Evi -

Evi is a new iPhone and Android app which might just give Apple’s Siri a run for her money. She – we’ll call this Artificial Intelligence a she – returns amazing results when consulted. Evi might just be the Siri for the rest of the world, especially since Evi wil run on any Andoid or iPhone, and not just the 4S. I’ve seen her in action and Evi is very, very smart.

–

We stood alone. Not because we were all that was left. Not because our homes were gone, and with them our wives and our sons. We manned the city walls because she had asked. And bound by the debt of a billion favors over the years, we were honored to come to her aid in a time of need.

We readied ourselves, checked our ammunition. We planned and she whispered movements and tactics in our ears. During the siege, she spoke of courage. When they charged, an enormous mass of aluminum and black, Evi’s voice stayed steady, and true. To those locked in mortal combat, she offered encouragement. To the weak, she gave purpose. To those who lay broken, she softly told stories of events past, of friends, of mugs of crisp ale, and of family in the snow, until they heard no more.

When they ended her life, we too screamed in agony. We had asked for, and received, so much. She had asked only once, but even that we could not give her.

–
Part of my ongoing startup dystopia series. I try to publish one short story (<1,000 words) per week. Previous installments: Basis Science, Uber,  PlaceIQ , Flavo.rs, and Hard Drive.

Basis Science -

Basis tracks your heart rate, your movement, skin temperature and the ambient temperature, and your galvanic skin response (GSR, or how much you’re sweating). The sweat tells you how strenuous your activity is and how stressed out you are. All of the data gets uploaded to the cloud and Basis analyzes it. You wear the device 24 hours a day. The device knows whether you are asleep or awake.

–

I placed my belt in a Zappos-sponsored plastic bin. Onto yet another city. My life had recently turned into an exhausting mix of precisely measured 2.5oz bottles, TSA anal sniffing, and a series of seat backs in the upright and locked position.

I eyed the guy in front of me and popped another airborne, hoping the stuff worked. He had a tissue box cradled under his armpit, and was using and discarding one after the other in an almost overflowing grocery bag. I kneeled to untie my shoe. Something started to drip onto the ground in front of him. Blood. I jumped up and back. He turned and said something about a doctor. It was all over his hands, his shirt. I looked up. His nose, which I expected to be a mess, was fine. It was his eyes that were bursting.

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Uber:

Users of startup car service Uber got quite a shock from the company’s demand-based pricing on New Year’s Eve. The company implemented a New Year’s surcharge that stuck San Francisco-based Uber user Dan Darcy with a $63 bill for traveling 0.73 miles. (That’s a rate of $86.30 per mile.)

–

“Johnny, we have to get out of here.”

“I know honey, I know.”

Johnny continued to jam furiously on his phone. Red. He checked on Lainey. Less grime today. Mucked hair strung down both sides of her always surprisingly delicate face.

He tried to project confidence. He could make this work. They were going to be OK. But they really weren’t. Night was near, and with it came all the cold, violent, harsh things. Lainey was right. They had to get out of here.

The screen continued flashing multiples, but in red, signifying they were well above a level he could afford. The numbers changed every refresh, but always red.

In the outer affordability radiuses, there were hundreds of people waiting. But on their curving overpass, from which they could clearly see the tall, glass buildings, matte with years of unwashed filth, there were only a few other travelers. An older couple whose silence was one of loss, even as they peered together onto one screen, occasionally tapping the button and shivering. Next to them, a family of four had each member slamming their respective screens on a coordinated basis.

The stakes were high. If you did make it into the relative safety of New York City, the future was yours. You could sleep at night instead of standing guard. Days were productive. Sunshine washed shit away. Gold provided food.

But you had to get there.

Walking was the best way to get killed. If you could drive yourself, you would have already done it – assuming you could have made it through the bandit nests, and were able to pay your way through the tollbooths with the right mix of ID and money.

This left taking taxis. There weren’t many owner-operators left. Too complex and violent for that. Instead, a company called Uber had reinvented itself when the city gates had slammed shut. They standardized and took care of the entire NYC entry process for a hefty fee. Which, in the interest of fairness, was demand driven.

Of course, this meant it was only available to the rich, who found it a convenient means to commute from their enclaves. But every year, when the rich didn’t travel during the holidays, the rats came clambering out of the hives, frantically pushing their Uber button, fighting against dynamic skullfucking, hoping for a way out. It rarely worked out. But you had to try.

Johnny told himself, as he had done every preceding year, that if not this year, than next, he would get Lainey into New York City. He glanced at her burgeoning belly again. His will steeled. It had to be this year.

Screams interrupted his thoughts. He instinctively shielded her, placing himself between her and unseen danger. Ready for violence, again.

He relaxed when he saw it was one of the children. She had hit green. A car was on the way. Then it was there. Guns were leveled at the remaining travelers. Water bottles were handed to the lucky family. Excited murmuring. They trooped into the rusting black sedan. Then they were gone.

