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Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.

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Speaker 1: This week on the podcast, I have a special guest.

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Speaker 1: His name is Luis Perez Brava. He is a professor

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Speaker 1: at m I T where he directs the Innovation Team's program.

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Speaker 1: He is also UH the author of Innovating a Doer's Manifesto.

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Speaker 1: And this is really a fascinating conversation for those of

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Speaker 1: you who are interested in the direction that technology is

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Speaker 1: taking us and how startup Silicon Valley technology UH problem solving,

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Speaker 1: big data, artificial intelligence, et cetera is progressing. I think

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Speaker 1: you will find this to be a surprising and fascinating conversation. UH.

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Speaker 1: Luise approaches the idea of UM problem solving an innovation

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Speaker 1: in a different UH concept and a different order and

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Speaker 1: in a different construct than I think most people think

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Speaker 1: of in terms of technology and UM innovation. He really

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Speaker 1: looks at this as a problem solving an iterative process

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Speaker 1: as opposed to something driven purely by technology or purely

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Speaker 1: by capital UH. He in fact, he specifically thinks most

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Speaker 1: people are doing this wrong because they're focusing on the

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Speaker 1: concept of big ideas as opposed to thinking about hunches

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Speaker 1: and small incremental changes that solve problems as opposed to

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Speaker 1: what we discussed, the grand pivot that seems to be

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Speaker 1: so popular these days in Silicon Valley. So if you're

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Speaker 1: at all interested in venture capital or technology or the

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Speaker 1: future direct action of tech, I think you'll find this

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Speaker 1: conversation quite fascinating. With no further ado, my conversation with

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Speaker 1: Luis Perez Breva. I'm Barry Riholts. You're listening to Masters

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Speaker 1: in Business on Bloomberg Radio. My guest today is Luis

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Speaker 1: Perez Breva. He is an expert in the process of

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Speaker 1: technology innovation. He holds multiple degrees from multiple countries, including Physics,

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Speaker 1: Business artificial Intelligence from Spain, France, and the US. He

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Speaker 1: is a professor at m I T and he is

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Speaker 1: the author of Innovating a Doer's Manifesto for starting from

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Speaker 1: a hunch, prototyping problems, scaling up, and learning to be

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Speaker 1: productively wrong. He currently directs the m I T Innovation

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Speaker 1: Team's program and advises organizations on artificial intelligence and innovation.

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Speaker 1: Luis Perez Breva, Welcome to Bloomberg. Thank you, thank you

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Speaker 1: for having me. So you've said a number of things

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Speaker 1: that I find fascinating. Let's jump right into the comment

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Speaker 1: you made about impossible projects. You said, I'm drawn to

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Speaker 1: projects that look impossible. What are impossible projects? And if

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Speaker 1: they're impossible, what can you do with them? Well, they're

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Speaker 1: they're really not impossible. They just looked like that, And

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Speaker 1: it's mostly because of the way we think, we think

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Speaker 1: in our silos and our disciplines, and so a bunch

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Speaker 1: of stuff then all of a sudden, so it looks

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Speaker 1: like no one knows how to do it. But everything

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Speaker 1: you love about the innovation started out looking impossible. So

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Speaker 1: give us an example of something that looked impossible but

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Speaker 1: turns out not to be My favorite one because it's

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Speaker 1: been a long time and it's still mesmerizing is Henry Ford.

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Speaker 1: But I'll bring it to the present for you. So,

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Speaker 1: if Henry Ford was life today, what he would have

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Speaker 1: said is that he wanted to make jet planes private

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Speaker 1: jet planes affordable that because that's what a car was

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Speaker 1: back then. So this seemed like a completely impossible thing

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Speaker 1: to do. Actually, no one believed him. Everyone thought he

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Speaker 1: was wrong, and yet he somehow they didn't change the

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Speaker 1: America forever. So that kind of problems, whether they be

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Speaker 1: that big or smaller, but that people cannot seem to

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Speaker 1: be able to bring together what draws my attention every

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Speaker 1: single time. And I noticed you said affordable. It's not

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Speaker 1: just that he wants to manufacture cars. He wants to

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Speaker 1: manufacture cars. It's such a scale that they were affordable

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Speaker 1: to anyone working in his factories that that seems to

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Speaker 1: be an interesting That's the key when you actually look

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Speaker 1: at the history of him before, as historians tell it,

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Speaker 1: just different from how you may read it in a book.

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Speaker 1: In a business book, you hear that all he wanted

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Speaker 1: was make cars affordable because his experience living and growing

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Speaker 1: up in a farm made him realize how hard it

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Speaker 1: was to make supply runs to the city. So that

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Speaker 1: was the objective. Everything else, the scale, manufacturing, finance, increasing wages,

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Speaker 1: was a means to an end, which is make cars affordable.

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Speaker 1: It's like problem solving all along. So let's let's address

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Speaker 1: problem solving a little bit. The title of the book

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Speaker 1: you reference being productively wrong? How is a person productively wrong?

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Speaker 1: You know the way. I've actually even come to realize

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Speaker 1: that even more ever since I had kids. When you're

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Speaker 1: a kid, When we were children, you spend all your

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Speaker 1: day being fundamentally wrong, trying things out and mostly not

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Speaker 1: getting everything right, and just you don't really care, just experiments,

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Speaker 1: just experimenting, No one seems to care, and so on.

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Speaker 1: Then eventually we learned just enough to believe that everything

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Speaker 1: applies by formula, and then we destroy the magic. Effectively,

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Speaker 1: we destroyed the spirit of angry. But the way we

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Speaker 1: seem to learn as humans, and this we know from

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Speaker 1: neuroscience from how hard it is to do ourtificial intelligence

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Speaker 1: and so on, is by fundamentally being wrong, so officially

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Speaker 1: many times, until we figure out what was wrong about it.

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Speaker 1: So we don't learn by being right or by formula.

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Speaker 1: We learned by fundamentally being wrong. I'll give you one

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Speaker 1: quick example which I learned from my daughter. When she

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Speaker 1: was starting to play violin. She played out of tune

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Speaker 1: pretty much time until one day she didn't, and then

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Speaker 1: she figured out when violent itself was out of tune.

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Speaker 1: And this happened gradually to a point that she never

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Speaker 1: actually realized that she was playing out of tune until

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Speaker 1: that one day in which all of a sudden she did.

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Speaker 1: So she actually learned to play in tune mostly by

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Speaker 1: being wrung every single time. And I bring this up

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Speaker 1: because playing an instrument is such an intuitive thing depending

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Speaker 1: on how you learn it, that it brings this about

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Speaker 1: how we learn even when you're an adult. So I

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Speaker 1: started to learn violin with my daughter. Really, and you know,

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Speaker 1: the violin is unique, or or at least different from

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Speaker 1: a piano or something where each note is defined. It's fretless,

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Speaker 1: and so you could be off by a quarter of

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Speaker 1: a tone. It's a very different experience. But that leads

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Speaker 1: the question. We think of child's play as things kids

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Speaker 1: do for fun, but you're really implying that child's play

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Speaker 1: is a form of experimentation and learning. Yeah, or you

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Speaker 1: can look at it the other way around. We adults

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Speaker 1: should play much the same way, because that's how we

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Speaker 1: acquired a bunch of things we use every day. Language,

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Speaker 1: your surroundings, your sense of taste. Everything was acquired that

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Speaker 1: precise way through experimentation. Is experiment wrong play, but it

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Speaker 1: was play. Now we grow up and we kind of

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Speaker 1: are afraid of being wrong. We need to be right,

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Speaker 1: and so before you know it, you're just not using

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Speaker 1: your brain for what it was, for what it evolved

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Speaker 1: to do best, which is learning through this kind of play.

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Speaker 1: Let's talk about innovation a little bit. What is innovation

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Speaker 1: and how does it work? Funny that's innovation has been

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Speaker 1: said to me so many things. It's an overused word.

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Speaker 1: So I'll tell you what I think innovation is. Innovation

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Speaker 1: is the end result, meaning you go about trying to

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Speaker 1: solve some obstacle, solve a problem, and uh, most people

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Speaker 1: will think you're wrong, and one day they stop thinking

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Speaker 1: you're wrong and they'll call it an innovation. So innovation

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Speaker 1: is how people certify what you did. But at first

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Speaker 1: it looked preposterous, it was fundamentally wrong, and did it

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Speaker 1: because you felt there was something to it that other

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Speaker 1: people couldn't see. Give us an example of something that

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Speaker 1: was preposterous and then adapted by everybody I love. I'm

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Speaker 1: just going to use big examples. So let's see how

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Speaker 1: prepostors they wear. And hope logical isn't today. So Bill

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Speaker 1: Gates talks about a computer in every desk and at

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Speaker 1: the end at they were every home. Right. So I'll

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Speaker 1: go back to right where there are maybe a few

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Speaker 1: main frames here and there no internet, no no internet.

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Speaker 1: No one knows what to do with it. There isn't

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Speaker 1: even spreadsheet yet that would come a bit later you

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Speaker 1: can read the entire history I mean at the time

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Speaker 1: and and somehow today because you have a computer in

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Speaker 1: your pocket, and probably another in your desk, and another

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Speaker 1: at home, and maybe five iPads, and who knows what,

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Speaker 1: maybe your phone is a computer as well. Now it

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Speaker 1: seems perfectly natural. So that is what makes it look

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Speaker 1: like an innovation. It's even better because by the time

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Speaker 1: it's gone through its cycle, it actually looks old. The

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Speaker 1: idea of a computer and every desk and every home

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Speaker 1: seems old all of a sudden, And yet it was

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Speaker 1: a brutal innovation that has created so much economic progress

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Speaker 1: in all sorts of fields. Like we're sitting in a

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Speaker 1: room where there's a cusident of computers. The idea itself

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Speaker 1: was prepators in nineteen every actually so prepastors. It didn't matter.

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Speaker 1: That's fascinating. Let's talk a little bit about technology and innovation.

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Speaker 1: But I want to start with the idea of ideas.

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Speaker 1: Where do innovative ideas come from? It's that's a trick question.

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Speaker 1: It's a true question because over the last two years

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Speaker 1: we've placed such an importance on the word idea that

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Speaker 1: in my book, I decided to give up on it.

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Speaker 1: And I actually talk about hunches because now ideas have

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Speaker 1: become so important that it's actually burdening most of us.

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Speaker 1: We think we need to come up with this innovative

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Speaker 1: idea before we even do anything. So what's the difference

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Speaker 1: between an idea and a hunch other than people don't

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Speaker 1: judge hunches as harshly as they might judge idea. So

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Speaker 1: the basic difference is an admission practical sense. Nothing ideas

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Speaker 1: come from pretty much anywhere. And as I've told you,

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Speaker 1: the best ideas start out being just preposterous combined two

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Speaker 1: things that that are not meant to go together. But

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Speaker 1: from a perception standpoint, from how you go and see

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Speaker 1: people pitching ideas and investing in ideas, people should not

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Speaker 1: be investing in ideas. People should be investing in project

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Speaker 1: in organizations, in in dreams if you want, if you

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Speaker 1: want to take it to the extreme. But ideas just

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Speaker 1: they are born bad. All ideas are effectively born bad.

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Speaker 1: Ideas are born bad. Yes, go into more details about

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Speaker 1: what why do you believe all ideas are born bad? Well,

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Speaker 1: you know if you truly have one of those ideas

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Speaker 1: that in the futures, when someone will say they were

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Speaker 1: innovative right, What you realize is that there is a

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Speaker 1: lot to learn ahead of you, and it's just impossible

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Speaker 1: for you to have figured it out all in your head.

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Speaker 1: So the first thing that needs to happen to that

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Speaker 1: idea is change, because it's in your head and it

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Speaker 1: doesn't exist anywhere, and for it to exist, you need

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Speaker 1: to scale something up to the world. And it would

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Speaker 1: be amazing if you had figured everything out perfectly in

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Speaker 1: your head. So you need to almost assume that your

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Speaker 1: idea interesting, enticing as it seems to you, it's just

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Speaker 1: fundamentally wrong in more ways than it is actually right,

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Speaker 1: and that's how you figure out how to get it

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Speaker 1: the scale. So, in other words, an idea is almost

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Speaker 1: a conclusion, and you need all the intervening steps to

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Speaker 1: get there. And by the time you're done your idea

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Speaker 1: we only look like the initial idea to you, but

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Speaker 1: to no one else. So how does one develop innovations hunches?

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Speaker 1: I don't I won't say ideas. How does one develop

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Speaker 1: the skills to produce better hunches and lead yourself towards

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Speaker 1: greater innovation. It's actually much simpler than people think, once

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Speaker 1: you remove the obsession to come up with the earth

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Speaker 1: chattering or disruptive idea. Then you realize that all you

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Speaker 1: need to start is something that doesn't seem to be right,

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Speaker 1: So you might as well start by putting trying to

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Speaker 1: put together two things that actually don't simp to fit together.

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Speaker 1: And then that causes you to ask the question, Okay,

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Speaker 1: this doesn't work why, And as you start from there,

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Speaker 1: you realize that all you need to do is continue

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Speaker 1: to put these pieces, these parts together, to prove to

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Speaker 1: yourself that this will actually not work. Slowly, you increasingly

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Speaker 1: either build up scale just give up on it, mostly

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Speaker 1: because you lose interest. And that's fine. That's how we

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Speaker 1: humans operate. It's actually very simple way to do it,

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Speaker 1: and you can practice it. So as you practice, you

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Speaker 1: start to see opportunities that are essentially emerging things that

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Speaker 1: you and perhaps only you see that are meant to

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Speaker 1: go together. But then what else does just yet? That's

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Speaker 1: quite interesting. What do you think the differences between ideas

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Speaker 1: and execution, because they really are two completely different skill sets.

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Speaker 1: There are two completely different skill sets. But you know,

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Speaker 1: my experience when when I've taught over years, is that

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Speaker 1: paying too much attention to those two words actually gets

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Speaker 1: you into linear thinking, because then it because as you

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Speaker 1: to ask the question when once should I stop ideating

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Speaker 1: and then start executing, meaning to linear, not to line,

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Speaker 1: flexible or exactly. And what we know from innovations, and

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Speaker 1: every single treatise and innovation tells you this is that

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Speaker 1: innovation is fundamentally nonlinear. If you endeavor set you up

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Speaker 1: to try to predict the future so that you know

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Speaker 1: when to start executing, you are starting the wrong way.

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Speaker 1: So what I tell my students and what we the

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Speaker 1: way we teach it and the way I explained in

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Speaker 1: the book is your challenge is to scale up something

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Speaker 1: that's the only thing that matters. And you cannot scale

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Speaker 1: up by mostly dissociating an execution from idea. Execution will

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Speaker 1: change your idea, and your idea will change what you

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Speaker 1: execute on continuously at different scales. So is that from

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Speaker 1: whence comes the infamous pivot or you get all these

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Speaker 1: startup companies and Silicon Valley and they look like they're

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Speaker 1: going to be one thing, and then halfway through suddenly

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Speaker 1: the point in a different direction from a fundamentally similar

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Speaker 1: technology or idea. But but the problem they're trying to

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Speaker 1: solve seems to be something completely different, any original idea.

