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+title: Love in the Time of MapReduce
+date: 02/04/2012
+tags: notable
+
+> This piece was written for an internal Google fiction contest, for the 100th
+edition of the engineering newsletter. The call to arms arrived in my inbox like
+so:
+
+> > For this special Eng Newsletter issue, we're running a "google eng-y" short
+fiction contest. You can write about anything, but the story must begin with
+these two words: "The MapReduce".
+
+~
+
+> Please note that some meaning may be lost on non-Googler's, notably the bits
+concerning company hierarchy. All the opinions expressed are my own and
+obviously do not constitute the workings of an actual Google plan, etc. Jeff Dean
+is a very nice man. This is a piece of fiction in almost every sense.
+
+-------------------------------------------------------------------------------
+
+The MapReduce was a piece of technology whose existence its steward, Jeff Dean,
+sometimes begrudged. It was glamorous, in a way, to be the public face of the
+algorithm that had essentially rewritten interpersonal contact, but it was also
+draining and surreal.
+
+In one of Jeff's increasingly common attacks of perspective, he realized that
+his daughters, too, had been completely swept up by a thing that he himself had
+designed, built, and evangelized. They were, of course, perfectly happy with the
+product. Jeff noted this with a tinge of grim pride, remembering the long nights
+of trial runs. Victoria and Natalie were a bit too happy, Jeff mused, so
+completely satisfied with something they could never understand (indeed, that he
+himself no longer understood well), that he found their lack of doubt troubling.
+Why didn’t they care that it probably shouldn’t work, that time and computation
+could twist statistics in such a fundamentally disturbing way? It was probably
+due to both of them being so preoccupied with Natalie’s wedding, he concluded
+wearily.
+
+Later, outside of his office, Irina was waiting for him.
+
+“Jeff, you have a visitor waiting for you in your office,” she said. Something
+in her tone gave away the urgency of the situation, and Jeff nodded, having long
+grown used to trusting Irina to manage his calendar more deftly than he could
+tie his shoes.
+
+His suited visitor was a trim man of about sixty, which was unusual enough for
+the Googleplex in terms of both age and dress. He wore his graying hair swept
+back and neatly cropped. With a start, Jeff realized that his visitor was none
+other than a senator of Iowa.
+
+“I’m Robert Graves, and sorry about showing up so unannounced, Mr. Dean,” said
+the man, with a smile. Jeff paused for a moment to admire how finely
+countenanced the man was, and to feel a small thrill at being so delightfully
+underdressed, himself.
+
+“You’re the senator pushing for patent reform. I don’t watch TV much, but I’ve
+seen you on when my wife watches the news.” Jeff shook his visitor’s hand and
+seated himself behind his desk.
+
+“The very same. Look, I’ll spare you the pleasantries and get right to why I’ve
+come. I’m told that engineers prize truth and directness.” Jeff lifted an
+eyebrow at this, having found that lately he valued being left well enough alone
+better than both of those things. “As you well know, MapReduce is proving
+problematic, socially. FOX is filming a reality TV show at this very moment
+about an engaged couple who are convinced that after trying out their MapReduce
+partners, they’ll still want to get married.”
+
+“Jesus. How’s it looking for the couple?”
+
+“Not good. Even worse, they’re filming it in my hometown.” Graves massaged his
+temples.
+
+Jeff was not surprised. MapReduce rarely erred. Though it had begun as a general
+purpose framework for parallelizing search index updates, it eventually lent
+itself to analyzing the massive amounts of user generated social data Google+
+collected. In time, this would become all that MapReduce was known for (at least
+externally of Google), in a queer reversal of how the words escalator and
+aspirin came to describe all such contrivances, though they were once only
+brands.
+
+“Basically the right is getting as much fuel as it wants for its eternal fire of
+shouting about our perpetual moral decay. On top of that, MapReduce is having a
+powerful economic impact, which doesn’t help. We’re having an employment
+problem, as you’ve doubtless inferred by now, since you must have all the
+numbers on how many people are using MapReduce to pair up.”
+
+The first Jeff had heard about the phenomenon the media had dubbed as the
+“honeymoon effect,” had been from the news itself, but he nodded anyway. “My
+citizens are up and leaving jobs they’ve worked at for a decade to meet their
+dreamboat on the other side of the world. I mean, great for them, but our
+coffers weren’t in great shape before, and your invention is a drain we can’t
+possibly afford right now, never mind the bad press. As much as I am for the
+future, I desperately need you to stop operating in my state.”
+
+Jeff suppressed the urge to tell the man to just contact press@google.com, and
+instead reluctantly launched into a narrative he had delivered many times
+before. “I’m sure you’ve seen and read all the press releases about this. What
+we do isn’t terribly new. We provide a service that users want. In a sense, we
+provide nothing more than what eHarmony and Match.com have been providing for
+years, just with much less uncertainty and a bigger candidate pool.”
+
+Robert snorted. “I’d hardly call ‘every Google user on Earth’ a bigger pool.
+Your operation is different, too. You know all the things that people have
+searched for, and all the things they’re too ashamed to search for. You know why
+some actresses draw men to them, and which men women will wait hours to receive
+texts from. Those bankrupt dating sites had only the constructed personas of the
+desperate to work with. There’s a case that could be made here for unlawful
+invasion of privacy and monopolistic abuse of information.”
+
+A Googler rode past Jeff’s window on a small yellow bicycle. Jeff focused on the
+bright colors to briefly escape his current uncomfortable tension.
