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Speaker 1: Hello, Hello, I'm Malcolm Gladwell, and you're listening to Smart

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Speaker 1: Talks with IBM. This season, we've been bringing you stories

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Speaker 1: of how IBM works with its clients to solve complex problems,

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Speaker 1: like helping Loreel reimagine how scientists approach cosmetic formulation, or

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Speaker 1: enabling Scuderia FERRARIHP to connect with fans in new ways.

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Speaker 1: But in this episode, we're going to zoom out and

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Speaker 1: look at the bigger picture. Earlier this month, I had

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Speaker 1: the chance to meet the person who's shaping IBM's future.

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Speaker 1: It's CEO and Chairman Arvind Krishna. We sat down in

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Speaker 1: front of an intimate live audience at IBM's New York

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Speaker 1: City office and talked about his uncanny ability to anticipate

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Speaker 1: where technology is heading, the future of AI, and his

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Speaker 1: passion for quantum computing, which he says is as revolutionary

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Speaker 1: as a semiconductor. Thank you everyone, Thank you Arvin. You're

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Speaker 1: a difficult man to schedule for one of these things,

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Speaker 1: so we're enormously pleased that you could join us. Start

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Speaker 1: with a. I have all these cousins, two cousins who

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Speaker 1: work for IBM their entire career. I would ask them

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Speaker 1: what does IBM do? And they would always give me different, confusing,

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Speaker 1: complicated answers. What's your answer, what's your simple answer to

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Speaker 1: that question.

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Speaker 2: IBM's role is to help our clients improve their business

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Speaker 2: by deploying technology. That means you're not ever gated to

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Speaker 2: one product. It is what makes sense at that time,

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Speaker 2: but it is about improving their business, not just giving

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Speaker 2: them a commodity. Then to go to the next layer,

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Speaker 2: I would say we help them through a mixture of

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Speaker 2: hybrid cloud and artificial intelligence and a taste of quantum

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Speaker 2: coming down the road is kind of where I would

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Speaker 2: take it. That's that's what IBM is.

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Speaker 1: So you are technology agnostic in some sense.

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Speaker 2: I'm product agnostic, productnoy I'm not technology agnostic.

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Speaker 1: Yes, But if I twenty five years from now, IBM

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Speaker 1: could be doing things that would be unrecognizable to contemporary IBM,

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Speaker 1: it is completely possible.

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Speaker 2: Yeah, it could be there in twenty five years from now.

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Speaker 2: The only software IBM does his open source. It could

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Speaker 2: be the only computing you do is quantum computers. And

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Speaker 2: if I add those two people today say that's not

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Speaker 2: the IEM of today.

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Speaker 1: Is it even simpler to say you just IBM solves

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Speaker 1: problems at the highest technical level.

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Speaker 2: If you say highest technical level, yesh, Like the guy

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Speaker 2: who invented the bar code. He was solving a problem

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Speaker 2: retailers wanted to scale. Many of you wy not know

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Speaker 2: it was an IBM or who invented the bar code,

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Speaker 2: by the way, not somebody who was a PhD. Not

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Speaker 2: somebody who was a deep researcher. I think it was

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Speaker 2: actually a field engineer. Oh really yeah, And lasers were out,

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Speaker 2: and you could use lasers to scan things, but they

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Speaker 2: could be upside down, they could be muddy, they could

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Speaker 2: be partly scraped off. And he came up with the

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Speaker 2: idea of the bar code and that changed inventory management forever.

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Speaker 1: But the world needs to know that IBM invented the Parker.

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Speaker 1: You guys should do a better job. I'm deciding that.

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Speaker 2: I am sure our CMO will listen to this podcast

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Speaker 2: and we'll get that idea.

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Speaker 1: Tell me you started at Thomas Watson Research Center. What

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Speaker 1: were you doing when you first started it, IBM.

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Speaker 2: I started in nineteen ninety and that was the era

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Speaker 2: in which computers and networking but beginning to converge, And

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Speaker 2: for the first five years I was actually building networks

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Speaker 2: so let's remember this was pre laptops. Laptops came in

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Speaker 2: ninety two or ninety three, but it was clear to

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Speaker 2: us that they were going to come supportable computing, and

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Speaker 2: I spent my first five years building what today you

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Speaker 2: would call Wi Fi. We used to have these debates

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Speaker 2: can be builded? It's got to be small enough. I mean, like,

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Speaker 2: it can't be more than one hundred grams was kind

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Speaker 2: of our thought, because if it's more than that, you're

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Speaker 2: on a three thousand grand laptop. Wold anybody put this on?

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Speaker 2: And the debate used to be, why would anybody want

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Speaker 2: to walk around untethered? Won't you want to attach a

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Speaker 2: big thick cable into it and sit down? Because that

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Speaker 2: was the thought, that's how terminals worked. And I spent

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Speaker 2: five years having a lot of fun, building many iterations

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Speaker 2: of those and making progress on that.

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Speaker 1: If I had a conversation with your nineteen ninety self

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Speaker 1: about what the next thirty years were going to look like,

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Speaker 1: is it possible to reconstruct what you were What were

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Speaker 1: your predictions at that age about where the company, where

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Speaker 1: the industry was going.

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Speaker 2: It was more about where technology was going to go,

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Speaker 2: I would say, than where industry would go. I would

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Speaker 2: have told you that networking and computers would fuse in

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Speaker 2: nineteen ninety that was a weird thought that some researchers held.

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Speaker 2: By the late nineties, that was obvious that it became

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Speaker 2: the Internet. I would have told you that I believe

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Speaker 2: that video streaming will be the prime way people will

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Speaker 2: consume video. You would have said that in nineteen ninety. Absolutely,

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Speaker 2: Now that didn't take five years. That took twenty. But

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Speaker 2: it happened because you could do it technically, except it

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Speaker 2: just too expensive and too cumbersome. And if you've been

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Speaker 2: in technology, like in nineteen eighty five, I would have

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Speaker 2: told you the Internet is old because when I went

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Speaker 2: to grad school, every one of us had a those days,

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Speaker 2: an Apple, Mac or Lisa on our desks. They're all

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Speaker 2: connected by a network. We're happily sending email to people

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Speaker 2: all around the country. We were doing file transfer. So okay,

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Speaker 2: you had to be a little bit more aware of

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Speaker 2: the technology. And it didn't have a browser. That took

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Speaker 2: ten years to get the browser that took five years

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Speaker 2: to be a business. But when you see the speed

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Speaker 2: and the pace of technology in usually ten or fifteen years,

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Speaker 2: the cost point and the consumerization is at a scale

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Speaker 2: that you couldn't imagine ten years ago until you've seen

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Speaker 2: a few o those sites.

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Speaker 1: Wait, did you make the leap to Sorry, this is fascinating,

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Speaker 1: I'm curious, but how far did you take? That's a

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Speaker 1: really fundamental thing you have gotten right in nineteen ninety

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Speaker 1: I think.

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Speaker 2: You we were pretty convinced that what we used to

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Speaker 2: think of as linear television or broadcast would become digitized.

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Speaker 2: That was a given two with cable already the preponderance

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Speaker 2: of how people got it that if you put packet

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Speaker 2: television over cable, then that becomes the way it will go.

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Speaker 2: I fundamentally believed, actually way back eighty seven, that on

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Speaker 2: demand movies would become the way people would consume movies.

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Speaker 2: So those were all things that I could have predicted.

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Speaker 2: Nine didn't personally work on all those. I mean, after networking,

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Speaker 2: I moved on to doing other things. But those were

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Speaker 2: easy to predict.

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Speaker 1: If you had a conversation in those years with someone

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Speaker 1: in the television industry and you gave them those predictions,

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Speaker 1: did they see it? Were they convinced of this?

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Speaker 2: I'm actually going to take it back to wireless networking.

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Speaker 2: I think One of the reasons I do what I

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Speaker 2: do today, which is at the intersection of business and technology,

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Speaker 2: is because of what I saw happened with Wi Fi.

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Speaker 2: So you build these wireless networks and then you say, hey,

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Speaker 2: the market's going to be millions, tens of millions, billions

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Speaker 2: of users, and the business looks at it and says,

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Speaker 2: we think the market is confined to warehouse workers doing inventory.

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Speaker 2: You can look at them and say, why not people

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Speaker 2: in their homes because they could imagine outside how people

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Speaker 2: bought things at that time. And so I became convinced

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Speaker 2: that I can't just help invent it. I got to

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Speaker 2: think about, now, how do you market it? To whom

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Speaker 2: do you market it? What are their routs? How do

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Speaker 2: you make it easy enough? And that was probably I mean,

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Speaker 2: I'm making it simple now. That was probably a five

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Speaker 2: to ten year evolution of myself in those days.

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Speaker 1: You know what this reminds me of when the telephone

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Speaker 1: is invented in the eighteen seventies, It doesn't take off

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Speaker 1: for forty years because the people running a telephone business

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Speaker 1: they didn't and they didn't want women using it because

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Speaker 1: they were worried that women would gossip with their friends.

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Speaker 1: They didn't understand that that's actually what telephone is, right,

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Speaker 1: it's an exact parallel.

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Speaker 2: Yes it is, you see it again and again.

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Speaker 1: What is the source of that blindness? So there's a gap,

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Speaker 1: in other words, between the invention, the technological achievement, and

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Speaker 1: the social understanding of the technology. Why is there such

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Speaker 1: a gap?

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Speaker 2: I think that the gap is fundamental and rooted in

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Speaker 2: a lot of academic disciplines. So even channeling some of

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Speaker 2: your work, though you don't intend it to be used

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Speaker 2: that way, you can say a lot of things that

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Speaker 2: data driven. If it is data driven, then by definition,

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Speaker 2: you're looking at history. If you're looking at history, that

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Speaker 2: means you're looking at exact buying patterns. If you look

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Speaker 2: at existing buying patterns, you forget. All of those who

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Speaker 2: have created massive value in time have all created markets,

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Speaker 2: meaning they've all created new markets. And I think that

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Speaker 2: is why the world is fascinated with people like Steve Jobs,

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Speaker 2: for example, he imagined a market that didn't exist. So

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Speaker 2: I think that is the gap. And then if you

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Speaker 2: can get the technology the business acumen Scaler company and

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Speaker 2: that imagination of making the market is how you create

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Speaker 2: I think massive value. You got to get all three

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Speaker 2: pieces going.