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Speaker 1: I love that you called it infamous pivot. It wasn't me,

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Speaker 1: you did it. Yes, well, haven't we heard that phrase

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Speaker 1: a million times in the past. You know a lot

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Speaker 1: about the innovation we're just chatting about. It depends on

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Speaker 1: from where you're looking at it. So if you are

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Speaker 1: at the receiving end of it, and you're just looking

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Speaker 1: at what a company does without all the knowledge of

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Speaker 1: what they're doing every day, it looks at as though

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Speaker 1: they change direction all the time. Now that's just a

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Speaker 1: perception from the outside, which means it's not what you

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Speaker 1: should do. It's what people think you're doing. But what

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Speaker 1: you are doing is effectively continuously changed incrementally how you're

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Speaker 1: thinking about it, reformulating it continuously, and as you start

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Speaker 1: to get traction in different directions, it seems as though

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Speaker 1: you're doing different things. But every time you look at

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Speaker 1: any of those stories, the way they really happened, not

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Speaker 1: the way they are told. There was no pivot. Nobody

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Speaker 1: started pivoting and changing in their mind every ten seconds,

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Speaker 1: And the advice to pivot constantly makes no particular sense

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Speaker 1: to me. So they're they're working on something. It's evolving,

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Speaker 1: it's rating over time, and suddenly, I think you use

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Speaker 1: the phrase traction, something starts to gain traction and scale.

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Speaker 1: So what we're hearing after the fact as a pivot

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Speaker 1: is really a normal evolution towards something that's that's working. Yes,

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Speaker 1: except that some companies have taken as a mantra to

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Speaker 1: take pivoting as a as a thing they do. And

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Speaker 1: so every now and then they say they try to

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Speaker 1: cause them themselves to think, how should I change direction today?

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Speaker 1: And that's a really hard question. Actually, the field of entrepreneurship,

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Speaker 1: which is different from innovation, is full of really strange questions.

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Speaker 1: Give us another example we've just discussed, when should I

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Speaker 1: stop be the aating versus executing? I don't know how

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Speaker 1: to even pose that question meaningfully. Should I pivot today?

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Speaker 1: Maybe maybe not? I just I have no data, no

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Speaker 1: evidence to kind of grasp some other advice. You hear

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Speaker 1: a lot of this focus on the user, but really,

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Speaker 1: if what you're doing is that new, is there already

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Speaker 1: a user for it? If not, you're just making it up.

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Speaker 1: By the way, I'm all for making stuff up, I'm

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Speaker 1: not for ignoring the fact that you just made it up, right,

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Speaker 1: So it's concealing the assumption you make is actually the

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Speaker 1: big mistake you would do. So there's an apocryphal story

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Speaker 1: about Steve Jobs and the iPhone, and people at Apple

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Speaker 1: kind of challenged what he wanted to do, saying the

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Speaker 1: user doesn't have any idea what what a glass phone

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Speaker 1: is going to be like, and Jobs famously said, I'll

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Speaker 1: tell the user what they want. Is that what we're

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Speaker 1: talking about in terms of not focusing too much on

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Speaker 1: the user experience and instead, if you really want to innovate,

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Speaker 1: you have to forget about the user for a while.

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Speaker 1: So I think that we went back ten years ago, right,

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Speaker 1: maybe fifteen, and anybody came to us and said you

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Speaker 1: need to be listening to more podcasts, the answer would be,

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Speaker 1: what is this person talking about? Right? So I don't

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Speaker 1: think you can expect anybody, neither the innovator, nor the entrepreneur,

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Speaker 1: or the investor or anybody to predict the future, right.

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Speaker 1: And so at some point, if you truly are looking

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Speaker 1: for or five years ahead, and you're going to users

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Speaker 1: and hoping they have an answer of what the future

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Speaker 1: beholds five years had, I think you're delivering yourself like nobody,

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Speaker 1: nobody has that answer. Someone has to make up that future.

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Speaker 1: That's the way I hear what Steve Jobs might have said. Right,

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Speaker 1: I wasn't in the room. So let's talk a little

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Speaker 1: bit about what's going on in the world of innovation today.

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Speaker 1: How can we tell if a good idea or a

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Speaker 1: hunch is going to succeed? That's a question I get

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Speaker 1: every semester, and the answer, I'm afraid is you can't.

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Speaker 1: You just got to try it out. Just try it

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Speaker 1: out and see what happens. Now, the good news is

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Speaker 1: that you also get to define what success is. So

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Speaker 1: there's no way you can fail unless you make it

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Speaker 1: a purpose like many people do, meaning meaning that, well,

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Speaker 1: how do people define success if they have an idea? Well,

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Speaker 1: what I propose people do, and what I talk about

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Speaker 1: in my book is, you know you have a hunch.

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Speaker 1: How about we try to figure out what problem is

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Speaker 1: it that you three you really think you're solving. There's

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Speaker 1: a way to think about problems that's pretty straightforward. And

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Speaker 1: what I tell my my my students and they look

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Speaker 1: at me funny, is that, uh, if an alien species

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Speaker 1: where to come down to Earth and show you the solution,

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Speaker 1: would you even know that that's actually a solution to

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Speaker 1: your problem, because if not, then you will never find

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Speaker 1: it on your own, and that's how you fail. So

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Speaker 1: you start working backwards, what do you think solves the problem,

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Speaker 1: and then before you know you realize you actually get

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Speaker 1: to define everything. So you continue to do that, and

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Speaker 1: before you know it, you succeed. Maybe it's a different

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Speaker 1: way you thought you would, but that's how you succeed.

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Speaker 1: You try and you go through scale. You're the director

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Speaker 1: of the m I T Innovation Team's program. Yes, what

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Speaker 1: does that program do? What are its goals? And what

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Speaker 1: has come out of that program? So it's a very

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Speaker 1: usual entity in academia. A few years ago we thought about, well,

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Speaker 1: you know, we want to etiquate people to actually meaningful

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Speaker 1: to innovate as a career choice. How do we do that?

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Speaker 1: So most people think that you you want technologies from

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Speaker 1: academia to live and have an impact in the world,

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Speaker 1: and of course we want that. But what I really

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Speaker 1: want is for every single student at m I T

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Speaker 1: and wherever I've taught this now worldwide, to learn how

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Speaker 1: to do this as a career choice, and then they

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Speaker 1: can choose to do it or not. That's their choice.

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Speaker 1: So I thought, how do we do that? And it

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Speaker 1: occurred to us that you could actually get a Mighty Technologies,

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Speaker 1: which is a real tangible thing, and then ask students

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Speaker 1: from all domains. So I may get an engineering technology

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Speaker 1: and the students are from science, business and architecture, and

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Speaker 1: then try to teach them what it actually means to

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Speaker 1: innovate with that tangible starting point. And the process is

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Speaker 1: very simple. So someone in a lab at M I. T.

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Speaker 1: Has discovered that a new thing is actually possible. So

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Speaker 1: I of my students, Okay, somewhere they're hidden is a superpower.

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Speaker 1: We just don't know what the superpower is. And obviously

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Speaker 1: that superpower is going to solve a problem somewhere. We

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Speaker 1: just don't know where nor what the problem is. So

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Speaker 1: we need to solve for those two things. So people

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Speaker 1: are really approaching the concept of ideas and innovation backwards.

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Speaker 1: They're starting with the idea as opposed to what problems

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Speaker 1: to solve exactly. And that alone takes me about a

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Speaker 1: month for STEMS to appreciate that we're actually turning things

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Speaker 1: on its head because it's actually simpler problem and solve

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Speaker 1: it exactly. And of course there's no change we're going

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Speaker 1: to find the perfect problem at the start. But as

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Speaker 1: long as you understand how to do it, you can

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Speaker 1: do it increasingly more quickly. And that's what led me

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Speaker 1: to believe that you could actually practice this even when

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Speaker 1: you don't have a technology at the input, which is

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Speaker 1: what the book covers. Um. But beauty is that the

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Speaker 1: result is actually very clear path to action to take

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Speaker 1: this technology, change it and address a problem that even

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Speaker 1: the researchers hadn't hadn't about. And so a bunch of

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Speaker 1: startups have actually come out of this, even though it's

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Speaker 1: not my objective, right, My objective is to educate students,

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Speaker 1: so they do this continuously. Right, So you write in

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Speaker 1: the book that people and again to reference Silicon Valley,

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Speaker 1: they do it backwards. They as soon as they have

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Speaker 1: an idea, they try and um get funding, They try

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Speaker 1: and go out and raise capital. You suggest not to

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Speaker 1: do that until way later explain the thinking. So there's

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Speaker 1: the ways to think about about that. Um, I think

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Speaker 1: that I while you have is an idea, which as

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Speaker 1: you know, I think they tend to be born not

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Speaker 1: so great, not to say bad ah, and you obsess

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Speaker 1: about pitching it and selling it. You're putting yourself in

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Speaker 1: the worst negotiation position you can possibly ever be, which

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Speaker 1: is someone else is going to decide what your company

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Speaker 1: is going to look like. They may give you money,

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Speaker 1: You may look like great because you've got the money,

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Speaker 1: but then you may soon realize your idea. UH needed

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Speaker 1: a whole different process. So I asked in the book,

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Speaker 1: I asked everybody who has an idea to think of

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Speaker 1: themselves as investors zero because the first time he's going

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Speaker 1: to be spent by them. And instead of spending six

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Speaker 1: months touring vcs or asking for money, what can you

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Speaker 1: accomplish in those six months to spare yourself the agony

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Speaker 1: of going for money with a bad idea, and in

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Speaker 1: the process, can you actually make that idea better? And

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Speaker 1: that's what I mean by exploring at first. Now I've

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Speaker 1: got to say that nowadays, UH, and a good friend

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Speaker 1: of mine alerting it to this, UH, nowadays capital has

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Speaker 1: become cheap in the sense that there's an abundance of

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Speaker 1: capital for investors, and so now it's even harder for

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Speaker 1: people with good ideas or with a desire to produce

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Speaker 1: good ideas to not be drawn into this frenzy of

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Speaker 1: asking for money and then going and creating a company

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Speaker 1: on top of something that's really maybe not that great

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Speaker 1: starting point. The name of the program you wrecked has

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Speaker 1: the word teams right in it. What is it about

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Speaker 1: teams that's so important? And doesn't that kind of contradict

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Speaker 1: the idea of the loan inventor toiling away in his

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Speaker 1: lab or her lab until late at night. Interesting, So

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Speaker 1: I'm not sure the lon inventor at the lab will

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Speaker 1: actually become the innovator, right. Sometimes they do somethings they don't,

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Speaker 1: But in order to become an innovator, they have to

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Speaker 1: become something else, right. That's something else is they are

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Speaker 1: required to talk to other people for some purpose, even

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Speaker 1: if to understand what the world looks like outside of

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Speaker 1: the lab and learn about the stuff they haven't learned

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Speaker 1: in the lab. They have to be ready to change

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Speaker 1: the idea. They have to be ready to use science

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Speaker 1: or technology or their discipline, whatever it may be, a

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Speaker 1: different way, uh than they were used to do it

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Speaker 1: in the lab, or if they are in a company,

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Speaker 1: in their company. Right. And so once you do that,

431
00:23:53,640 --> 00:23:56,680
Speaker 1: sometime along the process, there's got to be a team

432
00:23:56,720 --> 00:24:00,159
Speaker 1: because there's no company of one, right, So it not

433
00:24:00,280 --> 00:24:02,359
Speaker 1: so much that the team is important is that you

434
00:24:02,400 --> 00:24:06,280
Speaker 1: need to learn how to connect with other people and

435
00:24:06,640 --> 00:24:09,639
Speaker 1: let other people go and along the path discover how

436
00:24:09,720 --> 00:24:11,720
Speaker 1: to interact with people to actually bring the idea at

437
00:24:11,720 --> 00:24:14,639
Speaker 1: the scale. So we call it teams to emphasize the

438
00:24:14,680 --> 00:24:16,840
Speaker 1: fact that we're going to bring people from different disciplines

439
00:24:16,880 --> 00:24:19,440
Speaker 1: together and they're going to learn because remember it's a

440
00:24:19,480 --> 00:24:23,800
Speaker 1: medicational experience, that's a trick. They're going to learn how

441
00:24:24,600 --> 00:24:27,160
Speaker 1: good or bad they are and interacting with other kinds

442
00:24:27,200 --> 00:24:28,800
Speaker 1: of people, and they're going to learn how to either

443
00:24:28,920 --> 00:24:30,879
Speaker 1: change that or they're going to learn who do not

444
00:24:31,000 --> 00:24:33,240
Speaker 1: to work with again in the future. Let's talk a

445
00:24:33,320 --> 00:24:37,000
Speaker 1: little bit about your career in academia. How did you

446
00:24:37,040 --> 00:24:39,840
Speaker 1: find your way to m I T the first time.

447
00:24:40,920 --> 00:24:45,679
Speaker 1: So I was finishing my master's degree in Spain and

448
00:24:45,720 --> 00:24:48,480
Speaker 1: I decided I wanted to try something new, and a

449
00:24:48,480 --> 00:24:50,320
Speaker 1: friend of my family said you should go to the

450
00:24:50,359 --> 00:24:54,120
Speaker 1: United States, and I said, okay, And so I looked

451
00:24:54,119 --> 00:24:57,080
Speaker 1: around and I found these people working on a mix

452
00:24:57,080 --> 00:25:00,400
Speaker 1: of artificial intelligence and math, and I really wanted do that,

453
00:25:00,480 --> 00:25:02,359
Speaker 1: even though I was just a chemical engineer at the

454
00:25:02,400 --> 00:25:05,320
Speaker 1: time or not even that yet. And so I sent

455
00:25:05,359 --> 00:25:10,399
Speaker 1: an email Ah, asking to meet, and then another and

456
00:25:10,440 --> 00:25:13,400
Speaker 1: then another, and I kept on being told a bunch

457
00:25:13,440 --> 00:25:15,639
Speaker 1: of things, but none of them was no. Everything was

458
00:25:15,640 --> 00:25:17,919
Speaker 1: a challenge. So well, we may not be able to

459
00:25:17,920 --> 00:25:19,680
Speaker 1: get you a visa, and then I answered, I'll figure

460
00:25:19,720 --> 00:25:23,200
Speaker 1: that out, even though I didn't know how, and blah

461
00:25:23,280 --> 00:25:25,320
Speaker 1: blah blah. You can imagine. That went for nine months.

462
00:25:25,480 --> 00:25:27,320
Speaker 1: And at the end of nine months, I responded to

463
00:25:27,400 --> 00:25:31,800
Speaker 1: this professor who's since become a good friend. Uh, you

464
00:25:31,840 --> 00:25:34,040
Speaker 1: know what, Just tell me now, I said, because if

465
00:25:34,040 --> 00:25:35,720
Speaker 1: you don't tell me now, I'm just going to keep

466
00:25:35,760 --> 00:25:39,840
Speaker 1: going at it. And I needed to say no, Ah,

467
00:25:39,920 --> 00:25:42,480
Speaker 1: And they don't like to say no. Dude, that apparently not.

468
00:25:43,119 --> 00:25:44,879
Speaker 1: I thought that I thought he would. I thought he

469
00:25:44,880 --> 00:25:48,000
Speaker 1: would immediately say no and then uh, And I told him,

470
00:25:48,000 --> 00:25:49,560
Speaker 1: you know, I have lots of offers to stay around

471
00:25:49,600 --> 00:25:52,480
Speaker 1: here and do my master's thesis. I just want to

472
00:25:52,480 --> 00:25:54,760
Speaker 1: go work with you. I've told you all these things.

