The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future (2016)



There has never been a better time to be a reader, a watcher, a listener, or a participant in human expression. An exhilarating avalanche of new stuff is created every year. Every 12 months we produce 8 million new songs, 2 million new books, 16,000 new films, 30 billion blog posts, 182 billion tweets, 400,000 new products. With little effort today, hardly more than a flick of the wrist, an average person can summon the Library of Everything. You could, if so inclined, read more Greek texts in the original Greek than the most prestigious Greek nobleman of classical times. The same regal ease applies to ancient Chinese scrolls; there are more available to you at home than to emperors of China past. Or Renaissance etchings, or live Mozart concertos, so rare to witness in their time, so accessible now. In every dimension, media today is at an all-time peak of glorious plentitude.

According to the most recent count I could find, the total number of songs that have been recorded on the planet is 180 million. Using standard MP3 compression, the total volume of recorded music for humans would fit onto one 20-terabyte hard disk. Today a 20-terabyte hard disk sells for $2,000. In five years it will sell for $60 and fit into your pocket. Very soon you’ll be able to carry around all the music of humankind in your pants. On the other hand, if this library is so minuscule, why even bother to carry it around when you could get all music of the world in the cloud streamed to you on demand?

What goes for music also goes for anything and everything that can be rendered in bits. In our lifetime, the entire library of all books, all games, all movies, every text ever printed will be available 24/7 on that same screen thingy or in the same cloud thread. And every day, the library swells. The number of possibilities we confront has been expanded by a growing population, then expanded further by technology that eases creation. There are three times as many people alive today as when I was born (1952). Another billion are due in the next 10 years. An increasing proportion of those extra 5 billion to 6 billion people since my birth have been liberated by the surplus and leisure of modern development to generate new ideas, create new art, make new things. It is 10 times easier today to make a simple video than 10 years ago. It is a hundred times easier to create a small mechanical part and make it real than a century ago. It is a thousand times easier today to write and publish a book than a thousand years ago.

The result is an infinite hall of options. In every direction, countless choices pile up. Despite obsolete occupations like buggy whip maker, the variety of careers to choose from expands. Possible places to vacation, to eat, or even kinds of food all stack up each year. Opportunities to invest explode. Courses to take, things to learn, ways to be entertained explode to astronomical proportions. There is simply not enough time in any lifetime to review the potential of each choice, one by one. It would consume more than a year’s worth of our attention to merely preview all the new things that have been invented or created in the previous 24 hours.

The vastness of the Library of Everything quickly overwhelms the very narrow ruts of our own consuming habits. We’ll need help to navigate through its wilds. Life is short, and there are too many books to read. Someone, or something, has to choose, or whisper in our ear to help us decide. We need a way to triage. Our only choice is to get assistance in making choices. We employ all manner of filtering to winnow the bewildering spread of options. Many of these filters are traditional and still serve well:

· We filter by gatekeepers: Authorities, parents, priests, and teachers shield the bad and selectively pass on “the good stuff.”

· We filter by intermediates: Sky high is the reject pile in the offices of book publishers, music labels, and movie studios. They say no much more often than yes, performing a filtering function for what gets wide distribution. Every headline in a newspaper is a filter that says yes to this information and ignores the rest.

· We filter by curators: Retail stores don’t carry everything, museums don’t show everything, public libraries don’t buy every book. All these curators select their wares and act as filters.

· We filter by brands: Faced with a shelf of similar goods, the first-time buyer retreats to a familiar brand because it is a low-effort way to reduce the risk of the purchase. Brands filter through the clutter.

· We filter by government: Taboos are prohibited. Hate speech or criticism of leaders or of religion is removed. Nationalistic matters are promoted.

· We filter by our cultural environment: Children are fed different messages, different content, different choices depending on the expectations of the schools, family, and society around them.

· We filter by our friends: Peers have great sway over our choices. We are very likely to choose what our friends choose.

· We filter by ourselves: We make choices based on our own preferences, by our own judgment. Traditionally this is the rarest filter.

None of these methods disappear in the rising superabundance. But to deal with the escalation of options in the coming decades, we’ll invent many more types of filtering.

What if you lived in a world where every great movie, book, and song ever produced was at your fingertips as if “for free,” and your elaborate system of filters had weeded out the crap, the trash, and anything that would remotely bore you. Forget about all the critically acclaimed creations that mean nothing to you personally. Focus instead on just the things that would truly excite you. Your only choices would be the absolute cream of the cream, the things your best friends would recommend, including a few “random” choices to keep you surprised. In other words, you would encounter only things perfectly matched to you at that moment. You still don’t have enough time in your life.

For instance, you could filter your selection of books by reading only the greatest ones. Just focus on the books chosen by experts who have read a lot of them and let them guide you to the 60 volumes considered the best of the very best in Western civilization—the canonical collection known as the Great Books of the Western World. It would take you, or the average reader, some 2,000 hours to completely read all 29 million words. And that’s just the Western world. Most of us are going to need further filtering.

The problem is that we start with so many candidates that, even after filtering out all but one in a million, you still have too many. There are more super great five-stars-for-you movies than you can ever watch in your lifetime. There are more useful tools ideally suited to you than you have time to master. There are more cool websites to linger on than you have attention to spare. There are, in fact, more great bands, and books, and gizmos aimed right at you, customized to your unique desires, than you can absorb, even if it was your full-time job.

