Latest / Masters in Business / Luis Perez-Breva Explains How to Be ‘Productively Wrong’

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Luis Perez-Breva Explains How to Be ‘Productively Wrong’ · Masters in Business

0:02
Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.
0:09
Speaker 1: This week on the podcast, I have a special guest.
0:12
Speaker 1: His name is Luis Perez Brava. He is a professor
0:16
Speaker 1: at m I T where he directs the Innovation Team's program.
0:20
Speaker 1: He is also UH the author of Innovating a Doer's Manifesto.
0:25
Speaker 1: And this is really a fascinating conversation for those of
0:29
Speaker 1: you who are interested in the direction that technology is
0:35
Speaker 1: taking us and how startup Silicon Valley technology UH problem solving,
0:44
Speaker 1: big data, artificial intelligence, et cetera is progressing. I think
0:49
Speaker 1: you will find this to be a surprising and fascinating conversation. UH.
0:55
Speaker 1: Luise approaches the idea of UM problem solving an innovation
1:00
Speaker 1: in a different UH concept and a different order and
1:05
Speaker 1: in a different construct than I think most people think
1:09
Speaker 1: of in terms of technology and UM innovation. He really
1:14
Speaker 1: looks at this as a problem solving an iterative process
1:20
Speaker 1: as opposed to something driven purely by technology or purely
1:25
Speaker 1: by capital UH. He in fact, he specifically thinks most
1:30
Speaker 1: people are doing this wrong because they're focusing on the
1:35
Speaker 1: concept of big ideas as opposed to thinking about hunches
1:41
Speaker 1: and small incremental changes that solve problems as opposed to
1:47
Speaker 1: what we discussed, the grand pivot that seems to be
1:51
Speaker 1: so popular these days in Silicon Valley. So if you're
1:54
Speaker 1: at all interested in venture capital or technology or the
1:59
Speaker 1: future direct action of tech, I think you'll find this
2:02
Speaker 1: conversation quite fascinating. With no further ado, my conversation with
2:07
Speaker 1: Luis Perez Breva. I'm Barry Riholts. You're listening to Masters
2:15
Speaker 1: in Business on Bloomberg Radio. My guest today is Luis
2:18
Speaker 1: Perez Breva. He is an expert in the process of
2:21
Speaker 1: technology innovation. He holds multiple degrees from multiple countries, including Physics,
2:28
Speaker 1: Business artificial Intelligence from Spain, France, and the US. He
2:33
Speaker 1: is a professor at m I T and he is
2:35
Speaker 1: the author of Innovating a Doer's Manifesto for starting from
2:40
Speaker 1: a hunch, prototyping problems, scaling up, and learning to be
2:45
Speaker 1: productively wrong. He currently directs the m I T Innovation
2:50
Speaker 1: Team's program and advises organizations on artificial intelligence and innovation.
2:57
Speaker 1: Luis Perez Breva, Welcome to Bloomberg. Thank you, thank you
3:00
Speaker 1: for having me. So you've said a number of things
3:02
Speaker 1: that I find fascinating. Let's jump right into the comment
3:07
Speaker 1: you made about impossible projects. You said, I'm drawn to
3:12
Speaker 1: projects that look impossible. What are impossible projects? And if
3:17
Speaker 1: they're impossible, what can you do with them? Well, they're
3:21
Speaker 1: they're really not impossible. They just looked like that, And
3:24
Speaker 1: it's mostly because of the way we think, we think
3:26
Speaker 1: in our silos and our disciplines, and so a bunch
3:29
Speaker 1: of stuff then all of a sudden, so it looks
3:31
Speaker 1: like no one knows how to do it. But everything
3:33
Speaker 1: you love about the innovation started out looking impossible. So
3:37
Speaker 1: give us an example of something that looked impossible but
3:40
Speaker 1: turns out not to be My favorite one because it's
3:42
Speaker 1: been a long time and it's still mesmerizing is Henry Ford.
3:46
Speaker 1: But I'll bring it to the present for you. So,
3:48
Speaker 1: if Henry Ford was life today, what he would have
3:50
Speaker 1: said is that he wanted to make jet planes private
3:52
Speaker 1: jet planes affordable that because that's what a car was
3:55
Speaker 1: back then. So this seemed like a completely impossible thing
3:59
Speaker 1: to do. Actually, no one believed him. Everyone thought he
4:02
Speaker 1: was wrong, and yet he somehow they didn't change the
4:04
Speaker 1: America forever. So that kind of problems, whether they be
4:07
Speaker 1: that big or smaller, but that people cannot seem to
4:11
Speaker 1: be able to bring together what draws my attention every
4:13
Speaker 1: single time. And I noticed you said affordable. It's not
4:16
Speaker 1: just that he wants to manufacture cars. He wants to
4:20
Speaker 1: manufacture cars. It's such a scale that they were affordable
4:23
Speaker 1: to anyone working in his factories that that seems to
4:26
Speaker 1: be an interesting That's the key when you actually look
4:30
Speaker 1: at the history of him before, as historians tell it,
4:33
Speaker 1: just different from how you may read it in a book.
4:35
Speaker 1: In a business book, you hear that all he wanted
4:37
Speaker 1: was make cars affordable because his experience living and growing
4:41
Speaker 1: up in a farm made him realize how hard it
4:44
Speaker 1: was to make supply runs to the city. So that
4:46
Speaker 1: was the objective. Everything else, the scale, manufacturing, finance, increasing wages,
4:51
Speaker 1: was a means to an end, which is make cars affordable.
4:55
Speaker 1: It's like problem solving all along. So let's let's address
4:58
Speaker 1: problem solving a little bit. The title of the book
5:01
Speaker 1: you reference being productively wrong? How is a person productively wrong?
5:07
Speaker 1: You know the way. I've actually even come to realize
5:10
Speaker 1: that even more ever since I had kids. When you're
5:12
Speaker 1: a kid, When we were children, you spend all your
5:15
Speaker 1: day being fundamentally wrong, trying things out and mostly not
5:20
Speaker 1: getting everything right, and just you don't really care, just experiments,
5:23
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
5:29
Speaker 1: applies by formula, and then we destroy the magic. Effectively,
5:33
Speaker 1: we destroyed the spirit of angry. But the way we
5:35
Speaker 1: seem to learn as humans, and this we know from
5:38
Speaker 1: neuroscience from how hard it is to do ourtificial intelligence
5:42
Speaker 1: and so on, is by fundamentally being wrong, so officially
5:45
Speaker 1: many times, until we figure out what was wrong about it.
5:48
Speaker 1: So we don't learn by being right or by formula.
5:50
Speaker 1: We learned by fundamentally being wrong. I'll give you one
5:53
Speaker 1: quick example which I learned from my daughter. When she
5:55
Speaker 1: was starting to play violin. She played out of tune
5:59
Speaker 1: pretty much time until one day she didn't, and then
6:02
Speaker 1: she figured out when violent itself was out of tune.
6:04
Speaker 1: And this happened gradually to a point that she never
6:07
Speaker 1: actually realized that she was playing out of tune until
6:10
Speaker 1: that one day in which all of a sudden she did.
6:13
Speaker 1: So she actually learned to play in tune mostly by
6:15
Speaker 1: being wrung every single time. And I bring this up
6:19
Speaker 1: because playing an instrument is such an intuitive thing depending
6:22
Speaker 1: on how you learn it, that it brings this about
6:26
Speaker 1: how we learn even when you're an adult. So I
6:28
Speaker 1: started to learn violin with my daughter. Really, and you know,
6:31
Speaker 1: the violin is unique, or or at least different from
6:35
Speaker 1: a piano or something where each note is defined. It's fretless,
6:40
Speaker 1: and so you could be off by a quarter of
6:42
Speaker 1: a tone. It's a very different experience. But that leads
6:47
Speaker 1: the question. We think of child's play as things kids
6:50
Speaker 1: do for fun, but you're really implying that child's play
6:54
Speaker 1: is a form of experimentation and learning. Yeah, or you
6:58
Speaker 1: can look at it the other way around. We adults
7:01
Speaker 1: should play much the same way, because that's how we
7:03
Speaker 1: acquired a bunch of things we use every day. Language,
7:06
Speaker 1: your surroundings, your sense of taste. Everything was acquired that
7:10
Speaker 1: precise way through experimentation. Is experiment wrong play, but it
7:14
Speaker 1: was play. Now we grow up and we kind of
7:16
Speaker 1: are afraid of being wrong. We need to be right,
7:19
Speaker 1: and so before you know it, you're just not using
7:21
Speaker 1: your brain for what it was, for what it evolved
7:25
Speaker 1: to do best, which is learning through this kind of play.
7:28
Speaker 1: Let's talk about innovation a little bit. What is innovation
7:31
Speaker 1: and how does it work? Funny that's innovation has been
7:35
Speaker 1: said to me so many things. It's an overused word.
7:38
Speaker 1: So I'll tell you what I think innovation is. Innovation
7:40
Speaker 1: is the end result, meaning you go about trying to
7:44
Speaker 1: solve some obstacle, solve a problem, and uh, most people
7:48
Speaker 1: will think you're wrong, and one day they stop thinking
7:50
Speaker 1: you're wrong and they'll call it an innovation. So innovation
7:53
Speaker 1: is how people certify what you did. But at first
7:56
Speaker 1: it looked preposterous, it was fundamentally wrong, and did it
8:00
Speaker 1: because you felt there was something to it that other
8:03
Speaker 1: people couldn't see. Give us an example of something that
8:05
Speaker 1: was preposterous and then adapted by everybody I love. I'm
8:09
Speaker 1: just going to use big examples. So let's see how
8:11
Speaker 1: prepostors they wear. And hope logical isn't today. So Bill
8:16
Speaker 1: Gates talks about a computer in every desk and at
8:21
Speaker 1: the end at they were every home. Right. So I'll
8:24
Speaker 1: go back to right where there are maybe a few
8:27
Speaker 1: main frames here and there no internet, no no internet.
8:31
Speaker 1: No one knows what to do with it. There isn't
8:33
Speaker 1: even spreadsheet yet that would come a bit later you
8:36
Speaker 1: can read the entire history I mean at the time
8:39
Speaker 1: and and somehow today because you have a computer in
8:41
Speaker 1: your pocket, and probably another in your desk, and another
8:43
Speaker 1: at home, and maybe five iPads, and who knows what,
8:46
Speaker 1: maybe your phone is a computer as well. Now it
8:48
Speaker 1: seems perfectly natural. So that is what makes it look
8:52
Speaker 1: like an innovation. It's even better because by the time
8:56
Speaker 1: it's gone through its cycle, it actually looks old. The
8:59
Speaker 1: idea of a computer and every desk and every home
9:01
Speaker 1: seems old all of a sudden, And yet it was
9:04
Speaker 1: a brutal innovation that has created so much economic progress
9:08
Speaker 1: in all sorts of fields. Like we're sitting in a
9:10
Speaker 1: room where there's a cusident of computers. The idea itself
9:14
Speaker 1: was prepators in nineteen every actually so prepastors. It didn't matter.
9:17
Speaker 1: That's fascinating. Let's talk a little bit about technology and innovation.
9:23
Speaker 1: But I want to start with the idea of ideas.
9:27
Speaker 1: Where do innovative ideas come from? It's that's a trick question.
9:33
Speaker 1: It's a true question because over the last two years
9:35
Speaker 1: we've placed such an importance on the word idea that
9:38
Speaker 1: in my book, I decided to give up on it.
9:40
Speaker 1: And I actually talk about hunches because now ideas have
9:43
Speaker 1: become so important that it's actually burdening most of us.
