on 14-Oct-2016 (Fri)

For if the Socratic dialectic is reassuring, in the sense understood by Foucault, it is so only in that it has already expulsed, excluded, objectified or (curi- ously amounting to the same thing) assimilated and mastered as one of its moments, “enveloped” the contrary of reason; and also only in that it has tranquilized and reassured itself into a pre-Cartesian certainty, a sophrosyne, a wisdom, a reasonable good sense and prudence

n fact, however, it could be demonstrated that as Foucault intends it, if not as intended by the historical current he is studying, the concept of madness overlaps everything that can be put under the rubric of negativity

Але подальше дослідження Ісусового пророцтва допомогло зрозуміти, що частина цього пророцтва про останні дні має два сповнення (Матв. 24:4—22). Вперше це пророцтво виконалося в I столітті в Юдеї, а за нашого часу воно має сповнитися у всесвітньому масштабі.

Тому що, згідно з біблійним пророцтвом, велике лихо почнеться не з війни між народами, а з нападу на фальшиву релігію. Отже, події, які стали розгортатися в 1914 році, були не початком великого лиха, а «початком нестерпних мук» (Матв. 24:8). Ці «нестерпні муки» є аналогією до того, що відбувалося в Єрусалимі та Юдеї з 33 по 66 рік н. е.

Recruiting is a core competency for any company. It should never be outsourced. You need people who are not just skilled on paper but who will work together cohesively after they’re hired. The first four or five might be attracted by large equity stakes or high-profile responsibilities. More important than those obvious offerings is your answer to this question: Why should the 20th employee join your company? Talented people don’t need to work for you; they have plenty of options. You should ask yourself a more pointed version of the question: Why would someone join your company as its 20th engineer when she could go work at Google for more money and more prestige? Here are some bad answers: “Your stock options will be worth more here than elsewhere.” “You’ll get to work with the smartest people in the world.” “You can help solve the world’s most challenging problems.” What’s wrong with valuable stock, smart people, or pressing problems? Nothing—but every company makes these same claims, so they won’t help you stand out. General and undifferentiated pitches don’t say anything about why a recruit should join your company instead of many others.

Talented people don’t need to work for you; they have plenty of options. You should ask yourself a more pointed version of the question: Why would someone join your company as its 20th engineer when she could go work at Google for more money and more prestige? Here are some bad answers: “Your stock options will be worth more here than elsewhere.” “You’ll get to work with the smartest people in the world.” “You can help solve the world’s most challenging problems.” What’s wrong with valuable stock, smart people, or pressing problems? Nothing—but every company makes these same claims, so they won’t help you stand out. General and undifferentiated pitches don’t say anything about why a recruit should join your company instead of many others.

The only good answers are specific to your company, so you won’t find them in this book. But there are two general kinds of good answers: answers about your mission and answers about your team. You’ll attract the employees you need if you can explain why your mission is compelling: not why it’s important in general, but why you’re doing something important that no one else is going to get done. That’s the only thing that can make its importance unique. At PayPal, if you were excited by the idea of creating a new digital currency to replace the U.S. dollar, we wanted to talk to you; if not, you weren’t the right fit.

However, even a great mission is not enough. The kind of recruit who would be most engaged as an employee will also wonder: “Are these the kind of people I want to work with?” You should be able to explain why your company is a unique match for him personally. And if you can’t do that, he’s probably not the right match. Above all, don’t fight the perk war. Anybody who would be more powerfully swayed by free laundry pickup or pet day care would be a bad addition to your team. Just cover the basics like health insurance and then promise what no others can: the opportunity to do irreplaceable work on a unique problem alongside great people. You probably can’t be the Google of 2014 in terms of compensation or perks, but you can be like the Google of 1999 if you already have good answers about your mission and team.

