WEBVTT - Smart Talks with IBM: IBM CEO Arvind Krishna: Creating Smarter Business with AI and Quantum

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

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<v Speaker 1>something a little bit different to share with you. It's

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<v Speaker 1>a new season of the Smart Talks with IBM podcast series.

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<v Speaker 2>This season on Smart Talks with IBM, Malcolm Gladwell is back,

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<v Speaker 2>and this time he's taking the show on the road.

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<v Speaker 2>Malcolm is stepping outside the studio to explore how IBM

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<v Speaker 2>clients are using artificial intelligence to solve real world challenges

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<v Speaker 2>and transform the way they do business.

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<v Speaker 1>From accelerating scientific breakthroughs to reimagining education. It's a fresh

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<v Speaker 1>look at innovation in action, where big ideas meet cutting

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<v Speaker 1>edge solutions.

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<v Speaker 2>You'll hear from industry leaders, creative thinkers, and of course

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<v Speaker 2>Malcolm Gladwell himself as he guides you through each story.

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<v Speaker 1>New episodes of Smart Talks with IBM drop every month

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<v Speaker 1>on the iHeartRadio app, Apple Podcasts, or wherever you get

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<v Speaker 1>your podcasts. Learn more at IBM dot com slash smart Talks.

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<v Speaker 1>This is a paid advertisement for IBM pushkin.

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<v Speaker 3>Hello.

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

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

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<v Speaker 4>how IBM works with its clients to solve complex problems

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<v Speaker 4>like helping Lreel reimagine how scientists approach cosmetic formulation, or

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<v Speaker 4>enabling Scuderia FERRARIHP to connect with fans in new ways.

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<v Speaker 4>But in this episode, we're going to zoom out and

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<v Speaker 4>look at the bigger picture. Earlier this month, I had

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<v Speaker 4>the chance to meet the person who's shaping IBM's future.

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<v Speaker 4>It's CEO and Chairman Arvind Krishna. We sat down in

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<v Speaker 4>front of an intimate live audience at IBM's New York

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<v Speaker 4>City office and talked about his uncanny ability to anticipate

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<v Speaker 4>where technology is heading, the future of AI, and his

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<v Speaker 4>passion for quantum computing, which he says is as revolutionary

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<v Speaker 4>as a semiconductor.

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<v Speaker 3>Thank you everyone, Thank you Arvin.

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<v Speaker 4>You're a difficult man to scare for what they said,

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<v Speaker 4>so we're we're enormously pleased that you.

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<v Speaker 3>Could join us.

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<v Speaker 4>Let's start with a I have all these cousins, two

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<v Speaker 4>cousins who work for IBM their entire career.

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<v Speaker 3>I would ask them what does IBM do?

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<v Speaker 4>And they would always give me different, confusing, complicated answers.

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<v Speaker 3>What's your answer? What's your simple answer to that question?

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<v Speaker 5>IBM's role is to help our clients improve their business

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<v Speaker 5>by deploying technology. That means you're not ever gated to

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<v Speaker 5>one product. It is what makes sense at that time,

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<v Speaker 5>but it is about improving their business, not just giving

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<v Speaker 5>them a commodity. Then to go to the next layer,

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<v Speaker 5>I would say we help them through a mixture of

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<v Speaker 5>hybrid cloud and artificial intelligence and a taste of quantum

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<v Speaker 5>coming down the road is kind.

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<v Speaker 3>Of where I would take it.

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<v Speaker 5>That's that's what IBM is.

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<v Speaker 3>So you are technology agnostic in some sense.

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<v Speaker 5>I'm product agnostic. Product I'm not technology agnostic.

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<v Speaker 4>Yes, But if I twenty five years from now, IBM

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<v Speaker 4>could be doing things that would be unrecognizable to contemporary IBM, it.

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<v Speaker 3>Is completely possible.

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<v Speaker 5>Yeah, it could be that in twenty five years from now,

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<v Speaker 5>the only software IBM does his open source. It could

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<v Speaker 5>be the only computing you do is quantum computers. And

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<v Speaker 5>if I add those two people today, WLD say that's

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<v Speaker 5>not the IEM of today.

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<v Speaker 4>Is it even simpler to say you just IBM solves

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<v Speaker 4>problems at the highest technical level.

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<v Speaker 5>If you say highest technical level, yes, yeah, Like the

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<v Speaker 5>guy who invented the bar code, he was solving a

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<v Speaker 5>problem retailers wanted to scale. Many of you may not

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<v Speaker 5>know it was an IBM or who invented the bar code,

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<v Speaker 5>by the way, not somebody who was a PhD. Not

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<v Speaker 5>somebody who was a deep researcher. I think it was

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<v Speaker 5>actually a field engineer. Oh really, Yeah, and lasers were

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<v Speaker 5>out and you could use lasers to scan things, but

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<v Speaker 5>they could be upside down, they could be muddy, they

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<v Speaker 5>could be partly scraped off. And he came up with

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<v Speaker 5>the idea of the bar code. Yeah, and that changed

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<v Speaker 5>inventory management forever. But the world needs to know that

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<v Speaker 5>IBM invented the barcode. You guys should do a better

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<v Speaker 5>job ombicizing that. I am sure our CMO will listen

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<v Speaker 5>to this podcast and we'll get that idea.

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<v Speaker 4>Tell me you started at the Thomas Watson Research Center.

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<v Speaker 4>What were you doing when you first started it, IBM.

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<v Speaker 5>I started in nineteen ninety and that was the era

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<v Speaker 5>in which computers and networking but beginning to converge. And

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<v Speaker 5>for the first five years I was actually building networks.

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<v Speaker 5>So let's remember this was pre laptops. Laptops came in

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<v Speaker 5>ninety two or ninety three, but it was clear to

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<v Speaker 5>us that they were going to come supportable computing, and

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<v Speaker 5>I spent my first five years building what today you

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<v Speaker 5>would call Wi Fi. We used to have these debates

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<v Speaker 5>can be builded, it's got to be small enough. I

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<v Speaker 5>mean like, it can't be more than one hundred grams

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

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<v Speaker 5>you're on a three thousand grand laptop. Why would anybody

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<v Speaker 5>put this on? And the debate used to be, why

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<v Speaker 5>would anybody want to walk around untethered? Won't you want

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<v Speaker 5>to attach a big thick cable into it and sit down?

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<v Speaker 5>Because that was the thought, that's how terminals worked. And

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<v Speaker 5>I spent five years having a lot of fun, building

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<v Speaker 5>many iterations of those and making progress on that.

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<v Speaker 4>If I had a conversation with your nineteen ninety self

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<v Speaker 4>about what the next thirty years were going to look like,

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<v Speaker 4>is it possible to reconstruct you? What were your predictions

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<v Speaker 4>at that age about where the company, where the industry

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<v Speaker 4>was going.

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<v Speaker 5>It was more about where technology was going to go,

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<v Speaker 5>I would say, than where industry would go. I would

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<v Speaker 5>have told you that networking and computers would fuse in

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<v Speaker 5>nineteen ninety. That was a weird thought that some researchers

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<v Speaker 5>held by the late nineties, that was obvious that it

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<v Speaker 5>became the Internet. I would have told you that I

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<v Speaker 5>believe that video streaming will be the primary way people

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

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<v Speaker 5>that didn't take five years. That took twenty. But it

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<v Speaker 5>happened because you could do it technically, except it just

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

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<v Speaker 5>like in nineteen eighty five, I would have told you

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<v Speaker 5>the Internet is old because when I went to grad school,

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<v Speaker 5>every one of us had a those days, an Apple,

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<v Speaker 5>Mac or Lisa on our desks. They're all connected by

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<v Speaker 5>a network. You're happily sending email to people all around

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<v Speaker 5>the country. We were doing file transfers. So okay, you

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<v Speaker 5>had to be a little bit more aware of the

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<v Speaker 5>technology and didn't have a browser that took ten years

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<v Speaker 5>to get the browser that took five years to be

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<v Speaker 5>a business. But when you see the speed and the

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<v Speaker 5>pace of technology in usually ten or fifteen years, the

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<v Speaker 5>cost point and the consumerization is at a scale that

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<v Speaker 5>you couldn't imagine ten years ago.

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<v Speaker 3>Until you've seen a few of those cycles.

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<v Speaker 4>Wait, did you make the lead to sorry, this is fascinating.

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

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<v Speaker 4>a really fundamental thing to have gotten right in nineteen

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<v Speaker 4>ninety I think you're that idea.

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<v Speaker 5>We were pretty convinced that what we used to think

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<v Speaker 5>of as linear television or broadcast would become digitized. That

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<v Speaker 5>was a given two with cable already the preponderance of

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<v Speaker 5>how people got it that if you put packet television

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<v Speaker 5>over cable, then that becomes the way it will go.

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<v Speaker 5>I fundamentally believed, actually way back eighty seven, that on

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<v Speaker 5>demand movies would become the way people would consume movies.

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<v Speaker 5>So those were all things that I could have predicted.

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

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<v Speaker 5>I moved on to doing other things. But those were

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<v Speaker 5>easy to predict.

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<v Speaker 4>If you had a converse in those years with someone

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<v Speaker 4>in the television industry and you gave them those predictions,

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<v Speaker 4>did they see it?

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<v Speaker 3>Were they convinced of this?

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<v Speaker 5>I'm actually going to take it back to wireless networking.

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<v Speaker 5>I think one of the reasons I do what I

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<v Speaker 5>do today, which is at the intersection of business and technology,

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<v Speaker 5>is because of what I saw happened with Wi Fi.

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<v Speaker 5>So you built these wireless networks and then you say, hey,

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<v Speaker 5>the market's going to be millions, tens of millions, billions

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<v Speaker 5>of users. And the business looks at it and says,

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<v Speaker 5>we think the market is confined to warehouse workers doing inventory.

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<v Speaker 5>You can look at them and say, why not people

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<v Speaker 5>in their homes because they could imagine outside how people

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<v Speaker 5>bought things at that time. And so I became convinced

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<v Speaker 5>that I can't just help invent it. I got to

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<v Speaker 5>think about, now, how do you market it? To whom

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<v Speaker 5>do you market it? What are their routs? How do

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<v Speaker 5>you make it easy enough? And that was I mean

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<v Speaker 5>not making it simple? Now, that was probably a five

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<v Speaker 5>to ten year evolution of myself in those days.

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<v Speaker 4>You know, this reminds me of when the telephone is

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<v Speaker 4>invented in the eighteen seventies. It doesn't take off for

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<v Speaker 4>forty years because the people running a telephone business and

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<v Speaker 4>they didn't want women using it because they were worried

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<v Speaker 4>that women would gossip with their friends. They didn't understand

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<v Speaker 4>that that's actually what telephone is.

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<v Speaker 3>Right, It's an.

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<v Speaker 5>Exact parallel, Yes, it is. You see it again and again.

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<v Speaker 3>What is the source of that blindness.

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<v Speaker 4>So there's a gap, in other words, between the invention,

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<v Speaker 4>the technological achievement, and the social understanding of the technology.

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<v Speaker 3>Why is there such a gap.

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<v Speaker 5>I think that the gap is fundamental and rooted in

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<v Speaker 5>a lot of academic disciplines. So even channeling some of

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<v Speaker 5>your work, though you don't intend it to be used

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<v Speaker 5>that way, you can say a lot of things are

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<v Speaker 5>data driven. If it is data driven, then by definition,

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<v Speaker 5>you're looking at history. If you're looking at history, that

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<v Speaker 5>means you're looking at existing buying patterns. If you look

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<v Speaker 5>at existing buying patterns, you forget. All of those who

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<v Speaker 5>have created massive value in time have all created markets,

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<v Speaker 5>meaning they've all created new markets. And I think that

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<v Speaker 5>is why the world is fascinated with people like Steve

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

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<v Speaker 5>So I think that is the gap. And then if

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<v Speaker 5>you can get the technology the business acumen scaler company

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<v Speaker 5>and that imagination of making the market is how you

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<v Speaker 5>create I think massive value.

