Technology is getting smarter, faster. Are you? Experts including the authors of The Second Machine Age, Erik Brynjolfsson and Andrew McAfee, examine the impact that “thinking” machines may have sầu on top-management roles.

The exact moment when computers got better than people at human tasks arrived in 2011, according lớn data scientist Jeremy Howard, at an otherwise inconsequential machine-learning competition in Germany. Conthử nghiệm participants were asked khổng lồ design an algorithm that could recognize street signs, many of which were a bit blurry or dark. Humans correctly identified them 98.5 percent of the time. At 99.4 percent, the winning algorithm did even better.

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Or maybe the moment came earlier that year, when IBM’s Watson computer defeated the two leading human Jeopardy! players on the planet. Whenever or wherever it was, it’s increasingly clear that the comparative advantage of humans over software has been steadily eroding. Machines và their learning-based algorithms have sầu leapt forward in pattern-matching ability & in the nuances of interpreting & communicating complex information. The long-standing debate about computers as complements or substitutes for human labor has been renewed.

The matter is more than academic. Many of the jobs that had once seemed the sole province of humans—including those of pathologists, petroleum geologists, and law clerks—are now being performed by computers.


Andrew McAfee is associate director and principal retìm kiếm scientist of the Center for Digital Business at the Massachusetts Institute of Technology’s Sloan School of Management.

Erik Brynjolfsson is the director of the Center of Digital Business và the Schussel Family Professor of Management Science at the Sloan School.


And so it must be asked: can software substitute for the responsibilities of senior managers in their roles at the top of today’s biggest corporations? In some activities, particularly when it comes to finding answers khổng lồ problems, software already surpasses even the best managers. Knowing whether khổng lồ assert your own expertise or lớn step out of the way is fast becoming a critical executive skill.

In this interview with’s Rik Kirklvà, Erik Brynjolfsson và Andrew McAfeeexplain the organizational challenge posed by the Second Machine Age.

Yet senior managers are far from obsolete. As machine learning progresses at a rapid pace, top executives will be called on to lớn create the innovative new organizational forms needed to lớn crowdsource the far-flung human talent that’s coming online around the globe. Those executives will have to lớn emphakích cỡ their creative abilities, their leadership skills, và their strategic thinking.

To sort out the exponential advance of deep-learning algorithms & what it means for managerial science,’s Rik Kirkland conducted a series of interviews in January at the World Economic Forum’s annual meeting in Davos. Aý muốn those interviewed were two leading business academics—Erik Brynjolfsson & Andrew McAfee, coauthors of The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (W. W. Norton, January 2014)—và two leading entrepreneurs: Anthony Goldbloom, the founder and CEO of Kaggle (the San Francisco start-up that’s crowdsourcing predictive-analysis contests khổng lồ help companies & researchers gain insights from big data); và data scientist Jeremy Howard. This edited transcript captures and combines highlights from those conversations.

The Second Machine Age

What is it and why does it matter?

Andrew McAfee: The Industrial Revolution was when humans overcame the limitations of our muscle power. We’re now in the early stages of doing the same thing to lớn our mental capacity—infinitely multiplying it by virtue of digital technologies. There are two discontinuous changes that will stiông xã in historians’ minds. The first is the development of artificial intelligence, & the kinds of things we’ve seen so far are the warm-up act for what’s to lớn come. The second big khuyến mãi is the global interconnection of the world’s population, billions of people who are not only becoming consumers but also joining the global pool of innovative talent.

Erik Brynjolfsson: The First Machine Age was about power systems and the ability khổng lồ move large amounts of mass. The Second Machine Age is much more about automating and augmenting mental power & cognitive sầu work. Humans were largely complements for the machines of the First Machine Age. In the Second Machine Age, it’s not so clear whether humans will be complements or machines will largely substitute for humans; we see examples of both. That potentially has some very different effects on employment, on incomes, on wages, và on the types of companies that are going to be successful.

