Farming on screen

Bodies, Minds, and the Artificial Intelligence Industrial Complex, part six
Also published on Resilience.

What does the future of farming look like? To some pundits the answer is clear: “Connected sensors, the Internet of Things, autonomous vehicles, robots, and big data analytics will be essential in effectively feeding tomorrow’s world. The future of agriculture will be smart, connected, and digital.”1

Proponents of artificial intelligence in agriculture argue that AI will be key to limiting or reversing biodiversity loss, reducing global warming emissions, and restoring resilience to ecosystems that are stressed by climate change.

There are many flavours of AI and thousands of potential applications for AI in agriculture. Some of them may indeed prove helpful in restoring parts of ecosystems.

But there are strong reasons to expect that AI in agriculture will be dominated by the same forces that have given the world a monoculture agri-industrial complex overwhelmingly dependent on fossil fuels. There are many reasons why we might expect that agri-industrial AI will lead to more biodiversity loss, more food insecurity, more socio-economic inequality, more climate vulnerability. To the extent that AI in agriculture bears fruit, many of these fruits are likely to be bitter.

Optimizing for yield

A branch of mathematics known as optimization has played a large role in the development of artificial intelligence. Author Coco Krumme, who earned a PhD in mathematics from MIT, traces optimization’s roots back hundreds of years and sees optimization in the development of contemporary agriculture.

In her book Optimal Illusions: The False Promise of Optimization, she writes,

“Embedded in the treachery of optimals is a deception. An optimization, whether it’s optimizing the value of an acre of land or the on-time arrival rate of an airline, often involves collapsing measurement into a single dimension, dollars or time or something else.”2

The “single dimensions” that serve as the building blocks of optimization are the result of useful, though simplistic, abstractions of the infinite complexities of our world. In agriculture, for example, how can we identify and describe the factors of soil fertility? One way would be to describe truly healthy soil as soil that contains a diverse microbial community, thriving among networks of fungal mycelia, plant roots, worms, and insect larvae. Another way would be to note that the soil contains sufficient amounts of at least several chemical elements including carbon, nitrogen, phosphorus, potassium. The second method is an incomplete abstraction, but it has the big advantage that it lends itself to easy quantification, calculation, and standardized testing. Coupled with the availability of similar simple quantified fertilizers, this method also allows for quick, “efficient,” yield-boosting soil amendments.

In deciding what are the optimal levels of certain soil nutrients, of course, we must also give an implicit or explicit answer to this question: “Optimal for what?” If the answer is, “optimal for soya production”, we are likely to get higher yields of soya – even if the soil is losing many of the attributes of health that we might observe through a less abstract lens. Krumme describes the gradual and eventual results of this supposedly scientific agriculture:

“It was easy to ignore, for a while, the costs: the chemicals harming human health, the machinery depleting soil, the fertilizer spewing into the downstream water supply.”3

The social costs were no less real than the environmental costs: most farmers, in countries where industrial agriculture took hold, were unable to keep up with the constant pressure to “go big or go home”. So they sold their land to the fewer remaining farmers who farmed bigger farms, and rural agricultural communities were hollowed out.

“But just look at those benefits!”, proponents of industrialized agriculture can say. Certainly yields per hectare of commodity crops climbed dramatically, and this food was raised by a smaller share of the work force.

The extent to which these changes are truly improvements is murky, however, when we look beyond the abstractions that go into the optimization models. We might want to believe that “if we don’t count it, it doesn’t count” – but that illusion won’t last forever.

Let’s start with social and economic factors. Coco Krumme quotes historian Paul Conkin on this trend in agricultural production: “Since 1950, labor productivity per hour of work in the non-farm sectors has increased 2.5 fold; in agriculture, 7-fold.”4

Yet a recent paper by Irena Knezevic, Alison Blay-Palmer and Courtney Jane Clause finds:

“Industrial farming discourse promotes the perception that there is a positive relationship—the larger the farm, the greater the productivity. Our objective is to demonstrate that based on the data at the centre of this debate, on average, small farms actually produce more food on less land ….”5

Here’s the nub of the problem: productivity statistics depend on what we count, and what we don’t count, when we tally input and output. Labour productivity in particular is usually calculated in reference to Gross Domestic Product, which is the sum of all monetary transactions.

Imagine this scenario, which has analogs all over the world. Suppose I pick a lot of apples, I trade a bushel of them with a neighbour, and I receive a piglet in return. The piglet eats leftover food scraps and weeds around the yard, while providing manure that fertilizes the vegetable garden. Several months later I butcher the pig and share the meat with another neighbour who has some chickens and who has been sharing the eggs. We all get delicious and nutritious food – but how much productivity is tallied? None, because none of these transactions are measured in dollars nor counted in GDP.

In many cases, of course, some inputs and outputs are counted while others are not. A smallholder might buy a few inputs such as feed grain, and might sell some products in a market “official” enough to be included in economic statistics. But much of the smallholder’s output will go to feeding immediate family or neighbours without leaving a trace in GDP.

If GDP had been counted when this scene was depicted, the sale of Spratt’s Pure Fibrine poultry feed may have been the only part of the operation that would “count”. Image: “Spratts patent “pure fibrine” poultry meal & poultry appliances”, from Wellcome Collection, circa 1880–1889, public domain.

Knezevic et al. write, “As farm size and farm revenue can generally be objectively measured, the productivist view has often used just those two data points to measure farm productivity.” However, other statisticians have put considerable effort into quantifying output in non-monetary terms, by estimating all agricultural output in terms of kilocalories.

This too is an abstraction, since a kilocalorie from sugar beets does not have the same nutritional impact as a kilocalorie from black beans or a kilocalorie from chicken – and farm output might include non-food values such as fibre for clothing, fuel for fireplaces, or animal draught power. Nevertheless, counting kilocalories instead of dollars or yuan makes possible more realistic estimates of how much food is produced by small farmers on the edge of the formal economy.

The proportions of global food supply produced on small vs. large farms is a matter of vigorous debate, and Knezevic et al. discuss some of widely discussed estimates. They defend their own estimate:

“[T]he data indicate that family farmers and smallholders account for 81% of production and food supply in kilocalories on 72% of the land. Large farms, defined as more than 200 hectares, account for only 15 and 13% of crop production and food supply by kilocalories, respectively, yet use 28% of the land.”6

They also argue that the smallest farms – 10 hectares (about 25 acres) or less – “provide more than 55% of the world’s kilocalories on about 40% of the land.” This has obvious importance in answering the question “How can we feed the world’s growing population?”7

Of equal importance to our discussion on the role of AI in agriculture, are these conclusions of Knezevic et al.: “industrialized and non-industrialized farming … come with markedly different knowledge systems,” and “smaller farms also have higher crop and non-crop biodiversity.”

Feeding the data machine

As discussed at length in previous installments, the types of artificial intelligence currently making waves require vast data sets. And in their paper advocating “Smart agriculture (SA)”, Jian Zhang et al. write, “The focus of SA is on data exploitation; this requires access to data, data analysis, and the application of the results over multiple (ideally, all) farm or ranch operations.”8

The data currently available from “precision farming” comes from large, well-capitalized farms that can afford tractors and combines equipped with GPS units, arrays of sensors tracking soil moisture, fertilizer and pesticide applications, and harvested quantities for each square meter. In the future envisioned by Zhang et al., this data collection process should expand dramatically through the incorporation of Internet of Things sensors on many more farms, plus a network allowing the funneling of information to centralized AI servers which will “learn” from data analysis, and which will then guide participating farms in achieving greater productivity at lower ecological cost. This in turn will require a 5G cellular network throughout agricultural areas.

Zhang et al. do not estimate the costs – in monetary terms, or in up-front carbon emissions and ecological damage during the manufacture, installation and operation of the data-crunching networks. An important question will be: will ecological benefits be equal to or greater than the ecological harms?

There is also good reason to doubt that the smallest farms – which produce a disproportionate share of global food supply – will be incorporated into this “smart agriculture”. Such infrastructure will have heavy upfront costs, and the companies that provide the equipment will want assurance that their client farmers will have enough cash outputs to make the capital investments profitable – if not for the farmers themselves, then at least for the big corporations marketing the technology.

A team of scholars writing in Nature Machine Intelligence concluded,

“[S]mall-scale farmers who cultivate 475 of approximately 570 million farms worldwide and feed large swaths of the so-called Global South are particularly likely to be excluded from AI-related benefits.”9

On the subject of what kind of data is available to AI systems, the team wrote,

“[T]ypical agricultural datasets have insufficiently considered polyculture techniques, such as forest farming and silvo-pasture. These techniques yield an array of food, fodder and fabric products while increasing soil fertility, controlling pests and maintaining agrobiodiversity.”

They noted that the small number of crops which dominate commodity crop markets – corn, wheat, rice, and soy in particular – also get the most research attention, while many crops important to subsistence farmers are little studied. Assuming that many of the small farmers remain outside the artificial intelligence agri-industrial complex, the data-gathering is likely to perpetuate and strengthen the hegemony of major commodities and major corporations.

Montreal Nutmeg. Today it’s easy to find images of hundreds varieties of fruit and vegetables that were popular more than a hundred years ago – but finding viable seeds or rootstock is another matter. Image: “Muskmelon, the largest in cultivation – new Montreal Nutmeg. This variety found only in Rice’s box of choice vegetables. 1887”, from Boston Public Library collection “Agriculture Trade Collection” on flickr.

Large-scale monoculture agriculture has already resulted in a scarcity of most traditional varieties of many grains, fruits and vegetables; the seed stocks that work best in the cash-crop nexus now have overwhelming market share. An AI that serves and is led by the same agribusiness interests is not likely, therefore, to preserve the crop diversity we will need to cope with an unstable climate and depleted ecosystems.

It’s marvellous that data servers can store and quickly access the entire genomes of so many species and sub-species. But it would be better if rare varieties are not only preserved but in active use, by communities who keep alive the particular knowledge of how these varieties respond to different weather, soil conditions, and horticultural techniques.

Finally, those small farmers who do step into the AI agri-complex will face new dangers:

“[A]s AI becomes indispensable for precision agriculture, … farmers will bring substantial croplands, pastures and hayfields under the influence of a few common ML [Machine Learning] platforms, consequently creating centralized points of failure, where deliberate attacks could cause disproportionate harm. [T]hese dynamics risk expanding the vulnerability of agrifood supply chains to cyberattacks, including ransomware and denial-of-service attacks, as well as interference with AI-driven machinery, such as self-driving tractors and combine harvesters, robot swarms for crop inspection, and autonomous sprayers.”10

The quantified gains in productivity due to efficiency, writes Coco Krumme, have come with many losses – and “we can think of these losses as the flip side of what we’ve gained from optimizing.” She adds,

“We’ll call [these losses], in brief: slack, place, and scale. Slack, or redundancy, cushions a system from outside shock. Place, or specific knowledge, distinguishes a farm and creates the diversity of practice that, ultimately, allows for both its evolution and preservation. And a sense of scale affords a connection between part and whole, between a farmer and the population his crop feeds.”11

AI-led “smart agriculture” may allow higher yields from major commodity crops, grown in monoculture fields on large farms all using the same machinery, the same chemicals, the same seeds and the same methods. Such agriculture is likely to earn continued profits for the major corporations already at the top of the complex, companies like John Deere, Bayer-Monsanto, and Cargill.

But in a world facing combined and manifold ecological, geopolitical and economic crises, it will be even more important to have agricultures with some redundancy to cushion from outside shock. We’ll need locally-specific knowledge of diverse food production practices. And we’ll need strong connections between local farmers and communities who are likely to depend on each other more than ever.

In that context, putting all our eggs in the artificial intelligence basket doesn’t sound like smart strategy.

Notes

1 “Achieving the Rewards of Smart Agriculture,” by Jian Zhang, Dawn Trautman, Yingnan Liu, Chunguang Bi, Wei Chen, Lijun Ou, and Randy Goebel, Agronomy, 24 February 2024.

2 Coco Krumme, Optimal Illusions: The False Promise of Optimization, Riverhead Books, 2023, pg 181 A hat tip to Mark Hurst, whose podcast Techtonic introduced me to the work of Coco Krumme.

3 Optimal Illusions, pg 23.

4 Optimal Illusions, pg 25, quoting Paul Conkin, A Revolution Down on the Farm.

5 Irena Knezevic, Alison Blay-Palmer and Courtney Jane Clause, “Recalibrating Data on Farm Productivity: Why We Need Small Farms for Food Security,” Sustainability, 4 October 2023.

6 Knezevic et al., “Recalibrating the Data on Farm Productivity.”

7 Recommended reading: two farmer/writers who have conducted more thorough studies of the current and potential productivity of small farms are Chris Smaje and Gunnar Rundgren.