473
00:25:54,760 --> 00:25:56,879
Speaker 1: I've said we would sort it out, but say no,

474
00:25:57,200 --> 00:26:00,320
Speaker 1: or I will continue forever. I will never graduate. And

475
00:26:00,400 --> 00:26:04,640
Speaker 1: so he he's said to me, Okay, give me three days,

476
00:26:05,119 --> 00:26:06,919
Speaker 1: which was not the response I was expecting. I was

477
00:26:07,000 --> 00:26:10,280
Speaker 1: respecting the response no, and then he in the end

478
00:26:10,320 --> 00:26:12,320
Speaker 1: he said, okay, you can come. And that was the

479
00:26:12,359 --> 00:26:15,119
Speaker 1: first time I made it to M I T, mostly

480
00:26:15,160 --> 00:26:19,399
Speaker 1: as a visitor to work on artificial intelligence, being a

481
00:26:19,440 --> 00:26:22,000
Speaker 1: chemical engineer, which was the first time I realized that

482
00:26:22,040 --> 00:26:25,919
Speaker 1: what your study should not constrain you, h makes a

483
00:26:25,960 --> 00:26:29,600
Speaker 1: lot of sense. And yet I'm not sure everybody would

484
00:26:29,640 --> 00:26:32,000
Speaker 1: actually realize how much they led their background constrain them

485
00:26:32,000 --> 00:26:35,199
Speaker 1: instead of actually empowering them. So so that's the way

486
00:26:35,240 --> 00:26:36,320
Speaker 1: I first made it to a M I T. And

487
00:26:36,320 --> 00:26:39,159
Speaker 1: ever since they've come and gone six or seven times, right,

488
00:26:39,320 --> 00:26:41,800
Speaker 1: so you gotta you ended up getting a graduate degree

489
00:26:41,840 --> 00:26:44,439
Speaker 1: from M I T. Yes, But later first I did

490
00:26:44,480 --> 00:26:47,160
Speaker 1: a startup in Silicon Valley. I actually spent some months

491
00:26:47,200 --> 00:26:49,000
Speaker 1: at M I T. Then they offered me to stay

492
00:26:49,000 --> 00:26:52,040
Speaker 1: for a PhD. I applied and I got accepted, and

493
00:26:52,080 --> 00:26:53,959
Speaker 1: then I said, no, I'm not doing it. I've studied

494
00:26:53,960 --> 00:26:56,320
Speaker 1: for too long. I need real world experience, and I

495
00:26:56,359 --> 00:26:58,920
Speaker 1: went need the startup in Silicon Valley. What was the

496
00:26:58,960 --> 00:27:01,560
Speaker 1: start up to look itself? Phones in case of emergency

497
00:27:01,600 --> 00:27:05,240
Speaker 1: actually exactly to this day, when you call in any

498
00:27:05,240 --> 00:27:07,240
Speaker 1: one one in most of the states in the United States.

499
00:27:07,280 --> 00:27:09,199
Speaker 1: You were being located by the company we founded with

500
00:27:09,240 --> 00:27:12,679
Speaker 1: the technology WIN and oh, that's fantastic, it's phenomenal. And

501
00:27:12,720 --> 00:27:16,240
Speaker 1: then and you're using triangulation between cell towers or some

502
00:27:17,200 --> 00:27:21,200
Speaker 1: formula based on that, I'm like, we're using AI. So

503
00:27:21,280 --> 00:27:26,040
Speaker 1: it's artificial intelligence based on factors, not the old way,

504
00:27:26,080 --> 00:27:28,760
Speaker 1: which was the way. It turns out the triangulation would

505
00:27:28,760 --> 00:27:31,280
Speaker 1: actually not work, but it would take me a long

506
00:27:31,280 --> 00:27:35,080
Speaker 1: time actually to persuade you that that doesn't work. But

507
00:27:35,160 --> 00:27:37,119
Speaker 1: it actually links to a lot of what I discovered

508
00:27:37,160 --> 00:27:39,879
Speaker 1: since that the way we use science is very constraining,

509
00:27:40,000 --> 00:27:41,520
Speaker 1: we use it as a model. We tried to feed

510
00:27:41,560 --> 00:27:43,840
Speaker 1: the world to the science. But with AI, you can

511
00:27:43,840 --> 00:27:45,520
Speaker 1: do it the other way around, and you can make

512
00:27:45,520 --> 00:27:47,600
Speaker 1: science much more playable. And that's what I've been doing

513
00:27:47,680 --> 00:27:51,160
Speaker 1: for years in my reality. And then you find out

514
00:27:51,160 --> 00:27:52,760
Speaker 1: where it takes you, and you find out where it

515
00:27:52,760 --> 00:27:54,720
Speaker 1: takes you, and you can actually work and operate directly

516
00:27:54,760 --> 00:27:57,480
Speaker 1: on problems, which is what we didn't did then back then.

517
00:27:57,800 --> 00:27:59,920
Speaker 1: The real problem was and this is like already twenty

518
00:27:59,880 --> 00:28:03,280
Speaker 1: years ago, so you've been located by AI ever since

519
00:28:03,800 --> 00:28:07,160
Speaker 1: to help you. Right, So we realized the only problem

520
00:28:07,280 --> 00:28:10,919
Speaker 1: was that we didn't know where you were and you

521
00:28:10,960 --> 00:28:13,280
Speaker 1: needed us to know, and then we don't care. If

522
00:28:13,320 --> 00:28:16,240
Speaker 1: triangulation works, right, the problem to solve is where are

523
00:28:16,280 --> 00:28:19,640
Speaker 1: you when you call anyone one? Right? That's it. If

524
00:28:19,680 --> 00:28:22,240
Speaker 1: triangulation that work, then we need something else. And there

525
00:28:22,280 --> 00:28:24,040
Speaker 1: is data, and the data we have is rich because

526
00:28:24,080 --> 00:28:27,520
Speaker 1: it contains everything that's surroundings. So we built an AI

527
00:28:27,640 --> 00:28:29,639
Speaker 1: that would actually learn from that data how to locate

528
00:28:29,720 --> 00:28:31,960
Speaker 1: you for the purpose of any one one of course,

529
00:28:32,520 --> 00:28:35,199
Speaker 1: and so that worked out to this day. It's the

530
00:28:35,280 --> 00:28:39,920
Speaker 1: most phenomenal technology that does this. It's incredibly scalable and

531
00:28:40,200 --> 00:28:43,000
Speaker 1: it works and it looks near magical because it's not

532
00:28:43,040 --> 00:28:46,480
Speaker 1: based on model thinking or science based thinking of those.

533
00:28:46,480 --> 00:28:48,160
Speaker 1: Science plays a role, but it's not the way we

534
00:28:48,240 --> 00:28:51,960
Speaker 1: typically use science. So after that company, did you then

535
00:28:52,000 --> 00:28:54,160
Speaker 1: go back and get your PhD from M I T

536
00:28:54,520 --> 00:28:56,240
Speaker 1: or I went back to I did everything in the

537
00:28:56,240 --> 00:28:57,960
Speaker 1: wrong order, So I got I did a company in

538
00:28:57,960 --> 00:28:59,400
Speaker 1: an AI, and then I went to do a pH

539
00:28:59,520 --> 00:29:03,480
Speaker 1: d in AI. People typically do a NBA, but somehow

540
00:29:03,520 --> 00:29:05,640
Speaker 1: I I got my cables crossed. Do you have a

541
00:29:05,680 --> 00:29:08,480
Speaker 1: business degree? Yeah? That I got after before I actually

542
00:29:08,520 --> 00:29:12,120
Speaker 1: did the company. So so as I said everything in

543
00:29:12,160 --> 00:29:14,640
Speaker 1: the wrong worder, I did my PhD. Half the way

544
00:29:14,680 --> 00:29:17,360
Speaker 1: through my PhD, I needed more challenge, so I took

545
00:29:17,400 --> 00:29:19,400
Speaker 1: a year of sabbatical from my PhD and went to

546
00:29:19,400 --> 00:29:23,200
Speaker 1: live in France, and I continued working for my company,

547
00:29:23,320 --> 00:29:24,840
Speaker 1: and at the same time, I took a degree in

548
00:29:24,880 --> 00:29:28,400
Speaker 1: quantum mechanics because I thought quantum computing was going to

549
00:29:28,440 --> 00:29:33,160
Speaker 1: be hot, and I needed to understand that hot. It

550
00:29:33,280 --> 00:29:37,640
Speaker 1: is becoming hot. It's actually a fascinating field and uh.

551
00:29:37,960 --> 00:29:40,080
Speaker 1: And then I went back to m I T, finished

552
00:29:40,080 --> 00:29:42,200
Speaker 1: my PhD, did a number of startups on the side,

553
00:29:42,200 --> 00:29:44,280
Speaker 1: and as I was leaving m I T I was

554
00:29:44,680 --> 00:29:47,600
Speaker 1: approached by a faculty who had taught me, who had

555
00:29:47,640 --> 00:29:52,040
Speaker 1: seen me teach in context, and said, would you help

556
00:29:52,120 --> 00:29:55,520
Speaker 1: us set up this program innovation teams that needs revamped

557
00:29:56,200 --> 00:29:58,240
Speaker 1: to truly do what it's meant to accomplish. And you've

558
00:29:58,280 --> 00:30:00,680
Speaker 1: done this, he said, because you've you understand you have

559
00:30:00,720 --> 00:30:03,200
Speaker 1: a PhD and the sounds to be rigorous. And at

560
00:30:03,200 --> 00:30:05,800
Speaker 1: the same time, you've done this in the real world

561
00:30:05,840 --> 00:30:09,600
Speaker 1: with real technologies from scratch, and you actually are not

562
00:30:09,680 --> 00:30:12,360
Speaker 1: afraid of any deep attack as you can imagine with physics,

563
00:30:12,800 --> 00:30:18,360
Speaker 1: engine chemical engineering, and and and intentions as a background,

564
00:30:18,800 --> 00:30:22,360
Speaker 1: I'm not scared of technology anymore if I ever was right, uh,

565
00:30:22,400 --> 00:30:25,360
Speaker 1: And so I see all kinds of fantastic technologies and

566
00:30:25,400 --> 00:30:28,080
Speaker 1: I actually can help through that process. And that's how

567
00:30:28,920 --> 00:30:33,040
Speaker 1: I stayed in academia. So that's a really interesting arc

568
00:30:33,080 --> 00:30:36,680
Speaker 1: of a career. M I t is well known for

569
00:30:36,840 --> 00:30:42,080
Speaker 1: having a very savvy, intelligent student body. What have you

570
00:30:42,160 --> 00:30:45,520
Speaker 1: learned from your students? All sorts of things. First of all,

571
00:30:45,600 --> 00:30:48,120
Speaker 1: that the better I get, or actually should put it

572
00:30:48,120 --> 00:30:51,240
Speaker 1: into reverse, the the more I see them do what

573
00:30:51,360 --> 00:30:53,320
Speaker 1: I teach them, the more I realize I can teach more.

574
00:30:53,840 --> 00:30:57,240
Speaker 1: I know that in the general world sometimes less is better,

575
00:30:57,560 --> 00:31:01,479
Speaker 1: but might t more is better. So so I've gotten

576
00:31:01,560 --> 00:31:05,440
Speaker 1: to actually teach so much more throughout a semester than

577
00:31:05,520 --> 00:31:07,840
Speaker 1: I thought possible before, to the point that I now

578
00:31:07,880 --> 00:31:11,080
Speaker 1: believe that I can anybody, no whether their background, can

579
00:31:11,120 --> 00:31:14,200
Speaker 1: be trained to innovate starting from what they have. And

580
00:31:14,240 --> 00:31:16,520
Speaker 1: that's why I wrote the book. And that I learned

581
00:31:16,520 --> 00:31:20,400
Speaker 1: from the students by actually pushing the envelope. Um. The

582
00:31:20,440 --> 00:31:24,000
Speaker 1: other thing I learned, and this is shocking, This is

583
00:31:24,040 --> 00:31:27,800
Speaker 1: still shocking to me. Student body changes every two years.

584
00:31:28,000 --> 00:31:30,600
Speaker 1: And what I mean by that is that the mindset

585
00:31:30,640 --> 00:31:34,480
Speaker 1: of students changes radically every two years. I would have

586
00:31:34,480 --> 00:31:38,480
Speaker 1: thought it ten years or five or generational twenty every

587
00:31:38,520 --> 00:31:41,400
Speaker 1: two years. If you have not completely done a blank

588
00:31:41,480 --> 00:31:43,560
Speaker 1: slate in your head as to who the people are

589
00:31:43,600 --> 00:31:45,640
Speaker 1: in front of you, you're going to miss the bout.

590
00:31:45,720 --> 00:31:48,080
Speaker 1: So is that a function of the state of the economy.

591
00:31:48,160 --> 00:31:51,440
Speaker 1: Is that a function of technology and social networks? What's

592
00:31:51,520 --> 00:31:56,120
Speaker 1: driving that those changes? Or is it just a perennial backdrop?

593
00:31:56,240 --> 00:31:58,560
Speaker 1: Things are always changing. I've tried to all sorts of

594
00:31:58,600 --> 00:32:01,240
Speaker 1: explanations in the back of my head. Sometimes I thought

595
00:32:01,280 --> 00:32:05,480
Speaker 1: it was emergency of cell phones and so on. Uh,

596
00:32:05,680 --> 00:32:07,400
Speaker 1: sure those play a role, but there is something new

597
00:32:07,480 --> 00:32:09,760
Speaker 1: like that every two years. So every year, every two

598
00:32:09,840 --> 00:32:13,360
Speaker 1: years is different. Every semester is different already, But every

599
00:32:13,360 --> 00:32:16,520
Speaker 1: two years you need to entirely clean the class and

600
00:32:16,600 --> 00:32:18,719
Speaker 1: redo it from the grounds aps so that you can

601
00:32:18,760 --> 00:32:22,600
Speaker 1: actually communicate meaningfully with students, which makes me better. Right,

602
00:32:23,200 --> 00:32:26,320
Speaker 1: that's how I get better. That's pretty pretty wild. Let's

603
00:32:26,400 --> 00:32:31,520
Speaker 1: let's talk about innovation around the world. Where are some

604
00:32:31,640 --> 00:32:37,440
Speaker 1: of the most significant innovations being created globally? It's hard

605
00:32:37,480 --> 00:32:40,200
Speaker 1: to answer that question because if you believe what I've

606
00:32:40,320 --> 00:32:42,360
Speaker 1: what I've said about innovation before, we won't know it

607
00:32:42,440 --> 00:32:44,560
Speaker 1: until it's done, and then we'll call it an innovation

608
00:32:44,560 --> 00:32:47,720
Speaker 1: in hindsight. But what it's doing it looks preposterous. So

609
00:32:47,720 --> 00:32:51,000
Speaker 1: so let me ask that question slightly differently. We're very

610
00:32:51,080 --> 00:32:53,960
Speaker 1: much biased, and here in the United States we think

611
00:32:54,000 --> 00:32:59,360
Speaker 1: of ourselves as a capital of innovation. Is America still

612
00:32:59,800 --> 00:33:06,840
Speaker 1: a leader in innovation compared to its recent history? If

613
00:33:06,840 --> 00:33:09,880
Speaker 1: you were to talk to a number of historians and economies,

614
00:33:09,880 --> 00:33:12,760
Speaker 1: they would tell you that innovation is over. Actually, every

615
00:33:12,880 --> 00:33:17,120
Speaker 1: thirty years someone says that, uh so, there is something

616
00:33:17,480 --> 00:33:21,680
Speaker 1: very unique and amazing about American culture. And I say

617
00:33:21,720 --> 00:33:24,160
Speaker 1: that from the perspective of someone who came here for

618
00:33:24,280 --> 00:33:26,520
Speaker 1: what he thought was just a year and could not

619
00:33:27,320 --> 00:33:31,360
Speaker 1: getting stuff to leave, actually because I love it. Ah,

620
00:33:31,640 --> 00:33:35,040
Speaker 1: American culture combines ingenuity with a kind of attitude, with

621
00:33:35,240 --> 00:33:39,080
Speaker 1: a trying things out and not being afraid to not

622
00:33:39,160 --> 00:33:41,719
Speaker 1: get things right the first time. And noticed I avoid

623
00:33:41,720 --> 00:33:44,840
Speaker 1: the word failing. Well, I was gonna say I look

624
00:33:44,880 --> 00:33:48,160
Speaker 1: at failure in the United States as not a black

625
00:33:48,280 --> 00:33:50,840
Speaker 1: mark that it is elsewhere. And if you look at

