by Jaron Lanier
Wal-Mart could practically dictate price and delivery targets, with the reduced risk and increased precision of an attack drone. Suppose you ran a service or parts company in the 1990s. You went to a company that sold products to Wal-Mart and stated your price for something needed by that company. That company would often find itself saying, Sorry; Wal-Mart has decreed a price for our product that doesn’t allow us to pay you as much as you want.
It turned out that Wal-Mart had calculated a pretty good guess before you showed up about what everyone’s real bottom lines would be. Often enough, you would realize that you could (barely) accept the counteroffer, even though it wasn’t what you were looking for.
Wal-Mart didn’t need to get direct information about everyone in the loop. A sampling of information about a system is good enough to form an approximate model of that system. That means that someone can be indirectly spied on without any information about that person being gathered directly. Instead, the behaviors of those who interact with a party might yield some clues, and a whole picture is roughly pieced together automatically.
Once other big retailers understood what Wal-Mart had achieved, they hired their own specialists and powered up their own big data centers. But it was too late. Wal-Mart had already repatterned the world, giving itself a special place in it. Vendors were often already coordinated with each other to offer the lowest prices in a particular way that was finely tuned to Wal-Mart’s needs. The supply chain had become optimized to deliver to Wal-Mart’s door.
Wal-Mart didn’t cheat, spy, or steal to get information.* It just applied the best available computers to calculate the best possible statistics using legitimately available data.
*Once again, perhaps my assessment is more charitable than others. I see a collective mistake rather than a class of villains.
Everyone else’s margins got slammed to the bare minimums. It was like playing blackjack with an idiot savant who can’t help but count cards. This is the moral puzzle of Siren Servers. In the network age there can be collusion without colluders, conspiracies without conspirators.
From the Customer’s Point of View
Wal-Mart confronted the ordinary shopper with two interesting pieces of news. One was that stuff they wanted to buy got cheaper, which of course was great. This news was delivered first, and caused cheering.
But there was another piece of news that emerged more gradually. It has often been claimed that Wal-Mart plays a role in the reduction of employment prospects for the very people who tend to be its customers.1 Wal-Mart has certainly made the world more efficient in a certain sense. It moved manufacturing to any spot in the world that could accomplish it at the very lowest cost; it rewarded vendors willing to cut corners to the maximum degree.
Wal-Mart’s defenders might acknowledge some churn in the labor market, but to paraphrase the familiar rebuttal, “making the market more efficient might have cost some people their jobs, but it saved even more people a lot of money by lowering prices. In the long term everybody wins because of efficiencies.”
It’s certainly reasonable to expect that making economic activities more efficient ought to increase opportunity for everyone in the longer term.* However, you can’t really compare the two sides of the equation, of lower prices and lowered job prospects.
*As I will explain, I strongly agree with the assertion, but only if we don’t remove massive amounts of value from our ledgers.
This is so obviously the case that it seems strange to point it out, but I have found that it is a hard truth to convey to people who have not experienced anything other than affluence. So: If you already have enough to live on, saving some money on a purchase is a nice perk. But if you haven’t reached that threshold, or if you had been there but lost your perch, then saving is not the equivalent of making; it instead becomes part of a day-to-day calculus of just getting by. You can never save enough to get ahead if you don’t have adequate career prospects.
To me this false trade-off, which was often stated in the 1990s, foreshadowed what we hear today about free Internet services. Tech companies have played similar games, said similar things, and pale in the same harsh light. “Sure there might be fewer jobs, but people are getting so much stuff for free. You can now find strangers’ couches to crash on when you travel instead of dealing with traditional hotels!” The claim is as wrong today as it was back then. No amount of cost lowering can foster economic dignity when it also means that there are fewer good jobs.
All Siren Servers deliver dual messages similar to the pair pioneered by Wal-Mart. On the one hand, “Good news! Treats await! Information systems have made the world more efficient for you.”
On the other hand, a little later: “It turns out you, your needs, and your expectations are not maximally efficient from the lofty point of view of our server. Therefore, we are reshaping the world so that in the long term, your prospects are being reduced.”
The initial benefits don’t remotely balance the long-term degradations. Initially you made some money day trading or getting an insanely easy loan, or saved some money couch-surfing or by using coupons from an Internet site, but then came the pink slip, the eviction notice, and the halving of your savings when the market drooped. Or you loved getting music for free, but then realized that you couldn’t pursue a music career yourself because there were hardly any middle-class, secure jobs left in what was once the music industry. Maybe you loved the supercheap prices at your favorite store, but then noticed that the factory you might have worked for closed up for good.
Financial Siren Servers
The world of financial servers and quants is even more secretive than the corporate empires like Wal-Mart or Google. I have also had a window into this world, though it’s hard to get a sense of how much of it I have seen relative to all that goes on.