Nonetheless, we’ll try to reduce this abundance to a scale that is satisfying. Let’s start with the ideal path. And I’ll make it personal. How would I like to choose what I give my attention to next?

First I’d like to be delivered more of what I know I like. This personal filter already exists. It’s called a recommendation engine. It is in wide use at Amazon, Netflix, Twitter, LinkedIn, Spotify, Beats, and Pandora, among other aggregators. Twitter uses a recommendation system to suggest who I should follow based on whom I already follow. Pandora uses a similar system to recommend what new music I’ll like based on what I already like. Over half of the connections made on LinkedIn arise from their follower recommender. Amazon’s recommendation engine is responsible for the well-known banner that “others who like this item also liked this next item.” Netflix uses the same to recommend movies for me. Clever algorithms churn through a massive history of everyone’s behavior in order to closely predict my own behavior. Their guess is partly based on my own past behavior, so Amazon’s banner should really say, “Based on your own history and the history of others similar to you, you should like this.” The suggestions are highly tuned to what I have bought and even thought about buying before (they track how long I dwell on a page deliberating, even if I don’t choose it). Computing the similarities among a billion past purchases enables their predictions to be remarkably prescient.

These recommendation filters are one of my chief discovery mechanisms. I find them far more reliable, on average, than recommendations from experts or friends. In fact, so many people find these filtered recommendations useful that these kinds of “more like this” offers are responsible for a third of Amazon sales—a difference amounting to about $30 billion in 2014. They are so valuable to Netflix that it has 300 people working on its recommendation system, with a budget of $150 million. There are of course no humans involved in guiding these filters once they are operational. The cognification is based on subtle details of my (and others’) behavior that only a sleepless obsessive machine might notice.

The danger of being rewarded with only what you already like, however, is that you can spin into an egotistical spiral, becoming blind to anything slightly different, even if you’d love it. This is called a filter bubble. The technical term is “overfitting.” You get stuck at a lower than optimal peak because you behave as if you have arrived at the top, ignoring the adjacent environment. There’s a lot of evidence this occurs in the political realm as well: Readers of one political stripe who depend only on a simple filter of “more like this” rarely if ever read books outside their stripe. This overfitting tends to harden their minds. This kind of filter-induced self-reinforcement also occurs in science, the arts, and culture at large. The more effective the “more good stuff like this” filter is, the more important it becomes to alloy it with other types of filters. For instance, some researchers from Yahoo! engineered a way to automatically map one’s position in the field of choices visually, to make the bubble visible, which made it easier for someone to climb out of their filter bubble by making small tweaks in certain directions.

Second in the ideal approach, I’d like to know what my friends like that I don’t know about. In many ways, Twitter and Facebook serve up this filter. By following your friends, you get effortless updates on the things they find cool enough to share. The ease of shouting out a recommendation via a text or photo is so easy from a phone that we are surprised when someone loves something new but doesn’t share it. But friends can also act like a filter bubble if they are too much like you. Close friends can make an echo chamber, amplifying the same choices. Studies show that going to the next circle, to friends of friends, is sometimes enough to enlarge the range of options away from the expected.

A third component in the ideal filter would be a stream that suggested stuff that I don’t like but would like to like. It’s a bit similar to me trying a least favorite cheese or vegetable every now and then just to see if my tastes have changed. I am sure I don’t like opera, but a few years ago I again tried one—Carmen at the Met—teleprojected real time in a cinema with prominent subtitles on the huge screen, and I was glad I went. A filter dedicated to probing one’s dislikes would have to be delicate, but could also build on the powers of large collaborative databases in the spirit of “people who disliked those, learned to like this one.” In somewhat the same vein I also, occasionally, want a bit of stuff I dislike but should learn to like. For me that might be anything related to nutritional supplements, details of political legislation, or hip-hop music. Great teachers have a knack for conveying unsavory packages to the unwilling in a way that does not scare them off; great filters can too. But would anyone sign up for such a filter?

Right now, no one signs up for any of these filters because filters are primarily installed by platforms. The 200 average friends of your average Facebook member already post such a torrent of updates that Facebook feels it must cut, edit, clip, and filter your news to a more manageable stream. You do not see all the posts your friends make. Which ones have been filtered out? By what criteria? Only Facebook knows, and it considers the formulas trade secrets. What it is optimizing for is not even communicated. The company talks about increasing the satisfaction of members, but a fair guess is that it is filtering your news stream to optimize the amount of time you spend on Facebook—a much easier thing to measure than your happiness. But that may not be what you want to optimize Facebook for.

Amazon uses filters to optimize for maximum sales, and that includes filtering the content on the pages you see. Not just what items are recommended, but the other material that appears on the page, including bargains, offers, messages, and suggestions. Like Facebook, Amazon performs thousands of experiments a day, altering their filters to test A over B, trying to personalize the content in response to actual use by millions of customers. They fine-tune the small things, but at such a scale (a hundred thousand subjects at a time) that their results are extremely useful. As a customer I keep returning to Amazon because it is trying to maximize the same thing I am: cheap access to things I will like. That alignment is not always present, but when it is, we return.

