The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future

by Kevin Kelly

These broad historical trends are crucial because the underlying conditions that birthed them are still active and developing, which strongly suggests that these trends will continue to increase in the next few decades. There is nothing on the horizon to decrease them. Even the forces we might think could derail them, like crime, war, or our own excesses, also follow these emerging patterns. In this book I describe a dozen of these inevitable technological forces that will shape the next 30 years.

We are morphing so fast that our ability to invent new things outpaces the rate we can civilize them.

Get the ongoing process right and it will keep generating ongoing benefits. In our new era, processes trump products.

This wide, fast-moving system of technology bends the culture subtly, but steadily, so it amplifies the following forces: Becoming, Cognifying, Flowing, Screening, Accessing, Sharing, Filtering, Remixing, Interacting, Tracking, Questioning, and then Beginning.

Brand-new computers will ossify. Apps weaken with use. Code corrodes. Fresh software just released will immediately begin to fray. On their own—nothing you did. The more complex the gear, the more (not less) attention it will require. The natural inclination toward change is inescapable, even for the most abstract entities we know of: bits.

Technological life in the future will be a series of endless upgrades.

A world without discomfort is utopia. But it is also stagnant. A world perfectly fair in some dimensions would be horribly unfair in others. A utopia has no problems to solve, but therefore no opportunities either. None of us have to worry about these utopia paradoxes, because utopias never work. Every utopian scenario contains self-corrupting flaws. My aversion to utopias goes even deeper. I have not met a speculative utopia I would want to live in. I’d be bored in utopia. Dystopias, their dark opposites, are a lot more entertaining. They are also much easier to envision. Who can’t imagine an apocalyptic last-person-on-earth finale, or a world run by robot overlords, or a megacity planet slowly disintegrating into slums, or, easiest of all, a simple nuclear Armageddon? There are endless possibilities of how the modern civilization collapses. But just because dystopias are cinematic and dramatic, and much easier to imagine, that does not make them likely.

Real dystopias are more like the old Soviet Union rather than Mad Max: They are stiflingly bureaucratic rather than lawless. Ruled by fear, their society is hobbled except for the benefit of a few, but, like the sea pirates two centuries ago, there is far more law and order than appears. In fact, in real broken societies, the outrageous outlawry we associate with dystopias is not permitted. The big bandits keep the small bandits and dystopian chaos to a minimum.

However, neither dystopia nor utopia is our destination. Rather, technology is taking us to protopia. More accurately, we have already arrived in protopia. Protopia is a state of becoming, rather than a destination. It is a process. In the protopian mode, things are better today than they were yesterday, although only a little better. It is incremental improvement or mild progress. The “pro” in protopian stems from the notions of process and progress. This subtle progress is not dramatic, not exciting. It is easy to miss because a protopia generates almost as many new problems as new benefits. The problems of today were caused by yesterday’s technological successes, and the technological solutions to today’s problems will cause the problems of tomorrow. This circular expansion of both problems and solutions hides a steady accumulation of small net benefits over time. Ever since the Enlightenment and the invention of science, we’ve managed to create a tiny bit more than we’ve destroyed each year. But that few percent positive difference is compounded over decades into what we might call civilization. Its benefits never star in movies.

Today we’ve become so aware of the downsides of innovations, and so disappointed with the promises of past utopias, that we find it hard to believe even in a mild protopian future—one in which tomorrow will be a little better than today. We find it very difficult to imagine any kind of future at all that we desire. Can you name a single science fiction future on this planet that is both plausible and desirable? (Star Trek doesn’t count; it’s in space.)

The problem with constant becoming (especially in a protopian crawl) is that unceasing change can blind us to its incremental changes. In constant motion we no longer notice the motion. Becoming is thus a self-cloaking action often seen only in retrospect. More important, we tend to see new things from the frame of the old. We extend our current perspective to the future, which in fact distorts the new to fit into what we already know.

Any promising new invention will have its naysayers, and the bigger the promises, the louder the nays. It’s not hard to find smart people saying stupid things about the web/internet on the morning of its birth. In late 1994, Time magazine explained why the internet would never go mainstream: “It was not designed for doing commerce, and it does not gracefully accommodate new arrivals.” Wow! Newsweek put the doubts more bluntly in a February 1995 headline: “The Internet? Bah!” The article was written by an astrophysicist and network expert, Cliff Stoll, who argued that online shopping and online communities were an unrealistic fantasy that betrayed common sense. “The truth is no online database will replace your newspaper,” he claimed. “Yet Nicholas Negroponte, director of the MIT Media Lab, predicts that we’ll soon buy books and newspapers straight over the Internet. Uh, sure.” Stoll captured the prevailing skepticism of a digital world full of “interacting libraries, virtual communities, and electronic commerce” with one word: “baloney.”

