Con Ed

by Matthew Klein

At which point Ed Napier is quick to agree, although of course the choice was never his to make.

  So he follows her in his cherry-red Mercedes, over to Menlo Park. They shoot down Willow and trace the curve of the Bay to the Dumbarton Bridge. They park in the Pythia lot and get out of their cars. I watch them from between the slats of my office window shade. It’s only ten o’clock, but already eighty degrees, and the asphalt has started to bake. Napier squints into the sun.

  Jess points to the entrance.

  I hear them in the vestibule near my office. Jess is rattling her keys around, searching for the right one. Finally the door opens, and I hear Jess finishing her sentence, “. . . just moved in. About a week ago.”

  “Where were you before?” Napier asks.

  “Oh it was all very glamorous. Working out of our apartments.”

  “You entrepreneurs. I admire what you do.”

  “Let’s see who’s around,” Jess says. She calls out, “Franklin? Peter?”

  That’s my cue. I start toward the reception area. When I turn the corner, I lurch to a stop and pretend to be surprised to see Napier.

  “Jess,” I say, “what’s—”

  “Relax, Franklin. I ran into Mr. Napier at Buck’s.”

  Napier says, much too loudly and much too friendly, “Hi, Franklin. Good to see you again!”

  “Good to see you, too,” I say, though my tone suggests otherwise. “Jess, I thought we agreed—”

  “That we shouldn’t talk about Pythia. We did. But we can trust Ed. He’s offered to help.”

  “Oh,” I snip. “Well why didn’t you just say that?”

  Napier’s voice is as warm as an old sweater. “I’ve seen a lot of business plans, Franklin. I know what works, what doesn’t. Even if you don’t want my money, maybe I can help.” He shrugs. “And who knows? Maybe I’ll be interested. Wouldn’t a million dollars or two help you guys out? Bring you to the next level?”

  I pretend to think about it. Finally I say: “Give me your word.”

  “On what?”

  “That whatever you see here, stays here. You don’t tell a soul. Not your partners, not the press, not even your wife.”

  Jess says, “Franklin, you’re being rude.”

  Napier waves her off. “No, no, he’s not. He’s being cautious. I admire that. Okay, Franklin, I give you my word. Whatever you show me today will be our secret.”

  “Okay, then.” I turn and walk out of the room.

  Behind me, I hear Jess say, “I guess that means we should follow him.”

  The tour starts at the server room. I unlock the door and swing it open so Ed Napier can take a look.

  Peter Room is there, sitting on the floor, pecking at a keyboard, a can of Dr Pepper by his feet. My son, Toby, hanging off a pair of crutches, stands beside him, chatting. I’m keeping Toby on a short leash. He can hang out at Pythia, I’ve explained, observing and listening—part of his con education. But his assigned role is computer geek. Quiet computer geek. I have instructed him to keep his witty repartee and winsome personality to himself. So far, so good.

  I step back to allow Napier an unobstructed view of the room. The space, which was empty when we moved in, has been transformed. Metal racks, mounted with computers, fill every inch of wall space. Hundreds of orange patch cords hang down in hopeless tangles like cyborg hair. Dozens of router switches display rows of blinking green and yellow lights. The effect of the blinking lights—thousands of them—in ruler-straight lines, is psychedelic, trippy. Then there’s the noise, of a hundred computer fans—surprisingly loud, like rushing water.

  I say over the noise, “This is the brain of Pythia. All the software runs here, in this small room.” I look at Peter. “Ed Napier, this is Peter Room. He’s our lead programmer.”

  Peter scrambles to his feet. He steps over his Dr Pepper, shakes Napier’s hand. “How do you do?”

  I say, “Peter, can you give Mr. Napier a brief description of what’s going on here?”

  “Sure.” He gestures at the wall of computers. He raises his voice over the rush of the fans. “You’re looking at one hundred Xeon-class Pentium machines, running the Linux operating system. Each machine is rated at one gigahertz. That may not sound like a lot, but the computers are networked together. You can think of it like one big mainframe. The effective processing speed, for software that has been properly designed to operate in parallel, reaches one teraflop.”

