Do you expect robots to have a bigger impact inside or outside of factories in the next 15 years, and what implications does your answer have for the kinds of strategies that will gain competitive advantage?

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Glistening-footed Thetis reached Hephaestus' house....There she found him, sweating, wheeling round his bellows, pressing the work on twenty threelegged cauldrons, an array to ring the walls inside his mansion. He'd bolted golden wheels to the legs of each so all on their own speed, at a nod from him, they could roll to halls where the gods convene then roll right home again a marvel to behold. Homer, The Iliad, 8th century BCE (translated by Robert Fagles) IT'S RARE FOR A MEAL TO BE SIMULTANEOUSLY NUTRITIOUS, tasty, and affordable. It's even more uncommon for it to also provide a glimpse of the future of automation. The first Eatsa restaurant opened in San Francisco's SoMa neighborhood in 2015. It offered a selection of vegetarian dishes with a main ingredient of quinoa, a grain of South American origin with excellent nutritional properties.* At Eatsa, quinoa was accompanied by ingredients like corn, beans, eggplant, and guacamole and served in bowls with names like "Southwestern Scramble" and "No Worry Curry." Processes without People Before Eatsa diners even tasted the food, however, they encountered something unusual: they ordered, paid for, and received their meals without encountering any employees. Upon entering the restaurant, customers saw a row of tablet computers. They used one of them to place their order and pay via credit card. (Eatsa had no ability to accept cash.) As their bowls were being prepared, customers' first names and last initials (taken from their credit cards) appeared on a large flat-screen display. As a name neared the top of the list, a number appeared next to it, corresponding to one of approximately twenty cubby holessmall openings in a wallcovered with panels. These panels were actually transparent liquid crystal displays; they showed the customer's name in the middle of the screen and a small bull's-eye symbol in the upper right-hand corner. When the customer double-tapped the bull's-eye, the panel opened to reveal the meal, packaged to go (the restaurant offered little indoor seating). A small staff of concierges was available to guide newcomers through the ordering process and answer questions, but most customers didn't need them. Eatsa's early reviews were excellent; one Yelper said, "It's a restaurant where you don't have to speak to or interact with a single human being and in mere minutes get a delicious, nutritional, affordable meal through a computer screen. Marry me." Eatsa's popularity illustrated an important phenomenon of the second machine age: many transactions and interactions that used to take place between people in the physical world are now completed via digital interfaces. And lots of business processes, it turns out, do not really require atoms to be transformed or moved from one place to another; instead, they are about moving and transforming bits, or pieces of information. Ordering an Eatsa meal, paying for it, and learning where to pick it up are examples of such processes. It's not quite correct to say that they've been automated; there's still a person involvednamely, the customer. It's more precise to say that they've been "virtualized." Virtualization Is Reality Virtualization is spreading. When we fly without checking a bag, we rarely talk to an airline employee until we arrive at the gate, since we download boarding passes to our phones or print them at the airport using a selfservice kiosk. When we land in the United States after traveling abroad, we use Global Entry kiosks to supply customs and immigration information and be cleared to reenter the country. And it looks like we'll soon have fully automated security lanes when we fly within the country; the Transportation Security Administration announced in July of 2016 a plan to install and evaluate them at five domestic airports. Virtualization accelerates when networks and convenient digital devices are almost everywhere. As ATMs proliferated, many people no longer went to bank tellers to withdraw cash from their accounts. PC-based online banking enabled customers to review their statements, transfer funds, pay bills, and accomplish many other tasks from home, and smartphones and apps enabled these same tasks to be done from anywhere. Many banking apps eventually added another convenience: they enabled customers to deposit checks by taking a picture of them using their phone's camera. The ever-growing power, reach, and convenience of virtualized banking is probably a major reason why the total number of bank tellers in the United States is now falling year after year, and has dropped nearly 20% from its peak of 608,000 in 2007. Will some transactions and processes remain largely unvirtualized? Many people and companies think so. Virginia Postrel, an insightful analyst of business and cultural shifts, believes that automated self-checkout kiosks at drugstores, supermarkets, and other retailers will never catch on, "because of technical problems. Nobody wants to listen to an endless loop of electronic reprimands while watching other shoppers move smoothly through the human-staffed queue." We see Postrel's point. Most self-checkout technologies are confusing and hence slow to use, and they seem to seize up frequently. We probably keep using them more because of our research interests than because of their actual convenience. But we've noticed that they have gotten better over time, as we should expect. As the developers of