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

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<v Speaker 4>In other words, you were thinking it's not enough to

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<v Speaker 4>invent something new. I need to make a business case

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

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<v Speaker 4>the path that leads you to this job.

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<v Speaker 5>Oh yeah, I'll tell you if you had met or

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<v Speaker 5>when the nineteen ninety four and you had talked about

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<v Speaker 5>the stock market or about a balance sheet, or looked

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<v Speaker 5>at you like, Okay, I got those words are I

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<v Speaker 5>can parse them. I have no idea what they are.

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<v Speaker 5>I have no intuition on what they are. I couldn't

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<v Speaker 5>tell you why it's relevant or why it's not. But

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<v Speaker 5>then you began to think, Okay, why do companies get

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<v Speaker 5>higher values? Okay, that's the stock what does that capture

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<v Speaker 5>if I have to spend working capital and that's the

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<v Speaker 5>balance sheet? Well, so you learn. I mean, I figure

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<v Speaker 5>I'm willing to learn. I'm willing to read. Howether the

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<v Speaker 5>best way I read is to go to balance sheets?

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<v Speaker 5>Here you can read the book. It's pretty damn dry.

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<v Speaker 5>Much easier to go talk to a financial expert who's

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<v Speaker 5>around the corner, and people are if you're curious about

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<v Speaker 5>what they do, they're really happy to share their expertise,

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<v Speaker 5>and over time you learn more and more and they

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<v Speaker 5>actually become part of your network within the company. And

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<v Speaker 5>that's how you can both learn and evolve yourself and

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<v Speaker 5>actually gain the extra.

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<v Speaker 4>Skills you are to be a successful business leader. Do

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<v Speaker 4>you have to unlearn or or deviate from some of

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<v Speaker 4>the things that made you a successful scientist.

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<v Speaker 5>I actually believe the exactly opposite.

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<v Speaker 3>Yeah, but use what.

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

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<v Speaker 5>it the only thing you use. So then how do

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

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<v Speaker 5>can have people that you trust who help you out

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<v Speaker 5>those skills. You can gain some intuition, maybe not the

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<v Speaker 5>depth of expertise. I want to be deeper on certain

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<v Speaker 5>areas of electrical engineering than I'm ever going to be,

0:12:31.160 --> 0:12:34.079
<v Speaker 5>let's say, in finance or marketing. But I want to

0:12:34.120 --> 0:12:36.600
<v Speaker 5>be curious about those. I don't want to dismiss them.

0:12:37.080 --> 0:12:40.640
<v Speaker 5>So you build on your skills, and then you have

0:12:40.720 --> 0:12:43.560
<v Speaker 5>to say, but I need a complete and holistic view.

0:12:44.160 --> 0:12:46.960
<v Speaker 5>So I'm going to be a little deep, not very deep,

0:12:47.000 --> 0:12:49.400
<v Speaker 5>in all of those. And you've also got to learn

0:12:49.440 --> 0:12:51.079
<v Speaker 5>to trust your intuition a little bit.

0:12:51.440 --> 0:12:54.120
<v Speaker 4>Yeah, but I forgot a question that I wanted to ask,

0:12:54.920 --> 0:12:58.880
<v Speaker 4>but about the predictions of nineteen ninety arvand what did

0:12:58.880 --> 0:12:59.520
<v Speaker 4>you get wrong?

0:13:00.960 --> 0:13:03.840
<v Speaker 5>All lots of things. I think that people were thinking

0:13:04.000 --> 0:13:08.640
<v Speaker 5>that in those days, and it started my phrase, but

0:13:08.679 --> 0:13:11.240
<v Speaker 5>I'll come back to it. I think most people thought

0:13:11.360 --> 0:13:14.840
<v Speaker 5>that the communication companies would turn out to be the

0:13:14.880 --> 0:13:18.360
<v Speaker 5>winners of how networking got carried. If you all think

0:13:18.360 --> 0:13:20.880
<v Speaker 5>through the nineties of the investments that were being done

0:13:21.360 --> 0:13:23.720
<v Speaker 5>by let's not take the names of all of the

0:13:24.000 --> 0:13:25.199
<v Speaker 5>telecom carriers.

0:13:26.440 --> 0:13:27.480
<v Speaker 3>Didn't turn out to.

0:13:27.400 --> 0:13:30.120
<v Speaker 5>Be the case. Actually, I think that's the business model case.

0:13:30.840 --> 0:13:33.560
<v Speaker 5>The reason is they all had in their heads that

0:13:33.640 --> 0:13:34.920
<v Speaker 5>you can charge people by the.

0:13:34.880 --> 0:13:38.880
<v Speaker 3>Minute, because they had been doing that already, because they

0:13:38.880 --> 0:13:40.120
<v Speaker 3>had been doing that for one hundred years.

0:13:40.200 --> 0:13:44.560
<v Speaker 5>Yeah, And in the end, the winners the networking were

0:13:44.559 --> 0:13:47.360
<v Speaker 5>those who said flat price thirty bucks a month or

0:13:47.360 --> 0:13:50.440
<v Speaker 5>fifty bucks a month or whatever, and that was just

0:13:50.600 --> 0:13:52.360
<v Speaker 5>too much of a.

0:13:51.880 --> 0:13:52.600
<v Speaker 3>Leap for them.

0:13:52.880 --> 0:13:56.400
<v Speaker 4>You think it's as simple. That is the most parsimonious

0:13:56.400 --> 0:13:58.640
<v Speaker 4>explanation for why you think they failed.

0:13:58.920 --> 0:14:01.120
<v Speaker 5>No, there were a couple of hours the more technical things.

0:14:01.120 --> 0:14:03.840
<v Speaker 5>The one was written by somebody who was actually inside

0:14:03.880 --> 0:14:07.280
<v Speaker 5>one of these telecom companies, and he labeled his article

0:14:07.320 --> 0:14:11.199
<v Speaker 5>the rise of the Stupid Network. So telephone people believe

0:14:11.200 --> 0:14:13.959
<v Speaker 5>that the network should be really smart, the end device

0:14:14.040 --> 0:14:17.319
<v Speaker 5>is dumb. If you think about the telephone, telephone is dumb.

0:14:17.320 --> 0:14:19.200
<v Speaker 5>It doesn't actually do anything. It's just about the relays.

0:14:19.760 --> 0:14:22.240
<v Speaker 5>And the network is smart. It routes you, it figures

0:14:22.240 --> 0:14:26.560
<v Speaker 5>out where to send it, it does echocacul backwards. And

0:14:27.600 --> 0:14:30.000
<v Speaker 5>the current Internet is completely dumb with the inside. It

0:14:30.120 --> 0:14:32.200
<v Speaker 5>just takes the bits and shoves them out the other end.

0:14:32.600 --> 0:14:35.280
<v Speaker 5>All the intelligence is the computer at the end. That's

0:14:35.600 --> 0:14:38.520
<v Speaker 5>probably a bit more of a found explanation. But business

0:14:38.560 --> 0:14:40.000
<v Speaker 5>model didn't help them either.

0:14:40.240 --> 0:14:40.480
<v Speaker 3>Yeah.

0:14:40.800 --> 0:14:44.160
<v Speaker 4>Wait, did nineteen ninety r of end think that the

0:14:44.200 --> 0:14:46.680
<v Speaker 4>network should be dumb or smart?

0:14:48.080 --> 0:14:48.600
<v Speaker 3>I'm not sure.

0:14:48.600 --> 0:14:51.240
<v Speaker 5>I thought about it deeply, but everything I worked on

0:14:51.320 --> 0:14:52.240
<v Speaker 5>the network was dumb.

0:14:52.960 --> 0:14:54.640
<v Speaker 3>The netwuck movements. That's all I did.

0:14:55.240 --> 0:14:59.120
<v Speaker 5>Yeah, because even I in those days understood I can't

0:14:59.160 --> 0:15:01.720
<v Speaker 5>imagine all the opp loss. So if all you do

0:15:01.840 --> 0:15:04.440
<v Speaker 5>is voice, maybe the network can be smart. But if

0:15:04.480 --> 0:15:06.200
<v Speaker 5>you're doing all those other things, how could the network

0:15:06.200 --> 0:15:08.880
<v Speaker 5>possibly know all those things that be smart for it?

0:15:09.080 --> 0:15:13.800
<v Speaker 3>Yeah, so you've been COO for five years. Five years.

0:15:14.040 --> 0:15:16.760
<v Speaker 4>Wait, so in your five year increment, what was your

0:15:16.840 --> 0:15:20.640
<v Speaker 4>most misunderstood decision? Well, you ended up being right, but

0:15:20.680 --> 0:15:21.800
<v Speaker 4>everyone thought you were crazy.

0:15:22.440 --> 0:15:26.360
<v Speaker 5>Twenty and eighteen, I proposed to our boat that we

0:15:26.400 --> 0:15:30.840
<v Speaker 5>should buy a company called rad Hat. IBM does proprietary

0:15:30.840 --> 0:15:34.440
<v Speaker 5>but that was open source. The stock twelve fifteen percent

0:15:34.520 --> 0:15:37.920
<v Speaker 5>of the day we announced it, and today most people

0:15:37.920 --> 0:15:39.880
<v Speaker 5>will turn around and say, this is the most successful

0:15:39.880 --> 0:15:43.600
<v Speaker 5>acquisition that IBM has done in all time, and probably

0:15:43.640 --> 0:15:48.360
<v Speaker 5>the most successful software acquisition in history. So it was

0:15:48.400 --> 0:15:51.880
<v Speaker 5>completely misunderstood because people didn't see that you actually did

0:15:52.040 --> 0:15:56.240
<v Speaker 5>need a platform that could make you agnostic across multiple

0:15:56.640 --> 0:15:59.800
<v Speaker 5>cloud platforms, across on premise environments.

0:16:00.080 --> 0:16:01.160
<v Speaker 3>So you've got to have a view of what.

0:16:01.200 --> 0:16:04.000
<v Speaker 5>It could be, and we drove it to a place

0:16:04.040 --> 0:16:07.120
<v Speaker 5>where I think today it stands as the leader in

0:16:07.160 --> 0:16:07.600
<v Speaker 5>its space.

0:16:08.360 --> 0:16:11.360
<v Speaker 4>So how did you come to believe this heretical notion?

0:16:13.560 --> 0:16:17.960
<v Speaker 5>So Cloud was happening, you could ask yourself the question,

0:16:18.480 --> 0:16:23.000
<v Speaker 5>should we spend a lot of capital and chase Cloud? Okay,

0:16:23.200 --> 0:16:27.400
<v Speaker 5>you're five years to be generous, maybe longer behind at

0:16:27.400 --> 0:16:30.800
<v Speaker 5>that point the two leaders, So you could spend maybe

0:16:30.840 --> 0:16:34.800
<v Speaker 5>ten billion a year, and a lot of businesses tend

0:16:34.840 --> 0:16:37.080
<v Speaker 5>to do that. Okay, it's so important, it's going to

0:16:37.080 --> 0:16:40.600
<v Speaker 5>be half the market. I can't not My view was

0:16:41.040 --> 0:16:43.080
<v Speaker 5>we'll always be five years behind. They're not dumb and

0:16:43.080 --> 0:16:45.720
<v Speaker 5>they're not slow. So if you're going to be there,

0:16:45.800 --> 0:16:49.240
<v Speaker 5>you're going to be best case, a distant third, worst

0:16:49.240 --> 0:16:51.280
<v Speaker 5>case maybe a fourth or a fifth. Because there's Chinese

0:16:51.280 --> 0:16:54.920
<v Speaker 5>also in the mix, why would you do that instead?

0:16:55.280 --> 0:16:55.680
<v Speaker 3>Is there a.

0:16:55.640 --> 0:16:58.760
<v Speaker 5>Different space you can occupy instead of competing with them?

