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A Leadership Vision for the Future

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What do you think is the biggest story unfolding in the world today? You know, when you look around the drumbeat of news coverage out there, you might well answer, the rise and spread of terrorism— terrorism that's happening both over there and increasingly and sadly over here as this great city recently experienced. In fact, if you toss in anxiety about the rising risk of cyberterrorism, a reasonable response might well be terrorism everywhere, the defining theme of our anxious era. Or if you pull back a bit, a student of history might point as a more precise root cause to a decade of hot wars in the Middle East, conflicts that have preoccupied American power, levied a high cost in lives and treasure, and then continue as they flare on to reshape this oil-rich region that's been long hewn by rivalries between Shia and Sunni. More recently we've seen a shocking outbreak of violence in the Crimea. This conflict between Russia and Ukraine has rekindled what we thought was the dead Cold War, but this time as a clash of civilizations. It's also an important economic challenge, and some people worry it's a dangerous back to the future distraction for US leadership because we're focusing on the old conflict instead of the real geopolitical challenge of our time, the rise of the bricks. I like that picture. High five guys. Now I'm not talking about the sort of dubious idea of the bricks as an investment theme as Dr. Roy noted rightly yesterday. I'm talking about it as a shorthand for the longer term economic shift in not only economics but finance and diplomacy that's happened as billions of people join the modern economy and reclaim their nation's historic role atop the world order, particularly China and to a lesser extent India. Or if we pull back even further, a reasonable deep trim candidate, especially for those of you in this room who played key roles in helping develop it might be the intensifying urbanization of human civilization. Right? This explosive growth and wealth, productivity, consumer purchasing power. It's occurring as we go from a fraction of the Earth 100 years ago in cities to more than 50% today to more than 80% tomorrow. Now my McKinsey colleague Richard Dobbs at MGI, our think tank, calls this trend, which is of course a migration that's vastly outpacing in scale and in pace what happened after the first Industrial Revolution, Richard calls this the largest disruption the world has ever seen as it goes global. But as you may have surmised by now, I have a different candidate. My friend Brian Arthur, who's the fellow that Jillian mentioned yesterday who wrote this McKinsey article a few years ago, Brian likes to say if you really want to get a perspective on today ask yourself, what's the story right now? Not the murders or the political ups and downs in the evening news. What's the story that looking back in 2100 we're going to say, "This is what mattered." Well I believe the biggest story unfolding around us today is that we're witnessing the dawn of the era of machine learning and artificial intelligence, a technological and sociological shift that as it advances really will fundamentally change everything as we used to say in the old bubble days of the Internet. And here's one memorable marker of that shift. Now I have to ask, how many of you saw this episode of Jeopardy live? Yeah, there aren't too many sci-fi nerds like me who are also gathering string on this theme. Well let me tell you, I was gripped to my TV on the night about seven o'clock on the night of June 6, 2011. That was the night when IBM's Watson, a set of servers with petabytes of data from Wikipedia and numerous other sources and able to respond to questions in natural language, puns, innuendo, word play and other complexities. He simply crushed the two best human Jeopardy players in history. This is one of the champions, Ken Jennings, welcoming our new computer overlords. Now the truth is Watson's triumph was just another marker in a long string of man-machine, left-brain face-offs where we continue to end up on the losing side. Some of you may recall about a decade earlier in 1997 when an earlier generation AI, also from IBM, Deep Blue defeated world chess champion Garry Kasparov, a hither-to unthinkable feat. Soon after someone asked a Danish grandmaster what his strategy would be if he had to face Deep Blue. He memorably replied, "Bring a hammer." Now I'm a layman and only qualified to talk about this in layman's terms, but let me just explain simply why I think most experts I talk to are increasingly convinced we have really entered what they call an AI spring. And by the way, these are also the reasons why Silicon Valley venture capitalists this year are pouring more money into startups in AI and machine learning than they ever have before. This is also probably the point where I should give you a quick definition. For most of this talk this afternoon, I'm referring to soft AI as it's known, which means an ever greater ability to automate, complement, and supplant human intensive knowledge work, but I'm not really talking about the prospect of hard AI which is the rise of fully independent, conscious computer intelligence. Right? Think Skynet in the Terminator films, although we might get to that. So naturally this advance starts with the continued march of Moore's Law, which is the steady doubling and processing power that's occurred roughly every two years for half a century and which shows no sign of slowing. Now you add to that advances over the last decade in the rise of vast networks of cheap parallel computer processing power distributed across the cloud. Networks with hundreds of millions of connections can crunch ever-more massive amounts of data ever faster. Feed those networks great heapings of big data, as every piece of our former analog world is digitizing. And finally toss in better algorithms that enable machines to learn independently by crunching data without always having to have their every step programmed. And well, you get where we are today. You get a world after decades of frustratingly slow advances in AI where Watson in just four years, from the time he beat Ken Jennings, has morphed from a brilliant machine the size of a small bedroom to last year four servers the size of four pizza boxes to just recently a distributed intelligence spread across a cloud of open standard servers that run several hundred instances of Watson at once and can be accessed by multiple customers at the same time on a range of devices—phones, desktops, or your own data servers. Now combine that distributed capacity with digital cameras, lasers, advanced sensors and you begin to get machines that literally can see as well as process and can react to changes in the physical world as well as crunch data. As a result, you get the Google self-driving car and soon the Mercedes and the Nissan and the GM self-driving car. Now Google alone has logged close to 1 million miles driven accident and human-free to date. And that's after being barely able to drive seven miles before breaking down about 10 years ago in response to the first Defense Department Challenge. You also get personalized mobile access for as we've heard these last couple of days what will soon be billions of people giving them access to much of the world's knowledge and devices whose intelligent agents like Siri and Google Now already respond to natural language commands. Sometimes admittedly a little stupidly and amusingly and frustratingly, but still they keep getting better. You also get in the past year or two advances that today allow Facebook deep learning algorithms to do a better job of identifying individual faces than humans do. You also get advances that enable Google at the end of last year to simply feed its machines a few hundred pictures of the hundreds of millions of images of locations here in France in its street view database, and in short order the computer figured out, "Oh, that's what I'm looking for," and it mapped every location with every business and every household in this country in under an hour. You also get AIs like Watson, which is now doing a better job at detecting images of certain types of cancer than teams of experienced oncologists with decades and decades and decades of training. In short, you get the imminent prospect of a world with a very active and intelligent global nervous system, a world where AI— the soft AI I'm talking about—looks a lot like advanced versions of today's Amazon web services or tomorrow's Google. You know, apparently Larry Page back in the