8 Zhang et al., “Achieving the Rewards of Smart Agriculture,” 24 February 2024.

9 Asaf Tzachor, Medha Devare, Brian King, Shahar Avin and Seán Ó hÉigeartaigh, “Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities,” Nature Machine Intelligence, 23 February 2022.

10 Asaf Tzachor et al., “Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities.”

11 Coco Krumme, Optimal Illusions, pg 34.

Image at top of post: “Alexander Frick, Jr. in his tractor/planter planting soybean seeds with the aid of precision agriculture systems and information,” in US Dep’t of Agriculture album “Frick Farms gain with Precision Agriculture and Level Fields”, photo for USDA by Lance Cheung, April 2021, public domain, accessed via flickr. 

Watching work

Bodies, Minds, and the Artificial Intelligence Industrial Complex, part five
Also published on Resilience.

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 Pasquinelli, Brian Merchant, Dan 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.

Notes

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

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

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

4 Christopher Mims, Arriving Today, 2021, page 143.

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

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

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

8 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.

9 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!” CleanTechnica.com, 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 Workforce, The Burning Glass Institute, 1 February 2024.

17 Pasquinelli, The Eye of the Master.

18 Crawford, Kate, Atlas of AI, Yale 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.

 

Image at top of post: “Mechanically controlled eyes see the controlled eyes in the mirror looking back”, photo from “human (un)limited”, 2019, a joint exhibition project of Hyundai Motorstudio and Ars Electronica, licensed under CC BY-NC-ND 2.0 DEED, accessed via flickr.

Beware of WEIRD Stochastic Parrots

Bodies, Minds, and the Artificial Intelligence Industrial Complex, part four
Also published on Resilience.

A strange new species is getting a lot of press recently. The New Yorker published the poignant and profound illustrated essay “Is My Toddler a Stochastic Parrot?” Wall Street Journal told us about “‘Stochastic Parrot’: A Name for AI That Sounds a Bit Less Intelligent”. And expert.ai warned of “GPT-3: The Venom-Spitting Stochastic Parrot”.

The American Dialect Society even selected “stochastic parrot” as the AI-Related Word of the Year for 2023.

Yet this species was unknown until March of 2021, when Emily Bender, Timnit Gebru, Angelina McMillan-Major, and (the slightly pseudonymous) Shmargaret Shmitchell published “On the Dangers of Stochastic Parrots.”1

The paper touched a nerve in the AI community, reportedly costing Timnit Gebru and Margaret Mitchell their jobs with Google’s Ethical AI team.2

Just a few days after Chat-GPT was released, Open AI CEO Sam Altman paid snarky tribute to the now-famous phrase by tweeting “i am a stochastic parrot, and so r u.”3

Just what, according to its namers, are the distinctive characteristics of a stochastic parrot? Why should we be wary of this species? Should we be particularly concerned about a dominant sub-species, the WEIRD stochastic parrot? (WEIRD as in: Western, Educated, Industrialized, Rich, Democratic.) We’ll look at those questions for the remainder of this installment.

Haphazardly probable

The first recognized chatbot was 1967’s Eliza, but many of the key technical developments behind today’s chatbots only came together in the last 15 years. The apparent wizardry of today’s Large Language Models rests on a foundation of algorithmic advances, the availability of vast data sets, super-computer clusters employing thousands of the latest Graphics Processing Unit (GPU) chips, and, as discussed in the last post, an international network of poorly paid gig workers providing human input to fill in gaps in the machine learning process. 

By the beginning of this decade, some AI industry figures were arguing that Large Language Models would soon exhibit “human-level intelligence”, could become sentient and conscious, and might even become the dominant new species on the planet.

The authors of the stochastic parrot paper saw things differently:

“Contrary to how it may seem when we observe its output, an LM is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning: a stochastic parrot.”4

Let’s start by focusing on two words in that definition: “haphazardly” and “probabilistic”. How do those words apply to the output of ChatGPT or similar Large Language Models?

In a lengthy paper published last year, Stephen Wolfram offers an initial explanation:

“What ChatGPT is always fundamentally trying to do is to produce a ‘reasonable continuation’ of whatever text it’s got so far, where by ‘reasonable’ we mean ‘what one might expect someone to write after seeing what people have written on billions of webpages, etc.’”5

He gives the example of this partial sentence: “The best thing about AI is its ability to”. The Large Language Model will have identified many instances closely matching this phrase, and will have calculated the probability of various words being the next word to follow. The table below lists five of the most likely choices.

The element of probability, then, is clear – but in what way is ChatGPT “haphazard”?

Wolfram explains that if the chatbot always picks the next word with the highest probability, the results will be syntactically correct, sensible, but stilted and boring – and repeated identical prompts will produce repeated identical outputs.

By contrast, if at random intervals the chatbot picks a “next word” that ranks fairly high in probability but is not the highest rank, then more interesting and varied outputs result.

Here is Wolfram’s sample of an output produced by a strict “pick the next word with the highest rank” rule: 

The above output sounds like the effort of someone who is being careful with each sentence, but with no imagination, no creativity, and no real ability to develop a thought.

With a randomness setting introduced, however, Wolfram illustrates how repeated responses to the same prompt produce a wide variety of more interesting outputs:

The above summary is an over-simplification, of course, and if you want a more in-depth exposition Wolfram’s paper offers a lot of complex detail. But Wolfram’s “next word” explanation concurs with at least part of the stochastic parrot thesis: “an LM is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine ….”

What follows, in Bender and Gebru’s formulation, is equally significant. An LLM, they wrote, strings together words “without any reference to meaning.”

Do LLM’s actually understand the meaning of the words, phrases, sentences and paragraphs they have read and which they can produce? To answer that question definitively, we’d need definitive answers to questions like “What is meaning?” and “What does it mean to understand?”

A brain is not a computer, and a computer is not a brain

For the past fifty years a powerful but deceptive metaphor has become pervasive. We’ve grown accustomed to describing computers by analogy to the human brain, and vice versa. As the saying goes, these models are always wrong even though they are sometimes useful.

“The Computational Metaphor,” wrote Alexis Barria and Keith Cross, “affects how people understand computers and brains, and of more recent importance, influences interactions with AI-labeled technology.”

The concepts embedded in the metaphor, they added, “afford the human mind less complexity than is owed, and the computer more wisdom than is due.”6

The human mind is inseparable from the brain which is inseparable from the body. However much we might theorize about abstract processes of thought, our thought processes evolved with and are inextricably tangled with bodily realities of hunger, love, fear, satisfaction, suffering, mortality. We learn language as part of experiencing life, and the meanings we share (sometimes incompletely) when we communicate with others depends on shared bodily existence.

Angie Wang put it this way: “A toddler has a life, and learns language to describe it. An L.L.M. learns language, but has no life of its own to describe.”7

In other terms, wrote Bender and Gebru, “languages are systems of signs, i.e. pairings of form and meaning. But the training data for LMs is only form; they do not have access to meaning.”

Though the output of a chatbot may appear meaningful, that meaning exists solely in the mind of the human who reads or hears that output, and not in the artificial mind that stitched the words together. If the AI Industrial Complex deploys “counterfeit people”8 who pass as real people, we shouldn’t expect peace and love and understanding. When a chatbot tries to convince us that it really cares about our faulty new microwave or about the time we are waiting on hold for answers, we should not be fooled.

“WEIRD in, WEIRD out”

There are no generic humans. As it turns out, counterfeit people aren’t generic either.

Large Language Models are created primarily by large corporations, or by university researchers who are funded by large corporations or whose best job prospects are with those corporations. It would be a fluke if the products and services growing out of these LLMs didn’t also favour those corporations.

But the bias problem embedded in chatbots goes deeper. For decades, the people who contribute the most to digitized data sets are those who have the most access to the internet, who publish the most books, research papers, magazine articles and blog posts – and these people disproportionately live in Western Educated Industrialized Rich Democratic countries. Even social media users, who provide terabytes of free data for the AI machine, are likely to live in WEIRD places.

We should not be surprised, then, when outputs from chatbots express common biases:

“As people in positions of privilege with respect to a society’s racism, misogyny, ableism, etc., tend to be overrepresented in training data for LMs, this training data thus includes encoded biases, many already recognized as harmful.”9

In 2023 a group of scholars at Harvard University investigated those biases. “Technical reports often compare LLMs’ outputs with ‘human’ performance on various tests,” they wrote. “Here, we ask, ‘Which humans?’”10

“Mainstream research on LLMs,” they added, “ignores the psychological diversity of ‘humans’ around the globe.”

Their strategy was straightforward: prompt Open AI’s GPT to answer the questions in the World Values Survey, and then compare the results to the answers that humans around the world gave to the same set of questions. The WVS documents a range of values including but not limited to issues of justice, moral principles, global governance, gender, family, religion, social tolerance, and trust. The team worked with data in the latest WVS surveys, collected from 2017 to 2022.

Recall that GPT does not give identical responses to identical prompts. To ensure that the GPT responses were representative, each of the WVS questions was posed to GPT 1000 times.11

The comparisons with human answers to the same surveys revealed striking similarities and contrasts. The article states:

“GPT was identified to be closest to the United States and Uruguay, and then to this cluster of cultures: Canada, Northern Ireland, New Zealand, Great Britain, Australia, Andorra, Germany, and the Netherlands. On the other hand, GPT responses were farthest away from cultures such as Ethiopia, Pakistan, and Kyrgyzstan.”

In other words, the GPT responses were similar to those of people in WEIRD societies.

The results are summarized in the graphic below. Countries in which humans gave WVS answers close to GPT’s answers are clustered at top left, while countries whose residents gave answers increasingly at variance with GPT’s answers trend along the line running down to the right.

“Figure 3. The scatterplot and correlation between the magnitude of GPT-human similarity and cultural distance from the United States as a highly WEIRD point of reference.” From Atari et al., “Which Humans?”

The team went on to consider the WVS responses in various categories including styles of analytical thinking, degrees of individualism, and ways of expressing and understanding personal identity. In these and other domains, they wrote, “people from contemporary WEIRD populations are an outlier in terms of their psychology from a global and historical perspective.” Yet the responses from GPT tracked the WEIRD populations rather than global averages.

Anyone who asks GPT a question with hopes of getting an unbiased answer is running a fool’s errand. Because the data sets include a large over-representation of WEIRD inputs, the outputs, for better or worse, will be no less WEIRD.

As Large Language Models are increasingly incorporated into decision-making tools and processes, their WEIRD biases become increasingly significant. By learning primarily from data that encodes viewpoints of dominant sectors of global society, and then expressing those values in decisions, LLMs are likely to further empower the powerful and marginalize the marginalized.

In the next installment we’ll look at the effects of AI and LLMs on employment conditions, now and in the near future.

Notes

1 Emily Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?”, Association for Computing Machinery Digital Library, 1 March 2021.

2 John Naughton, “Google might ask questions about AI ethics, but it doesn’t want answers”, The Guardian, 13 March 2021.

3 As quoted in Elizabeth Weil, “You Are Not a Parrot”, New York Magazine, March 1, 2023.

4 Bender, Gebru et al, “On the Dangers of Stochastic Parrots.”

5 Stephen Wolfram, “What Is ChatGPT Doing … and Why Does It Work?”, 14 February 2023.

6 Alexis T. Baria and Keith Cross, “The brain is a computer is a brain: neuroscience’s internal debate and the social significance of the Computational Metaphor”, arXiv, 18 July 2021.

7 Angie Wang, “Is My Toddler a Stochastic Parrot?”, The New Yorker, 15 November 2023.

8 The phrase “counterfeit people” is attributed to philosopher David Dennett, quoted by Elizabeth Weil in “You Are Not a Parrot”, New York Magazine.

9 Bender, Gebru et al, “On the Dangers of Stochastic Parrots.”

10 Mohammed Atari, Mona J. Xue, Peter S. Park, Damián E. Blasi, and Joseph Henrich, “Which Humans?”, arXiv, 22 September 2023.

11 Specifically, the team “ran both GPT 3 and 3.5; they were similar. The paper’s plots are based on 3.5.” Email correspondence with study author Mohammed Atari.

Image at top of post: “The Evolution of Intelligence”, illustration by Bart Hawkins Kreps, posted under CC BY-SA 4.0 DEED license, adapted from “The Yin and Yang of Human Progress”, (Wikimedia Commons), and from parrot illustration courtesy of Judith Kreps Hawkins.

“Warning. Data Inadequate.”

Bodies, Minds, and the Artificial Intelligence Industrial Complex, part three
Also published on Resilience.

“The Navy revealed the embryo of an electronic computer today,” announced a New York Times article, “that it expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.”1

A few paragraphs into the article, “the Navy” was quoted as saying the new “perceptron” would be the first non-living mechanism “capable of receiving, recognizing and identifying its surroundings without any human training or control.”

This example of AI hype wasn’t the first and won’t be the last, but it is a bit dated. To be precise, the Times story was published on July 8, 1958.