  Napier nods sagely.

  I say to Peter, “Why don’t you put that in terms us normal people can understand, Peter?”

  “Right,” Peter says, “okay. Let’s put it this way. The computers in this room are able to perform one trillion floating point operations per second. Just to give you a sense of what that means, the National Weather Service recently bought a Cray supercomputer to help predict the path of hurricanes. That single Cray computer cost twenty-five million dollars. The machines in this room, all together, cost around two hundred fifty thousand dollars. But this room contains four times the computing power of the Cray.”

  “Fascinating,” Napier says.

  “Basically, this is the world’s most powerful pattern-matching software. We use genetic algorithms and neural networks to analyze massive amounts of data.”

  I say, “Thank you, Peter. Why don’t you go to the conference room to get a demo set up for Mr. Napier.”

  Peter nods and scampers down the hall, ahead of us, toward the large conference room. Napier, Jess, and I leave the server room, and slowly follow.

  I say, “The only reason we stopped at one hundred computers was lack of space. If we added another hundred computers, the processing power would increase by a factor of ten.”

  “And what do the computers do?” Napier says.

  “Come,” I say. “I’ll show you.”

  We make our way to the conference room. Jess powers up the video projector and presses a wall switch. A motorized white screen whirrs down from the ceiling. Peter is hunched over a keyboard on the conference table. Pecking away, he says, “I’ll be ready in a sec.”

  I offer Napier a seat facing the screen. I gesture to the light switch. “Jess, would you?”

  Jess cuts the lights. The only thing visible on the screen is a single blinking cursor.

  I walk to the head of the table, stand in front of the screen. The cursor blinks on my cheek. I say, “It’s taken us twelve months to develop the software that Pythia uses. The idea behind Pythia is to assemble hundreds of off-the-shelf computing components, and then to write software specially designed to take advantage of them. The software models real-world chaos by breaking down complexity into tiny, simple equations. Anything that seems complicated—that seemingly can’t be represented in a linear fashion—Pythia can model accurately. From simplicity comes complexity.”

  “I see,” Napier says. He glances at his watch. We’ve lost him. He’s thinking that it was exciting to drive over here in the summer heat as long as there was a chance to get some of Jess’s poon, but all this talk about modeling and software and chaos theory is not what he had in mind. He says, “I should tell you that I have an eleven o’clock meeting back in the office.”

  Jess says to me, “Franklin, I think you’re boring Mr. Napier. Excuse me—Ed.” She gives him a winning smile. He smiles in return. She continues, “Why don’t we put this in everyday terms? Let’s talk about how the software can be used.”

  Napier nods. “Thank God for marketing people.”

  She laughs. “There are hundreds of possible uses for Pythia,” she says. “Predicting the weather, for example. More accurately forecasting tornado zones. Even predicting earthquakes.”

  “I see,” Napier says. I can see him doing the P&L in his head. He’s thinking: The profit in predicting earthquakes is zero.

  She says, “Of course, predicting earthquakes doesn’t make for very compelling product demonstrations. So Peter helped us come up with this one.” She nods to Peter.

  Peter taps a key. The projection scre
en is filled with a graph of undulating green lines—a daily stock price chart.

  I say, “What stock is this, Peter?”

  Peter says, “Symbol HSV. Let’s see, that’s Home Services of America.”

  I say, “What does Home Services of America do, Peter?”

  “No friggin’ clue,” Peter says.

  “Okay, let’s go tick-by-tick.”

  Peter taps a key. The chart changes to a real-time price display. The final green dot representing the latest price is continuously moving: up, down, then up again, in tiny penny increments. The motion seems random.

  I say, “Okay, Peter, turn on Pythia.”

  He taps another key. “Done.”

  For a moment, nothing happens. Then, a red circle appears to the right of the screen, far from the last green tick for the stock. The circle is labeled with small text: “90% conf.”

  I say, “This is Pythia’s projection for the stock price in the next fifteen seconds.”

  The actual price of the stock stutters downward, away from the circle that was just drawn. I say, “Any second now . . .”

