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Watching Work

February 22, 2024

Bodies, Minds, and the Artificial Intelligence Industrial Complex, part five

Consider a human vs computer triathlon. The first contest is playing a cognitively demanding game like chess. The second is driving a truck safely through a busy urban downtown. The third is grabbing packages, from warehouse shelves stocked with a great diversity of package types, and placing them safely into tote boxes.

Who would win, humans or computers?

So far the humans are ahead two-to-one. Though a computer program passed the best human chess players more than 25 years ago, replacing humans in the intellectually demanding tasks of truck-driving and package-packing has proved a much tougher challenge.

The reasons for the skills disparity can tell us a lot about the way artificial intelligence has developed and how it is affecting employment conditions.

Some tasks require mostly analytical thinking and perceptual skills, but many tasks require close, almost instantaneous coordination of fine motor control. Many of these latter tasks fall into the category that is often condescendingly termed “manual labour”. But as Antonio Gramsci argued,

“There is no human activity from which every form of intellectual participation can be excluded: Homo faber cannot be separated from homo sapiens.”1

All work involves, to some degree, both body and mind. This plays a major role in the degree to which AI can or cannot effectively replace human labour.

Yet even if AI can not succeed in taking away your job, it might succeed in taking away a big chunk of your paycheque.

Moravec’s paradox

By 2021, Amazon had developed a logistics system that could track millions of items and millions of shipments every day, from factory loading docks to shipping containers to warehouse shelves to the delivery truck that speeds to your door.

But for all its efforts, it hadn’t managed to develop a robot that could compete with humans in the delicate task of grabbing packages off shelves or conveyor belts.

Author Christopher Mims described the challenge in his book Arriving Today2. “Each of these workers is the hub of a three-dimensional wheel, where each spoke is ten feet tall and consists of mail slot-size openings. Every one of these sorters works as fast as they can. First they grab a package off the chute, then they pause for a moment to scan the item and read its destination off a screen …. Then they whirl and drop the item into a slot. Each of these workers must sort between 1,100 and 1,200 parcels per hour ….”

The problem was this: there was huge diversity not only in packaging types but in packaging contents. Though about half the items were concealed in soft poly bags, those bags might contain things that were light and soft, or light and hard, or light and fragile, or surprisingly heavy.

Humans have a remarkable ability to “adjust on the fly”. As our fingers close on the end of a package and start to lift, we can make nearly instantaneous adjustments to grip tighter – but not too tight – if we sense significant resistance due to unexpected weight. Without knowing what is in the packages, we can still grab and sort 20 packages per minute while seldom if ever crushing a package because we grip too tightly, and seldom losing control and having a package fly across the room.

Building a machine with the same ability is terribly difficult, as summed up by robotics pioneer Hans Moravec.

“One formulation of Moravec’s paradox goes like this,” Mims wrote: “it’s far harder to teach a computer to pick up and move a chess piece like its human opponent than it is to teach it to beat that human at chess.”

In the words of robotics scholar Thrishantha Nanayakkara,

“We have made huge progress in symbolic, data-driven AI. But when it comes to contact, we fail miserably. We don’t have a robot that we can trust to hold a hamster safely.”3

In 2021 even Amazon’s newest warehouses had robots working only on carefully circumscribed tasks, in carefully fenced-off and monitored areas, while human workers did most of the sorting and packing.

Amazon’s warehouse staffers still had paying jobs, but AI has already shaped their working conditions for the worse. Since Amazon is one of the world’s largest employers, as well as a major player in AI, their obvious eagerness to extract more value from a low-paid workforce should be seen as a harbinger of AI’s future effects on labour relations. We’ll return to those changing labour relations below.

Behind the wheel

One job which the artificial intelligence industrial complex has tried mightily to eliminate is the work of drivers. On the one hand, proponents of autonomous vehicles have pointed to the shocking annual numbers of people killed or maimed on highways and streets, claiming that self-driving cars and trucks will be much safer. On the other hand, in some industries the wages of drivers are a big part of the cost of business, and thus companies could swell their profit margins by eliminating those wages.

We’ve been hearing that full self-driving vehicles are just a few years away – for the past twenty years. But driving is one of those tasks that requires not only careful and responsive manipulation of vehicle controls, but quick perception and quick judgment calls in situations that the driver may have seldom – or never – confronted before.

Christopher Mims looked at the work of tuSimple, a San Diego-based firm hoping to market self-driving trucks. Counting all the sensors, controllers, and information processing devices, he wrote, “The AI on board TuSimple’s self-driving truck draws about four times as much power as the average American home ….”4

At the time, tuSimple was working on increasing their system’s reliability “from something like 99.99 percent reliable to 99.9999 percent reliable.” That improvement would not come easily, Sims explained: “every additional decimal point of reliability costs as much in time, energy, and money as all the previous ones combined.”

