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Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got

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Speaker 1: something a little bit different to share with you. It's

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Speaker 1: a new season of the Smart Talks with IBM podcast series.

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Speaker 2: This season on Smart Talks with IBM, Malcolm Gladwell is back,

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Speaker 2: and this time he's taking the show on the road.

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Speaker 2: Malcolm is stepping outside the studio to explore how IBM

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Speaker 2: clients are using artificial intelligence to solve real world challenges

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Speaker 2: and transform the way they do business.

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Speaker 1: From accelerating scientific breakthroughs to reimagining education. It's a fresh

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Speaker 1: look at innovation in action, where big ideas meet cutting

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Speaker 1: edge solutions.

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Speaker 2: You'll hear from industry leaders, creative thinkers, and of course

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Speaker 2: Malcolm Gladwell himself as he guides you through each story.

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Speaker 1: New episodes of Smart Talks with IBM drop every month

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Speaker 1: on the iHeartRadio app, Apple Podcasts, or wherever you get

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Speaker 1: your podcasts. Learn more at IBM dot com slash smart Talks.

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Speaker 1: This is a paid advertisement for IBM pushkin.

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Speaker 3: Hello.

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Speaker 4: Hello, I'm Malcolm Gladwell and you're listening to Smart Talks

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Speaker 4: with IBM. This season, we've been bringing you stories of

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Speaker 4: how IBM works with its clients to solve complex problems

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Speaker 4: like helping Lreel reimagine how scientists approach cosmetic formulation, or

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Speaker 4: enabling Scuderia FERRARIHP to connect with fans in new ways.

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Speaker 4: But in this episode, we're going to zoom out and

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Speaker 4: look at the bigger picture. Earlier this month, I had

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Speaker 4: the chance to meet the person who's shaping IBM's future.

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Speaker 4: It's CEO and Chairman Arvind Krishna. We sat down in

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Speaker 4: front of an intimate live audience at IBM's New York

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Speaker 4: City office and talked about his uncanny ability to anticipate

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Speaker 4: where technology is heading, the future of AI, and his

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Speaker 4: passion for quantum computing, which he says is as revolutionary

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Speaker 4: as a semiconductor.

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Speaker 3: Thank you everyone, Thank you Arvin.

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Speaker 4: You're a difficult man to scare for what they said,

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Speaker 4: so we're we're enormously pleased that you.

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Speaker 3: Could join us.

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Speaker 4: Let's start with a I have all these cousins, two

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Speaker 4: cousins who work for IBM their entire career.

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Speaker 3: I would ask them what does IBM do?

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Speaker 4: And they would always give me different, confusing, complicated answers.

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Speaker 3: What's your answer? What's your simple answer to that question?

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Speaker 5: IBM's role is to help our clients improve their business

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Speaker 5: by deploying technology. That means you're not ever gated to

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Speaker 5: one product. It is what makes sense at that time,

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Speaker 5: but it is about improving their business, not just giving

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Speaker 5: them a commodity. Then to go to the next layer,

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Speaker 5: I would say we help them through a mixture of

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Speaker 5: hybrid cloud and artificial intelligence and a taste of quantum

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Speaker 5: coming down the road is kind.

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Speaker 3: Of where I would take it.

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

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Speaker 3: So you are technology agnostic in some sense.

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Speaker 5: I'm product agnostic. Product I'm not technology agnostic.

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Speaker 4: Yes, But if I twenty five years from now, IBM

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Speaker 4: could be doing things that would be unrecognizable to contemporary IBM, it.

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Speaker 3: Is completely possible.

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Speaker 5: Yeah, it could be that in twenty five years from now,

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Speaker 5: the only software IBM does his open source. It could

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Speaker 5: be the only computing you do is quantum computers. And

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Speaker 5: if I add those two people today, WLD say that's

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Speaker 5: not the IEM of today.

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Speaker 4: Is it even simpler to say you just IBM solves

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Speaker 4: problems at the highest technical level.

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

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Speaker 5: guy who invented the bar code, he was solving a

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Speaker 5: problem retailers wanted to scale. Many of you may not

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Speaker 5: know it was an IBM or who invented the bar code,

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Speaker 5: by the way, not somebody who was a PhD. Not

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Speaker 5: somebody who was a deep researcher. I think it was

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Speaker 5: actually a field engineer. Oh really, Yeah, and lasers were

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Speaker 5: out and you could use lasers to scan things, but

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Speaker 5: they could be upside down, they could be muddy, they

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Speaker 5: could be partly scraped off. And he came up with

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Speaker 5: the idea of the bar code. Yeah, and that changed

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Speaker 5: inventory management forever. But the world needs to know that

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Speaker 5: IBM invented the barcode. You guys should do a better

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Speaker 5: job ombicizing that. I am sure our CMO will listen

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Speaker 5: to this podcast and we'll get that idea.

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Speaker 4: Tell me you started at the Thomas Watson Research Center.

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Speaker 4: What were you doing when you first started it, IBM.

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Speaker 5: I started in nineteen ninety and that was the era

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Speaker 5: in which computers and networking but beginning to converge. And

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Speaker 5: for the first five years I was actually building networks.

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Speaker 5: So let's remember this was pre laptops. Laptops came in

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Speaker 5: ninety two or ninety three, but it was clear to

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Speaker 5: us that they were going to come supportable computing, and

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Speaker 5: I spent my first five years building what today you

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Speaker 5: would call Wi Fi. We used to have these debates

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Speaker 5: can be builded, it's got to be small enough. I

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Speaker 5: mean like, it can't be more than one hundred grams

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Speaker 5: was kind of our thought, because if it's more than that,

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Speaker 5: you're on a three thousand grand laptop. Why would anybody

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Speaker 5: put this on? And the debate used to be, why

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Speaker 5: would anybody want to walk around untethered? Won't you want

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Speaker 5: to attach a big thick cable into it and sit down?

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Speaker 5: Because that was the thought, that's how terminals worked. And

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Speaker 5: I spent five years having a lot of fun, building

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Speaker 5: many iterations of those and making progress on that.

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Speaker 4: If I had a conversation with your nineteen ninety self

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Speaker 4: about what the next thirty years were going to look like,

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Speaker 4: is it possible to reconstruct you? What were your predictions

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Speaker 4: at that age about where the company, where the industry

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Speaker 4: was going.

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Speaker 5: It was more about where technology was going to go,

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Speaker 5: I would say, than where industry would go. I would

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Speaker 5: have told you that networking and computers would fuse in

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Speaker 5: nineteen ninety. That was a weird thought that some researchers

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Speaker 5: held by the late nineties, that was obvious that it

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Speaker 5: became the Internet. I would have told you that I

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Speaker 5: believe that video streaming will be the primary way people

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Speaker 5: will consume video. You would have said that in nineteen ninety. Absolutely, Now,

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Speaker 5: that didn't take five years. That took twenty. But it

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Speaker 5: happened because you could do it technically, except it just

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Speaker 5: too expensive and too cumbersome. And if you've been in technology,

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Speaker 5: like in nineteen eighty five, I would have told you

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Speaker 5: the Internet is old because when I went to grad school,

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Speaker 5: every one of us had a those days, an Apple,

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Speaker 5: Mac or Lisa on our desks. They're all connected by

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Speaker 5: a network. You're happily sending email to people all around

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Speaker 5: the country. We were doing file transfers. So okay, you

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Speaker 5: had to be a little bit more aware of the

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Speaker 5: technology and didn't have a browser that took ten years

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Speaker 5: to get the browser that took five years to be

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Speaker 5: a business. But when you see the speed and the

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Speaker 5: pace of technology in usually ten or fifteen years, the

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Speaker 5: cost point and the consumerization is at a scale that

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Speaker 5: you couldn't imagine ten years ago.

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Speaker 3: Until you've seen a few of those cycles.

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Speaker 4: Wait, did you make the lead to sorry, this is fascinating.

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Speaker 4: I'm curious about how far did you take that. That's

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Speaker 4: a really fundamental thing to have gotten right in nineteen

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Speaker 4: ninety I think you're that idea.

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Speaker 5: We were pretty convinced that what we used to think

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Speaker 5: of as linear television or broadcast would become digitized. That

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Speaker 5: was a given two with cable already the preponderance of

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Speaker 5: how people got it that if you put packet television

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Speaker 5: over cable, then that becomes the way it will go.

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Speaker 5: I fundamentally believed, actually way back eighty seven, that on

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Speaker 5: demand movies would become the way people would consume movies.

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Speaker 5: So those were all things that I could have predicted.

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Speaker 5: Nine didn't personally work on all those. I mean, after networking,

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Speaker 5: I moved on to doing other things. But those were

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Speaker 5: easy to predict.

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Speaker 4: If you had a converse in those years with someone

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Speaker 4: in the television industry and you gave them those predictions,

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Speaker 4: did they see it?

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Speaker 3: Were they convinced of this?

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Speaker 5: I'm actually going to take it back to wireless networking.

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Speaker 5: I think one of the reasons I do what I

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Speaker 5: do today, which is at the intersection of business and technology,

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Speaker 5: is because of what I saw happened with Wi Fi.

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Speaker 5: So you built these wireless networks and then you say, hey,

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Speaker 5: the market's going to be millions, tens of millions, billions

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Speaker 5: of users. And the business looks at it and says,

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Speaker 5: we think the market is confined to warehouse workers doing inventory.

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Speaker 5: You can look at them and say, why not people

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Speaker 5: in their homes because they could imagine outside how people

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Speaker 5: bought things at that time. And so I became convinced

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Speaker 5: that I can't just help invent it. I got to

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Speaker 5: think about, now, how do you market it? To whom

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Speaker 5: do you market it? What are their routs? How do

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Speaker 5: you make it easy enough? And that was I mean

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Speaker 5: not making it simple? Now, that was probably a five

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Speaker 5: to ten year evolution of myself in those days.

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Speaker 4: You know, this reminds me of when the telephone is

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Speaker 4: invented in the eighteen seventies. It doesn't take off for

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Speaker 4: forty years because the people running a telephone business and

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Speaker 4: they didn't want women using it because they were worried

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Speaker 4: that women would gossip with their friends. They didn't understand

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Speaker 4: that that's actually what telephone is.

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Speaker 3: Right, It's an.

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Speaker 5: Exact parallel, Yes, it is. You see it again and again.

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Speaker 3: What is the source of that blindness.

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Speaker 4: So there's a gap, in other words, between the invention,

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Speaker 4: the technological achievement, and the social understanding of the technology.

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Speaker 3: Why is there such a gap.