Due to its incorporation of a simple “neural network” loosely analogous to the human brain, the perceptron of 1958 is recognized as a forerunner of today’s most successful “artificial intelligence” projects – from facial recognition systems to text extruders like ChatGPT. It’s worth considering this early device in some detail.

In particular, what about the claim that the perceptron could identify its surroundings “without any human training or control”? Sixty years on, the descendants of the perceptron have “learned” a great deal, and can now identify, describe and even transform millions of images. But that “learning” has involved not only billions of transistors, and trillions of watts, but also millions of hours of labour in “human training and control.”

Seeing is not perceiving

When we look at a real-world object – for example, a tree – sensors in our eyes pass messages through a network of neurons and through various specialized areas of the brain. Eventually, assuming we are old enough to have learned what a tree looks like, and both our eyes and the required parts of our brains are functioning well, we might say “I see a tree.” In short, our eyes see a configuration of light, our neural network processes that input, and the result is that our brains perceive and identify a tree.

Accomplishing the perception with electronic computing, it turns out, is no easy feat.

The perceptron invented by Dr. Frank Rosenblatt in the 1950s used a 20 pixel by 20 pixel image sensor, paired with an IBM 704 computer. Let’s look at some simple images, and how a perceptron might process the data to produce a perception. 

Images created by the author.

In the illustration at left above, what the camera “sees” at the most basic level is a column of pixels that are “on”, with all the other pixels “off”. However, if we train the computer by giving it nothing more than labelled images of the numerals from 0 to 9, the perceptron can recognize the input as matching the numeral “1”. If we then add training data in the form of labelled images of the characters in the Latin-script alphabet in a sans serif font, the perceptron can determine that it matches, equally well, the numeral “1”, the lower-case letter “l”, or an upper-case letter “I”.

The figure at right is considerably more complex. Here our perceptron is still working with a low-resolution grid, but pixels can be not only “on” or “off” – black or white – but various shades of grey. To complicate things further, suppose more training data has been added, in the form of hand-written letters and numerals, plus printed letters and numerals in an oblique sans serif font. The perceptron might now determine the figure is a numeral “1” or a lower-case “l” or upper-case “I”, either hand-written or printed in an oblique font, each with an equal probability. The perceptron is learning how to be an optical character recognition (OCR) system, though to be very good at the task it would need the ability to use context to the rank the probabilities of a numeral “1”, a lower-case “l”, or an upper-case “I”.

The possibilities multiply infinitely when we ask the perceptron about real-world objects. In the figure below, a bit of context, in the form of a visual ground, is added to the images. 

Images created by the author.

Depending, again, on the labelled training data already input to the computer, the perceptron may “see” the image at left as a tall tower, a bare tree trunk, or the silhouette of a person against a bright horizon. The perceptron might see, on the right, a leaning tree or a leaning building – perhaps the Leaning Tower of Pisa. With more training images and with added context in the input image – shapes of other buildings, for example – the perceptron might output with high statistical confidence that the figure is actually the Leaning Tower of Leeuwarden.

Today’s perceptrons can and do, with widely varying degrees of accuracy and reliability, identify and name faces in crowds, label the emotions shown by someone in a recorded job interview, analyse images from a surveillance drone and indicate that a person’s activities and surroundings match the “signature” of terrorist operations, or identify a crime scene by comparing an unlabelled image with photos of known settings from around the world. Whether right or wrong, the systems’ perceptions sometimes have critical consequences: people can be monitored, hired, fired, arrested – or executed in an instant by a US Air Force Reaper drone.

As we will discuss below, these capabilities have been developed with the aid of millions of hours of poorly-paid or unpaid human labour.

The Times article of 1958, however, described Dr. Rosenblatt’s invention this way: “the machine would be the first device to think as the human brain. As do human beings, Perceptron will make mistakes at first, but will grow wiser as it gains experience ….” The kernel of truth in that claim lies in the concept of a neural network.

Rosenblatt told the Times reporter “he could explain why the machine learned only in highly technical terms. But he said the computer had undergone a ‘self-induced change in the wiring diagram.’”

I can empathize with that Times reporter. I still hope to find a person sufficiently intelligent to explain the machine learning process so clearly that even a simpleton like me can fully understand. However, New Yorker magazine writers in 1958 made a good attempt. As quoted in Matteo Pasquinelli’s book The Eye of the Master, the authors wrote:

“If a triangle is held up to the perceptron’s eye, the association units connected with the eye pick up the image of the triangle and convey it along a random succession of lines to the response units, where the image is registered. The next time the triangle is held up to the eye, its image will travel along the path already travelled by the earlier image. Significantly, once a particular response has been established, all the connections leading to that response are strengthened, and if a triangle of a different size and shape is held up to the perceptron, its image will be passed along the track that the first triangle took.”2

With hundreds, thousands, millions and eventually billions of steps in the perception process, the computer gets better and better at interpreting visual inputs.

Yet this improvement in machine perception comes at a high ecological cost. A September 2021 article entitled “Deep Learning’s Diminishing Returns” explained:

“[I]n 2012 AlexNet, the model that first showed the power of training deep-learning systems on graphics processing units (GPUs), was trained for five to six days using two GPUs. By 2018, another model, NASNet-A, had cut the error rate of AlexNet in half, but it used more than 1,000 times as much computing to achieve this.”

The authors concluded that, “Like the situation that Rosenblatt faced at the dawn of neural networks, deep learning is today becoming constrained by the available computational tools.”3

The steep increase in the computing demands of AI is illustrated in a graph by Anil Ananthaswamy.

“The Drive to Bigger AI Models” shows that AI models used for language and image generation have grown in size by several orders of magnitude since 2010.  Graphic from “In AI, is Bigger Better?”, by Anil Ananthaswamy, Nature, 9 March 2023.

Behold the Mechanical Turk

In the decades since Rosenblatt built the first perceptron, there were periods when progress in this field seemed stalled. Additional theoretical advances in machine learning, a many orders-of-magnitude increase in computer processing capability, and vast quantities of training data were all prerequisites for today’s headline-making AI systems. In Atlas of AI, Kate Crawford gives a fascinating account of the struggle to acquire that data.

Up to the 1980s artificial intelligence researchers didn’t have access to large quantities of digitized text or digitized images, and the type of machine learning that makes news today was not yet possible. The lengthy antitrust proceedings against IBM provided an unexpected boost to AI research, in the form of a hundred million digital words from legal proceedings. In the 1990s, court proceedings against Enron collected more than half a million email messages sent among Enron employees. This provided text exchanges in everyday English, though Crawford notes wording “represented the gender, race, and professional skews of those 158 workers.”

And the data floodgates were just beginning to open. As Crawford describes the change,

“The internet, in so many ways, changed everything; it came to be seen in the AI research field as something akin to a natural resource, there for the taking. As more people began to upload their images to websites, to photo-sharing services, and ultimately to social media platforms, the pillaging began in earnest. Suddenly, training sets could reach a size that scientists in the 1980s could never have imagined.”4

It took two decades for that data flood to become a tsunami. Even then, although images were often labelled and classified for free by social media users, the labels and classifications were not always consistent or even correct. There remained a need for humans to look at millions of images and create or check the labels and classifications.

Developers of the image database ImageNet collected 14 million images and eventually organized them into over twenty thousand categories. They initially hired students in the US for labelling work, but concluded that even at $10/hour, this work force would quickly exhaust the budget.

Enter the Mechanical Turk.

The original Mechanical Turk was a chess-playing scam originally set up in 1770 by a Hungarian inventor. An apparently autonomous mechanical human model, dressed in the Ottoman fashion of the day, moved chess pieces and could beat most human chess players. Decades went by before it was revealed that a skilled human chess player was concealed inside the machine for each exhibition, controlling all the motions.

In the early 2000s, Amazon developed a web platform by which AI developers, among others, could contract gig workers for many tasks that were ostensibly being done by artificial intelligence. These tasks might include, for example, labelling and classifying photographic images, or making judgements about outputs from AI-powered chat experiments. In a rare fit of honesty, Amazon labelled the process “artificial artificial intelligence”5 and launched its service, Amazon Mechanical Turk, in 2005.

screen shot taken 3 February 2024, from opening page at mturk.com.

Crawford writes,

“ImageNet would become, for a time, the world’s largest academic user of Amazon’s Mechanical Turk, deploying an army of piecemeal workers to sort an average of fifty images a minute into thousands of categories.”6

Chloe Xiang described this organization of work for Motherboard in an article entitled “AI Isn’t Artificial or Intelligent”:

“[There is a] large labor force powering AI, doing jobs that include looking through large datasets to label images, filter NSFW content, and annotate objects in images and videos. These tasks, deemed rote and unglamorous for many in-house developers, are often outsourced to gig workers and workers who largely live in South Asia and Africa ….”7

Laura Forlano, Associate Professor of Design at Illinois Institute of Technology, told Xiang “what human labor is compensating for is essentially a lot of gaps in the way that the systems work.”

Xiang concluded,

“Like other global supply chains, the AI pipeline is greatly imbalanced. Developing countries in the Global South are powering the development of AI systems by doing often low-wage beta testing, data annotating and labeling, and content moderation jobs, while countries in the Global North are the centers of power benefiting from this work.”

In a study published in late 2022, Kelle Howson and Hannah Johnston described why “platform capitalism”, as embodied in Mechanical Turk, is an ideal framework for exploitation, given that workers bear nearly all the costs while contractors take no responsibility for working conditions. The platforms are able to enroll workers from many countries in large numbers, so that workers are constantly low-balling to compete for ultra-short-term contracts. Contractors are also able to declare that the work submitted is “unsatisfactory” and therefore will not be paid, knowing the workers have no effective recourse and can be replaced by other workers for the next task. Workers are given an estimated “time to complete” before accepting a task, but if the work turns out to require two or three times as many hours, the workers are still only paid for the hours specified in the initial estimate.8

A survey of 700 cloudwork employees (or “independent contractors” in the fictive lingo of the gig work platforms) found about 34% of the time they spent on these platforms was unpaid. “One key outcome of these manifestations of platform power is pervasive unpaid labour and wage theft in the platform economy,” Howson and Johnston wrote.9 From the standpoint of major AI ventures at the top of the extraction pyramid, pervasive wage theft is not a bug in the system, it is a feature.

The apparently dazzling brilliance of AI-model creators and semi-conductor engineers gets the headlines in western media. But without low-paid or unpaid work by employees in the Global South, “AI systems won’t function,” Crawford writes. “The technical AI research community relies on cheap, crowd-sourced labor for many tasks that can’t be done by machines.”10

Whether vacuuming up data that has been created by the creative labour of hundreds of millions of people, or relying on tens of thousands of low-paid workers to refine the perception process for reputedly super-intelligent machines, the AI value chain is another example of extractivism.

“AI image and text generation is pure primitive accumulation,” James Bridle writes, “expropriation of labour from the many for the enrichment and advancement of a few Silicon Valley technology companies and their billionaire owners.”11

“All seven emotions”

New AI implementations don’t usually start with a clean slate, Crawford says – they typically borrow classification systems from earlier projects.

“The underlying semantic structure of ImageNet,” Crawford writes, “was imported from WordNet, a database of word classifications first developed at Princeton University’s Cognitive Science Laboratory in 1985 and funded by the U.S. Office of Naval Research.”12

But classification systems are unavoidably political when it comes to slotting people into categories. In the ImageNet groupings of pictures of humans, Crawford says, “we see many assumptions and stereotypes, including race, gender, age, and ability.”

She explains,

“In ImageNet the category ‘human body’ falls under the branch Natural Object → Body → Human Body. Its subcategories include ‘male body,’ ‘person,’ ‘juvenile body,’ ‘adult body,’ and ‘female body.’ The ‘adult body’ category contains the subclasses ‘adult female body’ and ‘adult male body.’ There is an implicit assumption here that only ‘male’ and ‘female’ bodies are recognized as ‘natural.’”13

Readers may have noticed that US military agencies were important funders of some key early AI research: Frank Rosenblatt’s perceptron in the 1950s, and the WordNet classification scheme in the 1980s, were both funded by the US Navy.

For the past six decades, the US Department of Defense has also been interested in systems that might detect and measure the movements of muscles in the human face, and in so doing, identify emotions. Crawford writes, “Once the theory emerged that it is possible to assess internal states by measuring facial movements and the technology was developed to measure them, people willingly adopted the underlying premise. The theory fit what the tools could do.”14

Several major corporations now market services with roots in this military-funded research into machine recognition of human emotion – even though, as many people have insisted, the emotions people express on their faces don’t always match the emotions they are feeling inside.

Affectiva is a corporate venture spun out of the Media Lab at Massachusetts Institute of Technology. On their website they claim “Affectiva created and defined the new technology category of Emotion AI, and evangelized its many uses across industries.” The opening page of affectiva.com spins their mission as “Humanizing Technology with Emotion AI.”

