“I…don’t have a job.” The thought was colorless in Dinesh’s head. The news had struck him like a concussion. “Thirteen years as a financial analyst and some AI can do my job better than I can.”
The office was nearly empty at this point. The announcement had cleared the entire floor, dozens of analysts suddenly lost in Midtown. The only voices came from the big screen on the wall, seemingly contractually locked on CNBC. A blond talking head on the screen was rambling wild-eyed about “the hyperinflation of work.”
“If you’re doing the same job next week as you are today,” she said, "someone like me is going to start thinking, ‘I bet I can build an AI to do that cheaper, faster, and better. The skills that bought your job security yesterday won’t be enough to keep it by tomorrow.”
“We can just retrain displaced workers,” opined another talking head beside her that seemed to be all mustache.
“The rate at which technology is developing means that there is no time for retraining. By the time you’ve gone through a degree program, or even just a job retraining, your new skill set will have already become obsolete.”
Dinesh wasn’t really listening to anything. The words from the screen washed over him as he tried to get a hold of his feelings.
The blond continued: “And this isn’t just about ‘upskilling’ low-skill workers. Nearly everyone is vulnerable in the face of cognitive automation. Radiologists or field workers, truck drivers or financial advisors–we don’t need those traditional jobs anymore. AIs do them better. Today, we only need problem-solvers.”
“They don’t need me…but I did everything right,” Dinesh thought to himself. He was smart and educated. He had worked hard, earned the right degrees, and learned all of those arcane tools that were supposed to assure indispensability in his field. Dinesh may never have loved being an analyst, but he could read a Bloomberg terminal the way a radiologist could read an x-ray. It turned out that an AI could do both of those things better than a human.
“The damn thing has actually been secretly watching you for the last six months,” his manager–now former manager–had told him while breaking the news earlier that day. “It’s some software-as-a-service company that sells a reinforcement learning deep neural network. You know, like that AlphaGo thing that beat that guy at Chinese Checkers a few years ago. Turns out you’ve actually been training it to do your job this whole time. The CEO decided to flip the switch when he saw how much more the firm would earn with fewer analysts on salary. Quite frankly, if he didn’t we would have probably been driven out of the market within a year, even if no one seems to understand the AI’s strategies half the time.”
Dinesh had heard that a couple of people were staying on, some even being promoted. He wasn’t surprised to hear Angelica was one of them. In the time they’d worked together she must have spent more nights at her desk than in her bed. He remembered having felt sorry for her; she always seemed so awkward on the rare occasions she socialized outside of work. Her financial models and data science were just who she was as a person. Now she’d be part of some elite team working on the bleeding edge, inventing new solutions for their clients.
“I guess I’m happy for her,” Dinesh mumbled to himself, “but I never wanted a life as some quant savante, anyway.” He’d had passions growing up–he had dreamed of someday helping people with traumatic brain injuries, people like his brother. Still, part of growing up was setting those dreams aside. He’d been taught by his parents and schools to earn the right grades, go to the right university, and get the right job. He had worked hard in this crappy office so that he could enjoy his life away from it. (And, he had to admit, to impress his family and friends.) “I can’t just start over again. I can’t build a whole new career from scratch.”
The blond on the news kept talking through Dinesh’s numbness. “Our data shows that jobs are becoming strongly bimodal as AIs have improved. Employees are either moving up into cutting-edge creative fields or down into mid-skill or even low-skilled service positions.”
Dinesh sat up. “That’s exactly what’s happening to me!” The company had offered to pay for retraining, but only at half the wages he was getting before. He’d basically be customer support at some call center. Dinesh had heard the complaints of lower-skilled workers–drivers, mechanics, warehouse laborers–protesting about losing their jobs to automation, but he’d always heard that they were getting retrained into…well, some other job.