The fear of commercialization was strongest among hard-core programmers who were actually building the web: the coders, Unix weenies, and selfless volunteer IT folk who kept the ad hoc network running. The techy administrators thought of their work as noble, a gift to humanity. They saw the internet as an open commons, not to be undone by greed or commercialization. It’s hard to believe now, but until 1991 commercial enterprise on the internet was strictly prohibited as an unacceptable use. There was no selling, no ads. In the eyes of the National Science Foundation (which ran the internet backbone), the internet was funded for research, not commerce. In what seems remarkable naiveté now, the rules favored public institutions and forbade “extensive use for private or personal business.” In the mid-1980s I was involved in shaping the WELL, an early text-only online system. We struggled to connect our private WELL network to the emerging internet because we were thwarted, in part, by the NSF’s “acceptable use” policy. The WELL couldn’t prove its users would not conduct commercial business on the internet, so we were not allowed to connect. We were all really blind to what was becoming.

It will also expand in time. Today’s web is remarkably ignorant of the past. It may supply you with a live webcam stream of Tahrir Square in Egypt, but accessing that square a year ago is nearly impossible. Viewing an earlier version of a typical website is not easy, but in 30 years we’ll have time sliders enabling us to see any past version. Just as your phone’s navigation directions through a city are improved by including previous days, weeks, and months of traffic patterns, so the web of 2050 will be informed by the context of the past. And the web will slide into the future as well.

servers that run several hundred “instances” of the AI at once. Like all things cloudy, Watson is served to simultaneous customers anywhere in the world, who can access it using their phones, their desktops, or their own data servers. This kind of AI can be scaled up or down on demand. Because AI improves as people use it, Watson is always getting smarter; anything it learns in one instance can be quickly transferred to the others. And instead of one single program, it’s an aggregation of diverse software engines—its logic-deduction engine and its language-parsing engine might operate on different code, on different chips, in different locations—all cleverly integrated into a unified stream of intelligence.

One of the early stage AI companies Google purchased is DeepMind, based in London. In 2015 researchers at DeepMind published a paper in Nature describing how they taught an AI to learn to play 1980s-era arcade video games, like Video Pinball. They did not teach it how to play the games, but how to learn to play the games—a profound difference. They simply turned their cloud-based AI loose on an Atari game such as Breakout, a variant of Pong, and it learned on its own how to keep increasing its score. A video of the AI’s progress is stunning. At first, the AI plays nearly randomly, but it gradually improves. After a half hour it misses only once every four times. By its 300th game, an hour into it, it never misses. It keeps learning so fast that in the second hour it figures out a loophole in the Breakout game that none of the millions of previous human players had discovered. This hack allowed it to win by tunneling around a wall in a way that even the game’s creators had never imagined. At the end of several hours of first playing a game, with no coaching from the DeepMind creators, the algorithms, called deep reinforcement machine learning, could beat humans in half of the 49 Atari video games they mastered. AIs like this one are getting smarter every month, unlike human players.

Here are other unlikely realms waiting to be cognitively enhanced: Cognified music—Music can be created in real time from algorithms, employed as the soundtrack for a video game or a virtual world. Depending on your actions, the music changes. Hundreds of hours of new personal music can be written by the AI for every player. Cognified laundry—Clothes that tell the washing machines how they want to be washed. The wash cycle would adjust itself to the contents of each load as directed by the smart clothes. Cognified marketing—The amount of attention an individual reader or watcher spends on an advertisement can be multiplied by their social influence (how many people followed them and what their influence was) in order to optimize attention and influence per dollar. Done at the scale of millions, this is a job for AI. Cognified real estate—Matching buyers and sellers via an AI that can prompt “renters who liked this apartment also liked these . . .” It could then generate a financing package that worked for your particular circumstances. Cognified nursing—Patients outfitted with sensors that track their bio markers 24 hours a day can generate highly personalized treatments that are adjusted and refined daily. Cognified construction—Imagine project management software that is smart enough to take into account weather forecasts, port traffic delays, currency exchange rates, accidents, in addition to design changes. Cognified ethics—Robo cars need to be taught priorities and be...

