Who Owns the Future? (25 page)

To understand how Siren Servers work, it’s useful to divide network effects into those that are “rewarding” and those that are “punishing.” Siren Servers gain dominance through rewarding network effects, but keep dominance through punishing network effects.

Here’s a classic example of a rewarding network effect: A cliché in the advertising world is that in the old days you knew you were wasting half of your advertising budget, but you didn’t know which half. For instance, you’d spend tens of millions of dollars on TV and print ads, and somehow there would be a benefit, but you never knew exactly how or why. Surely many of the ads were playing when people were going to the bathroom, laying waste to your precious spend.

An oft-repeated trope goes like this: Because of all of Google’s data and placement algorithms, an advertiser can now finally know which half is waste. Google can individually target ads, and document the click-throughs that follow.

The reason this is a rewarding network effect is that success breeds success. Because people use Google, other people benefit from using Google, creating a cycle of growth. The more advertisers use Google, the more Web pages are optimized for Google, for instance. Google is perhaps a confusing example, since it is part of the large phylum of Siren Servers in which the users are product, and the true customers, the so-called advertisers, might not always be apparent. (Varieties of Siren Servers will be listed later on.)

Apple provides a clearer example. People use Apple products in part because there are so many apps in its store. Developers are motivated to create lots of apps because there are a lot of people using the Apple store. That’s a classical rewarding network effect.

For Every Carrot a Stick

The most successful Siren Servers also benefit from punishing network effects. These are centered on a fear, risk, or cost that makes “captured” populations think twice if they want to stop engaging with a Siren Server. In Silicon Valley–speak this is also called “stickiness.” Players often can’t take on the burden of escaping the thrall of a Siren Server once a punishing network effect is in place.

Remember, Google sells ad placements based on auctions. Imagine once again that you’re an advertiser. In the old days, if you had been paying for, say, a billboard, you might decide to give that billboard up and instead buy more newspaper ads. Neither you nor anyone else would have had any idea who would place a new ad on the billboard you abandoned. It might be a furniture company or a perfume brand. The risk you took by giving up the billboard was vague and uncertain.

However, if you give up a position on Google’s ad placement system, you know for certain that your next-nearest competitor in the auction will inherit your position. This risk and cost of leaving
a position is made specifically scary and annoying. You are yielding to your archrival! An in-your-face loss must then be weighed against an inevitably more vague future alternative.

Human cognition is often spooked by a trade-off of this kind.
¹
Within businesses it can be even spookier. It’s very hard to leap into a crisp risk in pursuit of a fuzzy benefit. As a result, Google’s customers are effectively locked in, or maybe we should say “glued in,” since we call it sticky.

Another type of punishing lock-in is to get users to put data they value into your server in such a way that access to it will be lost—or at least expensive or labor-intensive to salvage—if they choose to leave. This is a common strategy.

After you’ve spent money in a particular online store, your value received is entirely dependent on your continued fealty to that single Siren Server. Once you’ve paid for music, movies, books, or apps on one Siren Server, you typically have to give up your investment if you leave. Then you have to respend it if you want access to similar stuff on a different Siren Server. This is precisely the
opposite
of a middle-class levee.

It’s not always necessary that the data be made absolutely unavailable; sometimes data can just be decontextualized enough to become less valuable. Facebook provides a fine example. If a great deal of personal creativity and life experience has been added to the site, it’s hard to give all that up. Even if you capture every little thing you had uploaded, you can’t save it in the context of interactions with other people. You have to lose a part of yourself to leave Facebook once you become an avid user. If you leave, it will become difficult for some people to contact you at all. Would you ever be willing to take the risk to sever a part of your own life’s context in order to disengage from a Siren Server that ogles you?

Denial of Service

Yet another way to create a punishing network effect involves control of routing and bandwidth. To understand this method, I refer you to your wireless bill. A particular Siren Server becomes the only
way to connect to the information world. (Companies with proprietary hardware, like Apple, do this as well.) To sever, you must often pay penalties, purchase new equipment, and therefore potentially lose investments tied to the old equipment, like apps, only to get into a new long-term contract.

Access-granting services need not be Siren Servers, since they could just be boring and bill for granting access, but they have caught the deliriously alluring scent of the game by now and are trying to become big data players as well. This has led to power struggles, such as whether a smartphone company or the wireless carrier is in charge of various services and revenue opportunities, and whether the principle of “net neutrality” will endure.

There is often a cascade of hardware lock-ins that cumulatively corner a particular person. You might be locked into one service that connects your home to the Internet with a cable, another that connects your phone or tablet to the wireless signal, and yet another that provides the devices you use and key services like an app store for it.

This demonstrates an interesting difference between Siren Servers and traditional monopolies. There is no reason that there can’t be a lot of Siren Servers. They form ecologies instead of company towns. The reason to be concerned about them is how they distort and shrink the overall economy by demonetizing more and more value. But they don’t necessarily turn into the only game in town in the way that an old-time railroad monopoly might have.

Arm’s-Length Blackmail

There are yet other punishing network effects that resemble a soft kind of blackmail. Some local retail review sites have periodically been accused of skewing or ruining the online visibility of local businesses that cease to buy “optional” premium placement services.
²
Social networking sites will sometimes extract fees to make someone more “visible” on the site.
³
This is particularly true for hookup services akin to the “where the babes are” app that was pitched by the Berkeley graduate students.
⁴

Readers of my previous book will recall an extended examination of how ideas and patterns of use and behavior get “locked into” networked software. This type of software lock-in is often employed to create or buttress a punishing network effect. If a small business designs its own processes and code around the cloud services from only one of the major cloud companies, then it can easily get locked into that company.