9:47
Speaker 1: We think we need to come up with this innovative
9:49
Speaker 1: idea before we even do anything. So what's the difference
9:51
Speaker 1: between an idea and a hunch other than people don't
9:56
Speaker 1: judge hunches as harshly as they might judge idea. So
10:00
Speaker 1: the basic difference is an admission practical sense. Nothing ideas
10:03
Speaker 1: come from pretty much anywhere. And as I've told you,
10:06
Speaker 1: the best ideas start out being just preposterous combined two
10:09
Speaker 1: things that that are not meant to go together. But
10:12
Speaker 1: from a perception standpoint, from how you go and see
10:15
Speaker 1: people pitching ideas and investing in ideas, people should not
10:18
Speaker 1: be investing in ideas. People should be investing in project
10:21
Speaker 1: in organizations, in in dreams if you want, if you
10:24
Speaker 1: want to take it to the extreme. But ideas just
10:27
Speaker 1: they are born bad. All ideas are effectively born bad.
10:30
Speaker 1: Ideas are born bad. Yes, go into more details about
10:35
Speaker 1: what why do you believe all ideas are born bad? Well,
10:40
Speaker 1: you know if you truly have one of those ideas
10:42
Speaker 1: that in the futures, when someone will say they were
10:45
Speaker 1: innovative right, What you realize is that there is a
10:49
Speaker 1: lot to learn ahead of you, and it's just impossible
10:52
Speaker 1: for you to have figured it out all in your head.
10:54
Speaker 1: So the first thing that needs to happen to that
10:56
Speaker 1: idea is change, because it's in your head and it
10:59
Speaker 1: doesn't exist anywhere, and for it to exist, you need
11:02
Speaker 1: to scale something up to the world. And it would
11:04
Speaker 1: be amazing if you had figured everything out perfectly in
11:07
Speaker 1: your head. So you need to almost assume that your
11:10
Speaker 1: idea interesting, enticing as it seems to you, it's just
11:13
Speaker 1: fundamentally wrong in more ways than it is actually right,
11:16
Speaker 1: and that's how you figure out how to get it
11:18
Speaker 1: the scale. So, in other words, an idea is almost
11:20
Speaker 1: a conclusion, and you need all the intervening steps to
11:24
Speaker 1: get there. And by the time you're done your idea
11:26
Speaker 1: we only look like the initial idea to you, but
11:29
Speaker 1: to no one else. So how does one develop innovations hunches?
11:35
Speaker 1: I don't I won't say ideas. How does one develop
11:38
Speaker 1: the skills to produce better hunches and lead yourself towards
11:44
Speaker 1: greater innovation. It's actually much simpler than people think, once
11:48
Speaker 1: you remove the obsession to come up with the earth
11:50
Speaker 1: chattering or disruptive idea. Then you realize that all you
11:53
Speaker 1: need to start is something that doesn't seem to be right,
11:56
Speaker 1: So you might as well start by putting trying to
11:58
Speaker 1: put together two things that actually don't simp to fit together.
12:02
Speaker 1: And then that causes you to ask the question, Okay,
12:04
Speaker 1: this doesn't work why, And as you start from there,
12:08
Speaker 1: you realize that all you need to do is continue
12:09
Speaker 1: to put these pieces, these parts together, to prove to
12:12
Speaker 1: yourself that this will actually not work. Slowly, you increasingly
12:15
Speaker 1: either build up scale just give up on it, mostly
12:18
Speaker 1: because you lose interest. And that's fine. That's how we
12:21
Speaker 1: humans operate. It's actually very simple way to do it,
12:24
Speaker 1: and you can practice it. So as you practice, you
12:27
Speaker 1: start to see opportunities that are essentially emerging things that
12:30
Speaker 1: you and perhaps only you see that are meant to
12:33
Speaker 1: go together. But then what else does just yet? That's
12:36
Speaker 1: quite interesting. What do you think the differences between ideas
12:41
Speaker 1: and execution, because they really are two completely different skill sets.
12:46
Speaker 1: There are two completely different skill sets. But you know,
12:49
Speaker 1: my experience when when I've taught over years, is that
12:52
Speaker 1: paying too much attention to those two words actually gets
12:55
Speaker 1: you into linear thinking, because then it because as you
12:58
Speaker 1: to ask the question when once should I stop ideating
13:00
Speaker 1: and then start executing, meaning to linear, not to line,
13:04
Speaker 1: flexible or exactly. And what we know from innovations, and
13:07
Speaker 1: every single treatise and innovation tells you this is that
13:10
Speaker 1: innovation is fundamentally nonlinear. If you endeavor set you up
13:13
Speaker 1: to try to predict the future so that you know
13:15
Speaker 1: when to start executing, you are starting the wrong way.
13:17
Speaker 1: So what I tell my students and what we the
13:20
Speaker 1: way we teach it and the way I explained in
13:21
Speaker 1: the book is your challenge is to scale up something
13:25
Speaker 1: that's the only thing that matters. And you cannot scale
13:27
Speaker 1: up by mostly dissociating an execution from idea. Execution will
13:31
Speaker 1: change your idea, and your idea will change what you
13:34
Speaker 1: execute on continuously at different scales. So is that from
13:38
Speaker 1: whence comes the infamous pivot or you get all these
13:42
Speaker 1: startup companies and Silicon Valley and they look like they're
13:45
Speaker 1: going to be one thing, and then halfway through suddenly
13:49
Speaker 1: the point in a different direction from a fundamentally similar
13:54
Speaker 1: technology or idea. But but the problem they're trying to
13:57
Speaker 1: solve seems to be something completely different, any original idea.
14:01
Speaker 1: I love that you called it infamous pivot. It wasn't me,
14:04
Speaker 1: you did it. Yes, well, haven't we heard that phrase
14:07
Speaker 1: a million times in the past. You know a lot
14:10
Speaker 1: about the innovation we're just chatting about. It depends on
14:13
Speaker 1: from where you're looking at it. So if you are
14:15
Speaker 1: at the receiving end of it, and you're just looking
14:17
Speaker 1: at what a company does without all the knowledge of
14:20
Speaker 1: what they're doing every day, it looks at as though
14:22
Speaker 1: they change direction all the time. Now that's just a
14:25
Speaker 1: perception from the outside, which means it's not what you
14:28
Speaker 1: should do. It's what people think you're doing. But what
14:31
Speaker 1: you are doing is effectively continuously changed incrementally how you're
14:35
Speaker 1: thinking about it, reformulating it continuously, and as you start
14:39
Speaker 1: to get traction in different directions, it seems as though
14:41
Speaker 1: you're doing different things. But every time you look at
14:44
Speaker 1: any of those stories, the way they really happened, not
14:47
Speaker 1: the way they are told. There was no pivot. Nobody
14:49
Speaker 1: started pivoting and changing in their mind every ten seconds,
14:52
Speaker 1: And the advice to pivot constantly makes no particular sense
14:55
Speaker 1: to me. So they're they're working on something. It's evolving,
14:59
Speaker 1: it's rating over time, and suddenly, I think you use
15:03
Speaker 1: the phrase traction, something starts to gain traction and scale.
15:07
Speaker 1: So what we're hearing after the fact as a pivot
15:10
Speaker 1: is really a normal evolution towards something that's that's working. Yes,
15:15
Speaker 1: except that some companies have taken as a mantra to
15:18
Speaker 1: take pivoting as a as a thing they do. And
15:20
Speaker 1: so every now and then they say they try to
15:22
Speaker 1: cause them themselves to think, how should I change direction today?
15:26
Speaker 1: And that's a really hard question. Actually, the field of entrepreneurship,
15:29
Speaker 1: which is different from innovation, is full of really strange questions.
15:33
Speaker 1: Give us another example we've just discussed, when should I
15:36
Speaker 1: stop be the aating versus executing? I don't know how
15:40
Speaker 1: to even pose that question meaningfully. Should I pivot today?
15:43
Speaker 1: Maybe maybe not? I just I have no data, no
15:45
Speaker 1: evidence to kind of grasp some other advice. You hear
15:50
Speaker 1: a lot of this focus on the user, but really,
15:52
Speaker 1: if what you're doing is that new, is there already
15:55
Speaker 1: a user for it? If not, you're just making it up.
15:58
Speaker 1: By the way, I'm all for making stuff up, I'm
16:00
Speaker 1: not for ignoring the fact that you just made it up, right,
16:03
Speaker 1: So it's concealing the assumption you make is actually the
16:05
Speaker 1: big mistake you would do. So there's an apocryphal story
16:08
Speaker 1: about Steve Jobs and the iPhone, and people at Apple
16:14
Speaker 1: kind of challenged what he wanted to do, saying the
16:17
Speaker 1: user doesn't have any idea what what a glass phone
16:22
Speaker 1: is going to be like, and Jobs famously said, I'll
16:26
Speaker 1: tell the user what they want. Is that what we're
16:28
Speaker 1: talking about in terms of not focusing too much on
16:33
Speaker 1: the user experience and instead, if you really want to innovate,
16:36
Speaker 1: you have to forget about the user for a while.
16:38
Speaker 1: So I think that we went back ten years ago, right,
16:43
Speaker 1: maybe fifteen, and anybody came to us and said you
16:46
Speaker 1: need to be listening to more podcasts, the answer would be,
16:49
Speaker 1: what is this person talking about? Right? So I don't
16:54
Speaker 1: think you can expect anybody, neither the innovator, nor the entrepreneur,
16:58
Speaker 1: or the investor or anybody to predict the future, right.
17:02
Speaker 1: And so at some point, if you truly are looking
17:04
Speaker 1: for or five years ahead, and you're going to users
17:07
Speaker 1: and hoping they have an answer of what the future
17:09
Speaker 1: beholds five years had, I think you're delivering yourself like nobody,
17:13
Speaker 1: nobody has that answer. Someone has to make up that future.
17:16
Speaker 1: That's the way I hear what Steve Jobs might have said. Right,
17:20
Speaker 1: I wasn't in the room. So let's talk a little
17:23
Speaker 1: bit about what's going on in the world of innovation today.
17:28
Speaker 1: How can we tell if a good idea or a
17:32
Speaker 1: hunch is going to succeed? That's a question I get
17:37
Speaker 1: every semester, and the answer, I'm afraid is you can't.
17:41
Speaker 1: You just got to try it out. Just try it
17:43
Speaker 1: out and see what happens. Now, the good news is
17:47
Speaker 1: that you also get to define what success is. So
17:49
Speaker 1: there's no way you can fail unless you make it
17:52
Speaker 1: a purpose like many people do, meaning meaning that, well,
17:57
Speaker 1: how do people define success if they have an idea? Well,
18:01
Speaker 1: what I propose people do, and what I talk about
18:03
Speaker 1: in my book is, you know you have a hunch.
18:05
Speaker 1: How about we try to figure out what problem is
18:07
Speaker 1: it that you three you really think you're solving. There's
18:10
Speaker 1: a way to think about problems that's pretty straightforward. And
18:14
Speaker 1: what I tell my my my students and they look
18:16
Speaker 1: at me funny, is that, uh, if an alien species
18:20
Speaker 1: where to come down to Earth and show you the solution,
18:22
Speaker 1: would you even know that that's actually a solution to
18:24
Speaker 1: your problem, because if not, then you will never find
18:27
Speaker 1: it on your own, and that's how you fail. So
18:30
Speaker 1: you start working backwards, what do you think solves the problem,
18:33
Speaker 1: and then before you know you realize you actually get
18:35
Speaker 1: to define everything. So you continue to do that, and
18:39
Speaker 1: before you know it, you succeed. Maybe it's a different
18:41
Speaker 1: way you thought you would, but that's how you succeed.