self-checkout systems gain more experience, they'll improve the technology and the user experience, and figure out how to reduce error rates and frustrations. This might mean future self-checkout machines and processes that look very different, but we predict that large-scale virtualization will arrive, despite unimpressive progress so far. When it does, it might look like Amazon Go, an 1,800-square-foot convenience store unveiled in Seattle by the online giant in December of 2016. It's a retailer with neither cashiers nor self-checkout kiosks. Instead, in-store sensors and cameras combine with machine learning technologies and a smartphone app to keep track of everything customers put in their shopping baskets, then bill them for whatever they leave the store with. Journalist Lloyd Alter observed that "Amazon Go is not a grocery store upgraded with online-style technology; it's an online experience surrounded by brick walls." In this experience, the shopping cart is real but the checkout counter becomes virtual. Another argument against very widespread virtualization is the idea that some interactions require a human touch to put the focal personthe customer, patient, sales prospect, and so onat ease and in the right frame of mind. We think there's truth to this, but we've also seen that at least some groups of people are willingand maybe even eagerto virtualize exactly those transactions where the human touch has long been considered crucial. The conventional wisdom within financial services has been that at least one face-to-face meeting is necessary to convince a person or family to turn over a large portion of their wealth to an investment adviser. Yet Wealthfront has taken more than $3 billion from over 35,000 households since it was founded in December of 2011, and all of this money was transferred to the company virtually, with no human investment adviser across the desk or in the loop. Wealthfront is a wealth management company that has not only turned away from using human judgment when making investment decisions, but also completely eliminated the classic staging and cast of characters of the wealth transfer transactionthe wellappointed office, the glossy brochures, the receptionist, the professionallooking adviserand replaced it with an online form. Self-Selection or Secular Trend? Wealthfront's clients tend to be younger and more tech-savvy than the clients of other investment advisory companies. Economists use the term "self-selection" to refer to phenomena like this: cases in which people sort themselves into different groups based on their preferences. Self-selection is likely to be a powerful force shaping virtualization. Some people will give their money to Wealthfront to invest, use self-checkout machines at supermarkets, and lunch at Eatsa. Others will want to meet a human investment adviser, have a cashier ring up their purchases, and order lunch from a person. At present, we see companies explicitly appealing to one side or the other of this self-selection. The fast-food chain McDonald's is, like Eatsa, increasing virtualization. By November 2016 it had installed digital self- service ordering and payment stations in 500 locations across New York, Florida, and southern California and announced plans to expand the touchscreen technology to all 14,000 of its American restaurants. The Discover credit card, in contrast, is stressing the human touch. A series of ads, first aired in 2013, featured phone conversations between customers and employees played by actors who look very similar. The idea, of course, was to convey that the company provided deeply personal and hence more authentic customer service. One of the ads even suggested that the company was more concerned about interpersonal connection than about making more money. Its narrator said that "with Discover Card you can talk to a real person in the US day or night, plus we're not going to waste your time trying to sell you a bunch of other products you don't really need." Eatsa, Wealthfront, McDonald's, Discover, and many other companies are chasing market segments defined by customer preferences for or against virtualization. This is a natural and appropriate thing to do, but we wonder how long the antivirtualization market will be a large one. The recent decline in the number of bank tellers in the United States indicates to us that once virtualization that is robust enough becomes available for a given process, many people will take advantage of it, especially as time passes and more and more of the population consists of "digital natives." This is especially true if the human option takes longer or is otherwise less efficient and pleasant. If completely automated and equally safe and private airport security suddenly became available, how many of us would choose to stand in line and be screened by a human TSA agent? After enough technical progress, enough experimentation, and enough iteration, we believe that automated and digitally mediated processes will become quite widespread and will take the place of many that are now mediated by people. We believe, in short, that virtualization is a secular trend, where "secular" is used in the way the finance industry uses it: to denote a long-term development that will unfold over several years, rather than a short-term fluctuation. Automatons Explode Eatsa wants to do more than virtualize the task of ordering meals; it also wants to automate how they're prepared. Food preparation in its kitchens is highly optimized and standardized, and the main reason the company uses human cooks instead of robots is that the objects being processed avocados, tomatoes, eggplants, and so onare both