Some of the world’s largest companies have tried, and so far failed, to achieve widespread regulatory approval for their entries in the autonomous-vehicle sweepstakes. Consider the saga of GM’s Cruise robotaxi subsidiary. After GM and other companies had invested billions in the venture, Cruise received permission in August 2023 to operate their robotaxis twenty-four hours a day in San Fransisco.5

Just over two months later, Cruise suddenly suspended its robotaxi operations nationwide following an accident in San Francisco.6

In the wake of the controversy, it was revealed that although Cruise taxis appeared to have no driver and to operate fully autonomously, things weren’t quite that simple. Cruise founder and CEO Kyle Vogt told CNBC that “Cruise AVs are being remotely assisted (RA) 2-4% of the time on average, in complex urban environments.”7

Perhaps “2–4% of the time” doesn’t sound like much. But if you have a fleet of vehicles needing help, on average, that often, you need to have quite a few remote operators on call to be reasonably sure they can provide timely assistance. According to the New York Times, the two hundred Cruise vehicles in San Francisco “were supported by a vast operations staff, with 1.5 workers per vehicle.”8 If a highly capitalized company can pay teams of AI and robotics engineers to build vehicles whose electronics cost several times more than the vehicle itself, and the vehicles still require 1.5 workers/vehicle, the self-driving car show is not yet ready for prime time.

In another indication of the difficulty in putting a virtual robot behind the wheel, Bloomberg News reported last month that Apple is delaying launch of its long-rumored vehicle until 2028 at earliest.9 Not only that, but the vehicle will boast no more than Level-2 autonomy. CleanTechnica reported that

“The prior design for the [Apple] vehicle called for a system that wouldn’t require human intervention on highways in approved parts of North America and could operate under most conditions. The more basic Level 2+ plan would require drivers to pay attention to the road and take over at any time — similar to the current standard Autopilot feature on Tesla’s EVs. In other words, it will offer no significant upgrades to existing driver assistance technology from most manufacturers available today.”10

As for self-driving truck companies still trying to tap the US market, most are focused on limited applications that avoid many of the complications involved in typical traffic. For example, Uber Freight targets the “middle mile” segment of truck journeys. In this model, human drivers deliver a trailer to a transfer hub close to a highway. A self-driving tractor then pulls the trailer on the highway, perhaps right across the country, to another transfer hub near the destination. A human driver then takes the trailer to the drop-off point.11

This model limits the self-driving segments to roads with far less complications than urban environments routinely present.

This simplification of the tasks inherent in driving may seem quintessentially twenty-first century. But it represents one step in a process of “de-skilling” that has been a hallmark of industrial capitalism for hundreds of years.

Jacquard looms, patented in France in 1803, were first brought to the U.S. in the 1820s. The loom is an ancestor of the first computers, using hundreds of punchcards to “program” intricate designs for the loom to produce. Photo by Maia C, licensed via CC BY-NC-ND 2.0 DEED, accessed at flickr.

Reshaping labour relations

Almost two hundred years ago computing pioneer Charles Babbage advised industrialists that “The workshops of [England] contain within them a rich mine of knowledge, too generally neglected by the wealthier classes.”12

Babbage is known today as the inventor of the Difference Engine – a working mechanical calculator that could manipulate numbers – and the Analytical Engine – a programmable general purpose computer whose prototypes Babbage worked on for many years.

But Babbage was also interested in the complex skeins of knowledge evidenced in the co-operative activities of skilled workers. In particular, he wanted to break down that working knowledge into small constituent steps that could be duplicated by machines and unskilled workers in factories.

Today writers including Matteo PasquinelliBrian MerchantDan McQuillan and Kate Crawford highlight factory industrialism as a key part of the history of artificial intelligence.

The careful division of labour not only made proto-assembly lines possible, but they also allowed capitalists to pay for just the quantity of labour needed in the production process:

“The Babbage principle states that the organisation of a production process into small tasks (the division of labour) allows for the calculation and precise purchase of the quantity of labour that is necessary for each task (the division of value).”13

Babbage turned out to be far ahead of his time with his efforts to build a general-purpose computer, but his approach to the division of labour became mainstream management economics.

In the early 20th century assembly-line methods reshaped labour relations even more, thanks in part to the work of management theorist Frederick Taylor.

Taylor carefully measured and noted each movement of skilled mechanics – and used the resulting knowledge to design assembly lines in which cars could be produced at lower cost by workers with little training.