From the outside, everyone in your company should be dif erent in the same way. Unlike people on the East Coast, who all wear the same skinny jeans or pinstripe suits depending on their industry, young people in Mountain View and Palo Alto go to work wearing T-shirts. It’s a cliché that tech workers don’t care about what they wear, but if you look closely at those T-shirts, you’ll see the logos of the wearers’ companies—and tech workers care about those very much. What makes a startup employee instantly distinguishable to outsiders is the branded T-shirt or hoodie that makes him look the same as his co-workers. The startup uniform encapsulates a simple but essential principle: everyone at your company should be different in the same way—a tribe of like-minded people fiercely devoted to the company’s mission. Max Levchin, my co-founder at PayPal, says that startups should make their early staff as personally similar as possible. Startups have limited resources and small teams. They must work quickly and efficiently in order to survive, and that’s easier to do when everyone shares an understanding of the world. The early PayPal team worked well together because we were all the same kind of nerd. We all loved science fiction: Cryptonomicon was required reading, and we preferred the capitalist Star Wars to the communist Star Trek. Most important, we were all obsessed with creating a digital currency that would be controlled by individuals instead of governments. For the company to work, it didn’t matter what people looked like or which country they came from, but we needed every new hire to be equally obsessed.

On the inside, every individual should be sharply distinguished by her work. When assigning responsibilities to employees in a startup, you could start by treating it as a simple optimization problem to efficiently match talents with tasks. But even if you could somehow get this perfectly right, any given solution would quickly break down. Partly that’s because startups have to move fast, so individual roles can’t remain static for long. But it’s also because job assignments aren’t just about the relationships between workers and tasks; they’re also about relationships between employees. The best thing I did as a manager at PayPal was to make every person in the company responsible for doing just one thing. Every employee’s one thing was unique, and everyone knew I would evaluate him only on that one thing. I had started doing this just to simplify the task of managing people. But then I noticed a deeper result: defining roles reduced conflict. Most fights inside a company happen when colleagues compete for the same responsibilities. Startups face an especially high risk of this since job roles are fluid at the early stages. Eliminating competition makes it easier for everyone to build the kinds of long-term relationships that transcend mere professionalism. More than that, internal peace is what enables a startup to survive at all. When a startup fails, we often imagine it succumbing to predatory rivals in a competitive ecosystem. But every company is also its own ecosystem, and factional strife makes it vulnerable to outside threats. Internal conflict is like an autoimmune disease: the technical cause of death may be pneumonia, but the real cause remains hidden from plain view.

In the most intense kind of organization, members hang out only with other members. They ignore their families and abandon the outside world. In exchange, they experience strong feelings of belonging, and maybe get access to esoteric “truths” denied to ordinary people. We have a word for such organizations: cults. Cultures of total dedication look crazy from the outside, partly because the most notorious cults were homicidal: Jim Jones and Charles Manson did not make good exits. But entrepreneurs should take cultures of extreme dedication seriously. Is a lukewarm attitude to one’s work a sign of mental health? Is a merely professional attitude the only sane approach? The extreme opposite of a cult is a consulting firm like Accenture: not only does it lack a distinctive mission of its own, but individual consultants are regularly dropping in and out of companies to which they have no long-term connection whatsoever. Every company culture can be plotted on a linear spectrum: The best startups might be considered slightly less extreme kinds of cults. The biggest difference is that cults tend to be fanatically wrong about something important. People at a successful startup are fanatically right about something those outside it have missed. You’re not going to learn those kinds of secrets from consultants, and you don’t need to worry if your company doesn’t make sense to conventional professionals. Better to be called a cult—or even a mafia.

EVEN THOUGH SALES is everywhere, most people underrate its importance. Silicon Valley underrates it more than most. The geek classic The Hitchhiker’s Guide to the Galaxy even explains the founding of our planet as a reaction against salesmen. When an imminent catastrophe requires the evacuation of humanity’s original home, the population escapes on three giant ships. The thinkers, leaders, and achievers take the A Ship; the salespeople and consultants get the B Ship; and the workers and artisans take the C Ship. The B Ship leaves first, and all its passengers rejoice vainly. But the salespeople don’t realize they are caught in a ruse: the A Ship and C Ship people had always thought that the B Ship people were useless, so they conspired to get rid of them. And it was the B Ship that landed on Earth. Distribution may not matter in fictional worlds, but it matters in ours. We underestimate the importance of distribution—a catchall term for everything it takes to sell a product—because we share the same bias the A Ship and C Ship people had: salespeople and other “middlemen” supposedly get in the way, and distribution should flow magically from the creation of a good product. The Field of Dreams conceit is especially popular in Silicon Valley, where engineers are biased toward building cool stuff rather than selling it. But customers will not come just because you build it. You have to make that happen, and it’s harder than it looks.

