, and the associated carbon footprint

849
00:44:12,160 --> 00:44:16,000
Speaker 4: climate change were in't less mileage on vague. I'm not

850
00:44:16,000 --> 00:44:19,719
Speaker 4: even counting all that. These are pretty attractive problems to

851
00:44:19,760 --> 00:44:22,560
Speaker 4: go after. So if I look at the interest recently,

852
00:44:23,800 --> 00:44:28,239
Speaker 4: New York started a whole program in some places. Illinois

853
00:44:28,680 --> 00:44:32,400
Speaker 4: stood up a quantum algorithm center between a number of

854
00:44:32,400 --> 00:44:37,319
Speaker 4: the universities. The governor there was heavily behind it, etc.

855
00:44:38,040 --> 00:44:41,080
Speaker 4: So I wouldn't say that this is widespread. This is

856
00:44:41,080 --> 00:44:42,960
Speaker 4: why I'm saying three to four years for that moment.

857
00:44:43,600 --> 00:44:47,200
Speaker 4: But there's enough people who are deeply cognizant who are saying,

858
00:44:47,200 --> 00:44:49,279
Speaker 4: wait a moment, we kind of get it.

859
00:44:49,320 --> 00:44:51,000
Speaker 5: This is a new kind of man. What are the

860
00:44:51,040 --> 00:44:52,120
Speaker 5: new problems we can solve?

861
00:44:52,840 --> 00:44:55,280
Speaker 4: And the fact that we have about roughly two hundred

862
00:44:55,280 --> 00:44:58,920
Speaker 4: clients who worked with us very early stage, small experiments

863
00:44:59,239 --> 00:45:01,600
Speaker 4: is because they're in is I could do something here

864
00:45:01,600 --> 00:45:04,040
Speaker 4: that I couldn't do in other places.

865
00:45:04,320 --> 00:45:05,960
Speaker 2: Three to four years is not a long time.

866
00:45:06,400 --> 00:45:06,600
Speaker 5: No.

867
00:45:08,520 --> 00:45:10,280
Speaker 2: But if I'm in the battery business.

868
00:45:10,520 --> 00:45:14,040
Speaker 3: And I don't have a line out to a quantum

869
00:45:14,239 --> 00:45:16,319
Speaker 3: computing experiment.

870
00:45:17,480 --> 00:45:19,359
Speaker 2: I have a problem. Don't have a problem.

871
00:45:19,800 --> 00:45:23,839
Speaker 4: Yeah, you'll probably be out of business in ten years. Well,

872
00:45:23,840 --> 00:45:25,680
Speaker 4: maybe you could write a big check and buy the

873
00:45:25,719 --> 00:45:27,040
Speaker 4: technology from somebody else.

874
00:45:27,719 --> 00:45:30,600
Speaker 3: You had to what is quantum of rank in the

875
00:45:30,680 --> 00:45:33,840
Speaker 3: kind of great inventions of the last one hundred and

876
00:45:33,880 --> 00:45:34,719
Speaker 3: fifty years.

877
00:45:34,480 --> 00:45:40,000
Speaker 4: Equal to something conductor? And I think that if sem

878
00:45:40,080 --> 00:45:42,160
Speaker 4: Conductor's vanished, modern life.

879
00:45:42,000 --> 00:45:43,840
Speaker 5: Would stop, like just stop.

880
00:45:44,560 --> 00:45:53,040
Speaker 4: Yeah, no electricity, no automobile, no streaming. You can imagine

881
00:45:53,080 --> 00:45:54,759
Speaker 4: the yells from all the kids who ever hear that

882
00:45:54,840 --> 00:45:55,800
Speaker 4: no streaming?

883
00:45:57,680 --> 00:46:01,719
Speaker 3: The and is that it's funny because don't. As someone

884
00:46:01,760 --> 00:46:05,480
Speaker 3: who's outside this world, I feel like quantum is underdiscussed

885
00:46:05,520 --> 00:46:09,520
Speaker 3: relative to its potential for transforming society.

886
00:46:09,480 --> 00:46:13,279
Speaker 4: Because I use my Internet example. Ninety five was the

887
00:46:13,280 --> 00:46:18,200
Speaker 4: moment with Netscape that Internet came on people's consciousness. I said,

888
00:46:18,239 --> 00:46:20,360
Speaker 4: when eighty five I considered it to be this is

889
00:46:20,400 --> 00:46:24,000
Speaker 4: a solve problem because it needs something that makes it

890
00:46:24,080 --> 00:46:25,760
Speaker 4: accessible easy.