626
00:33:51,240 --> 00:33:53,960
Speaker 1: some of the world's great innovators, but it's Steve Jobs

627
00:33:54,000 --> 00:33:57,480
Speaker 1: or Henry Ford or Toms Ederson. All of them have

628
00:33:57,600 --> 00:34:02,360
Speaker 1: had substantial failures and that failure didn't operate as a

629
00:34:02,440 --> 00:34:06,680
Speaker 1: constraint for them going forward. And I don't maybe this

630
00:34:06,760 --> 00:34:09,879
Speaker 1: is my hometown bias, but I think around the rest

631
00:34:09,920 --> 00:34:13,400
Speaker 1: of the world, I think failure carries more of a

632
00:34:13,440 --> 00:34:17,279
Speaker 1: stigma than it does in the US. So I've had

633
00:34:17,320 --> 00:34:19,560
Speaker 1: to do a lot with that question. So I think

634
00:34:19,640 --> 00:34:21,800
Speaker 1: the word failure, which has become incredibly popular in the

635
00:34:21,880 --> 00:34:24,040
Speaker 1: last ten years, and only in the last ten years,

636
00:34:24,440 --> 00:34:27,600
Speaker 1: is being overused and it doesn't mean exactly what what

637
00:34:27,680 --> 00:34:29,719
Speaker 1: I perceived my students and the people I see in

638
00:34:29,719 --> 00:34:33,239
Speaker 1: the United States actually do. So failure is is the

639
00:34:33,280 --> 00:34:35,759
Speaker 1: word the rest of the world actually use it to

640
00:34:35,840 --> 00:34:40,000
Speaker 1: describe their fear. Their fear. Yes. So the first time

641
00:34:40,040 --> 00:34:42,640
Speaker 1: I went and I started teaching abroad, Uh, they asked

642
00:34:42,680 --> 00:34:46,000
Speaker 1: me what about failure with this kind of panicky eyes,

643
00:34:46,239 --> 00:34:48,399
Speaker 1: and I said, what about it? I said, it makes

644
00:34:48,400 --> 00:34:50,440
Speaker 1: no sense, it has no meaning, And they looked at

645
00:34:50,440 --> 00:34:52,040
Speaker 1: me and said, what do you means that? Yeah, why

646
00:34:52,040 --> 00:34:54,680
Speaker 1: do you even worry about that? I said, like it's

647
00:34:54,719 --> 00:34:58,120
Speaker 1: just the world has no meaning. You try it, it works,

648
00:34:58,200 --> 00:35:01,640
Speaker 1: or it doesn't work. That's it. And this doesn't work,

649
00:35:01,680 --> 00:35:04,239
Speaker 1: you move on to the next. Either fix it right

650
00:35:04,400 --> 00:35:06,520
Speaker 1: or you figure out you can't fix it. But then

651
00:35:06,560 --> 00:35:08,319
Speaker 1: it probably means you are no longer interested and you

652
00:35:08,320 --> 00:35:11,080
Speaker 1: move on. So the keyword failure that speaks over years.

653
00:35:11,080 --> 00:35:13,920
Speaker 1: He's actually destructing you from what I believe the true

654
00:35:14,000 --> 00:35:17,440
Speaker 1: American values, which is you just dry it up. It works, great,

655
00:35:17,520 --> 00:35:20,000
Speaker 1: doesn't work, you fix it, you don't care. You move on.

656
00:35:20,680 --> 00:35:24,560
Speaker 1: We have been speaking with Professor Louise Perez Breva of

657
00:35:24,840 --> 00:35:29,000
Speaker 1: m I T, author of an Innovating a Doer's Manifesto.

658
00:35:29,520 --> 00:35:32,200
Speaker 1: If you enjoy this conversation, be sure and stick around

659
00:35:32,200 --> 00:35:35,280
Speaker 1: for the podcast extras, where we keep the tape rolling

660
00:35:35,280 --> 00:35:38,680
Speaker 1: and continue discussing all things innovation. Be sure and check

661
00:35:38,719 --> 00:35:41,520
Speaker 1: out my daily column. You can find that on Bloomberg

662
00:35:41,600 --> 00:35:45,319
Speaker 1: View dot com. Follow me on Twitter at rit Halts.

663
00:35:45,360 --> 00:35:49,160
Speaker 1: We love your comments, feedback and suggestions right to us

664
00:35:49,239 --> 00:35:53,480
Speaker 1: at m IB podcast at Bloomberg dot net. I'm Barry

665
00:35:53,560 --> 00:35:57,360
Speaker 1: rit Halts. You're listening to Masters in Business on Bloomberg Radio.

666
00:36:10,239 --> 00:36:13,000
Speaker 1: Welcome to the podcast. Thank you, Louis for doing this.

667
00:36:13,080 --> 00:36:17,120
Speaker 1: I find your work fascinating and I have so many

668
00:36:17,239 --> 00:36:21,480
Speaker 1: questions to get to. I don't even know where to start.

669
00:36:22,080 --> 00:36:24,880
Speaker 1: What motivated you to write the book? Because writing a

670
00:36:24,920 --> 00:36:27,960
Speaker 1: book is a challenge, and then going out and talking

671
00:36:28,000 --> 00:36:31,680
Speaker 1: about it afterwards is a slog. What made you say?

672
00:36:32,200 --> 00:36:35,640
Speaker 1: I know, I'll put three d and fifty words pages

673
00:36:35,680 --> 00:36:38,759
Speaker 1: of words down to sum up my past twenty years

674
00:36:38,760 --> 00:36:41,040
Speaker 1: worth of work. So I can give you two stories,

675
00:36:41,080 --> 00:36:44,919
Speaker 1: the one forward and the one in kindsight. So looking

676
00:36:44,920 --> 00:36:46,520
Speaker 1: for what they had a clue what I was doing.

677
00:36:46,680 --> 00:36:49,720
Speaker 1: Let's be seriously. I figured stuff as I go because

678
00:36:49,800 --> 00:36:53,279
Speaker 1: learning is my thing, so so I didn't know what

679
00:36:53,400 --> 00:36:55,839
Speaker 1: it would take to write a book, nor how much

680
00:36:55,840 --> 00:36:58,960
Speaker 1: more work there wasn't after you're turning the manuscript that

681
00:36:59,120 --> 00:37:02,600
Speaker 1: second that's second half is you think you're done and

682
00:37:02,600 --> 00:37:04,560
Speaker 1: it's like, oh no, you've got six more months of work.

683
00:37:04,920 --> 00:37:10,000
Speaker 1: But in hindsight, the motivation was the true motivation was

684
00:37:10,040 --> 00:37:14,520
Speaker 1: that I've been teaching this for now a decade, and uh,

685
00:37:14,719 --> 00:37:18,960
Speaker 1: I know of all the problems, confusions, paradoxes that forming

686
00:37:19,000 --> 00:37:21,840
Speaker 1: my students heads, and the month I spent trying to

687
00:37:21,880 --> 00:37:25,120
Speaker 1: disabuse them of prejudices so that they see it's actually

688
00:37:25,239 --> 00:37:28,160
Speaker 1: far simpler, which doesn't mean it doesn't take a lot

689
00:37:28,160 --> 00:37:30,759
Speaker 1: of work. It does take a lot of work, but

690
00:37:30,800 --> 00:37:34,760
Speaker 1: it's actually far simpler to do and you can actually

691
00:37:34,840 --> 00:37:37,640
Speaker 1: practice it. Whereas people start to think about are you born,

692
00:37:37,760 --> 00:37:41,720
Speaker 1: are you made? And strange questions. That was what motivated

693
00:37:41,760 --> 00:37:43,239
Speaker 1: me to say, Okay, fine, I need to write this

694
00:37:43,280 --> 00:37:45,399
Speaker 1: down because there is no way they could ever get

695
00:37:45,400 --> 00:37:48,520
Speaker 1: it anywhere because all the other books say ask you

696
00:37:48,560 --> 00:37:51,840
Speaker 1: to fail, and I rather they succeed. So so that

697
00:37:52,000 --> 00:37:55,000
Speaker 1: was one piece. In hindsight, what this allowed me to

698
00:37:55,040 --> 00:37:58,879
Speaker 1: do is realize why I've been camping in academia. Uh.

699
00:37:58,880 --> 00:38:01,240
Speaker 1: It kind of helped me bring a bunch of thoughts together.

700
00:38:01,360 --> 00:38:03,879
Speaker 1: So if you look at my funny story arc, there's

701
00:38:03,960 --> 00:38:07,800
Speaker 1: just one constant all throughout, which is at I've always

702
00:38:07,880 --> 00:38:11,680
Speaker 1: made computers do my bidding. I've always used computers ever

703
00:38:11,680 --> 00:38:14,040
Speaker 1: since I was eight to have them do what I wanted,

704
00:38:14,040 --> 00:38:15,959
Speaker 1: and I've been obsessed about how to make that dumb

705
00:38:15,960 --> 00:38:20,960
Speaker 1: machine work for me forever. So and every time everything

706
00:38:21,000 --> 00:38:23,680
Speaker 1: I've done has actually, to some degree or another involved computers.

707
00:38:23,880 --> 00:38:26,200
Speaker 1: When I got into AI, I want to take this

708
00:38:26,280 --> 00:38:30,680
Speaker 1: to the next level, which is I need AI to

709
00:38:30,800 --> 00:38:33,320
Speaker 1: have me do and continue and play with the science

710
00:38:33,360 --> 00:38:35,319
Speaker 1: I still don't know, so I can reach further and

711
00:38:35,360 --> 00:38:39,520
Speaker 1: make up new stuff. Um so I never got that

712
00:38:39,600 --> 00:38:41,600
Speaker 1: from my PhD, and I spent a number of years

713
00:38:41,600 --> 00:38:43,520
Speaker 1: trying to figure out what I thought I was missing,

714
00:38:43,520 --> 00:38:46,720
Speaker 1: which is, how do you actually solve a real world problem?

715
00:38:46,760 --> 00:38:49,120
Speaker 1: What does it actually even mean? And how did I

716
00:38:49,200 --> 00:38:52,360
Speaker 1: bring this into the equation? So that motivation is what

717
00:38:52,520 --> 00:38:54,840
Speaker 1: kept IN going for the last ten years, though I

718
00:38:54,880 --> 00:38:57,360
Speaker 1: could not phrase it as such until I finished the

719
00:38:57,360 --> 00:39:01,040
Speaker 1: book and said I figured it out right. So now

720
00:39:01,080 --> 00:39:03,520
Speaker 1: I can actually go to that next kind of AI

721
00:39:03,800 --> 00:39:05,520
Speaker 1: that no one is thinking about, because it really is

722
00:39:05,520 --> 00:39:08,560
Speaker 1: obsessed with data as opposed to what we can have

723
00:39:08,600 --> 00:39:10,840
Speaker 1: computers do for us, which is what I really want.

724
00:39:11,120 --> 00:39:15,799
Speaker 1: So you you wrote that innovation has been commoditized. What

725
00:39:15,840 --> 00:39:17,719
Speaker 1: do you mean by that? I actually mean it in

726
00:39:17,719 --> 00:39:20,040
Speaker 1: a in a in a very good way. It's like

727
00:39:20,600 --> 00:39:24,560
Speaker 1: it's never been so easy for you to get another

728
00:39:24,640 --> 00:39:29,239
Speaker 1: kind of education altogether. You go online and you can

729
00:39:29,280 --> 00:39:33,040
Speaker 1: find information about whatever you want. You can build any

730
00:39:33,080 --> 00:39:35,640
Speaker 1: kind of contraption, you can put it to your service.

731
00:39:35,920 --> 00:39:38,640
Speaker 1: The science were done, what everybody else has experimented before.

732
00:39:38,880 --> 00:39:41,920
Speaker 1: This was not available when I was a student. Actually,

733
00:39:41,960 --> 00:39:43,560
Speaker 1: I was one of the first people to play with

734
00:39:43,560 --> 00:39:47,239
Speaker 1: the Internet when I was a student in Barcelona, and

735
00:39:48,360 --> 00:39:50,400
Speaker 1: this was not there. If it had been there, it

736
00:39:50,560 --> 00:39:54,680
Speaker 1: was fantastic. Why I go through all these degrees and

737
00:39:54,880 --> 00:39:58,319
Speaker 1: so what what I what I feel is like if

738
00:39:58,360 --> 00:40:02,680
Speaker 1: people understand how easy it is to put technology to

739
00:40:02,800 --> 00:40:05,080
Speaker 1: your service, which I hope I can get to do

740
00:40:05,120 --> 00:40:08,880
Speaker 1: eventually with this form of AI technic science and technology

741
00:40:09,200 --> 00:40:13,400
Speaker 1: more playable, then it's never been easier to just drive

742
00:40:13,440 --> 00:40:16,080
Speaker 1: your education based on the project you want to do today.

743
00:40:16,640 --> 00:40:18,520
Speaker 1: And if you actually understand how to scale things up,

744
00:40:18,560 --> 00:40:20,440
Speaker 1: you can actually make a living of it. And I

745
00:40:20,480 --> 00:40:23,480
Speaker 1: think it's a responsibility of all of us to take

746
00:40:23,520 --> 00:40:25,960
Speaker 1: advantage of the fact that innovation or the tools for

747
00:40:26,000 --> 00:40:28,839
Speaker 1: innovation have been so commoditized, to bring it to all

748
00:40:28,880 --> 00:40:32,520
Speaker 1: of America right and kind of erase the chasm that

749
00:40:32,560 --> 00:40:36,239
Speaker 1: exists there today. So let's talk about that scaling up.

750
00:40:36,840 --> 00:40:40,560
Speaker 1: Because you could solve a problem, but that doesn't mean

751
00:40:40,600 --> 00:40:44,200
Speaker 1: it's going to scale. How do you take a solution

752
00:40:44,280 --> 00:40:47,440
Speaker 1: to an issue and and make it scale. This may

753
00:40:47,480 --> 00:40:51,560
Speaker 1: not sound immediately intuitive because there is nothing intuitive about

754
00:40:51,640 --> 00:40:55,000
Speaker 1: scale app so ideas, per say, don't scale. What scales

755
00:40:55,080 --> 00:40:59,200
Speaker 1: up is organizations, organizations organizations, which is that you started

756
00:40:59,239 --> 00:41:01,719
Speaker 1: with five people and doing a few of those. And

757
00:41:01,760 --> 00:41:03,959
Speaker 1: it's not about how many you do. Is being able

758
00:41:04,000 --> 00:41:07,160
Speaker 1: to do the same for less money with more people

759
00:41:08,040 --> 00:41:10,839
Speaker 1: in some way. So you get to fifty people and

760
00:41:10,920 --> 00:41:13,560
Speaker 1: you have more products, but each of them cost you

761
00:41:13,640 --> 00:41:17,080
Speaker 1: less money. This should not be logical, right, doing more

762
00:41:17,080 --> 00:41:20,680
Speaker 1: products should cost you more money. So even though we've

763
00:41:20,680 --> 00:41:24,560
Speaker 1: gotten used to the idea that with volume comes shippening up,

764
00:41:24,760 --> 00:41:29,160
Speaker 1: you know, kind of scale, we call it that way,

765
00:41:29,200 --> 00:41:32,760
Speaker 1: but we don't really understand why. I'll tell you an example.

766
00:41:32,880 --> 00:41:37,360
Speaker 1: You make eight cookies, you make sixteen cookies, you're spending

767
00:41:37,360 --> 00:41:40,000
Speaker 1: more money. Actually, the only reason why you're not spending

768
00:41:40,080 --> 00:41:42,280
Speaker 1: much more money is because you're still using the same women.