18:44
Speaker 1: You try and you go through scale. You're the director
18:48
Speaker 1: of the m I T Innovation Team's program. Yes, what
18:52
Speaker 1: does that program do? What are its goals? And what
18:56
Speaker 1: has come out of that program? So it's a very
19:00
Speaker 1: usual entity in academia. A few years ago we thought about, well,
19:07
Speaker 1: you know, we want to etiquate people to actually meaningful
19:09
Speaker 1: to innovate as a career choice. How do we do that?
19:13
Speaker 1: So most people think that you you want technologies from
19:16
Speaker 1: academia to live and have an impact in the world,
19:19
Speaker 1: and of course we want that. But what I really
19:22
Speaker 1: want is for every single student at m I T
19:25
Speaker 1: and wherever I've taught this now worldwide, to learn how
19:28
Speaker 1: to do this as a career choice, and then they
19:30
Speaker 1: can choose to do it or not. That's their choice.
19:32
Speaker 1: So I thought, how do we do that? And it
19:35
Speaker 1: occurred to us that you could actually get a Mighty Technologies,
19:38
Speaker 1: which is a real tangible thing, and then ask students
19:41
Speaker 1: from all domains. So I may get an engineering technology
19:45
Speaker 1: and the students are from science, business and architecture, and
19:47
Speaker 1: then try to teach them what it actually means to
19:51
Speaker 1: innovate with that tangible starting point. And the process is
19:54
Speaker 1: very simple. So someone in a lab at M I. T.
19:56
Speaker 1: Has discovered that a new thing is actually possible. So
19:59
Speaker 1: I of my students, Okay, somewhere they're hidden is a superpower.
20:04
Speaker 1: We just don't know what the superpower is. And obviously
20:07
Speaker 1: that superpower is going to solve a problem somewhere. We
20:09
Speaker 1: just don't know where nor what the problem is. So
20:12
Speaker 1: we need to solve for those two things. So people
20:14
Speaker 1: are really approaching the concept of ideas and innovation backwards.
20:19
Speaker 1: They're starting with the idea as opposed to what problems
20:22
Speaker 1: to solve exactly. And that alone takes me about a
20:25
Speaker 1: month for STEMS to appreciate that we're actually turning things
20:30
Speaker 1: on its head because it's actually simpler problem and solve
20:35
Speaker 1: it exactly. And of course there's no change we're going
20:38
Speaker 1: to find the perfect problem at the start. But as
20:40
Speaker 1: long as you understand how to do it, you can
20:42
Speaker 1: do it increasingly more quickly. And that's what led me
20:45
Speaker 1: to believe that you could actually practice this even when
20:47
Speaker 1: you don't have a technology at the input, which is
20:48
Speaker 1: what the book covers. Um. But beauty is that the
20:51
Speaker 1: result is actually very clear path to action to take
20:55
Speaker 1: this technology, change it and address a problem that even
20:58
Speaker 1: the researchers hadn't hadn't about. And so a bunch of
21:01
Speaker 1: startups have actually come out of this, even though it's
21:03
Speaker 1: not my objective, right, My objective is to educate students,
21:06
Speaker 1: so they do this continuously. Right, So you write in
21:10
Speaker 1: the book that people and again to reference Silicon Valley,
21:15
Speaker 1: they do it backwards. They as soon as they have
21:18
Speaker 1: an idea, they try and um get funding, They try
21:22
Speaker 1: and go out and raise capital. You suggest not to
21:26
Speaker 1: do that until way later explain the thinking. So there's
21:30
Speaker 1: the ways to think about about that. Um, I think
21:35
Speaker 1: that I while you have is an idea, which as
21:37
Speaker 1: you know, I think they tend to be born not
21:40
Speaker 1: so great, not to say bad ah, and you obsess
21:44
Speaker 1: about pitching it and selling it. You're putting yourself in
21:47
Speaker 1: the worst negotiation position you can possibly ever be, which
21:51
Speaker 1: is someone else is going to decide what your company
21:54
Speaker 1: is going to look like. They may give you money,
21:56
Speaker 1: You may look like great because you've got the money,
21:58
Speaker 1: but then you may soon realize your idea. UH needed
22:00
Speaker 1: a whole different process. So I asked in the book,
22:03
Speaker 1: I asked everybody who has an idea to think of
22:05
Speaker 1: themselves as investors zero because the first time he's going
22:09
Speaker 1: to be spent by them. And instead of spending six
22:12
Speaker 1: months touring vcs or asking for money, what can you
22:16
Speaker 1: accomplish in those six months to spare yourself the agony
22:20
Speaker 1: of going for money with a bad idea, and in
22:22
Speaker 1: the process, can you actually make that idea better? And
22:25
Speaker 1: that's what I mean by exploring at first. Now I've
22:27
Speaker 1: got to say that nowadays, UH, and a good friend
22:31
Speaker 1: of mine alerting it to this, UH, nowadays capital has
22:35
Speaker 1: become cheap in the sense that there's an abundance of
22:38
Speaker 1: capital for investors, and so now it's even harder for
22:41
Speaker 1: people with good ideas or with a desire to produce
22:45
Speaker 1: good ideas to not be drawn into this frenzy of
22:50
Speaker 1: asking for money and then going and creating a company
22:53
Speaker 1: on top of something that's really maybe not that great
22:56
Speaker 1: starting point. The name of the program you wrecked has
23:00
Speaker 1: the word teams right in it. What is it about
23:03
Speaker 1: teams that's so important? And doesn't that kind of contradict
23:07
Speaker 1: the idea of the loan inventor toiling away in his
23:10
Speaker 1: lab or her lab until late at night. Interesting, So
23:14
Speaker 1: I'm not sure the lon inventor at the lab will
23:17
Speaker 1: actually become the innovator, right. Sometimes they do somethings they don't,
23:21
Speaker 1: But in order to become an innovator, they have to
23:24
Speaker 1: become something else, right. That's something else is they are
23:27
Speaker 1: required to talk to other people for some purpose, even
23:30
Speaker 1: if to understand what the world looks like outside of
23:32
Speaker 1: the lab and learn about the stuff they haven't learned
23:36
Speaker 1: in the lab. They have to be ready to change
23:39
Speaker 1: the idea. They have to be ready to use science
23:41
Speaker 1: or technology or their discipline, whatever it may be, a
23:44
Speaker 1: different way, uh than they were used to do it
23:47
Speaker 1: in the lab, or if they are in a company,
23:49
Speaker 1: in their company. Right. And so once you do that,
23:53
Speaker 1: sometime along the process, there's got to be a team
23:56
Speaker 1: because there's no company of one, right, So it not
24:00
Speaker 1: so much that the team is important is that you
24:02
Speaker 1: need to learn how to connect with other people and
24:06
Speaker 1: let other people go and along the path discover how
24:09
Speaker 1: to interact with people to actually bring the idea at
24:11
Speaker 1: the scale. So we call it teams to emphasize the
24:14
Speaker 1: fact that we're going to bring people from different disciplines
24:16
Speaker 1: together and they're going to learn because remember it's a
24:19
Speaker 1: medicational experience, that's a trick. They're going to learn how
24:24
Speaker 1: good or bad they are and interacting with other kinds
24:27
Speaker 1: of people, and they're going to learn how to either
24:28
Speaker 1: change that or they're going to learn who do not
24:31
Speaker 1: to work with again in the future. Let's talk a
24:33
Speaker 1: little bit about your career in academia. How did you
24:37
Speaker 1: find your way to m I T the first time.
24:40
Speaker 1: So I was finishing my master's degree in Spain and
24:45
Speaker 1: I decided I wanted to try something new, and a
24:48
Speaker 1: friend of my family said you should go to the
24:50
Speaker 1: United States, and I said, okay, And so I looked
24:54
Speaker 1: around and I found these people working on a mix
24:57
Speaker 1: of artificial intelligence and math, and I really wanted do that,
25:00
Speaker 1: even though I was just a chemical engineer at the
25:02
Speaker 1: time or not even that yet. And so I sent
25:05
Speaker 1: an email Ah, asking to meet, and then another and
25:10
Speaker 1: then another, and I kept on being told a bunch
25:13
Speaker 1: of things, but none of them was no. Everything was
25:15
Speaker 1: a challenge. So well, we may not be able to
25:17
Speaker 1: get you a visa, and then I answered, I'll figure
25:19
Speaker 1: that out, even though I didn't know how, and blah
25:23
Speaker 1: blah blah. You can imagine. That went for nine months.
25:25
Speaker 1: And at the end of nine months, I responded to
25:27
Speaker 1: this professor who's since become a good friend. Uh, you
25:31
Speaker 1: know what, Just tell me now, I said, because if
25:34
Speaker 1: you don't tell me now, I'm just going to keep
25:35
Speaker 1: going at it. And I needed to say no, Ah,
25:39
Speaker 1: And they don't like to say no. Dude, that apparently not.
25:43
Speaker 1: I thought that I thought he would. I thought he
25:44
Speaker 1: would immediately say no and then uh, And I told him,
25:48
Speaker 1: you know, I have lots of offers to stay around
25:49
Speaker 1: here and do my master's thesis. I just want to
25:52
Speaker 1: go work with you. I've told you all these things.
25:54
Speaker 1: I've said we would sort it out, but say no,
25:57
Speaker 1: or I will continue forever. I will never graduate. And
26:00
Speaker 1: so he he's said to me, Okay, give me three days,
26:05
Speaker 1: which was not the response I was expecting. I was
26:07
Speaker 1: respecting the response no, and then he in the end
26:10
Speaker 1: he said, okay, you can come. And that was the
26:12
Speaker 1: first time I made it to M I T, mostly
26:15
Speaker 1: as a visitor to work on artificial intelligence, being a
26:19
Speaker 1: chemical engineer, which was the first time I realized that
26:22
Speaker 1: what your study should not constrain you, h makes a
26:25
Speaker 1: lot of sense. And yet I'm not sure everybody would
26:29
Speaker 1: actually realize how much they led their background constrain them
26:32
Speaker 1: instead of actually empowering them. So so that's the way
26:35
Speaker 1: I first made it to a M I T. And
26:36
Speaker 1: ever since they've come and gone six or seven times, right,
26:39
Speaker 1: so you gotta you ended up getting a graduate degree
26:41
Speaker 1: from M I T. Yes, But later first I did
26:44
Speaker 1: a startup in Silicon Valley. I actually spent some months
26:47
Speaker 1: at M I T. Then they offered me to stay
26:49
Speaker 1: for a PhD. I applied and I got accepted, and
26:52
Speaker 1: then I said, no, I'm not doing it. I've studied
26:53
Speaker 1: for too long. I need real world experience, and I
26:56
Speaker 1: went need the startup in Silicon Valley. What was the
26:58
Speaker 1: start up to look itself? Phones in case of emergency
27:01
Speaker 1: actually exactly to this day, when you call in any
27:05
Speaker 1: one one in most of the states in the United States.