The U.S. advertising industry collects annual revenues of $150 billion and employs more than 600,000 people. At $450 billion annually, the U.S. sales industry is even bigger. When they hear that 3.2 million Americans work in sales, seasoned executives will suspect the number is low, but engineers may sigh in bewilderment. What could that many salespeople possibly be doing? In Silicon Valley, nerds are skeptical of advertising, marketing, and sales because they seem superficial and irrational. But advertising matters because it works. It works on nerds, and it works on you. You may think that you’re an exception; that your preferences are authentic, and advertising only works on other people. It’s easy to resist the most obvious sales pitches, so we entertain a false confidence in our own independence of mind. But advertising doesn’t exist to make you buy a product right away; it exists to embed subtle impressions that will drive sales later. Anyone who can’t acknowledge its likely effect on himself is doubly deceived. Nerds are used to transparency. They add value by becoming expert at a technical skill like computer programming. In engineering disciplines, a solution either works or it fails. You can evaluate someone else’s work with relative ease, as surface appearances don’t matter much. Sales is the opposite: an orchestrated campaign to change surface appearances without changing the underlying reality. This strikes engineers as trivial if not fundamentally dishonest. They know their own jobs are hard, so when they look at salespeople laughing on the phone with a customer or going to two-hour lunches, they suspect that no real work is being done. If anything, people overestimate the relative difficulty of science and engineering, because the challenges of those fields are obvious. What nerds miss is that it takes hard work to make sales look easy

All salesmen are actors: their priority is persuasion, not sincerity. That’s why the word “salesman” can be a slur and the used car dealer is our archetype of shadiness. But we only react negatively to awkward, obvious salesmen—that is, the bad ones. There’s a wide range of sales ability: there are many gradations between novices, experts, and masters. There are even sales grandmasters. If you don’t know any grandmasters, it’s not because you haven’t encountered them, but rather because their art is hidden in plain sight. Tom Sawyer managed to persuade his neighborhood friends to whitewash the fence for him—a masterful move. But convincing them to actually pay him for the privilege of doing his chores was the move of a grandmaster, and his friends were none the wiser. Not much has changed since Twain wrote in 1876. Like acting, sales works best when hidden. This explains why almost everyone whose job involves distribution—whether they’re in sales, marketing, or advertising—has a job title that has nothing to do with those things. People who sell advertising are called “account executives.” People who sell customers work in “business development.” People who sell companies are “investment bankers.” And people who sell themselves are called “politicians.” There’s a reason for these redescriptions: none of us wants to be reminded when we’re being sold. Whatever the career, sales ability distinguishes superstars from also-rans. On Wall Street, a new hire starts as an “analyst” wielding technical expertise, but his goal is to become a dealmaker. A lawyer prides himself on professional credentials, but law firms are led by the rainmakers who bring in big clients. Even university professors, who claim authority from scholarly achievement, are envious of the self-promoters who define their fields. Academic ideas about history or English don’t just sell themselves on their intellectual merits. Even the agenda of fundamental physics and the future path of cancer research are results of persuasion. The most fundamental reason that even businesspeople underestimate the importance of sales is the systematic effort to hide it at every level of every field in a world secretly driven by it. The engineer’s grail is a product great enough that “it sells itself.” But anyone who would actually say this about a real product must be lying: either he’s delusional (lying to himself) or he’s selling something (and thereby contradicting himself). The polar opposite business cliché warns that “the best product doesn’t always win.” Economists attribute this to “path dependence”: specific historical circumstances independent of objective quality can determine which products enjoy widespread adoption. That’s true, but it doesn’t mean the operating systems we use today and the keyboard layouts on which we type were imposed by mere chance. It’s better to think of distribution as something essential to the design of your product. If you’ve invented something new but you haven’t invented an effective way to sell it, you have a bad business—no matter how good the product.