There was an initial phase, which I mostly missed, when digital networking first amplified ambitions at what had been the margins of the world of finance. Starting in the 1980s, but really blossoming in the 1990s, finance got networked, and schemes were for the first time able to exceed the pre-digital limitations of human deception.
The networking of finance occurred independently and in advance of the rise of the familiar Internet. There were different technical protocols over different infrastructure, though similar principles applied.
Some of the early, dimly remembered steps toward digitally networked finance included: 1987’s Black Monday (a market anomaly caused by automated trading systems), Long-Term Capital, and Enron. I will not recount these stories here, but those readers who are not familiar with them would do well to read up on these rehearsals of our current global troubles.
In all these cases there was a high-tech network scheme at play that seemed to concentrate wealth while at the same time causing volatility and trauma for ordinary people, particularly taxpayers who often ended up paying for a bailout.
In addition, a loosening of regulation was often involved. There’s a legitimate argument about whether the weakening of regulation was the cause of the failures, or if the regulations were weakened because the temptations of overcoming them became so great because of new technologies, that financiers put more effort into political influence than previously.
In either case, it is interesting that the lost regulations dated from market failures of old, particularly the Great Depression. That should not be taken to mean that the hazards that arose once finance was networked are precisely what they were before finance was regulated. I worry that regulators might be inclined to look only backward.
I knew a few people involved with Long-Term Capital, and I fielded calls from Enron when it wanted to buy a startup that ultimately went to Google. Mostly I got to know what I believe were second- and third-generation financial Siren Servers.
I have had many friends who worked as quants, and have also gotten to know a few very successful financiers at the helms of some of the more hermetic ventures. During the late 1990s and early 2000s, I was able to v
isit various power spots, and had many long conversations about the statistics and the architectures.
Usually there would be an unmarked technology center in one of the states surrounding New York City, or perhaps farther afield. There, a drowsy gaggle of mathematicians and computer scientists, often recently graduated from MIT or Stanford, would stare at screens, sipping espressos.
The schemes were remarkably similar to Silicon Valley designs. A few of them took as input everything they possibly could scrape from the Internet as well as other, proprietary networks. As in Google’s data centers, stupendous correlative algorithms would crunch on the whole ’net’s data overnight, looking for correlations. Maybe a sudden increase in comments about mosquito bites would cause an automatic, instant investment in a company that sold lotions. Actually, that’s an artificially sensible example. The real examples made no sense to humans. But money was made, and fairly reliably.
In most of the cases, the input wasn’t the whole ’net, but only streaming numerical financial data. Signal-processing algorithms would attempt to discern subtle but predictable fluctuations that had never been noticed before. Maybe a number wobbled just a little bit, but not entirely at random. By betting for and against that number rhythmically, a slight, but steady profit dripped out. If this was done a million times simultaneously, the result was an impressive haul.*
*It should be pointed out that if only one Siren Server is milking a particular fluctuation in this way, a reasonable argument could be made that a service is being performed, in that the fluctuation reveals inefficiency, and the Siren is canceling it out. However, when many Sirens milk the same fluctuation, they lock into a feedback system with each other and inadvertently conspire to milk the rest of the world to no purpose.
Yet other schemes didn’t rely so much on fancy analytic math as on the spectacular logistical capabilities of digital networks. For instance, banks settle accounts at particular times of day. With a sufficiently evolved network, money can be automatically wired in and out of accounts at precise moments, in order to enact elaborate rounds of perfectly timed transactions that cycle through many countries. At the end of each cycle, some money was reliably earned, not based on making bets about the unpredictable events of the world, but on the meticulous alignment of the quirks of the world’s local rules. For instance, the same money might earn interest at two different banks on opposite sides of the world at once. No one at any of the localities involved would have a clue.
Then there were the exquisitely positioned schemes. The most notorious of these are the servers that accomplish high-frequency trading. They tap directly into the hubs of markets and extract a profit before anyone else can even get a trade in edgewise. This sort of thing was just getting started when I bid Manhattan adieu. (My place was damaged in the 2001 attacks, and I moved out to crazy Berkeley.)
Every scheme I encountered was completely legal, as far as I know. Of course there are lingering questions about the legality of some of what happened at the most visible Wall Street firms—the ones that ended up receiving the most gargantuan bailouts at the public’s expense in the wake of the 2008 financial crisis.
The quiet world of the quirkiest financial Siren Servers was racking up numbers that compared to the big players, however. Some of them came out of the recession quite well and others did not.
The most successful runners of financial Siren Servers were often unconventional, or at least the more unconventional ones were the ones who wanted to talk to me. There was one guy whom I only ever saw in his silk robes, hanging out by the spa in his sybaritic, giant loft in TriBeCa.