27:07
Speaker 1: You were being located by the company we founded with
27:09
Speaker 1: the technology WIN and oh, that's fantastic, it's phenomenal. And
27:12
Speaker 1: then and you're using triangulation between cell towers or some
27:17
Speaker 1: formula based on that, I'm like, we're using AI. So
27:21
Speaker 1: it's artificial intelligence based on factors, not the old way,
27:26
Speaker 1: which was the way. It turns out the triangulation would
27:28
Speaker 1: actually not work, but it would take me a long
27:31
Speaker 1: time actually to persuade you that that doesn't work. But
27:35
Speaker 1: it actually links to a lot of what I discovered
27:37
Speaker 1: since that the way we use science is very constraining,
27:40
Speaker 1: we use it as a model. We tried to feed
27:41
Speaker 1: the world to the science. But with AI, you can
27:43
Speaker 1: do it the other way around, and you can make
27:45
Speaker 1: science much more playable. And that's what I've been doing
27:47
Speaker 1: for years in my reality. And then you find out
27:51
Speaker 1: where it takes you, and you find out where it
27:52
Speaker 1: takes you, and you can actually work and operate directly
27:54
Speaker 1: on problems, which is what we didn't did then back then.
27:57
Speaker 1: The real problem was and this is like already twenty
27:59
Speaker 1: years ago, so you've been located by AI ever since
28:03
Speaker 1: to help you. Right, So we realized the only problem
28:07
Speaker 1: was that we didn't know where you were and you
28:10
Speaker 1: needed us to know, and then we don't care. If
28:13
Speaker 1: triangulation works, right, the problem to solve is where are
28:16
Speaker 1: you when you call anyone one? Right? That's it. If
28:19
Speaker 1: triangulation that work, then we need something else. And there
28:22
Speaker 1: is data, and the data we have is rich because
28:24
Speaker 1: it contains everything that's surroundings. So we built an AI
28:27
Speaker 1: that would actually learn from that data how to locate
28:29
Speaker 1: you for the purpose of any one one of course,
28:32
Speaker 1: and so that worked out to this day. It's the
28:35
Speaker 1: most phenomenal technology that does this. It's incredibly scalable and
28:40
Speaker 1: it works and it looks near magical because it's not
28:43
Speaker 1: based on model thinking or science based thinking of those.
28:46
Speaker 1: Science plays a role, but it's not the way we
28:48
Speaker 1: typically use science. So after that company, did you then
28:52
Speaker 1: go back and get your PhD from M I T
28:54
Speaker 1: or I went back to I did everything in the
28:56
Speaker 1: wrong order, So I got I did a company in
28:57
Speaker 1: an AI, and then I went to do a pH
28:59
Speaker 1: d in AI. People typically do a NBA, but somehow
29:03
Speaker 1: I I got my cables crossed. Do you have a
29:05
Speaker 1: business degree? Yeah? That I got after before I actually
29:08
Speaker 1: did the company. So so as I said everything in
29:12
Speaker 1: the wrong worder, I did my PhD. Half the way
29:14
Speaker 1: through my PhD, I needed more challenge, so I took
29:17
Speaker 1: a year of sabbatical from my PhD and went to
29:19
Speaker 1: live in France, and I continued working for my company,
29:23
Speaker 1: and at the same time, I took a degree in
29:24
Speaker 1: quantum mechanics because I thought quantum computing was going to
29:28
Speaker 1: be hot, and I needed to understand that hot. It
29:33
Speaker 1: is becoming hot. It's actually a fascinating field and uh.
29:37
Speaker 1: And then I went back to m I T, finished
29:40
Speaker 1: my PhD, did a number of startups on the side,
29:42
Speaker 1: and as I was leaving m I T I was
29:44
Speaker 1: approached by a faculty who had taught me, who had
29:47
Speaker 1: seen me teach in context, and said, would you help
29:52
Speaker 1: us set up this program innovation teams that needs revamped
29:56
Speaker 1: to truly do what it's meant to accomplish. And you've
29:58
Speaker 1: done this, he said, because you've you understand you have
30:00
Speaker 1: a PhD and the sounds to be rigorous. And at
30:03
Speaker 1: the same time, you've done this in the real world
30:05
Speaker 1: with real technologies from scratch, and you actually are not
30:09
Speaker 1: afraid of any deep attack as you can imagine with physics,
30:12
Speaker 1: engine chemical engineering, and and and intentions as a background,
30:18
Speaker 1: I'm not scared of technology anymore if I ever was right, uh,
30:22
Speaker 1: And so I see all kinds of fantastic technologies and
30:25
Speaker 1: I actually can help through that process. And that's how
30:28
Speaker 1: I stayed in academia. So that's a really interesting arc
30:33
Speaker 1: of a career. M I t is well known for
30:36
Speaker 1: having a very savvy, intelligent student body. What have you
30:42
Speaker 1: learned from your students? All sorts of things. First of all,
30:45
Speaker 1: that the better I get, or actually should put it
30:48
Speaker 1: into reverse, the the more I see them do what
30:51
Speaker 1: I teach them, the more I realize I can teach more.
30:53
Speaker 1: I know that in the general world sometimes less is better,
30:57
Speaker 1: but might t more is better. So so I've gotten
31:01
Speaker 1: to actually teach so much more throughout a semester than
31:05
Speaker 1: I thought possible before, to the point that I now
31:07
Speaker 1: believe that I can anybody, no whether their background, can
31:11
Speaker 1: be trained to innovate starting from what they have. And
31:14
Speaker 1: that's why I wrote the book. And that I learned
31:16
Speaker 1: from the students by actually pushing the envelope. Um. The
31:20
Speaker 1: other thing I learned, and this is shocking, This is
31:24
Speaker 1: still shocking to me. Student body changes every two years.
31:28
Speaker 1: And what I mean by that is that the mindset
31:30
Speaker 1: of students changes radically every two years. I would have
31:34
Speaker 1: thought it ten years or five or generational twenty every
31:38
Speaker 1: two years. If you have not completely done a blank
31:41
Speaker 1: slate in your head as to who the people are
31:43
Speaker 1: in front of you, you're going to miss the bout.
31:45
Speaker 1: So is that a function of the state of the economy.
31:48
Speaker 1: Is that a function of technology and social networks? What's
31:51
Speaker 1: driving that those changes? Or is it just a perennial backdrop?
31:56
Speaker 1: Things are always changing. I've tried to all sorts of
31:58
Speaker 1: explanations in the back of my head. Sometimes I thought
32:01
Speaker 1: it was emergency of cell phones and so on. Uh,
32:05
Speaker 1: sure those play a role, but there is something new
32:07
Speaker 1: like that every two years. So every year, every two
32:09
Speaker 1: years is different. Every semester is different already, But every
32:13
Speaker 1: two years you need to entirely clean the class and
32:16
Speaker 1: redo it from the grounds aps so that you can
32:18
Speaker 1: actually communicate meaningfully with students, which makes me better. Right,
32:23
Speaker 1: that's how I get better. That's pretty pretty wild. Let's
32:26
Speaker 1: let's talk about innovation around the world. Where are some
32:31
Speaker 1: of the most significant innovations being created globally? It's hard
32:37
Speaker 1: to answer that question because if you believe what I've
32:40
Speaker 1: what I've said about innovation before, we won't know it
32:42
Speaker 1: until it's done, and then we'll call it an innovation
32:44
Speaker 1: in hindsight. But what it's doing it looks preposterous. So
32:47
Speaker 1: so let me ask that question slightly differently. We're very
32:51
Speaker 1: much biased, and here in the United States we think
32:54
Speaker 1: of ourselves as a capital of innovation. Is America still
32:59
Speaker 1: a leader in innovation compared to its recent history? If
33:06
Speaker 1: you were to talk to a number of historians and economies,
33:09
Speaker 1: they would tell you that innovation is over. Actually, every
33:12
Speaker 1: thirty years someone says that, uh so, there is something
33:17
Speaker 1: very unique and amazing about American culture. And I say
33:21
Speaker 1: that from the perspective of someone who came here for
33:24
Speaker 1: what he thought was just a year and could not
33:27
Speaker 1: getting stuff to leave, actually because I love it. Ah,
33:31
Speaker 1: American culture combines ingenuity with a kind of attitude, with
33:35
Speaker 1: a trying things out and not being afraid to not
33:39
Speaker 1: get things right the first time. And noticed I avoid
33:41
Speaker 1: the word failing. Well, I was gonna say I look
33:44
Speaker 1: at failure in the United States as not a black
33:48
Speaker 1: mark that it is elsewhere. And if you look at
33:51
Speaker 1: some of the world's great innovators, but it's Steve Jobs
33:54
Speaker 1: or Henry Ford or Toms Ederson. All of them have
33:57
Speaker 1: had substantial failures and that failure didn't operate as a
34:02
Speaker 1: constraint for them going forward. And I don't maybe this
34:06
Speaker 1: is my hometown bias, but I think around the rest
34:09
Speaker 1: of the world, I think failure carries more of a
34:13
Speaker 1: stigma than it does in the US. So I've had
34:17
Speaker 1: to do a lot with that question. So I think
34:19
Speaker 1: the word failure, which has become incredibly popular in the
34:21
Speaker 1: last ten years, and only in the last ten years,
34:24
Speaker 1: is being overused and it doesn't mean exactly what what
34:27
Speaker 1: I perceived my students and the people I see in
34:29
Speaker 1: the United States actually do. So failure is is the
34:33
Speaker 1: word the rest of the world actually use it to
34:35
Speaker 1: describe their fear. Their fear. Yes. So the first time
34:40
Speaker 1: I went and I started teaching abroad, Uh, they asked
34:42
Speaker 1: me what about failure with this kind of panicky eyes,
34:46
Speaker 1: and I said, what about it? I said, it makes
34:48
Speaker 1: no sense, it has no meaning, And they looked at
34:50
Speaker 1: me and said, what do you means that? Yeah, why
34:52
Speaker 1: do you even worry about that? I said, like it's
34:54
Speaker 1: just the world has no meaning. You try it, it works,
34:58
Speaker 1: or it doesn't work. That's it. And this doesn't work,
35:01
Speaker 1: you move on to the next. Either fix it right
35:04
Speaker 1: or you figure out you can't fix it. But then
35:06
Speaker 1: it probably means you are no longer interested and you
35:08
Speaker 1: move on. So the keyword failure that speaks over years.
35:11
Speaker 1: He's actually destructing you from what I believe the true
35:14
Speaker 1: American values, which is you just dry it up. It works, great,
35:17
Speaker 1: doesn't work, you fix it, you don't care. You move on.
35:20
Speaker 1: We have been speaking with Professor Louise Perez Breva of
35:24
Speaker 1: m I T, author of an Innovating a Doer's Manifesto.
35:29
Speaker 1: If you enjoy this conversation, be sure and stick around
35:32
Speaker 1: for the podcast extras, where we keep the tape rolling
35:35
Speaker 1: and continue discussing all things innovation. Be sure and check
35:38
Speaker 1: out my daily column. You can find that on Bloomberg
35:41
Speaker 1: View dot com. Follow me on Twitter at rit Halts.
35:45
Speaker 1: We love your comments, feedback and suggestions right to us
35:49
Speaker 1: at m IB podcast at Bloomberg dot net. I'm Barry
35:53
Speaker 1: rit Halts. You're listening to Masters in Business on Bloomberg Radio.
36:10
Speaker 1: Welcome to the podcast. Thank you, Louis for doing this.
36:13
Speaker 1: I find your work fascinating and I have so many
36:17
Speaker 1: questions to get to. I don't even know where to start.
36:22
Speaker 1: What motivated you to write the book? Because writing a
36:24
Speaker 1: book is a challenge, and then going out and talking
36:28
Speaker 1: about it afterwards is a slog. What made you say?
36:32
Speaker 1: I know, I'll put three d and fifty words pages
36:35
Speaker 1: of words down to sum up my past twenty years
36:38
Speaker 1: worth of work. So I can give you two stories,
36:41
Speaker 1: the one forward and the one in kindsight. So looking
36:44
Speaker 1: for what they had a clue what I was doing.