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Speaker 1: It's not enough. In other words, you were thinking, it's

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Speaker 1: not enough to invent something new, I need to make

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Speaker 1: a business case for it simultaneously, and that that's what

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Speaker 1: gets you thinking along the path that leads you to

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Speaker 1: this job.

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Speaker 2: Oh yeah, I'll tell you if you had met or

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Speaker 2: when the nineteen ninety four and you had talked about

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Speaker 2: the stock market or about a balance sheet, or looked

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Speaker 2: at you like, Okay, I got though those words are,

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Speaker 2: I can parse them. I have no I what they are.

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Speaker 2: I have no intuition on what they are. I couldn't

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Speaker 2: tell you why it's relevant or why it's not. But

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Speaker 2: then you began to think, Okay, why do companies get

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Speaker 2: higher values? Okay, that's the stock What does that capture

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Speaker 2: if I have to spend working capital and that's the

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Speaker 2: balance sheet? Well, so you learn. I mean, I figure,

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Speaker 2: I'm willing to learn. I'm willing to read either. The

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Speaker 2: best way I read is to go to balance sheets.

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Speaker 2: Here you can read the book. It's pretty dawn dry.

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Speaker 2: Much easier to go talk to a financial expert who's

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Speaker 2: around the corner. And people are if you're curious about

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Speaker 2: what they do, they're really happy to share their expertise,

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Speaker 2: and over time you learn more and more and they

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Speaker 2: actually become part of your network within the company. And

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Speaker 2: that's how you can both learn and evolve yourself and

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Speaker 2: actually gain the extra skills you nere.

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Speaker 1: To be a successful business leader. Do you have to

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Speaker 1: unlearn or deviate from some of the things that made

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Speaker 1: you a successful science.

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Speaker 2: I actually believe the exactly opposite. But use what you're

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Speaker 2: really good at as a foundation, but don't make it

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Speaker 2: the only thing you use. So then how do you

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Speaker 2: add the other skills? And there's many ways. You can

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Speaker 2: have people that you trust who help you out those skills.

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Speaker 2: You can gain some intuition, maybe not the depth of expertise.

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Speaker 2: I want to be deeper on certain areas of electrical

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Speaker 2: engineering than I'm ever going to be, let's say, in

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Speaker 2: finance or marketing. But I want to be curious about

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Speaker 2: those I don't want to dismiss them. So you build

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Speaker 2: on your skills, and then you have to say, but

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Speaker 2: I need a complete and holistic view, So I'm going

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Speaker 2: to be a little deep, not very deep, in all

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Speaker 2: of those. And you've also got to learn to trust

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Speaker 2: your intuition a little bit.

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Speaker 1: Yeah, but I forgot a question that I wanted to

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Speaker 1: ask about about the predictions of nineteen ninety arvand what

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Speaker 1: did you get wrong?

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Speaker 2: All lots of things. I think that people were thinking

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Speaker 2: that but in those days, and it started my phrase,

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Speaker 2: but I'll come back to it. I think most people

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Speaker 2: thought that the communication companies would turn out to be

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Speaker 2: the winners of how networking got carried. If you all

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Speaker 2: think through the nineties of the investments that were being

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Speaker 2: done by let's not take the names of all of

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Speaker 2: the telecom carriers. Didn't turn out to be the case. Actually,

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Speaker 2: I think that's the business model case. The reason is

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Speaker 2: they all had in their heads that you can charge

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Speaker 2: people by the minute.

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Speaker 1: Because they had been doing that already, because.

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Speaker 2: They'd been doing that for one hundred years. Yeah, And

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Speaker 2: in the end, the winners the networking were those who

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Speaker 2: said flat price thirty bucks a month or fifty bucks

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Speaker 2: a month or whatever, and that was just too much

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Speaker 2: of a leap for them.

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Speaker 1: I think it's as simples, that is the most parsimonious

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Speaker 1: explanation for why you think they failed.

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Speaker 2: No, there were a couple of other more technical things.

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Speaker 2: One was written by somebody who was actually inside one

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Speaker 2: of these outcom companies, and he leveled his article the

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Speaker 2: Rise of the Stupid Network. So telephone people believe that

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Speaker 2: the network should be really smart. The end device is dumb.

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Speaker 2: If you think about the telephone, telephone is dumb. It

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Speaker 2: doesn't actually do anything. It's just about the relays. And

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Speaker 2: the network is smart. It routes you, it figures out

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Speaker 2: where to send it, it does echocaculation backwards. And the

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Speaker 2: current Internet is completely dumb with the inside. It just

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Speaker 2: takes the bits and shoves them out the other end.

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Speaker 2: All the intelligence is the computer at the end. That's

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Speaker 2: probably a bit more of a found explanation, but business

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Speaker 2: model didn't help them either.

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Speaker 1: Yeah. Wait, did nineteen ninety r of End think that

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Speaker 1: the network should be dumb or smart?

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Speaker 2: I'm not sure I thought about it deeply. But everything

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Speaker 2: I worked on the network was dumb. The network movements.

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Speaker 2: That's all I did. Yeah, because even I in those

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Speaker 2: days understood I couldn't imagine all the applications. So if

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Speaker 2: all you do is voice, maybe the network can be smart.

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Speaker 2: But if you're doing all those other things, how could

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Speaker 2: the network possibly know all those things that be smart

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Speaker 2: for it?

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Speaker 1: Yeah, so you've been COO for five years.

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Speaker 2: Five years.

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Speaker 1: Wait, so in your five year increment, what was your

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Speaker 1: most misunderstood decision where you ended up being right but

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Speaker 1: everyone thought you were crazy.

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Speaker 2: Twenty and eighteen, I proposed to our boat that we

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Speaker 2: should buy a company called rad Hat. IBM does proprietary,

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Speaker 2: but that was open source. The stock fell fifteen percent

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Speaker 2: of the day we announced it, and today most people

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Speaker 2: will turn around and say, this is the most successful

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Speaker 2: acquisition that IBM has done in all time, and probably

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Speaker 2: the most successful software acquisition in history. So it was

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Speaker 2: completely misunderstood because people didn't see that you actually did

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Speaker 2: need a platform that could make you agnostic across multiple

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Speaker 2: cloud platforms, across on premise environments. So you've got to

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Speaker 2: have a view of what it could be, and we

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Speaker 2: drove it to a place where I think today it

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Speaker 2: stands as the leader in its space.

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Speaker 1: So how did you come to believe this heretical notion?

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Speaker 2: So Cloud was happening, you could ask yourself the question,

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Speaker 2: should we spend a lot of capital and chase Cloud? Okay,

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Speaker 2: you're five years to be generous, maybe longer behind at

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Speaker 2: that point the two leaders, So you could spend maybe

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Speaker 2: ten billion a year and a lot of businesses tend

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Speaker 2: to do that. Okay, it's so important, it's going to

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Speaker 2: be half the market. I can't not. My view was

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Speaker 2: we'll always be five years behind. They're not dumb and

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Speaker 2: they're not slow. So if you're going to be there,

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Speaker 2: you're going to be best case, a distant third, worst

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Speaker 2: case maybe a fourth or a fifth. Because there's Chinese

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Speaker 2: also in the mix, why would you do that instead?

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Speaker 2: Is there a different space you can occupy instead of

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Speaker 2: competing with them? Can you become their best partner, in

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Speaker 2: which case you write their success. If I want to

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Speaker 2: be the best partner, then what are the set of

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Speaker 2: technologies that would be useful so you can flip The

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Speaker 2: problem is how I thought about it.

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Speaker 1: How hard was it to convince people needed convincing before

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Speaker 1: that acquisition.

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Speaker 2: Probably six to nine months of breaking my head with

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Speaker 2: no success, and then six months of building the momentum

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Speaker 2: once a couple of people began to see it.

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Speaker 1: Yeah, you're very persistent, Oh yes, very Would you describe

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Speaker 1: that as you defining trade?

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Speaker 2: I am very persistent and I'm very patient. I'm also

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Speaker 2: probably very impatient, but I'm not a yeller and screamer.

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Speaker 2: I don't rant and rave. But as I say, if

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Speaker 2: I think we're going to do something, I can be

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Speaker 2: remarkably stubborn about it. We will do it.

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Speaker 1: If I got your family, put them up on stage

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Speaker 1: and asked them this exact question, is this how they

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Speaker 1: would answer?

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Speaker 2: As well, they will tell you I'm very stubborn. They

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Speaker 2: might not agree that I don't try and drave.

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Speaker 1: Well, you know, one of the principal observations of psychology

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Speaker 1: is that our home self and our work self are uncorrelated.

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Speaker 1: Once you know that, you know everything. Wait, I'm curious

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Speaker 1: one last question about that. How long does it take

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Speaker 1: for you to be vindicated with red Hat?

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Speaker 2: Probably took five maybe four years, I think by twenty

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Speaker 2: twenty three. So twenty eighteen we announced it, we took

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Speaker 2: the big start crop, it took a year to close

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Speaker 2: twenty nineteen, so if our count not that I'm counting

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Speaker 2: that much, but July ninth, twenty nineteen as the day

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Speaker 2: that we got all the approvals. Took another a few

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Speaker 2: weeks to actually transfer them. But from there, probably twenty

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Speaker 2: twenty three, the world woke up and said, hey, you

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Speaker 2: guys deserve credit for this was actually a great move,

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Speaker 2: not a bad move.

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Speaker 1: Yeah, but this is it's interesting because this is a

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Speaker 1: real gamble. If it doesn't work, you're not sitting in

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Speaker 1: this chair right now. Right.

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Speaker 2: Oh, for sure. There were two steps. One if it

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Speaker 2: was obviously not going to work, I wouldn't have been selected.