0:16:58.960 --> 0:17:02.040
<v Speaker 5>Can you become their best partner, in which case you

0:17:02.200 --> 0:17:05.960
<v Speaker 5>write their success. If I want to be the best partner,

0:17:06.440 --> 0:17:08.600
<v Speaker 5>then what are the set of technologies that would be

0:17:08.680 --> 0:17:13.760
<v Speaker 5>useful so you can flip The problem is how I

0:17:13.800 --> 0:17:14.440
<v Speaker 5>thought about it.

0:17:15.280 --> 0:17:18.960
<v Speaker 4>How hard was it to convince people needed convincing before

0:17:19.000 --> 0:17:20.760
<v Speaker 4>that acquisition.

0:17:21.200 --> 0:17:26.199
<v Speaker 5>Probably six to nine months of breaking my head with

0:17:26.280 --> 0:17:32.480
<v Speaker 5>no success, and then six months of building the momentum

0:17:32.720 --> 0:17:36.440
<v Speaker 5>once a couple of people began to see it.

0:17:36.920 --> 0:17:39.240
<v Speaker 3>Yeah, you're very persistent.

0:17:39.280 --> 0:17:44.680
<v Speaker 4>Oh yes, very Would you describe that as you defining trade?

0:17:46.040 --> 0:17:49.600
<v Speaker 5>I am very persistent and I am very patient. I'm

0:17:49.600 --> 0:17:53.560
<v Speaker 5>also probably very impatient, but I'm not a yeller and screamer.

0:17:53.640 --> 0:17:57.280
<v Speaker 5>I don't rant and rave. But as I say, if

0:17:57.320 --> 0:18:00.679
<v Speaker 5>I think we're going to do something, I can be

0:18:00.760 --> 0:18:03.240
<v Speaker 5>remarkably stubborn about it.

0:18:03.280 --> 0:18:03.919
<v Speaker 3>We will do it.

0:18:04.119 --> 0:18:06.399
<v Speaker 4>If I got your family, put them up on stage

0:18:06.440 --> 0:18:09.080
<v Speaker 4>and ask them this exact question, is this how they

0:18:09.080 --> 0:18:09.600
<v Speaker 4>would answer?

0:18:09.640 --> 0:18:15.639
<v Speaker 5>As well, they will tell you I'm very stubborn. They

0:18:15.720 --> 0:18:17.800
<v Speaker 5>might not agree that I don't rant and drave.

0:18:20.119 --> 0:18:23.719
<v Speaker 4>Well, you know, one of the principal observations of psychology

0:18:23.800 --> 0:18:27.240
<v Speaker 4>is that our home self and our work self are uncorrelated.

0:18:27.880 --> 0:18:29.119
<v Speaker 3>Once you know that, you know everything.

0:18:29.520 --> 0:18:32.600
<v Speaker 4>Wait, I'm curious one last question about that. How long

0:18:32.640 --> 0:18:36.000
<v Speaker 4>does it take for you to be vindicated with red Hat?

0:18:37.040 --> 0:18:41.960
<v Speaker 5>Probably took five maybe four years, I think by twenty

0:18:42.119 --> 0:18:44.680
<v Speaker 5>twenty three, So twenty eighteen we announced it, We took

0:18:44.720 --> 0:18:46.600
<v Speaker 5>the big shot crop. It took a year to close

0:18:46.640 --> 0:18:51.040
<v Speaker 5>twenty nineteen, so if O count not that I'm counting

0:18:51.080 --> 0:19:00.440
<v Speaker 5>that much, but July ninth, twenty nineteen as the day

0:19:00.480 --> 0:19:03.000
<v Speaker 5>that we got all the approvals. Took another few weeks

0:19:03.000 --> 0:19:07.119
<v Speaker 5>to actually transfer the money. But from there, probably twenty

0:19:07.160 --> 0:19:09.399
<v Speaker 5>twenty three, the world woke up and said, hey, you

0:19:09.440 --> 0:19:11.240
<v Speaker 5>guys deserve credit for this was actually.

0:19:11.000 --> 0:19:12.920
<v Speaker 3>A great move, not a bad move.

0:19:13.080 --> 0:19:17.280
<v Speaker 4>Yeah, but this is it's interesting because this is a

0:19:17.280 --> 0:19:21.359
<v Speaker 4>real gamble. If it doesn't work, you're not sitting in

0:19:21.359 --> 0:19:22.240
<v Speaker 4>the chair right now.

0:19:22.160 --> 0:19:24.280
<v Speaker 3>Right, Oh, for sure. There were two steps.

0:19:24.280 --> 0:19:26.960
<v Speaker 5>One if it was obviously not going to work, I

0:19:27.000 --> 0:19:29.560
<v Speaker 5>wouldn't have been selected, and two if it hadn't worked

0:19:29.560 --> 0:19:32.359
<v Speaker 5>after that, That's why CEOs can be short lived.

0:19:32.840 --> 0:19:34.960
<v Speaker 4>Can I ask you, sif a personal question, how much

0:19:35.000 --> 0:19:36.320
<v Speaker 4>sleep did you lose over this.

0:19:39.760 --> 0:19:42.240
<v Speaker 5>Once we had made the decision?

0:19:42.880 --> 0:19:43.200
<v Speaker 3>None?

0:19:45.840 --> 0:19:47.560
<v Speaker 4>Can you give me pointers on how you do this?

0:19:47.600 --> 0:19:50.840
<v Speaker 4>Because I wake up at two am every morning and

0:19:50.880 --> 0:19:53.000
<v Speaker 4>I over much more trivial things than this.

0:19:53.480 --> 0:19:55.440
<v Speaker 5>Once a week, I'll probably wake up at two or

0:19:55.440 --> 0:19:58.800
<v Speaker 5>three in the morning. I acknowledge it because I wake

0:19:58.880 --> 0:20:01.119
<v Speaker 5>up and my brain is runrunning, and once it's running,

0:20:01.119 --> 0:20:01.720
<v Speaker 5>I don't even.

0:20:01.600 --> 0:20:02.400
<v Speaker 3>Try to go back to sleep.

0:20:02.400 --> 0:20:05.280
<v Speaker 5>I mean, okay, go get up and do work and

0:20:05.280 --> 0:20:07.159
<v Speaker 5>make yourself productive. You're going to be tired before in

0:20:07.160 --> 0:20:10.320
<v Speaker 5>the afternoon, that's fine, you'll sleep well that night. I

0:20:10.400 --> 0:20:12.840
<v Speaker 5>have actually learned a long time back. You can't do

0:20:12.960 --> 0:20:16.480
<v Speaker 5>it across. You can't do it early morning, through the

0:20:16.560 --> 0:20:20.600
<v Speaker 5>day and late at night. So an hour before I

0:20:20.640 --> 0:20:23.040
<v Speaker 5>think I want to go to bed, I will actually

0:20:23.160 --> 0:20:27.480
<v Speaker 5>change what I'm doing, meaning I will start reading something

0:20:27.520 --> 0:20:31.480
<v Speaker 5>interesting to me but completely outside the scope of work.

0:20:31.520 --> 0:20:35.919
<v Speaker 5>I may read a biography, I might read somebody who's

0:20:36.040 --> 0:20:40.399
<v Speaker 5>spontefecating on demographics and population. But I won't read it

0:20:40.400 --> 0:20:43.240
<v Speaker 5>on leadership because that's too close. Now, twenty years ago

0:20:43.280 --> 0:20:46.160
<v Speaker 5>I might have that would have been different. I won't

0:20:46.200 --> 0:20:49.200
<v Speaker 5>read it on deep signs because that's too close to.

0:20:49.160 --> 0:20:50.119
<v Speaker 3>What we do for a living.

0:20:50.400 --> 0:20:53.400
<v Speaker 5>So it's got to be outside the things that will

0:20:53.440 --> 0:20:57.080
<v Speaker 5>make my brain churn about work. But it's got to

0:20:57.119 --> 0:21:00.720
<v Speaker 5>be something that is dense enough to occupy your brain.

0:21:01.359 --> 0:21:04.480
<v Speaker 3>So shifted gears. Sorry, I want to dwell on this

0:21:04.640 --> 0:21:05.880
<v Speaker 3>just for a moment. The red hat thing.

0:21:06.640 --> 0:21:09.160
<v Speaker 4>Was there someone or is there someone who you went

0:21:09.240 --> 0:21:13.400
<v Speaker 4>to and explained the logic of this and they saw

0:21:13.440 --> 0:21:15.640
<v Speaker 4>the logic of this, and that made a big difference

0:21:15.640 --> 0:21:15.840
<v Speaker 4>to you.

0:21:17.560 --> 0:21:21.200
<v Speaker 5>Getting their support made a big difference. You'd be surprised.

0:21:21.359 --> 0:21:28.160
<v Speaker 5>I'm remarkably open inside. I mean, when I have are

0:21:28.200 --> 0:21:31.160
<v Speaker 5>there probably a half dozen to a dozen people inside

0:21:31.200 --> 0:21:33.840
<v Speaker 5>that I'll talk to and I'll be completely open about, Hey,

0:21:33.880 --> 0:21:36.239
<v Speaker 5>this is what I'm thinking. I don't know. Here are

0:21:36.240 --> 0:21:38.159
<v Speaker 5>the risks. I'm open about those. Also, it's not just

0:21:38.200 --> 0:21:41.280
<v Speaker 5>the benefits. I think the other risks, but I think

0:21:41.320 --> 0:21:44.119
<v Speaker 5>the benefits outweigh the risks. I talk about that to

0:21:44.160 --> 0:21:49.280
<v Speaker 5>people all the time. So whether for example, I mean

0:21:49.359 --> 0:21:52.800
<v Speaker 5>i'll take names. I think our current chro O Nicol

0:21:52.800 --> 0:21:55.640
<v Speaker 5>who introduced us, she has been in that loop since

0:21:55.680 --> 0:21:59.000
<v Speaker 5>at least twenty fifteen. For me, if I look at

0:21:59.000 --> 0:22:02.359
<v Speaker 5>our CFO Jim Cavanaugh, he's been in that loop probably

0:22:02.359 --> 0:22:05.879
<v Speaker 5>since twenty thirteen. And the IBM's will probably wonder, what

0:22:05.920 --> 0:22:08.239
<v Speaker 5>the hell intersection do you guys have? It didn't when

0:22:08.280 --> 0:22:11.239
<v Speaker 5>I talked about learning finance. I will go to him

0:22:11.240 --> 0:22:13.280
<v Speaker 5>and say, hey, explain this to me. I don't understand

0:22:13.280 --> 0:22:16.760
<v Speaker 5>why it's like this, And to me it's okay. You're

0:22:17.200 --> 0:22:20.320
<v Speaker 5>a patient, you go learn. If I think about many

0:22:20.359 --> 0:22:23.200
<v Speaker 5>of the people in the software business, they've been having

0:22:23.240 --> 0:22:27.440
<v Speaker 5>these discussions with me for always. I mean, now I'll

0:22:27.440 --> 0:22:32.480
<v Speaker 5>acknowledge I can get probably impatient and a SERVIIK, but

0:22:32.520 --> 0:22:34.920
<v Speaker 5>it's meant to be a discussion. I mean, like, let's

0:22:34.920 --> 0:22:36.879
<v Speaker 5>have the discussion. If you have a strong point of view,

0:22:37.040 --> 0:22:39.800
<v Speaker 5>I got it. Nobody has a going to be perfectly correct,

0:22:40.080 --> 0:22:42.520
<v Speaker 5>but I always look for if you have a strong

0:22:42.560 --> 0:22:45.520
<v Speaker 5>point of view, that means it's from a different perspective

0:22:45.520 --> 0:22:48.520
<v Speaker 5>than mine. So what do I learn from that? Is

0:22:48.520 --> 0:22:51.720
<v Speaker 5>the question which helps to improve my point of view.

0:22:51.800 --> 0:22:55.760
<v Speaker 5>That makes sense. I actually think that each person should

0:22:55.760 --> 0:22:58.399
<v Speaker 5>try to build a community of one hundred people inside

0:22:58.440 --> 0:23:02.439
<v Speaker 5>your enterprise and outside that you can call up.