irregularly shaped and not completely rigid. These traits present no real problems for humans, who have always lived in a world full of softish blobs. Most of the robots created so far, however, are much better at handling things that are completely rigid and do not vary from one to the next. This is because robots' senses of vision and touch have historically been quite primitivefar inferior to oursand proper handling of a tomato generally entails seeing and feeling it with a lot of precision. It's also because it's been surprisingly hard to program robots to handle squishiness here again, we know more than we can tellso robot brains have lagged far behind ours, just as their senses have. But they're catching upfastand a few robot chefs have already appeared. At one restaurant in China's Heilongjiang Province, stir-fries and other wok dishes are cooked over a flame by an anthropomorphic purple robot, while humans still do the prep work. At the Hannover Messe Industrial Trade Fair in April 2015, the UK company Moley Robotics introduced a highly automated kitchen, the centerpiece of which was a pair of multijointed robotic arms that descended from the ceiling. These arms emulated movements made by master chefs as they prepared their signature dishes. At the fair, the arms whipped up a crab bisque developed by Tim Anderson, a winner of the UK's televised MasterChef competition. One online reviewer said of the dish, "It's good. If I was served it at a restaurant I wouldn't bat an eye." Here again, though, food preparation had to be done by a human, and the robot arms had no eyes, so they would fail if any ingredients or utensils were not exactly where they were expected to be. The most advanced robot cook the two of us have seen is the hamburger maker developed by Momentum Machines, a startup funded by venture capitalist Vinod Khosla. It takes in raw meat, buns, condiments, sauces, and seasonings, and converts these into finished, bagged burgers at rates as high as 400 per hour. The machine does much of its own food preparation, and to preserve freshness it does not start grinding, mixing, and cooking until each order is placed. It also allows diners to greatly customize their burgers, specifying not only how they'd like them cooked, but also the mix of meats in the patty. We can attest to their deliciousness. DANCE of the Robots These automatic chefs are early examples of what Gill Pratt, the CEO of the Toyota Research Institute (and our former MIT colleague) calls an unfolding "Cambrian Explosion" in robotics. The original Cambrian Explosion, which began more than 500 million years ago, was a relatively brief period of time during which most of the major forms of life on Earth the phylaappeared. Almost all the body types present on our planet today can trace their origins back to this burst of intense evolutionary innovation. Pratt believes we're about to experience something similarly transformative with robotic innovation. As he wrote in 2015, "Today, technological developments on several fronts are fomenting a similar explosion in the diversification and applicability of robotics. Many of the base hardware technologies on which robots dependparticularly computing, data storage, and communicationshave been improving at exponential growth rates." One of the most important enablers of the Cambrian Explosion was visionthe moment when biological species first developed the ability to see the world. This opened up a massive new set of capabilities for our ancestors. Pratt makes the point that we are now at a similar threshold for machines. For the first time in history, machines are learning to see, and thereby gain the many benefits that come with vision. Our conversations and investigations point to recent major developments in five parallel, interdependent, and overlapping areas: data, algorithms, networks, the cloud, and exponentially improving hardware. We remember them by using the acronym "DANCE." Data. Music CDs, movie DVDs, and web pages have been adding to the world's stock of digitally encoded information for decades, but in the past few years the rate of creation has exploded. IBM estimates, in fact, that 90% of all the digital data in the world was created within the last twentyfour months. Signals from sensors in smartphones and industrial equipment, digital photos and videos, a nonstop global torrent of social media, and many other sources combine to put us in an era of "big data" that is without precedent. Algorithms. The data deluge is important because it supports and accelerates the developments in artificial intelligence and machine learning described in the previous chapter. The algorithms and approaches that are now dominating the disciplineones like deep learning and reinforcement learningshare the basic property that their results get better as the amount of data they're given increases. The performance of most algorithms usually levels off, or "asymptotes," at some point, after which feeding it more data improves results very little or not at all. But this does not yet appear to be the case for many of the machine learning approaches in wide use today. Andrew Ng told us that with modern algorithms, "Moore's law and some very clever technical work keep pushing the asymptote out." Networks. Technologies and protocols for communicating wirelessly over both short and long distances are improving rapidly. Both AT&T and Verizon, for example, announced 2016 trials of wireless 5G technology with download speeds as high as 10 gigabits per second. This is fifty times faster than the average speed of LTE networks (the fastest networks currently in wide deployment), and LTE is itself ten times