Machine-learning experts Anthony Goldbloom & Jeremy Howard tell’s Rik Kirklvà how smart machines will impact employment.

Jeremy Howard: Today, machine-learning algorithms are actually as good as or better than humans at many things that we think of as being uniquely human capabilities. People whose job is lớn take boxes of legal documents và figure out which ones are discoverable— that job is rapidly disappearing because computers are much faster & better than people at it.

In 2012, a team of four expert pathologists looked through thousands of breast-cancer screening images, and identified the areas of what’s called mitosis, the areas which were the most active sầu parts of a tumor. It takes four pathologists khổng lồ vày that because any two only agree with each other 50 percent of the time. It’s that hard to look at these images; there’s so much complexity. So they then took this kind of consensus of experts and fed those breast-cancer images with those tags to lớn a machine-learning algorithm. The algorithm came baông chồng with something that agreed with the pathologists 60 percent of the time, so it is more accurate at identifying the very thing that these pathologists were trained for years to lớn vày. And this machine-learning algorithm was built by people with no background in life sciences at all. These are total tên miền newbies.

Andrew McAfee: We thought we knew, after a few decades of experience with computers & information giải pháp công nghệ, the comparative sầu advantages of human & digital labor. But just in the past few years, we have seen astonishing progress. A digital brain can now drive sầu a oto down a street and not hit anything or hurt anyone—that’s a high-stakes exercise in pattern matching involving lots of different kinds of data & a constantly changing environment.

Why now?

Computers have sầu been around for more than 50 years. Why is machine learning suddenly so important?

Erik Brynjolfsson: It’s been said that the greademo failing of the human mind is the inability to lớn understvà the exponential function. Daniela Rus—the chair of the Computer Science and Artificial Intelligence Lab at MIT—thinks that, if anything, our projections about how rapidly machine learning will become mainstream are too pessimistic. It’ll happen even faster. And that’s the way it works with exponential trends: they’re slower than we expect, then they catch us off guard and soar ahead.

Andrew McAfee: There’s a passage from a Hemingway novel about a man going broke in two ways: “gradually and then suddenly.” And that characterizes the progress of digital technologies. It was really slow & gradual & then, boom—suddenly, it’s right now.

Jeremy Howard: The difference here is each thing builds on each other thing. The data & the computational capability are increasing exponentially, and the more data you give these deep-learning networks and the more computational capability you give sầu them, the better the result becomes because the results of previous machine-learning exercises can be fed baông xã inlớn the algorithms. That means each layer becomes a foundation for the next layer of machine learning, và the whole thing scales in a multiplicative sầu way every year. There’s no reason lớn believe sầu that has a limit.

Erik Brynjolfsson: With the foundational layers we now have sầu in place, you can take a prior innovation and augment it khổng lồ create something new. This is very different from the comtháng idea that innovations get used up lượt thích low-hanging fruit. Now each innovation actually adds khổng lồ our stochồng of building blocks & allows us to lớn bởi vì new things.

One of my students, for example, built an ứng dụng on Facebook. It took hyên about three weeks to lớn build, and within a few months the ứng dụng had reached 1.3 million users. He was able khổng lồ bởi vì that with no particularly special skills and no company infrastructure, because he was building it on top of an existing platsize, Facebook, which of course is built on the website, which is built on the Internet. Each of the prior innovations provided building blocks for new innovations. I think it’s no accident that so many of today’s innovators are younger than innovators were a generation ago; it’s so much easier to lớn build on things that are preexisting.

Jeremy Howard: I think people are massively underestimating the impact, on both their organizations and on society, of the combination of data plus modern analytical techniques. The reason for that is very clear: these techniques are growing exponentially in capability, và the human brain just can’t conceive sầu of that.

There is no organization that shouldn’t be thinking about leveraging these approaches, because either you do—in which case you’ll probably surpass the competition—or somebody else will. And by the time the competition has learned lớn leverage data really effectively, it’s probably going to be too late for you to lớn try khổng lồ catch up. Your competitors will be on the exponential path, và you’ll still be on that linear path.