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Speaker 2: And two if it hadn't worked after that, that's why

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Speaker 2: CEOs can be short lived.

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Speaker 1: Can I ask you, Sif a personal question? How much

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Speaker 1: sleep did you lose over this.

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Speaker 2: Once we had made the decision? None?

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Speaker 1: Can you give me pointers on how you do this?

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Speaker 1: Because I wake up at two am every morning and

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Speaker 1: I over much more trivial things than this.

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Speaker 2: Once a week, I'll probably wake up at two or

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Speaker 2: three in the morning. I acknowledge it because I wake

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Speaker 2: up and my brain is running, and once it's running,

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Speaker 2: I don't even try to go back to sleep. I mean, okay,

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Speaker 2: get up and do work and make yourself productive. You're

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Speaker 2: gonna be tired before in the afternoon. That's fine, you'll

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Speaker 2: sleep well that night. I have actually learned a long

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Speaker 2: time back. You can't do it across. You can't do

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Speaker 2: it early morning, through the day and late at night.

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Speaker 2: So an hour before I think I want to go

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Speaker 2: to bed, I will actually change what I'm doing, meaning

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Speaker 2: I will start reading something interesting to me but completely

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Speaker 2: outside the scope of work. I may read a biography,

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Speaker 2: I might read somebody who's spontificating on demographics and population.

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Speaker 2: But I won't read it on leadership because that's too close.

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Speaker 2: Now twenty years ago I might have that would have

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Speaker 2: been different. I won't read it on deep signs because

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Speaker 2: that's too close to what we do for a living.

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Speaker 2: So it's got to be outside the things that will

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Speaker 2: make my brain churn about work. But it's got to

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Speaker 2: be something that is dense enough to occupy your brain,

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Speaker 2: so it shift gears.

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Speaker 1: I want to do. It's just for a moment. The

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Speaker 1: red hat thing. Was there someone or is there someone

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Speaker 1: who you went to and explained the logic of this

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Speaker 1: and they saw the logic of this, and that made

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Speaker 1: a big difference to you.

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Speaker 2: Getting their support made a big difference. You'd be surprised.

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Speaker 2: I'm remarkably open inside. I mean, when I have are

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Speaker 2: there probably a half dozen to a dozen people inside

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Speaker 2: that I'll talk to and I'll be completely open about, Hey,

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Speaker 2: this is what I'm thinking. I don't know. Here are

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Speaker 2: the risks. I'm open about those Also, it's not just

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Speaker 2: the benefits. I think the other risks, but I think

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Speaker 2: the benefits outweigh the risks. I talk about that to

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Speaker 2: people all the time. So whether for example, I mean

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Speaker 2: i'll take names. I think our current chro O Nicol

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Speaker 2: who introduced us, she has been in that loop since

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Speaker 2: at least twenty fifteen. For me, if I look at

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Speaker 2: our CFO Jim Cavanaugh, he's been in that loop probably

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Speaker 2: since twenty thirteen. And IBM's will probably wonder, what the

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Speaker 2: hell intersection do you guys have? It didn't when I

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Speaker 2: talked about learning finance. I will go to him and say, hey,

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Speaker 2: explain this to me. I don't understand why it's like this,

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Speaker 2: And to me it's okay, use a patient, you go learn.

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Speaker 2: If I think about many of the people in the

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Speaker 2: software business, they've been having these discussions with me for always.

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Speaker 2: I mean, now I'll acknowledge I can get probably impatient

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Speaker 2: and a servig, but it's meant to be a discussion.

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Speaker 2: I mean, like, let's have the discussion. If you have

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Speaker 2: a strong point of view, I got it. Nobody has

395
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Speaker 2: a will be perfectly correct, but I always look for

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Speaker 2: if you have a strong point of view. That means

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Speaker 2: it's from a different perspective than mine. So what do

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Speaker 2: I learn from that? Is the question which helps to

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Speaker 2: improve my point of view? That makes sense. I actually

400
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Speaker 2: think that each person should try to build a community

401
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Speaker 2: of one hundred people inside your enterprise and a hundred

402
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Speaker 2: outside that you can call up. I have no hesitation.

403
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Speaker 2: Somebody introduced me to a long time back, to a

404
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Speaker 2: CEO on the outside. I called them up all the

405
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Speaker 2: time and say, hey, do you have five minutes. I'm

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Speaker 2: just thinking about something. This way, the CEO of red

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Speaker 2: At who left IBM in twenty twenty one, we probably

408
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Speaker 2: talk every two or three months on a random topic.

409
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Speaker 2: Ony way, it becomes mutual. He'll asks me my opinion

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Speaker 2: on some things. Now, by the way, three or four

411
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Speaker 2: times he might do something different, but he wants my opinion.

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Speaker 2: Tour the other way around.

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Speaker 1: If I gave you my phone number, can I be

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Speaker 1: on that list? I would just be fascinating. I don't

415
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Speaker 1: know if I can help you, but I would be

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Speaker 1: really fine to get the call.

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Speaker 2: Sure you can. Do you think that we can ever

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Speaker 2: succeed unless people who influence opinions say things about us.

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Speaker 2: So you may not think deeply about maybe the physics

420
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Speaker 2: of quantum computing, but would you think deeply about why

421
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Speaker 2: and what moment may make it much more attractive to

422
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Speaker 2: a large audience. Sure you would. You'd be far better

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Speaker 2: as a thinker on that topic. And probably most of

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Speaker 2: the people.

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Speaker 1: I was thinking, you know, when you were making your

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Speaker 1: comments about your nineteen ninety self and streaming, that the

427
00:23:08,600 --> 00:23:11,919
Speaker 1: rational thing would have been for there have been a

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Speaker 1: reserved board seat for every television network from someone from

429
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Speaker 1: the world of technology, which I one hundred percent sure

430
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Speaker 1: they did not have that in nineteen ninety but they

431
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Speaker 1: they're board was probably composed of people like them. Let's

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Speaker 1: talk a little bit about technology now. There's so much,

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Speaker 1: so much of the changes going on right now are

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Speaker 1: accompanied by a great deal of hype. What are we overestimating?

435
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Speaker 1: What are we underestimating?

436
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Speaker 2: Okay, let's go back to ninety ninety five the Internet,

437
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Speaker 2: because I think that the current moment is very much

438
00:23:44,960 --> 00:23:47,240
Speaker 2: like the Internet moment. Actually, all the moments in the

439
00:23:47,240 --> 00:23:49,840
Speaker 2: middle were much smaller. I think mobile streaming or much

440
00:23:49,880 --> 00:23:52,919
Speaker 2: smaller Internet was the major moment. If you remember back

441
00:23:52,960 --> 00:23:54,960
Speaker 2: to ninety nine and two thousand people claimed there was

442
00:23:55,000 --> 00:23:57,800
Speaker 2: a lot of hype. Would we say that the Internet

443
00:23:57,800 --> 00:24:02,200
Speaker 2: of today has more than fulfilled all the expectations and more? Yes?

444
00:24:03,320 --> 00:24:06,840
Speaker 2: Along the way, did eight out of ten of the

445
00:24:06,880 --> 00:24:11,439
Speaker 2: companies that were invested in heavily go bankrupt? Yes, I

446
00:24:11,520 --> 00:24:15,040
Speaker 2: actually think of that as being the huge positive of

447
00:24:15,119 --> 00:24:20,360
Speaker 2: the United States capital system. That investment happened, eight out

448
00:24:20,359 --> 00:24:22,840
Speaker 2: of ten went broke. By the way, those acids didn't

449
00:24:22,880 --> 00:24:25,280
Speaker 2: go away. They got consumed at ten cents in the

450
00:24:25,320 --> 00:24:27,480
Speaker 2: dollar by somebody else who could then make a lot

451
00:24:27,480 --> 00:24:30,800
Speaker 2: of money. But the two out of ten, just take two,

452
00:24:31,440 --> 00:24:33,560
Speaker 2: it probably has paid for all the capitals. If we

453
00:24:33,840 --> 00:24:38,680
Speaker 2: just take Amazon and Alphabet aka Google, just those two

454
00:24:38,760 --> 00:24:41,480
Speaker 2: have probably paid for all the capital of that time.

455
00:24:42,119 --> 00:24:45,680
Speaker 2: So that's what's going to happen this time. There will

456
00:24:45,720 --> 00:24:49,040
Speaker 2: be a lot of tears, but in aggregate, there will

457
00:24:49,040 --> 00:24:50,879
Speaker 2: be a lot of success. And I think that's the

458
00:24:50,960 --> 00:24:55,000
Speaker 2: fundamental difference between the US model and almost all other countries.

459
00:24:55,600 --> 00:24:58,000
Speaker 2: On all other countries, they're desperate to keep all the

460
00:24:58,040 --> 00:25:01,879
Speaker 2: companies alive. So that means your dialute. But that's a

461
00:25:01,920 --> 00:25:05,760
Speaker 2: horrible thing. So to me, let the system works worked

462
00:25:05,840 --> 00:25:08,440
Speaker 2: really effectively, by the way, not just now, I mean

463
00:25:08,480 --> 00:25:12,119
Speaker 2: all the way back to railways and electrification, and you

464
00:25:12,200 --> 00:25:16,359
Speaker 2: mentioned telephone system. You can keep going on oil, I

465
00:25:16,359 --> 00:25:20,160
Speaker 2: mean consumer goods. It goes on and on. I think

466
00:25:20,160 --> 00:25:24,160
Speaker 2: this system is very effective. It deploys capital. Its census

467
00:25:24,200 --> 00:25:27,240
Speaker 2: is a big market is completely willing to over deploy

468
00:25:27,320 --> 00:25:30,320
Speaker 2: capital in the short term, not the long term. That

469
00:25:30,359 --> 00:25:34,240
Speaker 2: results in more competition. So it actually improves a rate

470
00:25:34,280 --> 00:25:37,600
Speaker 2: of innovation. That means what might have taken twenty years

471
00:25:37,680 --> 00:25:41,560
Speaker 2: takes five and the winners emerge exactly the same is

472
00:25:41,600 --> 00:25:44,679
Speaker 2: going to happen this time. Yeah, I saw that. I

473
00:25:44,680 --> 00:25:48,960
Speaker 2: grew up in Waterloo and BlackBerry curses from Waterloo. Yep.