0:23:02.840 --> 0:23:03.800
<v Speaker 3>I have no hesitation.

0:23:04.480 --> 0:23:08.159
<v Speaker 5>Somebody introduced me to a long time back, to a

0:23:08.200 --> 0:23:10.760
<v Speaker 5>CEO of the outside. I call them up all the

0:23:10.760 --> 0:23:12.239
<v Speaker 5>time and say, hey, do you have five minutes. I'm

0:23:12.280 --> 0:23:15.520
<v Speaker 5>just thinking about something. This way, the CEO of red

0:23:15.520 --> 0:23:18.520
<v Speaker 5>At who left IBM in twenty twenty one, we probably

0:23:18.600 --> 0:23:21.439
<v Speaker 5>talk every two or three months on a random topic.

0:23:21.760 --> 0:23:24.199
<v Speaker 5>One way it becomes mutual. He asked me my opinion

0:23:24.240 --> 0:23:26.760
<v Speaker 5>on some things. Now, by the way, three or four

0:23:26.760 --> 0:23:30.160
<v Speaker 5>times he might do something different, but he wants my opinion.

0:23:30.720 --> 0:23:31.879
<v Speaker 5>The tour the other way around.

0:23:32.040 --> 0:23:33.800
<v Speaker 4>If I gave you my phone number, can I be

0:23:33.880 --> 0:23:36.680
<v Speaker 4>on that list? I would just be fascinating. I don't

0:23:36.680 --> 0:23:37.800
<v Speaker 4>know if I can help you, but it would be

0:23:37.840 --> 0:23:38.800
<v Speaker 4>really fine to get the call.

0:23:38.840 --> 0:23:40.360
<v Speaker 5>Sure you can. Do you think that we can ever

0:23:40.440 --> 0:23:45.400
<v Speaker 5>succeed unless people who influence opinions say things about us.

0:23:46.320 --> 0:23:50.080
<v Speaker 5>So you may not think deeply about maybe the physics

0:23:50.119 --> 0:23:53.480
<v Speaker 5>of one of computing, but would you think deeply about

0:23:54.160 --> 0:23:57.280
<v Speaker 5>why and what moment may make it much more attractive

0:23:57.280 --> 0:24:00.199
<v Speaker 5>to a large audience. Sure you would, you'd be all

0:24:00.359 --> 0:24:03.320
<v Speaker 5>better as a thinker of that topic, and probably most

0:24:03.320 --> 0:24:04.160
<v Speaker 5>of the people.

0:24:04.880 --> 0:24:06.320
<v Speaker 4>I was thinking, you know, when you were making your

0:24:06.320 --> 0:24:12.159
<v Speaker 4>comments about your nineteen ninety self and streaming, that the

0:24:12.880 --> 0:24:16.199
<v Speaker 4>rational thing would have been for there have been a

0:24:16.320 --> 0:24:21.840
<v Speaker 4>reserved board seat for every television network from someone from

0:24:21.840 --> 0:24:25.160
<v Speaker 4>the world with technology, which I one hundred percent sure

0:24:25.160 --> 0:24:28.000
<v Speaker 4>they did not have that in nineteen ninety, but they

0:24:28.359 --> 0:24:30.960
<v Speaker 4>their board was probably composed of people like them. Let's

0:24:30.960 --> 0:24:35.320
<v Speaker 4>talk a little bit about technology now. There's so much,

0:24:35.400 --> 0:24:37.720
<v Speaker 4>so much of the changes going on right now are

0:24:38.400 --> 0:24:42.800
<v Speaker 4>accompanied by a great deal of hype. What are we overestimating?

0:24:42.840 --> 0:24:44.040
<v Speaker 3>What are we underestimating?

0:24:44.440 --> 0:24:47.240
<v Speaker 5>Okay, let's go back to ninety ninety five the Internet,

0:24:47.280 --> 0:24:49.160
<v Speaker 5>because I think that the current moment is very much

0:24:49.240 --> 0:24:51.520
<v Speaker 5>like the Internet moment. Actually, all the moments in the

0:24:51.560 --> 0:24:54.520
<v Speaker 5>middle were much smaller. I think mobile streaming were much smaller.

0:24:54.520 --> 0:24:57.280
<v Speaker 5>Internet was the major moment. If you remember back to

0:24:57.359 --> 0:24:59.359
<v Speaker 5>ninety nine and two thousand people claimed there was a

0:24:59.359 --> 0:25:02.199
<v Speaker 5>lot of hype. Would we say that the Internet of

0:25:02.200 --> 0:25:05.640
<v Speaker 5>today has more than fulfilled all those expectations and more?

0:25:06.160 --> 0:25:06.480
<v Speaker 3>Yes?

0:25:07.600 --> 0:25:11.120
<v Speaker 5>Along the way, did eight out of ten of the

0:25:11.160 --> 0:25:14.200
<v Speaker 5>companies that were invested in heavily go bankrupt.

0:25:14.359 --> 0:25:17.080
<v Speaker 3>Yes, I actually think of that as.

0:25:16.880 --> 0:25:21.280
<v Speaker 5>Being the huge positive of the United States capital system,

0:25:21.880 --> 0:25:25.480
<v Speaker 5>that that investment happened. Eight out of ten went broke.

0:25:25.920 --> 0:25:27.880
<v Speaker 5>By the way, those acids didn't go away. They got

0:25:28.200 --> 0:25:30.640
<v Speaker 5>consumed at ten cents in the dollar by somebody else

0:25:30.840 --> 0:25:32.800
<v Speaker 5>who could then make a lot of money. But the

0:25:32.880 --> 0:25:36.320
<v Speaker 5>two out of ten, just take two, it probably has

0:25:36.320 --> 0:25:39.280
<v Speaker 5>paid for all the capitals. If you just take Amazon

0:25:40.359 --> 0:25:43.760
<v Speaker 5>and Alphabet, ak, Google, just those two have probably paid

0:25:43.760 --> 0:25:47.600
<v Speaker 5>for all the capital of that time. So that's what's

0:25:47.640 --> 0:25:50.400
<v Speaker 5>going to happen this time. There will be a lot

0:25:50.440 --> 0:25:53.640
<v Speaker 5>of tears, but in aggregate, there will be a lot

0:25:53.640 --> 0:25:56.600
<v Speaker 5>of success. And I think that's the fundamental difference between

0:25:56.640 --> 0:26:00.239
<v Speaker 5>the US model and almost all other countries. In all

0:26:00.240 --> 0:26:03.040
<v Speaker 5>other countries, they're desperate to keep all the companies alive.

0:26:03.359 --> 0:26:06.800
<v Speaker 3>But that means you're dialuting. That's a horrible thing.

0:26:07.280 --> 0:26:11.040
<v Speaker 5>So to me, let the system works worked really effectively,

0:26:11.240 --> 0:26:12.960
<v Speaker 5>by the way, not just now, I mean all the

0:26:12.960 --> 0:26:17.680
<v Speaker 5>way back to railways and electrification, and you mentioned telephone system.

0:26:18.119 --> 0:26:22.760
<v Speaker 5>You can keep going on oil, I mean consumer goods.

0:26:23.119 --> 0:26:25.080
<v Speaker 5>It goes on and on. I think this system is

0:26:25.200 --> 0:26:28.840
<v Speaker 5>very effective. It deploys capital. Its census is a big

0:26:28.880 --> 0:26:32.720
<v Speaker 5>market is completely willing to over deploy capital in the short.

0:26:32.560 --> 0:26:34.080
<v Speaker 3>Term, not the long term.

0:26:34.440 --> 0:26:38.280
<v Speaker 5>That results in more competition, so it actually improves a

0:26:38.359 --> 0:26:41.640
<v Speaker 5>rate of innovation. That means what might have taken twenty

0:26:41.720 --> 0:26:45.760
<v Speaker 5>years takes five and the winners emerge exactly the same

0:26:45.800 --> 0:26:47.000
<v Speaker 5>is going to happen this time.

0:26:47.240 --> 0:26:48.640
<v Speaker 3>Yeah, I saw that.

0:26:48.880 --> 0:26:53.200
<v Speaker 4>I grew up in Waterloo and BlackBerry closs from Waterloo. Yep,

0:26:53.440 --> 0:26:56.720
<v Speaker 4>everyone used to work for BlackBerry. Ye, BlackBerry goes into

0:26:56.760 --> 0:26:58.920
<v Speaker 4>its die and that's the best thing that happened to

0:26:58.960 --> 0:27:00.800
<v Speaker 4>Waterloo because it was just capital with.

0:27:00.840 --> 0:27:04.480
<v Speaker 5>Talents, yep, talents many other companies. So all these smart.

0:27:04.280 --> 0:27:08.040
<v Speaker 4>People went on the next really more interesting thing. And yeah,

0:27:08.800 --> 0:27:11.919
<v Speaker 4>the wait, you haven't answered. So what is your an

0:27:11.960 --> 0:27:15.320
<v Speaker 4>idea that we are underestimating at the moment that's in

0:27:15.359 --> 0:27:17.760
<v Speaker 4>the current kind of suite of innovations.

0:27:17.960 --> 0:27:20.639
<v Speaker 5>So I don't think AI is being underestimated because when

0:27:20.640 --> 0:27:22.159
<v Speaker 5>you look at the amount of capital and the amount

0:27:22.160 --> 0:27:25.800
<v Speaker 5>of things chasing it, I think it's incredible. I do

0:27:25.880 --> 0:27:28.359
<v Speaker 5>think that a lot of enterprises are deploying it in

0:27:28.359 --> 0:27:31.959
<v Speaker 5>the wrong place they're running after shiny experiments. There's a

0:27:32.040 --> 0:27:35.160
<v Speaker 5>lot of basic things you can do to use AI

0:27:35.240 --> 0:27:38.080
<v Speaker 5>to improve the business today. So that's really just my

0:27:38.200 --> 0:27:41.359
<v Speaker 5>one advice to them. Pick areas you can scale, don't

0:27:41.400 --> 0:27:44.240
<v Speaker 5>pick the shiny little toys on the side.

0:27:45.040 --> 0:27:47.320
<v Speaker 3>Then I think, for example, that.

0:27:49.160 --> 0:27:54.520
<v Speaker 5>If anybody has more than ten percent of what they

0:27:54.560 --> 0:27:59.280
<v Speaker 5>had for customer service ten years ago, they're already five

0:27:59.359 --> 0:28:01.480
<v Speaker 5>years behind.

0:28:02.920 --> 0:28:04.679
<v Speaker 3>If anybody is not using.

0:28:04.440 --> 0:28:08.919
<v Speaker 5>AI to make their developers who write software thirty percent

0:28:09.040 --> 0:28:12.119
<v Speaker 5>more productive today with the goal of being seventy percent

0:28:12.240 --> 0:28:15.359
<v Speaker 5>more productive, that's not to say you will need less,

0:28:15.520 --> 0:28:16.920
<v Speaker 5>you'll just get more software done.

0:28:18.400 --> 0:28:20.639
<v Speaker 3>Then they're not. And I would turn.

0:28:20.480 --> 0:28:23.280
<v Speaker 5>Around and tell you I think only maybe five percent

0:28:23.359 --> 0:28:25.400
<v Speaker 5>of the enterprises on both.

0:28:25.240 --> 0:28:26.160
<v Speaker 3>Those metrics today.

0:28:26.400 --> 0:28:31.080
<v Speaker 5>Yeah, yeah, and the one that is completely underestimated. I

0:28:31.200 --> 0:28:35.280
<v Speaker 5>kind of put it like this, Quantum today is where

0:28:35.440 --> 0:28:39.080
<v Speaker 5>GPUs and AI war in twenty fifteen, and I bet

0:28:39.160 --> 0:28:42.800
<v Speaker 5>you every AI person is thinking and hoping I wish

0:28:42.840 --> 0:28:46.200
<v Speaker 5>I had started doing more in twenty fifteen as opposed

0:28:46.200 --> 0:28:49.440
<v Speaker 5>to wait until twenty twenty two. Quantum today is there.