Who might want to contract services for “Emotion AI”? Media companies, perhaps, want to “optimize content and media spend by measuring consumer emotional responses to videos, ads, movies and TV shows – unobtrusively and at scale.” Auto insurance companies, perhaps, might want to keep their (mechanical) eyes on you while you drive: “Using in-cabin cameras our AI can detect the state, emotions, and reactions of drivers and other occupants in the context of a vehicle environment, as well as their activities and the objects they use. Are they distracted, tired, happy, or angry?”

Affectiva’s capabilities, the company says, draw on “the world’s largest emotion database of more than 80,000 ads and more than 14.7 million faces analyzed in 90 countries.”15 As reported by The Guardian, the videos are screened by workers in Cairo, “who watch the footage and translate facial expressions to corresponding emotions.”6

There is a slight problem: there is no clear and generally accepted definition of an emotion, nor general agreement on just how many emotions there might be. But “emotion AI” companies don’t let those quibbles get in the way of business.

Amazon’s Rekognition service announced in 2019 “we have improved accuracy for emotion detection (for all 7 emotions: ‘Happy’, ‘Sad’, ‘Angry’, ‘Surprised’, ‘Disgusted’, ‘Calm’ and ‘Confused’)” – but they were proud to have “added a new emotion: ‘Fear’.”17

Facial- and emotion-recognition systems, with deep roots in military and intelligence agency research, are now widely employed not only by these agencies but also by local police departments. Their use is not confined to governments: they are used in the corporate world for a wide range of purposes. And their production and operation likewise crosses public-private lines; though much of the initial research was government-funded, the commercialization of the technologies today allows corporate interests to sell the resulting services to public and private clients around the world.

What is the likely impact of these AI-aided surveillance tools? Dan McQuillan sees it this way:

“We can confidently say that the overall impact of AI in the world will be gendered and skewed with respect to social class, not only because of biased data but because engines of classification are inseparable from systems of power.”18

In our next installment we’ll see that biases in data sources and classification schemes are reflected in the outputs of the GPT large language model.

Image at top of post: The Senture computer server facility in London, Ky, on July 14, 2011, photo by US Department of Agriculture, public domain, accessed on flickr.

Title credit: the title of this post quotes a lyric of “Data Inadequate”, from the 1998 album Live at Glastonbury by Banco de Gaia.

Notes

1 “New Navy Device Learns By Doing,” New York Times, July 8, 1958, page 25.

2 “Rival”, in The New Yorker, by Harding Mason, D. Stewart, and Brendan Gill, November 28, 1958, synopsis here. Quoted by Matteo Pasquinelli in The Eye of the Master: A Social History of Artificial Intelligence, Verso Books, October 2023, page 137.

3  “Deep Learning’s Diminishing Returns”, by Neil C. Thompson, Kristjan Greenewald, Keeheon Lee, and Gabriel F. Manso, IEEE Spectrum, 24 September 2021.

4 Crawford, Kate, Atlas of AI, Yale University Press, 2021.

5 This phrase is cited by Elizabeth Stevens and attributed to Jeff Bezos, in “The mechanical Turk: a short history of ‘artificial artificial intelligence’”, Cultural Studies, 08 March 2022.

6 Crawford, Atlas of AI.

7 Chloe Xiang, “AI Isn’t Artificial or Intelligent: How AI innovation is powered by underpaid workers in foreign countries,” Motherboard, 6 December 2022.

8 Kelle Howson and Hannah Johnston, “Unpaid labour and territorial extraction in digital value networks,” Global Network, 26 October 2022.

9 Howson and Johnston, “Unpaid labour and territorial extraction in digital value networks.”

10 Crawford, Atlas of AI.

11 James Bridle, “The Stupidity of AI”, The Guardian, 16 Mar 2023.

12 Crawford, Atlas of AI.

13 Crawford, Atlas of AI.

14 Crawford, Atlas of AI.

15 Quotes from Affectiva taken from www.affectiva.com on 5 February 2024.

16 Oscar Schwarz, “Don’t look now: why you should be worried about machines reading your emotions,” The Guardian, 6 March 2019.

17 From Amazon Web Services Rekognition website, accessed on 5 February 2024; italics added.

18 Dan McQuillan, “Post-Humanism, Mutual Aid,” in AI for Everyone? Critical Perspectives, University of Westminster Press, 2021.

Artificial Intelligence in the Material World

Bodies, Minds, and the Artificial Intelligence Industrial Complex, part two
Also published on Resilience.

Picture a relatively simple human-machine interaction: I walk two steps, flick a switch on the wall, and a light comes on.

Now picture a more complex interaction. I say, “Alexa, turn on the light” – and, if I’ve trained my voice to match the classifications in the electronic monitoring device and its associated global network, a light comes on.

“In this fleeting moment of interaction,” write Kate Crawford and Vladan Joler, “a vast matrix of capacities is invoked: interlaced chains of resource extraction, human labor and algorithmic processing across networks of mining, logistics, distribution, prediction and optimization.”

“The scale of resources required,” they add, “is many magnitudes greater than the energy and labor it would take a human to … flick a switch.”1

Crawford and Joler wrote these words in 2018, at a time when “intelligent assistants” were recent and rudimentary products of AI. The industry has grown by leaps and bounds since then – and the money invested is matched by the computing resources now devoted to processing and “learning” from data.

In 2021, a much-discussed paper found that “the amount of compute used to train the largest deep learning models (for NLP [natural language processing] and other applications) has increased 300,000x in 6 years, increasing at a far higher pace than Moore’s Law.”2

An analysis in 2023 backed up this conclusion. Computing calculations are often measured in Floating Point OPerations. A Comment piece in the journal Nature Machine Intelligence illustrated the steep rise in the number of FLOPs used in training recent AI models.

Changes in the number of FLOPs needed for state-of-the-art AI model training, graph from “Reporting electricity consumption is essential for sustainable AI”, Charlotte Debus, Marie Piraud, Achim Streit, Fabian Theis & Markus Götz, Nature Machine Intelligence, 10 November 2023. AlexNet is a neural network model used to great effect with the image classification database ImageNet, which we will discuss in a later post. GPT-3 is a Large Language Model developed by OpenAI, for which Chat-GPT is the free consumer interface.

With the performance of individual AI-specialized computer chips now measured in TeraFLOPs, and thousands of these chips harnessed together in an AI server farm, the electricity consumption of AI is vast.

As many researchers have noted, accurate electricity consumption figures are difficult to find, making it almost impossible to calculate the worldwide energy needs of the AI Industrial Complex.

However, Josh Saul and Dina Bass reported last year that

“Artificial intelligence made up 10 to 15% of [Google’s] total electricity consumption, which was 18.3 terawatt hours in 2021. That would mean that Google’s AI burns around 2.3 terawatt hours annually, about as much electricity each year as all the homes in a city the size of Atlanta.”3

However, researcher Alex de Vries reported if an AI system similar to ChatGPT were used for each Google search, electricity usage would spike to 29.2 TWh just for the search engine.4

In Scientific American, Lauren Leffer cited projections that Nvidia, manufacturer of the most sophisticated chips for AI servers, will ship “1.5 million AI server units per year by 2027.”

“These 1.5 million servers, running at full capacity,” she added, “would consume at least 85.4 terawatt-hours of electricity annually—more than what many small countries use in a year, according to the new assessment.”5

OpenAI CEO Sam Altman expects AI’s appetite for energy will continue to grow rapidly. At the Davos confab in January 2024 he told the audience, “We still don’t appreciate the energy needs of this technology.” As quoted by The Verge, he added, “There’s no way to get there without a breakthrough. We need [nuclear] fusion or we need like radically cheaper solar plus storage or something at massive scale.” Altman has invested $375 million in fusion start-up Helion Energy, which hopes to succeed soon with a technology that has stubbornly remained 50 years in the future for the past 50 years.

In the near term, at least, electricity consumption will act as a brake on widespread use of AI in standard web searches, and will restrict use of the most sophisticated AI models to paying customers. That’s because the cost of AI use can be measured not only in watts, but in dollars and cents.

Shortly after the launch of Chat-GPT,  Sam Altman was quoted as saying that Chat-GPT cost “probably single-digit cents per chat.” Pocket change – until you multiply it by perhaps 10 million users each day. Citing figures from SemiAnalysis, the Washington Post reported that by February 2023, “ChatGPT was costing OpenAI some $700,000 per day in computing costs alone.” Will Oremus concluded,

“Multiply those computing costs by the 100 million people per day who use Microsoft’s Bing search engine or the more than 1 billion who reportedly use Google, and one can begin to see why the tech giants are reluctant to make the best AI models available to the public.”6

In any case, Alex de Vries says, “NVIDIA does not have the production capacity to promptly deliver 512,821 A100 HGX servers” which would be required to pair every Google search with a state-of-the-art AI model. And even if Nvidia could ramp up that production tomorrow, purchasing the computing hardware would cost Google about $100 billion USD.

Detail from: Nvidia GeForce RTX 2080, (TU104 | Turing), (Polysilicon | 5x | External Light), photograph by Fritzchens Fritz, at Wikimedia Commons, licensed under Creative Commons CC0 1.0 Universal Public Domain Dedication

A 457,000-item supply chain

Why is AI computing hardware so difficult to produce and so expensive? To understand this it’s helpful to take a greatly simplified look at a few aspects of computer chip production.

That production begins with silicon, one of the most common elements on earth and a basic constituent of sand. The silicon must be refined to 99.9999999% purity before being sliced into wafers.

Image from Intel video From Sand to Silicon: The Making of a Microchip.

Eventually each silicon wafer will be augmented with an extraordinarily fine pattern of transistors. Let’s look at the complications involved in just one step, the photolithography that etches a microscopic pattern in the silicon.

As Chris Miller explains in Chip War, the precision of photolithography is determined by, among other factors, the wavelength of the light being used: “The smaller the wavelength, the smaller the features that could be carved onto chips.”7 By the early 1990s, chipmakers had learned to pack more than 1 million transistors onto one of the chips used in consumer-level desktop computers. To enable the constantly climbing transistor count, photolithography tool-makers were using deep ultraviolet light, with wavelengths of about 200 nanometers (compared to visible light with wavelengths of about 400 to 750 nanometers; a nanometer is one-billionth of a meter). It was clear to some industry figures, however, that the wavelength of deep ultraviolet light would soon be too long for continued increases in the precision of etching and for continued increases in transistor count.

Thus began the long, difficult, and immensely expensive development of Extreme UltraViolet (EUV) photolithography, using light with a wavelength of about 13.5 nanometers.

Let’s look at one small part of the complex EUV photolithography process: producing and focusing the light. In Miller’s words,

“[A]ll the key EUV components had to be specially created. … Producing enough EUV light requires pulverizing a small ball of tin with a laser. … [E]ngineers realized the best approach was to shoot a tiny ball of tin measuring thirty-millionths of a meter wide moving through a vacuum at a speed of around two hundred miles an hour. The tin is then struck twice with a laser, the first pulse to warm it up, the second to blast it into a plasma with a temperature around half a million degrees, many times hotter than the surface of the sun. This process of blasting tin is then repeated fifty thousand times per second to produce EUV light in the quantities necessary to fabricate chips.”8

Heating the tin droplets to that temperature, “required a carbon dioxide-based laser more powerful than any that previously existed.”9 Laser manufacturer Trumpf worked for 10 years to develop a laser powerful enough and reliable enough – and the resulting tool had “exactly 457,329 component parts.”10

Once the extremely short wavelength light could be reliably produced, it needed to be directed with great precision – and for that purpose German lens company Zeiss “created mirrors that were the smoothest objects ever made.”11

Nearly 20 years after development of EUV lithography began, this technique is standard for the production of sophisticated computer chips which now contain tens of billions of transistors each. But as of 2023, only Dutch company ASML had mastered the production of EUV photolithography machines for chip production. At more than $100 million each, Miller says “ASML’s EUV lithography tool is the most expensive mass-produced machine tool in history.”12

Landscape Destruction: Rio Tinto Kennecott Copper Mine from the top of Butterfield Canyon. Photographed in 665 nanometer infrared using an infrared converted Canon 20D and rendered in channel inverted false color infrared, photo by arbyreed, part of the album Kennecott Bingham Canyon Copper Mine, on flickr, licensed via CC BY-NC-SA 2.0 DEED.

No, data is not the “new oil”

US semi-conductor firms began moving parts of production to Asia in the 1960s. Today much of semi-conductor manufacturing and most of computer and phone assembly is done in Asia – sometimes using technology more advanced than anything in use within the US.

The example of EUV lithography indicates how complex and energy-intensive chipmaking has become. At countless steps from mining to refining to manufacturing, chipmaking relies on an industrial infrastructure that is still heavily reliant on fossil fuels.

