Uncanny Valley

When I arrived at the analytics startup’s headquarters, I was surprised to find that the whole enterprise was peanut-sized. The office, however, was huge—at least seven thousand square feet, with a polished-concrete floor, and mostly unfurnished. About fifteen employees were clustered together at the far end of the room, all gazing deeply into monitors. Some stood at elevated desks, legs spread sturdily, feet cushioned by little rubber mats. Every work space had its own colorful clutter: pots of succulents and other dying plants, anime figurines and stacks of books, bottles of nice liquor. On one was perched an obelisk of empty cans, husks of the same high-caffeine energy drink. The open-plan setup made it look like a classroom. No one there looked over the age of thirty.

I stood in the doorway and counted the women. There were three. They wore jeans and sneakers, oversized cardigans over T-shirts. I had dressed carefully, in a blue shift, heeled boots, and a thin blazer. It was what I always wore to interviews, and I thought it signaled professionalism and seriousness. In publishing, an ensemble like this was nice, but still dowdy enough to be nonthreatening. Inside the startup, I felt like a narc. I shrugged off the blazer as discreetly as possible and stuffed it into my tote bag.

The first interview was with the manager of the Solutions team, the customer-facing division. He was jolly and hirsute, wearing faded jeans and a company T-shirt that declared I AM DATA DRIVEN. I resisted asking if it needed a hyphen. He sat down in one of the ergonomic desk chairs, leaned back, and bounced lightly, like a baby. Through the glass door of the conference room, on which was taped a handwritten sign designating it THE PENTAGON, I watched a wiry man in a plaid shirt wiggle past on a RipStik, waving one arm for balance and shouting enthusiastically into a golden telephone receiver.

The solutions manager placed his elbows on the table and leaned toward me, explaining that we would be going through a series of questions together so I could demonstrate how I problem-solved. “So,” he said, as if he were asking me to let him in on a secret, “how would you calculate the number of people who work for the United States Postal Service?” We sat in silence for a moment. I wouldn’t, I thought; I would look it up on the internet. I wondered if perhaps this was actually a test of my tolerance for bullshit and inefficiency—if the sassy response could also be the right one. I had no idea what the solutions manager wanted. Then he handed me a marker and pointed toward the whiteboard. “Why don’t you get up on the board and show me how you’d work it out?” he asked. It wasn’t a suggestion.

In the four hours that followed, the solutions manager and the wiry man I had earlier seen cruising past, a sales engineer, walked me through a series of questions and puzzles. The sales engineer seemed to be about my age, and had an unaffected drawl and manic, infectious energy. His speech was littered with folksy aphorisms. “Butter my biscuits,” he said, when I complimented his oversized belt buckle. “Now we’re cooking with gas,” he said, teaching me how to reverse a string on the whiteboard.

Both the sales engineer and the solutions manager exclusively referred to the data-analytics software as “the tool.” Both of them asked questions that were self-conscious and infuriating. “What’s the hardest thing you’ve ever done?” asked the solutions manager, twisting his wedding ring around and around. “How would you explain the tool to your grandmother?”

“How would you describe the internet to a medieval farmer?” asked the sales engineer, opening and closing the pearl snaps on his shirt, sticking his hand thoughtfully down the back of his own waistband.

Because interviewing at the e-book startup had been breezy and comfortable, I expected the same from the analytics company. No one had warned me that in San Francisco and Silicon Valley interviewing was effectively punitive, more like a hazing ritual than an airtight vetting system. A search-engine giant down in Mountain View was famous for its interview brainteasers, and while it had already denounced the practice, finding it useless as an indicator of future job performance, others insisted on enshrining it as tradition: learning from another company’s mistakes took on a new meaning when those mistakes had proved lucrative. Across the Bay Area, applicants were routinely asked questions like “How many square feet of pizza are eaten in the United States every year?” and “How many Ping-Pong balls fit in an airplane?” Some of them, meant to determine whether an interviewee was a culture fit, were dredged in middle-school kitsch. “If you were a superhero, what would your superhero strength be?” asked straight-faced human resources professionals. “When you walk into a room, what’s your theme song?” That afternoon, mine was a dirge.