early part of this century, someone was asking him, "What's this deal with search and what are you trying to do with Google? Where's it going?" And he said, "Actually what I'm building is an AI." So very soon we're going to live in a world where we can deliver cheap, reliable, industrial grade digital smartness to almost every device. As Wired's Kevin Kelly, who's also the source of that story I just shared, recently put it, "This emerging AI will enliven inert objects," and I love this metaphor, "much as electricity did more than a century ago." So you think about AI. It's this big magic thing. Actually think about it as a utility like electricity. And what Kevin says is, "Everything that we formerly electrified, in the future we will cognitize." Interesting thought. So less than four years ago, Brian Arthur in that McKinsey Quarterly piece that Jillian mentioned, he described the collective impact of what I'm talking about here as the rise of a second economy. And I was delighted at Jillian's mention because just a little personal note, that story came about because I had met Brian at Davos and I got to talking about it. I said, "Wow, that's a fascinating idea," and I went and interviewed him and captured what he was thinking about in a transcript and then sent it back to him, and he edited it and turned it into that article. But here's a news flash. When I caught up with Brian in Singapore last year, he told me he wished he'd given his phenomenon a more precise and accurate name, which I will now unveil. He calls it the autonomous economy. Now the word autonomy gets at Brian's core idea that more and more of our economic activity will consist of machines talking to each other—and here's the key phrase—taking appropriate action. These brilliant machines will be driving our cars. They're already driving our planes. We have humans there to kind of bring them into the landing field. Or if Sully's around,there's a problem, he'll take you down. But by and large, they were flying by wire and have been for a long time. In the future they'll be sorting out packages in warehouses or widgets in factories, grading our exams, by the way, moving to knowledge work, and writing some of our news. I don't know how many of you know this one, but last year the Associated Press announced that henceforth all its earnings announcements were going to be assigned to computers. They formulaic enough that a computer can just do them, and they're actually turning them over to computers. Computers are also going to be crunching our medical records and suggesting diagnoses. I was having lunch right before Christmas with a McKinsey alumnus who's a distinguished advisor to a number of Fortune 50 health companies—Fortune 100 health companies—and he told me when I raised this topic, "You know, I predict as many as half—maybe it's 40%— "as many as half of the medical records that are being created in doctor's offices today will never be read by a human." So to sum it up and to play on a phrase that Jim Carville coined for President Bill Clinton when he was a candidate nearly 25 years ago, "It's the autonomous economy stupid." Now of course, this tectonic shift has been building for a while. We had these large scale industrial robots. They first burst on the scene decades ago, right, when manufacturing jobs were just 20%— were still 20% of US manufacturing jobs—sorry, of all jobs that were manufacturing. Wall Street began moving in the mid '80s from trading floors filled with people to largely invisible electronic trading. Today if you wander down to the New York Stock Exchange or the London Exchange, about the only humans you'll actually encounter work for CNBC. We also got used to a while ago at airports to self check-in. And now we're getting used to retailers to self check-out. We've already seen a big impact in recent decades as certain categories of jobs got automated away. But that fall in low-end white collar jobs, things like—you can read it— typist, telephone operators, bookkeeping jobs, secretaries, bank tellers, you name it. That shift doesn't really begin to capture a deeper underlying shift in the economy which is more reflected in this chart which shows the ratio of GDP going to corporate profits and capital as a percent as opposed to going to labor income. See the gap there. It's wider than it's ever been in history. And what lies underneath that growing gap are stats like the data that shows stagnant wage growth for all but the top 5, 10% of the income ladder in developed countries for the last decade or so. It also helps account for things like a record post-war low in the US despite the falling unemployment rate. The fact is the number of employed people in America as a percentage of the working population is at a record low. So I think those pain points are only going to intensify as we enter what's called the second half of the chessboard. Now let me ask another question. How many of you have read this book? Okay, it's an FT McKinsey Book Award of the Year— Book of the Year Finalist, so I recommend it to you. Not to mention that Dr. Roy mentioned Erik Brynjolfsson yesterday. And the second half of the chessboard is this memorable metaphor that the authors here use to explain the concept of exponential growth, which I am very grateful to them for, because let me tell you, I don't know if you're like me. If I tell you 40 years of doubling computer processing power creates a million-fold increase in capability, if you're like me you go, "Wow, that sounds really big!" You know? What does it mean exactly? So I think it's best to just tell a little story. Many centuries ago, an Indian emperor apparently wanted to reward the inventor of chess because he loved the game. So he said, "Name your price." And the fellow says, "All I really want to do is feed my family. "So if you just please give me one grain of rice and just double "the amount for each of the 64 squares on this game I've invented, I'd be very grateful." "Make it so," said the emperor. Except by the time they got halfway to square 32, the amount of rice, thanks to that constant doubling, amounted to 4 billion grains, which sounds like a lot. But that's still only the amount of grains you'd get from a large field. But from there it quickly became apparent, even to the emperor, that he'd been had because the target out there at square 64, is 18 quintillion grains of rice, which is more rice than has ever been produced in the history of the world. So being an emperor, he quickly had this guy beheaded before things got to the end. I'm afraid we don't have that option. So here's what happens with exponential growth. When we doubled—back when I was a kid. Some of you were a kid— from 5 transistors in a chip to 10 to 20 to 40, well that was one story. That was a story we could still imagine. But today after 40 years of doubling, we now have 5 billion transistors on a chip. So that means a year from now, we're going to have 10 billion, and then 10 billion is going to be 20 billion. And you just take that out 40 years. Feels like another story altogether. So right now we can sort of feel that acceleration, and if we look around, we can see it in action. From the past decade as we've moved from call it square 29 of the chessboard to about square 33, where we are today, we've gone from our friends the expensive, stationary, single purpose, industrial robots, to ever-cheaper all-purpose robots like this guy here. His name is Baxter, and that's his inventor Rodney Brooks in Cambridge. Now unlike his pricey predecessors, Baxter can learn multiple tasks and move between them. He can work safely alongside humans. He possesses fine motor skills like the ability to pick up credit cards and helpfully fold laundry. He's whip smart, and here's a key fact. He can be purchased for less than the total cost of one year's wages for a human working at minimum wage. Pretty significant. So in less than a decade we've also gone from dumb phones—no knocking Nokia, but you know, back then we thought they were pretty great, but all they could do was make calls— to smartphones like this marvel, which by the way just sold 75 million units in one quarter and tallied a cool $18 billion in net profit—not gross—net profit. The biggest quarter ever in history for a single company. Out there on the cutting edge of our physical roads, we went from this adapted traditional car which was the first DARPA Challenge car in that self-driving contest back in 2005, to this totally reconceived computer pod with seats on wheels, which actually doesn't even have a steering wheel anymore. Now I don't know. Have any of you ever been in a self-driving car? Okay, so I interviewed Sebastian Thrun at Davos a couple