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01 / 05
AI, Tractors, and the Slow Diffusion of Labor-Saving Devices

Blog Post | Science & Technology

AI, Tractors, and the Slow Diffusion of Labor-Saving Devices

Productivity and economic growth are a tide that lifts all boats, whether we're talking about agricultural machinery or ChatGPT.

Summary: The proliferation of large language models, such as ChatGPT, raises concerns about job displacement, particularly in professions like writing and law. However, historical transitions, like the shift from horses to tractors, suggest that the adoption of new technologies is gradual, with human labor and machines coexisting and evolving together. While AI may revolutionize certain sectors, its widespread impact on employment is likely to be evolutionary rather than revolutionary.


“You can see the computer age everywhere but in the productivity statistics,” wrote economist and Nobel laureate Robert Solow in a 1987 book review. While computing was making massive strides in the 1980s and ’90s—cumulating in the internet mania and dot-com bubble and its burst—it took a long time before we saw large, economy-wide impacts of this revolutionary general-purpose technology. Computers, internet, and (smart) screens have made the modern world unrecognizable from what it was just a few decades ago, but the process took a lot longer than most techno-optimists suggested then—and their followers are suggesting now.

Last year had a strong 1990s feel to it: 2023 became the year of AI worries, with ChatGPT and other large language models making a bid for disrupting all manner of industries. Writers, musicians, lawyers, editors, physicians, graphic designers, and more were some of the professions seemingly at risk of losing their livelihood to machines that quickly replicate their work at a fraction of the cost.

We should draw two conclusions from these large language models and past technological transitions: First, some of their features are truly remarkable and can very well compete with (some) lawyers, writers, musicians, or graphic designers over the next few years. Second, radical, labor-saving technologies are slow to diffuse across the economy, so these mainly middle-class professions are probably safe from mechanization and automation a while longer.

Take the iconic horses-to-tractor transition in agriculture over the 20th century. From invention and commercial availability, tractors took decades to overtake horses. The same was true for steam engines, that iconic energy invention powering the Industrial Revolution—which itself was both one of most radical and life-changing events in all of human history and pretty dull on account of its slow changes.

“Over the sweep of history the tractor has indeed had an immense impact on people’s lives,” The Economist said in December 2023, “but it conquered the world with a whimper, not a bang.” In all ages, tech innovations have defused across societies slowly: the old technology remains relevant for decades. Even in the 1930s, two to three decades after the first tractors were built, there was more equine than mechanical horsepower on American farms. Scholars usually put the cutoff point for when tractors overtook horses on farms after World War II, a generation or more after they were first employed.

The story in The Economist also highlights the connection to real wages and overall economy. Productivity and economic growth are a tide that lifts all boats. A rule of thumb is that labor-saving technology gets adopted only once it makes sense to replace labor with machines. That happens when the machines get good enough, the cost of running them falls sufficiently, or the alternative uses for labor get high enough.

For the first few decades of the tractor’s existence, they weren’t obviously better than the source of literal horsepower that preceded them. They were expensive to run, were not that powerful, and couldn’t do many things that a horse could. For one, they compelled farmers to purchase gasoline—a resource they didn’t have—rather than putting horses out to grazing on land or having children (or cheap labor) tend to the animals—resources that they did have. And during the Great Depression in America, cheap labor wasn’t that hard to come by.

In time, those trends reversed; real incomes and better-paid manufacturing jobs bid labor away from the farms, and the demographic transition to smaller families meant that children couldn’t be routinely relied upon to, for example, care for the animals. In economic speech, the optimal size of farms grew, and on these larger farms the now much better tractors came into their own right.

In contrast, the labor that large language models like ChatGPT purport to displace is often highly remunerated, while the costs of running the operations are pretty low, which suggest that artificial intelligence (AI) might displace some such workers.

Yann LeCun, chief AI scientist at Meta/Facebook, is less convinced. In an interview with Wired magazine’s Steven Levy, LeCun says that “chatbots can produce very fluent text with very good style. But they’re boring, and what they come up with can be completely false.” For Reason magazine last year, I reflected on the promise of ChatGPT along similar lines: “Bad writers, cheating students, lazy professors, and journalists echoing press releases have clearly met their match. But the more human, creative, authentic storytelling that is the center of all meaningful writing has not.”

The tractor attempted to compete directly with a farming industry routinely employing over a third of the labor force in the 1800s and early 1900s in the United States, constituting some 15 percent of gross domestic product. What is the total addressable market for large language models and by extension general AI? Statista estimates that the market size of AI in the United States, a $27 trillion economy, is some $100 billion—a fraction of a percent. Even if that grows seven-fold this decade in line with projections (nothing to scoff at!), that’s also not as revolutionary as most pundits have claimed in this the first year of their widespread adoption.