474
00:25:49,160 --> 00:25:50,639
Speaker 1: Everyone used to work for BlackBerry.

475
00:25:50,800 --> 00:25:50,960
Speaker 2: Yep.

476
00:25:51,000 --> 00:25:54,239
Speaker 1: BlackBerry goes into its dive. And that's the best thing

477
00:25:54,240 --> 00:25:56,320
Speaker 1: that happened to Waterloo because it was not just capital

478
00:25:56,359 --> 00:25:56,840
Speaker 1: but talent.

479
00:25:57,119 --> 00:25:59,280
Speaker 2: Yep. Challenge is to many other companies.

480
00:25:59,280 --> 00:26:01,920
Speaker 1: It's all the smart people went on the next really

481
00:26:02,000 --> 00:26:05,800
Speaker 1: more interesting thing. And yeah, but wait, you haven't answered.

482
00:26:06,760 --> 00:26:09,680
Speaker 1: So what is your an idea that we are underestimating

483
00:26:09,680 --> 00:26:12,359
Speaker 1: at the moment that's in the current kind of suite

484
00:26:12,440 --> 00:26:13,480
Speaker 1: of innovations.

485
00:26:13,680 --> 00:26:16,359
Speaker 2: So I don't think AI is being underestimated because when

486
00:26:16,359 --> 00:26:17,840
Speaker 2: you look at the amount of capital and the amount

487
00:26:17,880 --> 00:26:21,520
Speaker 2: of things chasing it. I think it's incredible. I do

488
00:26:21,560 --> 00:26:24,040
Speaker 2: think that a lot of enterprises are deploying it in

489
00:26:24,080 --> 00:26:27,679
Speaker 2: the wrong place. They're running after shining experiments. There's a

490
00:26:27,760 --> 00:26:30,879
Speaker 2: lot of basic things you can do to use AI

491
00:26:30,960 --> 00:26:33,800
Speaker 2: to improve the business today. So that's really just my

492
00:26:33,920 --> 00:26:37,080
Speaker 2: one advice to them. Pick areas you can scale, don't

493
00:26:37,119 --> 00:26:41,000
Speaker 2: pick the shiny little toys on the side. Then.

494
00:26:41,119 --> 00:26:43,280
Speaker 1: I think, for example, there.

495
00:26:44,880 --> 00:26:50,240
Speaker 2: If anybody has more than ten percent of what they

496
00:26:50,280 --> 00:26:55,000
Speaker 2: had for customer service ten years ago, they're already five

497
00:26:55,080 --> 00:27:01,000
Speaker 2: years behind. If anybody is not using AI to make

498
00:27:01,119 --> 00:27:05,760
Speaker 2: their developers who write software thirty percent more productive today

499
00:27:06,119 --> 00:27:09,840
Speaker 2: with the goal of being seventy percent more productive, that's

500
00:27:09,840 --> 00:27:11,800
Speaker 2: not to say you will need less, you'll just get

501
00:27:11,800 --> 00:27:16,200
Speaker 2: more software done. Then they're not. And I would turn

502
00:27:16,200 --> 00:27:19,000
Speaker 2: around and tell you I think only maybe five percent

503
00:27:19,080 --> 00:27:24,320
Speaker 2: of the enterprises on both those metrics today Yeah wow Yeah.

504
00:27:24,359 --> 00:27:27,159
Speaker 2: And the one that is completely underestimated. I kind of

505
00:27:27,200 --> 00:27:31,840
Speaker 2: put it like this, quantum today is where GPUs and

506
00:27:31,920 --> 00:27:35,399
Speaker 2: AI war in twenty fifteen, and I bet you every

507
00:27:35,760 --> 00:27:38,960
Speaker 2: AI person is thinking and hoping. I wish I had

508
00:27:39,240 --> 00:27:42,200
Speaker 2: started doing more in twenty fifteen, as opposed to wait

509
00:27:42,280 --> 00:27:45,840
Speaker 2: until twenty twenty two. Quantum today is there, so it's

510
00:27:46,080 --> 00:27:48,879
Speaker 2: not good enough that you can get a big advantage,

511
00:27:49,000 --> 00:27:51,359
Speaker 2: But if you learn how to use it, then in

512
00:27:51,400 --> 00:27:54,359
Speaker 2: five years you'll be ready to exploit what comes.

513
00:27:54,520 --> 00:27:56,720
Speaker 1: Yeah, we're gonna get to quantum in a moment. But

514
00:27:56,720 --> 00:27:59,399
Speaker 1: I have a couple other AI questions. You know, I

515
00:28:00,080 --> 00:28:02,679
Speaker 1: as you know where. This conversation is part of this

516
00:28:02,760 --> 00:28:05,920
Speaker 1: thing that we do with IBM Smart Talks, and I've

517
00:28:05,920 --> 00:28:10,160
Speaker 1: been The last episode I did was on Kenya, which

518
00:28:10,160 --> 00:28:13,919
Speaker 1: has a massive deforestation problem, and they got together IBM

519
00:28:14,160 --> 00:28:18,160
Speaker 1: to call the NASA satellite data, ran it through an LLM,

520
00:28:18,400 --> 00:28:22,160
Speaker 1: and gave them this incredibly precise ten meter by ten

521
00:28:22,200 --> 00:28:25,520
Speaker 1: meter analysis of what trees the plant, where to plant

522
00:28:25,560 --> 00:28:28,920
Speaker 1: them exactly where the you know, an astonishing kind of

523
00:28:28,960 --> 00:28:32,399
Speaker 1: blueprint about how to fix their country ecologically. And it

524
00:28:32,440 --> 00:28:36,879
Speaker 1: made me think, when we analyze the potential of AI,

525
00:28:37,680 --> 00:28:39,960
Speaker 1: are we making a mistake by spending too much thinking

526
00:28:39,960 --> 00:28:42,880
Speaker 1: about the developed world when it's actually the developing world

527
00:28:43,160 --> 00:28:45,560
Speaker 1: where the greatest ROI for this is to me.

528
00:28:45,680 --> 00:28:49,200
Speaker 2: Look, software technologies are wonderful and the sense they can

529
00:28:49,240 --> 00:28:51,440
Speaker 2: scare and they can be an ad so you don't

530
00:28:51,440 --> 00:28:55,080
Speaker 2: have to do one or the other. You use defraortestation.

531
00:28:55,640 --> 00:28:59,360
Speaker 2: How about the use of pesticides and fertilizers. We always

532
00:28:59,400 --> 00:29:02,680
Speaker 2: use it. We tend to for irrigation. We tend to

533
00:29:02,680 --> 00:29:05,160
Speaker 2: just flood everything, as opposed to say, okay, only that

534
00:29:05,200 --> 00:29:07,320
Speaker 2: one needs it. You could do all those things to

535
00:29:07,320 --> 00:29:09,720
Speaker 2: get it ten times effectiveness, and that all would apply

536
00:29:09,880 --> 00:29:14,040
Speaker 2: to the developing world. How about remote healthcare or telehealth

537
00:29:14,160 --> 00:29:18,320
Speaker 2: using an AI agent. So the examples are numerous in

538
00:29:18,400 --> 00:29:22,040
Speaker 2: the developed world. I believe we are running out of people.

539
00:29:23,480 --> 00:29:26,480
Speaker 2: I know that nobody likes to hear it. Most of

540
00:29:26,720 --> 00:29:29,440
Speaker 2: the Far East is going to have half the number

541
00:29:29,440 --> 00:29:31,959
Speaker 2: of people, But twenty seventy competed today, that's not that

542
00:29:32,000 --> 00:29:35,000
Speaker 2: far away. If I look at Europe, birth rates are

543
00:29:35,040 --> 00:29:41,240
Speaker 2: far under sustaining or keeping population flat. The US, also,

544
00:29:41,240 --> 00:29:42,960
Speaker 2: depending on which number you want to look at, is

545
00:29:42,960 --> 00:29:46,640
Speaker 2: either one point six births per women or two or

546
00:29:46,640 --> 00:29:49,400
Speaker 2: two point one. Why are the three numbers? One point

547
00:29:49,440 --> 00:29:51,600
Speaker 2: six is to women who were born in the United States.

548
00:29:52,200 --> 00:29:55,720
Speaker 2: It becomes two point two. If you include immigrant women,

549
00:29:56,120 --> 00:29:58,320
Speaker 2: it becomes two point one. If you include children who

550
00:29:58,360 --> 00:30:02,640
Speaker 2: immigranting in to decide where the trend is obvious, this

551
00:30:02,720 --> 00:30:06,280
Speaker 2: is going down. So AI in the developed world is

552
00:30:06,320 --> 00:30:09,560
Speaker 2: going to be essential because to keep our current quality

553
00:30:09,600 --> 00:30:12,800
Speaker 2: of life, you need more work done or what's going

554
00:30:12,840 --> 00:30:14,479
Speaker 2: to do the work if they aren't people to do

555
00:30:14,520 --> 00:30:17,760
Speaker 2: the work. So the problems are different in the places.

556
00:30:17,840 --> 00:30:20,880
Speaker 1: Yeah, it gives you in the developing world, you get

557
00:30:20,920 --> 00:30:25,000
Speaker 1: access to a suite of technologies and things at a

558
00:30:25,120 --> 00:30:27,479
Speaker 1: price you could never been able to afford.

559
00:30:27,640 --> 00:30:28,000
Speaker 2: Correct.

560
00:30:28,280 --> 00:30:30,240
Speaker 1: That was my in talking to the Kenyon thing. It

561
00:30:30,280 --> 00:30:32,840
Speaker 1: was like the whole it's this. It's maybe one of

562
00:30:32,880 --> 00:30:37,080
Speaker 1: the largest ecological projects of its kind, A fifteen billion

563
00:30:37,120 --> 00:30:38,200
Speaker 1: trees they want to plan, and.