Consider the logistics alone. A wide variety of metals, minerals, and rare earth elements, located at sites around the world, must be extracted, refined, and processed. These materials must then be transformed into the hundreds of thousands of parts that go into computers, phones, and routers, or which go into the machines that make the computer parts.

Co-ordinating all of this production, and getting all the pieces to where they need to be for each transformation, would be difficult if not impossible if it weren’t for container ships and airlines. And though it might be possible someday to run most of those processes on renewable electricity, for now those operations have a big carbon footprint.

It has become popular to proclaim that “data is the new oil”13, or “semi-conductors are the new oil”14. This is nonsense, of course. While both data and semi-conductors are worth a lot of money and a lot of GDP growth in our current economic context, neither one produces energy – they depend on available and affordable energy to be useful.

A world temporarily rich in surplus energy can produce semi-conductors to extract economic value from data. But warehouses of semi-conductors and petabytes of data will not enable us to produce surplus energy.

Artificial Intelligence powered by semi-conductors and data could, perhaps, help us to use the surplus energy much more efficiently and rationally. But that would require a radical change in the economic religion that guides our whole economic system, including the corporations at the top of the Artificial Intelligence Industrial Complex.

Meanwhile the AI Industrial Complex continues to soak up huge amounts of money and energy.

Open AI CEO Sam Altman has been in fund-raising mode recently, seeking to finance a network of new semi-conductor fabrication plants. As reported in Fortune, “Constructing a single state-of-the-art fabrication plant can require tens of billions of dollars, and creating a network of such facilities would take years. The talks with [Abu Dhabi company] G42 alone had focused on raising $8 billion to $10 billion ….”

This round of funding would be in addition to the $10 billion Microsoft has already invested in Open AI. Why would Altman want to get into the hardware production side of the Artificial Intelligence Industrial Complex, in addition to Open AI’s leading role in software operations? According to Fortune,

“Since OpenAI released ChatGPT more than a year ago, interest in artificial intelligence applications has skyrocketed among companies and consumers. That in turn has spurred massive demand for the computing power and processors needed to build and run those AI programs. Altman has said repeatedly that there already aren’t enough chips for his company’s needs.”15

Becoming data

We face the prospect, then, of continuing rapid growth in the Artificial Intelligence Industrial Complex, accompanied by continuing rapid growth in the extraction of materials and energy – and data.

How will major AI corporations obtain and process all the data that will keep these semi-conductors busy pumping out heat?

Consider the light I turned on at the beginning of this post. If I simply flick the switch on the wall and the light goes off, the interaction will not be transformed into data. But if I speak to an Echo, asking Alexa to turn off the light, many data points are created and integrated into Amazon’s database: the time of the interaction, the IP address and physical location where this takes place, whether I speak English or some other language, whether my spoken words are unclear and the device asks me to repeat, whether the response taken appears to meet my approval, or whether I instead ask for the response to be changed. I would be, in Kate Crawford’s and Vladan Joler’s words, “simultaneously a consumer, a resource, a worker, and a product.”15

By buying into the Amazon Echo world,

“the user has purchased a consumer device for which they receive a set of convenient affordances. But they are also a resource, as their voice commands are collected, analyzed and retained for the purposes of building an ever-larger corpus of human voices and instructions. And they provide labor, as they continually perform the valuable service of contributing feedback mechanisms regarding the accuracy, usefulness, and overall quality of Alexa’s replies. They are, in essence, helping to train the neural networks within Amazon’s infrastructural stack.”16

How will AI corporations monetize that data so they can cover their hardware and energy costs, and still return a profit on their investors’ money? We’ll turn to that question in coming installments.

Image at top of post: Bingham Canyon Open Pit Mine, Utah, photo by arbyreed, part of the album Kennecott Bingham Canyon Copper Mine, on flickr, licensed via CC BY-NC-SA 2.0 DEED.

Notes

1 Kate Crawford and Vladan Joler, Anatomy of an AI System: The Amazon Echo as an anatomical map of human labor, data and planetary resources”, 2018.

2 Emily M. Bender, Timnit Gebru and Angelina McMillan-Major, Shmargaret Shmitchell, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” ACM Digital Library, March 1, 2021. Thanks to Paris Marx for introducing me to the work of Emily M. Bender on the excellent podcast Tech Won’t Save Us.

3 “Artificial Intelligence Is Booming—So Is Its Carbon Footprint”, Bloomberg, 9 March 2023.

4 Alex de Vries, “The growing energy footprint of artificial intelligence,” Joule, 18 October 2023.

5 Lauren Leffer, “The AI Boom Could Use a Shocking Amount of Electricity,” Scientific American, 13 October 2023.

6 Will Oremus, “AI chatbots lose money every time you use them. That is a problem.” Washington Post, 5 June 2023.

7 Chris Miller, Chip War: The Fight for the World’s Most Critical Technology, Simon & Schuster, October 2022; page 183

8 Chip War, page 226.

9 Chip War, page 227.

10 Chip War, page 228.

11 Chip War, page 228.

12 Chip War, page 230.

13 For example, in “Data Is The New Oil — And That’s A Good Thing,” Forbes, 15 Nov 2019.

14  As in, “Semi-conductors may be to the twenty-first century what oil was to the twentieth,” Lawrence Summer, former US Secretary of the Treasury, in blurb to Chip War.

15  “OpenAI CEO Sam Altman is fundraising for a network of AI chips factories because he sees a shortage now and well into the future,” Fortune, 20 January 2024.

16 Kate Crawford and Vladan Joler, Anatomy of an AI System: The Amazon Echo as an anatomical map of human labor, data and planetary resources”, 2018.

Bodies, Minds, and the Artificial Intelligence Industrial Complex

Also published on Resilience.

This year may or may not be the year the latest wave of AI-hype crests and subsides. But let’s hope this is the year mass media slow their feverish speculation about the future dangers of Artificial Intelligence, and focus instead on the clear and present, right-now dangers of the Artificial Intelligence Industrial Complex.

Lost in most sensational stories about Artificial Intelligence is that AI does not and can not exist on its own, any more than other minds, including human minds, can exist independent of bodies. These bodies have evolved through billions of years of coping with physical needs, and intelligence is linked to and inescapably shaped by these physical realities.

What we call Artificial Intelligence is likewise shaped by physical realities. Computing infrastructure necessarily reflects the properties of physical materials that are available to be formed into computing machines. The infrastructure is shaped by the types of energy and the amounts of energy that can be devoted to building and running the computing machines. The tasks assigned to AI reflect those aspects of physical realities that we can measure and abstract into “data” with current tools. Last but certainly not least, AI is shaped by the needs and desires of all the human bodies and minds that make up the Artificial Intelligence Industrial Complex.

As Kate Crawford wrote in Atlas of AI,

“AI can seem like a spectral force — as disembodied computation — but these systems are anything but abstract. They are physical infrastructures that are reshaping the Earth, while simultaneously shifting how the world is seen and understood.”1

The metaphors we use for high-tech phenomena influence how we think of these phenomena. Take, for example, “the Cloud”. When we store a photo “in the Cloud” we imagine that photo as floating around the ether, simultaneously everywhere and nowhere, unconnected to earth-bound reality.

But as Steven Gonzalez Monserrate reminded us, “The Cloud is Material”. The Cloud is tens of thousands of kilometers of data cables, tens of thousands of server CPUs in server farms, hydroelectric and wind-turbine and coal-fired and nuclear generating stations, satellites, cell-phone towers, hundreds of millions of desktop computers and smartphones, plus all the people working to make and maintain the machinery: “the Cloud is not only material, but is also an ecological force.”2

It is possible to imagine “the Cloud” without an Artificial Intelligence Industrial Complex, but the AIIC, at least in its recent news-making forms, could not exist without the Cloud.

The AIIC relies on the Cloud as a source of massive volumes of data used to train Large Language Models and image recognition models. It relies on the Cloud to sign up thousands of low-paid gig workers for work on crucial tasks in refining those models. It relies on the Cloud to rent out computing power to researchers and to sell AI services. And it relies on the Cloud to funnel profits into the accounts of the small number of huge corporations at the top of the AI pyramid.

So it’s crucial that we reimagine both the Cloud and AI to escape from mythological nebulous abstractions, and come to terms with the physical, energetic, flesh-and-blood realities. In Crawford’s words,

“[W]e need new ways to understand the empires of artificial intelligence. We need a theory of AI that accounts for the states and corporations that drive and dominate it, the extractive mining that leaves an imprint on the planet, the mass capture of data, and the profoundly unequal and increasingly exploitative labor practices that sustain it.”3

Through a series of posts we’ll take a deeper look at key aspects of the Artificial Intelligence Industrial Complex, including:

  • the AI industry’s voracious and growing appetite for energy and physical resources;
  • the AI industry’s insatiable need for data, the types and sources of data, and the continuing reliance on low-paid workers to make that data useful to corporations;
  • the biases that come with the data and with the classification of that data, which both reflect and reinforce current social inequalities;
  • AI’s deep roots in corporate efforts to measure, control, and more effectively extract surplus value from human labour;
  • the prospect of “superintelligence”, or an AI that is capable of destroying humanity while living on without us;
  • the results of AI “falling into the wrong hands” – that is, into the hands of the major corporations that dominate AI, and which, as part of our corporate-driven economy, are driving straight towards the cliff of ecological suicide.

One thing this series will not attempt is providing a definition of “Artificial Intelligence”, because there is no workable single definition. The phrase “artificial intelligence” has come into and out of favour as different approaches prove more or less promising, and many computer scientists in recent decades have preferred to avoid the phrase altogether. Different programming and modeling techniques have shown useful benefits and drawbacks for different purposes, but it remains debatable whether any of these results are indications of intelligence.

Yet “artificial intelligence” keeps its hold on the imaginations of the public, journalists, and venture capitalists. Matteo Pasquinelli cites a popular Twitter quip that sums it up this way:

“When you’re fundraising, it’s Artificial Intelligence. When you’re hiring, it’s Machine Learning. When you’re implementing, it’s logistic regression.”4

Computers, be they boxes on desktops or the phones in pockets, are the most complex of tools to come into common daily use. And the computer network we call the Cloud is the most complex socio-technical system in history. It’s easy to become lost in the detail of any one of a billion parts in that system, but it’s important to also zoom out from time to time to take a global view.

The Artificial Intelligence Industrial Complex sits at the apex of a pyramid of industrial organization. In the next installment we’ll look at the vast physical needs of that complex.

Notes

1 Kate Crawford, Atlas of AI, Yale University Press, 2021.

2 Steven Gonzalez Monserrate, “The Cloud is Material” Environmental Impacts of Computation and Data Storage”, MIT Schwarzman College of Computing, January 2022.

3 Crawford, Atlas of AI, Yale University Press, 2021.

4 Quoted by Mateo Pasquinelli in “How A Machine Learns And Fails – A Grammar Of Error For Artificial Intelligence”, Spheres, November 2019.

Image at top of post: Margaret Henschel in Intel wafer fabrication plant, photo by Carol M. Highsmith, part of a collection placed in the public domain by the photographer and donated to the Library of Congress.

cloudy with sunny breaks

PHOTO POST

A long stretch of warm but gloomy weather finally made room for a week of old-fashioned winter, with brisk winds, the odd sunny day, and even some ice buildup on the shoreline.

Lightshower II

Lightshower

How cold did it get? Cold enough on Saturday that there was only one person fishing at the breakwater – but not cold enough for him to keep his gloves on.

Fishing at the edge

The cold weather was a delight to some of us, providing the kinds of sights we may only see for a few days a year.

Construction

Construction II

Waterline

We knew it wouldn’t last, of course. By Sunday night a warm wind picked up from the southwest, and by Monday morning the waves had chopped much of the shore ice into slush.

Splash at sunrise

By afternoon we were treated to a typical lakeshore squall, with warm fluffy snowflakes whipped along in a biting wet wind.

What Great Teeth

The snow drifted along with the sand, moving across the beach and straight into the harbour channel.

Heritage Winter

A storm like this might put you in mind of seeking shelter in a forest. If you’re small of stature, though – an ermine, perhaps, or a rabbit – quiet pathways through the lakeshore marsh are an even better place to get in out of the wind.

Shelter among the reeds

Photo at top of post: Cloudy with sunny breaks (full-size version here)

 

Finding safe paths through suburbia

Also published on Resilience

The post-WWII suburban settlement pattern assumes and reinforces car travel as the default transport choice for its residents. Do such settlements have a future when the temporary energy bonanza of the past 100 years falters? And can residents of suburbia begin to create that future today?

This series on the transition from car-dependency to walkability has examined the integral, sometimes convoluted relationship between land use planning and transportation planning. We’ve looked at major, top-down initiatives as well as small-scale efforts to intensify suburban land uses. This post will look primarily at small scale, from-the-ground-up efforts to make suburban travel safer for people who want to make more trips on foot or on bike.