faster than the previous generation, 3G technology. Such speed improvements mean better and faster data accumulation, and they also mean that robots and flying drones can be in constant communication and thus coordinate their work and react together on the fly to quickly-changing circumstances. The cloud. An unprecedented amount of computing power is now available to organizations and individuals. Applications, blank or preconfigured servers, and storage space can all be leased for a long time or rented for a few minutes over the Internet. This cloud computing infrastructure, largely less than a decade old, accelerates the robotic Cambrian Explosion in three ways. First, it greatly lowers barriers to entry, since the kinds of computing resources that were formerly found only in great research universities and multinationals' R&D labs are now available to startups and lone inventors. Second, it allows robot and drone designers to explore the important trade-off of local versus central computation: which information-processing tasks should be done in each robot's local brain, and which should be done by the great global brain in the cloud? It seems likely that the most resource-intensive work, such as replaying previous experiences to gain new insights from them, will be done in the cloud for some time to come. Third, and perhaps most important, the cloud means that every member of a robot or drone tribe can quickly know what every other member does. As Pratt puts it, "Human beings take decades to learn enough to add meaningfully to the compendium of common knowledge. However, robots not only stand on the shoulders of each other's learning, but can start adding to the compendium of robot knowledge almost immediately after their creation." An early example of this kind of universal "hive mind" is Tesla's fleet of cars, which share data about the roadside objects they pass. This information sharing helps the company build over time an understanding of which objects are permanent (they're the ones passed in the same spot by many different cars) and thus very unlikely to run out into the middle of the road. Exponential improvements in digital hardware. Moore's lawthe steady doubling in integrated circuit capability every eighteen to twentyfour monthscelebrated its fiftieth anniversary in 2015, at which time it was still going strong. Some have suggested recently that the law is running up against the limits of physics and thus the doubling will increasingly slow down in the years to come. This may be true, but even if the tech industry's scientists and engineers can't figure out how to etch silicon ever more finely in future decades, we are confident that we'll continue to enjoy simultaneously lower prices and higher performance from our digital gear processors, memory, sensors, storage, communications, and so onfor a long time to come. How can this be? Chris Anderson, CEO of drone maker 3D Robotics, gave us a vivid illustration of what's going on in the drone industry and, by extension, in many others. He showed us a metal cylinder about 1 inch in diameter and 3 inches long and said, "This is a gyro sensor. It is mechanical, it cost $10,000, it was made in the nineties by some very talented ladies in an aerospace factory and hand-wound, et cetera. And it takes care of one axis of motion. On our drones we have twenty-four sensors like this. That would have been $10,000 each. That would have been $240,000 of sensors, and by the way, it would be the size of a refrigerator. Instead, we have a tiny little chip or a few tiny little chips that cost three dollars and are almost invisible." Anderson's point is that the combination of cheap raw materials, mass global markets, intense competition, and large manufacturing scale economies is essentially a guarantee of sustained steep price declines and performance improvements. He calls personal drones the "peace dividend of the smartphone wars, which is to say that the components in a smartphonethe sensors, the GPS, the camera, the ARM core processors, the wireless, the memory, the batteryall that stuff, which is being driven by the incredible economies of scale and innovation machines at Apple, Google, and others, is available for a few dollars. They were essentially 'unobtainium' 10 years ago. This is stuff that used to be military industrial technology; you can buy it at RadioShack now." Together, the elements of DANCE are causing the Cambrian Explosion in robots, drones, autonomous cars and trucks, and many other machines that are deeply digital. Exponentially cheaper gear enables higher rates of innovation and experimentation, which generate a flood of data. This information is used to test and refine algorithms, and to help systems learn. The algorithms are put into the cloud and distributed to machines via robust networks. The innovators do their next round of tests and experiments, and the cycle continues. Where the Work Is Dull, Dirty, Dangerous, and Dear How, then, will robots, drones, and all the other digital machines that move in the physical world spread throughout the economy? What roles will they assume in the coming years? The standard view is that robots are best suited for work that is dull, dirty, and dangerous. We would add to this list one more "D"namely, "dear," or expensive. The more of these attributes a given task has, the more likely it is to be turned over to digital machines. Visiting construction sites to check on progress is an excellent example. These sites are usually dirty and sometimes dangerous, and the work of ensuring that the job is being done according to plan, dimensions are correct, lines are plumb, and so on can be dull. It's