Solow, who died late in 2023, might have repeated his statement about this new form of the computer age. Like the computer in the 1990s, the onset of AI and large language models make more media puff and noise than they have real-world, real-economy impact.

There is some suggestion that adoption of new technology and consumer items happen somewhat faster today than, say, in the 1940s or 1980s, but not by a lot. Still, what is more likely to happen than mass white-collar unemployment is that we’re going to have a symbiosis for a while. The old human-labor technology and the new machines will coexist, and human workers will gradually employ the machines in different ways. An evolution, not a revolution.

At the end of the day, writes Mike Munger for the American Institute for Economic Research, ChatGPT is just a calculator. And like calculators did, it will obsolete some human labor. And that will be fine.

Axios | Labor Productivity

AI Is Triaging Mothers’ Hidden Labor

“Mothers are using AI as a personal assistant to manage the invisible work of raising today’s kids…

ChatGPT’s gender gap appears to be closing. Immediately following its release, only 17.6% of active ChatGPT users had typically feminine first names. As of June 2025 the percentage of active users with feminine first names had jumped to 52.4%…

By the numbers: ‘Organizing my life’ was the second-most popular use case for genAI in 2025, according to new research from Harvard Business Review…

What they’re saying: ‘ChatGPT has basically become like an extended village of my parenting,’ Sandy Shakoor, a PR director with two young kids, told Axios.

‘Now there are three of us to do things,’ Sarah Dooley, founder of AI-Empowered Mom and mother of three, told Axios. ‘AI is the third supporter, the third leg of the stool in this little household.’ Dooley used to teach mothers how to use AI in classes she held in her living room. Now she hosts a podcast, writes a newsletter and is developing an AI assistant, all in the service of reducing the mental load of motherhood.”

From Axios.

Blog Post | Manufacturing

Grim Old Days: Virginia Postrel’s Fabric of Civilization

Beneath today’s abundance of clothing lies a long and brutal history.

Summary: Virginia Postrel’s book weaves a sweeping history of textiles as both drivers of innovation and toil. From ancient women spinning for months to make a single garment to brutal sumptuary laws and dye trades steeped in labor and odor, it is revealed how fabric shaped the foundations of human society.


Virginia Postrel’s The Fabric of Civilization: How Textiles Made the World is the riveting story of how humanity’s quest for thread, cloth, and clothing built modern civilization, by motivating achievements from the Neolithic Revolution to the Industrial Revolution and more. While much of the book contains inspiring tales of innovation, artistry, and entrepreneurship, the parts of the book about the preindustrial era also reveal some dark and disturbing facts about the past.

In the preindustrial era, clothing was often painstakingly produced at home. Postrel estimates that, in Roman times, it took a woman about 909 hours—or 114 days, almost 4 months—to spin enough wool into yarn for a single toga. With the later invention of the spinning wheel, the time needed to produce yarn for a similarly sized garment dropped to around 440 hours, or 50 days. Even in the 18th century, on the eve of industrialization, Yorkshire wool spinners using the most advanced treadle spinning wheels of the time would have needed 14 days to produce enough yarn for a single pair of trousers. Today, by contrast, spinning is almost entirely automated, with a single worker overseeing machines that are able to produce 75,000 pounds of yarn a year—enough to knit 18 million T-shirts.

Most preindustrial women devoted enormous amounts of time to producing thread, which they learned how to make during childhood. It is not an exaggeration to say, as Postrel does, “Most preindustrial women spent their lives spinning.” This was true across much of the world. Consider Mesoamerica:

At only four years old, an Aztec girl was introduced to spinning tools. By age six, she was making her first yarn. If she slacked off or spun poorly, her mother punished her by pricking her wrists with thorns, beating her with a stick, or forcing her to inhale chili smoke.

These girls often multitasked while spinning: “preindustrial spinners could work while minding children or tending flocks, gossiping or shopping, or waiting for a pot to boil.” The near-constant nature of the task meant that prior to the Industrial Revolution, “industry’s visual representation was a woman spinning thread: diligent, productive, and absolutely essential” to the functioning of society, and from antiquity onward cloth-making was viewed as a key feminine virtue. Ancient Greek pottery portrays spinning “as both the signature activity of the good housewife and something prostitutes do between clients,” showing that women of different social classes were bound to spend much of their lives engaged in this task.

Women of every background worked day and night, but still, their efforts were never enough. “Throughout most of human history, producing enough yarn to make cloth was so time-consuming that this essential raw material was always in short supply.”

Having sufficient spun yarn or thread was only the beginning; it still had to be transformed into cloth. “It took three days of steady work to weave a single bolt of silk, about thirteen yards long, enough to outfit two women in blouses and trousers,” although silk-weavers themselves could rarely afford to wear silk. According to Postrel, a Chinese poem from the year 1145, paired with a painting of a modestly dressed, barefoot peasant weaving silk, suggests that “the couple in damask silk . . . should think of the one who wears coarse hemp.”