Let me give sầu you an example. Google announced last month that it had just completed mapping the exact location of every business, every household, and every street number in the entirety of France. You’d think it would have needed to sover a team of 100 people out to each suburb & district to lớn go around with a GPS & that the whole thing would take maybe a year, right? In fact, it took Google one hour.

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Now, how did the company vì chưng that? Rather than programming a computer yourself lớn vày something, with machine learning you give sầu it some examples và it kind of figures out the rest. So Google took its street-view database—hundreds of millions of images—and had sometoàn thân manually go through a few hundred and circle the street numbers in them. Then Google fed that khổng lồ a machine-learning algorithm & said, “You figure out what’s unique about those circled things, find them in the other 100 million images, & then read the numbers that you find.” That’s what took one hour. So when you switch from a traditional to lớn a machine-learning way of doing things, you increase productivity & scalability by so many orders of magnitude that the nature of the challenges your organization faces totally changes.

The senior-executive role

How will top managers go about their day-to-day jobs?

Andrew McAfee: The First Machine Age really led khổng lồ the art & science and practice of management—khổng lồ management as a discipline. As we expanded these big organizations, factories, & railways, we had to lớn create organizations to lớn oversee that very complicated infrastructure. We had to invent what management was.

In the Second Machine Age, there are going to lớn be equally big changes lớn the art of running an organization.

I can’t think of a corner of the business world (or a discipline within it) that is immune to the astonishing technological progress we’re seeing. That clearly includes being at the top of a large global enterprise.

I don’t think this means that everything those leaders vì chưng right now becomes irrelevant. I’ve sầu still never seen a piece of công nghệ that could negotiate effectively. Or motivate và lead a team. Or figure out what’s going on in a rich social situation or what motivates people & how you get them to move in the direction you want.

These are human abilities. They’re going lớn stiông xã around. But if the people currently running large enterprises think there’s nothing about the công nghệ revolution that’s going to lớn affect them, I think they would be naïve sầu.

So the role of a senior manager in a deeply data-driven world is going to lớn shift. I think the job is going khổng lồ be khổng lồ figure out, “Where vày I actually add value and where should I get out of the way và go where the data take me?” That’s going lớn mean a very deep rethinking of the idea of the managerial “gut,” or intuition.

It’s striking how little data you need before you would want to switch over and start being data driven instead of intuition driven. Right now, there are a lot of leaders of organizations who say, “Of course I’m data driven. I take the data và I use that as an đầu vào lớn my final decision-making process.” But there’s a lot of research showing that, in general, this leads lớn a worse outcome than if you rely purely on the data. Now, there are a ton of wrinkles here. But on average, if you second-guess what the data tell you, you tover lớn have sầu worse results. And it’s very painful—especially for experienced, successful people—to walk away quickly from the idea that there’s something inherently magical or unsurpassable about our particular intuition.

Jeremy Howard: Top executives get where they are because they are really, really good at what they vị. And these executives trust the people around them because they are also good at what they vì and because of their tên miền expertise. Unfortunately, this now saddles executives with a real difficulty, which is how to lớn become data driven when your entire culture is built, by definition, on domain expertise. Everytoàn thân who is a domain name expert, everytoàn thân who is running an organization or serves on a senior-executive team, really believes in their capability & for good reason—it got them there. But in a sense, you are suffering from survivor bias, right?

You got there because you’re successful, và you’re successful because you got there. You are going khổng lồ underestimate, fundamentally, the importance of data. The only way to understand data is khổng lồ look at these data-driven companies like Facebook và Netflix và Amazon and Google and say, “OK, you know, I can see that’s a different way of running an organization.” It is certainly not the case that tên miền expertise is suddenly redundant. But data expertise is at least as important and will become exponentially more important. So this is the triông chồng. Data will tell you what’s really going on, whereas domain name expertise will always bias you toward the status quo, và that makes it very hard to lớn keep up with these disruptions.