36:46
Speaker 1: Let's be seriously. I figured stuff as I go because
36:49
Speaker 1: learning is my thing, so so I didn't know what
36:53
Speaker 1: it would take to write a book, nor how much
36:55
Speaker 1: more work there wasn't after you're turning the manuscript that
36:59
Speaker 1: second that's second half is you think you're done and
37:02
Speaker 1: it's like, oh no, you've got six more months of work.
37:04
Speaker 1: But in hindsight, the motivation was the true motivation was
37:10
Speaker 1: that I've been teaching this for now a decade, and uh,
37:14
Speaker 1: I know of all the problems, confusions, paradoxes that forming
37:19
Speaker 1: my students heads, and the month I spent trying to
37:21
Speaker 1: disabuse them of prejudices so that they see it's actually
37:25
Speaker 1: far simpler, which doesn't mean it doesn't take a lot
37:28
Speaker 1: of work. It does take a lot of work, but
37:30
Speaker 1: it's actually far simpler to do and you can actually
37:34
Speaker 1: practice it. Whereas people start to think about are you born,
37:37
Speaker 1: are you made? And strange questions. That was what motivated
37:41
Speaker 1: me to say, Okay, fine, I need to write this
37:43
Speaker 1: down because there is no way they could ever get
37:45
Speaker 1: it anywhere because all the other books say ask you
37:48
Speaker 1: to fail, and I rather they succeed. So so that
37:52
Speaker 1: was one piece. In hindsight, what this allowed me to
37:55
Speaker 1: do is realize why I've been camping in academia. Uh.
37:58
Speaker 1: It kind of helped me bring a bunch of thoughts together.
38:01
Speaker 1: So if you look at my funny story arc, there's
38:03
Speaker 1: just one constant all throughout, which is at I've always
38:07
Speaker 1: made computers do my bidding. I've always used computers ever
38:11
Speaker 1: since I was eight to have them do what I wanted,
38:14
Speaker 1: and I've been obsessed about how to make that dumb
38:15
Speaker 1: machine work for me forever. So and every time everything
38:21
Speaker 1: I've done has actually, to some degree or another involved computers.
38:23
Speaker 1: When I got into AI, I want to take this
38:26
Speaker 1: to the next level, which is I need AI to
38:30
Speaker 1: have me do and continue and play with the science
38:33
Speaker 1: I still don't know, so I can reach further and
38:35
Speaker 1: make up new stuff. Um so I never got that
38:39
Speaker 1: from my PhD, and I spent a number of years
38:41
Speaker 1: trying to figure out what I thought I was missing,
38:43
Speaker 1: which is, how do you actually solve a real world problem?
38:46
Speaker 1: What does it actually even mean? And how did I
38:49
Speaker 1: bring this into the equation? So that motivation is what
38:52
Speaker 1: kept IN going for the last ten years, though I
38:54
Speaker 1: could not phrase it as such until I finished the
38:57
Speaker 1: book and said I figured it out right. So now
39:01
Speaker 1: I can actually go to that next kind of AI
39:03
Speaker 1: that no one is thinking about, because it really is
39:05
Speaker 1: obsessed with data as opposed to what we can have
39:08
Speaker 1: computers do for us, which is what I really want.
39:11
Speaker 1: So you you wrote that innovation has been commoditized. What
39:15
Speaker 1: do you mean by that? I actually mean it in
39:17
Speaker 1: a in a in a very good way. It's like
39:20
Speaker 1: it's never been so easy for you to get another
39:24
Speaker 1: kind of education altogether. You go online and you can
39:29
Speaker 1: find information about whatever you want. You can build any
39:33
Speaker 1: kind of contraption, you can put it to your service.
39:35
Speaker 1: The science were done, what everybody else has experimented before.
39:38
Speaker 1: This was not available when I was a student. Actually,
39:41
Speaker 1: I was one of the first people to play with
39:43
Speaker 1: the Internet when I was a student in Barcelona, and
39:48
Speaker 1: this was not there. If it had been there, it
39:50
Speaker 1: was fantastic. Why I go through all these degrees and
39:54
Speaker 1: so what what I what I feel is like if
39:58
Speaker 1: people understand how easy it is to put technology to
40:02
Speaker 1: your service, which I hope I can get to do
40:05
Speaker 1: eventually with this form of AI technic science and technology
40:09
Speaker 1: more playable, then it's never been easier to just drive
40:13
Speaker 1: your education based on the project you want to do today.
40:16
Speaker 1: And if you actually understand how to scale things up,
40:18
Speaker 1: you can actually make a living of it. And I
40:20
Speaker 1: think it's a responsibility of all of us to take
40:23
Speaker 1: advantage of the fact that innovation or the tools for
40:26
Speaker 1: innovation have been so commoditized, to bring it to all
40:28
Speaker 1: of America right and kind of erase the chasm that
40:32
Speaker 1: exists there today. So let's talk about that scaling up.
40:36
Speaker 1: Because you could solve a problem, but that doesn't mean
40:40
Speaker 1: it's going to scale. How do you take a solution
40:44
Speaker 1: to an issue and and make it scale. This may
40:47
Speaker 1: not sound immediately intuitive because there is nothing intuitive about
40:51
Speaker 1: scale app so ideas, per say, don't scale. What scales
40:55
Speaker 1: up is organizations, organizations organizations, which is that you started
40:59
Speaker 1: with five people and doing a few of those. And
41:01
Speaker 1: it's not about how many you do. Is being able
41:04
Speaker 1: to do the same for less money with more people
41:08
Speaker 1: in some way. So you get to fifty people and
41:10
Speaker 1: you have more products, but each of them cost you
41:13
Speaker 1: less money. This should not be logical, right, doing more
41:17
Speaker 1: products should cost you more money. So even though we've
41:20
Speaker 1: gotten used to the idea that with volume comes shippening up,
41:24
Speaker 1: you know, kind of scale, we call it that way,
41:29
Speaker 1: but we don't really understand why. I'll tell you an example.
41:32
Speaker 1: You make eight cookies, you make sixteen cookies, you're spending
41:37
Speaker 1: more money. Actually, the only reason why you're not spending
41:40
Speaker 1: much more money is because you're still using the same women.
41:43
Speaker 1: So it's not necessarily intuitive about what it means to
41:46
Speaker 1: actually scale things up. But what scales is the endeavor,
41:49
Speaker 1: not the idea, And so what you need to do
41:51
Speaker 1: is think about it the way astronauts go to the
41:54
Speaker 1: moon or International Space Station, which is the only way
41:57
Speaker 1: you can scale up is you catch. If you catch
42:00
Speaker 1: everything that would look like a failure at the skate
42:02
Speaker 1: at which is only an ever and so you don't fail,
42:06
Speaker 1: So why do I bring astronauts? But that's what they do,
42:08
Speaker 1: according to Chris Hadfield, as I explain in the book
42:11
Speaker 1: and many others, they just go through every excruciating detail
42:14
Speaker 1: about what will kill them right, and they plan for it.
42:17
Speaker 1: They either suggest modifications to the engine, they come up
42:19
Speaker 1: with protocols, They do something so that by the time
42:22
Speaker 1: they bring their endeavor to scale, which is getting into
42:25
Speaker 1: the International Space Station, they are prepared for whatever may happen,
42:28
Speaker 1: so that if it all fails, it's because it was
42:31
Speaker 1: not predictable. That's what it means to actually achieve scale.
42:33
Speaker 1: And then it may well be that the problem your
42:35
Speaker 1: targeting just lives at the small scale. That's fine. You
42:38
Speaker 1: learn that through this process. You talk about leadership and innovation.
42:42
Speaker 1: Why is it that we see a lot of people
42:45
Speaker 1: who create ideas, who create haunches, who create concepts, aren't
42:49
Speaker 1: always the best person to lead organizations to implement the
42:55
Speaker 1: scaling of those ideas. And pick pick a company, be
42:59
Speaker 1: it it could be Uber, it could be the Weinstein Company,
43:03
Speaker 1: it doesn't matter. It seems that the people who create
43:06
Speaker 1: or or crucial to help drive the idea, only take
43:12
Speaker 1: it so far and there are better people to manage
43:15
Speaker 1: those scaling businesses or or or Is that wrong? I
43:18
Speaker 1: think it's neither right nor wrong. It's true for some people.
43:22
Speaker 1: Steve Jobs was able to, Bill Gates was able to.
43:26
Speaker 1: In the case of Uber, it didn't happen at least
43:28
Speaker 1: now that we know, maybe you know it will happened
43:31
Speaker 1: like with Steve Jobs and come back, come back in
43:34
Speaker 1: the future. It's hard to make those general statements about people.
43:37
Speaker 1: But it's also true that some people, and have plenty
43:39
Speaker 1: of friends that have shared this with me, entrepreneurs that
43:42
Speaker 1: that just like to build it and get it going
43:45
Speaker 1: and bringing to a given skill and then they're better
43:48
Speaker 1: after doing the next one than they are actually doing this,
43:50
Speaker 1: And that's actually a career choice. But I also want
43:52
Speaker 1: to distinguish I don't know who the innovator was in
43:55
Speaker 1: the Uber story. We talked about the CEO, which may
43:58
Speaker 1: be the entrepreneur, but was he the innovator he was
44:00
Speaker 1: the founder, we don't know if he was. So it's
44:03
Speaker 1: those two are necessarily always the same, right, And sometimes
44:07
Speaker 1: I would say that in the case of many daring innovators.
44:11
Speaker 1: They've actually started some and they've stayed. In some cases
44:13
Speaker 1: they have actually gotten. Bosniak left Apple and he was
44:16
Speaker 1: key to the innovations that god Apple started, as was
44:20
Speaker 1: the Steve Jobs right. So I think it's more of
44:22
Speaker 1: a personal choice and a career choice than it is
44:26
Speaker 1: a rule. So there's a there's a general fear in
44:30
Speaker 1: in the population about technology and that automation is taking
44:35
Speaker 1: jobs away. I don't get the sense you look at
44:38
Speaker 1: it quite that way. What do you think about big
44:42
Speaker 1: data AI and what it means for the broader economy?
44:46
Speaker 1: Is technology gonna turn us all into um unemployed or
44:51
Speaker 1: is technology going to help us continue creating the next
44:55
Speaker 1: generation of jobs. So on that when I'm rooting for
44:59
Speaker 1: human so I think we're going to see amazing things
45:03
Speaker 1: coming up. I would also like to bring up what
45:07
Speaker 1: we actually call technology, because the definition the t at
45:11
Speaker 1: M I T what God m I T started is
45:15
Speaker 1: the idea of using science and knowledge to help humankind
45:19
Speaker 1: reach further and gain control over nature. So if it
45:22
Speaker 1: is not helping us, it's not technology at least according
45:25
Speaker 1: to this definition. So the way I and many of
45:27
Speaker 1: my friends think about technology and what I think it's
45:30
Speaker 1: my next passion now is what we need to do
45:32
Speaker 1: is make a better effort to bring this technology so
45:35
Speaker 1: that more people can play with it and benefit from it.
