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CHAPTER 1
Worker
IT'S RUSH HOUR in New York. I stop by Hank's stand on 47th Street, spend a buck and a quarter for a sweetened coffee, carry it to the elevator, and ride high up in a Midtown skyscraper. A big pile of Wall Street Journals used to wait at reception, one for each of us. No more. Now we've been instructed to read the paper online. With that, even more of our work moves onto the computer.
I pry the lid off the coffee. I call up Yahoo, read my personal mail, and type a quick reply to an e-mail from my sister. Then I check the Philadelphia papers for baseball news. The Phillies got crushed ... It's 10 A.M., the coffee's a brown stain on the bottom of the cup, and I'm just getting to the Wall Street Journal online. Or maybe I'm not.
Office workers have had pleasant little stalling routines forever, and it hasn't mattered much. Other laborers haven't been so lucky. A century ago, men carrying notebooks and stopwatches made their way into factories and started to measure the movements of workers. They turned industrial production into a science, which reached its zenith in Japanese auto plants. They perfected Statistical Quality Control, and today they can analyze each spray gun, each furnace, and, by extension, each worker, minute by minute. If any one of these elements is missing a beat, they can adjust it on the spot. Many office dweebs, by comparison, luxuriate in privacy. Unless we happen to be snoring louder than usual in our cubicle when the boss strolls by, our work habits remain our own little secret. We're scored on results, not process. Sell a house, win a trial, wow the boss with elegant lines of software code, and we're golden.
Things are changing, though. In the past decade, much of the work we do has moved away from the piles on our desks, the notebooks and newspapers and Post-its stuck to the door. It has migrated right onto the computer, which is now linked to a network. We're tied to a workmate equipped with a phenomenal memory, an uncanny sense of time, and no loyalty to us. He works for the boss, who can measure our efforts with no need for a notebook or a stopwatch. The computer will rat on us, exposing each one of our online secrets without a nanosecond of hesitation or regret. At work, perhaps more than anywhere else, we are in danger of becoming data serfs — slaves to the information we produce. Every keystroke at the office can now be recorded and mathematically analyzed. We don't own them. If our bosses wanted to, they could order up an e-mail chart for each of us. It would display the words we write most often, in proportionally sized fonts. You could only pray that movies or beer wouldn't show up bigger on your chart than the medicines you sell or the stocks you recommend. That online version of the Wall Street Journal? Our employers can follow which articles we read. They can also buy software to create maps of the people we communicate with — our social networks. From these, they can draw powerful conclusions about our productivity, our happinessat work, and our relations with colleagues. Just what kind of team player are you, anyway? Microsoft even filed in 2006 to patent a technology to monitor the heart rate, blood pressure, galvanic skin response, and facial expressions of office workers. The idea, according to the application, is that managers would receive alerts if workers were experiencing heightened frustration or stress. Such systems are in the early stages of research. But even with today's technology, if your company is not scouring the patterns of your behavior at the keyboard, it's only because it doesn't choose to — or hasn't gotten around to it yet.
Why would companies intrude like this? Very simply, to boost our productivity. For centuries they've concentrated on results because, like the newspaper advertisers now rushing to Dave Morgan's offices at Tacoda, they haven't had the means to monitor and dissect what we actually do. Now the tools are at hand. Don't they have a responsibility to shareholders to put them to use and pump up productivity and profits? That's the way they see it.
Now as I look at the workplace through their purposeful eyes, I'm already feeling a trace of nostalgia for the idle moments and wasteful routines that brighten my days. Sitting in my 43rd-floor office, I call up YouTube and click on a silly Morphing Pug video. An animated dog dances and sings a ridiculous song. I wonder what that investment of 45 seconds of utter nonsense could possibly say to my bosses about me. Is there a correlation between Morphing Pug watchers and prizewinning journalism? It's doubtful. And it's a matter of time before management starts recording such behavior. The very thought fills me with such regret that I click on the video once more, not so much to laugh at the dog as to soak up the on-the-job freedom it represents.
On a late spring morning I drive over the Tappan Zee Bridge, across the wide expanse of the Hudson. Then I hook left, away from New York City and up into the forests of Westchester County, to the headquarters of IBM's Thomas J. Watson Research Laboratory. It sits like a fortress atop a hill, a long, curved wall of glass reflecting the cotton-ball clouds floating above. I have a date there with Samer Takriti, the Syrian-born mathematician who launched me on this entire project. He was the one who described to me early on how his team was building mathematical models of thousands of IBM's tech consultants. The idea, he said, was to piece together inventories of all of their skills and then to calculate, mathematically, how best to deploy them. I came away from that meeting convinced that if Takriti could model people as workers, then eventually we'd also be modeled as shoppers and patients — in short, in a whole range of our activities as humans. Now I'm going back to find out how Takriti and his team plan to turn IBM's workers into numbers — and what they'll do with them (and with us) if they succeed.