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Speaker 5: I think that the gap is fundamental and rooted in

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Speaker 5: a lot of academic disciplines. So even channeling some of

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Speaker 5: your work, though you don't intend it to be used

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Speaker 5: that way, you can say a lot of things are

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Speaker 5: data driven. If it is data driven, then by definition,

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Speaker 5: you're looking at history. If you're looking at history, that

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Speaker 5: means you're looking at existing buying patterns. If you look

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Speaker 5: at existing buying patterns, you forget. All of those who

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Speaker 5: have created massive value in time have all created markets,

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Speaker 5: meaning they've all created new markets. And I think that

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Speaker 5: is why the world is fascinated with people like Steve

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Speaker 5: Jobs for example, he imagined a market that didn't exist.

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Speaker 5: So I think that is the gap. And then if

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Speaker 5: you can get the technology the business acumen scaler company

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Speaker 5: and that imagination of making the market is how you

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Speaker 5: create I think massive value.

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Speaker 3: You got to get all three pieces going. It's not enough.

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Speaker 4: In other words, you were thinking it's not enough to

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Speaker 4: invent something new. I need to make a business case

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Speaker 4: for it. Simultaneously, and that's what gets you thinking along

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Speaker 4: the path that leads you to this job.

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Speaker 5: Oh yeah, I'll tell you if you had met or

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Speaker 5: when the nineteen ninety four and you had talked about

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Speaker 5: the stock market or about a balance sheet, or looked

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Speaker 5: at you like, Okay, I got those words are I

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Speaker 5: can parse them. I have no idea what they are.

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Speaker 5: I have no intuition on what they are. I couldn't

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Speaker 5: tell you why it's relevant or why it's not. But

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Speaker 5: then you began to think, Okay, why do companies get

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Speaker 5: higher values? Okay, that's the stock what does that capture

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Speaker 5: if I have to spend working capital and that's the

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Speaker 5: balance sheet? Well, so you learn. I mean, I figure

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Speaker 5: I'm willing to learn. I'm willing to read. Howether the

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Speaker 5: best way I read is to go to balance sheets?

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Speaker 5: Here you can read the book. It's pretty damn dry.

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Speaker 5: Much easier to go talk to a financial expert who's

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Speaker 5: around the corner, and people are if you're curious about

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Speaker 5: what they do, they're really happy to share their expertise,

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Speaker 5: and over time you learn more and more and they

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Speaker 5: actually become part of your network within the company. And

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Speaker 5: that's how you can both learn and evolve yourself and

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Speaker 5: actually gain the extra.

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Speaker 4: Skills you are to be a successful business leader. Do

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Speaker 4: you have to unlearn or or deviate from some of

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Speaker 4: the things that made you a successful scientist.

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Speaker 5: I actually believe the exactly opposite.

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Speaker 3: Yeah, but use what.

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Speaker 5: You're really good at as a foundation, but don't make

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Speaker 5: it the only thing you use. So then how do

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Speaker 5: you add the other skills? And there's many ways. You

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Speaker 5: can have people that you trust who help you out

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Speaker 5: those skills. You can gain some intuition, maybe not the

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Speaker 5: depth of expertise. I want to be deeper on certain

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Speaker 5: areas of electrical engineering than I'm ever going to be,

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Speaker 5: let's say, in finance or marketing. But I want to

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Speaker 5: be curious about those. I don't want to dismiss them.

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Speaker 5: So you build on your skills, and then you have

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Speaker 5: to say, but I need a complete and holistic view.

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Speaker 5: So I'm going to be a little deep, not very deep,

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Speaker 5: in all of those. And you've also got to learn

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Speaker 5: to trust your intuition a little bit.

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Speaker 4: Yeah, but I forgot a question that I wanted to ask,

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Speaker 4: but about the predictions of nineteen ninety arvand what did

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Speaker 4: you get wrong?

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Speaker 5: All lots of things. I think that people were thinking

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Speaker 5: that in those days, and it started my phrase, but

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Speaker 5: I'll come back to it. I think most people thought

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Speaker 5: that the communication companies would turn out to be the

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Speaker 5: winners of how networking got carried. If you all think

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Speaker 5: through the nineties of the investments that were being done

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Speaker 5: by let's not take the names of all of the

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Speaker 5: telecom carriers.

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Speaker 3: Didn't turn out to.

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Speaker 5: Be the case. Actually, I think that's the business model case.

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Speaker 5: The reason is they all had in their heads that

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Speaker 5: you can charge people by the.

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Speaker 3: Minute, because they had been doing that already, because they

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Speaker 3: had been doing that for one hundred years.

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Speaker 5: Yeah, And in the end, the winners the networking were

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Speaker 5: those who said flat price thirty bucks a month or

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Speaker 5: fifty bucks a month or whatever, and that was just

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Speaker 5: too much of a.

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Speaker 3: Leap for them.

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Speaker 4: You think it's as simple. That is the most parsimonious

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Speaker 4: explanation for why you think they failed.

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Speaker 5: No, there were a couple of hours the more technical things.

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Speaker 5: The one was written by somebody who was actually inside

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Speaker 5: one of these telecom companies, and he labeled his article

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Speaker 5: the rise of the Stupid Network. So telephone people believe

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Speaker 5: that the network should be really smart, the end device

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Speaker 5: is dumb. If you think about the telephone, telephone is dumb.

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Speaker 5: It doesn't actually do anything. It's just about the relays.

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Speaker 5: And the network is smart. It routes you, it figures

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Speaker 5: out where to send it, it does echocacul backwards. And

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Speaker 5: the current Internet is completely dumb with the inside. It

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Speaker 5: just takes the bits and shoves them out the other end.

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Speaker 5: All the intelligence is the computer at the end. That's

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Speaker 5: probably a bit more of a found explanation. But business

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Speaker 5: model didn't help them either.

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Speaker 3: Yeah.

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Speaker 4: Wait, did nineteen ninety r of end think that the

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Speaker 4: network should be dumb or smart?

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Speaker 3: I'm not sure.

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Speaker 5: I thought about it deeply, but everything I worked on

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Speaker 5: the network was dumb.

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Speaker 3: The netwuck movements. That's all I did.

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Speaker 5: Yeah, because even I in those days understood I can't

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Speaker 5: imagine all the opp loss. So if all you do

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Speaker 5: is voice, maybe the network can be smart. But if

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Speaker 5: you're doing all those other things, how could the network

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Speaker 5: possibly know all those things that be smart for it?

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Speaker 3: Yeah, so you've been COO for five years. Five years.

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Speaker 4: Wait, so in your five year increment, what was your

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Speaker 4: most misunderstood decision? Well, you ended up being right, but

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Speaker 4: everyone thought you were crazy.

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Speaker 5: Twenty and eighteen, I proposed to our boat that we

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Speaker 5: should buy a company called rad Hat. IBM does proprietary

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Speaker 5: but that was open source. The stock twelve fifteen percent

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Speaker 5: of the day we announced it, and today most people

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Speaker 5: will turn around and say, this is the most successful

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Speaker 5: acquisition that IBM has done in all time, and probably

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Speaker 5: the most successful software acquisition in history. So it was

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Speaker 5: completely misunderstood because people didn't see that you actually did

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Speaker 5: need a platform that could make you agnostic across multiple

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Speaker 5: cloud platforms, across on premise environments.

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Speaker 3: So you've got to have a view of what.

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Speaker 5: It could be, and we drove it to a place

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Speaker 5: where I think today it stands as the leader in

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Speaker 5: its space.

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Speaker 4: So how did you come to believe this heretical notion?

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Speaker 5: So Cloud was happening, you could ask yourself the question,

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Speaker 5: should we spend a lot of capital and chase Cloud? Okay,

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Speaker 5: you're five years to be generous, maybe longer behind at

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Speaker 5: that point the two leaders, So you could spend maybe

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Speaker 5: ten billion a year, and a lot of businesses tend

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Speaker 5: to do that. Okay, it's so important, it's going to

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Speaker 5: be half the market. I can't not My view was

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Speaker 5: we'll always be five years behind. They're not dumb and

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Speaker 5: they're not slow. So if you're going to be there,

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Speaker 5: you're going to be best case, a distant third, worst

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Speaker 5: case maybe a fourth or a fifth. Because there's Chinese

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Speaker 5: also in the mix, why would you do that instead?

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Speaker 3: Is there a.

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Speaker 5: Different space you can occupy instead of competing with them?

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Speaker 5: Can you become their best partner, in which case you

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Speaker 5: write their success. If I want to be the best partner,

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Speaker 5: then what are the set of technologies that would be

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Speaker 5: useful so you can flip The problem is how I

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Speaker 5: thought about it.

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Speaker 4: How hard was it to convince people needed convincing before

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Speaker 4: that acquisition.

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Speaker 5: Probably six to nine months of breaking my head with

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Speaker 5: no success, and then six months of building the momentum

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Speaker 5: once a couple of people began to see it.

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Speaker 3: Yeah, you're very persistent.

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Speaker 4: Oh yes, very Would you describe that as you defining trade?

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

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Speaker 5: also probably very impatient, but I'm not a yeller and screamer.

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Speaker 5: I don't rant and rave. But as I say, if

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Speaker 5: I think we're going to do something, I can be

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Speaker 5: remarkably stubborn about it.

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Speaker 3: We will do it.

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Speaker 4: If I got your family, put them up on stage

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Speaker 4: and ask them this exact question, is this how they

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Speaker 4: would answer?

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Speaker 5: As well, they will tell you I'm very stubborn. They

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Speaker 5: might not agree that I don't rant and drave.

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Speaker 4: Well, you know, one of the principal observations of psychology

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Speaker 4: is that our home self and our work self are uncorrelated.

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Speaker 3: Once you know that, you know everything.

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Speaker 4: Wait, I'm curious one last question about that. How long

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Speaker 4: does it take for you to be vindicated with red Hat?

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Speaker 5: Probably took five maybe four years, I think by twenty

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Speaker 5: twenty three, So twenty eighteen we announced it, We took

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Speaker 5: the big shot crop. It took a year to close

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Speaker 5: twenty nineteen, so if O count not that I'm counting

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Speaker 5: that much, but July ninth, twenty nineteen as the day

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Speaker 5: that we got all the approvals. Took another few weeks

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Speaker 5: to actually transfer the money. But from there, probably twenty

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Speaker 5: twenty three, the world woke up and said, hey, you

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Speaker 5: guys deserve credit for this was actually.

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Speaker 3: A great move, not a bad move.

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Speaker 4: Yeah, but this is it's interesting because this is a

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Speaker 4: real gamble. If it doesn't work, you're not sitting in

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Speaker 4: the chair right now.

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Speaker 3: Right, Oh, for sure. There were two steps.