45:37
Speaker 1: And that needs to be done. It needs to be
45:39
Speaker 1: brought to scale. I think we don't haven't done such
45:41
Speaker 1: a great job at that for the last ten twenty years,
45:44
Speaker 1: but we can. But I don't think that on its own,
45:47
Speaker 1: big data or AI the way you see it implemented
45:50
Speaker 1: today is going to do any of those things that
45:54
Speaker 1: the fearmongers say are going to happen. So what does
45:58
Speaker 1: it mean in terms of artificial intelligence? Can we really
46:03
Speaker 1: help machines acquire intelligence in a human like fashion? If
46:08
Speaker 1: you have to look out fifty or a hundred years,
46:11
Speaker 1: what what are these machines going to look like. I'm
46:14
Speaker 1: actually going to surprise you with this. The idea of
46:19
Speaker 1: artificial general or generalized artificial intelligence was proposed at the
46:24
Speaker 1: beginning of the field, and the presumption was that if
46:28
Speaker 1: we ever got a computer to beat a human a
46:32
Speaker 1: chance or strategy game would have achieved general I started
46:35
Speaker 1: dificial intelligence because back then we thought that that was
46:39
Speaker 1: super hard for a computer. And I guess what we
46:41
Speaker 1: now know is that you didn't need artificial general generalized
46:46
Speaker 1: division intelligence to that, even though the progress has been remarkable,
46:49
Speaker 1: but it's only been machine learning. And it also means
46:52
Speaker 1: that we're done in an odd way. We need a
46:54
Speaker 1: new objective because the way we define that generalized started
46:58
Speaker 1: difficial intelligence, which is the touring test and these problems
47:02
Speaker 1: problems is gone. I mean, like we don't know what
47:04
Speaker 1: we're doing anymore. So I would rather think of an
47:06
Speaker 1: AI that actually helps us directly in the sense of
47:10
Speaker 1: we need a new objective, and to me, the objective
47:12
Speaker 1: is AI that helps us the way Jarvis helps Tony
47:15
Speaker 1: Stark become a superhero iron Man. So in that movie
47:20
Speaker 1: Iron Man, everybody talks about Tony Stark and whatever, but
47:23
Speaker 1: the great, great, great tool is Jarvis, which is the
47:26
Speaker 1: AI that powers the suit and helps Tony Stark build stuff. Now,
47:30
Speaker 1: imagine you had some kind of a system that's way
47:32
Speaker 1: more powerful than Google but still is helping new problem
47:35
Speaker 1: to alve things so that you can accomplish something new.
47:38
Speaker 1: That's what we need an I to do. At least
47:40
Speaker 1: that's what I want an eye to do. So that
47:43
Speaker 1: that's quite fascinating if, in other words, are our focus
47:48
Speaker 1: is really on the wrong thing, and we should actually
47:51
Speaker 1: be thinking about AI as a problem solving tool and
47:55
Speaker 1: not a means in and of itself. Am I am
47:58
Speaker 1: I getting that right? I think? So that's the way.
48:01
Speaker 1: But by the way, that's actually much closer to the
48:03
Speaker 1: way you are benefiting from AI today. So, with the
48:07
Speaker 1: limited capabilities of Netflix in terms of intelligence as broadly understood,
48:12
Speaker 1: you're benefiting from how Netflix collates what other people have
48:15
Speaker 1: looked at without having watched a single one of those
48:18
Speaker 1: movies Netflix itself, and you're getting new recommendations of movies.
48:23
Speaker 1: The same for Google. You're actually accessing Google typing a
48:25
Speaker 1: few keywords, and you're getting to see how other people
48:28
Speaker 1: have sorted out the web. Right. So the way AI
48:31
Speaker 1: has been helping us has already been that way, even
48:34
Speaker 1: though it's limited, it's still only machine learning. One of
48:36
Speaker 1: the questions that someone I mentioned somebody I was interviewing you,
48:40
Speaker 1: and they said, ask him if innovation has become concentrated
48:44
Speaker 1: in too few hands? Oh, I think money has become
48:49
Speaker 1: concentrated into few hands. Uh, money in terms of capital
48:55
Speaker 1: for technology or money generally or both money generally. And
48:59
Speaker 1: what you see is that um, if you don't have
49:02
Speaker 1: good ideas that you've actually kicked the tires for yourself
49:05
Speaker 1: before going out for money. You're just going to be
49:08
Speaker 1: subjected to how those people think about what innovation should
49:11
Speaker 1: be right, and so you're going to go through a
49:13
Speaker 1: path that may not be the best now because money
49:15
Speaker 1: is more concentrated, either it being companies that are pushing
49:20
Speaker 1: more research I would say, not so much innovation, or
49:24
Speaker 1: in the hands of a few philanthropists. UM, you sort
49:28
Speaker 1: of need to figure out a way to make a
49:30
Speaker 1: case appealing that even though it may not match how
49:32
Speaker 1: they think about innovation, they agree with you that the
49:35
Speaker 1: problem can get solved. So if you do your homework,
49:37
Speaker 1: it's okay. But I think the problem is that money
49:40
Speaker 1: has gotten too concentrated in the hands and that may
49:43
Speaker 1: create the perception that it's harder. So let's let me
49:48
Speaker 1: pull an example out from the real world. UM. Google
49:53
Speaker 1: very famously tells its engineers you could take of your
49:58
Speaker 1: time one day a week and think about problems that
50:01
Speaker 1: have nothing to do with the work you're you're doing.
50:05
Speaker 1: Can you organize and structure a workforce to be innovative
50:09
Speaker 1: that way? Or is are they just wasting time? What?
50:13
Speaker 1: What does that directive from management actually end up accomplishing.
50:21
Speaker 1: It boils down to what you asked them to present
50:23
Speaker 1: at the end of those Uh, time is spent on
50:26
Speaker 1: on ideas if they are just presenting ideas, I don't
50:30
Speaker 1: think it's efficient. But you can actually guide that works
50:33
Speaker 1: an entire workforce that actually to innovate continuously if you
50:38
Speaker 1: place the interest in can you please tell me how
50:41
Speaker 1: we will direct resources to prove this thing that you
50:44
Speaker 1: have at the next scale. And that's actually a different
50:47
Speaker 1: way to think about the innovation. We've actually done it
50:48
Speaker 1: outside of m I T and proving it to myself
50:51
Speaker 1: that you can truly get highly motivated people and prepare
50:55
Speaker 1: them to do this and innovate continuously in this way
50:57
Speaker 1: and create the organizations that work. Whenever we think of
51:00
Speaker 1: innovative companies, the same couple of names seemed to come
51:04
Speaker 1: up over and over again. I was struck not too
51:07
Speaker 1: long ago when someone had asked Jeff Bezos about the
51:11
Speaker 1: Amazon phone which failed, and instead of getting defensive about it,
51:16
Speaker 1: he very much embraced it and said, we need more failures.
51:20
Speaker 1: If we're not failing frequently, then we're not trying enough stuff.
51:24
Speaker 1: We're playing it too safe. I want to see a
51:26
Speaker 1: lot of things tried, and by definition a lot of
51:30
Speaker 1: them are going to fail. I think that philosophies is
51:34
Speaker 1: quite insightful. What companies or leaders do you look at
51:40
Speaker 1: that you think understand innovation and know how to uh
51:44
Speaker 1: solve problems using technology and are likely to be the
51:48
Speaker 1: innovators of the future. Uh. It's always hard to know
51:54
Speaker 1: exactly what's going on inside the company, right. We have
51:56
Speaker 1: unusual visibility unsum and no no visibility thought or not,
52:00
Speaker 1: so it's hard to make a blanket statement. The few
52:03
Speaker 1: examples we know of our companies that are still starting
52:07
Speaker 1: up and so they are not so good at keeping secrets,
52:10
Speaker 1: and so you actually get to see more. So there's
52:13
Speaker 1: a lot to like and not to like about how
52:15
Speaker 1: things are developing from many I certainly love everything Jeff
52:18
Speaker 1: Bezos does, and even though I will not use the
52:20
Speaker 1: word failure because they kept moving, they just moved on,
52:24
Speaker 1: I agree and sympathize with the idea of try, and
52:27
Speaker 1: of course if everything works out, you're not trying hard enough,
52:29
Speaker 1: right um so uh I am I admire the way
52:35
Speaker 1: Elon Musk has gotten around to create the company he
52:39
Speaker 1: has created, but I've actually developed the appreciation by reading
52:42
Speaker 1: the biography more than what the news say about about him.
52:47
Speaker 1: I am not particularly impressed by any web company that
52:50
Speaker 1: we see nowadays, even though there is lots of incubators
52:53
Speaker 1: and things out there. I actually feel that they've proven
52:57
Speaker 1: kind of the opposite that if you just start with
53:00
Speaker 1: have web idea, it takes very long, it is too costly,
53:04
Speaker 1: and uh, no one makes a profit. So I'm actually
53:08
Speaker 1: if at all they've proven that there is a way
53:10
Speaker 1: to the web companies if everybody thinks they're easier and
53:14
Speaker 1: only create perceived value but not real value. Well, Airbnb,
53:18
Speaker 1: I guess you can hold out an app company like Uber.
53:21
Speaker 1: What about Netflix, they pretty much exist. Well, Netflix is
53:24
Speaker 1: from a different breed, right of companies. They started physical right,
53:28
Speaker 1: so sending DVDs, sending DVDs and which, if we remember
53:31
Speaker 1: the idea, sounded ridiculous. It sounded completely ridiculous at first. Actually, Netflix,
53:36
Speaker 1: which I talk about in the book, is a great
53:37
Speaker 1: example because they too have been exposed in the press,
53:40
Speaker 1: and every four years someone says they're ridiculous and they're wrong,
53:43
Speaker 1: and then it four years later it turns out they
53:44
Speaker 1: weren't so that's why the mechanic is so great. But
53:47
Speaker 1: Netflix also shows you the idea that there's a difference
53:49
Speaker 1: between coming up with an app and just counting users
53:52
Speaker 1: and actually scaling up a company. The way Netflix gets
53:56
Speaker 1: to where it is today is by actually becoming various
53:58
Speaker 1: different companies on the way. When you look at what
54:00
Speaker 1: they actually do today, they produce movies, right, and the
54:03
Speaker 1: day they sold DVDs, actually they rented div so they
54:05
Speaker 1: went from DVD rental to content streaming to content manufact
54:09
Speaker 1: and some things are kept along the way. So in
54:11
Speaker 1: their case, it's a very clever use of machine learning
54:13
Speaker 1: that's kept along the way and allows them to think
54:16
Speaker 1: about the next scale up, and that's the piece they
54:18
Speaker 1: keep on growing. So they are building a very incredible
54:21
Speaker 1: capability that gets to be incredibly versatile. It's like the
54:25
Speaker 1: perfect example of proper financial wisdom, which is, you know,
54:29
Speaker 1: you want to diversify in the face of uncertainty, You
54:32
Speaker 1: want to actually diversify and not do the opposite, which
54:35
Speaker 1: is focus just on one idea and spend all your
54:37
Speaker 1: money in it. That sounds financially wrong advice, and so uh,
54:42
Speaker 1: there's companies like that that are incredibly nov that if that,
54:44
Speaker 1: we just don't know what they're doing. Apple, for instance,
54:48
Speaker 1: is what is the prototypical example. The typical question is
54:51
Speaker 1: with Steve Jobs, are they still innovative or not? I
54:54
Speaker 1: don't know the answer to that. But in the meantime,
54:56
Speaker 1: while we're not looking, they became a services company, right,
55:00
Speaker 1: so they keep on reinventing themselves. So rather than looking
55:02
Speaker 1: at who tries and fails a lot, which implies a
55:05
Speaker 1: degree of visibility onto their process that we may not have,
55:09
Speaker 1: just look at how subtly they actually change the company
55:13
Speaker 1: they are. And every time you look at it, it
55:15
Speaker 1: seems like perfectly incremental. And yet it's completely different Amazon
55:18
Speaker 1: the Everything store than producing movies the elastic cloud along
55:22
Speaker 1: the way. Uh, they use what they have to change
55:25
Speaker 1: who they are and we may not see how they
55:28
Speaker 1: do it, but that's what happens every single time. So
55:31
Speaker 1: what about Facebook, which is a web or I guess
55:35
Speaker 1: you could go on an app company, but it's certainly
55:37
Speaker 1: an internet company. Um, and they have a bunch of
55:41
Speaker 1: other properties like Instagram and go down the list of
55:46
Speaker 1: things that they've purchased. Or are they a company that's
55:49
Speaker 1: innovative or they is social? Their only thing and and
55:53
Speaker 1: that's going to be Um. They'll either live or die
55:56
Speaker 1: on how popular social networks are. I've got I mean,
56:00
Speaker 1: I have to confess that when it comes to companies
56:03
Speaker 1: that are just purely social, I have a hard time understanding,
56:09
Speaker 1: ah why they have any competitive advantage, also whatsoever acceptive
56:14
Speaker 1: where they are already, which means that if someone else
56:16
Speaker 1: comes around with something that's fundamentally different, it might be
56:19
Speaker 1: wiped out. But that's my own shortcoming. It's other shortcoming
56:23
Speaker 1: of Facebook. Uh uh. In the case of Facebook, I
56:27
Speaker 1: see lots of ideas, they see lots of chaos in
56:30
Speaker 1: many ideas, many companies, a use of machine learning for
56:34
Speaker 1: many things. But I also see mostly an advertising company
56:38
Speaker 1: right in everything they do, much to some degree like Google,
56:42
Speaker 1: though Google has tried more actively to branch out from that.