Superior sales and distribution by itself can create a monopoly, even with no product differentiation. The converse is not true. No matter how strong your product—even if it easily fits into already established habits and anybody who tries it likes it immediately—you must still support it with a strong distribution plan. Two metrics set the limits for effective distribution. The total net profit that you earn on average over the course of your relationship with a customer (Customer Lifetime Value, or CLV) must exceed the amount you spend on average to acquire a new customer (Customer Acquisition Cost, or CAC). In general, the higher the price of your product, the more you have to spend to make a sale—and the more it makes sense to spend it. Distribution methods can be plotted on a continuum

If your average sale is seven figures or more, every detail of every deal requires close personal attention. It might take months to develop the right relationships. You might make a sale only once every year or two. Then you’ll usually have to follow up during installation and service the product long after the deal is done. It’s hard to do, but this kind of “complex sales” is the only way to sell some of the most valuable products. SpaceX shows that it can be done. Within just a few years of launching his rocket startup, Elon Musk persuaded NASA to sign billion-dollar contracts to replace the decommissioned space shuttle with a newly designed vessel from SpaceX. Politics matters in big deals just as much as technological ingenuity, so this wasn’t easy. SpaceX employs more than 3,000 people, mostly in California. The traditional U.S. aerospace industry employs more than 500,000 people, spread throughout all 50 states. Unsurprisingly, members of Congress don’t want to give up federal funds going to their home districts. But since complex sales requires making just a few deals each year, a sales grandmaster like Elon Musk can use that time to focus on the most crucial people—and even to overcome political inertia.

Complex sales works best when you don’t have “salesmen” at all. Palantir, the data analytics company I co-founded with my law school classmate Alex Karp, doesn’t employ anyone separately tasked with selling its product. Instead, Alex, who is Palantir’s CEO, spends 25 days a month on the road, meeting with clients and potential clients. Our deal sizes range from $1 million to $100 million. At that price point, buyers want to talk to the CEO, not the VP of Sales. Businesses with complex sales models succeed if they achieve 50% to 100% year-over-year growth over the course of a decade. This will seem slow to any entrepreneur dreaming of viral growth. You might expect revenue to increase 10x as soon as customers learn about an obviously superior product, but that almost never happens. Good enterprise sales strategy starts small, as it must: a new customer might agree to become your biggest customer, but they’ll rarely be comfortable signing a deal completely out of scale with what you’ve sold before. Once you have a pool of reference customers who are successfully using your product, then you can begin the long and methodical work of hustling toward ever bigger deals.

Most sales are not particularly complex: average deal sizes might range between $10,000 and $100,000.

The challenge here isn’t about how to make any particular sale, but how to establish a process by which a sales team of modest size can move the product to a wide audience.

Box had a good way for companies to store their data safely and accessibly in the cloud. But no one knew they needed that.

Starting with small groups of users who had the most acute file sharing problems, Box’s sales reps built relationships with more and more users in each client company.

They sold a small Box account to the Stanford Sleep Clinic, where researchers needed an easy, secure way to store experimental data logs. Today the university offers a Stanford-branded Box account to every one of its students and Stanford Hospital runs on Box.

If it had started off by trying to sell the president of the university on an enterprise-wide solution, Box would have sold nothing.

In between personal sales (salespeople obviously required) and traditional advertising (no salespeople required) there is a dead zone. Suppose you create a software service that helps convenience store owners track their inventory and manage ordering. For a product priced around $1,000, there might be no good distribution channel to reach the small businesses that might buy it. Even if you have a clear value proposition, how do you get people to hear it? Advertising would either be too broad (there’s no TV channel that only convenience store owners watch) or too inefficient (on its own, an ad in Convenience Store News probably won’t convince any owner to part with $1,000 a year). The product needs a personal sales effort, but at that price point, you simply don’t have the resources to send an actual person to talk to every prospective customer. This is why so many small and medium-sized businesses don’t use tools that bigger firms take for granted. It’s not that small business proprietors are unusually backward or that good tools don’t exist: distribution is the hidden bottleneck.