As Christopher Mims wrote, “Taylorism” is now “the dominant ideology of the modern world and the root of all attempts at increasing productivity ….” Indeed,

“While Taylorism once applied primarily to the factory floor, something fundamental has shifted in how we live and work. … the walls of the factory have dissolved. Every day, more and more of what we do, how we consume, even how we think, has become part of the factory system.”14

We can consume by using Amazon’s patented 1-Click ordering system. When we try to remember a name, we can start to type a Google search and get an answer – possibly even an appropriate answer – before we have finished typing our query. In both cases, of course, the corporations use their algorithms to capture and sort the data produced by our keystrokes or vocal requests.

But what about remaining activities on the factory floor, warehouse or highway? Can Taylorism meet the wildest dreams of Babbage, aided today by the latest forms of artificial intelligence? Can AI not only measure our work but replace human workers?

Yes, but only in certain circumstances. For work in which mind-body, hand-eye coordination is a key element, AI-enhanced robots have limited success. As we have seen, where a work task can be broken into discrete motions, each one repeated with little or no variation, it is sometimes economically efficient to develop and build robots. But where flexible and varied manual dexterity is required, or where judgement calls must guide the working hands to deal with frequent but unpredicted contingencies, AI robotization is not up to the job.

A team of researchers at MIT recently investigated jobs that could potentially be replaced by AI, and in particular jobs in which computer vision could play a significant role. They found that “at today’s costs U.S. businesses would choose not to automate most vision tasks that have “AI Exposure,” and that only 23% of worker wages being paid for vision tasks would be attractive to automate. … Overall, our findings suggest that AI job displacement will be substantial, but also gradual ….”15

A report released earlier this month, entitled Generative Artificial Intelligence and the Workforce, found that “Blue-collar jobs are unlikely to be automated by GenAI.” However, many job roles that are more cerebral and less hands-on stand to be greatly affected. The report says many jobs may be eliminated, at least in the short term, in categories including the following:

  • “financial analysts, actuaries and accountants [who] spend much of their time crunching numbers …;”
  • auditors, compliance officers and lawyers who do regulatory compliance monitoring;
  • software developers who do “routine tasks—such as generating code, debugging, monitoring systems and optimizing networks;”
  • administrative and human resource managerial roles.

The report also predicts that

“Given the broad potential for GenAI to replace human labor, increases in productivity will generate disproportionate returns for investors and senior employees at tech companies, many of whom are already among the wealthiest people in the U.S., intensifying wealth concentration.”16

It makes sense that if a wide range of mid-level managers and professional staff can be cut from payrolls, those at the top of the pyramid stand to gain. But even though, as the report states, blue-collar workers are unlikely to lose their jobs to AI-bots, the changing employment trends are making work life more miserable and less lucrative at lower rungs on the socio-economic ladder.

Pasquinelli puts it this way:

“The debate on the fear that AI fully replaces jobs is misguided: in the so-called platform economy, in reality, algorithms replace management and multiply precarious jobs.”17

And Crawford writes:

“Instead of asking whether robots will replace humans, I’m interested in how humans are increasingly treated like robots and what this means for the role of labor.”18

The boss from hell does not have an office

Let’s consider some of the jobs that are often discussed as prime targets for elimination by AI.

The taxi business has undergone drastic upheaval due to the rise of Uber and Lyft. These companies seem driven by a mission to solve a terrible problem: taxi drivers have too much of the nations’ wealth and venture capitalists have too little. The companies haven’t yet eliminated driving jobs, but they have indeed enriched venture capitalists while making the chauffeur-for-hire market less rewarding and less secure. It’s hard for workers to complain to or negotiate with the boss, now that the boss is an app.

How about Amazon warehouse workers? Christopher Mims describes the life of a worker policed by Amazon’s “rate”. Every movement during every warehouse worker’s day is monitored and fed into a data management system. The system comes back with a “rate” of tasks that all workers are expected to meet. Failure to match that rate puts the worker at immediate risk of firing. In fact, the lowest 25 per cent of the workers, as measured by their “rate”, are periodically dismissed. Over time, then, the rate edges higher, and a worker who may have been comfortably in the middle of the pack must keep working faster to avoid slipping into the bottom 25th percentile and thence into the ranks of the unemployed.

“The company’s relentless measurement, drive for efficiency, loose hiring standards, and moving targets for hourly rates,” Mims writes, “are the perfect system for ingesting as many people as possible and discarding all but the most physically fit.”19 Since the style of work lends itself to repetitive strain injuries, and since there are no paid sick days, even very physically fit warehouse employees are always at risk of losing their jobs.