891
00:46:26,080 --> 00:46:28,680
Speaker 5: That was the browser. The Netscape browser.

892
00:46:28,400 --> 00:46:32,640
Speaker 4: Is what brought it easy to understand. We have probably,

893
00:46:32,680 --> 00:46:35,600
Speaker 4: as I said, about four to five years from that moment.

894
00:46:35,640 --> 00:46:38,560
Speaker 4: That's why it's under discussed, because the moment I say,

895
00:46:38,560 --> 00:46:40,480
Speaker 4: and you got a math, I've probably lost ninety nine

896
00:46:40,520 --> 00:46:43,280
Speaker 4: percent of the audience. If I go to quantum mechanics.

897
00:46:43,320 --> 00:46:46,680
Speaker 4: I've probably lost nine percent of the audience.

898
00:46:48,480 --> 00:46:51,640
Speaker 3: So you, as c over the last five years, have

899
00:46:51,800 --> 00:46:54,680
Speaker 3: been really the birth mother for a lot of the

900
00:46:54,760 --> 00:46:59,319
Speaker 3: quantum computing work. I'm curious, so you come in When

901
00:46:59,360 --> 00:47:03,800
Speaker 3: you started this, CEO, was this your first priority.

902
00:47:04,320 --> 00:47:06,840
Speaker 4: I had already started investing in it back in twenty

903
00:47:06,880 --> 00:47:12,520
Speaker 4: fifteen when I was leading IBM research. So let me

904
00:47:12,560 --> 00:47:14,360
Speaker 4: acknowledge and like nobody should try to copy it. And

905
00:47:14,400 --> 00:47:16,960
Speaker 4: I've had a i'll call it a weird career.

906
00:47:17,400 --> 00:47:19,359
Speaker 5: I was a researcher at some point.

907
00:47:19,360 --> 00:47:20,640
Speaker 4: If he had asked me out, I said, I'm one

908
00:47:20,680 --> 00:47:22,239
Speaker 4: of those people, you know, throw a pizza under our

909
00:47:22,280 --> 00:47:23,560
Speaker 4: door and like leave me alone.

910
00:47:23,719 --> 00:47:24,839
Speaker 5: I don't want to talk to people.

911
00:47:25,360 --> 00:47:27,719
Speaker 4: Then I decided I was interested in the business. Then

912
00:47:27,760 --> 00:47:30,759
Speaker 4: I went and started acquiring companies and doing that. Then

913
00:47:30,800 --> 00:47:32,840
Speaker 4: somebody told me, hey, why did you start doing some

914
00:47:32,880 --> 00:47:37,560
Speaker 4: business strategy. Then I went back to research and led

915
00:47:37,560 --> 00:47:40,359
Speaker 4: our research division for a couple of years. And when

916
00:47:40,400 --> 00:47:44,160
Speaker 4: the people described it to me, I asked some questions.

917
00:47:44,239 --> 00:47:46,480
Speaker 4: So it wasn't a big investment.

918
00:47:46,000 --> 00:47:46,399
Speaker 5: At that time.

919
00:47:46,440 --> 00:47:48,719
Speaker 4: It was hey, can we make a computer not just

920
00:47:48,760 --> 00:47:52,239
Speaker 4: a science experiment? Can it run by itself all night?

921
00:47:52,760 --> 00:47:55,839
Speaker 4: Can you think about software so that even people who

922
00:47:55,840 --> 00:47:58,440
Speaker 4: are not deeper quantum mechanics can begin to use it?

923
00:47:59,360 --> 00:48:00,760
Speaker 5: And they began to do those things.

924
00:48:01,040 --> 00:48:05,640
Speaker 4: So over three four years, did I get enough confidence, Yeah, okay,

925
00:48:05,640 --> 00:48:07,320
Speaker 4: this is something that can really work.

926
00:48:07,840 --> 00:48:09,279
Speaker 5: And then you've got to nurture it to.

927
00:48:10,000 --> 00:48:12,759
Speaker 4: Where it gets bigger and bigger until you get the

928
00:48:12,800 --> 00:48:14,479
Speaker 4: confidence that okay, now it's a big bet.

929
00:48:15,280 --> 00:48:18,120
Speaker 2: And what was the moment when you when you realize now.

930
00:48:17,960 --> 00:48:22,240
Speaker 5: It's a big bet, probably two or three years ago.