Google is the foremost filterer in the world, making all kinds of sophisticated judgments about what search results you see. In addition to filtering the web, it processes 35 billion emails a day, filtering out spam very effectively, assigning labels and priorities. Google is the world’s largest collaborative filter, with thousands of interdependent dynamic sieves. If you opt in, it personalizes search results for you and will customize them for your exact location at the time you ask. It uses the now proven principles of collaborative filtering: People who found this answer valuable also found this next one good too (although they don’t label it that way). Google filters the content of 60 trillion pages about 2 million times every minute, but we don’t often question how it recommends. When I ask it a query, should it show me the most popular, or the most trusted, or the most unique, or the options most likely to please me? I don’t know. I say to myself I’d probably like to have the choice to rank results each of those four different ways, but Google knows that all I’d do is look at the first few results and then click. So they say, “Here’s the top few we think are the best based on our deep experience in answering 3 billion questions a day.” So I click. Google is trying to optimize the chance I’ll return to ask it again.

As they mature, filtering systems will be extended to other decentralized systems beyond media, to services like Uber and Airbnb. Your personal preferences in hotel style, status, and service can easily be ported to another system in order to increase your satisfaction when you are matched to a room in Venice. Heavily cognified, incredibly smart filters can be applied to any realm with a lot of choices—which will be more and more realms. Anywhere we want personalization, filtering will follow.

Twenty years ago many pundits anticipated the immediate arrival of large-scale personalization. A 1992 book called Mass Customization by Joseph Pine laid out the plan. It seemed reasonable that custom-made work—which was once the purview of the rich—could be widened to the middle class with the right technology. For instance, an ingenious system of digital scans and robotic flexible manufacturing could provide personally tailored shirts for the middle class, instead of just bespoke shirts for the gentry. A few startups tried to execute “mass customization” for jeans, shirts, and baby dolls in the late 1990s, but they failed to catch on. The main hurdle was that, except in trivial ways (choosing a color or length), it was very difficult to capture or produce significant uniqueness without raising prices to the luxury level. The vision was too far ahead of the technology. But now the technology is catching up. The latest generation of robots are capable of agile manufacturing, and advanced 3-D printers can rapidly produce units of one. Ubiquitous tracking, interacting, and filtering means that we can cheaply assemble a multidimensional profile of ourselves, which can guide any custom services we desire.

Here is a picture of where this force is taking us. My day in the near future will entail routines like this: I have a pill-making machine in my kitchen, a bit smaller than a toaster. It stores dozens of tiny bottles inside, each containing a prescribed medicine or supplement in powdered form. Every day the machine mixes the right doses of all the powders and stuffs them all into a single personalized pill (or two), which I take. During the day my biological vitals are tracked with wearable sensors so that the effect of the medicine is measured hourly and then sent to the cloud for analysis. The next day the dosage of the medicines is adjusted based on the past 24-hour results and a new personalized pill produced. Repeat every day thereafter. This appliance, manufactured in the millions, produces mass personalized medicine.

My personal avatar is stored online, accessible to any retailer. It holds the exact measurements of every part and curve of my body. Even if I go to a physical retail store, I still try on each item in a virtual dressing room before I go because stores carry only the most basic colors and designs. With the virtual mirror I get a surprisingly realistic preview of what the clothes will look like on me; in fact, because I can spin my simulated dressed self around, it is more revealing than a real mirror in a dressing room. (It could be better in predicting how comfortable the new clothes feel, though.) My clothing is custom fit based on the specifications (tweaked over time) from my avatar. My clothing service generates new variations of styles based on what I’ve worn in the past, or on what I spend the most time wishfully gazing at, or on what my closest friends have worn. It is filtering styles. Over years I have trained an in-depth profile of my behavior, which I can apply to anything I desire.

My profile, like my avatar, is managed by Universal You. It knows that I like to book inexpensive hostels when I travel on vacation, but with a private bath, maximum bandwidth, and always in the oldest part of the town, except if it is near a bus station. It works with an AI to match, schedule, and reserve the best rates. It is more than a mere stored profile; rather it is an ongoing filter that is constantly adapting to wherever I have already gone, what kind of snapshots and tweets I made about past visits, and it weighs my new interests in reading and movies since books and movies are often a source for travel desires. It pays a lot of attention to the travels of my best friends and their friends, and from that large pool of data often suggests specific restaurants and hostels to visit. I generally am delighted by its recommendations.

Because my friends let Universal You track their shopping, eating out, club attendance, movie streaming, news screening, exercise routines, and weekend excursions, it can make very detailed recommendations for me—with minimal effort on their part. When I wake in the morning, Universal filters through my update stream to deliver the most vital news of the type I like in the morning. It filters based on the kinds of things I usually forward to others, or bookmark, or reply to. In my cupboard I find a new kind of cereal with saturated nutrition that my friends are trying this week, so Universal ordered it for me yesterday. It’s not bad. My car service notices where the traffic jams are this morning, so it schedules my car later than normal and it will try an unconventional route to the place I’ll work today, based on several colleagues’ commutes earlier. I never know for sure where my office will be since our startup meets in whatever coworking space is available that day. My personal device turns the space’s screens into my screen. My work during the day entails tweaking several AIs that match doctoring and health styles with clients. My job is to help the AIs understand some of the outlier cases (such as folks with faith-healing tendencies) in order to increase the effectiveness of the AIs’ diagnoses and recommendations.