worth it, however, to regularly send a person to the site to perform these checks because small mistakes can amplify over time and become very expensive. It seems, though, that this work could soon be automated. In the fall of 2015 the ninety-five-year-old Japanese firm Komatsu, the second largest construction equipment company in the world, announced a partnership with the US drone startup Skycatch. The American company's small aerial vehicles would fly over a site, precisely mapping it in three dimensions. They would continuously send this information to the cloud, where software would match it against the plans for a site and use the resulting information to direct an autonomous fleet of bulldozers, dump trucks, and other earth-moving equipment. Agriculture, too, could soon be transformed by drones. Chris Anderson asked us to imagine a farm where drones fly over the fields every day, scanning them in the near-infrared wavelengths of light. These wavelengths provide a great deal of information about crop health, and current drone sensors are accurate enough to assess each square foot of land separately (and, given exponential improvement in the sensors, soon it will probably be possible to look at each plant individually). Flying a plane over the fields every day would be both dull and dear, but both of these barriers vanish with the arrival of small, cheap drones. Information gained from these daily flyovers enables a much deeper understanding of change over time with a given crop, and also enables much more precise targeting of water, fertilizer, and pesticides. Modern agricultural equipment often has the capability to deliver varying amounts of these critical ingredients foot by foot, rather than laying down a uniform amount. Drone data helps make the most of this capability, enabling farmers to move deeper into the era of precision agriculture. It's likely that drones will soon also be used by insurance companies to assess how badly a roof was damaged after a tornado, to help guard herds of endangered animals against poaching and remote forests against illegal logging, and for many other tasks. They're already being used to inspect equipment that would be dull, dirty, dangerous, or dear to get to. Sky Futures, a UK company, specializes in flying its drones around oil rigs in the North Sea, where metal and cement are no match over time for salt water and harsh weather. Sky Futures' drones fly around and through structures in all conditions so that human roughnecks don't have to climb and dangle from them in order to see what's going on. We see this patternmachines assuming the dull, dirty, dangerous, or dear tasksover and over at present: In 2015, Rio Tinto became the first company to utilize a fleet of fully remote-controlled trucks to move all the iron ore at its mine in Western Australia's Pilbara region. The driverless vehicles run twenty-four hours a day, 365 days a year and are supervised from a control center a thousand miles away. The savings from breaks, absenteeism, and shift changes enable the robotic fleet to be 12% more efficient than the human-driven one. Automated milking systems milk about one-quarter of the cows in leading dairy countries such as Denmark and the Netherlands today. Within ten years, this figure is expected to rise to 50%. Ninety percent of all crop spraying in Japan is currently done by unmanned helicopters. Of course, this pattern of machines taking over tasks has been unfolding for many decades inside factories, where engineers can achieve high levels of what our MIT colleague David Autor calls "environmental control," or "radically simplify[ing] the environment in which machines work to enable autonomous operation, as in the familiar example of a factory assembly line." Environmental control is necessary when pieces of automation have primitive brains and no ability to sense their environments. As all the elements of DANCE improve together, however, pieces of automation can leave the tightly controlled environment of the factory and head out into the wide world. This is exactly what robots, drones, autonomous vehicles, and many other forms of digital machines are doing at present. They'll do much more of it in the near future. What Humans Do in a World Full of Robots How will our minds and bodies work in tandem with these machines? There are two main ways. First, as the machines are able to do more work in the physical world, we'll do less and less of it, and instead use our brains in the ways described in earlier chapters, and in the next one. This is clearly what's happening in agriculture, humanity's oldest industry. Working the land to bring forth a crop has long been some of the most labor-intensive work done by people. It's now some of the most knowledgeintensive. As Brian Scott, an Indiana farmer who writes the blog The Farmer's Life, puts it, "Do you think when my grandfather was running...harvesters and combines...he could've imagined how...today's machines would be...driving themselves via invisible GPS signals while creating printable maps of things like yield and grain moisture? Amazing!" Similarly, workers in the most modern factories no longer need to be physically strong and hardy. Instead, they need to be comfortable with both words and numbers, adept at troubleshooting problems, and able to work as part of a team. The second way people will work with robots and their kin is, quite literally, side by side with them. Again, this is nothing new; factory workers have long been surrounded by machines, often working in close quarters with them. Our combination of sharp minds, acute senses, dexterous hands, and sure feet have not yet been matched by