+
+If Graves was right about anything, it was that MapReduce was uncannily
+effective. Through what some people might call sorcery, or what Jeff's team
+leads described as “massively parallel Bayesian-adapted machine learning plus
+deep social mining,” it was able to identify, with nearly 97% confidence, a
+lifetime romantic partner for any given user. The algorithm could even supply
+just the right amount of shared interests as conversation starters, while
+leaving enough unsaid for the nascent couple to discover independently, leaving
+them feeling as if they had come to know each other intimately of their own
+volition. Some people found this deeply unnerving.
+
+Even those people commonly derided by society could find love in this way,
+though MapReduce might take weeks instead of seconds to produce a suitable
+pairing. People of every sexual deviancy and every personal vice were being
+matched up, to the horror of the many people alienated by the brutal efficiency
+of MapReduce’s perfect lack of bias.
+
+In short, romantic fulfillment was, for most people, little more work than
+clicking “I’m feeling lucky” and buying a plane ticket. This is what the people
+wanted more than anything else. Graves knew it, and Jeff knew that Graves knew
+it. Furthermore, Jeff knew that Graves was powerless to do anything about it, so
+strongly did the public crave MapReduce’s presence in the world. Yet Jeff felt
+sympathy for Grave’s willingness to shoulder the impossible task of squaring the
+budget against falling revenues and changing social tides.
+
+“Mr. Graves, I understand your dilemma. The last thing you need right now is the
+income rug pulled out from under you. But look at it this way: about half of
+those people who have gone and paired off will probably come back to their
+hometown, bride or husband in tow, so your population will probably end up about
+even. After these couples outgrow their honeymoon period, they’ll settle down,
+work, have kids, and spend with an intensity that only the truly content can
+bring to bear. In the coming decade, your books might even make it into the
+black.”
+
+Robert was not easily placated. “Can you say for certain that this is the way
+its going to play out? The world has never seen this kind of mass social
+movement. What if the people become complacent instead of motivated? What if
+your algorithms can’t guarantee long term stability?”
+
+Jeff had an inner conflict. As usual, the side favoring the least amount of
+social friction won out. “We’re the ones who managed to pair everyone up so well
+in the first place, aren’t we? The models say the population will eventually
+converge on a higher level of stable productivity. I can’t promise you it’s
+going to happen, of course, but here at Google we have pretty high hopes for the
+future.”
+
+The two men talked in this way for some time. The elder statesman pushed and the
+younger (but not exactly young) engineer deflected until the senator grew weary
+or satisfied enough to defer discussion to a later date. Jeff had managed to end
+the meeting with only vague promises, a surprising talent that had earned him
+his relative autonomy from Larry Page’s inner circle. Later he would have to
+file a report, naturally, detailing the intricacies of his conversation with the
+senator, but for now Page trusted him to keep third parties at arm’s length on
+his own.
+
+Later that evening, after a quiet supper with Heidi, Jeff lay in bed thinking.
+The models actually didn’t say much about the economic reality of the future.
+The social data that allowed his team to pair people so effectively seemed to
+shrug mutely at the problem of what the future might be like. He had assured
+Graves that everything would be fine, but by the time Jeff could be proved
+wrong, he would be long retired.
+
+Sleep took him. He dreamt, which was not unusual (though he didn’t know it), but
+he also remembered his dreams from that night, which was. He dreamt of a young
+man smashing a perfect chalice in a decrepit hallway, and of women who laughed
+while they danced away from their homes.
+
+When he woke, Jeff knew what he had to do.
+
+Sanjay probably could be trusted, but Jeff couldn’t take the chance. He would
+split his change into pieces, and sneak them into other, tangentially related
+changelists. The other developers on his team would probably rubber stamp these,
+anyway, since Jeff was one of the most prolific programmers there was. Who would
+look at yet another Jeff Dean code review too closely?
+
+What did it mean to adhere to Google’s famed “Don’t be Evil” policy, when it
+came to arranging marriages? The standard Google answer would be to make the
+user as happy as possible without violating their trust. But what trust was
+there to violate if users themselves didn’t know what they wanted in
+relationships, or what would truly make them happy? Marriages are long lived
+beasts, Jeff reasoned, subject to slowly building changes in the macroeconomic
+climate. If marriages affect the economy, and the economy affects all marriages,
+what should you optimize for, and how?
+
+Jeff’s changelists were approved, as a matter of course. Years later, he
+retired. The day he first started noticing what might have been the fruits of
+his subversion ripen, he remembered a thing that his old mentor Urs loved to
+say, before Urs had left him in charge.
+
+“It is better to ask forgiveness than permission,” Hölzle would often chuckle,
+in a particularly German way. Jeff chuckled now, too.
+
+Wired was doing a bio piece on a recently minted tech millionaire. The man was
+one of the few people for whom MapReduce’s pairing hadn’t worked out in the long
+term. When asked what had motivated him to start the company he had just sold,
+the man somewhat abashedly said that he wanted to prove to his ex-wife that
+dumping him was a mistake.
+
+Ambition and talent sometimes survive contact with love, Jeff mused, but are
+more often dulled by it. MapReduce could identify those individuals who are
+defined by intelligence, drive, and pride. In other words, the archetypal
+entrepreneur. A few modified terms in a complex linear algebra equation could
+yield surprising results, Jeff had discovered, like optimizing for romantic
+partners that would net the largest increase in a person’s ambition, rather than
+happiness. A lot of the unfortunate people of talent singled out by Jeff’s
+modification would probably yield little value, but one, he hoped, would build
+the next Google. Jeff longed to see that day.
+
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