0:28:49.680 --> 0:28:52.080
<v Speaker 5>So it's not good enough that you can get a

0:28:52.160 --> 0:28:54.800
<v Speaker 5>big advantage. But if you learn how to use it,

0:28:55.240 --> 0:28:58.680
<v Speaker 5>then in five years you'll be ready to exploit what comes.

0:28:59.200 --> 0:29:00.920
<v Speaker 3>We're going to get to contum in a moment.

0:29:00.920 --> 0:29:03.480
<v Speaker 4>But I had a couple other AI questions, you know,

0:29:03.560 --> 0:29:06.840
<v Speaker 4>I as you know where this conversation is part of

0:29:06.880 --> 0:29:10.000
<v Speaker 4>this thing that we do with IBM Smart Talks, and

0:29:10.080 --> 0:29:13.560
<v Speaker 4>I've been The last episode I did was on Kenya,

0:29:14.240 --> 0:29:17.040
<v Speaker 4>which has a massive deforestation problem, and they got together.

0:29:17.120 --> 0:29:21.480
<v Speaker 4>IBM took all the NASA satellite data, ran it through

0:29:21.800 --> 0:29:25.960
<v Speaker 4>an LLM, and gave them this incredibly precise ten meter

0:29:26.040 --> 0:29:29.280
<v Speaker 4>by ten meter analysis of what trees to plant, where

0:29:29.320 --> 0:29:32.800
<v Speaker 4>to plant them exactly where the you know, an astonishing

0:29:33.000 --> 0:29:36.080
<v Speaker 4>kind of blueprint about how to fix their country ecologically.

0:29:36.480 --> 0:29:40.760
<v Speaker 4>And it made me think, when we analyze the potential

0:29:40.760 --> 0:29:43.640
<v Speaker 4>of AI, are we making a mistake by spending too

0:29:43.680 --> 0:29:46.160
<v Speaker 4>much thinking about the developed world when it's actually the

0:29:46.200 --> 0:29:49.200
<v Speaker 4>developing world where the greatest ROI for this.

0:29:49.280 --> 0:29:49.840
<v Speaker 3>Is to me.

0:29:49.960 --> 0:29:53.360
<v Speaker 5>Look, software technology is are wonderful and the sense they

0:29:53.360 --> 0:29:55.480
<v Speaker 5>can scare and they can be an ad so you

0:29:55.480 --> 0:29:59.360
<v Speaker 5>don't have to do one or the other. You use defrautestation,

0:30:00.000 --> 0:30:04.040
<v Speaker 5>how about the use of pesticides and fertilizers, We overuse it.

0:30:04.440 --> 0:30:06.080
<v Speaker 3>We tend to for.

0:30:05.920 --> 0:30:09.120
<v Speaker 5>Irrigation, we tend to just flood everything, as opposed to say, okay,

0:30:09.160 --> 0:30:11.320
<v Speaker 5>only that one needs it. You could do all those

0:30:11.320 --> 0:30:13.479
<v Speaker 5>things to get a ten times effectiveness, and that all

0:30:13.520 --> 0:30:17.520
<v Speaker 5>would apply to the developing world. How about remote healthcare

0:30:17.720 --> 0:30:21.760
<v Speaker 5>or telehealth using an AI agent. So the examples are numerous.

0:30:22.520 --> 0:30:25.959
<v Speaker 5>In the developed world, I believe we are running out

0:30:26.000 --> 0:30:29.400
<v Speaker 5>of people. I know that nobody likes to hear it.

0:30:30.320 --> 0:30:33.280
<v Speaker 5>Most of the Far East is going to have half

0:30:33.320 --> 0:30:35.960
<v Speaker 5>the number of people by twenty seventy competed today. That's

0:30:36.000 --> 0:30:38.760
<v Speaker 5>not that far away. If I look at Europe, birth

0:30:38.840 --> 0:30:44.000
<v Speaker 5>rates are far under sustaining or keeping population flat. How

0:30:44.200 --> 0:30:46.760
<v Speaker 5>the US, also, depending on which number you want to

0:30:46.760 --> 0:30:49.080
<v Speaker 5>look at, is either one point six births per women

0:30:49.800 --> 0:30:51.680
<v Speaker 5>or two or two point one.

0:30:52.280 --> 0:30:53.280
<v Speaker 3>Why are the three numbers?

0:30:53.320 --> 0:30:55.080
<v Speaker 5>One point six is to women who were born in

0:30:55.080 --> 0:30:58.200
<v Speaker 5>the United States, It becomes two point two. If you

0:30:58.360 --> 0:31:01.800
<v Speaker 5>include immigrant women, it becomes two point one if you

0:31:01.840 --> 0:31:04.920
<v Speaker 5>include children or remigrating in So you got to decide

0:31:04.960 --> 0:31:08.120
<v Speaker 5>where the trend is obvious, This is going down. So

0:31:08.320 --> 0:31:11.720
<v Speaker 5>AI in the developed world is going to be essential

0:31:12.000 --> 0:31:14.880
<v Speaker 5>because to keep our current quality of life you need

0:31:14.960 --> 0:31:17.680
<v Speaker 5>more work done, or what's going to do the work

0:31:17.720 --> 0:31:20.000
<v Speaker 5>if there aren't people to do the work. So the

0:31:20.040 --> 0:31:22.040
<v Speaker 5>problems are different in the places.

0:31:22.120 --> 0:31:25.160
<v Speaker 4>Yeah, it gives you in the developing world, you get

0:31:25.200 --> 0:31:29.320
<v Speaker 4>access to a suite of technologies and things at a

0:31:29.400 --> 0:31:31.760
<v Speaker 4>price you could never been able to afford.

0:31:31.920 --> 0:31:34.400
<v Speaker 3>Correct. That was my in talking to the Kenyon thing.

0:31:34.440 --> 0:31:37.120
<v Speaker 4>It was like the whole it's it's maybe one of

0:31:37.160 --> 0:31:41.360
<v Speaker 4>the largest ecological projects of its kind at fifteen billion

0:31:41.400 --> 0:31:42.480
<v Speaker 4>trees they want to plan, and.

0:31:42.440 --> 0:31:44.360
<v Speaker 5>That is one country that might get it done because

0:31:44.360 --> 0:31:47.560
<v Speaker 5>they do take a lot of pride in their ecology

0:31:47.800 --> 0:31:51.120
<v Speaker 5>and in sort of returning to the land and giving back.

0:31:51.360 --> 0:31:51.600
<v Speaker 3>Yeah.

0:31:51.760 --> 0:31:56.800
<v Speaker 4>Yeah, what's different about IBM's version of AI versus some

0:31:56.880 --> 0:31:57.400
<v Speaker 4>of your.

0:31:57.880 --> 0:31:59.760
<v Speaker 5>So we are not a consumer company, so we have

0:31:59.760 --> 0:32:03.880
<v Speaker 5>no focus on a B two C chatbot. And the

0:32:03.920 --> 0:32:06.520
<v Speaker 5>reason I say that is, if you're making a B

0:32:06.560 --> 0:32:09.480
<v Speaker 5>two C chat bot, does it help you to make

0:32:09.520 --> 0:32:13.080
<v Speaker 5>it even bigger and more competitionally inefficient? And the short

0:32:13.120 --> 0:32:15.360
<v Speaker 5>answer is yes, because you have a certain number of

0:32:15.480 --> 0:32:18.000
<v Speaker 5>users and you kind of say, I kind of say

0:32:18.040 --> 0:32:22.040
<v Speaker 5>this jokingly. If I add finished to French capabilities, I

0:32:22.080 --> 0:32:23.880
<v Speaker 5>can probably add five million users.

0:32:24.440 --> 0:32:25.560
<v Speaker 3>If I add.

0:32:25.880 --> 0:32:27.840
<v Speaker 5>Writing a high coup I might be able to add

0:32:27.840 --> 0:32:31.840
<v Speaker 5>another five million users. If I add writing an email

0:32:31.920 --> 0:32:34.000
<v Speaker 5>in the voice of Steinbeck, I can probably add another

0:32:34.000 --> 0:32:34.920
<v Speaker 5>five million users.

0:32:35.360 --> 0:32:36.200
<v Speaker 3>Do all those things.

0:32:36.920 --> 0:32:40.200
<v Speaker 5>If my goal is to get help a company summarize

0:32:40.200 --> 0:32:43.800
<v Speaker 5>the legal documents in English, that can be a model

0:32:43.840 --> 0:32:48.360
<v Speaker 5>that's one hundred size as effective, probably higher quality.

0:32:48.680 --> 0:32:50.400
<v Speaker 3>But I don't need to go wide.

0:32:50.800 --> 0:32:54.720
<v Speaker 5>So if you're focusing on the enterprise, that actually takes

0:32:54.760 --> 0:32:58.920
<v Speaker 5>away the focus of having to go to extremely large models,

0:32:58.920 --> 0:33:02.560
<v Speaker 5>which but definition going to be computationally expensive, power hungry,

0:33:03.520 --> 0:33:06.360
<v Speaker 5>and demand lots and lots of data. So I can

0:33:06.400 --> 0:33:08.280
<v Speaker 5>turn onto the enterprise. You don't need to worry about

0:33:08.320 --> 0:33:11.040
<v Speaker 5>copyright issues, about all those because you can train on

0:33:11.080 --> 0:33:12.520
<v Speaker 5>a much smaller amount of data.

0:33:12.920 --> 0:33:14.000
<v Speaker 3>And now, by the.

0:33:14.000 --> 0:33:17.000
<v Speaker 5>Way, tuning it for you yourself is a weekend exercise,

0:33:17.240 --> 0:33:20.560
<v Speaker 5>it's not a six month on a big super computer

0:33:20.640 --> 0:33:23.600
<v Speaker 5>cluster somewhere out there. That's one big difference of what

0:33:23.720 --> 0:33:29.360
<v Speaker 5>we do. Second, we are very focused on helping those

0:33:29.440 --> 0:33:32.600
<v Speaker 5>problems that can give people immediate benefit where we have

0:33:32.640 --> 0:33:37.000
<v Speaker 5>domain knowledge. So our domain knowledge is around operations, is

0:33:37.080 --> 0:33:46.240
<v Speaker 5>around programming, encoding, is around customer service, is around customer experience, logistics, procurement,

0:33:46.720 --> 0:33:50.040
<v Speaker 5>let's change the areas where we have a lot of expertise,

0:33:50.720 --> 0:33:53.920
<v Speaker 5>and then three we kind of apply it to ourselves,

0:33:54.640 --> 0:33:57.040
<v Speaker 5>and so we are not asking our clients to be

0:33:57.160 --> 0:33:59.800
<v Speaker 5>the first experiment on it. We say you can have

0:34:00.280 --> 0:34:03.280
<v Speaker 5>what we did. We're happy to bring out all our learnings,

0:34:03.320 --> 0:34:06.160
<v Speaker 5>including what needs to change in the process, because the

0:34:06.200 --> 0:34:09.160
<v Speaker 5>biggest change is not technology, is getting people to accept

0:34:09.880 --> 0:34:11.879
<v Speaker 5>that there's a different way to do things.

0:34:12.440 --> 0:34:16.880
<v Speaker 4>Other challenges to explaining what makes you different to potential customers.

0:34:16.920 --> 0:34:19.880
<v Speaker 5>For sure, the shining object is always attractive. Oh, I

0:34:19.920 --> 0:34:23.279
<v Speaker 5>can go and try chat GPT. Why don't you have

0:34:23.360 --> 0:34:24.360
<v Speaker 5>your GPT version?

0:34:25.920 --> 0:34:27.080
<v Speaker 3>Do you use chat ChiPT?

0:34:28.040 --> 0:34:28.839
<v Speaker 5>I have used it.