Today neural nets running on GPUs are routinely used by cloud-enabled companies such as Facebook to identify your friends in photos or for Netflix to make reliable recommendations for its more than 50 million subscribers.

The rocket engine is the learning algorithms but the fuel is the huge amounts of data we can feed to these algorithms.”

Imagine we land on an alien planet. How would we measure the level of the intelligences we encounter there? This is an extremely difficult question because we have no real definition of our own intelligence, in part because until now we didn’t need one.

Some possible new minds: A mind like a human mind, just faster in answering (the easiest AI mind to imagine). A very slow mind, composed primarily of vast storage and memory. A global supermind composed of millions of individual dumb minds in concert. A hive mind made of many very smart minds, but unaware it/they are a hive. A borg supermind composed of many smart minds that are very aware they form a unity. A mind trained and dedicated to enhancing your personal mind, but useless to anyone else. A mind capable of imagining a greater mind, but incapable of making it. A mind capable of creating a greater mind, but not self-aware enough to imagine it. A mind capable of successfully making a greater mind, once. A mind capable of creating a greater mind that can create a yet greater mind, etc. A mind with operational access to its source code, so it can routinely mess with its own processes. A superlogic mind without emotion. A general problem-solving mind, but without any self-awareness. A self-aware mind, but without general problem solving. A mind that takes a long time to develop and requires a protector mind until it matures. An ultraslow mind spread over large physical distance that appears “invisible” to fast minds. A mind capable of cloning itself exactly many times quickly. A mind capable of cloning itself and remaining in unity with its clones. A mind capable of immortality by migrating from platform to platform. A rapid, dynamic mind capable of changing the process ...

Today, many scientific discoveries require hundreds of human minds to solve, but in the near future there may be classes of problems so deep that they require hundreds of different species of minds to solve. This will take us to a cultural edge because it won’t be easy to accept the answers from an alien intelligence.

But we haven’t just been redefining what we mean by AI—we’ve been redefining what it means to be human. Over the past 60 years, as mechanical processes have replicated behaviors and talents we thought were unique to humans, we’ve had to change our minds about what sets us apart. As we invent more species of AI, we will be forced to surrender more of what is supposedly unique about humans. Each step of surrender—we are not the only mind that can play chess, fly a plane, make music, or invent a mathematical law—will be painful and sad. We’ll spend the next three decades—indeed, perhaps the next century—in a permanent identity crisis, continually asking ourselves what humans are good for. If we aren’t unique toolmakers, or artists, or moral ethicists, then what, if anything, makes us special? In the grandest irony of all, the greatest benefit of an everyday, utilitarian AI will not be increased productivity or an economics of abundance or a new way of doing science—although all those will happen. The greatest benefit of the arrival of artificial intelligence is that AIs will help define humanity. We need AIs to tell us who we are.

The alien minds that we’ll pay the most attention to in the next few years are the ones we give bodies to. We call them robots. They too will come in all shapes, sizes, and configurations—manifesting in diverse species, so to speak. Some will roam like animals, but many will be immobile like plants or diffuse like a coral reef. Robots are already here, quietly. Very soon louder, smarter ones are inevitable. The disruption they cause will touch our core.

It may be hard to believe, but before the end of this century, 70 percent of today’s occupations will likewise be replaced by automation—including the job you hold. In other words, robots are inevitable and job replacement is just a matter of time. This upheaval is being led by a second wave of automation, one that is centered on artificial cognition, cheap sensors, machine learning, and distributed smarts. This broad automation will touch all jobs, from manual labor to knowledge work.

The robot takeover will be epic.

Humans can weave cotton cloth with great effort, but automated looms make perfect cloth by the mile for a few cents per pound. The only reason to buy handmade cloth today is because you want the imperfections humans introduce. There’s very little reason to want an imperfect car. We no longer value irregularities while traveling 70 miles per hour on a highway—so we figure that the fewer humans touching our car as it is being made, the better.

Humans have trouble making a single brass screw unassisted, but automation can produce a thousand exact ones per hour. Without automation, we could not make a single computer chip—a job that requires degrees of precision, control, and unwavering attention that our animal bodies don’t possess. Likewise no human—indeed no group of humans, no matter their education—can quickly search through all the web pages in the world to uncover the one page revealing the price of eggs in Kathmandu yesterday. Every time you click on the search button you are employing a robot to do something we as a species are unable to do alone.