Subdued colors often defined the clothing of the masses. “‘Any weed can be a dye,’ fifteenth-century Florentine dyers used to say. But that’s only if you want yellows, browns, or grays—the colors yielded by the flavonoids and tannins common in shrubs and trees.” Other dye colors were harder to produce.

In antiquity, Tyrian purple was a dye derived from crushed sea snails, and the notoriously laborious and foul-smelling production process made it expensive. As a result, it became a status symbol, despite the repulsive stench that clung to the fabric it colored. In fact, according to Postrel, the poet Martial included “a fleece twice drenched in Tyrian dye” in a list of offensive odors, with a joke that a wealthy woman wore the reeking color to conceal her own body odor. The fetor became a status symbol. “Even the purple’s notorious stench conveyed prestige, because it proved the shade was the real thing, not an imitation fashioned from cheaper plant dyes.” The color itself was not purple, despite the name, but a dark hue similar to the color of dried blood. Later, during the Renaissance, Italian dyers yielded a bright red from crushed cochineal insects imported from the Americas, as well as other colors that were created by using acidic bran water that was said to smell “like vomit.”

Numerous laws strictly regulated what people were allowed to wear. Italian city-states issued more than 300 sumptuary laws between 1300 and 1500, motivated in part by revenue-hungry governments’ appetite for fines. For example, in the early 1320s, Florence forbade women from owning more than four outfits that were considered presentable enough to wear outside. Postrel quotes the Florentine sumptuary law official Franco Sacchetti as writing that women often ignored the rules and argued with officials until the latter gave up on enforcement; he ends his exasperated account with the saying, “What woman wants the Lord wants, and what the Lord wants comes to pass.” But enough fines were collected to motivate officials to enact ever more restrictions.

In Ming Dynasty China, punishment for dressing above one’s station could include corporal punishment or penal servitude. Yet, as in Florence, and seemingly nearly everywhere that sumptuary laws were imposed, such regulations were routinely flouted, with violators willing to risk punishment or fines. In France in 1726, the authorities harshened the penalty for trafficking certain restricted cotton fabrics, which were made illegal in 1686, to include the death penalty. The French law was not a traditional sumptuary law, but an economic protectionist measure intended to insulate the domestic cloth industry from foreign competition. Postrel quotes the French economist André Morellet lamenting the barbarity of this rule, writing in 1758,

Is it not strange that an otherwise respectable order of citizens solicits terrible punishments such as death and the galleys against Frenchmen, and does so for reasons of commercial interest? Will our descendants be able to believe that our nation was truly as enlightened and civilized as we now like to say when they read that in the middle of the eighteenth century a man in France was hanged for buying [banned cloth] to sell in Grenoble for 58 [coins]?

Despite such disproportionate punishments, the textile-smuggling trade continued.

Postrel’s book exposes the brutal realities woven into the history of textiles; stories not just of uplifting innovation, but of relentless toil, repression, and suffering. Her book fosters a deeper appreciation for the wide range of fabrics and clothes that we now take for granted, and it underscores the human resilience that made such abundance and choice possible.

Bloomberg | Labor Productivity

Chick-Fil-A’s Lemon-Squeezing Robots Save 10,000 Hours of Work

“In a plant north of Los Angeles, machines now squeeze as many as 1.6 million pounds of the fruit with hardly any human help. The facility, larger than the average Costco store at roughly 190,000 square feet, then ships bags of juice to Chick-fil-A locations, where workers add water and sugar to whip up the chain’s trademark lemonade.

The automated plant frees up in-store staff to serve customers faster, according to the company. Squeezing lemons was a tedious task that added up to 10,000 hours of work a day across all locations and resulted in many injured fingers.”

From Bloomberg.

Wall Street Journal | Labor Productivity

The American Worker Is Becoming More Productive

“Productivity in the U.S., as measured by how much the average worker gets done in an hour, has been on the rise. That matters because the faster that productivity grows, the faster the economy can grow as well. The success of the U.S. economy, and why it has grown so much compared with other countries over the past century and more, has hinged on its productivity. 

Productivity—the total output of the economy divided by hours worked—rose 2% in the third quarter compared with a year earlier, according to the Labor Department. That marked the fifth quarter in a row with an increase of 2% or better. In the five years before the pandemic, there were only two such quarters.

The gains in part reflect massive changes in the U.S. economy since the onset of Covid-19. Companies learned new ways of doing things and adopted new technologies, while an upheaval in the labor market moved workers into more productive jobs.

Another big change in the American labor force—a massive influx of immigration—might also have played a role. Immigrants are often slotted into manual-intensive jobs, which could allow other workers to move up to more highly skilled jobs.”

From The Wall Street Journal.