When I get home, I really look forward to watching the string of amusing 3-D videos and fun games that Albert lines up for me. That’s the name I gave to the avatar from Universal who filters my media for me. Albert always gets the coolest stuff because I’ve trained him really well. Ever since high school I would spend at least 10 minutes every day correcting his selections and adding obscure influences, really tuning the filters, so that by now, with all the new AI algos and the friends of friends of friends’ scores, I have the most amazing channel. I have a lot of people who follow my Albert daily. I am at the top of the leaderboard for the VR worlds filter. My mix is so popular that I’m earning some money from Universal—well, at least enough to pay for all my subscriptions.

• • •

We are still at the early stages in how and what we filter. These powerful computational technologies can be—and will be—applied to the internet of everything. The most trivial product or service could be personalized if we wanted it (but many times we won’t). In the next 30 years the entire cloud will be filtered, elevating the degree of personalization.

Yet every filter throws something good away. Filtering is a type of censoring, and vice versa. Governments can implement nationwide filters to remove unwanted political ideas and restrict speech. Like Facebook or Google, they usually don’t disclose what they are filtering. Unlike social media, citizens don’t have an alternative government to switch to. But even in benign filtering, by design we see only a tiny fraction of all there is to see. This is the curse of the postscarcity world: We can connect to only a thin thread of all there is. Each day maker-friendly technologies such as 3-D printing, phone-based apps, and cloud services widen the sky of possibilities another few degrees. So each day wider filters are needed to access this abundance at human scale. There is no retreat from more filtering. The inadequacies of a filter cannot be remedied by eliminating filters. The inadequacies of a filter can be remedied only by applying countervailing filters upon it.

From the human point of view, a filter focuses content. But seen in reverse, from the content point of view, a filter focuses human attention. The more content expands, the more focused that attention needs to become. Way back in 1971 Herbert Simon, a Nobel Prize–winning social scientist, observed, “In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention.” Simon’s insight is often reduced to “In a world of abundance, the only scarcity is human attention.”

Our attention is the only valuable resource we personally produce without training. It is in short supply and everyone wants some of it. You can stop sleeping altogether and you will still have only 24 hours per day of potential attention. Absolutely nothing—no money or technology—will ever increase that amount. The maximum potential attention is therefore fixed. Its production is inherently limited while everything else is becoming abundant. Since it is the last scarcity, wherever attention flows, money will follow.

Yet for being so precious, our attention is relatively inexpensive. It is cheap, in part, because we have to give it away each day. We can’t save it up or hoard it. We have to spend it second by second, in real time.

In the United States, TV still captures most of our attention, followed by radio, and then the internet. These three take the majority of our attention, while the others—books, newspapers, magazines, music, home video, games—consume only slivers of the total pie.

But not all attention is equal. In the advertising business, quantity of attention is often reflected in a metric called CPM, or cost per thousand (M is Latin for “thousand”). That’s a thousand views, or a thousand readers or listeners. The estimated average CPM of various media platforms ranges widely. Cheap outdoor billboards average $3.50, TV is $7, magazines earn $14, and newspapers $32.50.

There’s another way to calculate how much our attention is worth. We can tally up the total annual revenue earned by each of the major media industries, and the total amount of time spent on each media, and then calculate how much revenue each hour of attention generates in dollars per hour. The answer surprised me.

First, it is a low number. The ratio of dollars earned by the industry per hour of attention spent by consumers shows that attention is not worth very much to media businesses. While half a trillion hours are devoted to TV annually (just in the U.S.), it generates for its content owners, on average, only 20 cents per hour. If you were being paid to watch TV at this rate, you would be earning a third-world hourly wage. Television watching is coolie labor. Newspapers occupy a smaller slice of our attention, but generate more revenue per hour spent with them—about 93 cents per hour. The internet, remarkably, is relatively more expensive, increasing its quality of attention each year, garnering on average $3.60 per hour of attention.

A lousy 20 cents per hour of attention that we watchers “earn” for TV companies, or even a dollar an hour for upscale newspapers, reflects the worth of what I call “commodity attention.” The kind of attention we pay to entertainment commodities that are easily duplicated, easily transmitted, nearly ubiquitous, and always on is not worth much. When we inspect how much we have to pay to purchase commodity content—all the content that can easily be copied—such as books, movies, music, news, etc.—the rates are higher, but still don’t reflect the fact that our attention is the last scarcity. Take a book, for instance. The average hardcover book takes 4.3 hours to read and $23 to buy. Therefore the average consumer cost for that reading duration is $5.34 per hour. A music CD is, on average, listened to dozens of times over its lifetime, so its retail price is divided by its total listening time to arrive at its hourly rate. A two-hour movie in a theater is seen only once, so its per hour rate is half the ticket price. These rates can be thought of as mirroring how much we, as the audience, value our attention.

In 1995 I calculated the average hourly costs for various media platforms, including music, books, newspapers, and movies. There was some variation between media, but the price stayed within the same order of magnitude, converging on a mean of $2.00 per hour. In 1995 we tended to pay, on average, two bucks per hour for media use.