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Speaker 5: One if it was obviously not going to work, I

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Speaker 5: wouldn't have been selected, and two if it hadn't worked

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Speaker 5: after that, That's why CEOs can be short lived.

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Speaker 4: Can I ask you, sif a personal question, how much

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Speaker 4: sleep did you lose over this.

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Speaker 5: Once we had made the decision?

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Speaker 3: None?

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Speaker 4: Can you give me pointers on how you do this?

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Speaker 4: Because I wake up at two am every morning and

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Speaker 4: 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. I acknowledge it because I wake

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Speaker 5: up and my brain is runrunning, and once it's running,

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Speaker 5: I don't even.

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Speaker 3: Try to go back to sleep.

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Speaker 5: I mean, okay, go get up and do work and

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Speaker 5: make yourself productive. You're going to be tired before in

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Speaker 5: the afternoon, that's fine, you'll sleep well that night. I

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Speaker 5: have actually learned a long time back. You can't do

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Speaker 5: it across. You can't do it early morning, through the

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Speaker 5: day and late at night. So an hour before I

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Speaker 5: think I want to go to bed, I will actually

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Speaker 5: change what I'm doing, meaning I will start reading something

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Speaker 5: interesting to me but completely outside the scope of work.

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Speaker 5: I may read a biography, I might read somebody who's

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Speaker 5: spontefecating on demographics and population. But I won't read it

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Speaker 5: on leadership because that's too close. Now, twenty years ago

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Speaker 5: I might have that would have been different. I won't

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Speaker 5: read it on deep signs because that's too close to.

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Speaker 3: What we do for a living.

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Speaker 5: So it's got to be outside the things that will

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Speaker 5: make my brain churn about work. But it's got to

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Speaker 5: be something that is dense enough to occupy your brain.

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Speaker 3: So shifted gears. Sorry, I want to dwell on this

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Speaker 3: just for a moment. The red hat thing.

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Speaker 4: Was there someone or is there someone who you went

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Speaker 4: to and explained the logic of this and they saw

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Speaker 4: the logic of this, and that made a big difference

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Speaker 4: to you.

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Speaker 5: Getting their support made a big difference. You'd be surprised.

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Speaker 5: I'm remarkably open inside. I mean, when I have are

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Speaker 5: there probably a half dozen to a dozen people inside

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Speaker 5: that I'll talk to and I'll be completely open about, Hey,

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Speaker 5: this is what I'm thinking. I don't know. Here are

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Speaker 5: the risks. I'm open about those. Also, it's not just

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Speaker 5: the benefits. I think the other risks, but I think

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Speaker 5: the benefits outweigh the risks. I talk about that to

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Speaker 5: people all the time. So whether for example, I mean

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Speaker 5: i'll take names. I think our current chro O Nicol

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Speaker 5: who introduced us, she has been in that loop since

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Speaker 5: at least twenty fifteen. For me, if I look at

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Speaker 5: our CFO Jim Cavanaugh, he's been in that loop probably

415
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Speaker 5: since twenty thirteen. And the IBM's will probably wonder, what

416
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Speaker 5: the hell intersection do you guys have? It didn't when

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Speaker 5: I talked about learning finance. I will go to him

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Speaker 5: and say, hey, explain this to me. I don't understand

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Speaker 5: why it's like this, And to me it's okay. You're

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Speaker 5: a patient, you go learn. If I think about many

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Speaker 5: of the people in the software business, they've been having

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Speaker 5: these discussions with me for always. I mean, now I'll

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Speaker 5: acknowledge I can get probably impatient and a SERVIIK, but

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Speaker 5: it's meant to be a discussion. I mean, like, let's

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Speaker 5: have the discussion. If you have a strong point of view,

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Speaker 5: I got it. Nobody has a going to be perfectly correct,

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Speaker 5: but I always look for if you have a strong

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Speaker 5: point of view, that means it's from a different perspective

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Speaker 5: than mine. So what do I learn from that? Is

430
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Speaker 5: the question which helps to improve my point of view.

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Speaker 5: That makes sense. I actually think that each person should

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Speaker 5: try to build a community of one hundred people inside

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Speaker 5: your enterprise and outside that you can call up.

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Speaker 3: I have no hesitation.

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Speaker 5: Somebody introduced me to a long time back, to a

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Speaker 5: CEO of the outside. I call them up all the

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Speaker 5: time and say, hey, do you have five minutes. I'm

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Speaker 5: just thinking about something. This way, the CEO of red

439
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Speaker 5: At who left IBM in twenty twenty one, we probably

440
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Speaker 5: talk every two or three months on a random topic.

441
00:23:21,760 --> 00:23:24,199
Speaker 5: One way it becomes mutual. He asked me my opinion

442
00:23:24,240 --> 00:23:26,760
Speaker 5: on some things. Now, by the way, three or four

443
00:23:26,760 --> 00:23:30,160
Speaker 5: times he might do something different, but he wants my opinion.

444
00:23:30,720 --> 00:23:31,879
Speaker 5: The tour the other way around.

445
00:23:32,040 --> 00:23:33,800
Speaker 4: If I gave you my phone number, can I be

446
00:23:33,880 --> 00:23:36,680
Speaker 4: on that list? I would just be fascinating. I don't

447
00:23:36,680 --> 00:23:37,800
Speaker 4: know if I can help you, but it would be

448
00:23:37,840 --> 00:23:38,800
Speaker 4: really fine to get the call.

449
00:23:38,840 --> 00:23:40,360
Speaker 5: Sure you can. Do you think that we can ever

450
00:23:40,440 --> 00:23:45,400
Speaker 5: succeed unless people who influence opinions say things about us.

451
00:23:46,320 --> 00:23:50,080
Speaker 5: So you may not think deeply about maybe the physics

452
00:23:50,119 --> 00:23:53,480
Speaker 5: of one of computing, but would you think deeply about

453
00:23:54,160 --> 00:23:57,280
Speaker 5: why and what moment may make it much more attractive

454
00:23:57,280 --> 00:24:00,199
Speaker 5: to a large audience. Sure you would, you'd be all

455
00:24:00,359 --> 00:24:03,320
Speaker 5: better as a thinker of that topic, and probably most

456
00:24:03,320 --> 00:24:04,160
Speaker 5: of the people.

457
00:24:04,880 --> 00:24:06,320
Speaker 4: I was thinking, you know, when you were making your

458
00:24:06,320 --> 00:24:12,159
Speaker 4: comments about your nineteen ninety self and streaming, that the

459
00:24:12,880 --> 00:24:16,199
Speaker 4: rational thing would have been for there have been a

460
00:24:16,320 --> 00:24:21,840
Speaker 4: reserved board seat for every television network from someone from

461
00:24:21,840 --> 00:24:25,160
Speaker 4: the world with technology, which I one hundred percent sure

462
00:24:25,160 --> 00:24:28,000
Speaker 4: they did not have that in nineteen ninety, but they

463
00:24:28,359 --> 00:24:30,960
Speaker 4: their board was probably composed of people like them. Let's

464
00:24:30,960 --> 00:24:35,320
Speaker 4: talk a little bit about technology now. There's so much,

465
00:24:35,400 --> 00:24:37,720
Speaker 4: so much of the changes going on right now are

466
00:24:38,400 --> 00:24:42,800
Speaker 4: accompanied by a great deal of hype. What are we overestimating?

467
00:24:42,840 --> 00:24:44,040
Speaker 3: What are we underestimating?

468
00:24:44,440 --> 00:24:47,240
Speaker 5: Okay, let's go back to ninety ninety five the Internet,

469
00:24:47,280 --> 00:24:49,160
Speaker 5: because I think that the current moment is very much

470
00:24:49,240 --> 00:24:51,520
Speaker 5: like the Internet moment. Actually, all the moments in the

471
00:24:51,560 --> 00:24:54,520
Speaker 5: middle were much smaller. I think mobile streaming were much smaller.

472
00:24:54,520 --> 00:24:57,280
Speaker 5: Internet was the major moment. If you remember back to

473
00:24:57,359 --> 00:24:59,359
Speaker 5: ninety nine and two thousand people claimed there was a

474
00:24:59,359 --> 00:25:02,199
Speaker 5: lot of hype. Would we say that the Internet of

475
00:25:02,200 --> 00:25:05,640
Speaker 5: today has more than fulfilled all those expectations and more?

476
00:25:06,160 --> 00:25:06,480
Speaker 3: Yes?

477
00:25:07,600 --> 00:25:11,120
Speaker 5: Along the way, did eight out of ten of the

478
00:25:11,160 --> 00:25:14,200
Speaker 5: companies that were invested in heavily go bankrupt.

479
00:25:14,359 --> 00:25:17,080
Speaker 3: Yes, I actually think of that as.

480
00:25:16,880 --> 00:25:21,280
Speaker 5: Being the huge positive of the United States capital system,

481
00:25:21,880 --> 00:25:25,480
Speaker 5: that that investment happened. Eight out of ten went broke.

482
00:25:25,920 --> 00:25:27,880
Speaker 5: By the way, those acids didn't go away. They got

483
00:25:28,200 --> 00:25:30,640
Speaker 5: consumed at ten cents in the dollar by somebody else

484
00:25:30,840 --> 00:25:32,800
Speaker 5: who could then make a lot of money. But the

485
00:25:32,880 --> 00:25:36,320
Speaker 5: two out of ten, just take two, it probably has

486
00:25:36,320 --> 00:25:39,280
Speaker 5: paid for all the capitals. If you just take Amazon

487
00:25:40,359 --> 00:25:43,760
Speaker 5: and Alphabet, ak, Google, just those two have probably paid

488
00:25:43,760 --> 00:25:47,600
Speaker 5: for all the capital of that time. So that's what's

489
00:25:47,640 --> 00:25:50,400
Speaker 5: going to happen this time. There will be a lot

490
00:25:50,440 --> 00:25:53,640
Speaker 5: of tears, but in aggregate, there will be a lot

491
00:25:53,640 --> 00:25:56,600
Speaker 5: of success. And I think that's the fundamental difference between

492
00:25:56,640 --> 00:26:00,239
Speaker 5: the US model and almost all other countries. In all

493
00:26:00,240 --> 00:26:03,040
Speaker 5: other countries, they're desperate to keep all the companies alive.

494
00:26:03,359 --> 00:26:06,800
Speaker 3: But that means you're dialuting. That's a horrible thing.

495
00:26:07,280 --> 00:26:11,040
Speaker 5: So to me, let the system works worked really effectively,

496
00:26:11,240 --> 00:26:12,960
Speaker 5: by the way, not just now, I mean all the

497
00:26:12,960 --> 00:26:17,680
Speaker 5: way back to railways and electrification, and you mentioned telephone system.