The problems of suburbia arise not only at a local level, but are also due to national laws and subsidies that favour car ownership, state and provincial funding and specifications for expressways and major arterial roads, a housing development industry whose bread and butter is clearing land on the urban fringe for cookie-cutter subdivisions, and an entrenched culture within municipal governments that prioritizes throughput of vehicles in transportation plans.

Changes are needed at all of those levels – and some of those changes will take a lot of time, money, and political will. At a local level, though, political will can implement some important changes in very little time and with modest expenditures.

The Strong Towns organization promotes an approach that de-emphasizes large, comprehensive, expensive projects that will take years to produce results. By contrast, they advocate a simple, bottom-up approach to making small changes, starting right away:

“1. Humbly observe where people in the community struggle.

2. Ask the question: What is the next smallest thing we can do right now to address that struggle?

3. Do that thing. Do it right now.

4. Repeat.”1

Some of the barriers to walkability are small and can be quickly fixed – but in some cases they are left unfixed for years because “we are doing a transportation masterplan” which will, hopefully, propose a solution to be implemented years from now. A good example would be installing curb cuts that could make crossings accessible to someone pushing a stroller or traveling in a wheelchair. Simple improvements like this, when repeated at dozens of locations, can make life easier for many citizens and build hope and confidence that a municipality is moving in the right direction – even if larger and more elaborate changes are also needed.

A related approach, known as “tactical urbanism”, has been popularized by Mike Lydon and put into practice in many cities. (For an excellent introduction to Lydon’s approach see the video Tactical Urbanism: Transform your City Today! hosted by Gil Penalosa of 8/80 Cities.) Tactical Urbanism also looks for projects that can be implemented quickly and cheaply, though they might fit into a grand vision for much larger change to follow. By implementing changes quickly, on a pilot-project basis, this approach also allows much more effective public consultation.

As Lydon explains, typical public consultation processes fail to reach many of the people most affected by projects. The advantage of rapidly implemented pilot projects is that they allow public consultation to happen outdoors, onsite, where the people most affected by a change can see how the change is affecting their daily lives.

An example would be a “road diet”, in which a section of a four-lane collector road is reduced to three lanes – one travel lane in each direction, plus a shared center lane for left turns – thus freeing enough space for a protected bike lane on each side. Another example would be installing a “bump-out” at an intersection to reduce the unprotected distance a pedestrian needs to cross. These pilot projects can typically be done with nothing more expensive than paint and flexible, temporary plastic bollards. Following onsite consultations during the pilot project, the plan can be scrapped, modified, or implemented on a more durable permanent basis – all in less time than a comprehensive masterplan process would need to get to a draft stage.

Regular but temporary “open streets” programs – that is, closing streets to cars so they are open to people – have helped millions of people envision and understand how they could experience their cities in safer, more enjoyable, more pro-social ways. The most famous of these experiments began decades ago in Bogotá, Colombia. Today Bogotá’s program includes more than 100 km of city streets which are opened every Sunday, to a vast range of activities including exercise classes, street theatre, children’s games. The Open Streets program has spread to scores of cities, including many in North America, and has often led to permanent establishment of pedestrian blocks.2

“We’ll work with anyone – but we won’t wait for anyone”

Tactical urbanism programs often get their impetus from small groups of residents proposing changes to city staff. In some cases, though, tactical urbanist improvements are made directly by citizens who have tired of waiting for the slow wheels of bureaucracy to turn. This was the subject of a fascinating webinar entitled “Direct Action Gets the Goods: The Rise of Illicit Tactical Urbanism.”3 Led by Jessie Singer, author of There Are No Accidents, the webinar heard from anonymous direct action activists in Los Angeles, San Francisco, and Chattanooga. Their activities have included painting city-standard crosswalks at locations suggested by community members through a website form; installing benches at bus stops that lacked any nod to user comforts; and installing temporary bollards to convert a dangerous right-turn lane into a traffic-calming bump-out.

As the panelists explained, sometimes the citizen-installed crosswalks or benches were quickly removed by city staff. Just as often, however, city staff received so many messages of support for the new improvements that they were left in place, or quickly upgraded to a higher standard. In either case, the publicity the groups receive on social media ensures that important issues get a boost in visibility. Although advocacy work is sometimes seen as a win-or-lose game, a Crosswalks Collective Los Angeles member explained, “with guerrilla urbanism, there is no such thing as losing.”

“Where the sidewalk ends”, North St. Louis, photo by Paul Sableman, May 9, 2012, licensed via CC BY 2.0 DEED, accessed on Flickr.

Follow the footsteps

When city staff take a close look at what citizens are accomplishing or attempting to accomplish on their own, they may discover ways their suburban environments can be improved. In an article entitled “Walking to the Strip Mall,” Nico Larco notes that informal pedestrian routes are common around suburban strip malls, indicating that even without good infrastructure, significant numbers of people walk to these malls. He notes that:

“Pedestrian networks in suburbia are much more than just sidewalks along streets. They include sidewalks within private property, cut-throughs, the streets themselves, paved and unpaved bike paths, informal goat paths, makeshift gates in fences, and kickdowns.”4

While these routes make it easier for some residents to get to and from these malls, they are far from ideal. The routes may be muddy, rough, impassable for people pushing strollers, strewn with garbage, routed through ditches, vacant lots, woods, and may be unlit at night. They often also lead to the rear loading-dock area of a strip mall, rather than the parking lot side where store entrances are located.

However, city staff should be looking at each case to see whether it is feasible to formalize some of these informal routes to make them useful and safe for a greater number of nearby residents. For example, it may be possible to secure an easement on a strip of private land, so that an informal pedestrian route can be formalized, paved or otherwise maintained, and lighted. Perhaps a public access doorway can be installed at the rear of a building, providing straight-through access for pedestrians who would benefit from a formalized pathway from their homes to the commercial entrances of the mall.

Clearly, each case will be different and not all of the informal pedestrian paths are likely to be good candidates for upgrading. But if they don’t take seriously the “votes” of citizens who are already marking out paths with their steps, municipal officials will miss an important chance to learn and to improve their suburban environments.

Walkable, bikeable, or both?

Jeff Speck has written,

“Walkable cities are also bikeable cities, because bicycles thrive in environments that support pedestrians and also because bikeability makes driving less necessary.”5

Once supportive and safe infrastructure is provided, rates of walking and biking go up dramatically. But biking is likely to be even more significant in suburban contexts, simply because distances tend to be greater. For the foreseeable future, many suburban trips are likely to be too long for walking to be a practical option – but the range of bicycles is growing due to electrification.

With the widespread availability of electric-assist bikes, a big share of suburban trips are now fully within the range of adults of average fitness. E-bikes can be a convenient, healthy, and economical transportation choice for individuals. Several US states and cities are now providing subsidies to residents for purchases of e-bikes.6

A study of e-bike potential noted that in England, an average person could comfortably use a bike for a trip of 11 km (6.8 miles), while the same average person could go 20 km (12.4 miles) on an electric-assist bike.7 One conclusion is that e-bikes could reduce car use even more in rural and suburban areas, where transit services are poor and distances are longer, than in urban cores where there are many options for the mostly short trips.

According to the United States Office of Energy Efficiency & Renewable Energy, in 2021 just over 50% of all trips were three miles or less.8

Source: Estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources.

If the average resident of the US or Canada is as physically capable as the average resident of England, then even the trips in the third and fourth categories on the chart above would be feasible for many people on e-assist bikes. That would make bikes and e-bikes practical options for about 80% of trips – as long as there is safe infrastructure on which to ride those e-bikes.

The benefits of a switch by a significant segment of the population to e-bikes for many of their daily journeys would include not only a substantial reduction in traffic, but also a reduction in CO2 emissions, better health for the people making that lifestyle change, and significant cost reductions both for individuals and for cities.

Citing AAA figures, Michael Thomas wrote this month that

“After fuel, maintenance, insurance, taxes, and the like, owning and driving a new car in America costs $10,728 a year. My e-bike, by comparison, cost $2,000 off the rack and has near-negligible recurring charges.”

If a typical two-car family can trade one of their cars for an e-bike, that can make suburban housing suddenly much more affordable. But even the cost savings aren’t “the real reason you should get an e-bike,” Thomas wrote, because

“Study after study shows that people with longer car commutes are more likely to experience poor health outcomes and lower personal well-being—and that cyclists are the happiest commuters.”9

Should your municipality consider offering subsidies to encourage e-bike use? Consider that a $400 (US) subsidy could cover from 20% to 40% of the cost of a good e-bike, while that amount would be too small to be relevant to the potential buyer of an electric car. Consider also that e-bike charging stations could be installed at libraries, schools, shopping malls, and other destinations at a small fraction of the cost of electric car chargers, with little or no need to install electric grid upgrades.

* * *

There are a host of complications in transforming car-dependent suburbs. When I started this series on car-dependent suburbs, I planned to finish with one post on making the transformation to walkable, bikeable communities. That concluding post has now stretched to three long posts and I’ve just scratched the surface.

Clearly the best option would be to stop digging ourselves into these holes: stop building car-dependent suburbs now. But if you’re already in a car-dependent suburb, the time to start the transition to a walkable community is also now.

Notes

1 In “The Strong Towns Approach to Public Investment,” by Charles Marohn, Strong Towns, Sept 23, 2019.

2 See The Open Streets Project for information on these programs.

3 Part of the Vision Zero Cities 2023 conference sponsored by Transportation Alternatives, Oct 18, 2023.

4 “Walking to the Strip Mall: Retrofitting Informal Pedestrian Paths,” by Nico Larco, in Retrofitting Sprawl: Addressing Seventy Years of Failed Urban Form, edited by Emily Talen, University of Georgia Press, 2015.

5 Walkable City, 10th Anniversary Edition, by Jeff Speck, Picador, 2022, page 72.

6 “Free electric bikes? How many US cities and states are handling e-bike subsidies,” electric.co, 19 Feb 2023.

7 “E-bikes and their capability to reduce car CO2 emissions,” by Ian Philips, Jillian Anable and Tim Chatterton, Transport Policy, February 2022.

8 “More than Half of all Daily Trips Were Less than Three Miles in 2021,” US Office of Energy Efficiency & Renewable Energy, March 21, 2022.

9 “The real reason you should get an e-bike,” by Michael Thomas, The Atlantic, 20 Oct 2023.

Photo at top of page: “A man walks south on Cobb Parkway just south of Southern Polytechnic State University and Life University, a stretch of US 41 lacking sidewalks almost entirely. He’s got a long walk ahead to find the next crosswalk, which is 0.9 miles from the last one at Highway 120 — a stretch that is also almost completely devoid of sidewalks on both sides of the street.” Photo by Transportation For America, Metro Atlanta Pedestrians series, on Flickr, taken March 30, 2012, licensed via CC 2 BY-NC-ND 2.0 DEED.

Turning a new leaf in suburbia

Also published on Resilience

Social critic James Howard Kunstler referred to suburban sprawl as “the greatest misallocation of resources in history.”1 In his view, “The suburbs have three destinies – as slums, salvage yards, and ruins.”2

While agreeing that suburbs in their current form are “hopelessly maladapted to the coming world of energy descent,” permaculture pioneer David Holmgren nevertheless believes that “Low-density detached housing with gardens is the ideal place for beginning a bottom-up revolution to recreate the household and community non-monetary economies that our recent forebears took for granted as the basis for an adequate, even comfortable, life.”3

Suburbs have not come to an end – I’m my region, in fact, they are still adding suburban sprawl like there’s no tomorrow. Signs of positive transformations of suburban developments exist across North America, but you might need to look carefully to notice.

This post will look at some of those signs of transformation and how they might be accelerated. In contrast to the last post, Can car-dependent suburbs become walkable communities?, this post and the next will focus mostly on small-scale initiatives.

The major theme of this series of posts has been the contrast between car-dependency and walkable communities. Walkability is a transportation issue, of course, but it is more than that.

It is often said that transportation planning and land use planning are two sides of the same coin.4 It’s important to look at both issues, not only as they are addressed in government policies, but also as they are addressed by individuals or small groups of neighbours.

For the purposes of this discussion, three key features of suburbia are:

  1. zoning rules that mandate the separation of residential districts from commercial districts and industrial districts;
  2. the default assumption that people will drive cars from their homes to workplaces, stores, cultural events, and recreational facilities; and
  3. the organization of the resulting car traffic into maze-like local residential streets, larger collector streets, six-to-eight lane major arterials, and expressways.

These basic parameters have many implications as discussed in previous posts. The practice of driving everywhere means there also needs to be parking at every location, so that a typical suburban district has several parking spaces for every car. (See How parking ate North American cities.)

The funneling of traffic to bigger but more widely spaced roads leads to traffic jams during every rush hour, and dangerous speeding when traffic volumes are low. The dangerous collector and arterial roads put vulnerable road users, such as pedestrians and cyclists, at risk of death or serious injury in getting from their own immediate neighbourhoods to other neighbourhoods. (See Building car-dependent neighbourhoods).