years ago, who's the guy that developed this for Google. And I said, "You've got to let me go for a ride." And my wife is really interested because she's a New Yorker, and she's never learned to drive. And so she was thinking, "Finally I can get something to take me to the mall or wherever I want to go without having to wait for you to stop working." Right? So we went out to Google and we took a ride in the car and it was pretty amazing. She was disappointed, but I was—I couldn't believe it. We were out on Highway 1. It's accelerating. It's slowing. You know, as something merged, it kind of edged over. It was like the most awesome cruise control you've ever seen. But now—but there was still a human in there. There was still a steering wheel, still a recognizable car. But now Google's already moving on to think, "If this really works, why do you need all that old stuff? Let's reconceive this thing." Now the fact is if you're thinking about buying one of these, one, they're not available, and two, they probably cost a couple hundred thousand dollars, at least more than the highest-end Tesla out there. Right? But the thing that I was so blown away by a couple years ago, it's becoming standard equipment. I was about, just a few weeks ago, on the roads outside of New York City in a new Nissan Q50 which comes with an autonomy— there's that word again—an autonomy feature that only adds an extra $7000 to the car's price, and it does exactly what that Google thing did three years ago. You turn it on, still the steering wheel so you don't have to panic. You can kind of like have a nap. You can look at your phone or your notes. It accelerates. You set a pace like three car lengths behind whatever I'm behind. It speeds up, slows down as needed. It adjusts as it gets crowded. $7000, it's going to be a standard feature, I predict, in pretty much every new car and it's going to happen long before we make the shift over to a new kind of transport system. Or consider the latest Air Force drone. Now in the interest of time, all I'm going to say about this baby is one, it's got a lot of intelligence and firepower and you don't want to mess with it. But two, you know, again this is the change we've gone through in the last decade. We're not aware of it. It's like frogs boiling in a pot. But we had none of these—we had about 10 of these in the US Air Force. I don't know the exact number, but it was in the low digits—when the first Iraq War—when the Iraq War 2 kicked off. We have over 20,000 now. The military in the US today trains more drone operators than fighter and bomber pilots combined, and they still have problems maintaining enough crews to keep them all flying. And oh yeah, here's this. The Pentagon last year, it was reported, is studying taking the standard brigade down from 4000 people to 3000 and making up the difference with drones and robots. In short, it's already getting weird. I don't know if you can see this, but these are camels racing robots, which happened in Dubai last year. And it's about to get a lot weirder. History shows us what can happen in a decade when price and consumer demand for something radically new hit an inflection point. So 100 years ago in the US around 1910, Ford was selling fewer than 1000 cars a year. But a decade later Ford was selling a million. And of course, it went up and up and up from there. Now, here's a conservative estimate from my own McKinsey Global Institute, about what happens to jobs as automation and soft AI roll out over the next decade. First, on the knowledge high end, computers will easily be doing the work of 140 million of today's knowledge workers globally. And robots will likely be doing the work of another 75 million. And again, I think we could be on the conservative end of this, but none of us know for sure. Right? The point is, hello productivity gains but bye-bye jobs or traditional incomes for more than 220 million people at both the high and low end of the spectrum. This is what's shocking to me as I've had these conversations in the last few years. As a business journalist, I grew up, lessons of history, what every economist told me, was don't worry about technology. Technology always creates more jobs than it destroys. That was—the Luddites were wrong. We saw, as we mentioned yesterday with agriculture, manufacturing, it happened and we moved to services. But the problem is economists today are no longer sure. There was a recent survey that the Pew Group did in the US, and half the economists—half the economists surveyed— thought automation is going to destroy more jobs than it creates over the next decade. That blows my mind because I guarantee you, it was never more than 5, 7, 8%—a bunch of wackos on the far end of the curve. So how many of you guys are aware, for example, of what's happening in the legal field? You aware of what's happening at—I don't know if anybody's got someone in law school, and if you do I'm sure they're going to be fine because I'm sure they're going to be really great and they go to a top school and they're going to get a great job with a great clerkship with a Supreme Court justice or somebody. But at the median, tuitions and admissions and entry level salaries in law again in the US have been falling at least 15, 20% for the last for the last couple years. It's mainly due to the rapid adoption of intelligent algorithms that are moving from doing unrivaled basic discovery to doing things like actually predicting Supreme Court decisions better than human experts. The stuff allows one smart lawyer, they say, to do the work of 500. That same kind of phenomenon could well happen in any profession with premium price talent and a lot of data to crunch. So how does this play out 20, 30, 40 years from now? Well, I've been talking to a lot of experts willing to put on their thinking caps. Bio-engineered humans anyone? I mean, this stuff gets—as I said—gets weird pretty quickly. But let's be honest. I certainly don't think I can predict, and I don't believe anyone can predict anything more than a decade out with any useful certainty. What I can tell you is, when you start thinking about the scale of the impact of exponential advances in infotech that we may be facing, it could make you feel like this. But my simple message today to you is it's time to stop screaming and start thinking about how to deal with the early stages of these previously unimaginable changes. Again, what are the kinds of things we're talking about? There's actually serious talk now— this is Vivek Wadhwa who is a venture capitalist and entrepreneur and now runs Singularity University out in California. He says he told us recently at a McKinsey convened event, "Every way I've sliced it and every industry I've looked at," he literally sees a jobless future. Now, he's out on the end of that but still it got a lot of attention when he said it. I mentioned I'm not going to do hard AI, but I can't help but note some really smart people have been raising this prospect recently and saying it's not inconceivable. Stephen Hawking in the FT last fall said, "A successful AI would be the biggest event in human history. Unfortunately it would also—might also be the last." The man who brought you PayPal, Tesla, SpaceX, Elon Musk, was on Twitter this summer saying, "We need to be super careful with AI. It's potentially more dangerous than nukes." And just an interesting little detail. I have a colleague at McKinsey who was telling me right before I flew over here that there was a conference—I think a little of this came out so I'm not revealing something untoward—in the Bay Area and Hawking, Musk, two Google founders, a bunch of brilliant academics, a couple of policy people were there. And the basic point of discussion was to talk about what is the rule-making framework for encouraging beneficial AI— that's their phrase, beneficial AI—by which they meant— and these are the exact words they used in their paper— "We must ensure our AI systems do what we want them to do." They're actually talking about this. But anyway, I'm not going there today. Long before we have to cope with the potential of the Singularity, which is at least happily at least many decades out, more and more sober non-tech economists I talk to believe we already face what Brian said in McKinsey four years ago was the biggest change in the economy ever because there's no upper limit. There's no place where this kind of neural network that we're seeing develop has to end. Now those are strong words—biggest change. So let me just show you what a very real world-based former Treasury Secretary, Larry Summers, repeated to me just at Davos this year and also put this in a speech he gave last year. Larry