After a few hours, the technical cofounder entered the conference room, looking confidently unprepared. He noted, with an apology, that he hadn’t done many interviews before and didn’t have any questions to ask. Still, he said, the office manager had slated an hour for our conversation.

This seemed okay: I figured we would talk about the company, I would ask routine follow-up questions, and they would finally let me out, like an elementary-school student, and the city would absorb me and my humiliation. Then the technical cofounder told me that his girlfriend was applying to law school, and he’d been helping her prep. Instead of a conventional interview, he said, he was just going to have me take a section of the LSAT. I searched his baby face to see if he was kidding.

“If it’s cool with you, I’m just going to hang out here and check my email,” he said, sliding the test across the table and opening a laptop. He set a timer on his phone.

I finished early, ever the overachiever. I checked the test twice. I joked that it was the closest I’d ever come to applying to law school—my mother would be so proud. The technical cofounder shot me a thin smile, slipped the papers under his computer, and left the room.

I sat in his wake, wondering what I was waiting for. There was not a doubt in my mind that I would not get the job. Not only had I surely demonstrated that I was unemployable, but I felt certain I’d been a vivid caricature of the dotty, linty liberal arts major—the antithesis of all that the tech industry stood for.

Still, though the interviews had been inane, they only served to fuel me. Here was a character flaw on parade, my industry origin story: I had always responded well to negging.

I would wonder for years if the analytics startup offered me the job because the entire interview process had revealed a degree of obedience desirable in a customer support representative, and in an employee—if they knew I would ultimately be a pushover, loyal and easily controlled. Eventually, I learned that it was actually just because I had managed a perfect score on the LSAT section they had administered. This knowledge would make me feel simultaneously cocky and displaced, of superior intelligence and crushing foolishness. A part of me had hoped they’d seen something latent and unique, some potential. I was always overthinking things.

The offer included medical and dental coverage, a four-thousand-dollar relocation stipend, and a starting salary of sixty-five thousand dollars a year. The manager informed me that the salary was above market and nonnegotiable. I couldn’t fathom being someone who made that much money, much less someone who would try to negotiate for more. With my skill set, or lack thereof, I couldn’t believe anyone wanted to pay me that much to do anything.

The solutions manager did not mention equity, and I didn’t ask. I did not know that early access to equity was a reason people joined private companies at the startup stage—that it was the only way anyone other than VCs and founders got rich. I did not even know that equity was an option. The company’s in-house recruiter would eventually intervene, to recommend that I negotiate to include even a small stake. His rationale was simple: all the other guys had some. No one told me how much it was worth, or how big the pool was, and I did not know to ask.

High on the feeling of being professionally desirable, I told the solutions manager I would sleep on it.

The analytics startup gave me three weeks. Back in Brooklyn, I invited friends over as I packed up the apartment. One evening, after a few drinks, a close friend asked whether I was sure I was making the right decision. I’d enjoyed working in publishing, she reminded me, idly popping bubble wrap between her fingers. Was it perhaps premature to throw in the towel? She promised not to judge if I decided, last minute, not to go. “Mobile analytics,” she said, trying on a pair of vintage spectator pumps that I had purchased in the midst of an identity crisis. “What is that? Do you care about it? And customer support—aren’t you worried it’ll be soul ruining?”

I was worried about a lot of things: loneliness, failure, earthquakes. But I wasn’t too worried about my soul. There had always been two sides to my personality. One side was sensible and organized, good at math; appreciative of order, achievement, authority, rules. The other side did everything it could to undermine the first. I behaved as if the first side dominated, but it did not. I wished it did: practicality, I thought, was a safe hedge against failure. It seemed like an easier way to move through the world.

Still, I had trouble admitting to my social group that I was moving across the country solely to work at a startup. It was embarrassing to articulate how excited I was to see what the fuss was about—it seemed, among my countercultural and creative friends, shrewd and cynical to be curious about business. I was selling out. In reality, I was not paying attention: those who understood our cultural moment saw that selling out—corporate positions, partnerships, sponsors—would become our generation’s premier aspiration, the best way to get paid.