Marketing and advertising work for relatively low-priced products that have mass appeal but lack any method of viral distribution. Procter & Gamble can’t afford to pay salespeople to go door-todoor selling laundry detergent. (P&G does employ salespeople to talk to grocery chains and large retail outlets, since one detergent sale made to these buyers might mean 100,000 one-gallon bottles.) To reach its end user, a packaged goods company has to produce television commercials, print coupons in newspapers, and design its product boxes to attract attention. Advertising can work for startups, too, but only when your customer acquisition costs and customer lifetime value make every other distribution channel uneconomical. Consider e-commerce startup Warby Parker, which designs and sells fashionable prescription eyeglasses online instead of contracting sales out to retail eyewear distributors. Each pair starts at around $100, so assuming the average customer buys a few pairs in her lifetime, the company’s CLV is a few hundred dollars. That’s too little to justify personal attention on every transaction, but at the other extreme, hundreddollar physical products don’t exactly go viral. By running advertisements and creating quirky TV commercials, Warby is able to get its better, less expensive offerings in front of millions of eyeglasswearing customers. The company states plainly on its website that “TV is a great big megaphone,” and when you can only afford to spend dozens of dollars acquiring a new customer, you need the biggest megaphone you can find. Every entrepreneur envies a recognizable ad campaign, but startups should resist the temptation to compete with bigger companies in the endless contest to put on the most memorable TV spots or the most elaborate PR stunts. I know this from experience. At PayPal we hired James Doohan, who played Scotty on Star Trek, to be our official spokesman. When we released our first software for the PalmPilot, we invited journalists to an event where they could hear James recite this immortal line: “I’ve been beaming people up my whole career, but this is the first time I’ve ever been able to beam money!” It flopped—the few who actually came to cover the event weren’t impressed. We were all nerds, so we had thought Scotty the Chief Engineer could speak with more authority than, say, Captain Kirk. (Just like a salesman, Kirk was always showboating out in some exotic locale and leaving it up to the engineers to bail him out of his own mistakes.) We were wrong: when Priceline.com cast William Shatner (the actor who played Kirk) in a famous series of TV spots, it worked for them. But by then Priceline was a major player. No early-stage startup can match big companies’ advertising budgets. Captain Kirk truly is in a league of his own.

A product is viral if its core functionality encourages users to invite their friends to become users too. This is how Facebook and PayPal both grew quickly: every time someone shares with a friend or makes a payment, they naturally invite more and more people into the network. This isn’t just cheap— it’s fast, too. If every new user leads to more than one additional user, you can achieve a chain reaction of exponential growth. The ideal viral loop should be as quick and frictionless as possible. Funny YouTube videos or internet memes get millions of views very quickly because they have extremely short cycle times: people see the kitten, feel warm inside, and forward it to their friends in a matter of seconds. A t PayPal, our initial user base was 24 people, all of whom worked at PayPal. Acquiring customers through banner advertising proved too expensive. However, by directly paying people to sign up and then paying them more to refer friends, we achieved extraordinary growth. This strategy cost us $20 per customer, but it also led to 7% daily growth, which meant that our user base nearly doubled every 10 days. After four or five months, we had hundreds of thousands of users and a viable opportunity to build a great company by servicing money transfers for small fees that ended up greatly exceeding our customer acquisition cost. Whoever is first to dominate the most important segment of a market with viral potential will be the last mover in the whole market. At PayPal we didn’t want to acquire more users at random; we wanted to get the most valuable users first. The most obvious market segment in email-based payments was the millions of emigrants still using Western Union to wire money to their families back home. Our product made that effortless, but the transactions were too infrequent. We needed a smaller niche market segment with a higher velocity of money—a segment we found in eBay “PowerSellers,” the professional vendors who sold goods online through eBay’s auction marketplace. There were 20,000 of them. Most had multiple auctions ending each day, and they bought almost as much as they sold, which meant a constant stream of payments. And because eBay’s own solution to the payment problem was terrible, these merchants were extremely enthusiastic early adopters. Once PayPal dominated this segment and became the payments platform for eBay, there was no catching up—on eBay or anywhere else.

One of these methods is likely to be far more powerful than every other for any given business: distribution follows a power law of its own. This is counterintuitive for most entrepreneurs, who assume that more is more. But the kitchen sink approach—employ a few salespeople, place some magazine ads, and try to add some kind of viral functionality to the product as an afterthought— doesn’t work. Most businesses get zero distribution channels to work: poor sales rather than bad product is the most common cause of failure. If you can get just one distribution channel to work, you have a great business. If you try for several but don’t nail one, you’re finished.

