John Seely Brown Commencement Speech - Singapore Management University, 2013 | Big Think

Right now, we would like to invite our guest of honor, Professor John Cely Brown, who was conferred an honorary Doctor of Information Systems degree in recognition of his outstanding record as a scientist, scholar, and innovator, on the stage to say a few words.

Professor Brown:

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[Applause]

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Good [Applause] afternoon. So, as you've already seen, you can learn a tremendous amount, but the ability to improvise is a very good skill if you want to be a Dean. Steve, I felt for you up here. I had a brief conversation, uh, with Steve coming over.

Um, and you heard this morning all these magnif things that I supposedly have done, uh, I got this wonderful award for, but he was surprised when I kind of came up in a conversation, like, how did such a hardcore geek as I was turning into such a softy, into such a person that believes in the power of social science? And I said, “Steve, you know, it's actually quite simple. You didn't talk about my first job. My first job is I happen to have been the youngest licensed bookie in New York State.”

I got that job at 18 years old because I was a geek; I could compute all kinds of amazing things in my head with blinding speed. But within the first day of my job, I discovered being able to compute amazingly complex things in my head didn't really matter. What really mattered was being able to read the people approaching me to make book because, if you could read them, you suddenly realize which ones are going to cheat you and which ones weren't.

And it was kind of the first moment that I began to realize that maybe it was more important to read context than just be able to compute content. Um, and that's where I kind of, Steve, started down this path. But that's not why I'm here today. Today, we live in, in some way, a terrifying world but also an exciting World. We've already heard left, right, and sideways the amazing kinds of disruptions that are happening.

But we, we here in this room have the unusual property that, yes, we are creating the tools that lead to radical disruption around the world, but simultaneously, we're creating the tools that let us to build new ways to learn, new ways to work, work, new ways to innovate, new ways to create meaning.

So, in some sense, we're causing the disruptions, but we're also creating the tools in which to reinvent the industrial world, the corporate world, the learning world, the world of actually making meaning. Left, right, and sideways, some of us call this the big shift. We call it a big shift because, perhaps for the first time in the history of civilization, we're moving from a world one might call an S-curve world where you have a moment of radical punctuation with 50, 60, 70 years or more less stability in terms of the infrastructures and defining how we work, learn, interact, socialize, etc., etc.

That has been the history over the last 300 years. Don't worry, everyone, I won't bore you with taking you through 300 years. But we now live in what we all in this room would call the exponential world where, basically, we no longer have these long periods of stability with which to reinvent how we work, how we learn, and so on.

What we're entering is a world in which, in fact, we can't even tell our students what they should know five years from now because, in fact, we're moving into a world in which the average half-life of a skill is moving from about 30 years to about 5 years. That means that we're going to have to do most of our learning after we leave here today, and we're going to have to do most of our learning in the workplace itself in one form or another.

In fact, those of us that design corporate architectures, institutional architectures have to step back and realize that, for the last 100 years, the West became powerful, the East became powerful because we could actually build institutions that understood how to leverage scalable efficiency. We knew how to scale things, but to make things scalable, they had to become predictable. Predictability was the coin of the realm, and virtually every single corporate strategy I've ever looked at found new ways to leverage, if you wish, scalable efficiency.

But here's the dirty secret: those very things that made us so successful for the last 50 or 60 years are the very things that stand in our way now because they no longer work. Because we don't have predictability, we can't predict customer needs; we can't predict what object to build in massive quantities, store up in warehouses, and distribute through superior transportation infrastructures, and so on and so forth.

So, some of us have been looking at the issue of how do we actually move from basically an underlying strategic basis of all our institutions of scalable efficiency to the notion of scalable learning? How do we actually think about reinventing corporations, public institutions, and so on and so forth whose job it is to make sure that those of us that work in those institutions learn faster than almost anywhere else?

For example, why do kids today go to Google? It, by the way, is not because of money. Uh, believe it or not. It's because at Google, in at least the Bay Area where I come from, you learn faster there; you learn newer technologies there than virtually any other place in the country and probably the world.

In fact, there's a very interesting book out called "The Race Against the Machine," written by two economists from MIT that are trying to explain why, in fact, maybe there are going to be so few jobs or fewer jobs in the new normal, so to call, so to speak.

Um, because basically, most of our jobs, they claim, can now be done by machines. That probably is somewhat true, but some of us are reframing the Race Against the Machine to be the race with the machine. How do we actually think about how do we and the machine work together better than either one by ourselves, by themselves?

In fact, that causes us to radically reframe what we think about in terms of augmented intelligence, augmented capabilities to innovate, and so on and so forth. In fact, if you play chess, you may be surprised to know that, of course, a few years ago, IBM built this mega machine that actually ended up beating the world champion.

But here's what you might not know: you take the world's best chess players, you take the world's best mega machines, and guess what? Both can be easily beat by a moderately good chess team playing with a moderately good machine. What these kids have figured out is how to use a machine to augment what they do so well. The pair of those things working together, man and machine, now beats the hell out of the world's best chess machine and the world's best chess players.

So, I want to suggest, as we go forward, we have to be very creative in how we think better about augmenting human intelligence and how we can work more effectively with machines. But I want to suggest equally challenging because some of you here are also going to be architecting organizations of the future.

We ought to be thinking about how do we reinvent institutional architectures to actually lead to scalable learning in the workplace and in our governments. And I think that is a new type of design challenge, and we add to that what we're now capable of doing with cloud computing and now how we can actually rethink learning on demand as well so that anytime we get stuck, we can pull information to us.

But that's pulling content; that's not pulling context. That's not pulling dispositions and so on. So, we got to kind of be careful to keep in mind the difference between character, the difference between dispositions, and the difference between knowledge.

I'm fond of using the term "the entrepreneurial learner," not because I'm concerned about how to train entrepreneurs per se, but I'm very interested in the disposition that says every moment of the day I personally have a chance to learn something new.

And if you can actually do something, put yourself out on the edge, often get stuck, and then what do you do? You pull information. We're moving from an education based on push to a learning on demand pull. But now I pull information into a context, into a situation that's personally meaningful to me because I'm stuck, and I'm trying to use that information as fast as can be to see if I can get myself unstuck.

So, that's how suddenly all of a sudden learning becomes so real, and I can make it so personal that whatever you do, this way, you basically never forget.

So, I do want to suggest that a couple of things we should be thinking about as we leave today. I think one is to make sure we ourselves don't get stuck in ruts. My single biggest advice to most corporate CEOs is: what are you doing to make sure you aren't stuck in a rut? And if this game is changing every five years as opposed to every 35 or 40 years, that means the things that made us successful yesterday may be exactly the things that blind us today.

So, how do we keep from being stuck in ruts? I want to suggest a second thing: as we move in increasingly from thinking about machines as things we can take apart, like clocks, to we're now living in a world where everything is interconnected, the smallest moves can now propagate around the world.

Um, we may have to shift from thinking about machines and mechanisms to thinking about biology as a fundamental metaphor. How do we think about emerging systems? How do we begin to understand sometimes certain moves seem so positive on the surface but end up having all kinds of secondary consequences, which is always a way that many complex ecological systems actually work?

So, I think we're actually going to be moving, if you wish, as core head from a world of clocks, mechanical systems that can be deconstructed and tinkered with gear by gear, to the world of clouds. Not as a pun, not as cloud computing—oh, I could talk forever about cloud computing—but as clouds, as these things in the sky that are emergent. How do we start designing emerging systems?

Because clouds are complex adaptive systems, and as soon as you touch it, something changes. Those are the challenges we face tomorrow. Good luck in helping us all figure out how to live in the new world.

Thank you.

[Applause]