any machine, and it remains a hugely valuable combination. Andy's favorite demonstration of it came on a tour of the storied Ducati motorcycle factory in Bologna, Italy. Ducati engines are particularly complex, and he was interested to see how much automation was involved in assembling them. The answer, it turned out, was almost none. Each engine was put together by a single person, who walked alongside a slow-moving conveyor belt. As the belt passed by the engine parts that were needed at each stage of assembly, the worker picked them up and put them where they belonged, fastening them in place and adjusting as necessary. Ducati engine assembly required locomotion, the ability to manipulate objects in a variety of tight spaces, good eyesight, and a highly refined sense of touch. Ducati's assessment was that no automation possessed all of these capabilities, so engine assembly remained a human job. Similar capabilities are required in the warehouses of many retailers, especially those like Amazon that sell products of all shapes, sizes, and consistencies. Amazon has not yet found or developed a digitally driven hand or other "grabber" that can reliably pick all products off the shelf and put them in a box. So the company has hit on a clever solution: it brings the shelves to a human, who grabs the right products and boxes them for shipment. Racks of shelves are whisked around the company's huge distribution centers by knee-high orange robots originally made by Bostonbased Kiva Systems (Kiva was bought by Amazon in 2012). These robots scoot underneath a rack, lift it up, and bring it to a stationary human. When this person has taken the items needed, the rack-and-robot unit scoots away, and another one takes its place. This arrangement allows the people to use their skills of vision and dexterity, where they have an advantage over machines, and avoid the physical exertion and lost time that comes from walking from one shelf to another. How much longer will we maintain our advantages over robots and drones? It's a hard question to answer with any confidence, especially since the elements of DANCE continue to advance individually and together. It seems, though, that our senses, hands, and feet will be a hard combination for machines to beat, at least for a few more years. Robots are making impressive progress, but they're still a lot slower than we are when they try to do humanlike things. After all, our brains and bodies draw on millions of years of evolution, rewarding the designs that solved well the problems posed by the physical world. When Gill Pratt was a program manager at DARPA, the US Defense Department's R&D lab, he oversaw its 2015 robotics challenge. Its automaton contestants moved at such a careful pace that he compared being a spectator at the competition to watching a golf match. Still, this represented a big improvement over the original 2012 competition. Watching that one, according to Pratt, was more like watching grass grow. The Shapes of Things to Come As the examples in this chapter show, progress with all things digital is enabling us to build machines that go beyond the universe of bits and interact with people and things in the world of atoms. The same progress is also taking this one big step further: it's enabling us to arrange atomsto build thingsin ways that were never before possible. We can see this happening with what are almost certainly the most common human-made objects in the world: plastic parts. Global plastics production in 2015 was 250 million tons, and a single modern car has over 2,000 plastic parts of all shapes and sizes. To manufacture most of these parts, it is first necessary to make a molda metal part that the hot plastic will be injected, pressed, or otherwise forced into. The contours and hollow spaces of the mold determine the final shape of the part. The need for a mold has three important implications. First, it's extremely important to get the mold right, since it will be the template for the thousands or millions of parts that come out of it. Molds thus tend to be durable, heavy, and very precisely engineereda combination that also makes them expensive. Second, the need for a mold imposes limitations on the kinds of parts that can be made. It's easy to make a simple plastic gear with a mold, for example, but impossible to have a set of interlocking gears on a base pop out of a single mold, ready to turn. More complex parts generally require more complex moldswith some of the greatest complexity arising from the engineering required to get all the plastic into the mold, and to make sure that the hot material fills the space evenly and fully. Third, the thermodynamics of moldsthe way they heat up and cool down with each partare critically important. It's clearly a bad idea to remove parts while they're still hot enough to deform, but it's also inefficient to have the full mold cooling down longer than necessary. Yet different parts of the mold may cool at different rates. So designers and engineers have to balance a range of factors to ensure both high-quality parts and high-productivity molds. About thirty years ago, a diverse group of technologists began asking, essentially, why have a mold at all? They took inspiration from laser printers, which work by using the laser to fuse a very thin layer of ink onto a piece of paper in the desired pattern of text and images. But why stop at one layer? Why not repeat this process over and over, thereby gradually building up not just a two-dimensional pattern, but instead a three-dimensional structure? It would take a while, since each layer was so thin, but making things this way would open up a huge range of