Most businesses get zero distribution channels to work: poor sales rather than bad product is the most common cause of failure. If you can get just one distribution channel to work, you have a great business. If you try for several but don’t nail one, you’re finished.

Your company needs to sell more than its product. You must also sell your company to employees and investors. There is a “human resources” version of the lie that great products sell themselves: “This company is so good that people will be clamoring to join it.” And there’s a fundraising version too: “This company is so great that investors will be banging down our door to invest.” Clamor and frenzy are very real, but they rarely happen without calculated recruiting and pitching beneath the surface. Selling your company to the media is a necessary part of selling it to everyone else. Nerds who instinctively mistrust the media often make the mistake of trying to ignore it. But just as you can never expect people to buy a superior product merely on its obvious merits without any distribution strategy, you should never assume that people will admire your company without a public relations strategy. Even if your particular product doesn’t need media exposure to acquire customers because you have a viral distribution strategy, the press can help attract investors and employees. Any prospective employee worth hiring will do his own diligence; what he finds or doesn’t find when he googles you will be critical to the success of your company

Nerds might wish that distribution could be ignored and salesmen banished to another planet. All of us want to believe that we make up our own minds, that sales doesn’t work on us. But it’s not true. Everybody has a product to sell—no matter whether you’re an employee, a founder, or an investor. It’s true even if your company consists of just you and your computer. Look around. If you don’t see any salespeople, you’re the salesperson.

AS MATURE INDUSTRIES stagnate, information technology has advanced so rapidly that it has now become synonymous with “technology” itself. Today, more than 1.5 billion people enjoy instant access to the world’s knowledge using pocket-sized devices. Every one of today’s smartphones has thousands of times more processing power than the computers that guided astronauts to the moon. And if Moore’s law continues apace, tomorrow’s computers will be even more powerful. Computers already have enough power to outperform people in activities we used to think of as distinctively human. In 1997, IBM’s Deep Blue defeated world chess champion Garry Kasparov. Jeopardy!’s best-ever contestant, Ken Jennings, succumbed to IBM’s Watson in 2011. And Google’s self-driving cars are already on California roads today. Dale Earnhardt Jr. needn’t feel threatened by them, but the Guardian worries (on behalf of the millions of chauffeurs and cabbies in the world) that self-driving cars “could drive the next wave of unemployment.” Everyone expects computers to do more in the future—so much more that some wonder: 30 years from now, will there be anything left for people to do? “Software is eating the world,” venture capitalist Marc Andreessen has announced with a tone of inevitability. VC Andy Kessler sounds almost gleeful when he explains that the best way to create productivity is “to get rid of people.” Forbes captured a more anxious attitude when it asked readers: Will a machine replace you? Futurists can seem like they hope the answer is yes. Luddites are so worried about being replaced that they would rather we stop building new technology altogether. Neither side questions the premise that better computers will necessarily replace human workers. But that premise is wrong: computers are complements for humans, not substitutes. The most valuable businesses of coming decades will be built by entrepreneurs who seek to empower people rather than try to make them obsolete

Fifteen years ago, American workers were worried about competition from cheaper Mexican substitutes. And that made sense, because humans really can substitute for each other. Today people think they can hear Ross Perot’s “giant sucking sound” once more, but they trace it back to server farms somewhere in Texas instead of cut-rate factories in Tijuana. Americans fear technology in the near future because they see it as a replay of the globalization of the near past. But the situations are very different: people compete for jobs and for resources; computers compete for neither. Globalization Means Substitution When Perot warned about foreign competition, both George H. W. Bush and Bill Clinton preached the gospel of free trade: since every person has a relative strength at some particular job, in theory the economy maximizes wealth when people specialize according to their advantages and then trade with each other. In practice, it’s not unambiguously clear how well free trade has worked, for many workers at least. Gains from trade are greatest when there’s a big discrepancy in comparative advantage, but the global supply of workers willing to do repetitive tasks for an extremely small wage is extremely large. People don’t just compete to supply labor; they also demand the same resources. While American consumers have benefited from access to cheap toys and textiles from China, they’ve had to pay higher prices for the gasoline newly desired by millions of Chinese motorists. Whether people eat shark fins in Shanghai or fish tacos in San Diego, they all need food and they all need shelter. And desire doesn’t stop at subsistence—people will demand ever more as globalization continues. Now that millions of Chinese peasants can finally enjoy a secure supply of basic calories, they want more of them to come from pork instead of just grain. The convergence of desire is even more obvious at the top: all oligarchs have the same taste in Cristal, from Petersburg to Pyongyang.