Fifteen years later, in 2010, and then again in 2015, I recalculated the values for a similar set of media using the same method. When I adjusted for inflation and translated into 2015 dollars, the average cost to consume one hour of media in 1995, 2010, and 2015 is respectively $3.08, $2.69, and $3.37. That means that the value of our attention has been remarkably stable over 20 years. It seems we have some intuitive sense of what a media experience “should” cost, and we don’t stray much from that. It also means that companies making money from our attention (such as many high-profile tech companies) are earning only an average of $3 per hour of attention—if they include high-quality content.

In the coming two decades the challenge and opportunity is to harness filtering technologies to cultivate higher quality attention at scale. Today, the bulk of the internet economy is fueled by trillions of hours of low-grade commodity attention. A single hour by itself is not worth much, but en masse it can move mountains. Commodity attention is like a wind or an ocean tide: a diffuse force that must be captured with large instruments.

• • •

The brilliance behind Google, Facebook, and other internet platforms’ immense prosperity is a massive infrastructure that filters this commodity attention. Platforms use serious computational power to match the expanding universe of advertisers to the expanding universe of consumers. Their AIs seek the optimal ad at the optimal time in the optimal place and the optimal frequency with the optimal way to respond. While this is sometimes termed personalized advertising, it is in fact far more complex than just targeting ads to individuals. It represents an ecosystem of filterings, which have consequences beyond just advertising.

Anyone can sign up to be an advertiser on Google by filling out an online form. (Most of the ads are text, like a classified ad.) That means the number of potential advertisers might be in the billions. You could be a small-time businessperson advertising a cookbook for vegan backpackers or a new baseball glove you invented. On the other side of the equation, anyone running a web page for any reason can allow an advertiser to place an ad on their page and potentially earn income from this advertising. The web page could be a personal blog or a company home page. For about eight years I ran Google AdSense ads on my own personal blogs. The hundred dollars or so I earned each month for showing ads was small potatoes for a billion-dollar company, but the tiny size of these transactions didn’t matter to Google because it was all automated, and the tiny sums would add up. The AdSense network embraces all comers no matter how small, so the potential places an ad could run swells to the billions. To mathematically match these billions of possibilities—of billions of people wanting to advertise and billions of places willing to run ads—an astronomical number of potential solutions are needed. In addition, the optimal solutions can shift by time of day or geographical location—and so Google (and other search companies like Microsoft and Yahoo!) need their gigantic cloud computers to sort through them.

To match advertiser with reader, Google’s computers roam the web 24 hours a day and collect all the content on every one of the 60 trillion pages on the web and store that information in its huge database. That’s how Google delivers you an instant answer whenever you query it. It has already indexed the location of every word, phrase, and fact on the web. So when a web owner wants to allow a small AdSense ad to run on their blog page, Google summons up its record of what material is on that page and then uses its superbrain to find someone—right that minute—who wants to place an ad related to that material. When the match consummates, the ad on the web page will reflect the editorial content of the page. Suppose the website belongs to a small-town softball team; the ads for an innovative baseball mitt would be very appropriate for that context. Readers are much more likely to click on it than an ad for snorkeling gear. So Google, guided by the context of the material, will place mitt ads on softball websites.

But that’s just the start of the complexity, because Google will try to make it a three-way match. Ideally, the ads not only match the context of the web page, but also the interest of the reader visiting the page. If you arrive at a general news site—say, CNN—and it knows you play in a softball league, you might see more ads for sports equipment than for furniture. How does it know about you? Unbeknownst to most people, when you arrive at a website you arrive with a bunch of invisible signs hanging around your neck that display where you just came from. These signs (technically called cookies) can be read not just by the website you have arrived at, but by many of the large platforms—like Google—who have their fingers all over the web. Since almost every commercial website uses a Google product, Google is able to track your journey as you visit one page after another all across the web. And of course if you google anything, it can follow you from there as well. Google does not know your name, address, or email (yet), but it does remember your web behavior. So if you arrive at a news site after visiting a softball team page, or after googling “softball mitt,” it can make some assumptions. It takes these guesses and adds them to the calculation of figuring out what ads to place on a web page that you’ve just arrived at. It’s almost magical, but the ads you see on a website today are not added until the moment after you land there. So in real time Google and the news site will select the ad that you see, so that you see a different ad than I would. If the whole ecosystem of filters is working, the ad you see will reflect your recent web visit history and will incline more to your interests.

But wait—there’s more! Google itself becomes a fourth party in this multisided market. In addition to satisfying the advertisers, the web page publisher, and the reader, Google is also trying to optimize its own score. Some audiences’ attention is worth more to advertisers than others. Readers of health-related websites are valuable because they may potentially spend a lot of money on pills and treatments over a long period of time, whereas readers of a walking club forum buy shoes only once in a while. So behind each placement is a very complicated auction that matches the value of key context words (“asthma” will cost a lot more than “walking”) with the price an advertiser is willing to pay along with the performance level of readers who actually click on the ad. The advertiser pays a few cents to the web page owner (and to Google) if someone clicks on the ad, so the algorithms try to optimize the placement of the ads, the rates that are charged, and the rate they are engaged. A 5-cent ad for a softball glove that gets clicked 12 times is worth more than a 65-cent ad for an asthma inhaler that gets clicked once. But then the next day the softball team blog posts a warning about the heavy pollen count this spring, and suddenly advertising inhalers on the softball blog is worth 85 cents. Google may have to juggle hundreds of millions of factors all at once, in real time, in order to settle on the optimal arrangement for that hour. When everything works in this very fluid four-part match, Google’s income is also optimized. In 2014, 21 percent of Google’s total revenue, or $14 billion, came through this system of AdSense ads.

