Clay Chandler interviews Mick Mountz about the future of Artificial Intelligence.
We’ve heard so much in the past year about technological change, the “Fourth Industrial Revolution,” and how everyone from Elon Musk to Steven Hawking is afraid of robots taking over the universe. What’s going on?
Society is trying to come to terms with the increasing pace of technological change and this exponential rise of new technologies, and put those concepts into understandable frameworks and metaphors. Calling what’s happening right now the Fourth Industrial Revolution brings to mind previous patterns of change and side effects—especially relating to jobs and employment.
Current discussions about robotics and artificial intelligence (AI) generally involve extrapolation of unchecked technology curves—which may be a relevant limit case, but is unlikely to be the probable outcome. These scenarios don’t often acknowledge how hard these complex technologies are to master, and the market and regulatory responses that impede and alter adoption trajectories.
What’s really new in all these discussions about robots and AI? Technological change has been a constant since the original Industrial Revolution. Don’t we always just adapt and move on?
The pace of change is quickening, but folks are also starting to see the convergence of hardware and software into seamless new solutions. At Kiva, we were the first material handling company to create the whole solution—a single integrated complex adaptive hardware and software system coordinating thousands of mobile robots in the storage, movement, and sortation of warehouse inventory in support of order fulfillment processes. This was an eye-opener for the industry, as the efficiency of these deployments would improve year-over-year via better software algorithms and optimizations, new hardware modifications, and changes to workflow processes. A second source of improvements, though, would stem from the “living, breathing” system adapting and tuning itself to better efficiencies. That is the fun and exciting part and the strangely interesting phenomenon that gives rise to these AI speculations. It should be noted, however, that those adaptive improvements were often an order of magnitude smaller than improvements conceived and developed by humans involving rigorously tested new features and algorithms deployed in software updates.
To generalize that observation—concerns around AI and robotics seemingly revolve around not understanding or knowing how these new complex adaptive hardware-software solutions will behave. The increased noise and discussion around the topic simply reflect the fact that we are finally starting to see more fielded examples of such systems. The Internet of Things (IoT) is the same old stuff (hardware) but now with improved, embedded, and connected software that turns this collection into a system capable of useful actions. My car now automatically closes the garage door as I leave the driveway (thanks to a software update earlier this year).
We keep hearing from pessimists about the idea that AI and robots are going to wipe out jobs, and optimists about how technology is going to make us all more productive. And yet neither of those things seems to have happened yet. U.S. unemployment is around 5% (of which probably half is “structural”). And for the past two decades, we haven’t seen any dramatic improvement in U.S. labor productivity. How come the impact of technology—whether positive or negative—doesn’t show up in the economic data?
Though I’m not an economist by any stretch, I would suggest that the impact of technology does show up in the data if you look in the right places. The price of oil was in the $20’s per barrel earlier this year—near the lowest inflation adjusted price ever. This was explained as an oversupply of oil, which I think reflects our technological progress in obtaining the oil that’s been there all along, together with demand-side efficiencies in cars, homes, mowers, and power plants that now use energy more efficiently as well.
When I was a kid growing up, I paid about 50¢ to fill my one-gallon gas can and mow three to four lawns for income ($3.51 during 1981 in 2015 dollars). Last summer I would have been paying about 34¢ inflation adjusted ($2.36 in 2015 dollars), and could probably mow five to six yards before needing a refill. My “mowing business” would be reporting better top and bottom lines today! Maybe corporate profits are where productivity ultimately shows up.
To make another point on productivity, we should be basing our metrics on the number of people employed, rather than unemployed as you simply can’t prove a negative. I suspect that the total output of the U.S. economy over the number of paid hours worked would show that we get more done each year per hour worked (though we probably need to take a fresh look at the GDP numerator too). At the same time, technology also provides a lot of new distractions; binge-watching shows because we can, Facebook, etc., can counterbalance productivity gains.
Is technology going to wipe out jobs? Or is it just going to change them? The McKinsey guys are always telling me not to confuse jobs with “tasks.” They argue you can probably automate half of all tasks, but not that many jobs. Does this sound true to you? And if so, what does it mean?