931
00:48:22,760 --> 00:48:25,280
Speaker 3: How do you decide, as the head of a company

932
00:48:25,360 --> 00:48:29,319
Speaker 3: like this, how much money, how many resources, and how

933
00:48:29,360 --> 00:48:31,399
Speaker 3: many people and how what kind of prominence to give

934
00:48:31,440 --> 00:48:32,239
Speaker 3: to an idea like that?

935
00:48:33,000 --> 00:48:37,040
Speaker 4: So three layers the set of people who actually have

936
00:48:37,200 --> 00:48:41,680
Speaker 4: the knowledge and the intensity to fundamentally advance the technology.

937
00:48:42,400 --> 00:48:44,080
Speaker 5: If I could find more out higher then.

938
00:48:44,080 --> 00:48:47,359
Speaker 4: So I'm constrained on people on that one because normally

939
00:48:47,400 --> 00:48:49,600
Speaker 4: there's only so many people who can do these things.

940
00:48:50,320 --> 00:48:52,960
Speaker 5: Two, you got to be careful.

941
00:48:53,239 --> 00:48:56,000
Speaker 4: If you push too hard on timing, you will get

942
00:48:56,000 --> 00:48:58,719
Speaker 4: people to take so much risk that actually the thing

943
00:48:58,760 --> 00:49:02,560
Speaker 4: will fail. So that's the art of between the leadership

944
00:49:02,600 --> 00:49:06,279
Speaker 4: on the project and me to say, Okay, how hard

945
00:49:06,320 --> 00:49:08,959
Speaker 4: can you push? But not so hard that you cause

946
00:49:09,000 --> 00:49:12,160
Speaker 4: it to fail because then they get compelled to commit

947
00:49:12,200 --> 00:49:14,239
Speaker 4: timelines that are just impossible.

948
00:49:14,800 --> 00:49:17,480
Speaker 2: Yeah, how do you This is fascinating.

949
00:49:17,600 --> 00:49:20,759
Speaker 3: So it's ultimately a question of judgment trying to figure

950
00:49:20,760 --> 00:49:23,399
Speaker 3: out what's the sweet spot between enough pressure to keep

951
00:49:23,440 --> 00:49:25,960
Speaker 3: them ahead of the pack, but not too much pressure

952
00:49:26,360 --> 00:49:29,759
Speaker 3: so that they start taking risks. How do you calibrate

953
00:49:30,040 --> 00:49:32,839
Speaker 3: whether you're hitting that sweet spot? I mean, do you

954
00:49:33,040 --> 00:49:36,239
Speaker 3: reassess every few months and say I think I'm over

955
00:49:36,280 --> 00:49:37,960
Speaker 3: correcting or undercorrecting at this moment.

956
00:49:38,800 --> 00:49:42,160
Speaker 4: So one you got to have what I call and

957
00:49:42,239 --> 00:49:44,920
Speaker 4: this is channeling a word from one of my favorite books,

958
00:49:44,960 --> 00:49:48,879
Speaker 4: The Geekway, How open can you be? So I want

959
00:49:48,920 --> 00:49:52,399
Speaker 4: to press hard, but the team knows that they're allowed

960
00:49:52,440 --> 00:49:56,600
Speaker 4: to push back and really argue back hard. That means

961
00:49:56,600 --> 00:50:02,360
Speaker 4: you'll get to probably that correct goldilocks pressure do the

962
00:50:02,480 --> 00:50:05,240
Speaker 4: people themselves should want to go as hard as possible,

963
00:50:05,760 --> 00:50:09,320
Speaker 4: but not harder than possible. So that is then personality

964
00:50:09,719 --> 00:50:12,000
Speaker 4: of leadership that makes sense.

965
00:50:12,239 --> 00:50:14,800
Speaker 2: But you have to be someone who people feel comfortable

966
00:50:14,840 --> 00:50:15,440
Speaker 2: being honest with.

967
00:50:15,640 --> 00:50:19,200
Speaker 5: Yes, absolutely, and people feel.

968
00:50:18,960 --> 00:50:20,040
Speaker 2: Comfortable being honest with you.

969
00:50:20,239 --> 00:50:20,880
Speaker 5: I believe so.

970
00:50:21,239 --> 00:50:25,719
Speaker 2: Yeah. When has there been a moment in this path

971
00:50:25,760 --> 00:50:28,560
Speaker 2: with quantum where you did think you were pushing too hard?