769
00:41:43,320 --> 00:41:46,440
Speaker 1: So it's not necessarily intuitive about what it means to

770
00:41:46,440 --> 00:41:49,200
Speaker 1: actually scale things up. But what scales is the endeavor,

771
00:41:49,320 --> 00:41:51,719
Speaker 1: not the idea, And so what you need to do

772
00:41:51,920 --> 00:41:54,319
Speaker 1: is think about it the way astronauts go to the

773
00:41:54,360 --> 00:41:57,600
Speaker 1: moon or International Space Station, which is the only way

774
00:41:57,640 --> 00:41:59,960
Speaker 1: you can scale up is you catch. If you catch

775
00:42:00,000 --> 00:42:02,480
Speaker 1: everything that would look like a failure at the skate

776
00:42:02,520 --> 00:42:05,279
Speaker 1: at which is only an ever and so you don't fail,

777
00:42:06,040 --> 00:42:08,600
Speaker 1: So why do I bring astronauts? But that's what they do,

778
00:42:08,640 --> 00:42:11,080
Speaker 1: according to Chris Hadfield, as I explain in the book

779
00:42:11,120 --> 00:42:14,240
Speaker 1: and many others, they just go through every excruciating detail

780
00:42:14,280 --> 00:42:17,319
Speaker 1: about what will kill them right, and they plan for it.

781
00:42:17,400 --> 00:42:19,719
Speaker 1: They either suggest modifications to the engine, they come up

782
00:42:19,719 --> 00:42:22,160
Speaker 1: with protocols, They do something so that by the time

783
00:42:22,480 --> 00:42:25,000
Speaker 1: they bring their endeavor to scale, which is getting into

784
00:42:25,000 --> 00:42:28,560
Speaker 1: the International Space Station, they are prepared for whatever may happen,

785
00:42:28,760 --> 00:42:30,960
Speaker 1: so that if it all fails, it's because it was

786
00:42:31,040 --> 00:42:33,720
Speaker 1: not predictable. That's what it means to actually achieve scale.

787
00:42:33,800 --> 00:42:35,839
Speaker 1: And then it may well be that the problem your

788
00:42:35,920 --> 00:42:38,120
Speaker 1: targeting just lives at the small scale. That's fine. You

789
00:42:38,239 --> 00:42:41,960
Speaker 1: learn that through this process. You talk about leadership and innovation.

790
00:42:42,360 --> 00:42:45,200
Speaker 1: Why is it that we see a lot of people

791
00:42:45,239 --> 00:42:49,400
Speaker 1: who create ideas, who create haunches, who create concepts, aren't

792
00:42:49,440 --> 00:42:55,000
Speaker 1: always the best person to lead organizations to implement the

793
00:42:55,040 --> 00:42:59,400
Speaker 1: scaling of those ideas. And pick pick a company, be

794
00:42:59,560 --> 00:43:03,200
Speaker 1: it it could be Uber, it could be the Weinstein Company,

795
00:43:03,239 --> 00:43:06,320
Speaker 1: it doesn't matter. It seems that the people who create

796
00:43:06,960 --> 00:43:12,399
Speaker 1: or or crucial to help drive the idea, only take

797
00:43:12,440 --> 00:43:15,360
Speaker 1: it so far and there are better people to manage

798
00:43:15,360 --> 00:43:18,759
Speaker 1: those scaling businesses or or or Is that wrong? I

799
00:43:18,800 --> 00:43:22,520
Speaker 1: think it's neither right nor wrong. It's true for some people.

800
00:43:22,840 --> 00:43:25,480
Speaker 1: Steve Jobs was able to, Bill Gates was able to.

801
00:43:26,080 --> 00:43:28,640
Speaker 1: In the case of Uber, it didn't happen at least

802
00:43:28,640 --> 00:43:31,440
Speaker 1: now that we know, maybe you know it will happened

803
00:43:31,440 --> 00:43:34,520
Speaker 1: like with Steve Jobs and come back, come back in

804
00:43:34,560 --> 00:43:37,680
Speaker 1: the future. It's hard to make those general statements about people.

805
00:43:37,840 --> 00:43:39,840
Speaker 1: But it's also true that some people, and have plenty

806
00:43:39,880 --> 00:43:42,279
Speaker 1: of friends that have shared this with me, entrepreneurs that

807
00:43:42,880 --> 00:43:45,359
Speaker 1: that just like to build it and get it going

808
00:43:45,440 --> 00:43:48,080
Speaker 1: and bringing to a given skill and then they're better

809
00:43:48,120 --> 00:43:50,040
Speaker 1: after doing the next one than they are actually doing this,

810
00:43:50,080 --> 00:43:52,560
Speaker 1: And that's actually a career choice. But I also want

811
00:43:52,600 --> 00:43:55,400
Speaker 1: to distinguish I don't know who the innovator was in

812
00:43:55,440 --> 00:43:58,480
Speaker 1: the Uber story. We talked about the CEO, which may

813
00:43:58,520 --> 00:44:00,880
Speaker 1: be the entrepreneur, but was he the innovator he was

814
00:44:00,920 --> 00:44:03,680
Speaker 1: the founder, we don't know if he was. So it's

815
00:44:03,719 --> 00:44:07,320
Speaker 1: those two are necessarily always the same, right, And sometimes

816
00:44:07,320 --> 00:44:10,319
Speaker 1: I would say that in the case of many daring innovators.

817
00:44:11,120 --> 00:44:13,880
Speaker 1: They've actually started some and they've stayed. In some cases

818
00:44:13,880 --> 00:44:16,640
Speaker 1: they have actually gotten. Bosniak left Apple and he was

819
00:44:16,760 --> 00:44:20,480
Speaker 1: key to the innovations that god Apple started, as was

820
00:44:20,520 --> 00:44:22,880
Speaker 1: the Steve Jobs right. So I think it's more of

821
00:44:22,880 --> 00:44:26,120
Speaker 1: a personal choice and a career choice than it is

822
00:44:26,160 --> 00:44:30,200
Speaker 1: a rule. So there's a there's a general fear in

823
00:44:30,200 --> 00:44:35,520
Speaker 1: in the population about technology and that automation is taking

824
00:44:35,640 --> 00:44:38,359
Speaker 1: jobs away. I don't get the sense you look at

825
00:44:38,400 --> 00:44:42,359
Speaker 1: it quite that way. What do you think about big

826
00:44:42,440 --> 00:44:46,320
Speaker 1: data AI and what it means for the broader economy?

827
00:44:46,520 --> 00:44:51,719
Speaker 1: Is technology gonna turn us all into um unemployed or

828
00:44:51,840 --> 00:44:55,840
Speaker 1: is technology going to help us continue creating the next

829
00:44:55,840 --> 00:44:59,600
Speaker 1: generation of jobs. So on that when I'm rooting for

830
00:44:59,680 --> 00:45:03,839
Speaker 1: human so I think we're going to see amazing things

831
00:45:03,880 --> 00:45:07,759
Speaker 1: coming up. I would also like to bring up what

832
00:45:07,840 --> 00:45:11,520
Speaker 1: we actually call technology, because the definition the t at

833
00:45:11,600 --> 00:45:14,440
Speaker 1: M I T what God m I T started is

834
00:45:15,080 --> 00:45:18,920
Speaker 1: the idea of using science and knowledge to help humankind

835
00:45:19,360 --> 00:45:22,319
Speaker 1: reach further and gain control over nature. So if it

836
00:45:22,400 --> 00:45:24,960
Speaker 1: is not helping us, it's not technology at least according

837
00:45:25,000 --> 00:45:27,279
Speaker 1: to this definition. So the way I and many of

838
00:45:27,320 --> 00:45:30,560
Speaker 1: my friends think about technology and what I think it's

839
00:45:30,600 --> 00:45:32,880
Speaker 1: my next passion now is what we need to do

840
00:45:32,960 --> 00:45:35,000
Speaker 1: is make a better effort to bring this technology so

841
00:45:35,040 --> 00:45:37,360
Speaker 1: that more people can play with it and benefit from it.

842
00:45:37,400 --> 00:45:39,000
Speaker 1: And that needs to be done. It needs to be

843
00:45:39,000 --> 00:45:41,359
Speaker 1: brought to scale. I think we don't haven't done such

844
00:45:41,400 --> 00:45:44,400
Speaker 1: a great job at that for the last ten twenty years,

845
00:45:44,400 --> 00:45:47,359
Speaker 1: but we can. But I don't think that on its own,

846
00:45:47,560 --> 00:45:50,200
Speaker 1: big data or AI the way you see it implemented

847
00:45:50,239 --> 00:45:54,040
Speaker 1: today is going to do any of those things that

848
00:45:54,120 --> 00:45:58,799
Speaker 1: the fearmongers say are going to happen. So what does

849
00:45:58,840 --> 00:46:03,080
Speaker 1: it mean in terms of artificial intelligence? Can we really

850
00:46:03,120 --> 00:46:08,839
Speaker 1: help machines acquire intelligence in a human like fashion? If

851
00:46:08,840 --> 00:46:11,120
Speaker 1: you have to look out fifty or a hundred years,

852
00:46:11,840 --> 00:46:14,440
Speaker 1: what what are these machines going to look like. I'm

853
00:46:14,440 --> 00:46:18,600
Speaker 1: actually going to surprise you with this. The idea of

854
00:46:19,920 --> 00:46:24,960
Speaker 1: artificial general or generalized artificial intelligence was proposed at the

855
00:46:24,960 --> 00:46:28,520
Speaker 1: beginning of the field, and the presumption was that if

856
00:46:28,520 --> 00:46:32,160
Speaker 1: we ever got a computer to beat a human a

857
00:46:32,280 --> 00:46:35,600
Speaker 1: chance or strategy game would have achieved general I started

858
00:46:35,600 --> 00:46:39,000
Speaker 1: dificial intelligence because back then we thought that that was

859
00:46:39,600 --> 00:46:41,759
Speaker 1: super hard for a computer. And I guess what we

860
00:46:41,840 --> 00:46:46,120
Speaker 1: now know is that you didn't need artificial general generalized

861
00:46:46,160 --> 00:46:49,160
Speaker 1: division intelligence to that, even though the progress has been remarkable,

862
00:46:49,200 --> 00:46:52,080
Speaker 1: but it's only been machine learning. And it also means

863
00:46:52,120 --> 00:46:54,680
Speaker 1: that we're done in an odd way. We need a

864
00:46:54,680 --> 00:46:58,120
Speaker 1: new objective because the way we define that generalized started

865
00:46:58,120 --> 00:47:01,920
Speaker 1: difficial intelligence, which is the touring test and these problems

866
00:47:02,000 --> 00:47:04,440
Speaker 1: problems is gone. I mean, like we don't know what

867
00:47:04,440 --> 00:47:06,919
Speaker 1: we're doing anymore. So I would rather think of an

868
00:47:06,920 --> 00:47:09,640
Speaker 1: AI that actually helps us directly in the sense of

869
00:47:10,120 --> 00:47:12,240
Speaker 1: we need a new objective, and to me, the objective

870
00:47:12,360 --> 00:47:15,760
Speaker 1: is AI that helps us the way Jarvis helps Tony

871
00:47:15,760 --> 00:47:20,479
Speaker 1: Stark become a superhero iron Man. So in that movie

872
00:47:20,520 --> 00:47:23,239
Speaker 1: Iron Man, everybody talks about Tony Stark and whatever, but

873
00:47:23,320 --> 00:47:26,160
Speaker 1: the great, great, great tool is Jarvis, which is the

874
00:47:26,200 --> 00:47:29,960
Speaker 1: AI that powers the suit and helps Tony Stark build stuff. Now,

875
00:47:30,000 --> 00:47:32,839
Speaker 1: imagine you had some kind of a system that's way

876
00:47:32,840 --> 00:47:35,720
Speaker 1: more powerful than Google but still is helping new problem

877
00:47:35,719 --> 00:47:38,640
Speaker 1: to alve things so that you can accomplish something new.

878
00:47:38,719 --> 00:47:40,200
Speaker 1: That's what we need an I to do. At least

879
00:47:40,200 --> 00:47:43,000
Speaker 1: that's what I want an eye to do. So that

880
00:47:43,160 --> 00:47:47,960
Speaker 1: that's quite fascinating if, in other words, are our focus

881
00:47:48,040 --> 00:47:50,640
Speaker 1: is really on the wrong thing, and we should actually

882
00:47:51,200 --> 00:47:55,759
Speaker 1: be thinking about AI as a problem solving tool and

883
00:47:55,840 --> 00:47:58,719
Speaker 1: not a means in and of itself. Am I am

884
00:47:58,719 --> 00:48:01,439
Speaker 1: I getting that right? I think? So that's the way.

885
00:48:01,680 --> 00:48:03,920
Speaker 1: But by the way, that's actually much closer to the

886
00:48:03,920 --> 00:48:07,719
Speaker 1: way you are benefiting from AI today. So, with the

887
00:48:07,760 --> 00:48:11,920
Speaker 1: limited capabilities of Netflix in terms of intelligence as broadly understood,

888
00:48:12,520 --> 00:48:15,799
Speaker 1: you're benefiting from how Netflix collates what other people have

889
00:48:15,880 --> 00:48:18,359
Speaker 1: looked at without having watched a single one of those

890
00:48:18,400 --> 00:48:22,640
Speaker 1: movies Netflix itself, and you're getting new recommendations of movies.

891
00:48:23,000 --> 00:48:25,920
Speaker 1: The same for Google. You're actually accessing Google typing a

892
00:48:25,920 --> 00:48:28,040
Speaker 1: few keywords, and you're getting to see how other people

893
00:48:28,080 --> 00:48:31,680
Speaker 1: have sorted out the web. Right. So the way AI

894
00:48:31,719 --> 00:48:34,040
Speaker 1: has been helping us has already been that way, even

895
00:48:34,120 --> 00:48:36,840
Speaker 1: though it's limited, it's still only machine learning. One of

896
00:48:36,840 --> 00:48:39,839
Speaker 1: the questions that someone I mentioned somebody I was interviewing you,

897
00:48:40,000 --> 00:48:44,280
Speaker 1: and they said, ask him if innovation has become concentrated

898
00:48:44,640 --> 00:48:49,560
Speaker 1: in too few hands? Oh, I think money has become

899
00:48:49,600 --> 00:48:54,960
Speaker 1: concentrated into few hands. Uh, money in terms of capital

900
00:48:55,000 --> 00:48:59,279
Speaker 1: for technology or money generally or both money generally. And

901
00:48:59,320 --> 00:49:02,640
Speaker 1: what you see is that um, if you don't have

902
00:49:02,760 --> 00:49:05,760
Speaker 1: good ideas that you've actually kicked the tires for yourself

903
00:49:05,800 --> 00:49:08,440
Speaker 1: before going out for money. You're just going to be

904
00:49:08,480 --> 00:49:11,120
Speaker 1: subjected to how those people think about what innovation should

905
00:49:11,120 --> 00:49:13,080
Speaker 1: be right, and so you're going to go through a

906
00:49:13,120 --> 00:49:15,800
Speaker 1: path that may not be the best now because money

907
00:49:15,840 --> 00:49:20,080
Speaker 1: is more concentrated, either it being companies that are pushing

908
00:49:20,680 --> 00:49:23,480
Speaker 1: more research I would say, not so much innovation, or

909
00:49:24,040 --> 00:49:28,480
Speaker 1: in the hands of a few philanthropists. UM, you sort

910
00:49:28,480 --> 00:49:29,920
Speaker 1: of need to figure out a way to make a

911
00:49:30,000 --> 00:49:32,520
Speaker 1: case appealing that even though it may not match how

912
00:49:32,560 --> 00:49:34,960
Speaker 1: they think about innovation, they agree with you that the

913
00:49:35,000 --> 00:49:37,320
Speaker 1: problem can get solved. So if you do your homework,

914
00:49:37,680 --> 00:49:40,280
Speaker 1: it's okay. But I think the problem is that money

915
00:49:40,320 --> 00:49:43,080
Speaker 1: has gotten too concentrated in the hands and that may

916
00:49:43,120 --> 00:49:48,320
Speaker 1: create the perception that it's harder. So let's let me

917
00:49:48,480 --> 00:49:53,200
Speaker 1: pull an example out from the real world. UM. Google

918
00:49:53,440 --> 00:49:58,239
Speaker 1: very famously tells its engineers you could take of your

919
00:49:58,280 --> 00:50:01,880
Speaker 1: time one day a week and think about problems that

920
00:50:01,960 --> 00:50:04,760
Speaker 1: have nothing to do with the work you're you're doing.