says, "This set of developments," by which he means this kind of autonomous, AI-enabled IT, "is going to be the defining economic feature of our era." And I think that's my takeaway today. This is the defining economic feature of our era. So guys, as the man says in Death of a Salesman, "Attention must be paid." Okay, so if we accept all that's true, what do we do about it? I've been stalling because this is the hard part. So let me just stick to the next 5 or 10 years and share a few high-level thoughts, and I'd love to hear yours. I think first from policy makers and business leaders who help shape the thinking and policy makers, I think we desperately need to start thinking and having serious discussions about new enabling regulations and some creative policy reforms. Now to me, this goes well beyond the kind of privacy debate that currently dominates today's dialogues. You know, think about it. If you go back to history, the first car market—when I showed you that Ford data—that didn't begin to happen until we agreed on the rules of the road. Right? There was no agreement. at least in the US, that you drove on the right. We didn't have stop signs. We didn't have traffic lights. We didn't have insurance. We didn't have standardization of basic parts. And we also established federal regulatory agencies. So in the same way no matter how great the technology gets, self-driving cars and their huge promise—unless it's the kind of cruise control I described where I guess the liability is still going to be on the manufacturer—we're to going to have the potential of this technology to create more dense and more people friendly urban landscapes and more sustainable landscapes any time soon, except beyond a few maybe pilots in places like Palo Alto, unless business experts and legislators can come together and agree on a whole raft of new rules and new regulations. And to me, that's—I mean, when I hear people give predictions on when we're going to have self-driving cars, this is the gating factor. Right? It's a little bit cultural behavior, but I think it's more the regulatory framework. That's going to determine whether this is 5 years away or 20 years away. The technology is happening. On a more mundane level, as you guys mentioned in your vision statement, I think the rise of the sharing economy which has been enabled by these fundamental technologies, fast mobile networks, brilliant algorithms, huge shifts in consumer behavior as people come to trust what they hear online and receive online has had huge implications for your industry. Of course, everybody knows the digital car service Uber has a $41 billion market cap and is disrupting traditional cab networks right and left. More relevant to you, Airbnb has gone in six years from one dude's apartment to more than 1 billion listings in 34,000 cities with an implied market cap bigger than the market cap of some of today's major hotel chains. But how these and other disruptors like them who are using data and technology to leverage existing private capital assets to meet demand instead of building new assets, it's going to be highly influenced by regulatory decisions and highly relevant, I think, to all of you. The same thing applies to things like realizing the full potential of the Internet of Things, the commercial use of drones, another hot topic. I don't know if you know this, but the FAA has promised—they made this promise two years ago— they're pledging to come out with rule making this fall on the first round of what we do with commercial drones. We'll see if they make their deadline. A little further out, I was talking to a Carnegie Mellon professor, very smart guy, who predicted that in the next decade we're going to see established a federal robotics commission, and that will be a big deal if that happens. It's head spinning stuff. A lot of these conversations and in the initial planning, we're going to need folks like you in this room to be engaged to get the best outcomes. In addition to smart regulation, we're going to need a host of creative new policy reforms and also new cross industry fora to debate and share best practices as you've been doing here. Let me ask a question. How many of you guys are still paying college tuition? Yeah, a few more. Okay. So you're okay. Again, kids are going to be fine. But I have a memo to a lot of American parents, particularly given the cost of education in the US. The returns to higher education except for advanced degrees and professional degrees, and with law we're already having it erode there. The returns have all basically plateaued over a decade ago. So we can't keep encouraging people to go into evermore debt to get a BA for careers with less certain payoffs. We've got to figure out, how are we going to structure education to deliver value in a more economical way? We also are having problems right now moving people with less than college educations into fields where there are semi-skilled jobs that are still going begging. So we need education reform, we need to move people from education to employment, and all of this calls out for new public-private partnerships as well as new approaches by providers. And the same need is going to be recurring again and again in any field where aggregate productivity has for the past decades really lagged behind the general productivity numbers. And this is things like education, healthcare, and in addition a field you guys know a lot about, construction, which has actually got some of the lowest productivity gains of any industry we studied for the last three or four decades. Longer term, to address continued income stagnation as well as the accelerated automation and fractionalization of work, we may need to consider radical enhancements to our social safety net that will enable us to allow continuing to let these market-driven productivity enhancing disruptions occur. I mean, whenever I hear somebody say a company like Uber needs to provide healthcare and pensions, I think, "Well no, no, no. That game's over. No companies are doing that anymore." Either it's going to be individuals or it's going to be government. I mean, you have one or the other but it's not going to be done through employers. When I was at Davos last year, Nobel economist Bob Shiller—when I was getting interested in this topic I asked him just to come do an interview. And he told me he actually considers the rise of this AI phenomenon "the most important problem facing the world today." This is a guy who has nothing to do with technology. And he talked to me about how one response he thought might be to create new forms of what he called livelihood insurance. Now this is many years away from being considered, I think, in a place like Washington. But it still—it's like when you see the history of ideas, these things start and they build like a little cloud becoming bigger. So let me move to companies, which is really what we want to talk about for the rest of the day here. I think what we need from executives, as we've said in some of our discussions already, is to really actively rethink what that leadership equation is in this new world. That's just way too much ground to cover, and I'm happy to try to delve into more of this in the Q&A. But for now let me just suggest seven broad principles for you to think about. One, I want you to remember, despite all the stuff I've been saying, that while machines will keep getting better than we do at if not everything a heck of a lot of things, humans working with machines are likely to continue outperforming machines alone. Now I personally find this notion hugely comforting speaking as a human, or at least one who plays the role on TV. When Garry Kasparov got crushed in 1997, he went out and started training with AIs and quickly discovered that people and machines could actually beat the algorithms. He then created this thing that sounds like ultimate fighting called chess freestyle battles where you can bring whatever you want to the game. And in a recent contest open to all modes of players, the pure chess AI engines won 42, but human-machine combos, which are also known as centaurs, won 53 games. And the current best chess player alive is a team of humans working on several different chess programs. What to me is even more inspiring is that the advent of these super smart, cheap chess programs has actually gotten more people than ever to play chess, and they play better. So Wired's Kevin Kelly reports there are now more than twice as many people who qualify as grandmaster as there were when Deep Blue first beat Kasparov. The top-ranked human player who trains with AIs also has the highest human grandmaster ratings of all time. So as Kelly