The Inevitable Page 20
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 spend $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 cr
ammed 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.
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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.
8
REMIXING
Paul Romer, an economist at New York University who specializes in the theory of economic growth, says real sustainable economic growth does not stem from new resources but from existing resources that are rearranged to make them more valuable. Growth comes from remixing. Brian Arthur, an economist at the Santa Fe Institute who specializes in the dynamics of technological growth, says that all new technologies derive from a combination of existing technologies. Modern technologies are combinations of earlier primitive technologies that have been rearranged and remixed. Since one can combine hundreds of simpler technolog
ies with hundreds of thousands of more complex technologies, there is an unlimited number of possible new technologies—but they are all remixes. What is true for economic and technological growth is also true for digital growth. We are in a period of productive remixing. Innovators recombine simple earlier media genres with later complex genres to produce an unlimited number of new media genres. The more new genres, the more possible newer ones can be remixed from them. The rate of possible combinations grows exponentially, expanding the culture and the economy.
We live in a golden age of new mediums. In the last several decades hundreds of media genres have been born, remixed out of old genres. Former mediums such as a newspaper article, or a 30-minute TV sitcom, or a 4-minute pop song still persist and enjoy immense popularity. But digital technology unbundles those forms into their elements so they can be recombined in new ways. Recent newborn forms include a web list article (a listicle) or a 140-character tweet storm. Some of these recombined forms are now so robust that they serve as a new genre. These new genres themselves will be remixed, unbundled, and recombined into hundreds of other new genres in the coming decades. Some are already mainstream—they encompass at least a million creators, and hundreds of millions in their audience.
For instance, behind every bestselling book are legions of fans who write their own sequels using their favorite author’s characters in slightly altered worlds. These extremely imaginative extended narratives are called fan fiction, or fanfic. They are unofficial—without the original authors’ cooperation or approval—and may mix elements from more than one book or author. Their chief audience is other avid fans. One fanfic archive lists 1.5 million fan-created works to date.
Extremely short snips (six seconds or less) of video quickly recorded on a phone can easily be shared and reshared with an app called Vine. Six seconds is enough for a joke or a disaster to spread virally. These brief recorded snips may be highly edited for maximum effect. Compilations of a sequence of six-second vines are a popular viewing mode. In 2013, 12 million Vine clips were posted to Twitter every day, and in 2015 viewers racked up 1.5 billion daily loops. There are stars on Vine with a million followers. But there is another kind of video that is even shorter. An animated gif is a seemingly still graphic that loops through its small motion again and again and again. The cycle lasts only a second or two, so it could be thought of as a one-second video. Any gesture can be looped. A gif might be a quirky expression on a face that is repeated, or a famous scene from a movie put on a loop, or it could be a repeating pattern. The endless repetition encourages it to be studied closely until it transcends into something bigger. Of course, there are entire websites devoted to promoting gifs.