Some sites have gotten fairly large with mostly rewarding network effects and barely any punishing ones. eBay is mostly based on rewarding effects, for instance. No one’s really punished for buying or selling elsewhere.
*
(This is in contrast to Amazon, which will sometimes lower prices on an item to undercut you if you sell the same item at a lower price elsewhere.)

*
Twitter’s lack of a plausible revenue growth plan as I write this is similarly due to offering carrot without a commensurate serving of stick. By the time you read this, that might have changed.

When you are subject to someone else’s punishing network effect, every decision becomes strategic. If you plan to break out of the gravitational field of a Siren Server, you often have to swallow hard and go all the way. The burden of that big leap creates a new kind of social immobility.

Who’s the Customer and Who Are All Those Other People?

To understand a particular Siren Server, it is critical to distinguish between distinct populations connected to the venture in different ways. Siren Servers often pit these populations against each other.

Once a Siren Server becomes dominant in its niche, after the Local/Global Flip, it treats those who connect with it as data sources and as subjects for behavior modification. However, there are usually sub-populations subject to different mixes of rewarding and punishing network effects. One sub-population might be shown carrot and stick in equal measure, for example, while another might mostly be offered carrots.

In the cases of Google and Facebook, this difference tracks the distinction between users and customers. Some people, the users, are valued mostly as data and potential for behavior modification, while others, the advertisers, are also sources of money. It is crucial, obviously, to capture money if the Siren Server is to be a business.

This bifurcation can lead to confusion, as when Siren Servers are scrutinized in the terms of old-fashioned antitrust. When a service like Google is evaluated, one of the first observations is that users are free to leave. That is true.
*
From a typical user’s perspective, Google is mostly carrot. But the other population—the true customers, the advertisers—is less free. It is captured because of punishing network effects.

*
True for search, that is. Not so true if a user has put personal data in Google’s tools.

In the case of Wal-Mart, the captured population was the supply chain. Google’s true customers are the advertisers, who are captured. Wal-Mart’s customers weren’t the critical population for it to capture, however. Retail customers gradually became a little captured in some locations where retail choice was eventually reduced, but for the most part they could shop elsewhere if they were so inclined, but it was the optimization of the global supply chain through the use of punishing network effects that really empowered and enriched Wal-Mart.

CHAPTER 14

Obscuring the Human Element

Noticing the New Order

Every tale of adventure lately seems to include a scene in which characters are attempting to crack the security of someone else’s computer. That’s the popular image of how power games are played out in the digital age, but such “cracking” is only a tactic, not a strategy. The big game is the race to create ascendant Siren Servers, or, much more often, to get close to those that are taking off and ascending in ways that no one predicted.

Networked contests for wealth and power tend to follow a pattern. Each particular scheme launched over a network, each purported golden goblet, tends to follow a well-worn course. Networked information, when it is about business instead of science (or, if you like, about human behavior instead of nature), follows a characteristic life cycle.
*

*
Oh how I hate using the term
life cycle
for something that isn’t alive. We are fascinated by ritualistic declarations that we have created new life in our artifices. In the case of big human data, it’s a mistake to perceive even an object, much less a living thing. This is the way these matters are talked about in my community, however, so I occasionally use the terminology despite my objection.

Since I prefer to see the faces instead of the goblet, I find that following the ways in which servers obscure the real people who are the sources of value is also a good way of noticing how the struggle for power proceeds.

Who Orders the Data?

Some Siren Servers relish a world in which data starts out as a mess, decontextualized and mysterious, until it is brought to order by the server’s analytics. Google is probably the best-known example. A Siren Server in this position will do all it can to promote every manner of “open” activity. Data made available for free with inadequate documentation on the open Internet is the ideal raw material for such a venture.

Later on I’ll describe how a remarkably simple idea in network architecture, which was the motivation for the very first digital media designs, was lost, and how that loss created much of the chaos that search engines attempt to undo today.

Other Siren Servers enjoy data that is ordered either at the time of entry or later on, but in either case for free. Facebook is a great example. Google must find patterns in chaos, while Facebook expects you to enter fairly contextualized information in the first place, essentially filling in the blanks of provided forms. However, Facebook also derives additional order through analysis, results that are hidden away in a dungeon.

A “content” site in which almost all contributions are unpaid, like the
Huffington Post,
shares this quality with Facebook. Online retailers like Amazon and eBay are also examples, since they don’t have to pay for reviews or the design of product presentations. Those who sell through these schemes are mostly responsible for creating and tending their own presentations, unlike in traditional retail, where the retailer has to figure out how to present each product.

This is a key sign of a Siren Server. The lowly non-Sirens are as responsible as possible, while the Siren Server presides from an arm’s length.
*

*
Another example is Wikipedia. I am not condemning it, and in my previous book have discussed what I see as its strengths and weaknesses. As I argued earlier, however, it does reduce markets for certain kinds of scholars in the long term in order to demonetize scholarship in the short term, so it qualifies as a Siren Server. It creates the kinds of false efficiencies that thwart levees.