498
00:26:18,119 --> 00:26:22,760
Speaker 5: You can keep going on oil, I mean consumer goods.

499
00:26:23,119 --> 00:26:25,080
Speaker 5: It goes on and on. I think this system is

500
00:26:25,200 --> 00:26:28,840
Speaker 5: very effective. It deploys capital. Its census is a big

501
00:26:28,880 --> 00:26:32,720
Speaker 5: market is completely willing to over deploy capital in the short.

502
00:26:32,560 --> 00:26:34,080
Speaker 3: Term, not the long term.

503
00:26:34,440 --> 00:26:38,280
Speaker 5: That results in more competition, so it actually improves a

504
00:26:38,359 --> 00:26:41,640
Speaker 5: rate of innovation. That means what might have taken twenty

505
00:26:41,720 --> 00:26:45,760
Speaker 5: years takes five and the winners emerge exactly the same

506
00:26:45,800 --> 00:26:47,000
Speaker 5: is going to happen this time.

507
00:26:47,240 --> 00:26:48,640
Speaker 3: Yeah, I saw that.

508
00:26:48,880 --> 00:26:53,200
Speaker 4: I grew up in Waterloo and BlackBerry closs from Waterloo. Yep,

509
00:26:53,440 --> 00:26:56,720
Speaker 4: everyone used to work for BlackBerry. Ye, BlackBerry goes into

510
00:26:56,760 --> 00:26:58,920
Speaker 4: its die and that's the best thing that happened to

511
00:26:58,960 --> 00:27:00,800
Speaker 4: Waterloo because it was just capital with.

512
00:27:00,840 --> 00:27:04,480
Speaker 5: Talents, yep, talents many other companies. So all these smart.

513
00:27:04,280 --> 00:27:08,040
Speaker 4: People went on the next really more interesting thing. And yeah,

514
00:27:08,800 --> 00:27:11,919
Speaker 4: the wait, you haven't answered. So what is your an

515
00:27:11,960 --> 00:27:15,320
Speaker 4: idea that we are underestimating at the moment that's in

516
00:27:15,359 --> 00:27:17,760
Speaker 4: the current kind of suite of innovations.

517
00:27:17,960 --> 00:27:20,639
Speaker 5: So I don't think AI is being underestimated because when

518
00:27:20,640 --> 00:27:22,159
Speaker 5: you look at the amount of capital and the amount

519
00:27:22,160 --> 00:27:25,800
Speaker 5: of things chasing it, I think it's incredible. I do

520
00:27:25,880 --> 00:27:28,359
Speaker 5: think that a lot of enterprises are deploying it in

521
00:27:28,359 --> 00:27:31,959
Speaker 5: the wrong place they're running after shiny experiments. There's a

522
00:27:32,040 --> 00:27:35,160
Speaker 5: lot of basic things you can do to use AI

523
00:27:35,240 --> 00:27:38,080
Speaker 5: to improve the business today. So that's really just my

524
00:27:38,200 --> 00:27:41,359
Speaker 5: one advice to them. Pick areas you can scale, don't

525
00:27:41,400 --> 00:27:44,240
Speaker 5: pick the shiny little toys on the side.

526
00:27:45,040 --> 00:27:47,320
Speaker 3: Then I think, for example, that.

527
00:27:49,160 --> 00:27:54,520
Speaker 5: If anybody has more than ten percent of what they

528
00:27:54,560 --> 00:27:59,280
Speaker 5: had for customer service ten years ago, they're already five

529
00:27:59,359 --> 00:28:01,480
Speaker 5: years behind.

530
00:28:02,920 --> 00:28:04,679
Speaker 3: If anybody is not using.

531
00:28:04,440 --> 00:28:08,919
Speaker 5: AI to make their developers who write software thirty percent

532
00:28:09,040 --> 00:28:12,119
Speaker 5: more productive today with the goal of being seventy percent

533
00:28:12,240 --> 00:28:15,359
Speaker 5: more productive, that's not to say you will need less,

534
00:28:15,520 --> 00:28:16,920
Speaker 5: you'll just get more software done.

535
00:28:18,400 --> 00:28:20,639
Speaker 3: Then they're not. And I would turn.

536
00:28:20,480 --> 00:28:23,280
Speaker 5: Around and tell you I think only maybe five percent

537
00:28:23,359 --> 00:28:25,400
Speaker 5: of the enterprises on both.

538
00:28:25,240 --> 00:28:26,160
Speaker 3: Those metrics today.

539
00:28:26,400 --> 00:28:31,080
Speaker 5: Yeah, yeah, and the one that is completely underestimated. I

540
00:28:31,200 --> 00:28:35,280
Speaker 5: kind of put it like this, Quantum today is where

541
00:28:35,440 --> 00:28:39,080
Speaker 5: GPUs and AI war in twenty fifteen, and I bet

542
00:28:39,160 --> 00:28:42,800
Speaker 5: you every AI person is thinking and hoping I wish

543
00:28:42,840 --> 00:28:46,200
Speaker 5: I had started doing more in twenty fifteen as opposed

544
00:28:46,200 --> 00:28:49,440
Speaker 5: to wait until twenty twenty two. Quantum today is there.

545
00:28:49,680 --> 00:28:52,080
Speaker 5: So it's not good enough that you can get a

546
00:28:52,160 --> 00:28:54,800
Speaker 5: big advantage. But if you learn how to use it,

547
00:28:55,240 --> 00:28:58,680
Speaker 5: then in five years you'll be ready to exploit what comes.

548
00:28:59,200 --> 00:29:00,920
Speaker 3: We're going to get to contum in a moment.

549
00:29:00,920 --> 00:29:03,480
Speaker 4: But I had a couple other AI questions, you know,

550
00:29:03,560 --> 00:29:06,840
Speaker 4: I as you know where this conversation is part of

551
00:29:06,880 --> 00:29:10,000
Speaker 4: this thing that we do with IBM Smart Talks, and

552
00:29:10,080 --> 00:29:13,560
Speaker 4: I've been The last episode I did was on Kenya,

553
00:29:14,240 --> 00:29:17,040
Speaker 4: which has a massive deforestation problem, and they got together.

554
00:29:17,120 --> 00:29:21,480
Speaker 4: IBM took all the NASA satellite data, ran it through

555
00:29:21,800 --> 00:29:25,960
Speaker 4: an LLM, and gave them this incredibly precise ten meter

556
00:29:26,040 --> 00:29:29,280
Speaker 4: by ten meter analysis of what trees to plant, where

557
00:29:29,320 --> 00:29:32,800
Speaker 4: to plant them exactly where the you know, an astonishing

558
00:29:33,000 --> 00:29:36,080
Speaker 4: kind of blueprint about how to fix their country ecologically.

559
00:29:36,480 --> 00:29:40,760
Speaker 4: And it made me think, when we analyze the potential

560
00:29:40,760 --> 00:29:43,640
Speaker 4: of AI, are we making a mistake by spending too

561
00:29:43,680 --> 00:29:46,160
Speaker 4: much thinking about the developed world when it's actually the

562
00:29:46,200 --> 00:29:49,200
Speaker 4: developing world where the greatest ROI for this.

563
00:29:49,280 --> 00:29:49,840
Speaker 3: Is to me.

564
00:29:49,960 --> 00:29:53,360
Speaker 5: Look, software technology is are wonderful and the sense they

565
00:29:53,360 --> 00:29:55,480
Speaker 5: can scare and they can be an ad so you

566
00:29:55,480 --> 00:29:59,360
Speaker 5: don't have to do one or the other. You use defrautestation,

567
00:30:00,000 --> 00:30:04,040
Speaker 5: how about the use of pesticides and fertilizers, We overuse it.

568
00:30:04,440 --> 00:30:06,080
Speaker 3: We tend to for.

569
00:30:05,920 --> 00:30:09,120
Speaker 5: Irrigation, we tend to just flood everything, as opposed to say, okay,

570
00:30:09,160 --> 00:30:11,320
Speaker 5: only that one needs it. You could do all those

571
00:30:11,320 --> 00:30:13,479
Speaker 5: things to get a ten times effectiveness, and that all

572
00:30:13,520 --> 00:30:17,520
Speaker 5: would apply to the developing world. How about remote healthcare

573
00:30:17,720 --> 00:30:21,760
Speaker 5: or telehealth using an AI agent. So the examples are numerous.

574
00:30:22,520 --> 00:30:25,959
Speaker 5: In the developed world, I believe we are running out

575
00:30:26,000 --> 00:30:29,400
Speaker 5: of people. I know that nobody likes to hear it.

576
00:30:30,320 --> 00:30:33,280
Speaker 5: Most of the Far East is going to have half

577
00:30:33,320 --> 00:30:35,960
Speaker 5: the number of people by twenty seventy competed today. That's

578
00:30:36,000 --> 00:30:38,760
Speaker 5: not that far away. If I look at Europe, birth

579
00:30:38,840 --> 00:30:44,000
Speaker 5: rates are far under sustaining or keeping population flat. How

580
00:30:44,200 --> 00:30:46,760
Speaker 5: the US, also, depending on which number you want to

581
00:30:46,760 --> 00:30:49,080
Speaker 5: look at, is either one point six births per women

582
00:30:49,800 --> 00:30:51,680
Speaker 5: or two or two point one.

583
00:30:52,280 --> 00:30:53,280
Speaker 3: Why are the three numbers?

584
00:30:53,320 --> 00:30:55,080
Speaker 5: One point six is to women who were born in

585
00:30:55,080 --> 00:30:58,200
Speaker 5: the United States, It becomes two point two. If you

586
00:30:58,360 --> 00:31:01,800
Speaker 5: include immigrant women, it becomes two point one if you

587
00:31:01,840 --> 00:31:04,920
Speaker 5: include children or remigrating in So you got to decide

588
00:31:04,960 --> 00:31:08,120
Speaker 5: where the trend is obvious, This is going down. So

589
00:31:08,320 --> 00:31:11,720
Speaker 5: AI in the developed world is going to be essential

590
00:31:12,000 --> 00:31:14,880
Speaker 5: because to keep our current quality of life you need

591
00:31:14,960 --> 00:31:17,680
Speaker 5: more work done, or what's going to do the work

592
00:31:17,720 --> 00:31:20,000
Speaker 5: if there aren't people to do the work. So the

593
00:31:20,040 --> 00:31:22,040
Speaker 5: problems are different in the places.

594
00:31:22,120 --> 00:31:25,160
Speaker 4: Yeah, it gives you in the developing world, you get

595
00:31:25,200 --> 00:31:29,320
Speaker 4: access to a suite of technologies and things at a

596
00:31:29,400 --> 00:31:31,760
Speaker 4: price you could never been able to afford.