At the time, though, it was corny to be openly enthusiastic about technology or the internet. For the most part, my friends were late and reluctant adopters. They had accounts on the social network everyone hated, but only used them to RSVP to poetry readings and DIY shows they had no intention of attending. Some defiantly carried flip phones without internet access, preferring to call those of us with desk jobs whenever they were out and needed directions. No one owned an e-reader. As the tides turned digital, my milieu was grounding itself firmly in the embodied, tangible world.

Out of self-protection, I stuck to the narrative that I was moving across the country just to try something new. I had never even lived outside of the tristate area. San Francisco had a great music scene, I said, unconvincingly, to anyone who would listen. It had medical marijuana. Working in analytics would be an experiment in separating my professional life from my personal interests. The startup gig was just a day job, I claimed, something to support me while I was otherwise creatively productive. Maybe I would start the short-story collection I had always wanted to write. Maybe I would take up pottery. I could finally learn bass.

It was easier, in any case, to fabricate a romantic narrative than admit that I was ambitious—that I wanted my life to pick up momentum, go faster.

When I arrived back in San Francisco, with a fresh haircut and two fraying duffel bags, I felt intrepid and pioneering. I did not know that thousands of people had already headed west for a crack at the new American dream, that they had been doing so for years. I was, by many standards, late.

It was a moment of corporate obsequiousness to young men. Tech companies were importing freshly graduated computer science majors from all over the world, putting them up in furnished apartments, paying their cable and internet and cell phone bills, and offering hundred-thousand-dollar signing bonuses as tokens of thanks. The programmers arrived with a flood of nontechnical carpetbaggers: former Ph.D. students and middle-school teachers, public defenders and chamber music singers, financial analysts and assembly-line operators, me.

I had booked another bedroom using the home-sharing platform, this time in the South of Market neighborhood, several blocks from the office. The room was on the garden level of a duplex, adjacent to a concrete patio and accessed through an alley, just past the recycling bins. It was decorated with the same lightweight, self-assembly furniture as my friends’ bedrooms back in Brooklyn. The woman who operated the rental was an entrepreneur in the renewable-energy space and described herself as never home.

A few small boxes of my books, bedding, and clothing were already at the analytics startup, stacked in a supply closet. I had been self-conscious about spending down the relocation stipend, wanting to save money for the company. A part of me worried that if I spent too much, the offer would be rescinded. I didn’t want my new manager to think I was frivolous. Others had expensed new furniture, meals, weeks of rent, but I didn’t know that. I was still operating according to publishing austerity.

The home-sharing platform offered an aspirational fantasy that I appreciated. Across the world, people were squeezing out the last of strangers’ toothpaste, picking up strangers’ soap in the shower, wiping their noses on strangers’ pillowcases. There I was as I had always been, only sleeping in a stranger’s bed, fumbling to replace a stranger’s spring-loaded toilet-paper holder, ordering sweaters off a stranger’s Wi-Fi network. I liked examining someone else’s product selections, judging their clutter. I wasn’t thinking about how the home-sharing platform might also be driving up rents, displacing residents, or undermining the very authenticity that it purported to sell. Mostly, the fact that it functioned, and nobody had murdered me, seemed like a miracle.

I had given myself a few days to get adjusted before starting the job. In the mornings, I bought coffee at a laundromat, consulted a crowdsourced reviewing app to find something to eat, and returned to my bedroom to spend the rest of the day reading technical documentation for the analytics software and panicking. The documentation was indecipherable to me. I didn’t know what an API was, or how to use one. I didn’t know how I would possibly provide technical support to engineers—I couldn’t even fake it.

The night before my first day of work, too unmoored and overwhelmed to sleep, I scrolled through previous guests’ reviews of my room and realized that the apartment was owned by one of the founders of the home-sharing platform. I looked up the founder’s name and read an interview in which he detailed how designers could follow in his footsteps and become entrepreneurs. He called them “designpreneurs.” I watched a video of him delivering the keynote at a tech conference, breathing excitedly into the mic. I learned that he and his two cofounders had raised over a hundred million dollars, and investors were desperate to give them more.

I looked around me at the blank walls, the closet door tilted on its hinges, the bars on the window, eager to identify hints of his success. But the designpreneur hadn’t slept in the room for years. He had moved into a gleaming, art-filled warehouse conversion close to his office. He’d left nothing behind.

The analytics startup made a pickax-during-the-Gold-Rush product, the kind venture capitalists loved to get behind. History saw the Gold Rush as a cautionary tale, but in Silicon Valley, people used its metaphors proudly, provided they were on the right side of things. Pickaxes were usually business-to-business products. Infrastructure, not services. Just as startups in New York were eager to build off their city’s existing cultural legacy, by creating services for media and finance—or, more commonly, creating sleek interfaces to sell things that would require more time, money, energy, or taste to buy elsewhere—the same was true of the Bay Area, where software engineers sought to usurp older technology companies by building tools for other software engineers.

It was the era of big data, complex data sets facilitated by exponentially faster computer processing power and stored, fashionably, in the cloud. Big data encompassed industries: science, medicine, farming, education, policing, surveillance. The right findings could be golden, inspiring new products or revealing user psychology, or engendering ingenious, hypertargeted advertising campaigns.

Not everyone knew what they needed from big data, but everyone knew that they needed it. Just the prospect incited lust in product managers, advertising executives, and stock-market speculators. Data collection and retention were unregulated. Investors salivated over predictive analytics, the lucrative potential of steroidal pattern-matching, and the prospect of bringing machine-learning algorithms to the masses—or, at least, to Fortune 500 companies. Transparency for the masses wasn’t ideal: better that the masses not see what companies in the data space had on them.

The analytics startup wasn’t disrupting anything so much as unseating big-data incumbents: slow-moving corporate behemoths whose products were technically unsophisticated and bore distinctly nineties user interfaces. The startup not only enabled other companies to collect customized data on their users’ behavior without having to write much code or pay for storage, but it also offered ways to analyze that data in colorful, dynamic dashboards. The cofounders had prioritized aesthetics and hired two graphic designers off the bat: men with signature hairstyles and large followings on a social network for people who referred to themselves as creatives and got excited about things like font sizing and hero images. In general, it was hard to say what, exactly, the designers did all day, but the dashboards were both friendly and elegant. The software looked especially pleasing, trustworthy, airtight. Good interface design was like magic, or religion: it cultivated the mass suspension of disbelief.

I had no qualms about disrupting extant corporations in the big-data space, no inherited nostalgia or fondness for business. I liked the underdog. I liked the idea of working for two kids younger than I was, who had dropped out of college and were upending the script for success. It was thrilling, in that sense, to see a couple of twentysomethings go up against middle-aged leaders of industry. It looked like they could win.

I was employee number twenty, and the fourth woman. Prior to my arrival, the Solutions team—four men, including the manager—had handled customer tickets themselves, attacking the support queue in shifts at the end of the workday, relaying the responsibility to avoid consecutive office-bound midnights. This strategy was effective for a while, but the user base was ballooning. The men couldn’t sustain the practice; they had their own jobs to do. They rearranged their desktop belongings and cleared a space for me.

The men on the Solutions team weren’t like the men from the e-book startup. They were weirder, wilder, funnier, harder to keep up with. They wore Australian work boots and flannel and durable, recycled polyester athletic vests, drank energy shots in the late afternoon and popped vitamin D in the mornings to stay focused and alert. They chewed powdered Swedish tobacco, packing it juicily behind their gums. Deep house and EDM leaked from their oversized headphones. At team gatherings they drank whiskey, neat, and, the following mornings, were prone to pounding a viscous liquid jacked up with electrolytes—sold as a remedy for small children with diarrhea—to flush away their hangovers. They had gone to top-tier private colleges and were fluent in the jargon of media studies and literary theory. They reminded me of my friends who had left San Francisco, but more adaptable and opportunistic, happier.