  The red circle grows darker. It changes to “93% conf.” Then: “95% conf.”

  I say, “Pythia is telling us that she is ninety-five percent confident stock HSV will reach price twenty-two and a nickel in the next ten seconds.”

  Now, like magic, the stock stops descending, and starts back upward.

  Pythia says, “98% conf.”

  The stock ticks upward again, toward the red circle that Pythia initially drew. Finally, the stock price climbs again, and the green dot reaches the center of the Pythia target circle.

  The red circle brightens. Text appears: “Target reached.”

  “There,” I say. “Pythia accurately predicted the price of HSV.”

  Napier is speechless. He stares at the screen, his jaw slack.

  I say, “Of course, Pythia wasn’t designed for financial services. It’s really meant to work on complex computing tasks—as I said: weather, volcanoes, fault lines. You can see that it might even be helpful in the biotech industry, analyzing drug molecules.”

  “Wait a second,” Napier says. He’s staring at the screen. “Did it just predict how that stock would move?”

  “Yes,” I say. “Well, fifteen seconds out. Any more than a minute or two, and it loses accuracy.”

  “Can you do it again?”

  “Sure,” I say, but I sound uncertain. “But that’s not really what the software—”

  “With any stock?” Napier says.

  “Yeah, sure. Name a stock.”

  “GM.”

  “Fine.” I turn to Peter. “GM, Peter. Know how to spell that?”

  He glares at me. “Got it,” he says. He types something, and then the first stock chart is replaced by a chart of GM. “Here’s the daily chart,” Peter says. He clicks a key. “And, here’s the tick-by-tick . . .”

  Again, the screen changes to a magnified tick-by-tick graph of the price of GM. The movement of GM is more frenetic than the previous stock. The price flits around the seventy-dollar mark. The green dot rises and falls, dancing epileptically as thousands of shares trade hands each second. The price stutter-steps around: first up, then down, then down again.

  “All right,” I say, “let’s run this through Pythia.”

  “Hang on,” Peter says. A few more keystrokes, and then a red circle appears on the far right of the screen, near the 70.25 price level. It is labeled: “92% conf.”

  We watch the screen as the price of General Motors fluctuates wildly: back down to 69.50, then up again. The confidence level grows with each passing second: 93% confident . . . 94% confident . . .

  Napier says, mostly to himself, “This is the damndest thing . . .”

  The price falls further away from Pythia’s red target circle. Regardless, the confidence level increases: 95% confident . . . 96% confident.

  “Looks like she’s wrong on this one,” Napier says.

  As if to bitch-slap him, the green dot reverses direction and starts shooting back up. It rockets past 69.90, then 70 dollars.

  Pythia’s confidence is now 97% . . . 98% . . .

  The price of GM continues its rise. It lands in the red target circle: at $70.25. The screen says: “Target reached.”

  “You’re shitting me,” Napier says.

  I nod to Jess. She turns on the lights. We look at Napier. He’s still staring at the screen.

  “Those are real-time prices?”

  “Yes,” I say. “You can check them yourself when you get back to your office. What time is it now?” We all look at our watches. “Okay, it’s twenty after ten. When you get back, look up at where GM was at this time. It’ll be seventy and a quarter.”

  “My God,” Napier says, quietly.

  “You like it?” Jess says.

  “Like it?” Napier stands up. “It’s amazing.” He turns to me. “How many people know about this? Besides me?”

  I pretend to do some counting in my head. “Let’s see. There’s me, Peter, and Jess. And Toby. Some of the computer guys.”

  “Have you talked to any other venture capitalists about this?”

  “No,” I say.

  “Good. Let’s keep it that way.”

  I say stupidly, “I don’t understand.”

  “I’m willing to invest in your company. Hell, buy it from you outright. Whatever.”

  Jess says softly, “You see, Franklin?”

  I ignore her. “But why?” I ask.

  “Don’t you see?” Napier says. He swivels in his chair to stare at me. “It’s going to make you rich.”