597
00:31:31,920 --> 00:31:34,400
Speaker 3: Correct. That was my in talking to the Kenyon thing.

598
00:31:34,440 --> 00:31:37,120
Speaker 4: It was like the whole it's it's maybe one of

599
00:31:37,160 --> 00:31:41,360
Speaker 4: the largest ecological projects of its kind at fifteen billion

600
00:31:41,400 --> 00:31:42,480
Speaker 4: trees they want to plan, and.

601
00:31:42,440 --> 00:31:44,360
Speaker 5: That is one country that might get it done because

602
00:31:44,360 --> 00:31:47,560
Speaker 5: they do take a lot of pride in their ecology

603
00:31:47,800 --> 00:31:51,120
Speaker 5: and in sort of returning to the land and giving back.

604
00:31:51,360 --> 00:31:51,600
Speaker 3: Yeah.

605
00:31:51,760 --> 00:31:56,800
Speaker 4: Yeah, what's different about IBM's version of AI versus some

606
00:31:56,880 --> 00:31:57,400
Speaker 4: of your.

607
00:31:57,880 --> 00:31:59,760
Speaker 5: So we are not a consumer company, so we have

608
00:31:59,760 --> 00:32:03,880
Speaker 5: no focus on a B two C chatbot. And the

609
00:32:03,920 --> 00:32:06,520
Speaker 5: reason I say that is, if you're making a B

610
00:32:06,560 --> 00:32:09,480
Speaker 5: two C chat bot, does it help you to make

611
00:32:09,520 --> 00:32:13,080
Speaker 5: it even bigger and more competitionally inefficient? And the short

612
00:32:13,120 --> 00:32:15,360
Speaker 5: answer is yes, because you have a certain number of

613
00:32:15,480 --> 00:32:18,000
Speaker 5: users and you kind of say, I kind of say

614
00:32:18,040 --> 00:32:22,040
Speaker 5: this jokingly. If I add finished to French capabilities, I

615
00:32:22,080 --> 00:32:23,880
Speaker 5: can probably add five million users.

616
00:32:24,440 --> 00:32:25,560
Speaker 3: If I add.

617
00:32:25,880 --> 00:32:27,840
Speaker 5: Writing a high coup I might be able to add

618
00:32:27,840 --> 00:32:31,840
Speaker 5: another five million users. If I add writing an email

619
00:32:31,920 --> 00:32:34,000
Speaker 5: in the voice of Steinbeck, I can probably add another

620
00:32:34,000 --> 00:32:34,920
Speaker 5: five million users.

621
00:32:35,360 --> 00:32:36,200
Speaker 3: Do all those things.

622
00:32:36,920 --> 00:32:40,200
Speaker 5: If my goal is to get help a company summarize

623
00:32:40,200 --> 00:32:43,800
Speaker 5: the legal documents in English, that can be a model

624
00:32:43,840 --> 00:32:48,360
Speaker 5: that's one hundred size as effective, probably higher quality.

625
00:32:48,680 --> 00:32:50,400
Speaker 3: But I don't need to go wide.

626
00:32:50,800 --> 00:32:54,720
Speaker 5: So if you're focusing on the enterprise, that actually takes

627
00:32:54,760 --> 00:32:58,920
Speaker 5: away the focus of having to go to extremely large models,

628
00:32:58,920 --> 00:33:02,560
Speaker 5: which but definition going to be computationally expensive, power hungry,

629
00:33:03,520 --> 00:33:06,360
Speaker 5: and demand lots and lots of data. So I can

630
00:33:06,400 --> 00:33:08,280
Speaker 5: turn onto the enterprise. You don't need to worry about

631
00:33:08,320 --> 00:33:11,040
Speaker 5: copyright issues, about all those because you can train on

632
00:33:11,080 --> 00:33:12,520
Speaker 5: a much smaller amount of data.

633
00:33:12,920 --> 00:33:14,000
Speaker 3: And now, by the.

634
00:33:14,000 --> 00:33:17,000
Speaker 5: Way, tuning it for you yourself is a weekend exercise,

635
00:33:17,240 --> 00:33:20,560
Speaker 5: it's not a six month on a big super computer

636
00:33:20,640 --> 00:33:23,600
Speaker 5: cluster somewhere out there. That's one big difference of what

637
00:33:23,720 --> 00:33:29,360
Speaker 5: we do. Second, we are very focused on helping those

638
00:33:29,440 --> 00:33:32,600
Speaker 5: problems that can give people immediate benefit where we have

639
00:33:32,640 --> 00:33:37,000
Speaker 5: domain knowledge. So our domain knowledge is around operations, is

640
00:33:37,080 --> 00:33:46,240
Speaker 5: around programming, encoding, is around customer service, is around customer experience, logistics, procurement,

641
00:33:46,720 --> 00:33:50,040
Speaker 5: let's change the areas where we have a lot of expertise,

642
00:33:50,720 --> 00:33:53,920
Speaker 5: and then three we kind of apply it to ourselves,

643
00:33:54,640 --> 00:33:57,040
Speaker 5: and so we are not asking our clients to be

644
00:33:57,160 --> 00:33:59,800
Speaker 5: the first experiment on it. We say you can have

645
00:34:00,280 --> 00:34:03,280
Speaker 5: what we did. We're happy to bring out all our learnings,

646
00:34:03,320 --> 00:34:06,160
Speaker 5: including what needs to change in the process, because the

647
00:34:06,200 --> 00:34:09,160
Speaker 5: biggest change is not technology, is getting people to accept

648
00:34:09,880 --> 00:34:11,879
Speaker 5: that there's a different way to do things.

649
00:34:12,440 --> 00:34:16,880
Speaker 4: Other challenges to explaining what makes you different to potential customers.

650
00:34:16,920 --> 00:34:19,880
Speaker 5: For sure, the shining object is always attractive. Oh, I

651
00:34:19,920 --> 00:34:23,279
Speaker 5: can go and try chat GPT. Why don't you have

652
00:34:23,360 --> 00:34:24,360
Speaker 5: your GPT version?

653
00:34:25,920 --> 00:34:27,080
Speaker 3: Do you use chat ChiPT?

654
00:34:28,040 --> 00:34:28,839
Speaker 5: I have used it.

655
00:34:30,280 --> 00:34:32,279
Speaker 4: I asked it a question recently which I thought was

656
00:34:32,280 --> 00:34:35,680
Speaker 4: really simple, and it made up about ten people.

657
00:34:37,080 --> 00:34:38,280
Speaker 3: Anyway, I had a bad experience.

658
00:34:38,280 --> 00:34:41,560
Speaker 5: I actually think that that's the fundamental issue with all

659
00:34:41,880 --> 00:34:44,600
Speaker 5: lms as they get larger, because you had to ask

660
00:34:44,719 --> 00:34:49,680
Speaker 5: what was the original insight that led to these It

661
00:34:49,840 --> 00:34:54,040
Speaker 5: was a reward function with intent. So if it has

662
00:34:54,239 --> 00:34:58,440
Speaker 5: learned by using a reward function, it's reward function comes

663
00:34:58,760 --> 00:35:02,080
Speaker 5: from giving an answer that satisfies you. So if it

664
00:35:02,280 --> 00:35:04,319
Speaker 5: thinks that if it makes up an answer that will

665
00:35:04,360 --> 00:35:07,560
Speaker 5: satisfy you, how will you stop it? Why do we

666
00:35:07,640 --> 00:35:11,480
Speaker 5: think this is different than the clever college kid who

667
00:35:11,480 --> 00:35:14,640
Speaker 5: doesn't know an answer? What bullships the way to an answer? Well,

668
00:35:14,800 --> 00:35:16,080
Speaker 5: it's exactly the same.

669
00:35:16,120 --> 00:35:18,600
Speaker 4: It's like the example of clever Hands during that story

670
00:35:18,920 --> 00:35:21,759
Speaker 4: the horse that they thought could speak, then all it.

671
00:35:21,840 --> 00:35:24,680
Speaker 3: Was doing was pleasing it. It's master. Yes, it is

672
00:35:24,719 --> 00:35:26,760
Speaker 3: a little bit of clever hands. Yeah, it's like dogs

673
00:35:26,840 --> 00:35:28,080
Speaker 3: kind of imitating and looking.

674
00:35:29,320 --> 00:35:34,480
Speaker 4: What would you identify as the most significant bottleneck in

675
00:35:34,520 --> 00:35:35,520
Speaker 4: the development of AI?

676
00:35:35,760 --> 00:35:37,040
Speaker 3: What's slowing us down right now?

677
00:35:38,880 --> 00:35:43,400
Speaker 5: I am not convinced that LLMS is the way to

678
00:35:43,560 --> 00:35:48,719
Speaker 5: get much beyond where we'll get incremental improvements, But I,

679
00:35:49,200 --> 00:35:52,160
Speaker 5: for one, don't believe that LMS are going to get

680
00:35:52,200 --> 00:35:58,319
Speaker 5: us to super intelligence or AGI. So I'll park that

681
00:35:58,440 --> 00:36:00,919
Speaker 5: on the side and simply say, we have to find

682
00:36:00,960 --> 00:36:04,760
Speaker 5: a way to fuse knowledge and how do you represent

683
00:36:04,920 --> 00:36:08,600
Speaker 5: knowledge as opposed to have to statistically rediscover it each

684
00:36:08,640 --> 00:36:11,520
Speaker 5: time I ask a question? And how do we fuse

685
00:36:11,560 --> 00:36:18,560
Speaker 5: knowledge with LLM? Maybe then we'll get to leaves and beyond.

686
00:36:18,600 --> 00:36:23,200
Speaker 5: Today on LLMS alone, my view is I think we

687
00:36:23,280 --> 00:36:27,400
Speaker 5: can get a thousand x efficiency in power and cost

688
00:36:27,480 --> 00:36:30,880
Speaker 5: and compute from today. So if you make something a

689
00:36:30,960 --> 00:36:34,200
Speaker 5: thousand times cheaper, would people use a lot more of it?

690
00:36:35,320 --> 00:36:35,680
Speaker 3: Yes?

691
00:36:36,560 --> 00:36:39,279
Speaker 5: And I think those answers lie as is usually in

692
00:36:39,320 --> 00:36:44,880
Speaker 5: compute through advances in semiconductors, advances in software, and advances

693
00:36:44,880 --> 00:36:47,719
Speaker 5: in algorithmic techniques, all three. But how come we're not

694
00:36:47,719 --> 00:36:49,160
Speaker 5: working in any of those three? Were just taking the

695
00:36:49,200 --> 00:36:52,600
Speaker 5: current semic conductor and going more. We're taking the current

696
00:36:53,600 --> 00:36:56,160
Speaker 5: algorithmic techniques and not really trying to invent new ones.