says, "It stands to reason if AI can help humans "become better chess players, they can help us become better pilots, better doctors, better judges, better teachers," and I would add better executives as well. Indeed when it comes to high-end knowledge work, I don't think any standalone algorithm is going to soon knock off the likes of Goldman Sachs or my own McKinsey any time soon. But that's partly because we and Goldman and other industry leaders are accelerating our use of brilliant machines to enhance the services we deliver. Right now—and we've got a long way to go—but right now, about 40% or so and rising of all our engagements at McKinsey involve some kind of data analytics. So that said, watch out for metaphors. This is a lovely line which my friend Ibarra at INSEAD— she's got a new book out and she says, "Leadership in the future is more like being a good chef than following a recipe." It's a lovely thought, and I tend to agree. But keep in mind Watson has lately been expanding its repertoire into cuisine trolling databases, USDA nutritional facts, and flavor research, and he's come up with some new compounds that no human would ever think of. This is what I've read anyway. I haven't tasted this yet. Apple kabobs, beets with a roasted prune dressing, and this morsel which I tried to research but I can't tell what it is. But it looks yummy and apparently this is Watson's concoction. So my point is even actual chefs are going to rely more on machines in the future. Second takeaway is in a world with robust and ubiquitous soft AI, what a leader knows is less valuable than his or her ability to ask questions and find things out. And again, I think this came up in some of our discussions. There's a great YouTube video that supercomputer pioneer Danny Hillis—where he's talking about— he's a geekly little kid who won all these contests when he was a young guy. He was so proud—and I remember feeling this way too as a kid growing up in Alabama—so proud of what I knew. You could just ask me any question. I could spew out answers. But he says, "Knowing is such a 20th century skill." So think about actually just to kind of reinforce that, think about London cabbies. As many of you know, they have this thing which they call the knowledge—literally call it the knowledge— which was you had to kind of remember the entire street map of London. And it took them years and it took countless versions of taking the test to get the knowledge. This training is kind of like if you've ever read Joshua Foer's wonderful book "Moonwalking with Einstein." Before there was printing, there was this whole technique called memory palaces. You would remember things tied to some image of some place you'd been. You could remember the Bible practically with that kind of thing. So this was an old human technique. But you know what? In the world of GPS and Uber, it's over. So we just don't need to do that anymore. Does it mean we don't want people with knowledge and experience? We need great judgment more than ever. But having killer left brain skills in a particular domain is less important in the future—is now and will be less important in the future for most leaders thanks to these nerdy, super smart specialist algorithms. Jim Smith, who's the CEO of Thomson Reuters told me recently, "You know, in legal research, "you used to have to worry no matter how much you researched if there's a case, had you found every precedent?" He said, "But now you don't have to worry. With these programs, you know they haven't missed one." But it still doesn't tell you if you asked the right questions about alternatives. Are you probing what might be missing? And they can't figure what data might be misused to rush to conclusions or to allow unsustainable risk. The classic example of that, of course, that we all know is what the quants were doing on Wall Street and management didn't always know what kind of risk they were actually taking. So to ask those questions and to make judgments amid uncertainty, we're going to continue to look to leaders. This is just a nice line from Mark Benioff that reinforces this: "The quality of your innovation is directly proportional to the quality of your questions going forward." So principle number three: As everything digitizes, constant experimenting and rapid adaptation are far more important than deep studies and detailed strategic plans. It's not enough to ask questions. More and more you can and should design experiments that test hypotheses and surface fresh insights. This is already something natural to born digital companies like Amazon, Google, Facebook, Netflix, or folks like Uber, Airbnb. They have this in their DNA. They're constantly testing their pricing for whom and what situations, what time of day, tweaking their websites, running experiments. But the CEO chief experiments officer's playbook is probably clearest in retailing as Jim Thompkins was so expertly discussing yesterday. The constant online experiments are helping give rise to these whole new highly interactive customer relations experiences which require both online and offline. This is also going to happen in finance, insurance. I can talk about that in some of the Q&A. But let me just give you one example. As we go with the Internet of Things, right now about 1% of all physical objects have sensors in them. But we're going to go to billions and billions of these things over the next few years. And lots of industrial companies are learning a new game. GE, which is the last surviving company from the original Dow 30 over 100 years ago, now has 1100 people in Silicon Valley, and they provide predictive analytics to customers on things like when their maintenance is due or how to optimize an engine's performance based on constant feedback and experiments with the data they get from their sensors. Today two-thirds of the value of GE's $250 billion order backlog is based on leveraging the value of this kind of mathematical intellectual property. Now I admit most people have a long way to go. A lot of our clients at McKinsey are struggling to realize the potential of big data. I think part of the answer is going to be to get the analytics out of the hands of the experts and into the hands of the front line business units because if you don't change executive behaviors, as we all know, nothing changes. Companies like American Express, Proctor & Gamble, Walmart are all making major investments in better visualization and other tools that will help democratize the use of analytics and rotating their leaders through new analytic centers where they learn the basics just so they can take it back and apply it to their business. It's not so they can become data scientists. So you guys are going to know how to do this better than I do, but I would expect and predict that in your future gatherings, you'll be talking more about best practices and applying data analytics to solving urban planning and real estate issues. My quote here is from Ram Charan. I don't know if you know Ram. He's a terrific writer and consultant, and he's not a tech guy. But in his new book, "The Attacker's Advantage," which is coming out I think next month, he has this line, "Any organization that's not a math house," which basically is using these algorithms I'm talking about, "or is not able to become one soon is a legacy company." Maybe that's too strong, but you know, it's interesting that he's saying that. All right. Principle four. And this one is really encouraging. And again it's very much in line with some of your leadership conversations. As machines take on more of what we do with our left brains, what remains uniquely human is more valuable than ever, our right brain skills—relationship building, teaming and partnering, co-creating, being sensitive to cultural differences, being able to manage diverse employees. For a long time to come, we humans are going to have major advantages over computers and things like ideation and brainstorming ideas or shifting flexibly between whole new problem sets. If you ever have the chance to play Jeopardy with Watson, and it annoys you that you lose so quickly, just ask him to take you on in a game of Wheel of Fortune. You'll kill him because they have to reprogram him, and he can't do what you do. But to get the best ideas, we're going to need as broad and diverse a set of employees as possible, and we're going to need a richer ecosystem—I think you guys used this word—of external relationships. Now just one more question. Has anybody ever heard the expression Joy's law? So Bill Joy—this is a good one—Bill Joy is one of the founders of Sun Microsystems. He invented Java programming. Bill likes to say, "No matter who you are, most