Technology Means Complementarity Now think about the prospect of competition from computers instead of competition from human workers. On the supply side, computers are far more different from people than any two people are different from each other: men and machines are good at fundamentally different things. People have intentionality—we form plans and make decisions in complicated situations. We’re less good at making sense of enormous amounts of data. Computers are exactly the opposite: they excel at efficient data processing, but they struggle to make basic judgments that would be simple for any human. To understand the scale of this variance, consider another of Google’s computer-for-human substitution projects. In 2012, one of their supercomputers made headlines when, after scanning 10 million thumbnails of YouTube videos, it learned to identify a cat with 75% accuracy. That seems impressive—until you remember that an average four-year-old can do it flawlessly. When a cheap laptop beats the smartest mathematicians at some tasks but even a supercomputer with 16,000 CPUs can’t beat a child at others, you can tell that humans and computers are not just more or less powerful than each other—they’re categorically different.

The stark differences between man and machine mean that gains from working with computers are much higher than gains from trade with other people. We don’t trade with computers any more than we trade with livestock or lamps. And that’s the point: computers are tools, not rivals. The differences are even deeper on the demand side. Unlike people in industrializing countries, computers don’t yearn for more luxurious foods or beachfront villas in Cap Ferrat; all they require is a nominal amount of electricity, which they’re not even smart enough to want. When we design new computer technology to help solve problems, we get all the efficiency gains of a hyperspecialized trading partner without having to compete with it for resources. Properly understood, technology is the one way for us to escape competition in a globalizing world. As computers become more and more powerful, they won’t be substitutes for humans: they’ll be complements.

Complementarity between computers and humans isn’t just a macro-scale fact. It’s also the path to building a great business. I came to understand this from my experience at PayPal. In mid-2000, we had survived the dot-com crash and we were growing fast, but we faced one huge problem: we were losing upwards of $10 million to credit card fraud every month. Since we were processing hundreds or even thousands of transactions per minute, we couldn’t possibly review each one—no human quality control team could work that fast. So we did what any group of engineers would do: we tried to automate a solution. First, Max Levchin assembled an elite team of mathematicians to study the fraudulent transfers in detail. Then we took what we learned and wrote software to automatically identify and cancel bogus transactions in real time. But it quickly became clear that this approach wouldn’t work either: after an hour or two, the thieves would catch on and change their tactics. We were dealing with an adaptive enemy, and our software couldn’t adapt in response. The fraudsters’ adaptive evasions fooled our automatic detection algorithms, but we found that they didn’t fool our human analysts as easily. So Max and his engineers rewrote the software to take a hybrid approach: the computer would flag the most suspicious transactions on a well-designed user interface, and human operators would make the final judgment as to their legitimacy. Thanks to this hybrid system—we named it “Igor,” after the Russian fraudster who bragged that we’d never be able to stop him—we turned our first quarterly profit in the first quarter of 2002 (as opposed to a quarterly loss of $29.3 million one year before). The FBI asked us if we’d let them use Igor to help detect financial crime. And Max was able to boast, grandiosely but truthfully, that he was “the Sherlock Holmes of the Internet Underground.” This kind of man-machine symbiosis enabled PayPal to stay in business, which in turn enabled hundreds of thousands of small businesses to accept the payments they needed to thrive on the internet. None of it would have been possible without the man-machine solution—even though most people would never see it or even hear about it. I continued to think about this after we sold PayPal in 2002: if humans and computers together could achieve dramatically better results than either could attain alone, what other valuable businesses could be built on this core principle? The next year, I pitched Alex Karp, an old Stanford classmate, and Stephen Cohen, a software engineer, on a new startup idea: we would use the humancomputer hybrid approach from PayPal’s security system to identify terrorist networks and financial fraud. We already knew the FBI was interested, and in 2004 we founded Palantir, a software company that helps people extract insight from divergent sources of information. The company is on track to book sales of $1 billion in 2014, and Forbes has called Palantir’s software the “killer app” for its rumored role in helping the government locate Osama bin Laden. We have no details to share from that operation, but we can say that neither human intelligence by itself nor computers alone will be able to make us safe. America’s two biggest spy agencies take opposite approaches: The Central Intelligence Agency is run by spies who privilege humans. The National Security Agency is run by generals who prioritize computers. CIA analysts have to wade through so much noise that it’s very difficult to identify the most serious threats. NSA computers can process huge quantities of data, but machines alone cannot authoritatively determine whether someone is plotting a terrorist act. Palantir aims to transcend these opposing biases: its software analyzes the data the government feeds it—phone records of radical clerics in Yemen or bank accounts linked to terror cell activity, for instance—and flags suspicious activities for a trained analyst to review. In addition to helping find terrorists, analysts using Palantir’s software have been able to predict where insurgents plant IEDs ...