We aren’t giving “good jobs” to robots. Most of the time we are giving them jobs we could never do. Without them, these jobs would remain undone.

This is the greatest genius of the robot takeover: With the assistance of robots and computerized intelligence, we already can do things we never imagined doing 150 years ago. We can today remove a tumor in our gut through our navel, make a talking-picture video of our wedding, drive a cart on Mars, print a pattern on fabric that a friend mailed to us as a message through the air. We are doing, and are sometimes paid for doing, a million new activities that would have dazzled and shocked the farmers of 1800. These new accomplishments are not merely chores that were difficult before. Rather they are dreams created chiefly by the capabilities of the machines that can do them. They are jobs the machines make up.

It is a safe bet that the highest-earning professions in the year 2050 will depend on automations and machines that have not been invented yet. That is, we can’t see these jobs from here, because we can’t yet see the machines and technologies that will make them possible. Robots create jobs that we did not even know we wanted done.

Here are the Seven Stages of Robot Replacement: 1. A robot/computer cannot possibly do the tasks I do. 2. [Later.] OK, it can do a lot of those tasks, but it can’t do everything I do. 3. [Later.] OK, it can do everything I do, except it needs me when it breaks down, which is often. 4. [Later.] OK, it operates flawlessly on routine stuff, but I need to train it for new tasks. 5. [Later.] OK, OK, it can have my old boring job, because it’s obvious that was not a job that humans were meant to do. 6. [Later.] Wow, now that robots are doing my old job, my new job is much more interesting and pays more! 7. [Later.] I am so glad a robot/computer cannot possibly do what I do now. [Repeat.]

This is not a race against the machines. If we race against them, we lose. This is a race with the machines. You’ll be paid in the future based on how well you work with robots. Ninety percent of your coworkers will be unseen machines. Most of what you do will not be possible without them. And there will be a blurry line between what you do and what they do. You might no longer think of it as a job, at least at first, because anything that resembles drudgery will be handed over to robots by the accountants.

It is inevitable. Let the robots take our jobs, and let them help us dream up new work that matters.

The digital economy runs on this river of freely flowing copies. In fact, our digital communication network has been engineered so that copies flow with as little friction as possible. Copies flow so freely we could think of the internet as a superconductor, where once a copy is introduced it will continue to flow through the network forever, much like electricity in a superconductive wire. This is what it means when something goes viral. The copies are recopied, and those duplications ripple outward launching new copies, in an endless contagious wave. Once a copy has touched the internet, it never leaves.

We can’t stop massive indiscriminate copying. Not only would that sabotage the engine of wealth if we could, but it would halt the internet itself. Free-flowing copies are baked into the nature of this global communications system. The technology of the net needs to copy without constraint. The flow of copies is inevitable.

Our civilization’s previous economy was built upon warehouses of fixed goods and factories stockpiled with solid cargo. These physical stocks are still necessary, but they are no longer sufficient for wealth and happiness. Our attention has moved away from stocks of solid goods to flows of intangibles, like copies. We value not only the atoms in a thing, but their immaterial arrangement and design and, even more, their ability to adapt and flow in response to our needs.

The initial age of computing borrowed from the industrial age. As Marshall McLuhan observed, the first version of a new medium imitates the medium it replaces. The first commercial computers employed the metaphor of the office. Our screens had a “desktop” and “folders” and “files.” They were hierarchically ordered, like much of the industrial age that the computer was overthrowing. The second digital age overturned the office metaphor and brought us the organizing principle of the web. The basic unit was no longer files but “pages.” Pages were not organized into folders, but were arranged into a networked web. The web was a billion hyperlinked pages which contained everything, both stored information and active knowledge. The desktop interface was replaced by a “browser,” a uniform window that looked into any and all pages. This web of links was flat. Now we are transitioning into the third age of computation. Pages and browsers are far less important. Today the prime units are flows and streams. We constantly monitor Twitter streams and the flows of posts on our Facebook wall. We stream photos, movies, and music. News banners stream across the bottom of TVs. We subscribe to YouTube streams, called channels. And RSS feeds from blogs. We are bathed in streams of notifications and updates. Our apps improve in a flow of upgrades. Tags have replaced links. We tag and “like” and “favorite” moments in the streams. Some streams, like Snapchat, Facebook Live, and Periscope, operat...