The mustache interjected, “But the new economy is hungry for talent. Competition to recruit the right employees is brutal. Their agents are making a killing! I’ve said it before, this is just like the Agricultural and Industrial Revolutions. As jobs are destroyed, vastly more jobs, even whole new economies, are created from retrained weavers and iron smiths.”
Dinesh had heard the protestors on the news saying that as soon as they learned a new skill it was already obsolete. He thought they were just being lazy; now he was one of them. The company had even offered to send him to a coding bootcamp to become a web developer.
“Programming?” he’d thought. Just last year the company had gone through a different wave of layoffs after buying a DaaS (development-as-a-service) platform. It was great: you could describe what you want to some chatbot and five minutes later you have a new feature, new analysis, sometimes a whole new product. Not only did they not need programmers to make new apps or run analyses, they didn’t even need human hands to type.
Dinesh knew that there were still a few teams of elite research engineers somewhere in the building doing cutting edge work with algorithms and databases, but he was never joining their ranks at this point in his life. The simple truth was that the value of Dinesh’s work was worth less than an AI’s. His mind was filled with the scene from Blazing Saddles where Slim Pickens saves the railroad cart from the quicksand while leaving the slaves to sink to their deaths.
“No. It isn’t like the Industrial Revolution,” the blond pounced. “Most of those new jobs are also being filled by AIs. We don’t need lever-pullers; we need creative, adaptive problem explorers. Whatever your industry, that will be the only job description of the future. The rest is just details.”
“That’s exactly my point! AIs are taking up these dull, repetitive jobs,” the mustache responded, “freeing people to pursue their passions, to become scientists and artists. Like Burning Man…or something,” his techno-utopian inspiration running out of steam.
“You’re right that AI is amazing, and its development shouldn’t be hobbled out of fear,” the blond agreed. “We sure as hell shouldn’t be sending people down mine shafts if we don’t have to. But we also need social institutions to keep pace. People aren’t magically creative or gritty or any of the other qualities that make us ‘robot-proof’. It takes 20 years to ‘build’ a problem solver, but instead we keep building people with static skill sets to fill specific jobs. All of that misinvestment has turned human capital into a toxic asset. We have no idea what those skills will be worth five years from now, but it’s almost certainly much less than we invested. Even a World Economic Forum report wrongly predicted that more ‘cognitive’ jobs would be better protected. If we start now, though, we do know how to grow problem solvers…”
Dinesh tuned them out again. What did it matter? No one was “magically” changing him at this point. He’d taught some damned machine to do his job better than he could without even realizing it. The only job he had any hope of getting now was as a caring voice on the other side of a telephone.
The mustache wasn’t giving up the fight. “With the huge increase in connectivity and access to information over the past few years, a kid anywhere in the world can become a data scientist, conducting analyses using Cloud resources such as Amazon web services and R, an open source language for statistics. Overnight we’ve turned them into knowledge creators, problem solvers, and innovators.”
“Wait,” Dinesh thought, “weren’t those lines right out of that book, The Second Machine Age?” He remembered being blown away by the book. “But I grew up in Palo Alto and never built an R model on AWS…or anywhere else, for that matter. How many kids just spontaneously become self-taught data scientists? It’s like this guy thinks everyone else is just like him, richly educated and motivated, but stuck on a desert island, just needing access to a mobile phone to unleash their pent-up creative potential on the world.” Maybe it wasn’t enough to just make information available. Maybe that was only the start to solving the problem.
“What the fuck am I going to do!” Dinesh shouted at the empty office. “Am I just useless now?” He suddenly felt a divide that, deep down, he knew to have always split the world...and a wrenching alienation at unexpectedly being on the losing side of it.
“Next up,” the host was saying on the screen, “the primaries are over. Can a campaign with the slogan ‘Burn It Down’ truly win the White House?”
Dinesh watched the clip and noticed the anger of the political rallies on screen. He’d always felt that those people were just the lunatic fringe, but watching them now made him finally feel something for the first time that day. A purpose, a reason to keep going. It was more than anyone else was offering him.