564
00:30:38,160 --> 00:30:40,040
Speaker 2: That is one country that might get it done because

565
00:30:40,080 --> 00:30:43,280
Speaker 2: they do take a lot of pride in their ecology

566
00:30:43,520 --> 00:30:46,840
Speaker 2: and the sort of returning to the land and giving back.

567
00:30:47,040 --> 00:30:52,400
Speaker 1: Yeah. Yeah, what's different about IBM's version of AI versus

568
00:30:52,400 --> 00:30:53,120
Speaker 1: some of your.

569
00:30:53,600 --> 00:30:55,479
Speaker 2: So we are not a consumer company, so we have

570
00:30:55,520 --> 00:30:59,480
Speaker 2: no focus on a B two C chat pot. And

571
00:30:59,520 --> 00:31:02,120
Speaker 2: the reason I say that is if you're making a

572
00:31:02,160 --> 00:31:04,960
Speaker 2: B two C chat bot, does it help you to

573
00:31:05,040 --> 00:31:08,600
Speaker 2: make it even bigger and more computationally inefficient? And the

574
00:31:08,600 --> 00:31:11,000
Speaker 2: short answer is yes, because you have a certain number

575
00:31:11,040 --> 00:31:13,520
Speaker 2: of users, and you kind of say, I kind of

576
00:31:13,520 --> 00:31:17,440
Speaker 2: say this jokingly. If I add finished to French capabilities,

577
00:31:17,680 --> 00:31:20,920
Speaker 2: I can probably add five million users. If I add

578
00:31:21,600 --> 00:31:23,520
Speaker 2: writing a high coup I might be able to add

579
00:31:23,560 --> 00:31:27,560
Speaker 2: another five million users. If I add writing an email

580
00:31:27,640 --> 00:31:29,680
Speaker 2: in the voice of Steinbeck, I can probably add another

581
00:31:29,720 --> 00:31:33,360
Speaker 2: five million users. Do all those things. If my goal

582
00:31:33,600 --> 00:31:37,000
Speaker 2: is to get help a company summarize the legal documents

583
00:31:37,000 --> 00:31:40,240
Speaker 2: in English, that can be a model that's one hundred

584
00:31:40,360 --> 00:31:45,080
Speaker 2: size as effective, probably higher quality. But I don't need

585
00:31:45,120 --> 00:31:48,880
Speaker 2: to go wide. So if you're focusing on the enterprise,

586
00:31:49,520 --> 00:31:52,080
Speaker 2: that actually takes away the focus of having to go

587
00:31:52,320 --> 00:31:55,840
Speaker 2: to extremely large models, which by definition are going to

588
00:31:55,880 --> 00:32:01,000
Speaker 2: be computationally expensive, power hungry, and demand and lots of data.

589
00:32:01,480 --> 00:32:03,479
Speaker 2: So I can turn ontell the enterprise you don't need

590
00:32:03,480 --> 00:32:06,160
Speaker 2: to worry about copyright issues, about all those because you

591
00:32:06,200 --> 00:32:09,440
Speaker 2: can train on a much smaller amount of data. And now,

592
00:32:09,480 --> 00:32:12,720
Speaker 2: by the way, turning it for you yourself is a weekend exercise,

593
00:32:12,960 --> 00:32:16,280
Speaker 2: It's not a six month on a big super computer

594
00:32:16,360 --> 00:32:19,320
Speaker 2: cluster somewhere out there. That's one big difference of what

595
00:32:19,440 --> 00:32:25,080
Speaker 2: we do. Second, we are very focused on helping those

596
00:32:25,160 --> 00:32:28,280
Speaker 2: problems that can give people immediate benefit where we have

597
00:32:28,360 --> 00:32:32,720
Speaker 2: domain knowledge. So our domain knowledge is around operations, is

598
00:32:32,760 --> 00:32:41,960
Speaker 2: around programming, encoding, is around customer service, is around customer experience, logistics, procurement.

599
00:32:42,400 --> 00:32:45,719
Speaker 2: Let's stage the areas where we have a lot of expertise,

600
00:32:46,400 --> 00:32:49,640
Speaker 2: and then three we kind of apply it to ourselves

601
00:32:50,360 --> 00:32:52,760
Speaker 2: and so we are not asking our clients to be

602
00:32:52,880 --> 00:32:55,920
Speaker 2: the first experiment on it. We say you can leverage

603
00:32:55,920 --> 00:32:59,000
Speaker 2: what we did. We're happy to bring out all our learnings,

604
00:32:59,000 --> 00:33:01,920
Speaker 2: including what needs to change in the process, because the

605
00:33:01,920 --> 00:33:04,880
Speaker 2: biggest change is not technology, is getting people to accept

606
00:33:05,600 --> 00:33:07,200
Speaker 2: that there's a different way to do things.

607
00:33:08,160 --> 00:33:12,600
Speaker 1: Other challenges to explaining what makes you different to potential customers.

608
00:33:12,640 --> 00:33:15,600
Speaker 2: For sure, the shiny object is always attractive. Well, I

609
00:33:15,640 --> 00:33:19,000
Speaker 2: can go and try chat GPT. Why don't you have

610
00:33:19,080 --> 00:33:20,080
Speaker 2: your GPT version?

611
00:33:21,640 --> 00:33:22,800
Speaker 1: Do you use chat ChiPT?

612
00:33:23,760 --> 00:33:24,560
Speaker 2: I have used it.

613
00:33:26,000 --> 00:33:28,000
Speaker 1: I asked you a question recently which I thought was

614
00:33:28,000 --> 00:33:33,160
Speaker 1: really simple, and it made up about ten people. Anyway,

615
00:33:33,200 --> 00:33:34,000
Speaker 1: I had a bad experience.

616
00:33:34,000 --> 00:33:37,280
Speaker 2: I actually think that that's the fundamental issue with all

617
00:33:37,600 --> 00:33:40,040
Speaker 2: llms as they get larger. Yeah, because you had to

618
00:33:40,080 --> 00:33:44,120
Speaker 2: ask what was the original insight that led to these

619
00:33:45,280 --> 00:33:49,479
Speaker 2: It was a reward function with intent. So if it

620
00:33:49,560 --> 00:33:53,560
Speaker 2: has learned by using a reward function, it's reward function

621
00:33:53,720 --> 00:33:57,680
Speaker 2: comes from giving an answer that satisfies you. So if

622
00:33:57,720 --> 00:33:59,800
Speaker 2: it thinks that if it makes up an answer that

623
00:33:59,840 --> 00:34:03,160
Speaker 2: will satisfy you, how will you stop it? Why do

624
00:34:03,240 --> 00:34:07,040
Speaker 2: we think this is different than the clever college kid

625
00:34:07,040 --> 00:34:09,040
Speaker 2: who doesn't know an answer? What a bullships the way

626
00:34:09,080 --> 00:34:11,799
Speaker 2: to an answer? Well, it's exactly the same.

627
00:34:11,800 --> 00:34:13,880
Speaker 1: It's like the example of clever Hands. You know that

628
00:34:13,960 --> 00:34:17,359
Speaker 1: story the horse that they thought could speak, and all

629
00:34:17,400 --> 00:34:20,239
Speaker 1: it was doing was pleasing it. It's master. Yes, it

630
00:34:20,320 --> 00:34:21,479
Speaker 1: is a little bit of clever hands.

631
00:34:21,600 --> 00:34:25,160
Speaker 2: Yeah, it's like dogs kind of imitating and looking. What

632
00:34:25,160 --> 00:34:25,600
Speaker 2: would you.

633
00:34:25,560 --> 00:34:31,160
Speaker 1: Identify as the most significant bottleneck in the development of AI?

634
00:34:31,480 --> 00:34:32,759
Speaker 1: What's slowing us down right now?

635
00:34:34,600 --> 00:34:39,120
Speaker 2: I am not convinced that LLMS is the way to

636
00:34:39,239 --> 00:34:44,440
Speaker 2: get much beyond where we'll get incremental improvements. But I,

637
00:34:44,920 --> 00:34:47,879
Speaker 2: for one, don't believe that LMS are going to get

638
00:34:47,920 --> 00:34:54,040
Speaker 2: us to super intelligence or AGI. So I'll park that

639
00:34:54,160 --> 00:34:56,640
Speaker 2: on the side and simply say, we have to find

640
00:34:56,680 --> 00:35:00,480
Speaker 2: a way to fuse knowledge. And how do you represent

641
00:35:00,640 --> 00:35:04,520
Speaker 2: knowledge as opposed to have to statistically rediscover it each time?

642
00:35:04,560 --> 00:35:07,800
Speaker 2: I ask a question, and how do we fuse knowledge

643
00:35:07,840 --> 00:35:13,840
Speaker 2: with LLM. Maybe then we'll get to leafs and beyond

644
00:35:14,000 --> 00:35:18,799
Speaker 2: beyond today on LMS alone, my view is, I think

645
00:35:18,840 --> 00:35:22,600
Speaker 2: we can get a thousand x efficiency in power and

646
00:35:22,800 --> 00:35:26,520
Speaker 2: cost and compute from today. So if you make something

647
00:35:26,560 --> 00:35:29,560
Speaker 2: a thousand times cheaper, would people use a lot more

648
00:35:29,600 --> 00:35:29,879
Speaker 2: of it?

649
00:35:31,040 --> 00:35:31,400
Speaker 1: Yes?

650
00:35:32,280 --> 00:35:35,600
Speaker 2: And I think those answers lie as is usually in compute.

651
00:35:35,800 --> 00:35:40,640
Speaker 2: So advances in semiconductors, advances in software, and advances in

652
00:35:40,680 --> 00:35:43,680
Speaker 2: agorthic techniques all three. But how come we're not working

653
00:35:43,680 --> 00:35:45,120
Speaker 2: in any of those three. We're just taking the current

654
00:35:45,160 --> 00:35:49,880
Speaker 2: sevic conductor and going more. We're taking the current algorithmic

655
00:35:49,920 --> 00:35:52,040
Speaker 2: techniques and not really trying to invent new ones. So

656
00:35:52,120 --> 00:35:55,000
Speaker 2: I think those are all happen less than five years.