And the low residential and employment density of sprawl makes it difficult and expensive to build public transit systems that run frequently and within a short walk of most residents. The result is that suburban sprawl seldom has good transit, which in turn strongly reinforces car-dependency. (See Recipes for car dependency.)

Change will not be optional

Notwithstanding the difficulties of transforming the suburban pattern, I believe it will happen for this simple reason:

That which is not sustainable will not be sustained.

First, suburban sprawl is not financially sustainable, particularly in the governance arrangements we have in North America. As Strong Towns has demonstrated through numerous articles, podcasts and videos, North American suburban expansion has been a Ponzi scheme. While expansion infrastructure is usually paid for through a combination of federal government and developer funding, local municipalities are left with the liabilities for infrastructure maintenance and eventual replacement. That wouldn’t be a problem if the new districts could raise sufficient property tax revenue to cover these liabilities. But they don’t.

Low-density housing tracts, interspersed with one-story shopping centers and strip malls, all surrounded by expansive parking, don’t bring in nearly as much property tax/acre as denser, multi-story developments in older downtown districts do. The low tax revenue, coupled with very high maintenance-replacement liabilities for extensive roadways, parking lots, and utilities, eventually catch up with municipalities. And then? Some can keep the game going, simply by getting more funding grants for even further sprawl – thus the “Ponzi scheme” moniker – but eventually they run out of room to expand.

As Charles Marohn has written, “Decades into this experiment, American cities have a ticking time bomb of unfunded liability for infrastructure maintenance. The American Society of Civil Engineers (ASCE) estimates deferred maintenance at multiple trillions of dollars, but that’s just for major infrastructure, not the local streets, curbs, walks, and pipes that directly serve our homes.”5

Worth noting is that as climate instability forces infrastructure reconstructions to happen more frequently and to higher standards, the pressure on municipal governments will be even more intense. And as energy costs spike higher, fewer residents will be able to afford the long commutes in private cars that they now take for granted.

When suburban municipalities face bankruptcy, what will the choices be? Certainly one choice is to abandon some areas to become, in Kunster’s words “slums, salvage yards, and ruins.” For reasons explained below, I think it’s more likely that municipalities will allow more varied and denser developments than are currently permitted by zoning codes, so that a larger property tax base can help cover infrastructure liabilities.

Suburban sprawl is also likely to prove unsustainable at the level of individual homes. Debt has grown rapidly in recent decades, and a great deal of that debt is in the form of mortgages by homeowners – many of whom live in the far reaches of suburbia.

Jeff Speck wrote “The typical American working family now lives in suburbia, where the practice of drive-’til-you-qualify reigns supreme.”6 Due to a dearth of affordable homes inside American cities (and in Canadian cities as well), new home buyers have only been able to qualify for mortgages far from urban cores. The price for somewhat cheaper housing, however, is that each working member of the family is likely to need a car to get to and from work. In Speck’s words,

“The average American family now spends about $14,000 per year driving multiple cars. … Remarkably, the typical ‘working’ family, with an income of $20,000 to $50,000, pays more for transportation than for housing.”7

When families are paying for the biggest mortgage they qualify for plus the cost of keeping two or more cars on the road, the shock of higher interest rates, a rise in unemployment, and/or higher gas costs can be too much to sustain. Referring to the 2007-2009 oil price spike and economic downturn, Speck explains that “as gasoline broke $4.00 per gallon and the housing bubble burst, the epicenter of foreclosures occurred at the urban periphery.”8

In coming economic crises, on a collective scale or an individual scale, I wouldn’t expect the suburbs to be abandoned or to be torn down en masse and rebuilt. Frankly, I don’t expect society to be wealthy enough to simply start over in other places or following other patterns. Instead, I would expect both municipal governments and individuals to muddle through by making a wide range of adjustments. And some of those are starting already.

The household as a place of production, just consumption

As Samuel Alexander and Brendan Gleeson have written, “Built environment change is slow and contested. In a developed city, turnover (additions and alterations) in the built stock is typically much less than five per cent per annum.”9 But while buildings, lots and streets may change slowly, the activities that go on there may change more rapidly.

One significant change has been happening already, in spite of zoning rules that typically disallow the change.

In a post titled “Your Home Office Might Be Illegal,” Edward Erfurt wrote,

“The frontline zoning battle for the right to work out of your home hit center stage during COVID. Under most zoning codes, we are all breaking the law.”10

He adds that “Working from home and working out of a home has become normalized. … Others have even taken the next incremental step of leaving a corporate job to open a new business in our homes.”

Simply turning a blind eye to zoning violations is one thing, but Erfurt urges municipalities to take a proactive approach:

“Home Occupations should be permitted by right in every zoning category in your community. Whether you are working remotely for a large corporation or running your own business, you should have the right to do this within your home. Cities should encourage home occupations as a tenet of their economic development strategy, and a single line could be added to any code to focus the planners.”

Robert Rice describes how the dynamic is now playing out in Houston:

“This is how the Suburban Experiment really ends: not with explosive legislation, but with regular people making the best of what they have. In Houston, what we have is houses. I propose that these new house-businesses, home offices, and de-facto multifamily residences are the first increment of intensity for a suburban neighborhood.”11

Some of these changes are taking place in accord with current law and some in defiance of current law. However, many jurisdictions across North America are now changing rules to allow modestly greater density in residential areas, including in suburbs. Travis Beck recently wrote:

“Minneapolis, for example, ended single-family zoning effective January 2020, allowing the construction of duplexes and triplexes on all residential lots. Oregon passed legislation in 2019 requiring cities with populations above 25,000 to allow construction of duplexes, triplexes, and fourplexes on all residential lots. And California’s 2021 Senate Bill 9 allows the construction of duplexes on residential lots and the splitting of sufficiently large lots into two parcels, effectively allowing four housing units to be built in place of one.”12

Even the province of Ontario, infamous for bungled attempts to enrich land speculators by fast-tracking sprawl on previously protected lands, recent legislation specifies that “up to three residential units are permitted ‘as of right’ on most land zoned for one home in residential areas without needing a municipal by-law amendment.”13

Intermittent additions of one or two residences per lot may seem insignificant compared with the scope of the housing crisis; such zoning changes are certainly not sufficient to make suburbia sustainable. Yet such changes provide for greater flexibility in housing options and promote actions by individual property owners and small contractors, in contrast to the large developers who are often spoken of as the only actors who can solve the housing crisis. Paradoxically, the pace of densification on a lot-by-lot basis could pick up in an economic downturn, if significant numbers of homeowners decide it makes sense to downsize their overly-large residences by creating one or two rental units.

It’s not only the number of residential units and the number of residences that matter, but also the kinds of activities that happen in residential neighbourhoods. As discussed above, a large number of suburban homes are now de facto workplaces. The work done in and around homes, whether or not that work is counted in official economic statistics, could become a greater factor in the suburban economy.

The Victory Garden movements of the last century encouraged people to raise food in their own yards, whether they lived in cities, the nascent suburbs, small towns or rural areas. In the US, during WW I about one-third of US vegetables came from Victory Gardens. By 1943 during WW II, there were 12 million Victory Gardens in cities. A Wikipedia article notes that “While Victory Gardens were portrayed as a patriotic duty, 54% of Americans polled said they grew gardens for economic reasons while only 20% mentioned patriotism.” (Image on left is a WWI-era poster from Canada; at right is WWII image from use. Images and data from Wikipedia article Victory garden.)

One of the key features of most suburbs, visible from the street or from the air, is the small- or medium-size plot of lawn adjacent to each single-family dwelling. But the biological desert of the standard lawn can easily be replaced with something much more life-giving. Alexander and Gleeson write:

“Digging up backyards and front yards and planting fruit and vegetables, keeping chickens, and composting, are important practices, reconnecting people with the seasons, the soil, and the food on their plates. In the words of permaculture activist and educator, Adam Grubb, we should ‘eat the suburbs’.”14

A frequent objection to this idea is that few people could raise all their own food on a typical suburban lot. Quite true, and quite beside the point. More relevant is that many and perhaps most suburban residents could raise a significant portion of their fruits, vegetables, herbs, eggs, and other foods if they choose. In the process, they and their communities would become more resilient while promoting greater local biodiversity.

Suburban landscapes often include many other strips of green, kept semi-alive through regular mowing and sometimes watering: strips between areas of parking lots, in front of strip malls, on medians within major arterials, within the “cloverleafs” of expressway interchanges. Alexander and Gleeson invite us to imagine the transformation of these areas:

“Over time, we can imagine food production crossing beyond household boundaries, too, re-commoning public space. This is already under way, as people reclaim nature strips for food production, plant fruit trees in the neighborhood, establish community gardens, and cultivate unused land through “guerrilla gardening.’”15

Alexander and Gleeson write in an Australian context. In North America, a great example of similar change is the work of permaculture proponent Jan Spencer in Eugene, Oregon. Over the past twenty-three years he has transformed his quarter-acre suburban lot into an oasis. Starting with an 1,100 square foot home fronted by a driveway big enough to park six cars, Spencer gradually turned the driveway and surrounding spaces into three-dimensional gardens, added enough water tanks to collect thousands of gallons of rainwater to keep his gardens happy through the typically dry local summer, and built a 400 square foot living space for himself so he could rent out three rooms in the house.16

As Spencer explains, a key permaculture principle is to design each change so that it meets multiple purposes. With his changes he has, among other things, increased the residential density of his property, provided an income for himself, taken major steps toward food security, added carbon storage, buffered the effects of extreme heat, drought, and rainfall, and reduced the draw on city utilities such as the water system.

Such activities hold the potential of turning the suburban household “into a place of production, not merely consumption.”17

Trip generation

What do home offices and front-yard gardens have to do with transportation? Recall the incantation of traffic engineers: “trip generation.”

A home with, for example, two adult residents “generates” fewer trips when one of those adults can work at home most days instead of commuting. The home will generate fewer trips to buy groceries if the household grows a lot of their own vegetables in the summer, and perhaps puts up some of those vegetables for the winter too.

A family with two or three cars for each working member may find they can trade one of those cars for a bike, taking the bike on grocery runs much of the time. Each family which reduces the number of cars they own not only reduces traffic, but also reduces the number of parking spaces needed both in their immediate neighbourhood and at the stores, schools or workplaces they can reach without driving. Which, in turn, makes it more feasible to gradually increase the number of residences in a neighbourhood or the number of stores in a shopping plaza, as the need to devote precious space to parking is reduced.

Obviously, not every suburban resident can make these type of lifestyle changes at present. Just as obviously, we don’t need all, or even most, suburban residents to become car-free before we see a major impact on traffic patterns and usage of public transit. Finally and obviously, only a limited number of people will willingly bike or walk outside of their immediate neighbourhoods until we make the roads safe for them, and few people will willingly switch to public transit if the service is slow, infrequent, or unreliable.

So zoning and land use changes, while necessary, are not sufficient to transform car-dependent suburbia into sustainable, walkable communities. Many changes to transportation policy and infrastructure are also needed. Some of these will require governments to play a major role, but many can be initiated by small groups of neighbours who see immediate problems and advocate or demonstrate simple solutions. Those changes will be the subject of the next post in this series.

Notes

1 TED talk transcript, April 20, 2007.

2 Quoted by Leigh Gallagher in The End of the Suburbs, Penguin Books, 2013; page 206. As an aside, it was in Gallagher’s book that I first learned of the Strong Towns movement; I have been learning from their blog posts, books, podcasts and videos ever since.

3 Foreword to Degrowth in the Suburbs, by Samuel Alexander and Brendan Gleeson, Palgrave Macmillan, 2019; page vii.

4 E.g., see Land Use Impacts on Transport: How Land Use Factors Affect Travel Behavior, by Todd Litman, Victoria Transport Policy Institute, Victoria, BC. Page 3.

5 “America’s Growth Ponzi Scheme,” Strong Towns, May 18, 2020.

6 Walkable City, 10th Anniversary Edition, by Jeff Speck, Picador, 2022; page 30.

7 Walkable City, page 30.

8 Walkable City, page 30.

9 Degrowth in the Suburbs, page 12.

10 Edward Erfurt, “Your Home Office Might Be Illegal”, on Strong Towns blog, Oct 13, 2023.

11 Robert Rice, “The End of Suburbia Starts with Disobedience,” on Strong Towns blog, Oct 13, 2023. Rice explains both the differences and similarities between the deed restrictions that are common in Houston, and the zoning-based restrictions much more common in most American cities.

12 In “ADUs Can Help Address The Lack Of Housing. But They’re Bad Urban Design.” by Travis Beck, Next City, Oct 5, 2023.

13 From “Backgrounder: More Homes Built Faster Act, 2022”, Ontario Government Newsroom, November 28, 2022.

14 Degrowth in the Suburbs, page 133.

15 From “Suburban Practices of Energy Descent,” by Samuel Alexander and Brendan Gleeson, Energy Transition and Economic Sufficiency, Kreps & Cobb, editors, Post Carbon Institute, 2021; page 189.