This complicated zoo of different types of interacting attention was nearly unthinkable before the year 2000. The degree of cognification and computation required to track, sort, and filter each vector was beyond practical. But as systems of tracking and cognifying and filtering keep growing, ever more possible ways to arrange attention—both giving and receiving—are made feasible. This period is analogous to the Cambrian era of evolution, when life was newly multicellular. In a very brief period (geologically speaking), life incarnated many previously untried possibilities. It racked up so many new, and sometimes strange, living arrangements so fast that we call this historical period of biological innovation the Cambrian explosion. We are at a threshold of a Cambrian explosion in attention technology, as novel and outlandish versions of attention and filtering are given a try.

For instance, what if advertising followed the same trend of decentralization as other commercial sectors have? What if customers created, placed, and paid for ads?

Here is one way to think of this strange arrangement. Each enterprise that is supported by advertising—which is currently the majority of internet companies—needs to convince advertisers to place their ads with them in particular. The argument a publisher, conference, blog, or platform makes to companies is that no one else can reach the particular audience they reach, or reach them within as good a relationship. The advertisers have the money, so they are picky about who gets to run their ads. While a publication will try to persuade the most desirable advertisers, the publications don’t get to select which ads run. The advertisers, or their agents, do. A magazine fat with ads or a TV show crammed with commercials usually considers itself lucky to have been picked as the vehicle for the ads.

But what if anyone with an audience could choose the particular ads they wanted to display, without having to ask permission? Say you saw a really cool commercial for a running shoe and you wanted to include it in your stream—and get paid for it just as a TV station would. What if any platform could simply gather the best ads that appealed to them and then were paid for the ones they ran—and were watched—according to the quality and quantity of traffic brought to them? Ads that were videos, still images, audio files would contain embedded codes that kept track of where they were shown and how often they were viewed, so that no matter how often they were copied, the host at the time would get paid. The very best thing that can happen to an ad is that it goes viral, getting placed and replayed on as many platforms as possible. Because an ad played on your site might generate some revenue for your site, you’ll be on the lookout for memorable ads to host. Imagine a Pinterest board that collected ads. Any ad in the collection that was played or viewed by readers would generate revenue for the collector. If done well, the audience might come not only for cool content but for cool ads—in the way millions of people show up for the Super Bowl on TV in large part to watch the commercials.

The result would create a platform that curated ads as well as content. Editors would spend as much time hunting down unknown, little-seen, attention-focusing ads as they might spend on finding news articles. However, wildly popular ads may not pay as much as niche ads. Obnoxious ads might pay more than humorous ones. So there will be a trade-off between cool-looking ads that make no money versus square but profitable ones. And of course, fun, high-paying ads would be likely shown a lot, both decreasing their coolness and probably decreasing their price. There might be magazines/publications/online websites that contained nothing but artfully arranged ads—and they would make money. There are websites today that feature only movie trailers or great commercials, but they don’t earn anything from the sources for hosting them. Soon enough they will.

This arrangement completely reverses the power of the established ad industry. Like Uber and other decentralized systems, it takes what was once a highly refined job performed by a few professionals and spreads it across a peer-to-peer network of amateurs. No advertising professional in 2016 believes it could work, and even reasonable people think it sounds crazy, but one thing we know about the last 30 years is that seemingly impossible things can be accomplished by peers of amateurs when connected smartly.

A couple of maverick startups in 2016 are trying to disrupt the current attention system, but it may take a number of tries before some of the radical new modes stick. The missing piece between this fantasy and reality is the technology to track the visits, to weed out fraud, and quantify the attention that a replicating ad gets, and then to exchange this data securely in order to make a correct payment. This is a computational job for a large multisided platform such as Google or Facebook. It would require a lot of regulation because the money would attract fraudsters and creative spammers. But once the system was up and running, advertisers would release ads to virally zip around the web. You catch one and embed it in a site. It then triggers a payment if a reader clicks on it.

This new regime puts the advertisers in a unique position. Ad creators no longer control where an ad will show up. This uncertainty would need to be compensated in some way by the ad’s construction. Some would be designed to replicate quickly and to induce action (purchases) by the viewers. Other ads may be designed to sit monumentally where they are, not travel, and to slowly affect branding. Since an ad could, in theory, be used like an editorial, it might resemble editorial material. Not all ads would be released into the wilds. Some, if not many, ads might be saved for traditional directed placement (making them rare). The success of this system would only prosper in addition to, and layered on top of, the traditional advertising modes.

The tide of decentralization floods every corner. If amateurs can place ads, why can’t the customers and fans create the ads themselves? Technology may be able to support a peer-to-peer ad creation network.

A couple of companies have experimented with limited versions of user-created ads. Doritos solicited customer-generated video commercials to be aired on the 2006 Super Bowl. It received 2,000 video ads and more than 2 million people voted on the best, which was aired. Every year since then it has received on average 5,000 user-made submissions. Doritos now awards $1 million to the winner, which is far less than what professional ads cost. In 2006, GM solicited user-created ads for its Chevy Tahoe SUV and received 21,000 of them (4,000 were negative ads complaining about SUVs). These examples are limited because the only ads that ran had to be approved and processed through company headquarters, which is not truly peer to peer.