697
00:36:56,239 --> 00:36:59,280
Speaker 5: So I think those are all happen less than five years.

698
00:37:00,200 --> 00:37:00,640
Speaker 3: Mm hmm.

699
00:37:01,040 --> 00:37:04,200
Speaker 4: But why you say there is a we're in a

700
00:37:04,200 --> 00:37:09,360
Speaker 4: moment where people are not pursuing the the optimal strategy

701
00:37:09,400 --> 00:37:10,880
Speaker 4: for exploiting this technology.

702
00:37:11,200 --> 00:37:15,919
Speaker 5: Why because when you see a few people running really

703
00:37:15,960 --> 00:37:19,040
Speaker 5: hard and they're willing to invest any amount of money,

704
00:37:19,040 --> 00:37:23,040
Speaker 5: so efficiency is not the focus. People feel if we

705
00:37:23,120 --> 00:37:24,720
Speaker 5: don't do the same, you'll get left behind.

706
00:37:25,960 --> 00:37:28,719
Speaker 4: So is this a case where there's too much money

707
00:37:28,760 --> 00:37:29,960
Speaker 4: humans have never had for more?

708
00:37:30,080 --> 00:37:30,200
Speaker 2: Right?

709
00:37:30,239 --> 00:37:30,439
Speaker 3: Ever?

710
00:37:31,040 --> 00:37:36,319
Speaker 4: Yeah, but this is this a consequence of overinvestment in

711
00:37:36,360 --> 00:37:37,400
Speaker 4: the in the field.

712
00:37:37,520 --> 00:37:39,800
Speaker 5: Going back to my internet allology, if two out of

713
00:37:39,840 --> 00:37:43,239
Speaker 5: ten are going to succeed. How do you guarantee or

714
00:37:43,280 --> 00:37:44,960
Speaker 5: how do you improve the orders that you are one

715
00:37:44,960 --> 00:37:47,960
Speaker 5: of those two. So if you pause to say I

716
00:37:47,960 --> 00:37:50,000
Speaker 5: want to become more efficient, that's not the way to win.

717
00:37:50,360 --> 00:37:52,480
Speaker 3: So first you win, then you become efficient.

718
00:37:53,560 --> 00:37:57,359
Speaker 4: Yeah, let's talk about what is I was told your

719
00:37:57,400 --> 00:37:59,560
Speaker 4: favorite topic it's quantum?

720
00:38:00,160 --> 00:38:03,920
Speaker 3: Is what? Boy even go any further? Why is quantum

721
00:38:04,000 --> 00:38:04,760
Speaker 3: your favorite topic?

722
00:38:06,000 --> 00:38:08,920
Speaker 5: We've only had two kinds of compute in the history,

723
00:38:09,760 --> 00:38:13,160
Speaker 5: so nineteen forty five was to use that year for

724
00:38:13,239 --> 00:38:15,839
Speaker 5: any act. All the way till twenty twenty, we had

725
00:38:15,880 --> 00:38:18,040
Speaker 5: one kind of computerlassical what today you would call a

726
00:38:18,040 --> 00:38:24,200
Speaker 5: classical computer. Then GPUs and AI came around, so you

727
00:38:24,239 --> 00:38:27,480
Speaker 5: would say the intuition there is you went from sort

728
00:38:27,480 --> 00:38:30,680
Speaker 5: of bits, which is algebra or high school algebra, to

729
00:38:30,840 --> 00:38:35,000
Speaker 5: including neurons, which is captured in linear algebra. But that

730
00:38:35,200 --> 00:38:37,920
Speaker 5: gives you a different kind But it can do problems

731
00:38:37,960 --> 00:38:40,760
Speaker 5: that are really hard to do. I don't say impossible,

732
00:38:40,840 --> 00:38:45,279
Speaker 5: just hard to do on normal computers. Quantum adds a

733
00:38:45,320 --> 00:38:51,120
Speaker 5: third kind of math. Yes, the physics properties which really

734
00:38:51,840 --> 00:38:55,080
Speaker 5: get people energized and the imagination going. And we use

735
00:38:55,120 --> 00:38:59,480
Speaker 5: all these words about entanglement and silver position, but maybe

736
00:38:59,560 --> 00:39:02,359
Speaker 5: because I'm a bit of a math guy. The real

737
00:39:02,440 --> 00:39:05,000
Speaker 5: thing is that does a third kind of math to

738
00:39:05,040 --> 00:39:07,880
Speaker 5: make it really simple, a third kind of math that

739
00:39:08,000 --> 00:39:11,880
Speaker 5: comes from the field of abstract algebra. It does the

740
00:39:11,920 --> 00:39:15,680
Speaker 5: math you can use Hamiltonians for those who like physics,

741
00:39:16,200 --> 00:39:18,319
Speaker 5: or you can use the word Lee algebras for those

742
00:39:18,320 --> 00:39:22,359
Speaker 5: who like abstract mathematics. If you can do a third

743
00:39:22,400 --> 00:39:25,359
Speaker 5: kind of math, which algorithms are suited to that third

744
00:39:25,440 --> 00:39:28,560
Speaker 5: kind of math. So it excites me because we can

745
00:39:28,640 --> 00:39:32,080
Speaker 5: now approach algorithms that you just could never do on

746
00:39:32,120 --> 00:39:34,839
Speaker 5: the other two it's impossible. Now it's different than AI.

747
00:39:34,960 --> 00:39:38,359
Speaker 5: It's not data intensive. It's compute intensive. So we kind

748
00:39:38,360 --> 00:39:41,080
Speaker 5: of had compute and supercomputers. Then we went to data

749
00:39:41,160 --> 00:39:43,280
Speaker 5: which is AI. And now if you say there's another

750
00:39:43,320 --> 00:39:45,360
Speaker 5: class of problems, it require lots of compute.

751
00:39:45,520 --> 00:39:47,280
Speaker 3: That's quantum.

752
00:39:47,320 --> 00:39:49,960
Speaker 4: A couple months ago was at the to watch some

753
00:39:50,000 --> 00:39:52,080
Speaker 4: research center and they have you know, on the ground floor,

754
00:39:52,080 --> 00:39:56,239
Speaker 4: they have those behind the glass. There's incredibly exciting looking machines.

755
00:39:56,840 --> 00:39:59,279
Speaker 4: But where are we in the timeline of this.

756
00:40:00,800 --> 00:40:05,640
Speaker 5: Three to five years away from shocking people? What does

757
00:40:05,680 --> 00:40:08,880
Speaker 5: shocking people mean do something that nobody thought was possible

758
00:40:08,920 --> 00:40:09,839
Speaker 5: in that timeline.

759
00:40:10,120 --> 00:40:11,520
Speaker 3: Does an example come to mind?

760
00:40:12,400 --> 00:40:16,919
Speaker 5: I was actually pleasantly surprised. So one of our clients, HSBC,

761
00:40:18,000 --> 00:40:22,080
Speaker 5: last week published a result that using a quantum computer

762
00:40:23,360 --> 00:40:27,480
Speaker 5: bond trading was thirty four percent more accurate than their

763
00:40:27,560 --> 00:40:28,360
Speaker 5: prior technique.

764
00:40:29,280 --> 00:40:31,400
Speaker 3: Thirty four percent. Thirty four percent.

765
00:40:31,920 --> 00:40:35,280
Speaker 4: This is an industry that's used to one percent correct,

766
00:40:35,600 --> 00:40:36,840
Speaker 4: zero point five percent.

767
00:40:37,320 --> 00:40:39,720
Speaker 3: Yes, that's astonishing.

768
00:40:40,000 --> 00:40:42,839
Speaker 5: Now that was not at a scale when they could

769
00:40:42,840 --> 00:40:45,440
Speaker 5: turn it into production today, but that was sort of

770
00:40:45,880 --> 00:40:49,240
Speaker 5: their original thought experiment, and that's what they did. Now

771
00:40:49,520 --> 00:40:54,280
Speaker 5: can you imagine when will somebody so you were correct.

772
00:40:54,280 --> 00:40:57,200
Speaker 5: You talk about an industry where one basis point, if

773
00:40:57,200 --> 00:41:00,640
Speaker 5: I remember I may be wrong, like thirteen trillion dollars

774
00:41:00,640 --> 00:41:03,000
Speaker 5: of money kind of moves around in the financial industry

775
00:41:03,040 --> 00:41:10,280
Speaker 5: each day, right, so basis point would be thirteen billion

776
00:41:10,360 --> 00:41:14,920
Speaker 5: something like that, right, one over ten thousand. So when

777
00:41:14,960 --> 00:41:17,520
Speaker 5: you think about the kind of profit that people can make,

778
00:41:18,239 --> 00:41:19,920
Speaker 5: if you can tell somebody that you can come up

779
00:41:19,960 --> 00:41:24,960
Speaker 5: with a better price than your competition by just one

780
00:41:25,000 --> 00:41:29,120
Speaker 5: basis point, they would actually gain the market share. Yeah,

781
00:41:29,320 --> 00:41:33,440
Speaker 5: So I think something around there or something in the

782
00:41:33,440 --> 00:41:37,560
Speaker 5: world of materials. Can we make a better battery? Could

783
00:41:37,600 --> 00:41:41,279
Speaker 5: we make a solid state battery? Which means your risk

784
00:41:41,320 --> 00:41:45,080
Speaker 5: of fires heating decrease dramatically.

785
00:41:45,120 --> 00:41:47,360
Speaker 4: And the reason, sorry to ask a really nive question,

786
00:41:48,760 --> 00:41:51,120
Speaker 4: why is it that a quantum computer would be better

787
00:41:51,160 --> 00:41:55,680
Speaker 4: at solving a battery problem than our existing methods of computing?

788
00:41:55,760 --> 00:42:00,120
Speaker 5: So the equations of quantum mechanics and chemistry and how

789
00:42:00,480 --> 00:42:04,839
Speaker 5: things interact or well known to solve them, that are

790
00:42:04,840 --> 00:42:07,120
Speaker 5: no known techniques. So these are not like closed form,

791
00:42:07,160 --> 00:42:09,000
Speaker 5: you know, it's not like the square root of a

792
00:42:09,080 --> 00:42:12,279
Speaker 5: quarter equation. So the only way to solve them is

793
00:42:12,360 --> 00:42:15,279
Speaker 5: to explore the state space. So if you have a

794
00:42:15,320 --> 00:42:19,560
Speaker 5: few hundred electrons, you need two to the one hundred states.