921
00:50:05,440 --> 00:50:09,960
Speaker 1: Can you organize and structure a workforce to be innovative

922
00:50:09,960 --> 00:50:13,720
Speaker 1: that way? Or is are they just wasting time? What?

923
00:50:13,719 --> 00:50:19,799
Speaker 1: What does that directive from management actually end up accomplishing.

924
00:50:21,320 --> 00:50:23,160
Speaker 1: It boils down to what you asked them to present

925
00:50:23,200 --> 00:50:26,640
Speaker 1: at the end of those Uh, time is spent on

926
00:50:26,640 --> 00:50:30,080
Speaker 1: on ideas if they are just presenting ideas, I don't

927
00:50:30,080 --> 00:50:33,120
Speaker 1: think it's efficient. But you can actually guide that works

928
00:50:33,520 --> 00:50:38,560
Speaker 1: an entire workforce that actually to innovate continuously if you

929
00:50:38,680 --> 00:50:41,879
Speaker 1: place the interest in can you please tell me how

930
00:50:41,920 --> 00:50:44,200
Speaker 1: we will direct resources to prove this thing that you

931
00:50:44,280 --> 00:50:47,120
Speaker 1: have at the next scale. And that's actually a different

932
00:50:47,120 --> 00:50:48,759
Speaker 1: way to think about the innovation. We've actually done it

933
00:50:48,800 --> 00:50:51,120
Speaker 1: outside of m I T and proving it to myself

934
00:50:51,400 --> 00:50:55,000
Speaker 1: that you can truly get highly motivated people and prepare

935
00:50:55,040 --> 00:50:57,319
Speaker 1: them to do this and innovate continuously in this way

936
00:50:57,320 --> 00:51:00,359
Speaker 1: and create the organizations that work. Whenever we think of

937
00:51:00,600 --> 00:51:03,960
Speaker 1: innovative companies, the same couple of names seemed to come

938
00:51:04,040 --> 00:51:07,080
Speaker 1: up over and over again. I was struck not too

939
00:51:07,120 --> 00:51:11,279
Speaker 1: long ago when someone had asked Jeff Bezos about the

940
00:51:11,320 --> 00:51:16,200
Speaker 1: Amazon phone which failed, and instead of getting defensive about it,

941
00:51:16,520 --> 00:51:19,640
Speaker 1: he very much embraced it and said, we need more failures.

942
00:51:20,080 --> 00:51:24,400
Speaker 1: If we're not failing frequently, then we're not trying enough stuff.

943
00:51:24,440 --> 00:51:26,880
Speaker 1: We're playing it too safe. I want to see a

944
00:51:26,880 --> 00:51:30,160
Speaker 1: lot of things tried, and by definition a lot of

945
00:51:30,200 --> 00:51:34,440
Speaker 1: them are going to fail. I think that philosophies is

946
00:51:34,560 --> 00:51:39,520
Speaker 1: quite insightful. What companies or leaders do you look at

947
00:51:40,000 --> 00:51:44,480
Speaker 1: that you think understand innovation and know how to uh

948
00:51:44,680 --> 00:51:48,719
Speaker 1: solve problems using technology and are likely to be the

949
00:51:48,760 --> 00:51:54,000
Speaker 1: innovators of the future. Uh. It's always hard to know

950
00:51:54,040 --> 00:51:56,279
Speaker 1: exactly what's going on inside the company, right. We have

951
00:51:56,360 --> 00:51:59,920
Speaker 1: unusual visibility unsum and no no visibility thought or not,

952
00:52:00,200 --> 00:52:03,640
Speaker 1: so it's hard to make a blanket statement. The few

953
00:52:03,640 --> 00:52:07,279
Speaker 1: examples we know of our companies that are still starting

954
00:52:07,360 --> 00:52:10,240
Speaker 1: up and so they are not so good at keeping secrets,

955
00:52:10,760 --> 00:52:13,799
Speaker 1: and so you actually get to see more. So there's

956
00:52:13,840 --> 00:52:15,640
Speaker 1: a lot to like and not to like about how

957
00:52:15,680 --> 00:52:18,799
Speaker 1: things are developing from many I certainly love everything Jeff

958
00:52:18,840 --> 00:52:20,560
Speaker 1: Bezos does, and even though I will not use the

959
00:52:20,560 --> 00:52:23,600
Speaker 1: word failure because they kept moving, they just moved on,

960
00:52:24,560 --> 00:52:27,120
Speaker 1: I agree and sympathize with the idea of try, and

961
00:52:27,160 --> 00:52:29,520
Speaker 1: of course if everything works out, you're not trying hard enough,

962
00:52:29,760 --> 00:52:35,520
Speaker 1: right um so uh I am I admire the way

963
00:52:35,760 --> 00:52:39,800
Speaker 1: Elon Musk has gotten around to create the company he

964
00:52:39,880 --> 00:52:42,560
Speaker 1: has created, but I've actually developed the appreciation by reading

965
00:52:42,840 --> 00:52:47,520
Speaker 1: the biography more than what the news say about about him.

966
00:52:47,560 --> 00:52:50,120
Speaker 1: I am not particularly impressed by any web company that

967
00:52:50,160 --> 00:52:53,560
Speaker 1: we see nowadays, even though there is lots of incubators

968
00:52:53,600 --> 00:52:57,080
Speaker 1: and things out there. I actually feel that they've proven

969
00:52:57,160 --> 00:53:00,080
Speaker 1: kind of the opposite that if you just start with

970
00:53:00,160 --> 00:53:03,480
Speaker 1: have web idea, it takes very long, it is too costly,

971
00:53:04,360 --> 00:53:08,760
Speaker 1: and uh, no one makes a profit. So I'm actually

972
00:53:08,880 --> 00:53:10,480
Speaker 1: if at all they've proven that there is a way

973
00:53:10,520 --> 00:53:13,479
Speaker 1: to the web companies if everybody thinks they're easier and

974
00:53:14,160 --> 00:53:18,200
Speaker 1: only create perceived value but not real value. Well, Airbnb,

975
00:53:18,520 --> 00:53:20,919
Speaker 1: I guess you can hold out an app company like Uber.

976
00:53:21,040 --> 00:53:24,480
Speaker 1: What about Netflix, they pretty much exist. Well, Netflix is

977
00:53:24,480 --> 00:53:28,120
Speaker 1: from a different breed, right of companies. They started physical right,

978
00:53:28,200 --> 00:53:31,360
Speaker 1: so sending DVDs, sending DVDs and which, if we remember

979
00:53:31,480 --> 00:53:36,120
Speaker 1: the idea, sounded ridiculous. It sounded completely ridiculous at first. Actually, Netflix,

980
00:53:36,120 --> 00:53:37,640
Speaker 1: which I talk about in the book, is a great

981
00:53:37,640 --> 00:53:40,359
Speaker 1: example because they too have been exposed in the press,

982
00:53:40,360 --> 00:53:42,759
Speaker 1: and every four years someone says they're ridiculous and they're wrong,

983
00:53:43,120 --> 00:53:44,920
Speaker 1: and then it four years later it turns out they

984
00:53:44,920 --> 00:53:47,480
Speaker 1: weren't so that's why the mechanic is so great. But

985
00:53:47,560 --> 00:53:49,720
Speaker 1: Netflix also shows you the idea that there's a difference

986
00:53:49,719 --> 00:53:51,840
Speaker 1: between coming up with an app and just counting users

987
00:53:52,640 --> 00:53:56,160
Speaker 1: and actually scaling up a company. The way Netflix gets

988
00:53:56,200 --> 00:53:58,480
Speaker 1: to where it is today is by actually becoming various

989
00:53:58,520 --> 00:54:00,640
Speaker 1: different companies on the way. When you look at what

990
00:54:00,680 --> 00:54:03,440
Speaker 1: they actually do today, they produce movies, right, and the

991
00:54:03,520 --> 00:54:05,560
Speaker 1: day they sold DVDs, actually they rented div so they

992
00:54:05,640 --> 00:54:09,880
Speaker 1: went from DVD rental to content streaming to content manufact

993
00:54:09,960 --> 00:54:11,480
Speaker 1: and some things are kept along the way. So in

994
00:54:11,520 --> 00:54:13,840
Speaker 1: their case, it's a very clever use of machine learning

995
00:54:13,840 --> 00:54:16,080
Speaker 1: that's kept along the way and allows them to think

996
00:54:16,080 --> 00:54:18,719
Speaker 1: about the next scale up, and that's the piece they

997
00:54:18,800 --> 00:54:21,279
Speaker 1: keep on growing. So they are building a very incredible

998
00:54:21,600 --> 00:54:25,359
Speaker 1: capability that gets to be incredibly versatile. It's like the

999
00:54:25,400 --> 00:54:29,520
Speaker 1: perfect example of proper financial wisdom, which is, you know,

1000
00:54:29,800 --> 00:54:32,400
Speaker 1: you want to diversify in the face of uncertainty, You

1001
00:54:32,400 --> 00:54:35,040
Speaker 1: want to actually diversify and not do the opposite, which

1002
00:54:35,040 --> 00:54:37,600
Speaker 1: is focus just on one idea and spend all your

1003
00:54:37,600 --> 00:54:42,719
Speaker 1: money in it. That sounds financially wrong advice, and so uh,

1004
00:54:42,760 --> 00:54:44,880
Speaker 1: there's companies like that that are incredibly nov that if that,

1005
00:54:44,920 --> 00:54:47,960
Speaker 1: we just don't know what they're doing. Apple, for instance,

1006
00:54:48,080 --> 00:54:51,200
Speaker 1: is what is the prototypical example. The typical question is

1007
00:54:51,600 --> 00:54:54,279
Speaker 1: with Steve Jobs, are they still innovative or not? I

1008
00:54:54,320 --> 00:54:56,560
Speaker 1: don't know the answer to that. But in the meantime,

1009
00:54:56,560 --> 00:54:59,640
Speaker 1: while we're not looking, they became a services company, right,

1010
00:55:00,040 --> 00:55:02,640
Speaker 1: so they keep on reinventing themselves. So rather than looking

1011
00:55:02,640 --> 00:55:05,840
Speaker 1: at who tries and fails a lot, which implies a

1012
00:55:05,880 --> 00:55:08,680
Speaker 1: degree of visibility onto their process that we may not have,

1013
00:55:09,280 --> 00:55:13,120
Speaker 1: just look at how subtly they actually change the company

1014
00:55:13,160 --> 00:55:15,239
Speaker 1: they are. And every time you look at it, it

1015
00:55:15,239 --> 00:55:18,840
Speaker 1: seems like perfectly incremental. And yet it's completely different Amazon

1016
00:55:18,960 --> 00:55:22,240
Speaker 1: the Everything store than producing movies the elastic cloud along

1017
00:55:22,239 --> 00:55:25,440
Speaker 1: the way. Uh, they use what they have to change

1018
00:55:25,480 --> 00:55:28,600
Speaker 1: who they are and we may not see how they

1019
00:55:28,640 --> 00:55:31,319
Speaker 1: do it, but that's what happens every single time. So

1020
00:55:31,400 --> 00:55:35,560
Speaker 1: what about Facebook, which is a web or I guess

1021
00:55:35,600 --> 00:55:37,640
Speaker 1: you could go on an app company, but it's certainly

1022
00:55:37,680 --> 00:55:41,880
Speaker 1: an internet company. Um, and they have a bunch of

1023
00:55:41,880 --> 00:55:46,080
Speaker 1: other properties like Instagram and go down the list of

1024
00:55:46,120 --> 00:55:49,680
Speaker 1: things that they've purchased. Or are they a company that's

1025
00:55:49,680 --> 00:55:53,680
Speaker 1: innovative or they is social? Their only thing and and

1026
00:55:53,760 --> 00:55:56,759
Speaker 1: that's going to be Um. They'll either live or die

1027
00:55:56,840 --> 00:56:00,160
Speaker 1: on how popular social networks are. I've got I mean,

1028
00:56:00,239 --> 00:56:03,480
Speaker 1: I have to confess that when it comes to companies

1029
00:56:03,480 --> 00:56:08,160
Speaker 1: that are just purely social, I have a hard time understanding,

1030
00:56:09,480 --> 00:56:14,160
Speaker 1: ah why they have any competitive advantage, also whatsoever acceptive

1031
00:56:14,160 --> 00:56:16,680
Speaker 1: where they are already, which means that if someone else

1032
00:56:16,719 --> 00:56:19,560
Speaker 1: comes around with something that's fundamentally different, it might be

1033
00:56:19,600 --> 00:56:23,080
Speaker 1: wiped out. But that's my own shortcoming. It's other shortcoming

1034
00:56:23,120 --> 00:56:27,279
Speaker 1: of Facebook. Uh uh. In the case of Facebook, I

1035
00:56:27,400 --> 00:56:30,359
Speaker 1: see lots of ideas, they see lots of chaos in

1036
00:56:30,640 --> 00:56:34,120
Speaker 1: many ideas, many companies, a use of machine learning for

1037
00:56:34,239 --> 00:56:38,200
Speaker 1: many things. But I also see mostly an advertising company

1038
00:56:38,920 --> 00:56:42,240
Speaker 1: right in everything they do, much to some degree like Google,

1039
00:56:42,280 --> 00:56:44,640
Speaker 1: though Google has tried more actively to branch out from that.

1040
00:56:45,440 --> 00:56:48,839
Speaker 1: So how long can we withstand that? You know, if

1041
00:56:48,840 --> 00:56:51,160
Speaker 1: you were in in in business school ten years ago,

1042
00:56:51,719 --> 00:56:53,360
Speaker 1: they would have told you that the secret of Google

1043
00:56:53,480 --> 00:56:56,320
Speaker 1: was that they ended the banner tyranny of the Internet.

1044
00:56:56,719 --> 00:56:59,560
Speaker 1: You should remember that in the nineties Internet was just

1045
00:56:59,640 --> 00:57:03,759
Speaker 1: mostly ad banners and eventually some content. Now look at

1046
00:57:03,760 --> 00:57:06,960
Speaker 1: the Internet today, it's exactly the same as it was

1047
00:57:08,320 --> 00:57:10,600
Speaker 1: a bunch of ads over there of stuff that most

1048
00:57:10,600 --> 00:57:13,120
Speaker 1: likely you bought already, and that's why how they know

1049
00:57:13,200 --> 00:57:15,960
Speaker 1: you like it, and so they advertise it again. So

1050
00:57:16,200 --> 00:57:18,120
Speaker 1: we're getting I believe we're getting to the same point.

1051
00:57:18,200 --> 00:57:20,280
Speaker 1: Maybe I'm wrong, which, as you know, I don't care

1052
00:57:20,880 --> 00:57:23,560
Speaker 1: much about. But so when I look at these companies,

1053
00:57:23,760 --> 00:57:29,320
Speaker 1: I just don't know how to assess what they're doing.

