Who Owns the Future? (11 page)

Is there really anything new under the sun? Maybe the way finance went nuts over networks in the early 21st century was really just a repeat of what happened just before the Great Depression of
the 1930s, or during the spates of gilded economic chaos at the end of the 19th century. Maybe Siren Servers have always been with us. When I recall what I have seen, I am not speaking as a historian, but as a witness. I leave it to historians to determine how much the recent past has in common with other historical periods.

What is of primary interest to me is whether there are new options for solutions available now that were not available in other eras.

Wal-Mart Considered as Software

One early example of computer networks transforming an industry on a global scale did not come from a social networking site, or from search, or any den of mathematicians working in Silicon Valley or Wall Street. Instead consider Wal-Mart.

Wal-Mart is a real-world, “brick and mortar” concern that succumbed early to the allure of pure networked information. The company’s supply chain was driven by real-time data and enormous amounts of computation well in advance of the appearance of search engines, the dot-com boom, or social networking.

Overall, Wal-Mart has brought about much good. Consider that in the decades before the explosion of Chinese imports to the United States, one of the greatest anxieties in American thinking concerned the “awakening” of the sleeping giant China. It was vastly more inscrutable even than the Soviet Union. I recall many chilling conversations about the potential for a third world war.

Instead, Wal-Mart’s servers helped coordinate the demand side of the rise of China as a manufacturing powerhouse. Economic interdependence had been faintly imagined, occasionally, as a way to avoid a new, hot superpower confrontation, but back in the 1980s that was barely imaginable. And yet it happened. This was certainly one of the more dramatic positive effects of digital networking on the unfolding of history thus far.
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To be clear, I am not at all saying today’s China is above criticism!

So Siren Servers can achieve good. My argument is not that Siren Servers always do harm. Often they accomplish great good in the
short term. We are, however, using the power of networks to optimize for the wrong things overall.

From the Supply Chain’s Point of View

I had a peephole into Wal-Mart’s world through an occasional consulting assignment in the 1990s, via a Silicon Valley think tank. What I saw was a prototypical version of what has become the familiar pattern.

Wal-Mart recognized early that information is power, and that with digital networking you could consolidate extraordinary power. Wal-Mart’s fledgling servers gathered information about simple but valuable conditions out in the world at large: what could be made where and when; what could be moved where and when; who would buy what, and when and for how much. Any little portion of this database would previously have been of value only to a few local players directly affected by it, but by collecting a lot of such information in one place, an overall, global picture emerged. This is the wild change of perspective that network technology can give you. The company gradually became the sculptor of its own environment.

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.
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It just applied the best available computers to calculate the best possible statistics using legitimately available data.

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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.
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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.
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However, you can’t really compare the two sides of the equation, of lower prices and lowered job prospects.

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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 visit 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.
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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.