795
00:42:20,000 --> 00:42:22,560
Speaker 3: Well, I'm sorry you don't have that much memory. It's impossible.

796
00:42:22,960 --> 00:42:25,840
Speaker 5: So it takes a really really long time on a

797
00:42:25,880 --> 00:42:29,520
Speaker 5: normal computer to solve those problems, right, that's simple a problem.

798
00:42:30,200 --> 00:42:34,680
Speaker 5: If a quantum computer operates in the equation domain, it

799
00:42:34,719 --> 00:42:36,960
Speaker 5: doesn't need to explore the state space. It can actually

800
00:42:36,960 --> 00:42:39,239
Speaker 5: solve it. That's why I call it a different kind

801
00:42:39,239 --> 00:42:42,200
Speaker 5: of math. That's the kind of math it does. So

802
00:42:43,239 --> 00:42:45,439
Speaker 5: in a couple of seconds it can tell you this

803
00:42:45,520 --> 00:42:46,919
Speaker 5: is how that material will be here.

804
00:42:48,400 --> 00:42:49,800
Speaker 3: Oh, I see, so you've.

805
00:42:49,600 --> 00:42:52,839
Speaker 5: Taken what could take years to a few seconds. Yeah,

806
00:42:52,880 --> 00:42:54,440
Speaker 5: that's a pretty big change.

807
00:42:54,600 --> 00:42:57,839
Speaker 4: Yeah. Yeah, it's speaking a different language, different line. So

808
00:42:58,080 --> 00:43:00,520
Speaker 4: any kind of problem that comes along that's specific to

809
00:43:00,560 --> 00:43:02,000
Speaker 4: that language correctly.

810
00:43:01,719 --> 00:43:04,840
Speaker 5: Which is not all problems. Yeah, just as I call it,

811
00:43:04,840 --> 00:43:06,000
Speaker 5: it's one more kind of math.

812
00:43:06,280 --> 00:43:10,520
Speaker 4: Yeah, what's an example? So so many questions. L give

813
00:43:10,560 --> 00:43:14,239
Speaker 4: me another example of a of a of a kind

814
00:43:14,239 --> 00:43:16,279
Speaker 4: of problem that a quantum computer would love.

815
00:43:18,080 --> 00:43:21,360
Speaker 5: This one is a bit more speculative, and I'm going

816
00:43:21,440 --> 00:43:24,640
Speaker 5: to use a little bit of poetic license. So let's

817
00:43:24,680 --> 00:43:28,160
Speaker 5: take a post office in a mid sized country. They

818
00:43:28,200 --> 00:43:30,920
Speaker 5: probably burn a billion gallons of fuel per year delivering

819
00:43:30,960 --> 00:43:34,480
Speaker 5: packages and letters because most posts and advanced country says

820
00:43:34,800 --> 00:43:40,279
Speaker 5: every house, every address, each day. The way to optimize

821
00:43:40,320 --> 00:43:42,640
Speaker 5: this is we can formulate the problem. It's called the

822
00:43:42,719 --> 00:43:46,840
Speaker 5: proveling sales and problem solving. It is really hard, so

823
00:43:46,880 --> 00:43:51,080
Speaker 5: people have heuristics. Let's suppose today our heuristics get us

824
00:43:51,080 --> 00:43:54,720
Speaker 5: to within twenty percent of the optimal answer. Let's suppose

825
00:43:54,719 --> 00:43:58,680
Speaker 5: a quantum computer can get you the next ten percent. Well,

826
00:43:58,719 --> 00:44:00,960
Speaker 5: if I can get ten percent of a billion gallons

827
00:44:01,000 --> 00:44:02,719
Speaker 5: that I think is one hundred million gallans of my

828
00:44:02,800 --> 00:44:05,680
Speaker 5: math is right, and in the country I'm thinking about,

829
00:44:05,920 --> 00:44:09,160
Speaker 5: that could be eight hundred million pounds of saving to

830
00:44:09,280 --> 00:44:14,920
Speaker 5: one entity in one year. And the associated carbon footprint

831
00:44:15,000 --> 00:44:18,800
Speaker 5: climate change were in't less mileage on vague. I'm not

832
00:44:18,840 --> 00:44:22,560
Speaker 5: even counting all that. These are pretty attractive problems to

833
00:44:22,600 --> 00:44:25,400
Speaker 5: go after. So if I look at the interest recently,

834
00:44:26,640 --> 00:44:29,960
Speaker 5: New York has started a whole program in some places.

835
00:44:30,440 --> 00:44:35,120
Speaker 5: Illinois stood up a quantum algorithm center between a number

836
00:44:35,160 --> 00:44:40,160
Speaker 5: of the universities. The governor there was heavily behind it, etc.

837
00:44:40,880 --> 00:44:43,920
Speaker 5: So I wouldn't say that this is widespread. This is

838
00:44:43,920 --> 00:44:45,760
Speaker 5: why I'm saying three to four years for that moment.

839
00:44:46,440 --> 00:44:50,000
Speaker 5: But there's enough people who are deeply cognizant who are saying,

840
00:44:50,040 --> 00:44:52,120
Speaker 5: wait a moment, we kind of get it.

841
00:44:52,160 --> 00:44:53,840
Speaker 3: This is a new kind of man. What are the

842
00:44:53,880 --> 00:44:54,960
Speaker 3: new problems we can solve?

843
00:44:55,640 --> 00:44:58,080
Speaker 5: And the fact that we have bought roughly two hundred

844
00:44:58,120 --> 00:45:02,160
Speaker 5: clients who worked with us early stage small experiments is

845
00:45:02,200 --> 00:45:04,520
Speaker 5: because the intuition is I can do something here that

846
00:45:04,560 --> 00:45:06,880
Speaker 5: I couldn't do in other places.

847
00:45:07,160 --> 00:45:09,439
Speaker 4: Three to four years is not a long time, no,

848
00:45:11,320 --> 00:45:14,120
Speaker 4: But if I'm in the battery business, and I don't

849
00:45:14,160 --> 00:45:18,600
Speaker 4: have a line out to a quantum computing experiment.

850
00:45:20,280 --> 00:45:22,160
Speaker 3: I have a problem, don't have a problem.

851
00:45:22,600 --> 00:45:25,959
Speaker 5: Yeah, you'll probably be out of business in ten years.

852
00:45:26,520 --> 00:45:28,439
Speaker 5: Well maybe you could write a big check and buy

853
00:45:28,440 --> 00:45:29,879
Speaker 5: the technology from somebody else.

854
00:45:30,560 --> 00:45:33,719
Speaker 4: You had to What is quantum rank in the kind

855
00:45:33,760 --> 00:45:37,560
Speaker 4: of great inventions of the last one hundred and fifty years.

856
00:45:37,320 --> 00:45:44,040
Speaker 5: Equal to semic conductor? And I think that if semiconductor's vanished,

857
00:45:44,320 --> 00:45:44,919
Speaker 5: modern life.

858
00:45:44,840 --> 00:45:46,680
Speaker 3: Would stop, like just stop.

859
00:45:47,360 --> 00:45:55,880
Speaker 5: Yeah, no electricity, no automobile, no streaming. You can imagine

860
00:45:55,920 --> 00:45:57,600
Speaker 5: the yells from all the kids. Who ever hear that

861
00:45:57,680 --> 00:45:58,640
Speaker 5: no streaming?

862
00:46:01,560 --> 00:46:04,799
Speaker 4: And is that it's funny because don't. As someone who's

863
00:46:04,840 --> 00:46:08,800
Speaker 4: outside this world, I feel like quantum is underdiscussed relative

864
00:46:08,840 --> 00:46:12,480
Speaker 4: to its potential for transforming society.

865
00:46:12,280 --> 00:46:16,120
Speaker 5: Because I use my Internet example. Ninety five was the

866
00:46:16,120 --> 00:46:20,839
Speaker 5: moment with Netscape that Internet came on people's consciousness. I

867
00:46:20,840 --> 00:46:23,080
Speaker 5: said in eighty five I considered it to be this

868
00:46:23,120 --> 00:46:26,719
Speaker 5: is a solved problem because it needs something that makes

869
00:46:26,719 --> 00:46:31,120
Speaker 5: it accessible easy. That was the browser. The Netscape browser

870
00:46:31,200 --> 00:46:35,480
Speaker 5: is what brought it easy to understand. We have probably,

871
00:46:35,520 --> 00:46:38,440
Speaker 5: as I said, about four to five years from that moment.

872
00:46:38,480 --> 00:46:41,359
Speaker 5: That's why it's under discussed because the moment I say

873
00:46:41,480 --> 00:46:43,560
Speaker 5: you got a math, I've probably lost ninety nine percent

874
00:46:43,640 --> 00:46:46,279
Speaker 5: of the audience. If I go to quantum mechanics, I've

875
00:46:46,280 --> 00:46:49,520
Speaker 5: probably lost nine percent of the audience.

876
00:46:51,320 --> 00:46:54,480
Speaker 4: So you, as c over the last five years, have

877
00:46:54,640 --> 00:46:57,480
Speaker 4: been really the birth mother for a lot of the

878
00:46:57,600 --> 00:47:02,160
Speaker 4: quantum computing work. I'm curious, so you come in. When

879
00:47:02,200 --> 00:47:06,640
Speaker 4: you started a CEO, was this your first priority.

880
00:47:07,160 --> 00:47:09,680
Speaker 5: I had already started investing in it back in twenty

881
00:47:09,719 --> 00:47:15,360
Speaker 5: fifteen when I was leading IBM research. So let me

882
00:47:15,400 --> 00:47:17,359
Speaker 5: acknowledge and like nobody should try to copy. And I've

883
00:47:17,360 --> 00:47:20,680
Speaker 5: had I'll call it a weird career. I was a

884
00:47:20,719 --> 00:47:22,920
Speaker 5: researcher at some point. If it had asked me out,

885
00:47:22,960 --> 00:47:24,359
Speaker 5: I said, I'm one of those people, you know, throw

886
00:47:24,360 --> 00:47:25,160
Speaker 5: a pizza under.

887
00:47:25,000 --> 00:47:27,040
Speaker 3: Our door and like leave me alone. I don't want

888
00:47:27,080 --> 00:47:27,680
Speaker 3: to talk to people.

