The Inevitable
Page 19
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.
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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.
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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, whi
ch 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 t
hat 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.