657
00:35:56,760 --> 00:35:59,840
Speaker 1: But why you say there is a we're in a

658
00:36:00,239 --> 00:36:05,279
Speaker 1: where people are not pursuing the the optimal strategy for

659
00:36:05,320 --> 00:36:06,719
Speaker 1: exploiting this technology.

660
00:36:06,920 --> 00:36:11,640
Speaker 2: Why Because when you see a few people running really

661
00:36:11,680 --> 00:36:14,719
Speaker 2: hard and they're willing to invest any amount of money,

662
00:36:14,760 --> 00:36:18,799
Speaker 2: so efficiency is not the focus. People feel if you

663
00:36:18,840 --> 00:36:20,440
Speaker 2: don't do the same, you'll get left behind.

664
00:36:21,680 --> 00:36:24,439
Speaker 1: So is this a case where there's too much money?

665
00:36:24,480 --> 00:36:26,200
Speaker 2: Humans have never had for more right ever?

666
00:36:26,760 --> 00:36:32,040
Speaker 1: Yeah, but this is this a consequence of overinvestment in

667
00:36:32,080 --> 00:36:33,120
Speaker 1: the in the field.

668
00:36:33,200 --> 00:36:35,520
Speaker 2: Going back to my internet allology, if two out of

669
00:36:35,560 --> 00:36:38,840
Speaker 2: ten are going to succeed, yeah, how do you guarantee

670
00:36:38,920 --> 00:36:40,480
Speaker 2: or how do you improve the odds that you are

671
00:36:40,520 --> 00:36:43,600
Speaker 2: one of those two? So if you pause to say,

672
00:36:43,600 --> 00:36:45,200
Speaker 2: I want to make a more efficient, that's not the

673
00:36:45,200 --> 00:36:48,200
Speaker 2: way to win. So first you win, then you become efficient.

674
00:36:48,520 --> 00:36:53,080
Speaker 1: Yeah, let's talk about what is I was told your

675
00:36:53,120 --> 00:36:58,360
Speaker 1: favorite topic it's quantum? It is what? Boy even go

676
00:36:58,360 --> 00:37:00,480
Speaker 1: any further? Why is quantum your favorite topic?

677
00:37:01,719 --> 00:37:04,640
Speaker 2: We've only had two kinds of compute in the history,

678
00:37:05,480 --> 00:37:08,880
Speaker 2: so nineteen forty five was to use that year for

679
00:37:08,960 --> 00:37:11,719
Speaker 2: anyac all the way till twenty twenty we had one

680
00:37:11,760 --> 00:37:13,759
Speaker 2: kind of compute, classical what today you would call a

681
00:37:13,760 --> 00:37:19,920
Speaker 2: classical computer. Then GPUs and AI came around, so you

682
00:37:19,920 --> 00:37:23,200
Speaker 2: would say the intuition there is you went from sort

683
00:37:23,200 --> 00:37:26,400
Speaker 2: of bits, which is algebra or high school algebra, to

684
00:37:26,560 --> 00:37:30,680
Speaker 2: including neurons, which is captured in linear algebra. But that

685
00:37:30,920 --> 00:37:33,640
Speaker 2: gives you a different kind but it can do problems

686
00:37:33,680 --> 00:37:36,480
Speaker 2: that are really hard to do. I don't say impossible,

687
00:37:36,560 --> 00:37:41,000
Speaker 2: just hard to do on normal computers. Quantum adds a

688
00:37:41,040 --> 00:37:46,800
Speaker 2: third kind of math. Yes, the physics properties which really

689
00:37:47,560 --> 00:37:50,759
Speaker 2: get people energized and the imagination going. And we use

690
00:37:50,840 --> 00:37:55,200
Speaker 2: all these words about entanglement and silver position, but maybe

691
00:37:55,280 --> 00:37:58,080
Speaker 2: because I'm a better of a math guy. The real

692
00:37:58,160 --> 00:38:00,719
Speaker 2: thing is it does a third kind of math to

693
00:38:00,760 --> 00:38:03,600
Speaker 2: make it really simple, a third kind of math that

694
00:38:03,719 --> 00:38:07,600
Speaker 2: comes from the field of abstract algebra. It does the

695
00:38:07,640 --> 00:38:11,360
Speaker 2: math you can use Hamiltonians for those who like physics,

696
00:38:11,920 --> 00:38:14,000
Speaker 2: or you can use the word Lee algebras for those

697
00:38:14,040 --> 00:38:18,040
Speaker 2: who like abstract mathematics. If you can do a third

698
00:38:18,120 --> 00:38:21,120
Speaker 2: kind of math, which algorithms are suited to that third

699
00:38:21,160 --> 00:38:24,279
Speaker 2: kind of math. So it excites me because we can

700
00:38:24,360 --> 00:38:27,759
Speaker 2: now approach algorithms that you just could never do on

701
00:38:27,840 --> 00:38:30,560
Speaker 2: the other two it's impossible. Now it's different than AI.

702
00:38:30,680 --> 00:38:34,080
Speaker 2: It's not data intensive. It's compute intensive. So we kind

703
00:38:34,080 --> 00:38:36,800
Speaker 2: of had compute and supercomputers. Then we went to data,

704
00:38:36,880 --> 00:38:39,000
Speaker 2: which is AI. And now if you say there's another

705
00:38:39,040 --> 00:38:43,000
Speaker 2: class of problems that require lots of compute, that's quantum.

706
00:38:43,040 --> 00:38:45,640
Speaker 1: A couple months ago was at the to watch some

707
00:38:45,719 --> 00:38:47,759
Speaker 1: research center and they have you know, on the ground floor,

708
00:38:47,800 --> 00:38:51,960
Speaker 1: they have those behind the glass. There's incredibly exciting looking machines.

709
00:38:52,560 --> 00:38:55,200
Speaker 1: But where are we in the timeline.

710
00:38:54,600 --> 00:39:00,000
Speaker 2: Of this three to five years away from shocking people?

711
00:39:01,040 --> 00:39:02,359
Speaker 1: What does shocking people mean?

712
00:39:02,719 --> 00:39:05,520
Speaker 2: Do something that nobody thought was possible in that timeline?

713
00:39:05,800 --> 00:39:07,240
Speaker 1: Does an example come to mind?

714
00:39:08,120 --> 00:39:12,640
Speaker 2: I was actually pleasantly surprised. So one of our clients, HSBC,

715
00:39:13,719 --> 00:39:17,800
Speaker 2: last week published a result that using a quantum computer

716
00:39:19,080 --> 00:39:23,200
Speaker 2: bond trading was thirty four percent more accurate than their

717
00:39:23,239 --> 00:39:24,200
Speaker 2: prior technique.

718
00:39:25,000 --> 00:39:26,200
Speaker 1: Thirty four percent.

719
00:39:26,320 --> 00:39:27,120
Speaker 2: Thirty four percent.

720
00:39:27,640 --> 00:39:31,000
Speaker 1: This is an industry that's used to one percent correct,

721
00:39:31,280 --> 00:39:35,440
Speaker 1: zero point five percent. Yes, that's astonishing.

722
00:39:35,719 --> 00:39:38,560
Speaker 2: Now, that was not at a scale when they could

723
00:39:38,560 --> 00:39:41,120
Speaker 2: turn it into production today, but that was sort of

724
00:39:41,560 --> 00:39:44,960
Speaker 2: their original thought experiment, and that's what they did. Now

725
00:39:45,239 --> 00:39:49,960
Speaker 2: can you imagine when will somebody so you were correct.

726
00:39:50,000 --> 00:39:52,920
Speaker 2: You talk about an industry where one basis point, if

727
00:39:52,920 --> 00:39:56,360
Speaker 2: I remember, I may be wrong, like thirteen trillion dollars

728
00:39:56,360 --> 00:39:58,720
Speaker 2: of money kind of moves around in the financial industry

729
00:39:58,760 --> 00:40:05,960
Speaker 2: each day, right, so basis point would be thirteen billion

730
00:40:06,080 --> 00:40:10,640
Speaker 2: something like that, right, one over ten thousand. So when

731
00:40:10,680 --> 00:40:13,000
Speaker 2: you think about the kind of profit that people can

732
00:40:13,040 --> 00:40:15,480
Speaker 2: make if you can tell somebody that you can come

733
00:40:15,560 --> 00:40:20,400
Speaker 2: up with a better price than your competition by just

734
00:40:20,520 --> 00:40:23,879
Speaker 2: one basis point, they would actually gain the market share.

735
00:40:25,040 --> 00:40:29,160
Speaker 2: So I think something around there, or something in the

736
00:40:29,160 --> 00:40:33,319
Speaker 2: world of materials. Can we make a better battery? Could

737
00:40:33,320 --> 00:40:36,960
Speaker 2: we make a solid state battery which means your risk

738
00:40:37,040 --> 00:40:40,799
Speaker 2: of fires heating decrease dramatically.

739
00:40:40,840 --> 00:40:43,080
Speaker 1: And the reason, sorry to ask a really nice question,

740
00:40:44,480 --> 00:40:46,840
Speaker 1: why is it that a quantum computer would be better

741
00:40:46,840 --> 00:40:51,400
Speaker 1: at solving a battery problem than our existing methods of computing.

742
00:40:51,480 --> 00:40:55,600
Speaker 2: So the equations of quantum mechanics and chemistry and how

743
00:40:56,200 --> 00:41:00,560
Speaker 2: things interact are well known. To solve them, that are

744
00:41:00,560 --> 00:41:02,839
Speaker 2: no known techniques, So these are not like closed form,

745
00:41:02,880 --> 00:41:04,719
Speaker 2: you know, it's not like the square root of a

746
00:41:04,800 --> 00:41:08,000
Speaker 2: quarterly equation. So the only way to solve them is

747
00:41:08,040 --> 00:41:11,000
Speaker 2: to explore the state space. So if you have a

748
00:41:11,040 --> 00:41:15,919
Speaker 2: few hundred electrons, you need two to the one hundred states. Well,

749
00:41:16,040 --> 00:41:18,279
Speaker 2: I'm sorry, you don't have that much memory. It's impossible.