Over the past 40 years the work of a long-distance trucker hasn’t changed much, but the work conditions and remuneration have changed greatly. Mims writes, “The average trucker in the United States made $38,618 a year in 1980, or $120,000 in 2020 dollars. In 2019, the average trucker made about $45,000 a year – a 63 percent decrease in forty years.”

There are many reasons for that redistribution of income out of the pockets of these workers. Among them is the computerization of a swath of supervisory tasks. In Mims words, “Drivers must meet deadlines that are as likely to be set by an algorithm and a online bidding system as a trucking company dispatcher or an account handler at a freight-forwarding company.”

Answering to a human dispatcher or payroll officer isn’t always pleasant or fair, of course – but at least there is the possibility of a human relationship with a human supervisor. That possibility is gone when the major strata of middle management are replaced by AI bots.

Referring to Amazon’s 25th percentile rule and steadily rising “rate”, Mims writes, “Management theorists have known for some time that forcing bosses to grade their employees on a curve is a recipe for low morale and unnecessarily high turnover.” But low morale doesn’t matter among managers who are just successions of binary digits. And high turnover of warehouse staff isn’t a problem for companies like Amazon – little is spent on training, new workers are easy enough to find, and the short average duration of employment makes it much harder for workers to get together in union organizing drives.

Uber drivers, many long-haul truckers, and Amazon packagers have this in common: their cold and heartless bosses are nowhere to be found; they exist only as algorithms. Management-by-AI, Dan McQuillan says, results in “an amplification of casualized and precarious work.”20

Management-by-AI could be seen, then, as just another stage in the development of a centuries-old “counterfeit person” – the legally recognized “person” that is the modern corporation. In the coinage of Charlie Stross, for centuries we’ve been increasingly governed by “old, slow AI”21 – the thinking mode of the corporate personage. We’ll return to the theme of “slow AI” and “fast AI” in a future post.


Antonio Gramsci, The Prison Notebooks, 1932. Quoted in The Eye of the Master: A Social History of Artificial Intelligence, by Matteo Pasquinelli, Verso, 2023.

Christopher Mims, Arriving Today: From Factory to Front Door – Why Everything Has Changed About How and What We Buy, Harper Collins, 2021; reviewed here.

Tom Chivers, “How DeepMind Is Reinventing the Robot,” IEEE Spectrum, 27 September 2021.

Christopher Mims, Arriving Today, 2021, page 143.

Johana Bhuiyan, “San Francisco to get round-the-clock robo taxis after controversial vote,” The Guardian, 11 Aug 2023.

David Shepardson, “GM Cruise unit suspends all driverless operations after California ban,” Reuters, 27 October 2023.

Lora Kolodny, “Cruise confirms robotaxis rely on human assistance every four to five miles,” CNBC, 6 Nov 2023.

Tripp Mickle, Cade Metz and Yiwen Lu, “G.M.’s Cruise Moved Fast in the Driverless Race. It Got Ugly.” New York Times, 3 November 2023.

Mark Gurman, “Apple Dials Back Car’s Self-Driving Features and Delays Launch to 2028”, Bloomberg, 23 January 2024.

10 Steve Hanley, “Apple Car Pushed Back To 2028. Autonomous Driving? Forget About It!”, 27 January 2024.

11 Marcus Law, “Self-driving trucks leading the way to an autonomous future,” Technology, 6 October 2023.

12 Charles Babbage, On the Economy of Machinery and Manufactures, 1832; quoted in Pasquinelli, The Eye of the Master, 2023.

13 Pasquinelli, The Eye of the Master.

14 Christopher Mims, Arriving Today, 2021.

15 Neil Thompson et al., “Beyond AI Exposure: Which Tasks are Cost-Effective to Automate with Computer Vision?”, MIT FutureTech, 22 January 2024.

16 Gad Levanon, Generative Artificial Intelligence and the WorkforceThe Burning Glass Institute, 1 February 2024.

17 Pasquinelli, The Eye of the Master.

18 Crawford, Kate, Atlas of AIYale University Press, 2021.

19 Christopher Mims, Arriving Today, 2021.

20 Dan McQuillan, Resisting AI: An Anti-Fascist Approach to Artificial Intelligence,” Bristol University Press, 2022.

21 Charlie Stross, “Dude, you broke the future!”, Charlie’s Diary, December 2017.

Bart Hawkins Kreps

Bart Hawkins Kreps is a long-time bicycling advocate and free-lance writer. His views have been shaped by work on highway construction and farming in the US Midwest, nine years spent in the Canadian arctic, and twenty years of involvement in the publishing industry in Ontario. Currently living on the outermost edge of the Toronto megalopolis, he blogs most often about energy, economics and ecology, at