Takriti, a slim 40-year-old with wide, languid eyes, opens the door of his small office. He wears a rugby shirt tucked tightly into blue jeans. He's on a conference call but waves me in. On one wall of his windowless office is a whiteboard covered with math calculations that mean nothing to me. Takriti is quiet on the call, just saying, "A hum, a hum." I look to the other wall, which is decorated with an electricity grid of New York and Pennsylvania. This is an artifact from Takriti's previous life, when he used math to model chunks of the old economy, things like steel mills and power plants. Story has it, Takriti says after he hangs up, that the original Takritis were warriors who marched from Saddam's native city, Tikrit, in Iraq. His branch of the family, he tells me, eventually settled in Syria. A top engineering student in Damascus, Takriti won a fellowship in the mid-1980s to study at the University of Michigan. He fell head over heels for math. In 1996, by then a Ph.D., he landed a research job at IBM's fabled Watson Research Center, a half-hour drive north of New York City. This son of Tikrit warriors now walked among the gods of math.
Takriti's specialty was stochastic analysis. This is the math that attempts to tie predictions to random events. Say it rains in Tucson from zero to six times per month, and you listen to the weather report, which has been right 19 of the past 20 days, only three times a week. One of your three jackets is suede. What are the chances it'll get drenched tomorrow? Imagine that same question with one thousand variables, and you've stepped into the stochastic world.
A generation ago, a crew of math whizzes led by Myron Scholes and Fischer Black focused their mastery of probability on finance, where they calculated risk and put prices on it. This led to a panoply of new financial products, from options to hedging strategies. It was a math revolution on Wall Street. The mathematicians were replacing hunches, wholesale, with science. Takriti says that by the time he reached IBM, many of the same math tools were being refitted for other industries.
Like energy. Takriti doesn't like to broadcast it, but he left Big Blue in 1999 for Houston, where he worked for Enron. Back then, Enron was not only innovating the kind of corporate fraud that would lead to its collapse. It also ran a world-class mathematics laboratory. The entire world, as Enron saw it (and was soon to demonstrate all too vividly) was awash in uncertainty. People had trillions of dollars riding on chance. If you looked at weather as a topsy- turvy market, for example, theme parks were betting on sunshine, farmers on rain. Enron'smath team could calculate the weather risks and then develop indexes and financial options for cold fronts and dog days. Everyone could hedge the weather, and Enron would turn this into a business. Given enough mathematicians, it seemed, every chancy element in the world could eventually be quantified, modeled, and turned into a financial instrument.
Takriti's stock soared at Enron. And when IBM called in late 2000, they offered him the top job in stochastic analysis. Takriti jumped. He got out of Houston, it turned out, barely a year before Enron's collapse. His new focus at IBM would be every bit as hard to quantify and predict as flash floods in the Mojave Desert or the looming corporate bankruptcy in Houston. Takriti would be modeling human workers.
I tell Takriti that being modeled doesn't sound like much fun. I picture an all-knowing boss anticipating my every move, perhaps sending me an e-mail with the simple message, "No!" before I even get up my nerve to ask for a raise. But Takriti focuses on the positive. Imagine that your boss finally recognizes your strengths, he says — maybe ones that are hidden even to you. Then he "puts you into situations where you will thrive."
If your performance is stellar, companies eventually could wield your mathematical model as a specimen of workplace DNA. And they could use it, in a sense, to clone you. Imagine, says Aleksandra Mojsilovic, one of Takriti's modelers, that the company has a superior worker named Joe Smith. Management could use two or three others just like him, or even a dozen. Once the company has built rich mathematical profiles of their employees, it shouldn't be too hard to sift through them to identify the experiences or routines that make Joe Smith so good. "If you had the full employment history, you could even compute the steps to become a Joe Smith," she says. Most of this, of course, would involve training programs, not genetic manipulation. And the real Joe Smith may have intuitive smarts or a knack for design that just cannot be replicated. "I'm not saying you can re-create a scientist, or a painter, or a musician," Mojsilovic says. "But there are a lot of job roles that are really commodities." And if people turn out to be poorly designed for these jobs, they'll be reconfigured, first mathematically and then in life.