750
00:41:18,680 --> 00:41:21,560
Speaker 2: So it takes a really, really long time on a

751
00:41:21,600 --> 00:41:25,240
Speaker 2: normal computer to solve those problems. Right, that's simpler problem.

752
00:41:25,920 --> 00:41:30,400
Speaker 2: If a quantum computer operates in the equation domain, it

753
00:41:30,440 --> 00:41:32,600
Speaker 2: doesn't need to explore the state space, it can actually

754
00:41:32,680 --> 00:41:34,919
Speaker 2: solve it. That's why I call it a different kind

755
00:41:34,960 --> 00:41:37,920
Speaker 2: of math. That's the kind of math it does. So

756
00:41:38,960 --> 00:41:41,120
Speaker 2: in a couple of seconds, it can tell you this

757
00:41:41,200 --> 00:41:44,799
Speaker 2: is how that material will be here. Oh I see,

758
00:41:44,960 --> 00:41:48,600
Speaker 2: so you've taken what could take years to a few seconds. Yeah,

759
00:41:48,600 --> 00:41:50,080
Speaker 2: that's a pretty big change.

760
00:41:50,320 --> 00:41:53,359
Speaker 1: Yeah. Yeah, it's speaking a different language, taking a different line.

761
00:41:53,440 --> 00:41:56,080
Speaker 1: So any kind of problem that comes along that's specific

762
00:41:56,160 --> 00:41:56,440
Speaker 1: to that.

763
00:41:56,440 --> 00:42:00,200
Speaker 2: Language correctly, which is not all problems. Yeah, just that's

764
00:42:00,200 --> 00:42:01,719
Speaker 2: I called it. It's one more kind of math.

765
00:42:02,000 --> 00:42:06,239
Speaker 1: Yeah, what's an example? So so many questions, Eric, give

766
00:42:06,280 --> 00:42:09,960
Speaker 1: me another example of a of a of a kind

767
00:42:09,960 --> 00:42:12,000
Speaker 1: of problem that a quantum computer would love.

768
00:42:13,800 --> 00:42:17,080
Speaker 2: This one is a bit more speculative, and I'm going

769
00:42:17,160 --> 00:42:20,359
Speaker 2: to use a little bit of poetic license. So let's

770
00:42:20,400 --> 00:42:23,879
Speaker 2: take a post office in a mid sized country. They

771
00:42:23,920 --> 00:42:26,640
Speaker 2: probably burn a billion gallons of fuel per year delivering

772
00:42:26,680 --> 00:42:30,200
Speaker 2: packages and letters because most post and advanced country says

773
00:42:30,520 --> 00:42:36,000
Speaker 2: every house, every address, each day. The way to optimize

774
00:42:36,000 --> 00:42:38,360
Speaker 2: this is we can formulate the problem. It's called the

775
00:42:38,440 --> 00:42:42,560
Speaker 2: traveling sales and problem solving it is really hard, so

776
00:42:42,600 --> 00:42:46,799
Speaker 2: people have heuristics. Let's suppose today our heuristics get us

777
00:42:46,800 --> 00:42:50,400
Speaker 2: to within twenty percent of the optimal answer. Let's suppose

778
00:42:50,440 --> 00:42:54,399
Speaker 2: a quantum computer can get you the next ten percent. Well,

779
00:42:54,440 --> 00:42:56,680
Speaker 2: if I can get ten percent of a billion gallons,

780
00:42:56,719 --> 00:42:58,440
Speaker 2: that I think is one hundred million gallons of my

781
00:42:58,520 --> 00:43:01,400
Speaker 2: math is right, and in the country i'm thinking about,

782
00:43:01,600 --> 00:43:04,880
Speaker 2: that could be eight hundred million pounds of saving to

783
00:43:05,000 --> 00:43:10,640
Speaker 2: one entity in one year. And the associated common footprint

784
00:43:10,719 --> 00:43:14,680
Speaker 2: climate change weren't less mileage on vanging. I'm not even

785
00:43:14,719 --> 00:43:18,640
Speaker 2: counting all that. These are pretty attractive problems to go after.

786
00:43:19,000 --> 00:43:22,799
Speaker 2: So if I look at the interest recently, New York

787
00:43:22,800 --> 00:43:27,520
Speaker 2: has started a whole program in some places. Illinois stood

788
00:43:27,600 --> 00:43:31,920
Speaker 2: up a quantum algorithm center between a number of the universities.

789
00:43:32,080 --> 00:43:36,920
Speaker 2: The governor there was heavily behind it, etc. So I

790
00:43:36,960 --> 00:43:39,960
Speaker 2: wouldn't say that this is widespread. This is why I'm

791
00:43:39,960 --> 00:43:42,520
Speaker 2: saying three to four years for that moment. But there's

792
00:43:42,640 --> 00:43:45,920
Speaker 2: enough people who are deeply cognizant who are saying, wait

793
00:43:45,960 --> 00:43:48,200
Speaker 2: a moment, we kind of get it. This is a

794
00:43:48,200 --> 00:43:50,160
Speaker 2: new kind of man. What are the new problems we

795
00:43:50,200 --> 00:43:53,240
Speaker 2: can solve? And the fact that we have about roughly

796
00:43:53,239 --> 00:43:56,440
Speaker 2: two hundred clients who worked with us very early stage

797
00:43:56,480 --> 00:43:59,640
Speaker 2: small experiments. Is because the intuition is I can do

798
00:43:59,680 --> 00:44:02,600
Speaker 2: something that I couldn't do in other places.

799
00:44:02,880 --> 00:44:04,520
Speaker 1: Three to four years is not a long time.

800
00:44:04,960 --> 00:44:05,120
Speaker 2: No.

801
00:44:07,040 --> 00:44:09,840
Speaker 1: But if I'm in the battery business and I don't

802
00:44:09,840 --> 00:44:16,080
Speaker 1: have a line out to a quantum computing experiment, I

803
00:44:16,080 --> 00:44:17,920
Speaker 1: have a problem. Don't have a problem.

804
00:44:18,320 --> 00:44:21,640
Speaker 2: Yeah, you'd probably be out of business in ten years.

805
00:44:22,239 --> 00:44:24,160
Speaker 2: Well maybe you could write a big check and buy

806
00:44:24,160 --> 00:44:26,640
Speaker 2: the technology from somebody else. You had to.

807
00:44:27,400 --> 00:44:30,880
Speaker 1: What is quantum rank in the kind of great inventions

808
00:44:30,920 --> 00:44:33,279
Speaker 1: of the last one hundred and fifty years.

809
00:44:33,040 --> 00:44:38,600
Speaker 2: Equal to some conductor? And I think that if semi

810
00:44:38,640 --> 00:44:43,440
Speaker 2: conductor's vanished, modern life would stop, like just stop. Yeah,

811
00:44:44,239 --> 00:44:51,640
Speaker 2: no electricity, no automobile, no streaming. You can imagine the

812
00:44:51,719 --> 00:44:54,440
Speaker 2: yells from all the kids who ever hear that no streaming?

813
00:44:56,239 --> 00:45:00,319
Speaker 1: The and is that it's funny because don't As someone

814
00:45:00,320 --> 00:45:04,040
Speaker 1: who's outside this world, I feel like quantum is underdiscussed

815
00:45:04,080 --> 00:45:08,200
Speaker 1: relative to its potential for transforming society.

816
00:45:08,000 --> 00:45:11,839
Speaker 2: Because I use my Internet example. Ninety five was the

817
00:45:11,840 --> 00:45:16,520
Speaker 2: moment with Netscape that Internet came on people's consciousness. I

818
00:45:16,560 --> 00:45:18,799
Speaker 2: said in eighty five I considered it to be this

819
00:45:18,840 --> 00:45:22,399
Speaker 2: is a solved problem because it needs something that makes

820
00:45:22,440 --> 00:45:26,840
Speaker 2: it accessible easy. That was the browser. The Netscape browser

821
00:45:26,920 --> 00:45:31,200
Speaker 2: is what brought it made easy to understand. We have probably,

822
00:45:31,239 --> 00:45:34,120
Speaker 2: as I said, about four to five years from that moment.

823
00:45:34,160 --> 00:45:37,120
Speaker 2: That's why it's under discussed because the moment I say

824
00:45:37,120 --> 00:45:39,040
Speaker 2: and you've got a math, I've probably lost ninety nine

825
00:45:39,040 --> 00:45:41,840
Speaker 2: percent of the audience. If I go to quantum mechanics,

826
00:45:41,840 --> 00:45:45,200
Speaker 2: I've probably lost nine percent of the audience.

827
00:45:47,040 --> 00:45:50,200
Speaker 1: So you, as CEO, over the last five years, have

828
00:45:50,360 --> 00:45:53,200
Speaker 1: been really the birth mother for a lot of the

829
00:45:53,280 --> 00:45:57,919
Speaker 1: quantum computing work. I'm curious, so you come in. When

830
00:45:57,920 --> 00:46:02,320
Speaker 1: you started as CEO, was this your first priority.

831
00:46:02,880 --> 00:46:05,320
Speaker 2: I had already started investing in it back in twenty

832
00:46:05,440 --> 00:46:11,080
Speaker 2: fifteen when I was leading IBM research. So let me

833
00:46:11,080 --> 00:46:13,080
Speaker 2: acknowledge and like nobody should try to copy. And I've

834
00:46:13,080 --> 00:46:16,279
Speaker 2: had a I'll call it a weird career. I was

835
00:46:16,280 --> 00:46:18,640
Speaker 2: a researcher at some point. If he'd asked me out,

836
00:46:18,680 --> 00:46:20,080
Speaker 2: I said, I'm one of those people, you know, throw

837
00:46:20,080 --> 00:46:22,080
Speaker 2: a pizza under our door and like, leave me alone.