Jobs versus tasks is a decent framework for thinking about employment and automation. At Kiva, we automated the walking task that consumed about 70% of the pickers’ job time, but we didn’t automate the pickers’ job. When you look around the office environment, you see plenty of repetitive processes that are being automated to improve productivity, thus enabling the knowledgeable worker to focus on the more value-added tasks that actually drive the company forward.
On this point, I always joke that the blame goes all the way back to Gutenberg in 1440 AD, who put thousands of monks out of business when he invented the moveable type printing press, and thus transcribing the Bible and other important works page-by-page by hand was no longer necessary. We know those monks didn’t tweet their grievances, instead, they may have even thrown a party (maybe that’s when they began to focus on beer brewing). And ultimately, civilization benefited from the faster proliferation of printed knowledge.
The fact is that every single new technology, not just industrial waves, change the employment landscape. RF toll tags displaced human collectors standing in smoggy booths. The loom displaced the Luddites. The horseless carriage displaced the blacksmiths. Microsoft Word displaced the traditional secretary. Even the ranks of brave New York City bicycle messengers are more scarce now as email and electronic document signatures have taken over.
In today’s headlines, cab and truck drivers (monks) are high on the speculative list to be sidelined by automated self-driving vehicles (printing presses). While they probably won’t throw a party (after tweeting and litigating their grievances), a few decades from now we’ll all be scratching our heads about the time we used to allow humans to control multi-ton lethal projectiles by hand, in the same way it now seems incredulous that kids once had free range of the back seat without car seats or even seat belts.
Amazon’s experience with Kiva suggests that robots actually make the workplace better, and can make work more meaningful. Do you think that will be true in other settings as well?
Yes—when you automate the mundane portion of a task, the overall job becomes more interesting and meaningful. The human is asked to engage in higher order thinking, creative tasks, and problem solving. That’s true regardless of the setting.
What CAN’T robots do?
Never say never, but today’s robots can’t even pick a t-shirt from a clothing bin, let alone bubble wrap a wine goblet, or fold a pack slip into that sticky window on the outside of an e-commerce box. Those are mechanical tasks that will likely be tackled eventually, but even at that point, the bot will not be smart enough to determine whether a unit is damaged versus sellable, let alone understand the why of the situation.
If I’m a new business school graduate, what should I do to prepare myself for a career in this brave new world? How can anyone train for a job these days when the definition of what employers need seems to change so rapidly? How do I even think about what constitutes an actual “job”?
Don’t think about getting a “job”—think about creating change, moving the needle, pursuing a passion, or changing the game—and develop and use those skills that further those trajectories. Creativity, problem solving, convening, and motivating are skills that won’t be replaced by automation anytime soon.
What can business schools do to prepare students for the new workplace?
Most B schools have started emphasizing the importance of group projects involving teamwork as central to learning the people skills necessary for success in the new workplace. Then layer on some entrepreneurial challenges and context, and you’re moving in the right direction.
What does all this new technology mean for CEOs? How will it force them to think differently about their companies?
CEOs will be those individuals who understand these workforce and marketplace dynamics, and will create companies and solutions that ride these undercurrents as opposed to getting washed away by them. They will place greater importance on finding flexible, adaptable, creative human talent, and provide them with the productivity tools that allow them to automate the mundane and focus on the strategic priorities that grow the business.
Does the “fourth revolution” (if we want to call it that) favor big companies or small companies?
Without even researching the point, I’m going to say smaller companies always have the advantage when things shift or when transformations take place. Each industrial revolution was characterized by new little companies, individuals, and ideas that eventually became big companies and huge industries. Think of Ford competing with hundreds of existing auto startups in the early 1900’s and breaking down the auto patent pooling control of the largest incumbents of the day, or Edison and Tesla/Westinghouse bringing electricity out of tiny labs in an early standards war, or Fairchild Semiconductor becoming a startup that grew too big and too slow, and thus giving rise to “Fair-children” including Intel, AMD, and others resulting in Silicon Valley.