of the smartest people work for someone else." And that's again reinforcing the idea it's really good to have a network. It's really good to go out there and troll for insights. And this is actually what I'm hearing in my CEO conversations. Again I just did about 12 interviews at Davos this year—and across a wide range of industries. And all these leaders are adjusting their calendars to spend considerably more time on two things. One, talking first and foremost to customers to pick up on how the world is changing for them, how they can interact with customers. They're also talking to other external stakeholders. A lot of them are having to talk to regulators but also NGOs. You do this to hone your ability to see where change is taking your customers so you can make sure your organization is following. So I think this notion of talking more to outside—fantastic, fantastic impulse. And many of them are also interestingly to me—they've elevated the notion of— we'll see if the HR function ever comes up. Remember CFOs in the '80s? That's when suddenly the finance guy or gal—mostly guys at that point—suddenly became the number two or three person in the company because people realized capital allocation mattered. I think people are going to figure out that people allocation and training and developing your best talent's going to matter. And I predict that you're going to see HR become either a lot more important as a function, or you're going to see the CEOs taking on more and more of this kind of people talent task. And the final thing I would note on this point is that I think communicating and inspiring people to change, which again is a topic that came up in your leadership sessions, is more critical than ever in an era where traditional industry boundaries are breaking down and we're getting the kind of technology disruption I'm talking about. Just one quick example—at McKinsey we recently applied data to this. So we wanted to ask what makes and effective leader? We surveyed 189,000 people in 81 organizations around the world to assess how frequently certain kinds of leadership behavior were applied. We divided this thing into organizations whose leadership performance was strong based on our own organizational health index. We got a top quartile. We got a bottom quartile. We love to geek it out by the way. Anyway, of these four qualities that emerged as the top most important by far, two you would expect—solve problems effectively, be results oriented. But two kind of interesting and surprising and fit with this theme. One, be supportive. The other, seek different perspectives. These literally came up in all this data crunching. Put it another way, empathy. Maybe empathy is the critical 21st century leadership skill. This is from a book my friend Geoff Colvin at Fortune is coming out with later this year. I'm going to also recommend that one to you. So on to principle five. To derive real change, you have to reallocate resources and break old orthodoxies. And just to quickly make this point, we've done a lot of studying of this problem at McKinsey. We studied more than 1600 large companies. We found the most aggressive reallocators of capital, those that shifted over 50% of their capital over a 15-year period, delivered 30% higher returns than their counterparts. Most people—and I don't know if this is true in your organizations but I know it's a common problem when I talk to executives— most people add 3%, take away 3%, add 5, take away 5%. Everyone gets the same baseline. You just adjust it slightly. And that's the reason why 50% of all transformation efforts fail according to our own research at McKinsey. Because it's not just the capital investment that fails to move. It's also the people and the organizational resources that new enterprises need. It just reinforces the fact that as you shift the data, don't let inertia, caution, internal politics keep you from, as we had said at McKinsey a few years ago, putting your money where your strategy is. Okay. Principle six. Every now and then with all this 24/7 constantly on, screen-driven world, unplug, reflect. It's the essential complement to always-on leadership. This is kind of my hope for a moment here if you're American. But you know what? It's really true. I mean, I have interviewed dozens and dozens of executives on leadership in the last few years, and it's striking how often the really successful ones—people like Amazon's Jeff Bezos or Microsoft founder Bill Gates or Larry Fink, who's the CEO of BlackRock, the world's biggest asset manager, but also many others—they all call out the vital importance of taking off a little time—some daily, some a few days every quarter— to be alone, turn off the machines, just read, think, write down ideas. This is, of course, one I particularly like because I'm a writer. Jeff Bezos famously manages digitally savvy Amazon by asking everyone to bring to meetings memos which they sit and actually read for 10 minutes before they start their discussions. I'm not sure you need to write a six-page narratively structured memo in order to be effective, but I do think the notion that you need to stop to reflect, to separate signal from noise, turn off the machines, I think that's really, really powerful. So before I give my seventh and final rule, let me pause briefly to recap my key takeaways today. All right. This one's pretty obvious. I've been saying it again and again. The first big takeaway—we are at the dawn of the biggest set of changes that we've ever faced in our careers. I really believe that, and arguably in human history. Now I'm not saying skepticism isn't warranted. Good Lord. I'm a journalist. How could I not be skeptical? Right? Or at least I was a journalist. I'm not a journalist now. There's going to be major bumps along the way. The timeline may slip. One thing I think about a lot is that we really haven't had yet a 9/11 with cybersecurity. Right? We've had some really awful incidents, some terrible scares, but nothing that really just blows the doors open. But I'll just point out when you do that thought exercise, you know, we had 9/11 and it didn't end up derailing the global economy. And neither did the bigger shock of 2008. So I believe that when you come to these world-changing disruptions, like the one I think we're facing, you kind of have to decide—skepticism. You take it, you don't stop being skeptical but you ask yourself, which side are you on? Say it's 1981. Are you betting on the PC like Bill Gates did? Or are you dismissing the potential like IBM did back then? Do you look at cell phones in 1980 as McKinsey famously did and tell AT&T that the potential US market is going to peak at 900,000? That's not one of our best predictions. Or do you realize actually pretty much everybody in the US and 5 billion people globally soon are going to have these things? Do you look at China in 1999—we were there for a Fortune conference on the 50th anniversary of the People's Republic—and a lot of smart China hands were telling me then, "Wow, look at this. "The corruption, lack of democracy, current real estate bubble. It's going to kill the last 20 years of growth." Or do you bet, as I suspect many of you did here in this room, the positives outweigh the negatives and conclude rightly as it turned out that China, in fact, has not yet begun to shake the world? We may still have a little problem at some point down the road, but the long trend is pretty clear. And this is my point. These real deep trends, they tend to follow what I think of as the Hemingway curve. Hemingway—the writer Ernest Hemingway— once described how a man goes bankrupt—slowly at first and then all at once. I think that's the way this stuff is going to work as well. Now, none of this is easy. It raises huge existential questions about meaning and purpose. But before I get to that— sorry I jumped ahead of my slide. Second big takeaway, and this is something I really want to emphasize. Solving the new leadership equation—if you can remember nothing else, remember this. First, you have to effectively leverage the rising power of the machines, but second and importantly, you have to focus more management time and attention on those right brain skills of social interaction, empathy, and creativity. This is not just a comforting thought. I think it's an essential response. It also speaks to the task that Jillian mentioned Davos gives you guys such credit for—urban planning. I mean, as you build new buildings and new spaces and cities, you have to do both and you're going to have to wire this stuff with the massive ability to handle all of this intelligence, like the mayor and his Google broadband effort. But you also have to create