838
00:46:22,280 --> 00:46:24,520
Speaker 2: I don't want to talk to people. Then I decided

839
00:46:24,520 --> 00:46:26,719
Speaker 2: I was interested in the business. Then I went and

840
00:46:26,760 --> 00:46:30,319
Speaker 2: started acquiring companies and doing that. Then somebody told me, hey,

841
00:46:30,320 --> 00:46:33,640
Speaker 2: why didn't you start doing some business strategy. Then I

842
00:46:33,719 --> 00:46:37,239
Speaker 2: went back to research and led our research division for

843
00:46:37,280 --> 00:46:40,239
Speaker 2: a couple of years, and when the people described it

844
00:46:40,280 --> 00:46:43,520
Speaker 2: to me, I asked some questions. So it wasn't a

845
00:46:43,560 --> 00:46:46,080
Speaker 2: big investment at that time. It was hey, can we

846
00:46:46,160 --> 00:46:48,960
Speaker 2: make a computer not just a science experiment? Can it

847
00:46:49,040 --> 00:46:53,160
Speaker 2: run by itself all night? Can you think about software

848
00:46:53,440 --> 00:46:55,960
Speaker 2: so that even people who are not deeper quantum mechanics

849
00:46:56,040 --> 00:46:58,680
Speaker 2: can begin to use it? And they began to do

850
00:46:58,760 --> 00:47:04,160
Speaker 2: those things three four years. Did they get enough confidence? Yeah, okay,

851
00:47:04,200 --> 00:47:06,759
Speaker 2: this is something that can really work. And then you've

852
00:47:06,760 --> 00:47:09,680
Speaker 2: got to nurture it to where it gets bigger and

853
00:47:09,680 --> 00:47:12,480
Speaker 2: bigger until you get the confidence that, okay, now it's

854
00:47:12,520 --> 00:47:13,000
Speaker 2: a big bet.

855
00:47:13,800 --> 00:47:16,360
Speaker 1: And what was the moment when you when you realize

856
00:47:16,360 --> 00:47:17,160
Speaker 1: now it's a big bet?

857
00:47:19,080 --> 00:47:20,760
Speaker 2: Probably two or three years ago.

858
00:47:21,120 --> 00:47:23,440
Speaker 1: And how do you decide, as the head of a

859
00:47:23,480 --> 00:47:27,759
Speaker 1: company like this, how much money, how many resources, and

860
00:47:27,800 --> 00:47:29,719
Speaker 1: how many people? And how what kind of prominence to

861
00:47:29,760 --> 00:47:30,759
Speaker 1: give to an idea like that?

862
00:47:31,560 --> 00:47:35,600
Speaker 2: So three layers the set of people who actually have

863
00:47:35,719 --> 00:47:40,239
Speaker 2: the knowledge and the intensity to fundamentally advance the technology.

864
00:47:40,960 --> 00:47:42,719
Speaker 2: If I could find more, I would higher them. So

865
00:47:42,800 --> 00:47:46,120
Speaker 2: I'm constrained of people on that one because normally there's

866
00:47:46,160 --> 00:47:49,240
Speaker 2: only so many people who could do these things. Two,

867
00:47:50,200 --> 00:47:53,040
Speaker 2: you got to be careful. If you push too hard

868
00:47:53,080 --> 00:47:55,960
Speaker 2: on timing, you will get people to take so much

869
00:47:56,040 --> 00:47:59,040
Speaker 2: risk that actually the thing will fail. So that's the

870
00:47:59,160 --> 00:48:01,920
Speaker 2: art of it in the leadership on the project and

871
00:48:02,960 --> 00:48:05,560
Speaker 2: me to say, Okay, how hard can you push? But

872
00:48:05,680 --> 00:48:08,440
Speaker 2: not so hard that you cause it to fail, because

873
00:48:08,440 --> 00:48:12,799
Speaker 2: then they get compelled to commit timelines that are just impossible.

874
00:48:13,360 --> 00:48:16,920
Speaker 1: Yeah, how do you This is fascinating. So it's ultimately

875
00:48:16,960 --> 00:48:19,719
Speaker 1: a question of judgment trying to figure out what's the

876
00:48:19,760 --> 00:48:22,920
Speaker 1: sweet spot between enough pressure to keep them ahead of

877
00:48:22,960 --> 00:48:25,279
Speaker 1: the pack, but not too much pressure so that they

878
00:48:25,360 --> 00:48:29,480
Speaker 1: start taking risks. How do you calibrate whether you're hitting

879
00:48:29,480 --> 00:48:32,640
Speaker 1: that sweet spot? I mean, do you reassess every few

880
00:48:32,640 --> 00:48:35,920
Speaker 1: months and say, I think I'm over correcting or undercorrecting

881
00:48:35,960 --> 00:48:36,520
Speaker 1: at this moment.

882
00:48:37,320 --> 00:48:40,759
Speaker 2: So one, you got to have what I call and

883
00:48:40,800 --> 00:48:43,160
Speaker 2: this is channeling a word from one of my favorite

884
00:48:43,160 --> 00:48:47,080
Speaker 2: books to geek away, how open can you be? So

885
00:48:47,120 --> 00:48:50,480
Speaker 2: I want to press hard, but the team knows that

886
00:48:50,520 --> 00:48:54,080
Speaker 2: they're allowed to push back and really argue back hard.

887
00:48:54,760 --> 00:48:59,280
Speaker 2: That means you'd get to probably that correct Goldilocks pressure.

888
00:49:00,840 --> 00:49:03,800
Speaker 2: The people themselves should want to go as hard as possible,

889
00:49:04,280 --> 00:49:07,880
Speaker 2: but not harder than possible. So that is then personality

890
00:49:08,239 --> 00:49:10,560
Speaker 2: of leadership that makes sense.

891
00:49:10,800 --> 00:49:13,360
Speaker 1: But you have to be someone who people feel comfortable

892
00:49:13,360 --> 00:49:18,040
Speaker 1: being honest with. Yes, absolutely, and people feel comfortable being

893
00:49:18,040 --> 00:49:22,200
Speaker 1: honest with you, I believe so. Yeah. When has there

894
00:49:22,200 --> 00:49:25,680
Speaker 1: been a moment in this path with quantum where you

895
00:49:25,680 --> 00:49:27,120
Speaker 1: did think you were pushing too hard?

896
00:49:28,880 --> 00:49:33,479
Speaker 2: No, because I think that the leadership there will argue

897
00:49:33,560 --> 00:49:36,480
Speaker 2: back with me any day of the week. I don't

898
00:49:36,520 --> 00:49:38,960
Speaker 2: think that they feel that they have to forward.

899
00:49:40,040 --> 00:49:43,560
Speaker 1: Do you drop by at Saturday night at ten pm

900
00:49:43,640 --> 00:49:44,600
Speaker 1: to see if people are working?

901
00:49:45,680 --> 00:49:51,600
Speaker 2: I tend to text people and ask questions and like

902
00:49:51,719 --> 00:49:54,440
Speaker 2: I'll read something and say, hey, are these people doing this?

903
00:49:55,000 --> 00:49:58,960
Speaker 2: And if they can answer me in reasonable terms, I

904
00:49:58,960 --> 00:50:01,880
Speaker 2: actually then say great. They are already watching the competition,

905
00:50:01,960 --> 00:50:05,120
Speaker 2: they are watching the literature, they're watching the science. I

906
00:50:05,120 --> 00:50:07,560
Speaker 2: don't need to push hard. If they are already ahead

907
00:50:07,560 --> 00:50:10,120
Speaker 2: of it, then me I can answer my question. I'll

908
00:50:10,120 --> 00:50:14,840
Speaker 2: say thoughtfully, not always completely accurately. You're thinking about it

909
00:50:14,880 --> 00:50:15,239
Speaker 2: on their own.

910
00:50:15,239 --> 00:50:18,080
Speaker 1: I don't need to push Yeah. One last question I

911
00:50:18,080 --> 00:50:20,800
Speaker 1: wanted to ask you, do you have the most interesting

912
00:50:20,840 --> 00:50:21,560
Speaker 1: job in America.

913
00:50:21,920 --> 00:50:24,279
Speaker 2: I believe that it's the most impressing job, which I

914
00:50:24,280 --> 00:50:25,400
Speaker 2: won't give up for anything.

915
00:50:26,400 --> 00:50:29,280
Speaker 1: It also sounds like you're enjoying yourself.

916
00:50:29,840 --> 00:50:33,120
Speaker 2: I enjoy it as long as. Look my role and

917
00:50:33,239 --> 00:50:37,279
Speaker 2: goal should be to make the enterprise thrive. As long

918
00:50:37,320 --> 00:50:40,600
Speaker 2: as than making the enterprise thrive, and are clients delighted?

919
00:50:42,200 --> 00:50:45,040
Speaker 2: I love it. If I don't, somebody else should do it.

920
00:50:46,080 --> 00:50:49,400
Speaker 1: Harvin, this has been so much fun. Thank you so

921
00:50:49,520 --> 00:50:55,000
Speaker 1: much taking the time and a fascinating, completely fascinating conversation.

922
00:50:55,040 --> 00:50:56,680
Speaker 1: I wish I was one of those people who could

923
00:50:56,680 --> 00:50:59,040
Speaker 1: help you out with quantum, but I'm afraid I'm not.

924
00:51:01,760 --> 00:51:02,040
Speaker 2: Good.

925
00:51:02,440 --> 00:51:14,840
Speaker 1: Thank you so much. Smart Talks with IBM is produced

926
00:51:14,840 --> 00:51:19,240
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927
00:51:19,600 --> 00:51:24,680
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928
00:51:25,080 --> 00:51:29,239
Speaker 1: Cassidy Meyer and Sophia Derlong. Smart Talks with IBM is

929
00:51:29,239 --> 00:51:32,959
Speaker 1: a production of Pushkin Industries and Ruby Studio at iHeartMedia.

930
00:51:33,520 --> 00:51:37,480
Speaker 1: To find more Pushkin podcasts. Listen on the iHeartRadio app,

931
00:51:37,640 --> 00:51:42,280
Speaker 1: Apple Podcasts, or wherever you listen to podcasts. I'm Malcolm Glawell.

932
00:51:43,080 --> 00:51:46,279
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933
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