Why do so many people miss the power of complementarity? It starts in school. Software engineers tend to work on projects that replace human efforts because that’s what they’re trained to do. Academics make their reputations through specialized research; their primary goal is to publish papers, and publication means respecting the limits of a particular discipline. For computer scientists, that means reducing human capabilities into specialized tasks that computers can be trained to conquer one by one. Just look at the trendiest fields in computer science today. The very term “machine learning” evokes imagery of replacement, and its boosters seem to believe that computers can be taught to perform almost any task, so long as we feed them enough training data. Any user of Netflix or Amazon has experienced the results of machine learning firsthand: both companies use algorithms to recommend products based on your viewing and purchase history. Feed them more data and the recommendations get ever better. Google Translate works the same way, providing rough but serviceable translations into any of the 80 languages it supports—not because the software understands human language, but because it has extracted patterns through statistical analysis of a huge corpus of text. The other buzzword that epitomizes a bias toward substitution is “big data.” Today’s companies have an insatiable appetite for data, mistakenly believing that more data always creates more value. But big data is usually dumb data. Computers can find patterns that elude humans, but they don’t know how to compare patterns from different sources or how to interpret complex behaviors. Actionable insights can only come from a human analyst (or the kind of generalized artificial intelligence that exists only in science fiction). We have let ourselves become enchanted by big data only because we exoticize technology. We’re impressed with small feats accomplished by computers alone, but we ignore big achievements from complementarity because the human contribution makes them less uncanny. Watson, Deep Blue, and ever-better machine learning algorithms are cool. But the most valuable companies in the future won’t ask what problems can be solved with computers alone. Instead, they’ll ask: how can computers help humans solve hard problems?

The future of computing is necessarily full of unknowns. It’s become conventional to see ever-smarter anthropomorphized robot intelligences like Siri and Watson as harbingers of things to come; once computers can answer all our questions, perhaps they’ll ask why they should remain subservient to us at all. The logical endpoint to this substitutionist thinking is called “strong AI”: computers that eclipse humans on every important dimension. Of course, the Luddites are terrified by the possibility. It even makes the futurists a little uneasy; it’s not clear whether strong AI would save humanity or doom it. Technology is supposed to increase our mastery over nature and reduce the role of chance in our lives; building smarter-than-human computers could actually bring chance back with a vengeance. Strong AI is like a cosmic lottery ticket: if we win, we get utopia; if we lose, Skynet substitutes us out of existence. But even if strong AI is a real possibility rather than an imponderable mystery, it won’t happen anytime soon: replacement by computers is a worry for the 22nd century. Indefinite fears about the far future shouldn’t stop us from making definite plans today. Luddites claim that we shouldn’t build the computers that might replace people someday; crazed futurists argue that we should. These two positions are mutually exclusive but they are not exhaustive: there is room in between for sane people to build a vastly better world in the decades ahead. As we find new ways to use computers, they won’t just get better at the kinds of things people already do; they’ll help us to do what was previously unimaginable.