0:34:30.280 --> 0:34:32.279
<v Speaker 4>I asked it a question recently which I thought was

0:34:32.280 --> 0:34:35.680
<v Speaker 4>really simple, and it made up about ten people.

0:34:37.080 --> 0:34:38.280
<v Speaker 3>Anyway, I had a bad experience.

0:34:38.280 --> 0:34:41.560
<v Speaker 5>I actually think that that's the fundamental issue with all

0:34:41.880 --> 0:34:44.600
<v Speaker 5>lms as they get larger, because you had to ask

0:34:44.719 --> 0:34:49.680
<v Speaker 5>what was the original insight that led to these It

0:34:49.840 --> 0:34:54.040
<v Speaker 5>was a reward function with intent. So if it has

0:34:54.239 --> 0:34:58.440
<v Speaker 5>learned by using a reward function, it's reward function comes

0:34:58.760 --> 0:35:02.080
<v Speaker 5>from giving an answer that satisfies you. So if it

0:35:02.280 --> 0:35:04.319
<v Speaker 5>thinks that if it makes up an answer that will

0:35:04.360 --> 0:35:07.560
<v Speaker 5>satisfy you, how will you stop it? Why do we

0:35:07.640 --> 0:35:11.480
<v Speaker 5>think this is different than the clever college kid who

0:35:11.480 --> 0:35:14.640
<v Speaker 5>doesn't know an answer? What bullships the way to an answer? Well,

0:35:14.800 --> 0:35:16.080
<v Speaker 5>it's exactly the same.

0:35:16.120 --> 0:35:18.600
<v Speaker 4>It's like the example of clever Hands during that story

0:35:18.920 --> 0:35:21.759
<v Speaker 4>the horse that they thought could speak, then all it.

0:35:21.840 --> 0:35:24.680
<v Speaker 3>Was doing was pleasing it. It's master. Yes, it is

0:35:24.719 --> 0:35:26.760
<v Speaker 3>a little bit of clever hands. Yeah, it's like dogs

0:35:26.840 --> 0:35:28.080
<v Speaker 3>kind of imitating and looking.

0:35:29.320 --> 0:35:34.480
<v Speaker 4>What would you identify as the most significant bottleneck in

0:35:34.520 --> 0:35:35.520
<v Speaker 4>the development of AI?

0:35:35.760 --> 0:35:37.040
<v Speaker 3>What's slowing us down right now?

0:35:38.880 --> 0:35:43.400
<v Speaker 5>I am not convinced that LLMS is the way to

0:35:43.560 --> 0:35:48.719
<v Speaker 5>get much beyond where we'll get incremental improvements, But I,

0:35:49.200 --> 0:35:52.160
<v Speaker 5>for one, don't believe that LMS are going to get

0:35:52.200 --> 0:35:58.319
<v Speaker 5>us to super intelligence or AGI. So I'll park that

0:35:58.440 --> 0:36:00.919
<v Speaker 5>on the side and simply say, we have to find

0:36:00.960 --> 0:36:04.760
<v Speaker 5>a way to fuse knowledge and how do you represent

0:36:04.920 --> 0:36:08.600
<v Speaker 5>knowledge as opposed to have to statistically rediscover it each

0:36:08.640 --> 0:36:11.520
<v Speaker 5>time I ask a question? And how do we fuse

0:36:11.560 --> 0:36:18.560
<v Speaker 5>knowledge with LLM? Maybe then we'll get to leaves and beyond.

0:36:18.600 --> 0:36:23.200
<v Speaker 5>Today on LLMS alone, my view is I think we

0:36:23.280 --> 0:36:27.400
<v Speaker 5>can get a thousand x efficiency in power and cost

0:36:27.480 --> 0:36:30.880
<v Speaker 5>and compute from today. So if you make something a

0:36:30.960 --> 0:36:34.200
<v Speaker 5>thousand times cheaper, would people use a lot more of it?

0:36:35.320 --> 0:36:35.680
<v Speaker 3>Yes?

0:36:36.560 --> 0:36:39.279
<v Speaker 5>And I think those answers lie as is usually in

0:36:39.320 --> 0:36:44.880
<v Speaker 5>compute through advances in semiconductors, advances in software, and advances

0:36:44.880 --> 0:36:47.719
<v Speaker 5>in algorithmic techniques, all three. But how come we're not

0:36:47.719 --> 0:36:49.160
<v Speaker 5>working in any of those three? Were just taking the

0:36:49.200 --> 0:36:52.600
<v Speaker 5>current semic conductor and going more. We're taking the current

0:36:53.600 --> 0:36:56.160
<v Speaker 5>algorithmic techniques and not really trying to invent new ones.

0:36:56.239 --> 0:36:59.280
<v Speaker 5>So I think those are all happen less than five years.

0:37:00.200 --> 0:37:00.640
<v Speaker 3>Mm hmm.

0:37:01.040 --> 0:37:04.200
<v Speaker 4>But why you say there is a we're in a

0:37:04.200 --> 0:37:09.360
<v Speaker 4>moment where people are not pursuing the the optimal strategy

0:37:09.400 --> 0:37:10.880
<v Speaker 4>for exploiting this technology.

0:37:11.200 --> 0:37:15.919
<v Speaker 5>Why because when you see a few people running really

0:37:15.960 --> 0:37:19.040
<v Speaker 5>hard and they're willing to invest any amount of money,

0:37:19.040 --> 0:37:23.040
<v Speaker 5>so efficiency is not the focus. People feel if we

0:37:23.120 --> 0:37:24.720
<v Speaker 5>don't do the same, you'll get left behind.

0:37:25.960 --> 0:37:28.719
<v Speaker 4>So is this a case where there's too much money

0:37:28.760 --> 0:37:29.960
<v Speaker 4>humans have never had for more?

0:37:30.080 --> 0:37:30.200
<v Speaker 2>Right?

0:37:30.239 --> 0:37:30.439
<v Speaker 3>Ever?

0:37:31.040 --> 0:37:36.319
<v Speaker 4>Yeah, but this is this a consequence of overinvestment in

0:37:36.360 --> 0:37:37.400
<v Speaker 4>the in the field.

0:37:37.520 --> 0:37:39.800
<v Speaker 5>Going back to my internet allology, if two out of

0:37:39.840 --> 0:37:43.239
<v Speaker 5>ten are going to succeed. How do you guarantee or

0:37:43.280 --> 0:37:44.960
<v Speaker 5>how do you improve the orders that you are one

0:37:44.960 --> 0:37:47.960
<v Speaker 5>of those two. So if you pause to say I

0:37:47.960 --> 0:37:50.000
<v Speaker 5>want to become more efficient, that's not the way to win.

0:37:50.360 --> 0:37:52.480
<v Speaker 3>So first you win, then you become efficient.

0:37:53.560 --> 0:37:57.359
<v Speaker 4>Yeah, let's talk about what is I was told your

0:37:57.400 --> 0:37:59.560
<v Speaker 4>favorite topic it's quantum?

0:38:00.160 --> 0:38:03.920
<v Speaker 3>Is what? Boy even go any further? Why is quantum

0:38:04.000 --> 0:38:04.760
<v Speaker 3>your favorite topic?

0:38:06.000 --> 0:38:08.920
<v Speaker 5>We've only had two kinds of compute in the history,

0:38:09.760 --> 0:38:13.160
<v Speaker 5>so nineteen forty five was to use that year for

0:38:13.239 --> 0:38:15.839
<v Speaker 5>any act. All the way till twenty twenty, we had

0:38:15.880 --> 0:38:18.040
<v Speaker 5>one kind of computerlassical what today you would call a

0:38:18.040 --> 0:38:24.200
<v Speaker 5>classical computer. Then GPUs and AI came around, so you

0:38:24.239 --> 0:38:27.480
<v Speaker 5>would say the intuition there is you went from sort

0:38:27.480 --> 0:38:30.680
<v Speaker 5>of bits, which is algebra or high school algebra, to

0:38:30.840 --> 0:38:35.000
<v Speaker 5>including neurons, which is captured in linear algebra. But that

0:38:35.200 --> 0:38:37.920
<v Speaker 5>gives you a different kind But it can do problems

0:38:37.960 --> 0:38:40.760
<v Speaker 5>that are really hard to do. I don't say impossible,

0:38:40.840 --> 0:38:45.279
<v Speaker 5>just hard to do on normal computers. Quantum adds a

0:38:45.320 --> 0:38:51.120
<v Speaker 5>third kind of math. Yes, the physics properties which really

0:38:51.840 --> 0:38:55.080
<v Speaker 5>get people energized and the imagination going. And we use

0:38:55.120 --> 0:38:59.480
<v Speaker 5>all these words about entanglement and silver position, but maybe

0:38:59.560 --> 0:39:02.359
<v Speaker 5>because I'm a bit of a math guy. The real

0:39:02.440 --> 0:39:05.000
<v Speaker 5>thing is that does a third kind of math to

0:39:05.040 --> 0:39:07.880
<v Speaker 5>make it really simple, a third kind of math that

0:39:08.000 --> 0:39:11.880
<v Speaker 5>comes from the field of abstract algebra. It does the

0:39:11.920 --> 0:39:15.680
<v Speaker 5>math you can use Hamiltonians for those who like physics,

0:39:16.200 --> 0:39:18.319
<v Speaker 5>or you can use the word Lee algebras for those

0:39:18.320 --> 0:39:22.359
<v Speaker 5>who like abstract mathematics. If you can do a third

0:39:22.400 --> 0:39:25.359
<v Speaker 5>kind of math, which algorithms are suited to that third

0:39:25.440 --> 0:39:28.560
<v Speaker 5>kind of math. So it excites me because we can

0:39:28.640 --> 0:39:32.080
<v Speaker 5>now approach algorithms that you just could never do on

0:39:32.120 --> 0:39:34.839
<v Speaker 5>the other two it's impossible. Now it's different than AI.

0:39:34.960 --> 0:39:38.359
<v Speaker 5>It's not data intensive. It's compute intensive. So we kind

0:39:38.360 --> 0:39:41.080
<v Speaker 5>of had compute and supercomputers. Then we went to data

0:39:41.160 --> 0:39:43.280
<v Speaker 5>which is AI. And now if you say there's another

0:39:43.320 --> 0:39:45.360
<v Speaker 5>class of problems, it require lots of compute.

0:39:45.520 --> 0:39:47.280
<v Speaker 3>That's quantum.

0:39:47.320 --> 0:39:49.960
<v Speaker 4>A couple months ago was at the to watch some

0:39:50.000 --> 0:39:52.080
<v Speaker 4>research center and they have you know, on the ground floor,

0:39:52.080 --> 0:39:56.239
<v Speaker 4>they have those behind the glass. There's incredibly exciting looking machines.

0:39:56.840 --> 0:39:59.279
<v Speaker 4>But where are we in the timeline of this.

0:40:00.800 --> 0:40:05.640
<v Speaker 5>Three to five years away from shocking people? What does

0:40:05.680 --> 0:40:08.880
<v Speaker 5>shocking people mean do something that nobody thought was possible

0:40:08.920 --> 0:40:09.839
<v Speaker 5>in that timeline.

0:40:10.120 --> 0:40:11.520
<v Speaker 3>Does an example come to mind?

0:40:12.400 --> 0:40:16.919
<v Speaker 5>I was actually pleasantly surprised. So one of our clients, HSBC,

0:40:18.000 --> 0:40:22.080
<v Speaker 5>last week published a result that using a quantum computer

0:40:23.360 --> 0:40:27.480
<v Speaker 5>bond trading was thirty four percent more accurate than their

0:40:27.560 --> 0:40:28.360
<v Speaker 5>prior technique.

0:40:29.280 --> 0:40:31.400
<v Speaker 3>Thirty four percent. Thirty four percent.

0:40:31.920 --> 0:40:35.280
<v Speaker 4>This is an industry that's used to one percent correct,

0:40:35.600 --> 0:40:36.840
<v Speaker 4>zero point five percent.