Erik Brynjolfsson: Pablo Picasso once made a great observation. He said, “Computers are useless. They can only give you answers.” I think he was half right. It’s true they give sầu you answers—but that’s not useless; that has some value. What he was stressing was the importance of being able khổng lồ ask the right questions, and that skill is going lớn be very important going forward và will require not just technical skills but also some domain knowledge of what your customers are demanding, even if they don’t know it. This combination of technical skills & domain knowledge is the sweet spot going forward.

Anthony Goldbloom: Two pieces are required to be able lớn vì a really good job in solving a machine-learning problem. The first is sometoàn thân who knows what problem to lớn solve & can identify the data sets that might be useful in solving it. Once you get to that point, the best thing you can possibly vì chưng is to lớn get rid of the domain expert who comes with preconceptions about what are the interesting correlations or relationships in the data and khổng lồ bring in somebody toàn thân who’s really good at drawing signals out of data.

The oil-and-gas industry, for instance, has incredibly rich data sources. As they’re drilling, a lot of their drill bits have sensors that follow the drill bit. And somewhere between every 2 and 15 inches, they’re collecting data on the rock that the drill bit is passing through. They also have sầu seismic data, where they shoot sound waves down into lớn the roông xã &, based on the time it takes for those sound waves to be captured by a recorder, they can get a sense for what’s under the earth. Now these are incredibly rich và complex data sets &, at the moment, they’ve sầu been mostly manually interpreted. And when you manually interpret what comes off a sensor on a drill bit or a seismic survey, you miss a lot of the richness that a machine-learning algorithm can pick up.

Andrew McAfee: The better you get at doing lots of iterations và lots of experimentation—each perhaps pretty small, each perhaps pretty low-risk và incremental—the more it all adds up over time. But the pilot programs in big enterprises seem to be very precisely engineered never to fail—& khổng lồ demonstrate the brilliance of the person who had the idea in the first place.

That makes for very shaky edifices, even though they’re designed to lớn not fall apart. By contrast, when you look at what truly innovative companies are doing, they’re asking, “How bởi I falsify my hypothesis? How vì chưng I bang on this idea really hard & actually see if it’s any good?” When you look at a lot of the brilliant web companies, they vì hundreds or thousands of experiments a day. It’s easy because they’ve sầu got this demo platkhung called the website. And they can bởi subtle changes & watch them add up over time.

So one of the implications of the manifested brilliance of the crowd applies lớn that ancient head-scratcher in economics: what the boundary of the firm should be. What should I be doing myself versus what should I be outsourcing? And, now, what should I be crowdsourcing?

Implications for talent & hiring

It’s important khổng lồ make sure that the organization has the right skills.

Jeremy Howard: Here’s how Google does HR. It has a unit called the human performance analytics group, which takes data about the performance of all of its employees và what interview questions were they asked, where was their office, how was that part of the organization’s structure, & so forth. Then it runs data analytics lớn figure out what interview methods work best and what career paths are the most successful.

Anthony Goldbloom: One huge limitation that we see with traditional Fortune 500 companies—& maybe this seems like a facile example, but I think it’s more profound than it seems at first glance—is that they have sầu very rigid pay scales.

And they’re competing with Google, which is willing lớn pay $5 million a year khổng lồ somebody who’s really great at building algorithms. The more rigid pay scales at traditional companies don’t allow them lớn vị that, & that’s irrational because the return on investment on a $5 million, incredibly capable data scientist is huge. The traditional Fortune 500 companies are always saying they can’t hire anyone. Well, one reason is they’re not willing khổng lồ pay what a great data scientist can be paid elsewhere. Not that it’s just about money; the best data scientists are also motivated by interesting problems and, probably most important, by the idea of working with other brilliant people.

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Machine learning and computers aren’t terribly good at creative thinking, so the idea that the rewards of most jobs và people will be based on their ability to think creatively is probably right.