Later, I heard the same thing from other masters of the universe: It ultimately comes down to having some special “in,” some special connection, or some special knowledge. You needed to know the right people to get the special data, or the special tap into the market’s computers, or the agreements to let your algorithms automatically enact trades in far-flung locations where such things had not happened before.
Ultimately, there was an old-fashioned old boys’ club obscured under the tangle of cables in the foundation of the newfangled digital network.
While there is never an absolutely sure thing, in the upper reaches of finance certain schemes come close to perfection. In the past, the perfect investment always rested on at least a touch of corruption. There was some chink in the law that you depended on.
There are certainly such legal maneuvers these days, such as tax loopholes for hedge fund managers. But the cores of these businesses, where the profit comes from, are in many cases more organic, more pure than previous “sure things.” If you can pull money out of sufficiently advanced math, then the law couldn’t keep up even if it tried.
Just as this book left my hands, regulators in Europe began to consider the regulation of high-frequency trading. I hope they appreciate the nature of the challenge they face. To an algorithm, a circuit breaker or timing limit is just another feature in the environment to be analyzed and exploited. Algorithms will “learn” to trip circuit breakers at the right millisecond to capture a profit, for instance. If the frequency of trades is limited, then some other parameter, like the phase, or relative timing, of trades, will be automatically refined in order to find an advantage. The cat-and-mouse game can go on forever. Undoing the Siren Server pattern is the only way back to a truer form of capitalism.
This is what computers appear to have made possible in some cases (though I can’t know enough to be absolutely sure): a new path to a “sure thing” that sidesteps the nasty old business of having to court politicians.
What is absolutely essential to a financial Siren Server, however, is a superior information position. If everyone else knew what you were doing, they could securitize you. If anyone could buy stock in a mathematical “sure thing” scheme, then the benefits of it would be copied like a shared music file, and spread out until it was nullified. So, in today’s world your mortgage can be securitized in someone else’s secretive bunker, but you can’t know about the bunker and securitize it. If it weren’t for that differential, the new kind of sure thing wouldn’t exist.
SECOND INTERLUDE (A PARODY)
If Life Gives You EULAs, Make Lemonade
The information economy that we are currently building doesn’t really embrace capitalism, but rather a new form of feudalism.
We aren’t creating enough opportunity for enough people online. The proof is simple. The wide adoption of transformative connecting technology should create a middle-class wealth boom, as happened when the Interstate Highway System gave rise to a world of new jobs in transportation and tourism, for instance, and generally widened commercial prospects. Instead we’ve seen recession, unemployment, and austerity.
I wonder if thinking about lemonade stands might help. A prominent political meme for the Republican half of America in 2012 went like this: “You built it.” This was a retort to an out-of-context attribution to President Barack Obama: “You didn’t build that,” originally referring to infrastructure like roads.
The contention was approximately that entrepreneurship is the most fundamental activity, and can close its own loops. Business would solve more problems if it were just left alone. Government taxes and regulation are the problem. Removing those things is the solution. Who needs infrastructure? Businesses would build their own roads if the government would just leave them alone.
Some girls who had created a lemonade stand were famously assembled on television by critics of the president and asked if they had built their business, or if the government had.1 I wish children could experience earning money online today, but that is harder to do than starting lemonade stands.
Can we compare the Internet to the road that must precede a lemonade stand? The government built the road. The whole idea of a public road is to push entrepreneurship up to a higher level.
Without the government there would have most likely been a set of incompatible digital networks,* mostly private, instead of a prominent unified In
ternet.2
*Al Gore played a crucial role in bringing that unity about when he was a senator, following in the footsteps of his father, who had facilitated the national system of interstate highways.
Without the public road, and utterly unencumbered access to it, a child’s lemonade stand would never turn a profit. The real business opportunity would be in privatizing other people’s roads.
Similarly, without an open, unified network, the whole notion of business online would have been entirely feudal from the start. Instead, it only took a feudal turn around the turn of the century. These days, instead of websites on the open Internet, people are more likely to create apps in proprietary stores or profiles on proprietary social media sites.
I have had more than one heated argument with Silicon Valley libertarians who believe that streets should be privatized. Here’s the EULA3 no one would read in the utopia they pine for:
Dear parents or legal guardians of ________________
As you may be aware, your daughter is one of _______ children in your neighborhood who recently applied for a jointly operated StreetApp® of the category “Lemonade Stand.”
As the owner/operator of the street on which you live, and on which this proposed app would operate, StreetBook is required by law to obtain parental consent. By clicking on the “yes” box at the bottom of this window, you acknowledge you are ________’s parent or legal guardian, and also agree to the following conditions: