My friends in publishing were skeptical when I told them where I was going to work. They had a lot of questions I felt uneasy answering. Wouldn’t a subscription model undercut author royalties? Wasn’t it basically a cynical, capitalist appropriation of the public library system? Wasn’t an app like this parasitic at best? Was it all that different from the online superstore, and wouldn’t the app’s success come at the expense of the literary culture and community? I didn’t have a good response to most of these concerns. Mostly, I tried not to think about them. Smug and self-congratulatory, I translated most of my friends’ questions to mean, simply, What about us?

The CEO did not acknowledge that the reason millennials might be interested in experiences—like the experience of renting things they could never own—was related to student loan debt, or the recession, or the plummeting market value of cultural products in an age of digital distribution. There were no crises in this vision of the future. There were only opportunities.

“She’s too interested in learning, not doing,” the CEO typed once into the company chat room. This was an accident—he meant it only for the other two cofounders. We huddled in the conference room and he apologized sincerely, while I looped the words over and over in my head. I had always been interested in learning, and I had always been rewarded for it; learning was what I did best. I wasn’t used to having the sort of professional license and latitude that the founders were given. I lacked their confidence, their entitlement. I did not know about startup maxims to experiment and “own” things. I had never heard the common tech incantation Ask forgiveness, not permission.

In an effort to hype myself up, I developed the theory, however flimsy, that analytics was a natural extension of my liberal arts education. The e-book startup had used the analytics software to track our alpha users through the app, and I had enjoyed looking at some of the data: what our investors were reading, and abandoning; whether or not people read public-domain books with cover art designed by the CPO, which we had added to bolster the library. In a certain light, I tried to convince myself, business analytics could be seen as a form of applied sociology.

Several months prior, a tech blog had published an article announcing the analytics startup’s first major fund-raising round of ten million dollars. When asked how he would spend the new funding, the CEO made his priorities clear: he would pay the first hundred employees far above market, he said, and spoil current employees to retain them. This was the language of customer acquisition, but I didn’t know. I didn’t think at all about the stratification, either; how the hundred-and-first employee might feel. I’d never worked anywhere with a hundred people—I’d never worked anywhere with twenty. I’d certainly never worked anywhere that wanted to spoil its employees and had the means to do it. Generous, I thought. I found the tenacity winning.

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.

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.

“This company is going to be worth a gajillion dollars,” the account manager said, taking a bite of potato salad. “We’re ripping up and to the right. We have the best and the brightest. We’re on an immutable path toward success. We’re all just fucking ready to give whatever needs to be given to make this thing happen. All anyone is asking is for us to pour our hearts and souls into this unstoppable behemoth.” He drained his iced coffee. “Frankly,” he said, “I think it’s a pretty good bargain.”

Our team was penned off in a corner of the office, at a cluster of tables marked with a sign that read SOLUTIONS ZONE. I stood in the Zone and felt powerful. Because the products of our labor were intangible, meeting customers felt amazing—validating. They approached, gave us their company names, and asked for help running reports. We never asked for their corporate ID cards or any sort of validation, and none of them ever questioned why it was so easy for us to pull up their data sets. Their companies had customer-support teams, too.

Even so, the enemy of a successful startup was complacency. To combat this, the CEO liked to instill fear. He was not a formidable physical presence—he had gelled, spiky hair; he was slight; he often wore a green jacket indoors, presumably to fight the chill—but he could scare the hell out of us. He spoke in military terms. “We are at war,” he would say, standing in front of us with his arms crossed and his jaw tensed. Across the world, Syria and Iraq and Israel raged. We were at war with competitors, for market share. We would look down at our bottles of kombucha or orange juice and nod along gravely.