0:40:37.320 --> 0:40:39.720
<v Speaker 3>Yes, that's astonishing.

0:40:40.000 --> 0:40:42.839
<v Speaker 5>Now that was not at a scale when they could

0:40:42.840 --> 0:40:45.440
<v Speaker 5>turn it into production today, but that was sort of

0:40:45.880 --> 0:40:49.240
<v Speaker 5>their original thought experiment, and that's what they did. Now

0:40:49.520 --> 0:40:54.280
<v Speaker 5>can you imagine when will somebody so you were correct.

0:40:54.280 --> 0:40:57.200
<v Speaker 5>You talk about an industry where one basis point, if

0:40:57.200 --> 0:41:00.640
<v Speaker 5>I remember I may be wrong, like thirteen trillion dollars

0:41:00.640 --> 0:41:03.000
<v Speaker 5>of money kind of moves around in the financial industry

0:41:03.040 --> 0:41:10.280
<v Speaker 5>each day, right, so basis point would be thirteen billion

0:41:10.360 --> 0:41:14.920
<v Speaker 5>something like that, right, one over ten thousand. So when

0:41:14.960 --> 0:41:17.520
<v Speaker 5>you think about the kind of profit that people can make,

0:41:18.239 --> 0:41:19.920
<v Speaker 5>if you can tell somebody that you can come up

0:41:19.960 --> 0:41:24.960
<v Speaker 5>with a better price than your competition by just one

0:41:25.000 --> 0:41:29.120
<v Speaker 5>basis point, they would actually gain the market share. Yeah,

0:41:29.320 --> 0:41:33.440
<v Speaker 5>So I think something around there or something in the

0:41:33.440 --> 0:41:37.560
<v Speaker 5>world of materials. Can we make a better battery? Could

0:41:37.600 --> 0:41:41.279
<v Speaker 5>we make a solid state battery? Which means your risk

0:41:41.320 --> 0:41:45.080
<v Speaker 5>of fires heating decrease dramatically.

0:41:45.120 --> 0:41:47.360
<v Speaker 4>And the reason, sorry to ask a really nive question,

0:41:48.760 --> 0:41:51.120
<v Speaker 4>why is it that a quantum computer would be better

0:41:51.160 --> 0:41:55.680
<v Speaker 4>at solving a battery problem than our existing methods of computing?

0:41:55.760 --> 0:42:00.120
<v Speaker 5>So the equations of quantum mechanics and chemistry and how

0:42:00.480 --> 0:42:04.839
<v Speaker 5>things interact or well known to solve them, that are

0:42:04.840 --> 0:42:07.120
<v Speaker 5>no known techniques. So these are not like closed form,

0:42:07.160 --> 0:42:09.000
<v Speaker 5>you know, it's not like the square root of a

0:42:09.080 --> 0:42:12.279
<v Speaker 5>quarter equation. So the only way to solve them is

0:42:12.360 --> 0:42:15.279
<v Speaker 5>to explore the state space. So if you have a

0:42:15.320 --> 0:42:19.560
<v Speaker 5>few hundred electrons, you need two to the one hundred states.

0:42:20.000 --> 0:42:22.560
<v Speaker 3>Well, I'm sorry you don't have that much memory. It's impossible.

0:42:22.960 --> 0:42:25.840
<v Speaker 5>So it takes a really really long time on a

0:42:25.880 --> 0:42:29.520
<v Speaker 5>normal computer to solve those problems, right, that's simple a problem.

0:42:30.200 --> 0:42:34.680
<v Speaker 5>If a quantum computer operates in the equation domain, it

0:42:34.719 --> 0:42:36.960
<v Speaker 5>doesn't need to explore the state space. It can actually

0:42:36.960 --> 0:42:39.239
<v Speaker 5>solve it. That's why I call it a different kind

0:42:39.239 --> 0:42:42.200
<v Speaker 5>of math. That's the kind of math it does. So

0:42:43.239 --> 0:42:45.439
<v Speaker 5>in a couple of seconds it can tell you this

0:42:45.520 --> 0:42:46.919
<v Speaker 5>is how that material will be here.

0:42:48.400 --> 0:42:49.800
<v Speaker 3>Oh, I see, so you've.

0:42:49.600 --> 0:42:52.839
<v Speaker 5>Taken what could take years to a few seconds. Yeah,

0:42:52.880 --> 0:42:54.440
<v Speaker 5>that's a pretty big change.

0:42:54.600 --> 0:42:57.839
<v Speaker 4>Yeah. Yeah, it's speaking a different language, different line. So

0:42:58.080 --> 0:43:00.520
<v Speaker 4>any kind of problem that comes along that's specific to

0:43:00.560 --> 0:43:02.000
<v Speaker 4>that language correctly.

0:43:01.719 --> 0:43:04.840
<v Speaker 5>Which is not all problems. Yeah, just as I call it,

0:43:04.840 --> 0:43:06.000
<v Speaker 5>it's one more kind of math.

0:43:06.280 --> 0:43:10.520
<v Speaker 4>Yeah, what's an example? So so many questions. L give

0:43:10.560 --> 0:43:14.239
<v Speaker 4>me another example of a of a of a kind

0:43:14.239 --> 0:43:16.279
<v Speaker 4>of problem that a quantum computer would love.

0:43:18.080 --> 0:43:21.360
<v Speaker 5>This one is a bit more speculative, and I'm going

0:43:21.440 --> 0:43:24.640
<v Speaker 5>to use a little bit of poetic license. So let's

0:43:24.680 --> 0:43:28.160
<v Speaker 5>take a post office in a mid sized country. They

0:43:28.200 --> 0:43:30.920
<v Speaker 5>probably burn a billion gallons of fuel per year delivering

0:43:30.960 --> 0:43:34.480
<v Speaker 5>packages and letters because most posts and advanced country says

0:43:34.800 --> 0:43:40.279
<v Speaker 5>every house, every address, each day. The way to optimize

0:43:40.320 --> 0:43:42.640
<v Speaker 5>this is we can formulate the problem. It's called the

0:43:42.719 --> 0:43:46.840
<v Speaker 5>proveling sales and problem solving. It is really hard, so

0:43:46.880 --> 0:43:51.080
<v Speaker 5>people have heuristics. Let's suppose today our heuristics get us

0:43:51.080 --> 0:43:54.720
<v Speaker 5>to within twenty percent of the optimal answer. Let's suppose

0:43:54.719 --> 0:43:58.680
<v Speaker 5>a quantum computer can get you the next ten percent. Well,

0:43:58.719 --> 0:44:00.960
<v Speaker 5>if I can get ten percent of a billion gallons

0:44:01.000 --> 0:44:02.719
<v Speaker 5>that I think is one hundred million gallans of my

0:44:02.800 --> 0:44:05.680
<v Speaker 5>math is right, and in the country I'm thinking about,

0:44:05.920 --> 0:44:09.160
<v Speaker 5>that could be eight hundred million pounds of saving to

0:44:09.280 --> 0:44:14.920
<v Speaker 5>one entity in one year. And the associated carbon footprint

0:44:15.000 --> 0:44:18.800
<v Speaker 5>climate change were in't less mileage on vague. I'm not

0:44:18.840 --> 0:44:22.560
<v Speaker 5>even counting all that. These are pretty attractive problems to

0:44:22.600 --> 0:44:25.400
<v Speaker 5>go after. So if I look at the interest recently,

0:44:26.640 --> 0:44:29.960
<v Speaker 5>New York has started a whole program in some places.

0:44:30.440 --> 0:44:35.120
<v Speaker 5>Illinois stood up a quantum algorithm center between a number

0:44:35.160 --> 0:44:40.160
<v Speaker 5>of the universities. The governor there was heavily behind it, etc.

0:44:40.880 --> 0:44:43.920
<v Speaker 5>So I wouldn't say that this is widespread. This is

0:44:43.920 --> 0:44:45.760
<v Speaker 5>why I'm saying three to four years for that moment.

0:44:46.440 --> 0:44:50.000
<v Speaker 5>But there's enough people who are deeply cognizant who are saying,

0:44:50.040 --> 0:44:52.120
<v Speaker 5>wait a moment, we kind of get it.

0:44:52.160 --> 0:44:53.840
<v Speaker 3>This is a new kind of man. What are the

0:44:53.880 --> 0:44:54.960
<v Speaker 3>new problems we can solve?

0:44:55.640 --> 0:44:58.080
<v Speaker 5>And the fact that we have bought roughly two hundred

0:44:58.120 --> 0:45:02.160
<v Speaker 5>clients who worked with us early stage small experiments is

0:45:02.200 --> 0:45:04.520
<v Speaker 5>because the intuition is I can do something here that

0:45:04.560 --> 0:45:06.880
<v Speaker 5>I couldn't do in other places.

0:45:07.160 --> 0:45:09.439
<v Speaker 4>Three to four years is not a long time, no,

0:45:11.320 --> 0:45:14.120
<v Speaker 4>But if I'm in the battery business, and I don't

0:45:14.160 --> 0:45:18.600
<v Speaker 4>have a line out to a quantum computing experiment.

0:45:20.280 --> 0:45:22.160
<v Speaker 3>I have a problem, don't have a problem.

0:45:22.600 --> 0:45:25.959
<v Speaker 5>Yeah, you'll probably be out of business in ten years.

0:45:26.520 --> 0:45:28.439
<v Speaker 5>Well maybe you could write a big check and buy

0:45:28.440 --> 0:45:29.879
<v Speaker 5>the technology from somebody else.

0:45:30.560 --> 0:45:33.719
<v Speaker 4>You had to What is quantum rank in the kind

0:45:33.760 --> 0:45:37.560
<v Speaker 4>of great inventions of the last one hundred and fifty years.

0:45:37.320 --> 0:45:44.040
<v Speaker 5>Equal to semic conductor? And I think that if semiconductor's vanished,

0:45:44.320 --> 0:45:44.919
<v Speaker 5>modern life.

0:45:44.840 --> 0:45:46.680
<v Speaker 3>Would stop, like just stop.

0:45:47.360 --> 0:45:55.880
<v Speaker 5>Yeah, no electricity, no automobile, no streaming. You can imagine

0:45:55.920 --> 0:45:57.600
<v Speaker 5>the yells from all the kids. Who ever hear that

0:45:57.680 --> 0:45:58.640
<v Speaker 5>no streaming?

0:46:01.560 --> 0:46:04.799
<v Speaker 4>And is that it's funny because don't. As someone who's

0:46:04.840 --> 0:46:08.800
<v Speaker 4>outside this world, I feel like quantum is underdiscussed relative

0:46:08.840 --> 0:46:12.480
<v Speaker 4>to its potential for transforming society.

0:46:12.280 --> 0:46:16.120
<v Speaker 5>Because I use my Internet example. Ninety five was the

0:46:16.120 --> 0:46:20.839
<v Speaker 5>moment with Netscape that Internet came on people's consciousness. I

0:46:20.840 --> 0:46:23.080
<v Speaker 5>said in eighty five I considered it to be this

0:46:23.120 --> 0:46:26.719
<v Speaker 5>is a solved problem because it needs something that makes

0:46:26.719 --> 0:46:31.120
<v Speaker 5>it accessible easy. That was the browser. The Netscape browser

0:46:31.200 --> 0:46:35.480
<v Speaker 5>is what brought it easy to understand. We have probably,

0:46:35.520 --> 0:46:38.440
<v Speaker 5>as I said, about four to five years from that moment.

0:46:38.480 --> 0:46:41.359
<v Speaker 5>That's why it's under discussed because the moment I say

0:46:41.480 --> 0:46:43.560
<v Speaker 5>you got a math, I've probably lost ninety nine percent

0:46:43.640 --> 0:46:46.279
<v Speaker 5>of the audience. If I go to quantum mechanics, I've

0:46:46.280 --> 0:46:49.520
<v Speaker 5>probably lost nine percent of the audience.