The union of a zillion streams of information intermingling, flowing into each other, is what we call the cloud. Software flows from the cloud to you as a stream of upgrades. The cloud is where your stream of texts go before they arrive on your friend’s screen. The cloud is where the parade of movies under your account rests until you call for them. The cloud is the reservoir that songs escape from. The cloud is the seat where the intelligence of Siri sits, even as she speaks to you. The cloud is the new organizing metaphor for computers. The foundational units of this third digital regime, then, are flows, tags, and clouds.

In a real sense, these uncopyable values are things that are “better than free.” Free is good, but these are better since you’ll pay for them. I call these qualities “generatives.” A generative value is a quality or attribute that must be generated at the time of the transaction. A generative thing cannot be copied, cloned, stored, and warehoused. A generative cannot be faked or replicated. It is generated uniquely, for that particular exchange, in real time. Generative qualities add value to free copies and therefore are something that can be sold. Here are eight generatives that are “better than free.”

As the old joke goes: “Software, free. User manual, $10,000.” But it’s no joke. A couple of high-profile companies, like Red Hat, Apache, and others make their living selling instruction and paid support for free software. The copy of code, being mere bits, is free. The lines of free code become valuable to you only through support and guidance.

You might be able to grab a popular software application for free on the dark net, but even if you don’t need a manual, you might want to be sure it comes without bugs, malware, or spam. In that case you’ll be happy to pay for an authentic copy. You get the same “free” software, but with an intangible peace of mind. You are not paying for the copy; you are paying for the authenticity.

You are not paying for the material; you are paying for the convenience of easy accessibility, without the obligations of maintaining it.

The book is free; the bodily talk is expensive. Live concert tours, live TED talks, live radio shows, pop-up food tours all speak to the power and value of a paid ephemeral embodiment of something you could download for free.

Deep down, avid audiences and fans want to pay creators. Fans love to reward artists, musicians, authors, actors, and other creators with the tokens of their appreciation, because it allows them to connect with people they admire.

The previous generatives resided within creative works. Discoverability, however, is an asset that applies to an aggregate of many works. No matter what its price, a work has no value unless it is seen. Unfound masterpieces are worthless.

Movie fans will pay Netflix because their recommendation engine finds gems they would not otherwise discover. They may be free somewhere else, but they are essentially lost and buried. In these examples, you are not paying for the copies, you are paying for the findability.

As we’ve learned from the steady democratization of other arts, soon you’ll be able to make music without being a musician. One hundred years ago, the only people technically capable of taking a photograph were a few dedicated experimenters. It was an incredibly elaborate and fussy process. It took great technical skill and greater patience before you could coax a picture worth looking at. An expert photographer might take a dozen photos per year. Today anyone with a phone—which is everyone—can instantly take a photo that is a hundred times better in most dimensions than one taken by the average professional a century ago.

four ways books embody fixity. Here’s my rendition of how books stay: Fixity of the page—The page stays the same. Whenever you pick it up, it’s the same. You can count on it. That means you can reference or cite it, certain it will say the same thing. Fixity of the edition—No matter which copy of the book you pick up, no matter where or when you purchased it, it will be the same (for that edition), so its text is shared between us. We can discuss a book sure that we are looking at the identical content. Fixity of the object—With proper care, paper books last a very long time (centuries longer than digital formats), and their text doesn’t change as they age. Fixity of completion—A paper book carries with it a sense of finality and closure. It is done. Complete. Part of the attraction of printed literature is that it is committed to paper, almost like a vow. The author stands upon it.

ebooks offer four fluidities to counter them: Fluidity of the page—The page is a flexible unit. Content will flow to fit any available space, from a tiny screen in a pair of glasses to a wall. It can adapt to your preferred reading device or reading style. The page fits you. Fluidity of the edition—A book’s material can be personalized. Your edition might explain new words if you are a student, or it could skip a recap of the previous books in the series if you’ve already read them. Customized “my books” are for me. Fluidity of the container—A book can be kept in the cloud at such low cost that it is “free” to store in an unlimited library and can be delivered instantly anywhere on earth at any time to anyone. Fluidity of growth—The book’s material can be corrected or improved incrementally. The never-done-ness of an ebook (at least in the ideal) resembles an animated creature more than a dead stone, and this living fluidity animates us as creators and readers.