889
00:47:28,200 --> 00:47:30,560
Speaker 5: Then I decided I was interested in the business. Then

890
00:47:30,600 --> 00:47:33,600
Speaker 5: I went and started acquiring companies and doing that. Then

891
00:47:33,640 --> 00:47:35,680
Speaker 5: somebody told me, hey, why did you start doing some

892
00:47:35,719 --> 00:47:40,359
Speaker 5: business strategy. Then I went back to research and led

893
00:47:40,400 --> 00:47:43,200
Speaker 5: our research division for a couple of years. And when

894
00:47:43,239 --> 00:47:46,960
Speaker 5: the people described it to me. I asked some questions.

895
00:47:47,080 --> 00:47:49,359
Speaker 5: So it wasn't a big investment at that time. It

896
00:47:49,480 --> 00:47:51,680
Speaker 5: was hey, can we make a computer not just a

897
00:47:51,680 --> 00:47:55,719
Speaker 5: science experiment? Can it run by itself all night? Can

898
00:47:55,800 --> 00:47:58,759
Speaker 5: you think about software so that even people who are

899
00:47:58,800 --> 00:48:01,279
Speaker 5: not deep of quantum mechanic can begin to use it?

900
00:48:02,239 --> 00:48:04,440
Speaker 5: And they begin to do those things. So over three

901
00:48:04,560 --> 00:48:08,560
Speaker 5: four years, did they get enough confidence Yeah, Okay, this

902
00:48:08,680 --> 00:48:11,239
Speaker 5: is something that can really work. And then you've got

903
00:48:11,239 --> 00:48:14,360
Speaker 5: to nurture it to where it gets bigger and bigger

904
00:48:14,960 --> 00:48:16,840
Speaker 5: until you get the confidence that okay, now it's a

905
00:48:16,840 --> 00:48:17,360
Speaker 5: big bet.

906
00:48:18,120 --> 00:48:20,640
Speaker 3: And what was the moment when you when you realize

907
00:48:20,640 --> 00:48:21,400
Speaker 3: now it's a big.

908
00:48:21,239 --> 00:48:25,040
Speaker 5: Bet, probably two or three years ago.

909
00:48:25,400 --> 00:48:27,719
Speaker 4: And how do you decide, as the head of a

910
00:48:27,760 --> 00:48:32,040
Speaker 4: company like this, how much money, how many resources, and

911
00:48:32,080 --> 00:48:34,000
Speaker 4: how many people? And how what kind of promise to

912
00:48:34,040 --> 00:48:35,080
Speaker 4: give to an idea like that?

913
00:48:35,840 --> 00:48:39,880
Speaker 5: So three layers the set of people who actually have

914
00:48:40,000 --> 00:48:44,520
Speaker 5: the knowledge and the intensity to fundamentally advance the technology.

915
00:48:45,239 --> 00:48:47,319
Speaker 5: If I could find more out higher then So I'm

916
00:48:47,440 --> 00:48:50,600
Speaker 5: constrained of people on that one, because normally there's only

917
00:48:50,600 --> 00:48:54,520
Speaker 5: so many people who can do these things. Two, you

918
00:48:54,640 --> 00:48:57,920
Speaker 5: got to be careful if you push too hard on timing,

919
00:48:58,400 --> 00:49:01,320
Speaker 5: you'll get people to take so much risk that actually

920
00:49:01,360 --> 00:49:04,520
Speaker 5: the thing will fail. So that's the art of between

921
00:49:04,560 --> 00:49:08,560
Speaker 5: the leadership on the project and me to say, Okay,

922
00:49:08,680 --> 00:49:11,239
Speaker 5: how hard can you push? But not so hard that

923
00:49:11,320 --> 00:49:14,400
Speaker 5: you cause it to fail because then they get compelled

924
00:49:14,440 --> 00:49:17,080
Speaker 5: to commit timelines that are just impossible.

925
00:49:17,640 --> 00:49:20,360
Speaker 3: Yeah, how do you This is fascinating.

926
00:49:20,440 --> 00:49:23,600
Speaker 4: So it's ultimately a question of judgment trying to figure

927
00:49:23,600 --> 00:49:26,239
Speaker 4: out what's the sweet spot between enough pressure to keep

928
00:49:26,280 --> 00:49:28,800
Speaker 4: them ahead of the pack, but not too much pressure

929
00:49:29,160 --> 00:49:32,600
Speaker 4: so that they start taking risks. How do you calibrate

930
00:49:32,880 --> 00:49:34,800
Speaker 4: whether you're hitting that sweet spot? I mean, do you

931
00:49:35,880 --> 00:49:39,080
Speaker 4: reassess every few months and say I think I'm over

932
00:49:39,120 --> 00:49:40,800
Speaker 4: correcting or undercorrecting at this moment.

933
00:49:41,600 --> 00:49:45,040
Speaker 5: So one you got to have what I call and

934
00:49:45,080 --> 00:49:47,720
Speaker 5: this is channeling a word from one of my favorite books,

935
00:49:47,760 --> 00:49:50,360
Speaker 5: The Geek Away, How open can you be?

936
00:49:51,200 --> 00:49:52,399
Speaker 3: So I want to press hard?

937
00:49:53,440 --> 00:49:55,959
Speaker 5: But the team knows that they're allowed to push back

938
00:49:56,400 --> 00:50:00,200
Speaker 5: and really argue back hard. That means you'd get do

939
00:50:00,640 --> 00:50:06,479
Speaker 5: probably that correct goldilocks pressure. Do the people themselves should

940
00:50:06,600 --> 00:50:09,440
Speaker 5: want to go as hard as possible, but not harder

941
00:50:09,440 --> 00:50:14,279
Speaker 5: than possible. So that is then personality of leadership that

942
00:50:14,320 --> 00:50:14,840
Speaker 5: makes sense.

943
00:50:15,080 --> 00:50:17,640
Speaker 4: But you have to be someone who people feel comfortable

944
00:50:17,640 --> 00:50:18,279
Speaker 4: being honest with.

945
00:50:18,480 --> 00:50:22,840
Speaker 3: Yes, absolutely, and people feel comfortable being honest with you,

946
00:50:23,080 --> 00:50:25,319
Speaker 3: I believe so. Yeah.

947
00:50:25,719 --> 00:50:28,680
Speaker 4: When has there been a moment in this path with

948
00:50:28,760 --> 00:50:31,399
Speaker 4: quantum where you did think you were pushing too hard?

949
00:50:33,200 --> 00:50:33,399
Speaker 3: No?

950
00:50:34,400 --> 00:50:38,040
Speaker 5: Because I think that the leadership there will argue back

951
00:50:38,080 --> 00:50:40,960
Speaker 5: with me any day of the week. I don't think

952
00:50:41,000 --> 00:50:43,240
Speaker 5: that they feel that they have to ford.

953
00:50:44,320 --> 00:50:47,400
Speaker 4: Do you drop by at sort of Saturday night at

954
00:50:47,440 --> 00:50:48,880
Speaker 4: ten pm to see if people are working.

955
00:50:49,960 --> 00:50:55,880
Speaker 5: I tend to text people and ask questions and like

956
00:50:56,000 --> 00:50:58,719
Speaker 5: I'll read something and say, hey, are these people doing this?

957
00:50:59,280 --> 00:51:03,239
Speaker 5: And if they can answer me in reasonable terms, I

958
00:51:03,280 --> 00:51:06,479
Speaker 5: actually then say great, they're already watching the competition, They're

959
00:51:06,520 --> 00:51:09,759
Speaker 5: watching the literature, they're watching the science. I don't need

960
00:51:09,800 --> 00:51:12,040
Speaker 5: to push hard. If they are already ahead of it,

961
00:51:12,080 --> 00:51:15,120
Speaker 5: then me I can answer my question. I'll say thoughtfully,

962
00:51:15,440 --> 00:51:19,520
Speaker 5: not always completely accurately, you're thinking about it on their own.

963
00:51:19,560 --> 00:51:22,560
Speaker 3: I don't need to push. One last question I wanted

964
00:51:22,600 --> 00:51:25,319
Speaker 3: to ask you, do you have the most interesting job

965
00:51:25,320 --> 00:51:26,040
Speaker 3: in America?

966
00:51:26,200 --> 00:51:28,560
Speaker 5: I believe that it's the most interesting job, which I

967
00:51:28,560 --> 00:51:29,680
Speaker 5: won't give up for anything.

968
00:51:30,680 --> 00:51:33,560
Speaker 3: It also sounds like you're enjoying yourself.

969
00:51:34,120 --> 00:51:37,440
Speaker 5: I enjoy it as long as look my role and

970
00:51:37,520 --> 00:51:41,560
Speaker 5: goal should be to make the enterprise thrive. As long

971
00:51:41,600 --> 00:51:44,880
Speaker 5: as than making the enterprise thrive, and are clients delighted?

972
00:51:46,480 --> 00:51:47,000
Speaker 3: I love it.

973
00:51:47,640 --> 00:51:49,000
Speaker 5: If I don't, somebody else.

974
00:51:48,840 --> 00:51:53,040
Speaker 3: Should do it. Harvin, this has been so much fun.

975
00:51:53,239 --> 00:51:57,320
Speaker 4: Thank you so much taking the time and a fascinating,

976
00:51:57,760 --> 00:52:00,200
Speaker 4: completely fascinating conversation. I wish I was one of those

977
00:52:00,239 --> 00:52:02,120
Speaker 4: people who could help you out with quantum, but I'm

978
00:52:02,160 --> 00:52:03,399
Speaker 4: afraid I'm not.

979
00:52:03,920 --> 00:52:07,480
Speaker 3: In a few years. Good Thank you so much.

980
00:52:17,520 --> 00:52:21,240
Speaker 4: Smart Talks with IBM is produced by Matt Ramano, Amy Gains, McQuaid,

981
00:52:21,719 --> 00:52:25,880
Speaker 4: Trina Menino, and Jake Harper. Mastering by Sarah Bugerer, Music

982
00:52:25,920 --> 00:52:31,720
Speaker 4: by Gramoscope, Strategy by Tatiana Lieberman, Cassidy Meyer and Sophia Derlong.

983
00:52:32,440 --> 00:52:35,040
Speaker 4: Smart Talks with IBM is a production of Pushkin Industries

984
00:52:35,280 --> 00:52:39,719
Speaker 4: and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,

985
00:52:40,040 --> 00:52:44,000
Speaker 4: listen on the iHeartRadio app, Apple Podcasts, or wherever you

986
00:52:44,080 --> 00:52:48,000
Speaker 4: listen to podcasts. I'm Malcolm Glawe. This is a paid

987
00:52:48,000 --> 00:52:52,560
Speaker 4: advertisement from IBM. The conversations on this podcast don't necessarily

988
00:52:52,600 --> 00:52:56,759
Speaker 4: represent IBM's positions, strategies, or opinions.

989
00:53:00,400 --> 00:53:03,359
Speaker 2: The take UNT