human spaces where people want to be. They want to interact. They want to mingle. It encourages that kind of interaction. You've got to do placemaking. Right? Is that the phrase? Placemaking, something I just learned this week from you guys. Okay, but as I said, it's not going to be easy. These are existential questions. I love this line from Kevin Kelly. It's pretty cosmic. "We're going to spend the next decade, "perhaps the next century as a result of all this in a permanent identity crisis constantly asking ourselves, 'What are humans for?'" All right? But here's my third and final takeaway and my seventh principle. I think you have to be an optimist—an eyes-wide-open optimist but an optimist—because it's the only way to bet. Let me just pull back again and remind you. When we went through all this the first time around 250 years ago, it was a lot rawer. Right? The world was not nearly as rich. Only a few countries scattered around the edge of the Atlantic were actually wealthy. Well, that wealth has now been dispersed. Now we have social safety nets which didn't exist back then. We can adjust them. We're not going to get poorer under any scenario as long as productivity rises, which seems very likely. Then the challenge we have as a society is not wealth creation but the significant problem of how it gets distributed. And the definition of human value—it's in our hands. And we're finally—and really I think correctly—we're surely going to need more intelligence, more analysis, and more man-machine interaction if we're going to solve some of these big global challenges you've been talking about the last couple of days, things like climate change, sustainable growth, coping with a rapidly aging world. So I want to close with a favorite quote from economic historian David Landes, a great man who after surveying centuries of economic history in his classic work, "The Wealth and Poverty of Nations" concludes with this thought, "In this world, the optimists have it, not because they're always right "but because they're positive and that is the way of achievement, correction, improvement, and success." Educated eyes-open optimism pays. Pessimism can only offer the empty consolation of sometimes being right. Take that Nouriel Roubini. So questions? Yeah? Let me—okay, go ahead. You've got the microphone. -I take it from your comments that they're mainly directed at the US. I have two questions. One is— - Not really. -Yeah, how widely spread is this in other parts of the world, number one? And number two, are there particular areas of the United States—cities—where more versus less work is being done on the development of artificial intelligence and the application of it? -So yeah. One, I think that in terms of developing the technology, there are obviously big locuses. Right? I mean, Silicon Valley is a huge one. I think Cambridge in US is another. I think places like Carnegie Mellon, places down in North Carolina, down in Austin, Texas. I mean, there is not—it is dispersed. And it's not just in the US. Baidu in China— now the big search engine, the Google of China, they've opened up a major AI research center in Silicon Valley. They're probably doing stuff in China, but they have a center in Silicon Valley and they've hired Andrew Ng, who's one of the Stanford University professors who helped again do early deep learning stuff. He's working with them. So—and this is happening in Oxford in England. It's happening—but I think the locus is the US, but the point is in a cloud-based mobile network, this is going to flow everywhere. I mean, just one example. I had a very inspiring interview a couple of years ago with Sal Khan, the Khan Academy guy. And he just was painting this vision of what it's going to be like as we get smartphones more distributed in India and they improve their broadband base. I mean, you're just going to radically transform—you're going to leapfrog the education from building a bunch of schools to just plugging these kids in and having hopefully some smart young people to come in and help lead the discussions, lead the conversations. The same thing is happening—again, I think it's happening in the US, which is the area I know best, but it's the same kind of model. And I heard Gates talking about this at a lunch a few years ago. You don't need people to replicate the lectures of the world's best minds because they're all available now online. So this notion of doing what I'm doing and being a classroom performer and replicating that down some sort of scale from great to kind of okay— just forget it. Just give people everywhere the best, but then make sure—because you need that right brain power— you have somebody coming in to lead discussions, to help people to elevate the issues, to engage in dialogue, to brainstorm. You can basically flip the classroom. So a lot more of our universities, I think, are going to be moving away from delivering—certainly starting at the community colleges and going up—move away from having to have everyone have the same set of assets to having access and then having that human touch. And so again, I think this is a global phenomenon. Yep? Lynn and then—yep. -I think it was your number three on leadership was constant experimentation and rapid adaptation. And we hear that all the time, that we need to be more creative and innovative. And yet, at least for my generation as we were in the business world, which is conservative and doesn't like errors and mistakes and, in fact, does performance evaluations and incentive compensation on exact—on not being wrong— how do companies change? What are you hearing? Is this a generational thing or are you hearing companies actually change their culture? And what do the leaders do to be willing to be those risk takers? -Well, like my friend Hermenia says, "All these chefs have different approaches to the kitchen, and there's not one recipe." But I think one, if you're not born digital and you don't start off this way, it's definitely a lot harder. Right? And no one's going to bet the organization on some big new—you probably can't have enough data to make a company killing bet. So a lot of what people are doing is there's a ton of Fortune 500 companies that have opened up labs in Silicon Valley, from Walmart—I mentioned GE as well as the Baidu. And I think they're getting— one, they're bringing in talent, and two, they're getting insights into problems they need to test, experiments they need to run. And then they're rolling that back into changing their model. So I think this notion of piloting is a big one. I'm looking at insurance. There's a bunch of things going on right now. You think about the Internet of Things, for example. Right now you base your pricing of insurance, say for cars, on somebody's driving history. But you could also use the feedback from—particularly in the rental area. Say somebody comes in and the car's dinged. And they say—they give you their story but the sensors are telling you the guys was driving 90 miles an hour. How do you price that? How do you deal with that? So there's just—I can see running— I can see a lot of people beginning to experiment with small-scale issues and then try to turn them into potentially larger business bets. In terms of being able to do this, again I see a lot of companies definitely purposely going out and scooping up new talent, changing the way they hire and develop talent. Yep? -My screensaver says, "Stay in the happy bubble." And I— -Stay in the happy bubble? That's good. -And I think that there's a potential, and I just would like to hear from you, that as people become more connected through cell phones and through information, is this going to create peace in our society? Are people going to be taken away from the totalitarian dogma that people are fed because their phones are going to be able to give them another opportunity and another way to reason? -Well, I mean, it's a cosmic question, and I think part of the challenge is in the short run, I think our technology often— there's all kinds of great examples of, you know, collective action in social media and stuff. A lot of it—I think Jillian said yesterday— makes you more aware of bad stuff happening everywhere because again, in the news business if it bleeds it leads, as they say. And the fact is that's where data will help you correct your misimpression because despite all the awful things going on in the world today, Steven Pinker wrote this wonderful book, "Better Angels," basically pointing out that using really rich, robust data series over hundreds and hundreds of centuries, the world has never been less violent. We're actually making progress as a society. Even despite World War II, the Holocaust