56:45
Speaker 1: So how long can we withstand that? You know, if
56:48
Speaker 1: you were in in in business school ten years ago,
56:51
Speaker 1: they would have told you that the secret of Google
56:53
Speaker 1: was that they ended the banner tyranny of the Internet.
56:56
Speaker 1: You should remember that in the nineties Internet was just
56:59
Speaker 1: mostly ad banners and eventually some content. Now look at
57:03
Speaker 1: the Internet today, it's exactly the same as it was
57:08
Speaker 1: a bunch of ads over there of stuff that most
57:10
Speaker 1: likely you bought already, and that's why how they know
57:13
Speaker 1: you like it, and so they advertise it again. So
57:16
Speaker 1: we're getting I believe we're getting to the same point.
57:18
Speaker 1: Maybe I'm wrong, which, as you know, I don't care
57:20
Speaker 1: much about. But so when I look at these companies,
57:23
Speaker 1: I just don't know how to assess what they're doing.
57:29
Speaker 1: And this is not to say that they are not
57:30
Speaker 1: doing great things. Maybe tomorrow Facebook will make an announcement
57:34
Speaker 1: and I'll say, oh, look, I was wrong in Bloomberg,
57:37
Speaker 1: and I'll move on right. So you keep coming back
57:41
Speaker 1: to the idea of not caring about being wrong. And
57:45
Speaker 1: before I get to my my favorite standard questions I
57:48
Speaker 1: ask all our guests, I have to ask you one
57:50
Speaker 1: more question about that. Most people have so much ego
57:55
Speaker 1: tied up in being right it prevents them from um
58:02
Speaker 1: either admitting error or trying things that have a low
58:05
Speaker 1: probability of success, or just you mentioned earlier, you're just
58:08
Speaker 1: playing and seeing what happens. How did you evolve to
58:12
Speaker 1: the point where you became very comfortable with I know
58:17
Speaker 1: failure is a loaded word, but being wrong, trying things out,
58:20
Speaker 1: moving on? I don't want to claim massive insight or
58:23
Speaker 1: foresight on the matter. So part of me I'm just
58:27
Speaker 1: I wasn't aware that I was supposed to not care.
58:30
Speaker 1: I mean, part of it was just I don't know
58:32
Speaker 1: something about my upbringing that that and then seriously, if
58:35
Speaker 1: I don't get to know every three days, uh, it's
58:39
Speaker 1: not my life. I've been told no about the craziestuff
58:43
Speaker 1: I proposed to do or literally every three days. Some
58:47
Speaker 1: of it is crazy, some of it I was right.
58:49
Speaker 1: But even super dear mentors that they take in high
58:53
Speaker 1: steam have told me time and again, what are you doing?
58:55
Speaker 1: This is not the normal path? And I tell them
58:57
Speaker 1: I just don't understand really the normal path. The fact
59:00
Speaker 1: that it's normal doesn't make it logical to me. So
59:04
Speaker 1: call it that my head is wired the opposite directions
59:08
Speaker 1: because I'm dyslexic and so certain things that seem obvious
59:10
Speaker 1: to other people are not obvious to me. But I
59:12
Speaker 1: don't claim to know to have a secret that all
59:14
Speaker 1: of a sudden I got inspired. Maybe it was just
59:18
Speaker 1: wrong one too many times to realize that it's not
59:20
Speaker 1: that bad. But you don't take no as I think
59:24
Speaker 1: most people's intended intention. When someone says no to you.
59:28
Speaker 1: It's almost the challenge. Yeah, what I what I tell
59:31
Speaker 1: you my class two students is that as long as
59:33
Speaker 1: no one is hurt, and that's very important, you should
59:36
Speaker 1: not take the first note for a note, right. And
59:39
Speaker 1: that doesn't apply to relationships, right, it disapplies you're talking technology, abology, business.
59:44
Speaker 1: You should not take the first note for an answer
59:46
Speaker 1: because sometimes people need a second chance to think it through.
59:49
Speaker 1: And so maybe two right is a good good starting
59:54
Speaker 1: point to get yourself trained. And then I develop all
59:57
Speaker 1: these funny games. I I call him credit Kid carate
1:00:02
Speaker 1: kid tasks for the movie. You probably remember the first
1:00:05
Speaker 1: Credate Kid movie in which there was walks on, walks
1:00:08
Speaker 1: off that stuff, and he the kid learned karate by
1:00:12
Speaker 1: actually kind of waxing cars and painting fences. So I
1:00:15
Speaker 1: developed a few create kid tasks to kind of train
1:00:19
Speaker 1: them to be around. One of them, I asked them
1:00:21
Speaker 1: to just call a pizza um, and I mean some
1:00:26
Speaker 1: pizzeria and ask for a doctor's appointment. Uh. Of course
1:00:31
Speaker 1: they think it's going to be embarrassing, but it's just
1:00:33
Speaker 1: a phone call, right, and uh, and then once they
1:00:37
Speaker 1: do it, you don't know who's more surprised if the
1:00:40
Speaker 1: person who called or the person who receives the call, right.
1:00:43
Speaker 1: I asked them to please not have used the pizzeria
1:00:45
Speaker 1: just to try it once, but to get the feeling
1:00:47
Speaker 1: of put themselves in in the shoes of that extress
1:00:51
Speaker 1: moment and realize it's not that bad. Right. And once
1:00:54
Speaker 1: you realize that, and you train yourself to these tiny mistakes,
1:00:57
Speaker 1: they call them near missus. If you want, uh, then
1:01:01
Speaker 1: you get a whole different sense of what the actually
1:01:02
Speaker 1: means to be wrong. It's the example I gave you earlier.
1:01:06
Speaker 1: If the violin is out of tune, it kind of
1:01:08
Speaker 1: still sounds Okay, it's not as great, right, But it's not.
1:01:11
Speaker 1: It needs to be really bad, bad, bad for you
1:01:13
Speaker 1: not to even recognize the song, and after a while,
1:01:16
Speaker 1: that helps you play better. So let me jump to
1:01:19
Speaker 1: my favorite questions. These are the ones I ask all
1:01:22
Speaker 1: my guests, UM, tell us the most important thing that
1:01:26
Speaker 1: people don't know about your background? Uh? How do people
1:01:33
Speaker 1: normally answer to that question? Well, usually it's some little
1:01:36
Speaker 1: tidbit that people um that is surprising. But you have
1:01:40
Speaker 1: a lot of interesting, surprising things in your background already.
1:01:43
Speaker 1: So I learned the program by accident. My mom had
1:01:48
Speaker 1: taken a class in a computer science because it wasn't
1:01:51
Speaker 1: and she went next door and took a class and
1:01:53
Speaker 1: we had the K computer and she told me, look,
1:01:57
Speaker 1: they've given me the master remind her game only that
1:02:00
Speaker 1: needs to be called in the computer. So the first
1:02:02
Speaker 1: time she wrote it in the computer for me, and
1:02:05
Speaker 1: I played, but it was one of those computers like
1:02:07
Speaker 1: really old, like you could not say so every time
1:02:11
Speaker 1: you want to play, you have to red to re
1:02:12
Speaker 1: write it. And so I wrote it and then wrote
1:02:17
Speaker 1: it again, and then wrote again and modified it so
1:02:20
Speaker 1: I could win always then and before you know it,
1:02:23
Speaker 1: I actually knew how to cod it. Basically, I was
1:02:25
Speaker 1: eight years old, mostly because I just wanted to play
1:02:28
Speaker 1: um Mastermind. And that has defined the way I think
1:02:32
Speaker 1: about AI and everything I've told you before in ways
1:02:34
Speaker 1: that keep on coming back to my head. The ability
1:02:37
Speaker 1: to play make things that seem complicated playable changes how
1:02:43
Speaker 1: we actually approached them. And that's define why I keep
1:02:45
Speaker 1: on jumping through field seemingly in a way that's quite fasting.
1:02:50
Speaker 1: Tell us about some of your early mentors. Hard to say.
1:02:55
Speaker 1: I've been through so many countries and I've met so
1:02:57
Speaker 1: many interesting people, and uh that it's it's hard for
1:03:03
Speaker 1: me to say it. Clearly the person and M I
1:03:05
Speaker 1: T who who you had a long email relationship before
1:03:10
Speaker 1: you has to be a significant mentor. He's a significant mentor.
1:03:14
Speaker 1: We haven't spoken in a few years now because directions
1:03:17
Speaker 1: takes different ways. Ah, but that's much later in life, right, So, uh,
1:03:22
Speaker 1: he was certainly a great mentor. More recently, what was
1:03:25
Speaker 1: his name, Tomaso Poggio Um, he's still a professor at
1:03:30
Speaker 1: m I t UM. The recently Charlie Cuney has been
1:03:35
Speaker 1: a great mentor because he sort of told me I
1:03:37
Speaker 1: think you have a book in your hands when I
1:03:39
Speaker 1: wasn't even thinking about it, and I guess I did. Uh.
1:03:42
Speaker 1: And even earlier a lot of family friends essentially a
1:03:45
Speaker 1: family the friend of my family was quoting earlier that
1:03:49
Speaker 1: I was talking to me and said, what are you
1:03:50
Speaker 1: going to do? The next time, I said, I don't
1:03:51
Speaker 1: really know. I haven't learned enough to what I want
1:03:54
Speaker 1: to accomplish. And he said, you you you don't know
1:03:57
Speaker 1: what you're doing. You need to leave this country and
1:03:58
Speaker 1: go to the United States. And said, okay, So I
1:04:01
Speaker 1: guess he's had me in this path So who um
1:04:05
Speaker 1: in the worlds of innovation? Who influenced your thinking? What philosopher,
1:04:10
Speaker 1: thinker technologists affected in a fundamental way your thoughts on innovation?
1:04:19
Speaker 1: Many I'll give you two. One is Thomas kun Um,
1:04:25
Speaker 1: which is very known in the United States and hardly
1:04:27
Speaker 1: known in Europe. Surprisingly, in Europe people talk about paper,
1:04:30
Speaker 1: which is the competing school of thought. Uh. I think
1:04:35
Speaker 1: Thomas kun explains the scientific method in a way that
1:04:37
Speaker 1: resonates with me. And Papa doesn't Um what is that?