The solutions manager assigned me an onboarding buddy, Noah, a curly-haired twenty-six-year-old with a forearm tattoo in Sanskrit and a wardrobe of workman’s jackets and soft fleeces. Noah was warm and loquacious, animated, handsome. He struck me as the kind of person who would invite women over to get stoned and look at art books and listen to Brian Eno, and then actually spend the night doing that. I had gone to college with men like this: men who would comfortably sit on the floor with their backs against the bed, men who self-identified as feminists and would never make the first move. I could immediately picture him making seitan stir-fry, suggesting a hike in the rain. Showing up in an emergency and thinking he knew exactly what to do. Noah spoke in absolutes and in the language of psychoanalysis, offering definitive narratives for everyone, everything. I had the uneasy feeling that he could persuade me to do anything: bike across America; join a cult.

Noah and I spent my first few weeks in various corners of the office, carting around an overflowing bowl of trail mix and a rolling whiteboard, on which he patiently diagrammed how cookie tracking worked, how data was sent server-side, how to send an HTTP request, how to prevent a race condition. He was patient and encouraging, and made direct eye contact as we pushed through problem sets of hypothetical customer questions, various scenarios in which the software—or, more realistically, the user—had a meltdown.

The product was actually deeply technical, though the company talked up its usability. The amount of information I needed to absorb, to be even marginally helpful to our customers, was intimidating. The learning curve looked unconquerable. Noah gave me homework and pep talks. He told me not to worry. Our teammates handed me beers in the late afternoon, and were confident and reassuring that I, too, would eventually scale up. I trusted them entirely.

I was happy; I was learning. For the first time in my professional life, I was not responsible for making anyone coffee. Instead, I was solving problems. My job involved surveying strangers’ codebases and telling them where they’d gone wrong in integrating our product with theirs, and how to fix it. The first time I looked at a block of code and understood what was happening, I felt like nothing less than a genius.

It did not take long for me to understand the fetish for big data. Data sets were mesmerizing: digital streams of human behavior, answers to questions I didn’t know I had. There was more every second. Our servers, and the company’s bank account, absorbed this unstoppable wave.

Our bread and butter was engagement: actions that demonstrated the ways users were interacting with a product. This was a turn away from the long-running industry standard, which prioritized metrics like page views and time on site, metrics that the CEO called bullshit. Engagement, he said, was distinguished from the bullshit because it was actionable. Engagement generated a feedback loop between the user and the company. User behavior could dictate product managers’ decisions. These insights would be fed back into an app or website, to dictate or predict subsequent user behavior.

The software was flexible, intended to function as easily for fitness trackers or payment processors as for photo-editing and ride-sharing apps. It could be integrated into online boutiques, digital megamalls, banks, social networks, streaming and gaming websites. It gathered data for platforms that enabled people to book flights or hotels or restaurant reservations or wedding venues; platforms for buying a house or finding a house cleaner, ordering takeout or arranging a date. Engineers and data scientists and product managers would inject snippets of our code into their own codebases, specify which behaviors they wanted to track, and begin collecting data immediately. Anything an app or website’s users did—tap a button, take a photograph, send a payment, swipe right, enter text—could be recorded in real time, stored, aggregated, and analyzed in those beautiful dashboards. Whenever I explained it to friends, I sounded like a podcast ad.

Depending on the metadata, users’ actions could be scrutinized down to the bone, at the most granular level imaginable. Data could be segmented by anything an app collected—age, gender, political affiliation, hair color, dietary restrictions, body weight, income bracket, favorite movies, education, kinks, proclivities—plus some IP-based defaults, like country, city, cell phone carrier, device type, and a unique device identification code. If women in Boise were using an exercise app primarily between the hours of nine and eleven in the morning—only once a month, mostly on Sunday, and for an average of twenty-nine minutes—the software could know. If people on a dating website were messaging everyone within walking distance who practiced yoga, trimmed their pubic hair, and were usually monogamous but looking for a threesome during a stint in New Orleans, the software could know that, too. All customers had to do was run a report; all they had to do was ask.