The Big Flip
AI is becoming ubiquitous, but not as robots taking our jobs, but instead, becoming our bosses
I’ve seen a number of compelling reports about AI being used to direct the work of the workforce.
In Robots aren’t taking our jobs — they’re becoming our bosses, Josh Dzieza notes that although there are many voices being raised about the dangers of automation to today's workforce, little is being done to slow it.
The robots are here, they’re working in management, and they’re grinding workers into the ground.
The robots are watching over hotel housekeepers, telling them which room to clean and tracking how quickly they do it. They’re managing software developers, monitoring their clicks and scrolls and docking their pay if they work too slowly. They’re listening to call center workers, telling them what to say, how to say it, and keeping them constantly, maximally busy. While we’ve been watching the horizon for the self-driving trucks, perpetually five years away, the robots arrived in the form of the supervisor, the foreman, the middle manager.
*These automated systems can detect inefficiencies that a human manager never would — a moment’s downtime between calls, a habit of lingering at the coffee machine after finishing a task, a new route that, if all goes perfectly, could get a few more packages delivered in a day. But for workers, what look like inefficiencies to an algorithm were their last reserves of respite and autonomy, and as these little breaks and minor freedoms get optimized out, their jobs are becoming more intense, stressful, and dangerous. Over the last several months, I’ve spoken with more than 20 workers in six countries. For many of them, their greatest fear isn’t that robots might come for their jobs: it’s that robots have already become their boss.
He chronicles what's happening on the ground for warehouse workers, delivery drivers, call center workers, and many other gears in the giant machinery of today's economy. And the algorithms running the machines have sped everything up.
It was Henry Ford who most fully demonstrated the [Taylor efficiency] approach’s power when he further simplified tasks and arranged them along an assembly line. The speed of the line controlled the pace of the worker and gave supervisors an easy way to see who was lagging. Laborers absolutely hated it. The work was so mindless and grueling that people quit in droves, forcing Ford to double wages. As these methods spread, workers frequently struck or slowed down to protest “speedups” — supervisors accelerating the assembly line to untenable rates.
We are in the midst of another great speedup. There are many factors behind it, but one is the digitization of the economy and the new ways of organizing work it enables. Take retail: workers no longer stand around in stores waiting for customers; with e-commerce, their roles are split. Some work in warehouses, where they fulfill orders nonstop, and others work in call centers, where they answer question after question. In both spaces, workers are subject to intense surveillance. Their every action is tracked by warehouse scanners and call center computers, which provide the data for the automated systems that keep them working at maximum capacity.
There was no single breakthrough in automated management, but as with the stopwatch, revolutionary technology can appear mundane until it becomes the foundation for a new way of organizing work. When rate-tracking programs are tied to warehouse scanners or taxi drivers are equipped with GPS apps, it enables management at a scale and level of detail that Taylor could have only dreamed of.
“The robot apocalypse is here,” said Joanna Bronowicka, a researcher with the Centre for Internet and Human Rights and a former candidate for European Parliament. “It’s just that the way we’ve crafted these narratives, and unfortunately people from the left and right and people like Andrew Yang and people in Europe that talk about this topic are contributing to it, they are using a language of the future, which obscures the actual lived reality of people right now.”
This isn’t to say that the future of AI shouldn’t worry workers. In the past, for jobs to be automatically managed, they had to be broken down into tasks that could be measured by machines — the ride tracked by GPS, the item scanned in a warehouse. But machine learning is capable of parsing much less structured data, and it’s making new forms of work, from typing at a computer to conversations between people, ready for robot bosses.
The big flip into an economy with surveillance-based AI monitoring workers at all times and acting like a foreman of old with a stopwatch, speeding the line to increase output. And where are the regulators, and the unions? Not yet paying attention.
Research shows that AI is now taking more jobs than it creates, which I am calling the big flip. In The Robots Are Coming for Phil in Accounting, Kevin Roose zooms in on the dark side of robotic process automation: it is happening so fast that workers may not have time to adapt and move into other jobs:
During the 19th and 20th centuries, some lamplighters and blacksmiths became obsolete, but more people were able to make a living as electricians and car dealers. And today’s A.I. optimists argue that while new technology may displace some workers, it will spur economic growth and create better, more fulfilling jobs, just as it has in the past.
But that is no guarantee, and there is growing evidence that this time may be different.
In a series of recent studies, Daron Acemoglu of M.I.T. and Pascual Restrepo of Boston University, two well-respected economists who have researched the history of automation, found that for most of the 20th century, the optimistic take on automation prevailed — on average, in industries that implemented automation, new tasks were created faster than old ones were destroyed.
Since the late 1980s, they found, the equation had flipped — tasks have been disappearing to automation faster than new ones are appearing.
This shift may be related to the popularity of what they call “so-so automation” — technology that is just barely good enough to replace human workers, but not good enough to create new jobs or make companies significantly more productive.
“The real danger for labor,” they wrote, “may come not from highly productive but from ‘so-so’ automation technologies that are just productive enough to be adopted and cause displacement.”
Only the most devoted Luddites would argue against automating any job, no matter how menial or dangerous. But not all automation is created equal, and much of the automation being done in white-collar workplaces today is the kind that may not help workers over the long run.
Roose turns to the issue of regulatory oversight and notes that the 2021 Covid-19 relief bill allocated zero dollars for reskilling a workforce to meet this threat. It’s not really on the agenda in Congress or at the White House.
During past eras of technological change, governments and labor unions have stepped in to fight for automation-prone workers, or support them while they trained for new jobs. But this time, there is less in the way of help. Congress has rejected calls to fund federal worker retraining programs for years, and while some of the money in the $1.9 trillion Covid-19 relief bill Democrats hope to pass this week will go to laid-off and furloughed workers, none of it is specifically earmarked for job training programs that could help displaced workers get back on their feet.
Another key difference is that in the past, automation arrived gradually, factory machine by factory machine. But today’s white-collar automation is so sudden — and often, so deliberately obscured by management — that few workers have time to prepare.
“The rate of progression of this technology is faster than any previous automation,” said Mr. Le Clair, the Forrester analyst, who thinks we are closer to the beginning than the end of the corporate A.I. boom.
“We haven’t hit the exponential point of this stuff yet,” he added. “And when we do, it’s going to be dramatic.”
Again, I believe the contrary: we are already in the exponential segment of the graph, but it’s hardly being noticed and as a result, little action is being taken to slow the curve of so-so work dominating much of the economy.
A reader suggested I take a look at Sarah O’Connor’s Do not let homeworking become digital piecework for the poor apropos of my post on The End Of Jobs. He also noted the Citrix scenarios have more of the end-of-jobs sort of thinking, as well.