972
00:50:30,360 --> 00:50:35,000
Speaker 4: No, because I think that the leadership there will argue

973
00:50:35,000 --> 00:50:37,959
Speaker 4: back with me any day of the week. I don't

974
00:50:37,960 --> 00:50:40,400
Speaker 4: think that they feel that they have to forward.

975
00:50:41,480 --> 00:50:44,600
Speaker 3: Do you drop by at sort of Saturday night at

976
00:50:44,600 --> 00:50:46,040
Speaker 3: ten pm to see if people are working?

977
00:50:47,120 --> 00:50:53,080
Speaker 4: I tend to text people and ask questions and like,

978
00:50:53,160 --> 00:50:55,880
Speaker 4: I'll read something and say, hey, are these people doing this?

979
00:50:56,440 --> 00:51:00,400
Speaker 4: And if they can answer me in reasonable terms, I

980
00:51:00,440 --> 00:51:03,640
Speaker 4: actually don't say great. They're already watching the competition, they're

981
00:51:03,680 --> 00:51:06,920
Speaker 4: watching the literature, they're watching the science. I don't need

982
00:51:06,960 --> 00:51:09,200
Speaker 4: to push hard. If they are already ahead of it,

983
00:51:09,239 --> 00:51:12,280
Speaker 4: then me I can answer my question. I'll say thoughtfully,

984
00:51:12,600 --> 00:51:16,719
Speaker 4: not always completely accurately. You're thinking about it on their own.

985
00:51:16,719 --> 00:51:17,919
Speaker 4: I don't need to push Yeah.

986
00:51:18,760 --> 00:51:21,239
Speaker 3: One last question I wanted to ask you, do you

987
00:51:21,320 --> 00:51:23,200
Speaker 3: have the most interesting job in America?

988
00:51:23,360 --> 00:51:25,719
Speaker 4: I believe that it's the most interesting job, which I

989
00:51:25,719 --> 00:51:26,880
Speaker 4: won't give up anything.

990
00:51:27,880 --> 00:51:30,760
Speaker 2: It also sounds like you're enjoying yourself.

991
00:51:31,320 --> 00:51:34,600
Speaker 4: I enjoy it as long as look my role and

992
00:51:34,719 --> 00:51:38,719
Speaker 4: goal should be to make the enterprise thrive as long

993
00:51:38,760 --> 00:51:42,040
Speaker 4: as than making the enterprise thrive and are clients delighted?

994
00:51:43,640 --> 00:51:46,520
Speaker 5: I love it. If I don't, somebody else should do it.

995
00:51:47,520 --> 00:51:50,840
Speaker 3: Harvin, this has been so much fun. Thank you so

996
00:51:50,960 --> 00:51:56,440
Speaker 3: much taking the time and a fascinating, completely fascinating conversation.

997
00:51:56,480 --> 00:51:58,120
Speaker 3: I wish I was one of those people who could

998
00:51:58,160 --> 00:52:00,440
Speaker 3: help you out with quantum, but I'm afraid I'm not

999
00:52:01,080 --> 00:52:01,759
Speaker 3: in a few years.

1000
00:52:03,200 --> 00:52:04,680
Speaker 2: Good. Thank you so much.

1001
00:52:14,719 --> 00:52:18,400
Speaker 3: Smart Talks with IBM is produced by Matt Ramano, Amy Gaines, McQuaid,

1002
00:52:18,880 --> 00:52:23,080
Speaker 3: Trina Menino, and Jake Harper. Mastering by Sarah Bugerer, music

1003
00:52:23,080 --> 00:52:28,880
Speaker 3: by Gramoscope, Strategy by Tatiana Lieberman, Cassidy Meyer and Sophia Derlong.

1004
00:52:29,600 --> 00:52:32,239
Speaker 3: Smart Talks with IBM is a production of Pushkin Industries

1005
00:52:32,440 --> 00:52:36,840
Speaker 3: and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,

1006
00:52:37,239 --> 00:52:41,160
Speaker 3: listen on the iHeartRadio app, Apple Podcasts, or wherever you

1007
00:52:41,239 --> 00:52:45,160
Speaker 3: listen to podcasts. I'm Malcolm Glabo. This is a paid

1008
00:52:45,160 --> 00:52:49,719
Speaker 3: advertisement from IBM. The conversations on this podcast don't necessarily

1009
00:52:49,800 --> 00:53:03,800
Speaker 3: represent IBM's positions, strategies, or opinions.