A fully decentralized peer-to-peer user-generated crowdsourced ad network would let users create ads, and then let user-publishers choose which ads they wanted to place on their site. Those user-generated ads that actually produced clicks would be kept and/or shared. Those that weren’t effective would be dropped. Users would become ad agencies, as they have become everything else. Just as there are amateurs making their living shooting stock photos or working tiny spreads on eBay auctions, there will surely be many folks who will earn a living churning out endless variations of ads for mortgages.

I mean, really, who would you rather make your ads? Would you rather employ the expensive studio pros who come up with a single campaign using their best guess, or a thousand creative kids endlessly tweaking and testing their ads of your product? As always, it will be a dilemma for the crowd: Should they work on an ad for a reliable bestseller—and try to better a thousand others with the same idea—or go for the long tail, where you might have an unknown product all to yourself if you get it right? Fans of products would love to create ads for it. Naturally they believe no one else knows it as well as they do, and that the current ads (if any) are lame, so they will be confident and willing to do a better job.

How realistic is it to expect big companies to let go of their advertising? Not very. Big companies are not going to be the first to do this. It will take many years of brash upstarts with small to no advertising budgets who have little to lose figuring this out. As with AdSense, big is not where the leverage is. Rather this new corner of ad space liberates the small to middle—a billion businesses who would have never thought of, let alone ever got around to, developing a cool advertising campaign. With a peer-to-peer system, these ads would be created by passionate (and greedy) users and unleashed virally into the blog wilds, where the best ads would evolve by testing and redesign until they were effective.

By tracing alternative routes of attention, we can see that there are many yet untapped formations of attention. Esther Dyson, an early internet pioneer and investor, has long complained of the asymmetry of attention in email. Since she has been active in forming the governance of the internet and financing many innovative startups, her inbox overflows with mail from people she doesn’t know. She says, “Email is a system that lets other people add things to my to-do list.” Right now there is no cost for adding an email in someone else’s queue. Twenty years ago she proposed a system that would enable someone to charge senders for reading their email. In other words, you’d have to pay Esther to read your email to her. She might charge as little as 25 cents for some senders—say, students—or more (say, $2) for a press release from a PR company. Friends and family are probably not charged, but a complicated pitch from an entrepreneur might warrant a $5 fee. Charges can also be forgiven retroactively once a piece of mail is read. Of course, Esther is a sought-after investor, so her default filter may be set high—say, $3 per email message she reads. An average person won’t command the same fee, but any charge acts as a filter. More important, a sufficient fee to read acts as a signal to the recipient that the message is deemed “important.”

The recipient doesn’t need to be as famous as Esther to be worth paying to read an email. It could involve a small-time influencer. An extremely powerful use of the cloud is to untangle the tangled network of followers and followed. Massive cognification can trace out every permutation of who is influencing whom. People who influence a small number of people who in turn influence others may get a different ranking than people who influence a whole lot of people who don’t influence others. Status is very local and specific. A teenage girl with a lot of loyal friends who follow her lead in fashion could have a much higher influence rank than a CEO of a tech company. This relationship network analysis can go to the third and fourth level (the friend of a friend of a friend) in an explosion of computational complexity. Out of this complexity various types of scores can be assigned for degrees of influence and attention. A high scorer may charge more to read an email, but may also choose to adjust what is charged based on the scores of the sender—which adds further complexity and costs to calculating the sum.

The principle of paying people directly for their attention can be extended to advertising as well. We spend our attention on ads for free. Why don’t we charge companies to watch their commercials? As in Esther’s scheme, different people might charge different fees depending on the source of the ad. And different people would have different desirability quotients for the vendors. Some watchers would be worth a lot. Retailers speak about the total lifetime spending of a customer; a customer predicted to spent $10,000 over his or her lifetime at a particular retailer’s store would be worth an early $200 discount bonus. There might also be a total lifetime influence for customers as well, as their influence ripples out to the followers of followers of followers, and so on. The sum could be tallied up and estimated for their lifespan. For those attention-givers with a high estimated lifetime influence, a company might find it worthwhile to pay them directly instead of paying advertisers. The company could pay in either cash or valuable goods and services. This is essentially what the swag bags given away at the Oscar Award ceremonies do. In 2015 the bags for some nominees were crammed with $168,000 worth of merchandise, a mixture of consumer commodities like lip gloss, lollipops, travel pillows, and luxury hotel and travel packages. Vendors make the reasonable calculation that Oscar nominees are high influencers. The recipients don’t need any of this stuff, but they might gab about their gifts to their fans.

The Oscars are obviously an outlier. But on a smaller scale, locally well-known people can gather a significantly loyal following and earn a sizable lifetime influence score. But until recently it was impossible to pinpoint the myriad microcelebrities in a population of hundreds of millions. Today, advances in filtering technology and sharing media enable these mavens to be spotted and reached in bulk. Instead of the Oscars, retailers can aim at a huge network of smaller influencers. Companies that normally advertise could skip ads altogether. They would take their million-dollar advertising budgets and directly pay the accounts of tens of thousands of small-time influencers for their attention.