and all that and all the things that are going on today, compared to the way it was on a per capita basis that adjusted, way less violent. So we're actually getting somewhere. I do think part of my answer to your question, though—and again, it goes back to my both/and thing—I think that this interesting data that shows too much technology isn't good for you. Right? Kids that just stay on their social network all the time and rather than go see their friend at the coffee shop or go out and throw a ball, there's careful studies that show they're less adjusted, they're less successful, they're less happy. So we do need—we still need that. We evolved all those millions of years ago as social animals out there on the savanna, and we need that interaction. So I think the balance in the future is going to be knowing how to use the technology to do great things but continue to have even richer right brain human interaction. I really think that's the formula. Yep? -So the World Economic Forum will launch an initiative on the future of construction and— -Yeah? Good. -You mentioned construction, and I'm sure McKinsey— and you mentioned also the low profitability of that sector. And for real estate, I think there are also similarities in terms of having a relatively conservative industry environment and just waiting for the next disruption which might be replaced— this kind of conservative industry by another, perhaps even coming from another industry area. And then, I would like to know your insight and perhaps also your ideas. I know it's difficult. But the future of construction in terms of that kind of artificial intelligence. -Well again, I'm not going to pretend to be an expert on that, but I just think it applies. My point is—you look at—first my big point is you look at cost productivity data. And one reason the average American doesn't feel particularly rich with stagnant wages is while their TVs are really, really cheap and their cars are going down, education is up 700% since 1982 and healthcare is up 500%. Again, construction costs in the physical world haven't gone anywhere. And so I just think that there's just no doubt in my mind that these technologies as they unfold—technology disruption is a productivity seeking missile. And where there is not productivity, it will go there and it will change structures of industries. I have colleagues much more knowledgeable than I. We just started a few years ago an infrastructure practice at McKinsey and we are having our third—small plug, if you want to go let me know— our third Global Infrastructure Initiative this fall in San Francisco. And one of the themes is technology disruption, and one of our points is this is coming. And so whether it's embedding intelligence, using more modular construction techniques, sharing best practice. I mean, a lot of productivity gains in the world are just about taking what somebody knows who knows how to do it well and making sure that gets shared with the people who are behind. And I think in this world of more seamless information sharing, we should be able to do that. So I can get you in touch with some folks who can give you real answers. But directionally that's how I look at it. -Rick, I work for an organization which has a sort of 350-year history. We built our first buildings in the 18th century. So we enjoy sort of speculating and looking forward. And I suppose it was 10 years ago, we debated the history of robots and the loss of all our jobs, and we've decided that probably politics and painting are the only things that might be left for us. -And physical therapy—physical therapy's really good. -I'm absolutely with you. -You don't want a machine giving you a massage, I don't think. -I'm with you with the direction. However, I mean, when I first joined this organization in the mid '70s, I think 3 out of 40 people had electronic typewriters. The rest had manual typewriters. So we've coped with an incredible amount of change. And I feel optimistic. And I'd ask you if I'm dangerously complacent, because I think this is one of the—this is still a sort of slow burn change. It's not like the oil price collapsing from $150 to $50. And I believe when you look back and see what we've coped with— And think of the Industrial Revolution. I mean, it's amazing what we've coped with. And the impact here I think will be slow enough—I'm sure they're accelerating—but slow enough that we're going to manage as leaders to adapt and cope. And we'll look back over 20 years and see the change, but it's not a sort of dramatic overnight— -If I gave that impression, I think it's wrong. Although I do think—I do think— and I don't know when that point is. I do think that with a lot of this stuff there's that going bankrupt slowly at first and then all at once. I do think there's a tipping point. And so I don't know if it's 5 years from now, 10 years from now. But I totally agree with you that particularly if we can get a little bit in front of it, we can adjust. I mean, we can adjust. As I said, it's not— you look at the history of the 19th century, which I'm a big fan of, and man, those early—those dark satanic mills sweeping across England and the Luddites and urban riots. And it took us about 45, 50 years, because again, things were slower then. To me, I was doing a book a few years ago on the history of capitalism, which I never finished because I joined the great capitalist institution, McKinsey, and decided to shelve that. And it was very clear that Marx might have been right. If we hadn't adjusted as a society, things were getting really ugly. These income gains didn't start the first 30 or 40 years. They kicked in later, and then what happened was you had to actually have the beginnings of social safety nets, which were those debates we had in the late 19 and early 20th century. So what is the new model for the 21st century? I think if we can solve that and we stay out in front of it, I'm totally with you. I think the fact that we've done it before, as they say—the song does. We've done it before and we can do it again. So d'accord, as we say in France. -Thank you first for your recommendations on individual books. I presume you're struggling like all of us to find time to read given the portion of our day— -I have a great forcing mechanism because I'm a judge for the FT McKinsey Book Award. So I had to read 15 books including Thomas Piketty. I'm the only person that raised his hand yesterday. I'll give you the Cliff Notes, by the way. Yeah. So it's tough. -Okay, so we're all struggling with email that's more time wasting than it is enlivening. Do you have a single recommendation beyond the books you mentioned? Is it Wired? Is there a single source to keep current? -I like Lynn's idea of McKinsey. But no. That would be very self promotional. There's no single source. I think you have to—in fact, I think part of what you have to do—we've never been in a richer information environment. If you're a news junkie like I am, if you move around, the world's in the best place it's ever been. But if you stay watching Fox, you stay watching MSNBC, you stay in you silos, you're not getting the story. So I think you've got to carve out some time for a little serendipity whether it's following interesting people on Twitter, shockingly asking your friends—asking your friends what they're reading. If your friends are interesting, smart people, you're going to get a great network effect. So I'd recommend that. And I'm happy to send around to anybody who wants some of these books I've been recommending. I think I'm getting the hook maybe. Yeah, right here. -How do you think the evolution of virtual currency or value such as Bitcoin fits into the second economy, the autonomous economy? -Yeah, it's an interesting question. I mean, a lot of the tech people I talk to are very excited, not about Bitcoin but about the notion of some kind of alternative encrypted currency. That's one where I go back to my point about regulation. Governments may let a lot of things go. They're really kind of sticky around their currencies, and so I think it's stickier than the technology optimists think. I don't dismiss it. I think these electronic pay systems, what Apple's getting into, changing the way we—I think currencies themselves tend to be a diminishing thing. And I think that's one to watch. I just don't have a formed view. Okay. Well good. Well thank you guys. I hope that was useful.

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Posted by: josephklem on Jun 8, 2015

Rik Kirkland, Director of Publishing at McKinsey & Company, speaking at the ULI Global Trustees and Key Leaders Midwinter Meeting 2015.

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