possibilities. For one thing, complexity would be free, as 3D printing researcher Luana Iorio puts it. In other words, it would cost no more to make an extraordinarily complex part than a very simple one, since both are, at base, simply a bunch of very thin layers. An assembly of interlocking gears, for example, would be as easy to create as a single 3D-printed component. Innovators have also brought the techniques of 3D printing to making metal parts, which are built up by having a laser melt successive thin layers of powdered metal onto the structure below (which is itself made up previous layers). This process gives rise to another highly desirable property: hardness becomes free. Extremely hard metals like titanium can be difficult and expensive to machine, but they're just about as easy as softer ones like aluminum to build up one layer at a time; all that's required is an adjustment of the power setting on the laser. When both complexity and hardness become free, many long-standing constraints are eased. It becomes easy, for example, to make molds for plastic parts that can be cooled down much more quickly. DTM Corporation of Austin, Texas, accomplished this by 3D-printing metal alloy molds that have many small, thin channels running through them in complex paths that could not have been created by conventional means. Hot plastic doesn't flow through these channels; cold liquids do, in order to quickly cool things down after each new part is formed. As a result, parts can be produced 20%-35% faster and with greater quality. A skeptic might ask at this point whether we want to generate innovations that keep flooding the world with more and more cheap plastic parts, stuffing our landfills and fouling our oceans with them. We see things differently. While we agree that overconsumption and inappropriate disposal of plastics are bad, we think that the advances in 3D printing are profoundly beneficial. Consider the case of the 3D-printed tumor model. Prior to the advent of 3D printing, surgeons simply had no realistic way to make an accurate representation of the mass of malignant tissue they were going after. They could not have afforded the dollars and time required to create a conventional mold, which makes economic sense only when you know you're going to make many copies of a part. But what if you want to make only a single model or prototype? Or a part fails and you want a single spare, quickly? Or you want to make a small set of parts, each one a bit different from the others? Conventional fabrication methods have been largely useless in these cases. 3D printing is ideal for them. The most profound benefit of 3D printing is probably that it makes experimentation and customization inexpensive. The path from idea or need to finished, useful part no longer has to include the time-consuming and expensive steps like mold making and other conventional manufacturing practices. Carl Bass, the former CEO of design and engineering software company Autodesk, sees 3D printing as only one part of a much larger story. As he told us, "I think additive manufacturing is a subset of what has really transformed manufacturing, which is the use of low-cost microprocessors to precisely control machinery." Bass's point is that sensors and code are not just being used now to precisely place very thin layers of material on top of each other; they're also being applied to just about every other fabrication technique, from cutting glass sheets and ceramic tiles to bending and milling all kinds of metals. The machines that do this workthat transform atoms into the final shapes we wantare improving these days, thanks to Moore's law. They might not be getting simultaneously better and less expensive as fast as CPUs and memory chips are, but their progress is still impressive. Compared to their equivalents of twenty years ago, they're cheaper, yet able to do more things at higher levels of quality. These advancements put them within reach of innovators of all kindsmore hobbyists, backyard inventors, students, engineers, and entrepreneursand gives people the ability to explore more possibilities. We're confident that the innovations that democratized high-quality tools will lead to a cascade of even more innovations in the near future. Chapter Summary Many business processes that today involve people are virtualizing: they're moving to digital channels and including fewer people. Often, the only person involved is the customer. Some people will continue to self-select human-to-human interactions, but we believe virtualization is a long-term trend that will generally increase over time as machines gain more capabilities. Robotics is undergoing a "Cambrian Explosion" as machines learn to see, as well as by many other kinds of digital progress. Automatons of all kindsrobots, drones, self-driving cars, and so onare becoming cheaper, more widely available, more capable, and more diverse all at the same time. Drivers of the robotic Cambrian Explosion include data, algorithms, networks, the cloud, and exponential improvements in hardware: DANCE. Robots and their kin will be increasingly used wherever work is dull, dirty, dangerous, and dear. People are still more agile and dexterous than even the most advanced robots, and they probably will be for some time to come. These abilities, combined with our senses and problem-solving skills, mean that we'll be working side by side with robots in many settings. 3D printing is important in its own right and also an example of a broader trend: the spread of digital tools into traditional manufacturing processes. This is an example of an innovation that itself leads to higher rates of innovation.