Lainey tried talking to the other couple, but it was too cold and windy to make much of it. Johnny continued working his phone. She crouched awkwardly and huddled against the side of the overpass, shielding her belly from the elements. Johnny curled up aside her as she fell asleep. Still, he hit the button. More hours trickled by. Red.

The sun disappeared over the landscape’s wreckage. Yellow gave way to violet to black. Johnny watched worriedly, and continued his battle against red. He absently brushed her hair. Red. Red. Red.

A sound. He saw shadowed creatures making their way up the overpass. No point in waking her or trying to run. Nowhere to go, especially in her condition. He felt the angry looking gun in his jacket pocket. Wished he had more ammunition. He turned to the older couple for help. No response. It looked like they didn’t have any protection anyway. Or, for that matter, life. He pulled the gun and aimed.

Suddenly, his phone flared in the darkness. Green. He stared incredulously. This was it. His life’s savings went one direction, and the map drew a little green marker coming closer and closer to him. He looked up again. His gun hand shook as dark shapes coalesced into seething humans.

Then the motor approached and guns fired above the violent mass. Lainey awoke with a start, and Johnny grabbed her arm, pushing her, ducking her, then him, into the back of the sedan. They collapsed into the seat as the car roared away.

Johnny turned. Watched the darkness encompass the forsaken couple. Gone. Lainey hugged him again, too tired and cold and hungry and scared to speak, strong enough to cry.

He faced forward. The lights, looming in the distance for so long, dead and calling, came alive as they drew nearer.

–
Part of my ongoing startup dystopia series. Read the previous installments, PlaceIQ here and Flavo.rs here.

PlaceIQ.

PlaceIQ sifts through tons of data about locations to give marketers a mini-zipcode-like profile of each block. The data comes from both open sources and commercial data sets, including place data, retail data, government data, event data, photo data, social data, and, crime data. This goes well beyond Facebook, Twitter, and Foursquare, but the company says it doesn’t use any personally identifiable information. Rather, it is making assumptions based on the contextual cues of a person’s location and time of day.

. . . It takes all of these various hyper-local data sources and maps it onto its 100 million map tiles. Then it normalizes the data and can guess what type of person is likely to be at that location at that time (a student, tourist, shopper, financial or tech worker, etc). It can also spit out information such as retail sales volume, events, foot traffic by time, and social media activity.

–

As published in the July 15, 2015 issue of Bloomberg Businessweek in the Etc. Hard Choices section.

Duncan McCall, CEO and Co-Founder of PlaceIQ recalls the challenges of reinventing the location intelligence business. 

When we first outlined the new vision for the business, the team was skeptical. The economy had just crashed and everyone wanted to keep doing what had been working instead of evolving. It’s a natural impulse, but one we had to resist. There were lots of late nights and loud arguments about that. I won in the end. The world was changing, and we had to keep up.

Transitioning was slow. Adapting a platform designed specifically not to isolate personal information, to do the exact opposite, was an arduous task. We had to spend our dwindling cash reserves. More importantly, it required a cultural shift. We had to reset moral expectations, sell a vision and guide people to be passionate about our future. I’m really proud of how my team did.

We tried the Department of Defense, but they were reeling from budget slashes. We worked with the intelligence agencies a bit, but they were playing it safe after what happened in Pakistan and Iran. We had some success with Homeland Security, but had a tough time cutting through all the red tape that the big four contractors had bought. This was a frustrating experience for us and morale was low, but we kept at it. One big contract is all we needed to prove the concept.

As it turns out, the strategy proved successful only when we shifted our focus from the federal government to the new wave of small scale security vendors. They immediately saw the value of our existing product and funded a pilot project. This was a big moment for the team. The future of the company suddenly crystalized, and everyone jumped in. It was like a whole new startup, though, to preserve some of our lineage, we decided to keep our motto, “Next-gen location intelligence.”

We got a lot done very quickly.  In 6 months, we had integrated the data from drone video and thermal feeds. The result was real-time, accurate, forecasting of when specific individuals would be where. In 9 months, we were able to loop our output back to the drones. We were effectively able to pinpoint, down to the block level, where someone would be at a certain time, and then deliver a payload – marketing collateral or weapons – within minutes. Our customers loved it. By the end of the year, we had doubled our revenues.

The kind of leadership my team showed was simply amazing. The entire staff came around, took ownership of the business, and now we’re just crushing it. We have now deployed in 83 communities around the country, with another 2 dozen in the works. We’re working on a suite of companion products to attract new customers while making our existing base even happier, and we’re expanding internationally over the next year into thriving markets, like Somalia and Mexico.

–

Part of my ongoing startup dystopia series. Read the previous installment, Flavo.rs here.