We have not yet explored all the possible ways to exchange and manage attention and influence. A blank continent is opening up. Many of the most interesting possible modes—like getting paid for your attention or influence—are still unborn. The future forms of attention will emerge from a choreography of streams of influence that are subject to tracking, filtering, sharing, and remixing. The scale of data needed to orchestrate this dance of attention reaches new heights of complexity.

Our lives are already significantly more complex than even five years ago. We need to pay attention to far more sources in order to do our jobs, to learn, to parent, or even to be entertained. The number of factors and possibilities we have to attend to rises each year almost exponentially. Thus our seemingly permanently distracted state and our endless flitting from one thing to another is not a sign of disaster, but is a necessary adaptation to this current environment. Google is not making us dumber. Rather we need to web surf to be agile, to remain alert to the next new thing. Our brains were not evolved to deal with zillions. This realm is beyond our natural capabilities, and so we have to rely on our machines to interface with it. We need a real-time system of filters upon filters in order to operate in the explosion of options we have created.

• • •

A major accelerant in this explosion of superabundance—the superabundance that demands constant increases in filtering—is the compounding cheapness of stuff. In general, on average, over time technology tends toward the free. That tends to make things abundant. At first it may be hard to believe that technology wants to be free. But it’s true about most things we make. Over time, if a technology persists long enough, its costs begin to approach (but never reach) zero. In the goodness of time any particular technological function will act as if it were free. This slide toward the free seems to be true for basic things like foodstuffs and materials (often called commodities), and complicated stuff like appliances, as well as services and intangibles. The costs of all these (per fixed unit) has been dropping over time, particularly since the industrial revolution. According to a 2002 paper published by the International Monetary Fund, “There has been a downward trend in real commodity prices of about 1 percent per year over the last 140 years.” For a century and a half prices have been headed toward zero.

This is not just about computer chips and high-tech gear. Just about everything we make, in every industry, is headed in the same economic direction, getting cheaper every day. Let’s take just one example: the dropping cost of copper. Plotted over the long term (since 1800), the graph of its price drifts downward. While it trends toward zero (despite ups and downs), the price will never reach its limit of the absolutely free. Instead it steadily creeps closer and closer to the ideal limit, in an infinite series of narrowing gaps. This pattern of paralleling the limit but never crossing it is called approaching the asymptote. The price here is not zero, but effectively zero. In the vernacular it is known as “too cheap to meter”—too close to zero to even keep track of.

That leaves the big question in an age of cheap plentitude: What is really valuable? Paradoxically, our attention to commodities is not worth much. Our monkey mind is cheaply hijacked. The remaining scarcity in an abundant society is the type of attention that is not derived or focused on commodities. The only things that are increasing in cost while everything else heads to zero are human experiences—which cannot be copied. Everything else becomes commoditized and filterable.

The value of experience is rising. Luxury entertainment is increasing 6.5 percent annually. Spending at restaurants and bars increased 9 percent in 2015 alone. The price of the average concert ticket has increased by nearly 400 percent from 1981 to 2012. Ditto for the price of health care in the United States. It rose 400 percent from 1982 to 2014. The average U.S. rate for babysitting is $15 per hour, twice the minimum wage. In big U.S. cities it is not unusual for parents to spend $100 for child care during an evening out. Personal coaches dispensing intensely personal attention for a very bodily experience are among the fastest growing occupations. In hospice care, the cost of drugs and treatments is in decline, but the cost of home visits—experiential—is rising. The cost of weddings has no limit. These are not commodities. They are experiences. We give them our precious, scarce, fully unalloyed attention. To the creators of these experiences, our attention is worth a lot. Not coincidentally, humans excel at creating and consuming experiences. This is no place for robots. If you want a glimpse of what we humans do when the robots take our current jobs, look at experiences. That’s where we’ll spend our money (because they won’t be free) and that’s where we’ll make our money. We’ll use technology to produce commodities, and we’ll make experiences in order to avoid becoming a commodity ourselves.

The funny thing about a whole class of technology that enhances experience and personalization is that it puts great pressure on us to know who we are. We will soon dwell smack in the middle of the Library of Everything, surrounded by the liquid presence of all existing works of humankind, just within reach of our fingertips, for free. The great filters will be standing by, quietly guiding us, ready to serve us our wishes. “What do you want?” the filters ask. “You can choose anything; what do you choose?” The filters have been watching us for years; they anticipate what we will ask. They can almost autocomplete it right now. Thing is, we don’t know what we want. We don’t know ourselves very well. To some degree we will rely on the filters to tell us what we want. Not as slave masters, but as a mirror. We’ll listen to the suggestions and recommendations that are generated by our own behavior in order to hear, to see who we are. The hundred million lines of code running on the million servers of the intercloud are filtering, filtering, filtering, helping us to distill ourselves to a unique point, to optimize our personality. The fears that technology makes us more uniform, more commoditized are incorrect. The more we are personalized, the easier it is for the filters because we become distinct, an actualized distinction they can reckon with. At its heart, the modern economy runs on distinction and the power of differences—which can be accentuated by filters and technology. We can use the mass filtering that is coming to sharpen who we are, for the personalization of our own person.

More filtering is inevitable because we can’t stop making new things. Chief among the new things we will make are new ways to filter and personalize, to make us more like ourselves.