Book Review: 'Automate This,' by Christopher Steiner

A new book traces the alarming rise of the all-powerful algorithm
This is the era of the algorithm, as big data transforms industries well outside of Wall Street and Silicon Valley Illustration by Joe Magee

Humans appear all too adept at building machines that suck the joy out of life. Great explorers like Neil Armstrong have been replaced by rovers that can do a bit of everything, including conducting scientific experiments and snapping breathtaking photos. Cars that, while dangerous, are awfully fun to drive are being turned into autonomous contraptions. Over at Google, some engineers even had the audacity to build a computerized brain that can find cats in YouTube videos—a feat that once united humanity.

Things will only get worse, according to Christopher Steiner and his new book, Automate This: How Algorithms Came to Rule Our World. Steiner traces the rise of algorithm-based trading on Wall Street, beginning in the late 1980s and carrying through to today, when 60 percent of trades occur via computer “with little or no real-time oversight from humans.” He says we can expect the Wall Street model to sweep across just about every field, as algorithms—complex sets of mathematical formulas—start to accomplish tasks with an insight and efficiency unmatched by people. Hooray! Steiner, though, can’t resist sounding the apocalyptic alarm that algorithms will soon put bright, creative people out of work and possibly even run amok, causing unimaginable mayhem. Oh …

Algorithms have been around at least since the ninth century Persian mathematician Abu Abdullah Muhammad ibn Musa al-Khwarizmi coined the term in a seminal mathematics text. But it’s only in the last 20 years or so that we’ve really seen the use of algorithms explode and the concept itself become fetishized. This is the result of the immense amount of cheap computing power now available and our ability to gather and store data on an unprecedented scale. We built algorithm machines—computer chips that speak “if this, then that” in 1s and 0s—and can now feed them.

If there’s a main character in Steiner’s book, it’s Thomas Peterffy, a Hungarian-born computer programmer who came to the U.S. in his twenties and ended up as one of the richest men in the country. Peterffy was one of the first traders to harness the power of computers to place huge batches of automated trades. He developed algorithms that would weigh various factors of securities and issue buy and sell orders when they spotted a discrepancy in the market. Peterffy is said to have spliced the wires on his Nasdaq trading terminal way back in 1987 to make a direct connection to his trading computer. When a Nasdaq official saw the setup, he deemed it unfair. So he invented something else. Steiner says that it took Peterffy one week to make a machine that could read security prices off the Nasdaq terminal using a camera and type in dozens of orders per minute with a mechanical hand.

Today algorithmic, high-frequency trading is the dominant force on Wall Street. People have developed computer systems that “read” news stories and issue trading commands based on their interpretation of the text. Investors have poured billions of dollars into digging trenches and blasting mountains to support fiber-optic cable links between trading hubs, so that a few nanoseconds can be shaved off trading times, allowing one group’s algorithm to beat another’s. This type of work has required the smarts of engineers, physicists, and statisticians who, over the past two decades, evolved into the famed “quants,” making huge bonuses working for Wall Street firms instead of being locked away in some university or lab’s nerdery.

The economy collapsed in 2008 in part under the weight of trading algorithms that were neither well thought out nor well analyzed. The recession left many of these smart people without work on Wall Street, but it also, Steiner suggests, made them rethink their way of life. And so began something of a quant diaspora. These algorithm-obsessed types showed up at Zynga to coax people into playing games longer online, and at One Kings Lane to get people to buy more throw pillows. (While reading this section of the book, I was surprised to find the text aligning so closely with a story I wrote for this magazine last year called “This Tech Bubble Is Different.” Even my feeble, human brain could spot the borrowed quotations and language. Steiner gives little credit in this edition but plans to amend the electronic version of the book and future printings to cite my reporting.)

It’s not just the technology industry, though, that is benefiting from quants seeking new lines of work. Steiner points to people who have developed algorithms that can predict which movies will end up as summer blockbusters and how much money they will gross. There are algorithms for picking music hits and even for writing symphonies with computers that fool trained ears into thinking a human composed them. Computers have also proven adept at spotting cancerous tumors, and with more accuracy than physicians earning $300,000 per year. Other machines can review legal documents more cheaply than Indian lawyers companies outsourced the work to. “Smart people assume that this creeping revolution of bot workers can’t touch them,” Steiner writes. “The notion is that algorithms can’t innovate, that a bot can’t create. We’re now learning, however, that these are dangerous assumptions.”

Steiner goes on to tell us that algorithms will basically control our entire lives, even telling us whom we can marry. There’s no doubt that this is the era of the algorithm, as software and big data transform industries well outside of Wall Street and Silicon Valley. But Steiner is blind to the bright side of this revolution. He does not visit the farms in the Midwest that are now buying algorithm-based crop insurance, nor does he talk to the drivers who, using the car service Uber’s algorithm-based reservation system, make more money than they ever have. In these cases, algorithms have created new jobs or made an old job better. Meanwhile, sites like LinkedIn and Facebook use algorithms to help us find better jobs and stay in touch with friends. Entire industries are arising on the back of these automated systems that study our DNA, spending patterns, and exercise habits.

Instead, the book spends the vast majority of its time on Wall Street’s algorithm obsession, while mostly providing eye-candy examples elsewhere that are then turned into horrific harbingers of mass unemployment. Steiner’s book could have used a true main character, and it should have given the algorithms the complexity and nuance they deserve.

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