1:04:42
Speaker 1: Papa is formulaic and uh And Quen essentially admits one
1:04:47
Speaker 1: thing that I've come to come believe as a mantra,
1:04:50
Speaker 1: which is that the beginning of any new theories fundamentally
1:04:55
Speaker 1: ill conceived, h mostly wrong by any traditional standard, barely
1:04:59
Speaker 1: work works, and yet somehow it becomes a standard afterwards.
1:05:03
Speaker 1: And that beginning starts with people putting together what seems preposterous,
1:05:07
Speaker 1: mostly because they have a hunch that something isn't working.
1:05:09
Speaker 1: I'm putting my own words on Kim, right, So so
1:05:12
Speaker 1: don't Qun doesn't use tounch and I'm not sure he
1:05:15
Speaker 1: would agree with the way I interpret his writings. And
1:05:18
Speaker 1: I think it's a real hard read a book to
1:05:20
Speaker 1: read it took me. I read it at first, at
1:05:22
Speaker 1: the age of twenty three, I didn't understand it. I
1:05:26
Speaker 1: only understood it at the age of twenty nine when
1:05:28
Speaker 1: I read it again. And by the way, that's because
1:05:30
Speaker 1: your brain keeps are involving, so you're not it's not
1:05:33
Speaker 1: fully matured and able to understand every single thing until
1:05:35
Speaker 1: you're close to twenty nine by some of the latest research.
1:05:39
Speaker 1: The other thing that actually influenced me is that the
1:05:42
Speaker 1: opposite extreme. I've had this question in my head about
1:05:46
Speaker 1: why does Warren Buffett. Why does Warren Buffett not invest
1:05:51
Speaker 1: in innovation? Not technology startups, but innovation? You know it
1:05:55
Speaker 1: should he should mean, I'm not, you know, I'm not
1:05:58
Speaker 1: want to say what he should do with his money,
1:06:00
Speaker 1: but it's a fascinating question. It's a seems like a
1:06:03
Speaker 1: bit of a dichonomy, right, but he invested in long
1:06:06
Speaker 1: term only for value creation, and he also does philanthropy right,
1:06:13
Speaker 1: and innovation is all about removing obstacles to progress in
1:06:15
Speaker 1: the long term that I actually create benefits from society
1:06:18
Speaker 1: and value. So it's like he should be investing in that,
1:06:22
Speaker 1: and yet he doesn't. I'm not I don't think he should.
1:06:25
Speaker 1: But trying to answer this question has cost me to
1:06:27
Speaker 1: think about more carefully about what innovation actually is and
1:06:31
Speaker 1: how it is different from just creating a company or
1:06:33
Speaker 1: doing a startup. It's much more ambitious. So you mentioned
1:06:37
Speaker 1: qun tell Us about some of your favorite books, so
1:06:40
Speaker 1: those change all the time. But the latest breed is
1:06:44
Speaker 1: The Hitchhiker's Guide to the Galaxy, which I keep I'm
1:06:46
Speaker 1: coming back to, and and the reason I love it
1:06:52
Speaker 1: is just it's just absurd in such a profoundly logical
1:06:55
Speaker 1: way that it's just hilarious. Another a book that I
1:07:01
Speaker 1: devoured was The Martian two years ago when I first
1:07:04
Speaker 1: got it, and I've still read it again because it
1:07:08
Speaker 1: is the way science really is, not the way it
1:07:12
Speaker 1: is being taught. It's not about the model. It's about
1:07:15
Speaker 1: using nature to help you. And that book, every page
1:07:18
Speaker 1: is literally a person trying to survive making science and
1:07:21
Speaker 1: nature work for him. Right. It's a series of problem solving,
1:07:26
Speaker 1: problem solving, no solution real and and and lots of
1:07:30
Speaker 1: what you might call failures. But really, if he failed,
1:07:33
Speaker 1: he died, right, So that was not an option. And
1:07:36
Speaker 1: and the mechanic of the book, the richness of it.
1:07:38
Speaker 1: Even if you don't fully understand the technology, you can
1:07:40
Speaker 1: tell the story. It's enormously vibrant. And the last book
1:07:44
Speaker 1: I keep on coming back to every now and then
1:07:46
Speaker 1: is what if? From random unders sure and you can
1:07:49
Speaker 1: tell the trend right it's I'm techie. I love sci fi,
1:07:53
Speaker 1: uh because I want to create the sci fi so
1:07:55
Speaker 1: it becomes real and but just a touch of absurdly.
1:07:59
Speaker 1: And it needs be absurd because otherwise you cannot get
1:08:01
Speaker 1: good ideas. They need to look absurd at first, not
1:08:04
Speaker 1: because just because and this is something I realized over
1:08:08
Speaker 1: over time, ideas look absurd because of your training. They
1:08:11
Speaker 1: were not absurd per se. So if your background taught
1:08:14
Speaker 1: you to ignore certain things and assume certain things, anything
1:08:17
Speaker 1: that challenges that will look absurd only until you show
1:08:20
Speaker 1: it works. So, since you came into the fields of
1:08:25
Speaker 1: filling the blank artificial intelligence innovation some years ago, what
1:08:31
Speaker 1: has changed? And is for the better or for the worst.
1:08:35
Speaker 1: Many things have changed. I'll give you one for the
1:08:39
Speaker 1: better and one for the worst. We've become way more siloed. Siloed, yes,
1:08:44
Speaker 1: and that creates enormous amount of inefficiencies in medication, in
1:08:48
Speaker 1: investing in innovation that I myself avoid with IT teams,
1:08:54
Speaker 1: which is crossed disciplinary across the entire institute. Um, that's
1:08:59
Speaker 1: that's something that I believe it's going to change. At
1:09:02
Speaker 1: the same time, the opposite has also become true. You
1:09:05
Speaker 1: could indicate yourself on whatever online if only you had
1:09:08
Speaker 1: a project or a means to guide you with a
1:09:10
Speaker 1: project to acquire that knowledge, which is the reason why
1:09:13
Speaker 1: I wrote the book to start with. And so those
1:09:15
Speaker 1: two things have happened. One people more siloed and more formulate.
1:09:20
Speaker 1: More students asked me today for formulas for innovation, and
1:09:23
Speaker 1: they did ten years ago, which makes no sense because
1:09:25
Speaker 1: if there is a formula, there is a recipe, it's
1:09:27
Speaker 1: then you're just going to bake the same cake someone day.
1:09:29
Speaker 1: How is that an innovation? It's a great copy by
1:09:32
Speaker 1: all means, eat the cake, But um, it's not an innovation,
1:09:36
Speaker 1: right so Um. But then on the other side, more
1:09:40
Speaker 1: and more people I believe can actually start to learn
1:09:43
Speaker 1: on their own and ignore recipes or said models because
1:09:46
Speaker 1: there is such an abundance of information that wasn't there
1:09:49
Speaker 1: ten years ago. And AI is only making that or
1:09:52
Speaker 1: will only make it if I get my way would
1:09:54
Speaker 1: only get it make it even easier to put that
1:09:57
Speaker 1: to your service, so you can solve whatever problem you anty.
1:10:00
Speaker 1: Tell us what you do outside of the office to
1:10:03
Speaker 1: relax or for fun. I spend as much time as
1:10:07
Speaker 1: i can with my kids for not just because I'm
1:10:11
Speaker 1: a family man. It's just they're hilarious, but it's it's
1:10:14
Speaker 1: but it's not going with my kids as in traditional playing.
1:10:17
Speaker 1: Of course, as you can imagine, we build contraptions. I'm
1:10:20
Speaker 1: having them build computers. There are five and eight. But
1:10:23
Speaker 1: it's okay, it's good early enough. Um So having them
1:10:26
Speaker 1: assemble the computers, we are doing all sorts of absurd things. Ah.
1:10:32
Speaker 1: And we played together and I'm even learning violin with
1:10:34
Speaker 1: both of them. Uh. And that's incredibly enriching for me.
1:10:40
Speaker 1: So if a millennial or recent college grad came up
1:10:43
Speaker 1: to you and said, I'm interested in a career in technology, innovations, startups,
1:10:51
Speaker 1: what sort of advice would you give them? Interesting question.
1:10:54
Speaker 1: So my first advice is find a problem, right, because
1:10:58
Speaker 1: technology is what you use to make the problem go away. Right.
1:11:01
Speaker 1: An innovation is the process of doing that to some degree. Uh.
1:11:05
Speaker 1: If you can find a problem and look harder, but
1:11:08
Speaker 1: if all you intend to do is get money, you
1:11:11
Speaker 1: know the current cycle is going to end. Eventually, it's
1:11:14
Speaker 1: going to become impossible to create this multibillion dollar investments
1:11:19
Speaker 1: that take ten or fifteen years to show a profit,
1:11:23
Speaker 1: if not more. So that's not going to be there forever.
1:11:26
Speaker 1: And when that goes away, if you just adopt that
1:11:28
Speaker 1: view of the world is not going to help you.
1:11:30
Speaker 1: So just find a problem and work backwards from that
1:11:33
Speaker 1: problem to get the education you want, rather than obsessing
1:11:36
Speaker 1: about what do I become computer scientist, stores, so on,
1:11:40
Speaker 1: so forth? And our final question, what is it that
1:11:43
Speaker 1: you know about technology and innovation today that you wish
1:11:47
Speaker 1: you knew years or so ago when you were first
1:11:50
Speaker 1: starting out. So I think of myself as a different
1:11:55
Speaker 1: person every so often, So I'm not sure what myself
1:11:58
Speaker 1: of twenty years ago really thought, right, I can't chat
1:12:02
Speaker 1: with him to figure that out. So I'm not sure
1:12:05
Speaker 1: that that's what I wanted. But what I wrote in
1:12:08
Speaker 1: the book gives me the answer. I think I was
1:12:10
Speaker 1: looking for what I didn't know what I was doing,
1:12:13
Speaker 1: So I guess that what I did is that answered.
1:12:16
Speaker 1: If not the question, I would have phrased what I
1:12:19
Speaker 1: thought I needed to understand to make it to the
1:12:21
Speaker 1: next leap for myself, and that has been a really
1:12:23
Speaker 1: enreaching experience. We have been speaking with Luis Perez Bravo.
1:12:28
Speaker 1: He is a professor at m I T and the
1:12:30
Speaker 1: author of Innovating a Doer's Manifesto. If you enjoy this conversation,
1:12:36
Speaker 1: be sure and look up an Inch or down an
1:12:38
Speaker 1: Inch on Apple iTunes, overcast, Bloomberg dot com wherever final
1:12:44
Speaker 1: podcasts are sold, and you could see any of the
1:12:46
Speaker 1: other nearly two hundred such conversations we've had over the
1:12:50
Speaker 1: past few years. We love your comments, feedback and suggestions
1:12:55
Speaker 1: right to us at m IB podcast at Bloomberg dot net.
1:12:59
Speaker 1: I would be remiss if I did not thank my
1:13:01
Speaker 1: crack staff who helps put together this podcast each week.
1:13:06
Speaker 1: Medina Parwana is our audio engineer and producer and keeps
1:13:10
Speaker 1: me honest each week and moving along as these conversations
1:13:15
Speaker 1: progress across ninety minutes. Taylor Riggs is our other producer
1:13:19
Speaker 1: slash booker. Michael Batnick is our head of research. Attica
1:13:24
Speaker 1: val Burne is our business producer. I'm Barry Retolts. You've
1:13:28
Speaker 1: been listening to Masters in Business on Bloomberg radio