  “But,” I say, “we’re talking about tiny price moves. Ten cents here. A nickel there.”

  Napier says: “But multiply that by ten thousand shares! By a hundred thousand shares. And if you can do that a hundred times a day . . .”

  I say, “Is that legal?”

  “Of course,” Napier says. “Why wouldn’t it be?”

  I say, “It’s kind of cheating.”

  Napier says, “It’s like card counting in Vegas. You try it in my place, and we’ll kick you out. But it’s not against the law to try.”

  “I see,” I say. I act as if this is the first time I considered using Pythia to make money. I say, “But it wasn’t really designed for that.”

  Napier turns to Peter Room. “You wrote the software?”

  “Most of it. Me and some guys.”

  “Can you add something? Make it place trades at a broker, automatically?”

  Peter shrugs. “With the right kind of broker . . .”

  “Can you use mine?” Napier asks. “Schwab?”

  Peter shakes his head. “No, we have to use something with automation hooks. FIX protocol.”

  I say, “I have an account at Datek.”

  “That’ll work,” Peter says.

  Napier turns to Jess. His gentle, flirtatious demeanor has vanished. Now he’s sharp edges—ruthless again, all business—on the hunt for money. Give men a choice between cooch and cash, they’ll choose the latter every time. “Jessica,” he says, “you have to keep this under wraps. You can’t go around talking about this.”

  “All right.”

  Napier rises from his chair. “Gentlemen.” He turns to Jess. “Jessica.” He points to the screen. “I’m willing to bankroll you. We go in as equal partners. I’ll shoulder the risk. You use my cash. And you can keep half of whatever we win.”

  “Whatever we win?” I say, stupidly. “But that’s not what the software was designed for . . .”

  Napier ignores me. He takes a checkbook from his suit pocket. He leans over the table, writing with his gold pen. He tears the check from the book, hands it to me.

  It’s made payable to “Cash” in the amount of fifty-thousand dollars.

  He says to me, “Deposit that at your broker.” He turns to Peter: “On Wednesday, we’ll do a little experiment. Test it with real money.”

  Peter looks as if he might object, but
then he thinks better of it. Napier is in charge now. Which is exactly what we want him to think.

  CHAPTER TWENTY

  In the 1890s, enterprising telegraph operators traveled around the country ripping people off. A crook would find a mark, a rich businessman, and explain how—as a telegraph operator at Western Union—he received the results of all the horse races that were being run on any given afternoon, and how it was his job to forward those results immediately to gambling parlors, so that bets could be paid out.

  The telegraph operator would have a proposition for his mark. The telegraph operator—at great personal risk—could delay telegraphing race results to a particular gambling parlor for a few minutes—just long enough to allow the mark to place a bet with certain knowledge about who won the race. Then the two partners would split the earnings.

  The rip-off spread quickly, growing in complexity. At the beginning, there may have been real telegraph operators who actually delivered on their promises. But soon the con was overrun by common criminals, men with no connection to the telegraph business whatsoever. They would spin a yarn about how—with the right kind of equipment—it was possible to “intercept” telegraph messages, and how—with a few minutes’ foreknowledge of race results—fortunes could be made. All that was required, explained the con man to his mark, was a modest amount of cash, enough to purchase the necessary telegraph equipment used to intercept telegraph messages. If the mark would fund the venture, the con man would buy the equipment, and the two would get rich . . .

  Of course no equipment was actually purchased, and no race results were actually “intercepted.” Nevertheless, the con man confidently delivered supposed “inside information” by telephone to his mark, who was standing by at a gambling parlor. The excited mark then placed a bet. If the “inside information” turned out to be correct (a one-in-seven chance), then the con man would collect additional money: his share of the lucky winning bet. Otherwise, if the “can’t-lose” information turned out to lose, the con man would disappear, never to be heard from again, pocketing the cash that was intended to buy telegraph interception equipment.

  This rudimentary con, in turn, evolved into something more sophisticated, in which elaborate—but entirely fictional—betting parlors were built and staffed by shills, and in which completely imaginary races were run, all for the benefit of a single mark.