When Samer Takriti sits down to define one of his colleagues in symbols, he looks to economists and industrial engineers for guidance. They've been modeling complex systems for decades. Economically, he regards us as components in a labor market. Our value rises and falls with demand. In that sense, we fit into the financial equations developed on Wall Street. And when we're employed, what do we do? We work with colleagues to build things and create value. So, boiled down to numbers, we share at least a few mathematical properties with the components that are unloaded every day at IBM's huge microprocessor factory up the road, in Fishkill, New York. Look at us one way, and we're stocks. Change the perspective, and we're machine parts.
Of course, this isn't entirely fair. We're more than stocks and parts, quite a bit more. Takriti is the first to admit it. It's because we're so different — so hard to predict — that Takriti needs a team of 40 Ph.D.'s, from data miners to linguists, to decode our behavior and our traits. They catalog what they find — each of our gestures, each of our skills — into symbols that a computer can digest. "Everything must be turned into numbers," Takriti says.
One of Takriti's challenges is to help IBM develop a taxonomy of the skills of its 300,000 employees. On its balance sheet, IBM lays out the value of many other assets, from supercomputers to swiveling Aeron desk chairs. When strategists at the company are figuring out whether to sell a division or invest more money, they pore over these figures. They sketch out rosy and grim scenarios. They do the numbers.
But how do they "do the numbers" on you and me? Yes, they know how much we cost. Anything that's counted in currency fits neatly into their equations. But what do they get for that money? How can that be measured? What's our potential? Will there be a glut of people like us in the next few years? A shortage? Planners want answers. To carry out these calculations, they have to turn us into something that, like financial instruments, can be measured over time. Picture an average worker in an industry that's chugging along at its usual pace. At the risk of seeming cold-hearted, let's give that imaginary laborer a rank based on his current value. Call him a C. If the industry heats up and more such workers are needed, his value rises, maybe to C+ or even B. If he picks up more skills or starts working harder, the same thing happens. His stock rises. But if the industry plunges into recession and companies shut down operations, our worker finds himself in a surplus market. His stock plummets, down to a D or even an F. We're all too familiar with this dynamic. Workers find jobs in boom times and get laid off in slumps. But often the process has little to do with a worker's value. In some companies, the last workers hired are the first to get the boot. That rewards longevity, not value. Sometimes it's the friendly workers who survive, or even workers who have a knee-breaking cousin in the mob. These are metrics a caveman could grasp. The Numerati have a different plan altogether. But how will they calculate our worth? How will they turn us into quantifiable financial instruments?
The first step is to break us down into little pieces. These are the characteristics we share with others, the bits of us that can be squeezed into columns and assigned numbers. Computers, after all, aren't yet capable of appreciating us as the integrated and complex beasts that Leo Tolstoy wrote about. You might have the nicest smile on earth and wonderful rapport with colleagues. Maybe you're mean, or smell like onions. There's no room, at least in the early versions of IBM's employee database, for those personal details. Some of them may be crucial. They may represent the real you. But the database understands us largely as a mosaic of résumé items, from job categories to mastery of the computer language C++ to fluency in Mandarin.
It's pathetically shallow. Consider what happens when you sit down in a room to, say, hammer out a new marketing campaign with five colleagues. This is life in the analog world. Your brain, by far the most sophisticated computing device known in the universe, processes an astonishing range of data. It perceives a wrinkled nose, a sideways glance, a hint of sarcasm, a flash of disdain. It ties together smells and sounds, and it links them with other memories and lessons from the past. Add up all the words and looks and gestures, and your brain picks up thousands, or even millions, of signals emanating from those five people. In his book Strangers to Ourselves, Timothy Wilson of the University of Virginia notes that as data streams in from our five senses, the brain grapples with more than 11 million disparate pieces of information per second. Today's computers cannot handle such complexity. IBM's mathematical system may scan each of us for a mere five or ten data points. I've had dogs that dig deeper into human nature. However, once we're represented as bits of math, the machine can do something superhuman. It can mix and match us in a fraction of a second with a million, or 100 million, others. That scale promises new efficiencies — and insights.
Imagine what IBM's bean counters will be able to do once all of the company's workers are classified by their skills. They'll start running ever more detailed numbers on workers — just as they do on other investments. They'll attempt to calculate the financial return for each job category and each skill, whether it's Java programmers or office managers. They'll compare productivity in ever-greater detail, worker by worker and region by region. This will help them decide which jobs to send offshore. And they'll be able to measure productivity based on dozens of yardsticks. How productive are workers in your category as they reach ages 45, 50, and 60? Once the company has those numbers, they might be able to calculate not just the present value of workers but also what they'll be worth down the road.
(Continues…)
Excerpted from "The Numerati"
by .
Copyright © 2008 Stephen Baker.
Excerpted by permission of Houghton Mifflin Harcourt Publishing Company.
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