Read Online Who Owns the Future? by Jaron Lanier - Free Book Online Page B

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. Thereal 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 ofsome 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