16 See Spencer’s description of this project in “Transforming suburbia,” on Resilience.org, October 6, 2023, and a video tour of Spencer’s property conducted by Laura Sweeny of Raintree Nursery.

17 “Suburban Practices of Energy Descent,” page 190.

Image at top of page: Levittown, PA, circa 1959, adapted from public domain image at Wikimedia Commons.

Can car-dependent suburbs become walkable communities?

Also published on Resilience

“The majority of urban areas in most cities today are car-dependent,” writes urban planner Tristan Cleveland, “leaving little room for walkable growth unless cities can convert large areas of existing suburbs into pedestrian-oriented neighbourhoods.”

Yet the processes of change are even more difficult in suburbs than in urban cores: “The barriers to walkable design are greatest in such suburban contexts, where the momentum for car-oriented design is most entrenched.”

Cleveland is an urban planning consultant who works with Happy Cities, a consultancy based in Halifax and Vancouver. His 2023 PhD thesis, Urban Intercurrence, is a thorough, enlightening, readable, jargon-lite study of how and why some suburban districts embark on the transition from car-dependent sprawl to walkable neighbourhoods. (A tip of the hat to Strong Towns, where I first learned of Tristan Cleveland’s work through this podcast interview.)

The work’s subtitle – “The Struggle To Build Walkable Downtowns In Car-Dependent Suburbia” – indicates an important limitation in scope. This is not a study of attempts to convert a car-dependent suburb as a whole, but more simply to develop a high-density, walkable district within a larger suburb. Even so, Cleveland demonstrates, the pitfalls are many and successes to date are partial at best.

Cleveland’s insights make for a good follow-up to recent posts here on car-dependent development. A first post, Recipes for Car-Dependency, looked at car-dependent development on a regional scale, in which a superfluity of highways and major arterial roads is matched with scarce, infrequent public transit. The second post, Building Car-Dependent Neighbourhoods, focused on car-dependent development at a neighbourhood scale.

But once car-dependent regions and neighbourhoods are established, is it possible to retrofit them, in whole or in part, to escape this car-dependency?

In my opinion it is not only possible, but is probably inevitable – though it may take a long time and it may involve difficult disruptions. Probably inevitable, because the suburban lifestyle is built on and presupposes cheap energy to power swarms of private cars which each carry one or two occupants many kilometers to work, school, and shopping on a daily basis. When this energy is no longer available and affordable to most residents, car-dependent lifestyles will change by necessity.

In the meantime, some residents and municipalities are already promoting car-lite or car-free lifestyles for other important reasons: to improve public health by simultaneously reducing air pollution and the diseases of sedentary lifestyles; to build social cohesion by encouraging more people to walk through their neighbourhoods to local shops; to cover rising infrastructure maintenance costs by achieving compact urban and suburban developments with a higher tax base; to make frequent and timely public transit possible in districts with sufficient population density.

As Cleveland notes, walkable neighbourhoods are in high demand but scarce supply, leading to sky-high rents and real estate prices in such districts. And since most North Americans now live in suburbs, providing the walkable neighbourhoods many people would prefer to live in will necessarily involve a significant degree of suburban retrofitting.

Urban Intercurrence provides detailed looks at four concerted attempts to build walkable downtown districts in suburbs. One is in a suburb of Vancouver, another in a suburb of Toronto, a third in a classic “edge city” in the orbit of Washington, DC, and one about ten miles from downtown Miami, Florida.

Before diving into the specifics of each project, Cleveland provides a valuable primer on a hundred years of car-prioritized developments. This history is essential to understanding why the retrofit examples have all had slow and limited success.

The history review and the examples are relevant and useful to transportation activists, environmental justice activists, municipal planners and officials.

Intercurrence and inverse feedback

For a PhD thesis Urban Intercurrence is remarkably light on specialist jargon, and Cleveland also defines clearly what he means by words or phrases that may be unfamiliar to a lay audience. Many of the issues he discusses will be familiar to any activist who has attended public meetings in favour of adding bike paths, reducing width of car lanes, or repurposing some of the vast area now devoted to car parking.

There is, to be sure, an out-of-the-ordinary word in the thesis title. Cleveland adopts the word “intercurrence” from political science, where it refers to “the ways in which multiple, contradictory paradigms of thought and practice can co-exist within institutions, and how their contradictions can shape policy.” (Pg 5. Unless noted otherwise, all quotes in this article are from Urban Intercurrence.)

The contradictory paradigms sometimes come from professionals who are educated with different orientations. In recent decades the urban planning profession has been strongly influenced by the movement to create safe, attractive, walkable districts, Cleveland says. Traffic engineering departments, on the other hand, tend to prioritize the swift and unimpeded movement of vehicles. Both groups are involved in suburban retrofits, and sometimes the result is a project that spends much public money to encourage walkability, and just as much or more money widening car lanes on more roadways, thereby discouraging walkability.

A paradigm like car-dependency tends to be self-reinforcing. If nearly all the residents in a district travel by car, then shopping centers have their doors opening to large parking areas, instead of opening directly to a sidewalk where the rare pedestrian might pass by. If each single-family home needs two or three parking spaces, then residents and their municipal councillors typically fear that even a mid-size apartment building will overwhelm the neighbourhood’s parking supply.

Nevertheless, car-dependency sometimes causes discontent with car-dependency. In many suburban areas today, roadways are so chronically congested that voters are ready to approve new public transit systems. At the same time, housing developers used to building low-density, car-dependent subdivisions may switch to advocating for high-density developments once they’ve used up most of the available land.

Cleveland writes, “I refer to these contradictory feedback processes — which undermine car-dependence, reinforce walkability, or at least enable a shift towards walkability — as ‘inverse feedback.’” (pg 5)

He cites clear examples of competing paradigms and inverse feedback in each of the four suburban retrofit case studies. In each case, inverse feedback provides an opening for walkability advocates to initiate change. Importantly, however, when car-oriented interests offer support to walkability, that support is limited and insufficient to result in a walkable neighbourhood:

“To complete a shift to walkability, it is necessary, at some point, for walkability to begin to reinforce itself on its own terms, at the expense of car-dependence. That is to say: it is necessary for walkable interests to identify as such, to defend their needs, to establish separate standards, and to normalize those standards. It is also essential for walkable development to achieve a sufficient scale that it can begin to attract other, similar growth. Car-dependence may cause backlash that inspires change, but to complete change, it is essential for those who have a direct stake in walkability to complete the transformation.” (pg 6)

The timeline is long, very long

Two important facts jump out when reading the four case studies of retrofits. First, change in these instances is primarily a top-down process, promoted and initiated by local governments, major developers, or both. Second, the move to walkability has taken thirty or forty years, with action stalled for years in some cases, and while significant progress has been made, none of the four projects have yet fully realized their original goals.

In Surrey, BC, a suburb of Vancouver, formal planning for a walkable downtown district began in the 1980s. Zoning changes alone failed to convince developers to build high-density projects geared to walkability. The city finally took major steps in the 21st century, building a new city hall and public library complex in a prime location. Even then developers hesitated, so in 2007 “The city established the Surrey City Development Corporation (SCDC), an arms-length company for which the city remained the sole shareholder, but which could raise capital, build market-oriented development projects, and partner with other development firms to help to encourage them to invest.” (pg 134)

The new developments were located adjacent to a station of the SkyTrain, a commuter train that goes to downtown Vancouver. The existence of the SkyTrain helped convince many car-dependent residents to support a walkable, high-density Surrey City Centre. However, this expensive transit line made it difficult to get funding for other intra-suburb lines that might have been of even more benefit in freeing Surrey residents from car-dependency. As Cleveland explains:

“A SkyTrain can appeal to otherwise car-dependent voters because it can replace the one trip that is most difficult to make by car — commuting through traffic to work — and it can also help to alleviate rush-hour traffic by replacing some of those car trips. And it does not consume road space. However, a SkyTrain to downtown does not meet the needs of people who rely on transit for everyday trips, such as going to daycare, visiting friends, or buying groceries. … A high-speed connection to the downtown makes one kind of trip faster, but does little to enable a complete transit-oriented lifestyle throughout one’s community.” (pg 145-146)

SkyTrain route from Surrey City Centre to downtown Vancouver (image via Apple Maps)

The interplay between transport decisions made by different levels of government has been a complicating factor in all four of the the suburban retrofits Cleveland examines.

Spontaneous generation

As Brian Eno sang on Before and After Science,

“If you study the logistics
and heuristics of the mystics
you will find that their minds rarely move in a line.”1

This aphorism comes to mind when considering the massive roadways that snake through the should-be-walkable suburban retrofits. The plans of the traffic engineers follow a curious logic indeed.

In his book Paved Paradise, Henry Grabar highlighted an assumption deeply embedded in North American traffic engineering. He discusses the Parking Generation Manual published in 1985 by the Institute of Traffic Engineers. Underpinning the nearly infinite specifications for required parking, Grabar says, “the premise is simple: every type of building creates car trips, and projects should be approved, streets designed, and parking constructed according to the science of trip generation.”2

The belief that a building itself somehow generates traffic, and a multi-unit building generates multi-traffic, guides not only parking requirements but also roadway planning. In this thinking, it is not a car-dependent lifestyle or urban layout that generates traffic, it is the mere existence of buildings where people live, work, or shop. As long as this thinking guides traffic engineers, urban planners’ hopes for dense, walkable districts get sidetracked.

In the Uptown Core project in Oakville, Ontario, Cleveland writes,

“Traffic studies … predicted the community would have high traffic demand, requiring wide roads throughout the community. Studies predicted high traffic, ironically, precisely because the community was dense: traffic models assume each unit produces a certain number of traffic trips, regardless of whether the community is designed to be walkable or not.” (pg 203)

Tysons, Virginia is the largest and most famous suburban retrofit project in North America. As a classic “edge city,” Tysons in 1993 had few homes but a forest of high-rise office buildings where 70,000 people worked. The only way to get to these buildings was by car. Two examples of “inverse feedback” helped to prompt a retrofit: prime development land was getting scarce, and roads choked with traffic were undermining the original locational advantage for this mega office park.

Following a wave of investment in high-density housing, the population of Tysons rose to 29,000 by 2021, of which 10,000 lived in transit-oriented developments near the new Silver Line commuter rail service to Washington, DC.

But the planning for a walkable district had to contend with traffic engineers at the county and state level. They insisted that, ideals of walkability notwithstanding, Tysons would need to accommodate ever greater numbers of private vehicles. As a result, “Tysons’ smaller collectors and minor avenues are larger than the widest highways in many cities, at seven to ten lanes.” (pg 166)

Multi-lane highways even run directly past the commuter rail stations, making it unattractive or impossible to build new developments in close proximity to the stations. Ringed and bisected by high-speed, high-volume, high-pollution, very wide roads, Tysons can be summarized as “islands of walkability amidst rivers of car-dependence,” Cleveland writes. (pg 151)

Intercurrence in Tysons is reflected in government expenditures that work at cross-purposes:

“I am aware of few examples where government has spent so heavily to achieve a goal while spending so heavily to undermine it: billions of dollars on subways, sidewalks, and bike lanes, and nearly a billion dollars for widening roads and onramps, and billions more on widening its highways.” (pg 193)

Another lesson to be drawn is that “if it is difficult to shift one path-dependent institution, it is more difficult to shift two simultaneously, Cleveland writes. “Multilevel governance can therefore create additional barriers to change, reducing the likelihood that all relevant institutions will shift to support walkability simultaneously.” (pg 180)

An all-or-nothing proposition?

Because the factors reinforcing suburban car-dependency are many and strong, and most suburban retrofits have had limited success to date, some urbanists have concluded that incremental approaches are doomed to failure.

Cleveland cites various authors who “argue it is better for a single developer to own enough land to build a full-scale walkable community at once, establishing a critical mass of dense housing, pedestrian-friendly streets, and high-quality public spaces, all within walking distance of local shops and services.” (pg 230)

But Cleveland concludes (correctly, I believe), that

“It is important for cities to learn how to implement incremental retrofits, because cities cannot achieve their most urgent goals by retrofitting those few exceptional sites where government owns a former airport, military base, or other large piece of land, and can redevelop it all at once.” (pg 230)

In a coming post we’ll look further at possibilities for incremental change toward walkable suburbs, including changes that are undertaken not by governments but directly by residents.

Photo at top of post: “Express Lanes at Tysons Corner ”, photograph by Trevor Wrayton for Virginia Department of Transportation, licensed by Creative Commons. accessed via flickr.

Notes

1   From the song “Backwater” on the album Before and After Science by Brian Eno, 1977.

2   Henry Grabar, Paved Paradise, Penguin Random House, 2023; pg 153.