0:46:51.320 --> 0:46:54.480
<v Speaker 4>So you, as c over the last five years, have

0:46:54.640 --> 0:46:57.480
<v Speaker 4>been really the birth mother for a lot of the

0:46:57.600 --> 0:47:02.160
<v Speaker 4>quantum computing work. I'm curious, so you come in. When

0:47:02.200 --> 0:47:06.640
<v Speaker 4>you started a CEO, was this your first priority.

0:47:07.160 --> 0:47:09.680
<v Speaker 5>I had already started investing in it back in twenty

0:47:09.719 --> 0:47:15.360
<v Speaker 5>fifteen when I was leading IBM research. So let me

0:47:15.400 --> 0:47:17.359
<v Speaker 5>acknowledge and like nobody should try to copy. And I've

0:47:17.360 --> 0:47:20.680
<v Speaker 5>had I'll call it a weird career. I was a

0:47:20.719 --> 0:47:22.920
<v Speaker 5>researcher at some point. If it had asked me out,

0:47:22.960 --> 0:47:24.359
<v Speaker 5>I said, I'm one of those people, you know, throw

0:47:24.360 --> 0:47:25.160
<v Speaker 5>a pizza under.

0:47:25.000 --> 0:47:27.040
<v Speaker 3>Our door and like leave me alone. I don't want

0:47:27.080 --> 0:47:27.680
<v Speaker 3>to talk to people.

0:47:28.200 --> 0:47:30.560
<v Speaker 5>Then I decided I was interested in the business. Then

0:47:30.600 --> 0:47:33.600
<v Speaker 5>I went and started acquiring companies and doing that. Then

0:47:33.640 --> 0:47:35.680
<v Speaker 5>somebody told me, hey, why did you start doing some

0:47:35.719 --> 0:47:40.359
<v Speaker 5>business strategy. Then I went back to research and led

0:47:40.400 --> 0:47:43.200
<v Speaker 5>our research division for a couple of years. And when

0:47:43.239 --> 0:47:46.960
<v Speaker 5>the people described it to me. I asked some questions.

0:47:47.080 --> 0:47:49.359
<v Speaker 5>So it wasn't a big investment at that time. It

0:47:49.480 --> 0:47:51.680
<v Speaker 5>was hey, can we make a computer not just a

0:47:51.680 --> 0:47:55.719
<v Speaker 5>science experiment? Can it run by itself all night? Can

0:47:55.800 --> 0:47:58.759
<v Speaker 5>you think about software so that even people who are

0:47:58.800 --> 0:48:01.279
<v Speaker 5>not deep of quantum mechanic can begin to use it?

0:48:02.239 --> 0:48:04.440
<v Speaker 5>And they begin to do those things. So over three

0:48:04.560 --> 0:48:08.560
<v Speaker 5>four years, did they get enough confidence Yeah, Okay, this

0:48:08.680 --> 0:48:11.239
<v Speaker 5>is something that can really work. And then you've got

0:48:11.239 --> 0:48:14.360
<v Speaker 5>to nurture it to where it gets bigger and bigger

0:48:14.960 --> 0:48:16.840
<v Speaker 5>until you get the confidence that okay, now it's a

0:48:16.840 --> 0:48:17.360
<v Speaker 5>big bet.

0:48:18.120 --> 0:48:20.640
<v Speaker 3>And what was the moment when you when you realize

0:48:20.640 --> 0:48:21.400
<v Speaker 3>now it's a big.

0:48:21.239 --> 0:48:25.040
<v Speaker 5>Bet, probably two or three years ago.

0:48:25.400 --> 0:48:27.719
<v Speaker 4>And how do you decide, as the head of a

0:48:27.760 --> 0:48:32.040
<v Speaker 4>company like this, how much money, how many resources, and

0:48:32.080 --> 0:48:34.000
<v Speaker 4>how many people? And how what kind of promise to

0:48:34.040 --> 0:48:35.080
<v Speaker 4>give to an idea like that?

0:48:35.840 --> 0:48:39.880
<v Speaker 5>So three layers the set of people who actually have

0:48:40.000 --> 0:48:44.520
<v Speaker 5>the knowledge and the intensity to fundamentally advance the technology.

0:48:45.239 --> 0:48:47.319
<v Speaker 5>If I could find more out higher then So I'm

0:48:47.440 --> 0:48:50.600
<v Speaker 5>constrained of people on that one, because normally there's only

0:48:50.600 --> 0:48:54.520
<v Speaker 5>so many people who can do these things. Two, you

0:48:54.640 --> 0:48:57.920
<v Speaker 5>got to be careful if you push too hard on timing,

0:48:58.400 --> 0:49:01.320
<v Speaker 5>you'll get people to take so much risk that actually

0:49:01.360 --> 0:49:04.520
<v Speaker 5>the thing will fail. So that's the art of between

0:49:04.560 --> 0:49:08.560
<v Speaker 5>the leadership on the project and me to say, Okay,

0:49:08.680 --> 0:49:11.239
<v Speaker 5>how hard can you push? But not so hard that

0:49:11.320 --> 0:49:14.400
<v Speaker 5>you cause it to fail because then they get compelled

0:49:14.440 --> 0:49:17.080
<v Speaker 5>to commit timelines that are just impossible.

0:49:17.640 --> 0:49:20.360
<v Speaker 3>Yeah, how do you This is fascinating.

0:49:20.440 --> 0:49:23.600
<v Speaker 4>So it's ultimately a question of judgment trying to figure

0:49:23.600 --> 0:49:26.239
<v Speaker 4>out what's the sweet spot between enough pressure to keep

0:49:26.280 --> 0:49:28.800
<v Speaker 4>them ahead of the pack, but not too much pressure

0:49:29.160 --> 0:49:32.600
<v Speaker 4>so that they start taking risks. How do you calibrate

0:49:32.880 --> 0:49:34.800
<v Speaker 4>whether you're hitting that sweet spot? I mean, do you

0:49:35.880 --> 0:49:39.080
<v Speaker 4>reassess every few months and say I think I'm over

0:49:39.120 --> 0:49:40.800
<v Speaker 4>correcting or undercorrecting at this moment.

0:49:41.600 --> 0:49:45.040
<v Speaker 5>So one you got to have what I call and

0:49:45.080 --> 0:49:47.720
<v Speaker 5>this is channeling a word from one of my favorite books,

0:49:47.760 --> 0:49:50.360
<v Speaker 5>The Geek Away, How open can you be?

0:49:51.200 --> 0:49:52.399
<v Speaker 3>So I want to press hard?

0:49:53.440 --> 0:49:55.959
<v Speaker 5>But the team knows that they're allowed to push back

0:49:56.400 --> 0:50:00.200
<v Speaker 5>and really argue back hard. That means you'd get do

0:50:00.640 --> 0:50:06.479
<v Speaker 5>probably that correct goldilocks pressure. Do the people themselves should

0:50:06.600 --> 0:50:09.440
<v Speaker 5>want to go as hard as possible, but not harder

0:50:09.440 --> 0:50:14.279
<v Speaker 5>than possible. So that is then personality of leadership that

0:50:14.320 --> 0:50:14.840
<v Speaker 5>makes sense.

0:50:15.080 --> 0:50:17.640
<v Speaker 4>But you have to be someone who people feel comfortable

0:50:17.640 --> 0:50:18.279
<v Speaker 4>being honest with.

0:50:18.480 --> 0:50:22.840
<v Speaker 3>Yes, absolutely, and people feel comfortable being honest with you,

0:50:23.080 --> 0:50:25.319
<v Speaker 3>I believe so. Yeah.

0:50:25.719 --> 0:50:28.680
<v Speaker 4>When has there been a moment in this path with

0:50:28.760 --> 0:50:31.399
<v Speaker 4>quantum where you did think you were pushing too hard?

0:50:33.200 --> 0:50:33.399
<v Speaker 3>No?

0:50:34.400 --> 0:50:38.040
<v Speaker 5>Because I think that the leadership there will argue back

0:50:38.080 --> 0:50:40.960
<v Speaker 5>with me any day of the week. I don't think

0:50:41.000 --> 0:50:43.240
<v Speaker 5>that they feel that they have to ford.

0:50:44.320 --> 0:50:47.400
<v Speaker 4>Do you drop by at sort of Saturday night at

0:50:47.440 --> 0:50:48.880
<v Speaker 4>ten pm to see if people are working.

0:50:49.960 --> 0:50:55.880
<v Speaker 5>I tend to text people and ask questions and like

0:50:56.000 --> 0:50:58.719
<v Speaker 5>I'll read something and say, hey, are these people doing this?

0:50:59.280 --> 0:51:03.239
<v Speaker 5>And if they can answer me in reasonable terms, I

0:51:03.280 --> 0:51:06.479
<v Speaker 5>actually then say great, they're already watching the competition, They're

0:51:06.520 --> 0:51:09.759
<v Speaker 5>watching the literature, they're watching the science. I don't need

0:51:09.800 --> 0:51:12.040
<v Speaker 5>to push hard. If they are already ahead of it,

0:51:12.080 --> 0:51:15.120
<v Speaker 5>then me I can answer my question. I'll say thoughtfully,

0:51:15.440 --> 0:51:19.520
<v Speaker 5>not always completely accurately, you're thinking about it on their own.

0:51:19.560 --> 0:51:22.560
<v Speaker 3>I don't need to push. One last question I wanted

0:51:22.600 --> 0:51:25.319
<v Speaker 3>to ask you, do you have the most interesting job

0:51:25.320 --> 0:51:26.040
<v Speaker 3>in America?

0:51:26.200 --> 0:51:28.560
<v Speaker 5>I believe that it's the most interesting job, which I

0:51:28.560 --> 0:51:29.680
<v Speaker 5>won't give up for anything.

0:51:30.680 --> 0:51:33.560
<v Speaker 3>It also sounds like you're enjoying yourself.

0:51:34.120 --> 0:51:37.440
<v Speaker 5>I enjoy it as long as look my role and

0:51:37.520 --> 0:51:41.560
<v Speaker 5>goal should be to make the enterprise thrive. As long

0:51:41.600 --> 0:51:44.880
<v Speaker 5>as than making the enterprise thrive, and are clients delighted?

0:51:46.480 --> 0:51:47.000
<v Speaker 3>I love it.

0:51:47.640 --> 0:51:49.000
<v Speaker 5>If I don't, somebody else.

0:51:48.840 --> 0:51:53.040
<v Speaker 3>Should do it. Harvin, this has been so much fun.

0:51:53.239 --> 0:51:57.320
<v Speaker 4>Thank you so much taking the time and a fascinating,

0:51:57.760 --> 0:52:00.200
<v Speaker 4>completely fascinating conversation. I wish I was one of those

0:52:00.239 --> 0:52:02.120
<v Speaker 4>people who could help you out with quantum, but I'm

0:52:02.160 --> 0:52:03.399
<v Speaker 4>afraid I'm not.

0:52:03.920 --> 0:52:07.480
<v Speaker 3>In a few years. Good Thank you so much.

0:52:17.520 --> 0:52:21.240
<v Speaker 4>Smart Talks with IBM is produced by Matt Ramano, Amy Gains, McQuaid,

0:52:21.719 --> 0:52:25.880
<v Speaker 4>Trina Menino, and Jake Harper. Mastering by Sarah Bugerer, Music

0:52:25.920 --> 0:52:31.720
<v Speaker 4>by Gramoscope, Strategy by Tatiana Lieberman, Cassidy Meyer and Sophia Derlong.

0:52:32.440 --> 0:52:35.040
<v Speaker 4>Smart Talks with IBM is a production of Pushkin Industries

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<v Speaker 4>and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,

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<v Speaker 4>listen on the iHeartRadio app, Apple Podcasts, or wherever you

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<v Speaker 4>listen to podcasts. I'm Malcolm Glawe. This is a paid

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0:52:52.600 --> 0:52:56.759
<v Speaker 4>represent IBM's positions, strategies, or opinions.

0:53:00.400 --> 0:53:03.359
<v Speaker 2>The take UNT