1054
00:57:29,400 --> 00:57:30,760
Speaker 1: And this is not to say that they are not

1055
00:57:30,800 --> 00:57:34,200
Speaker 1: doing great things. Maybe tomorrow Facebook will make an announcement

1056
00:57:34,440 --> 00:57:36,400
Speaker 1: and I'll say, oh, look, I was wrong in Bloomberg,

1057
00:57:37,640 --> 00:57:41,200
Speaker 1: and I'll move on right. So you keep coming back

1058
00:57:41,240 --> 00:57:45,000
Speaker 1: to the idea of not caring about being wrong. And

1059
00:57:45,240 --> 00:57:48,320
Speaker 1: before I get to my my favorite standard questions I

1060
00:57:48,360 --> 00:57:50,960
Speaker 1: ask all our guests, I have to ask you one

1061
00:57:50,960 --> 00:57:55,120
Speaker 1: more question about that. Most people have so much ego

1062
00:57:55,160 --> 00:58:01,960
Speaker 1: tied up in being right it prevents them from um

1063
00:58:02,000 --> 00:58:05,080
Speaker 1: either admitting error or trying things that have a low

1064
00:58:05,120 --> 00:58:08,840
Speaker 1: probability of success, or just you mentioned earlier, you're just

1065
00:58:08,920 --> 00:58:12,560
Speaker 1: playing and seeing what happens. How did you evolve to

1066
00:58:12,640 --> 00:58:17,280
Speaker 1: the point where you became very comfortable with I know

1067
00:58:17,360 --> 00:58:20,480
Speaker 1: failure is a loaded word, but being wrong, trying things out,

1068
00:58:20,600 --> 00:58:23,520
Speaker 1: moving on? I don't want to claim massive insight or

1069
00:58:23,680 --> 00:58:27,120
Speaker 1: foresight on the matter. So part of me I'm just

1070
00:58:27,600 --> 00:58:30,360
Speaker 1: I wasn't aware that I was supposed to not care.

1071
00:58:30,400 --> 00:58:32,280
Speaker 1: I mean, part of it was just I don't know

1072
00:58:32,360 --> 00:58:35,840
Speaker 1: something about my upbringing that that and then seriously, if

1073
00:58:35,920 --> 00:58:39,840
Speaker 1: I don't get to know every three days, uh, it's

1074
00:58:39,840 --> 00:58:43,400
Speaker 1: not my life. I've been told no about the craziestuff

1075
00:58:43,400 --> 00:58:47,040
Speaker 1: I proposed to do or literally every three days. Some

1076
00:58:47,120 --> 00:58:49,200
Speaker 1: of it is crazy, some of it I was right.

1077
00:58:49,840 --> 00:58:52,960
Speaker 1: But even super dear mentors that they take in high

1078
00:58:53,000 --> 00:58:55,600
Speaker 1: steam have told me time and again, what are you doing?

1079
00:58:55,640 --> 00:58:57,440
Speaker 1: This is not the normal path? And I tell them

1080
00:58:57,680 --> 00:59:00,600
Speaker 1: I just don't understand really the normal path. The fact

1081
00:59:00,600 --> 00:59:03,480
Speaker 1: that it's normal doesn't make it logical to me. So

1082
00:59:04,080 --> 00:59:08,160
Speaker 1: call it that my head is wired the opposite directions

1083
00:59:08,160 --> 00:59:10,440
Speaker 1: because I'm dyslexic and so certain things that seem obvious

1084
00:59:10,480 --> 00:59:12,680
Speaker 1: to other people are not obvious to me. But I

1085
00:59:12,720 --> 00:59:14,960
Speaker 1: don't claim to know to have a secret that all

1086
00:59:14,960 --> 00:59:18,520
Speaker 1: of a sudden I got inspired. Maybe it was just

1087
00:59:18,600 --> 00:59:20,800
Speaker 1: wrong one too many times to realize that it's not

1088
00:59:20,840 --> 00:59:24,160
Speaker 1: that bad. But you don't take no as I think

1089
00:59:24,200 --> 00:59:28,360
Speaker 1: most people's intended intention. When someone says no to you.

1090
00:59:28,440 --> 00:59:31,480
Speaker 1: It's almost the challenge. Yeah, what I what I tell

1091
00:59:31,520 --> 00:59:33,960
Speaker 1: you my class two students is that as long as

1092
00:59:33,960 --> 00:59:36,920
Speaker 1: no one is hurt, and that's very important, you should

1093
00:59:36,920 --> 00:59:39,440
Speaker 1: not take the first note for a note, right. And

1094
00:59:39,480 --> 00:59:44,280
Speaker 1: that doesn't apply to relationships, right, it disapplies you're talking technology, abology, business.

1095
00:59:44,840 --> 00:59:46,520
Speaker 1: You should not take the first note for an answer

1096
00:59:46,520 --> 00:59:49,240
Speaker 1: because sometimes people need a second chance to think it through.

1097
00:59:49,800 --> 00:59:54,640
Speaker 1: And so maybe two right is a good good starting

1098
00:59:54,680 --> 00:59:57,000
Speaker 1: point to get yourself trained. And then I develop all

1099
00:59:57,040 --> 01:00:01,920
Speaker 1: these funny games. I I call him credit Kid carate

1100
01:00:02,320 --> 01:00:05,439
Speaker 1: kid tasks for the movie. You probably remember the first

1101
01:00:05,480 --> 01:00:08,320
Speaker 1: Credate Kid movie in which there was walks on, walks

1102
01:00:08,360 --> 01:00:12,280
Speaker 1: off that stuff, and he the kid learned karate by

1103
01:00:12,360 --> 01:00:15,680
Speaker 1: actually kind of waxing cars and painting fences. So I

1104
01:00:15,800 --> 01:00:19,320
Speaker 1: developed a few create kid tasks to kind of train

1105
01:00:19,400 --> 01:00:21,760
Speaker 1: them to be around. One of them, I asked them

1106
01:00:21,760 --> 01:00:26,200
Speaker 1: to just call a pizza um, and I mean some

1107
01:00:26,520 --> 01:00:31,320
Speaker 1: pizzeria and ask for a doctor's appointment. Uh. Of course

1108
01:00:31,640 --> 01:00:33,640
Speaker 1: they think it's going to be embarrassing, but it's just

1109
01:00:33,760 --> 01:00:37,560
Speaker 1: a phone call, right, and uh, and then once they

1110
01:00:37,600 --> 01:00:40,880
Speaker 1: do it, you don't know who's more surprised if the

1111
01:00:40,920 --> 01:00:43,440
Speaker 1: person who called or the person who receives the call, right.

1112
01:00:43,480 --> 01:00:45,320
Speaker 1: I asked them to please not have used the pizzeria

1113
01:00:45,480 --> 01:00:47,560
Speaker 1: just to try it once, but to get the feeling

1114
01:00:47,560 --> 01:00:51,360
Speaker 1: of put themselves in in the shoes of that extress

1115
01:00:51,400 --> 01:00:54,560
Speaker 1: moment and realize it's not that bad. Right. And once

1116
01:00:54,600 --> 01:00:57,760
Speaker 1: you realize that, and you train yourself to these tiny mistakes,

1117
01:00:57,760 --> 01:01:00,919
Speaker 1: they call them near missus. If you want, uh, then

1118
01:01:01,000 --> 01:01:02,840
Speaker 1: you get a whole different sense of what the actually

1119
01:01:02,840 --> 01:01:05,600
Speaker 1: means to be wrong. It's the example I gave you earlier.

1120
01:01:06,400 --> 01:01:08,320
Speaker 1: If the violin is out of tune, it kind of

1121
01:01:08,360 --> 01:01:11,160
Speaker 1: still sounds Okay, it's not as great, right, But it's not.

1122
01:01:11,560 --> 01:01:13,400
Speaker 1: It needs to be really bad, bad, bad for you

1123
01:01:13,440 --> 01:01:15,880
Speaker 1: not to even recognize the song, and after a while,

1124
01:01:16,040 --> 01:01:19,560
Speaker 1: that helps you play better. So let me jump to

1125
01:01:19,760 --> 01:01:22,680
Speaker 1: my favorite questions. These are the ones I ask all

1126
01:01:22,760 --> 01:01:26,720
Speaker 1: my guests, UM, tell us the most important thing that

1127
01:01:26,800 --> 01:01:33,640
Speaker 1: people don't know about your background? Uh? How do people

1128
01:01:33,680 --> 01:01:36,640
Speaker 1: normally answer to that question? Well, usually it's some little

1129
01:01:36,640 --> 01:01:40,480
Speaker 1: tidbit that people um that is surprising. But you have

1130
01:01:40,560 --> 01:01:43,600
Speaker 1: a lot of interesting, surprising things in your background already.

1131
01:01:43,920 --> 01:01:48,000
Speaker 1: So I learned the program by accident. My mom had

1132
01:01:48,040 --> 01:01:51,080
Speaker 1: taken a class in a computer science because it wasn't

1133
01:01:51,280 --> 01:01:53,640
Speaker 1: and she went next door and took a class and

1134
01:01:53,720 --> 01:01:57,040
Speaker 1: we had the K computer and she told me, look,

1135
01:01:57,080 --> 01:01:59,880
Speaker 1: they've given me the master remind her game only that

1136
01:02:00,080 --> 01:02:02,120
Speaker 1: needs to be called in the computer. So the first

1137
01:02:02,160 --> 01:02:04,280
Speaker 1: time she wrote it in the computer for me, and

1138
01:02:05,200 --> 01:02:07,720
Speaker 1: I played, but it was one of those computers like

1139
01:02:07,800 --> 01:02:11,080
Speaker 1: really old, like you could not say so every time

1140
01:02:11,120 --> 01:02:12,840
Speaker 1: you want to play, you have to red to re

1141
01:02:12,920 --> 01:02:17,760
Speaker 1: write it. And so I wrote it and then wrote

1142
01:02:17,760 --> 01:02:20,120
Speaker 1: it again, and then wrote again and modified it so

1143
01:02:20,160 --> 01:02:23,480
Speaker 1: I could win always then and before you know it,

1144
01:02:23,520 --> 01:02:25,360
Speaker 1: I actually knew how to cod it. Basically, I was

1145
01:02:25,400 --> 01:02:28,280
Speaker 1: eight years old, mostly because I just wanted to play

1146
01:02:28,520 --> 01:02:32,800
Speaker 1: um Mastermind. And that has defined the way I think

1147
01:02:32,800 --> 01:02:34,840
Speaker 1: about AI and everything I've told you before in ways

1148
01:02:34,880 --> 01:02:37,840
Speaker 1: that keep on coming back to my head. The ability

1149
01:02:37,880 --> 01:02:43,080
Speaker 1: to play make things that seem complicated playable changes how

1150
01:02:43,120 --> 01:02:45,760
Speaker 1: we actually approached them. And that's define why I keep

1151
01:02:45,760 --> 01:02:50,000
Speaker 1: on jumping through field seemingly in a way that's quite fasting.

1152
01:02:50,160 --> 01:02:54,280
Speaker 1: Tell us about some of your early mentors. Hard to say.

1153
01:02:55,480 --> 01:02:57,440
Speaker 1: I've been through so many countries and I've met so

1154
01:02:57,440 --> 01:03:03,480
Speaker 1: many interesting people, and uh that it's it's hard for

1155
01:03:03,520 --> 01:03:05,840
Speaker 1: me to say it. Clearly the person and M I

1156
01:03:05,920 --> 01:03:10,840
Speaker 1: T who who you had a long email relationship before

1157
01:03:10,880 --> 01:03:14,320
Speaker 1: you has to be a significant mentor. He's a significant mentor.

1158
01:03:14,360 --> 01:03:17,040
Speaker 1: We haven't spoken in a few years now because directions

1159
01:03:17,040 --> 01:03:22,280
Speaker 1: takes different ways. Ah, but that's much later in life, right, So, uh,

1160
01:03:22,360 --> 01:03:25,600
Speaker 1: he was certainly a great mentor. More recently, what was

1161
01:03:25,640 --> 01:03:30,720
Speaker 1: his name, Tomaso Poggio Um, he's still a professor at

1162
01:03:30,800 --> 01:03:35,000
Speaker 1: m I t UM. The recently Charlie Cuney has been

1163
01:03:35,000 --> 01:03:37,080
Speaker 1: a great mentor because he sort of told me I

1164
01:03:37,120 --> 01:03:38,959
Speaker 1: think you have a book in your hands when I

1165
01:03:39,000 --> 01:03:42,480
Speaker 1: wasn't even thinking about it, and I guess I did. Uh.

1166
01:03:42,800 --> 01:03:45,760
Speaker 1: And even earlier a lot of family friends essentially a

1167
01:03:45,760 --> 01:03:48,080
Speaker 1: family the friend of my family was quoting earlier that

1168
01:03:49,400 --> 01:03:50,640
Speaker 1: I was talking to me and said, what are you

1169
01:03:50,680 --> 01:03:51,880
Speaker 1: going to do? The next time, I said, I don't

1170
01:03:51,920 --> 01:03:54,920
Speaker 1: really know. I haven't learned enough to what I want

1171
01:03:54,920 --> 01:03:57,000
Speaker 1: to accomplish. And he said, you you you don't know

1172
01:03:57,040 --> 01:03:58,560
Speaker 1: what you're doing. You need to leave this country and

1173
01:03:58,560 --> 01:04:01,120
Speaker 1: go to the United States. And said, okay, So I

1174
01:04:01,160 --> 01:04:05,400
Speaker 1: guess he's had me in this path So who um

1175
01:04:05,440 --> 01:04:10,520
Speaker 1: in the worlds of innovation? Who influenced your thinking? What philosopher,

1176
01:04:10,680 --> 01:04:17,120
Speaker 1: thinker technologists affected in a fundamental way your thoughts on innovation?

1177
01:04:19,040 --> 01:04:24,960
Speaker 1: Many I'll give you two. One is Thomas kun Um,

1178
01:04:25,000 --> 01:04:27,160
Speaker 1: which is very known in the United States and hardly

1179
01:04:27,200 --> 01:04:30,800
Speaker 1: known in Europe. Surprisingly, in Europe people talk about paper,

1180
01:04:30,840 --> 01:04:35,080
Speaker 1: which is the competing school of thought. Uh. I think

1181
01:04:35,080 --> 01:04:37,480
Speaker 1: Thomas kun explains the scientific method in a way that

1182
01:04:37,560 --> 01:04:41,640
Speaker 1: resonates with me. And Papa doesn't Um what is that?

1183
01:04:42,640 --> 01:04:47,680
Speaker 1: Papa is formulaic and uh And Quen essentially admits one

1184
01:04:47,720 --> 01:04:50,600
Speaker 1: thing that I've come to come believe as a mantra,

1185
01:04:50,720 --> 01:04:54,400
Speaker 1: which is that the beginning of any new theories fundamentally

1186
01:04:55,120 --> 01:04:59,760
Speaker 1: ill conceived, h mostly wrong by any traditional standard, barely

1187
01:04:59,760 --> 01:05:02,600
Speaker 1: work works, and yet somehow it becomes a standard afterwards.

1188
01:05:03,240 --> 01:05:06,840
Speaker 1: And that beginning starts with people putting together what seems preposterous,

1189
01:05:07,080 --> 01:05:09,360
Speaker 1: mostly because they have a hunch that something isn't working.

1190
01:05:09,840 --> 01:05:12,520
Speaker 1: I'm putting my own words on Kim, right, So so

1191
01:05:12,720 --> 01:05:15,160
Speaker 1: don't Qun doesn't use tounch and I'm not sure he

1192
01:05:15,160 --> 01:05:18,160
Speaker 1: would agree with the way I interpret his writings. And

1193
01:05:18,200 --> 01:05:20,240
Speaker 1: I think it's a real hard read a book to

1194
01:05:20,280 --> 01:05:22,440
Speaker 1: read it took me. I read it at first, at

1195
01:05:22,480 --> 01:05:26,160
Speaker 1: the age of twenty three, I didn't understand it. I

1196
01:05:26,200 --> 01:05:28,120
Speaker 1: only understood it at the age of twenty nine when

1197
01:05:28,160 --> 01:05:30,280
Speaker 1: I read it again. And by the way, that's because

1198
01:05:30,280 --> 01:05:32,920
Speaker 1: your brain keeps are involving, so you're not it's not

1199
01:05:33,000 --> 01:05:35,640
Speaker 1: fully matured and able to understand every single thing until

1200
01:05:35,680 --> 01:05:38,840
Speaker 1: you're close to twenty nine by some of the latest research.

1201
01:05:39,680 --> 01:05:42,080
Speaker 1: The other thing that actually influenced me is that the

1202
01:05:42,120 --> 01:05:45,600
Speaker 1: opposite extreme. I've had this question in my head about

1203
01:05:46,360 --> 01:05:51,440
Speaker 1: why does Warren Buffett. Why does Warren Buffett not invest

1204
01:05:51,720 --> 01:05:55,760
Speaker 1: in innovation? Not technology startups, but innovation? You know it

1205
01:05:55,880 --> 01:05:58,080
Speaker 1: should he should mean, I'm not, you know, I'm not

1206
01:05:58,200 --> 01:05:59,840
Speaker 1: want to say what he should do with his money,

1207
01:06:00,280 --> 01:06:03,920
Speaker 1: but it's a fascinating question. It's a seems like a

1208
01:06:03,920 --> 01:06:06,720
Speaker 1: bit of a dichonomy, right, but he invested in long

1209
01:06:06,840 --> 01:06:12,760
Speaker 1: term only for value creation, and he also does philanthropy right,

1210
01:06:13,200 --> 01:06:15,840
Speaker 1: and innovation is all about removing obstacles to progress in

1211
01:06:15,880 --> 01:06:18,240
Speaker 1: the long term that I actually create benefits from society

1212
01:06:18,320 --> 01:06:21,600
Speaker 1: and value. So it's like he should be investing in that,

1213
01:06:22,440 --> 01:06:24,640
Speaker 1: and yet he doesn't. I'm not I don't think he should.

1214
01:06:25,040 --> 01:06:27,680
Speaker 1: But trying to answer this question has cost me to

1215
01:06:27,960 --> 01:06:30,960
Speaker 1: think about more carefully about what innovation actually is and

1216
01:06:31,000 --> 01:06:33,400
Speaker 1: how it is different from just creating a company or

1217
01:06:33,440 --> 01:06:36,880
Speaker 1: doing a startup. It's much more ambitious. So you mentioned

1218
01:06:37,000 --> 01:06:40,440
Speaker 1: qun tell Us about some of your favorite books, so

1219
01:06:40,960 --> 01:06:44,200
Speaker 1: those change all the time. But the latest breed is

1220
01:06:44,880 --> 01:06:46,920
Speaker 1: The Hitchhiker's Guide to the Galaxy, which I keep I'm

1221
01:06:46,920 --> 01:06:52,680
Speaker 1: coming back to, and and the reason I love it

1222
01:06:52,720 --> 01:06:55,800
Speaker 1: is just it's just absurd in such a profoundly logical

1223
01:06:55,840 --> 01:07:01,000
Speaker 1: way that it's just hilarious. Another a book that I

1224
01:07:01,320 --> 01:07:04,520
Speaker 1: devoured was The Martian two years ago when I first

1225
01:07:04,520 --> 01:07:08,280
Speaker 1: got it, and I've still read it again because it

1226
01:07:08,560 --> 01:07:12,680
Speaker 1: is the way science really is, not the way it

1227
01:07:12,800 --> 01:07:14,959
Speaker 1: is being taught. It's not about the model. It's about

1228
01:07:15,040 --> 01:07:18,320
Speaker 1: using nature to help you. And that book, every page

1229
01:07:18,600 --> 01:07:21,680
Speaker 1: is literally a person trying to survive making science and

1230
01:07:21,760 --> 01:07:26,480
Speaker 1: nature work for him. Right. It's a series of problem solving,

1231
01:07:26,680 --> 01:07:30,760
Speaker 1: problem solving, no solution real and and and lots of

1232
01:07:30,800 --> 01:07:33,200
Speaker 1: what you might call failures. But really, if he failed,

1233
01:07:33,200 --> 01:07:36,560
Speaker 1: he died, right, So that was not an option. And

1234
01:07:36,560 --> 01:07:38,560
Speaker 1: and the mechanic of the book, the richness of it.

1235
01:07:38,600 --> 01:07:40,680
Speaker 1: Even if you don't fully understand the technology, you can

1236
01:07:40,800 --> 01:07:44,560
Speaker 1: tell the story. It's enormously vibrant. And the last book

1237
01:07:44,560 --> 01:07:46,200
Speaker 1: I keep on coming back to every now and then

1238
01:07:46,320 --> 01:07:49,400
Speaker 1: is what if? From random unders sure and you can

1239
01:07:49,440 --> 01:07:52,200
Speaker 1: tell the trend right it's I'm techie. I love sci fi,

1240
01:07:53,000 --> 01:07:55,080
Speaker 1: uh because I want to create the sci fi so

1241
01:07:55,160 --> 01:07:58,960
Speaker 1: it becomes real and but just a touch of absurdly.

1242
01:07:59,040 --> 01:08:01,520
Speaker 1: And it needs be absurd because otherwise you cannot get

1243
01:08:01,520 --> 01:08:04,040
Speaker 1: good ideas. They need to look absurd at first, not

1244
01:08:04,240 --> 01:08:08,120
Speaker 1: because just because and this is something I realized over

1245
01:08:08,160 --> 01:08:11,480
Speaker 1: over time, ideas look absurd because of your training. They

1246
01:08:11,480 --> 01:08:14,040
Speaker 1: were not absurd per se. So if your background taught

1247
01:08:14,080 --> 01:08:17,280
Speaker 1: you to ignore certain things and assume certain things, anything

1248
01:08:17,360 --> 01:08:20,479
Speaker 1: that challenges that will look absurd only until you show

1249
01:08:20,520 --> 01:08:25,599
Speaker 1: it works. So, since you came into the fields of

1250
01:08:25,640 --> 01:08:30,840
Speaker 1: filling the blank artificial intelligence innovation some years ago, what

1251
01:08:31,040 --> 01:08:33,839
Speaker 1: has changed? And is for the better or for the worst.

1252
01:08:35,360 --> 01:08:39,120
Speaker 1: Many things have changed. I'll give you one for the

1253
01:08:39,120 --> 01:08:44,439
Speaker 1: better and one for the worst. We've become way more siloed. Siloed, yes,

1254
01:08:44,479 --> 01:08:48,559
Speaker 1: and that creates enormous amount of inefficiencies in medication, in

1255
01:08:48,560 --> 01:08:54,439
Speaker 1: investing in innovation that I myself avoid with IT teams,

1256
01:08:54,479 --> 01:09:00,000
Speaker 1: which is crossed disciplinary across the entire institute. Um, that's

1257
01:08:59,800 --> 01:09:02,680
Speaker 1: that's something that I believe it's going to change. At

1258
01:09:02,680 --> 01:09:05,200
Speaker 1: the same time, the opposite has also become true. You

1259
01:09:05,200 --> 01:09:08,559
Speaker 1: could indicate yourself on whatever online if only you had

1260
01:09:08,560 --> 01:09:10,560
Speaker 1: a project or a means to guide you with a

1261
01:09:10,680 --> 01:09:13,280
Speaker 1: project to acquire that knowledge, which is the reason why

1262
01:09:13,320 --> 01:09:15,880
Speaker 1: I wrote the book to start with. And so those

1263
01:09:15,880 --> 01:09:19,920
Speaker 1: two things have happened. One people more siloed and more formulate.

1264
01:09:20,800 --> 01:09:23,200
Speaker 1: More students asked me today for formulas for innovation, and

1265
01:09:23,240 --> 01:09:25,479
Speaker 1: they did ten years ago, which makes no sense because

1266
01:09:25,479 --> 01:09:27,360
Speaker 1: if there is a formula, there is a recipe, it's

1267
01:09:27,520 --> 01:09:29,519
Speaker 1: then you're just going to bake the same cake someone day.

1268
01:09:29,680 --> 01:09:32,040
Speaker 1: How is that an innovation? It's a great copy by

1269
01:09:32,080 --> 01:09:36,240
Speaker 1: all means, eat the cake, But um, it's not an innovation,

1270
01:09:36,520 --> 01:09:40,479
Speaker 1: right so Um. But then on the other side, more

1271
01:09:40,520 --> 01:09:43,639
Speaker 1: and more people I believe can actually start to learn

1272
01:09:43,680 --> 01:09:46,639
Speaker 1: on their own and ignore recipes or said models because

1273
01:09:46,680 --> 01:09:48,880
Speaker 1: there is such an abundance of information that wasn't there

1274
01:09:49,120 --> 01:09:52,200
Speaker 1: ten years ago. And AI is only making that or

1275
01:09:52,280 --> 01:09:54,800
Speaker 1: will only make it if I get my way would

1276
01:09:54,840 --> 01:09:57,479
Speaker 1: only get it make it even easier to put that

1277
01:09:57,520 --> 01:10:00,720
Speaker 1: to your service, so you can solve whatever problem you anty.

1278
01:10:00,840 --> 01:10:03,200
Speaker 1: Tell us what you do outside of the office to

1279
01:10:03,320 --> 01:10:07,880
Speaker 1: relax or for fun. I spend as much time as

1280
01:10:07,880 --> 01:10:11,760
Speaker 1: i can with my kids for not just because I'm

1281
01:10:11,800 --> 01:10:14,880
Speaker 1: a family man. It's just they're hilarious, but it's it's

1282
01:10:14,920 --> 01:10:17,559
Speaker 1: but it's not going with my kids as in traditional playing.

1283
01:10:17,560 --> 01:10:20,320
Speaker 1: Of course, as you can imagine, we build contraptions. I'm

1284
01:10:20,360 --> 01:10:22,920
Speaker 1: having them build computers. There are five and eight. But

1285
01:10:23,000 --> 01:10:26,360
Speaker 1: it's okay, it's good early enough. Um So having them

1286
01:10:26,360 --> 01:10:32,240
Speaker 1: assemble the computers, we are doing all sorts of absurd things. Ah.

1287
01:10:32,280 --> 01:10:34,640
Speaker 1: And we played together and I'm even learning violin with

1288
01:10:34,760 --> 01:10:38,400
Speaker 1: both of them. Uh. And that's incredibly enriching for me.

1289
01:10:40,360 --> 01:10:43,599
Speaker 1: So if a millennial or recent college grad came up

1290
01:10:43,640 --> 01:10:50,600
Speaker 1: to you and said, I'm interested in a career in technology, innovations, startups,

1291
01:10:51,080 --> 01:10:54,400
Speaker 1: what sort of advice would you give them? Interesting question.

1292
01:10:54,600 --> 01:10:58,360
Speaker 1: So my first advice is find a problem, right, because

1293
01:10:58,400 --> 01:11:01,679
Speaker 1: technology is what you use to make the problem go away. Right.

1294
01:11:01,920 --> 01:11:05,320
Speaker 1: An innovation is the process of doing that to some degree. Uh.

1295
01:11:05,360 --> 01:11:08,760
Speaker 1: If you can find a problem and look harder, but

1296
01:11:08,800 --> 01:11:11,800
Speaker 1: if all you intend to do is get money, you

1297
01:11:11,800 --> 01:11:14,320
Speaker 1: know the current cycle is going to end. Eventually, it's

1298
01:11:14,320 --> 01:11:19,120
Speaker 1: going to become impossible to create this multibillion dollar investments

1299
01:11:19,920 --> 01:11:22,599
Speaker 1: that take ten or fifteen years to show a profit,

1300
01:11:23,160 --> 01:11:25,719
Speaker 1: if not more. So that's not going to be there forever.

1301
01:11:26,160 --> 01:11:28,840
Speaker 1: And when that goes away, if you just adopt that

1302
01:11:28,960 --> 01:11:30,360
Speaker 1: view of the world is not going to help you.

1303
01:11:30,360 --> 01:11:33,760
Speaker 1: So just find a problem and work backwards from that

1304
01:11:33,800 --> 01:11:36,799
Speaker 1: problem to get the education you want, rather than obsessing

1305
01:11:36,800 --> 01:11:40,040
Speaker 1: about what do I become computer scientist, stores, so on,

1306
01:11:40,160 --> 01:11:43,599
Speaker 1: so forth? And our final question, what is it that

1307
01:11:43,640 --> 01:11:47,479
Speaker 1: you know about technology and innovation today that you wish

1308
01:11:47,520 --> 01:11:50,599
Speaker 1: you knew years or so ago when you were first

1309
01:11:50,600 --> 01:11:55,120
Speaker 1: starting out. So I think of myself as a different

1310
01:11:55,120 --> 01:11:58,840
Speaker 1: person every so often, So I'm not sure what myself

1311
01:11:58,880 --> 01:12:02,439
Speaker 1: of twenty years ago really thought, right, I can't chat

1312
01:12:02,479 --> 01:12:05,800
Speaker 1: with him to figure that out. So I'm not sure

1313
01:12:05,880 --> 01:12:08,160
Speaker 1: that that's what I wanted. But what I wrote in

1314
01:12:08,200 --> 01:12:10,519
Speaker 1: the book gives me the answer. I think I was

1315
01:12:10,560 --> 01:12:12,439
Speaker 1: looking for what I didn't know what I was doing,

1316
01:12:13,120 --> 01:12:15,479
Speaker 1: So I guess that what I did is that answered.

1317
01:12:16,200 --> 01:12:19,120
Speaker 1: If not the question, I would have phrased what I

1318
01:12:19,160 --> 01:12:21,080
Speaker 1: thought I needed to understand to make it to the

1319
01:12:21,120 --> 01:12:23,080
Speaker 1: next leap for myself, and that has been a really

1320
01:12:23,160 --> 01:12:28,200
Speaker 1: enreaching experience. We have been speaking with Luis Perez Bravo.

1321
01:12:28,400 --> 01:12:30,760
Speaker 1: He is a professor at m I T and the

1322
01:12:30,840 --> 01:12:36,480
Speaker 1: author of Innovating a Doer's Manifesto. If you enjoy this conversation,

1323
01:12:36,560 --> 01:12:38,240
Speaker 1: be sure and look up an Inch or down an

1324
01:12:38,240 --> 01:12:44,080
Speaker 1: Inch on Apple iTunes, overcast, Bloomberg dot com wherever final

1325
01:12:44,120 --> 01:12:46,760
Speaker 1: podcasts are sold, and you could see any of the

1326
01:12:46,840 --> 01:12:50,600
Speaker 1: other nearly two hundred such conversations we've had over the

1327
01:12:50,640 --> 01:12:54,520
Speaker 1: past few years. We love your comments, feedback and suggestions

1328
01:12:55,160 --> 01:12:58,679
Speaker 1: right to us at m IB podcast at Bloomberg dot net.

1329
01:12:59,360 --> 01:13:01,600
Speaker 1: I would be remiss if I did not thank my

1330
01:13:01,760 --> 01:13:05,880
Speaker 1: crack staff who helps put together this podcast each week.

1331
01:13:06,400 --> 01:13:10,759
Speaker 1: Medina Parwana is our audio engineer and producer and keeps

1332
01:13:10,800 --> 01:13:15,400
Speaker 1: me honest each week and moving along as these conversations

1333
01:13:15,439 --> 01:13:19,200
Speaker 1: progress across ninety minutes. Taylor Riggs is our other producer

1334
01:13:19,320 --> 01:13:24,240
Speaker 1: slash booker. Michael Batnick is our head of research. Attica

1335
01:13:24,439 --> 01:13:28,880
Speaker 1: val Burne is our business producer. I'm Barry Retolts. You've

1336
01:13:28,920 --> 01:13:32,400
Speaker 1: been listening to Masters in Business on Bloomberg radio