Who gets the money when machines get faster?

What seven productivity revolutions tell us about AI and the future of software engineering

Every few generations, a tool comes along that makes a job dramatically more productive. A farmer feeds 150 people instead of 4. A single operator replaces a room full of typesetters. A container crane does the work of a hundred longshoremen.

Today, software engineers are living through their version of this moment. AI coding assistants like Copilot, Claude, Cursor, and the wave that follows are making individual developers measurably more productive. In a controlled trial run by Microsoft Research, developers using GitHub Copilot completed a coding task 55.8% faster than those without it. A larger 2024 study spanning Microsoft, Accenture, and a Fortune 100 company found an average 26% productivity boost, the equivalent of turning an 8-hour workday into 10 hours of output. As of 2025, 84% of developers are using or plan to use AI tools, and roughly 41% of all new code is AI-generated.

But “more productive” has never been a simple story. Productivity gains always flow somewhere: to the worker, the company, or the consumer. History has a lot to say about which way they go, and why. When you look across seven major productivity revolutions, a pattern emerges: the gains don’t land randomly. Who wins depends on power, institutions, and the nature of the technology itself.

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When capital wins: the long shadow of the loom

The darkest outcomes for workers share a common signature: technology that makes the individual replaceable. When that happens, capital captures nearly everything.

The textile revolution set the template. Between 1780 and 1840, output per worker in Britain rose by 46%, but real wages for the working class increased by only 12%. Economist Robert C. Allen coined the term “Engels’ Pause” to describe this gap. The profit rate doubled. Capital’s share of national income expanded at the expense of both labor and land. Friedrich Engels himself, the 24-year-old son of a textile industrialist, walked the streets of Manchester in 1842 and found mortality in industrial cities running at 1 in 30, compared to 1 in 45 in the countryside. Child mortality in the industrial town of Carlisle rose measurably after the introduction of mills. The skilled cottage weavers who had once commanded decent livelihoods found themselves redundant, replaced by factory hands who needed only enough skill to tend a machine.

The Bessemer process told the same story in steel. Andrew Carnegie’s adoption of new steelmaking technology made production dramatically cheaper, with the price of rolled-steel products dropping from $35 a gross ton to $22 between 1890 and 1892. But the workers didn’t share in the windfall. When the Amalgamated Association of Iron and Steel Workers’ contract came up for renewal in 1892, Carnegie’s operations manager Henry Clay Frick proposed an 18% wage cut. The skilled workers at Homestead had been among the best-paid in the industry, and Frick viewed that as the problem. “The mills have never been able to turn out the product they should, owing to being held back by the Amalgamated men,” he wrote to Carnegie. Carnegie, publicly pro-labor, privately told Frick to break the union: “The Firm has decided that the minority must give way to the majority.”

What followed was one of the bloodiest labor conflicts in American history. Three hundred Pinkerton agents arrived on barges. A twelve-hour gun battle left seven workers dead. The National Guard was called in with 8,500 troops. The union lost. In the fifteen years after Homestead, daily wages for skilled steelworkers at the plant fell by a fifth while shifts lengthened from eight hours to twelve. Amalgamated membership collapsed from 24,000 to 8,000 within three years. Carnegie Steel’s profits, meanwhile, rose to a staggering $106 million in the nine years after the strike. Union organizing among steelworkers was effectively crushed for 26 years, until the final months of World War I.

The common thread in both stories: technology reduced workers’ leverage by making them easier to replace. The spinning jenny didn’t need a skilled weaver. The Bessemer process didn’t need a craftsman with decades of experience. When the irreplaceable becomes replaceable, capital moves in. The gains flow not to the person operating the machine, but to the person who owns it.

For software engineers, this is the bear case. And it has a specific, data-driven shape. In the Microsoft Research productivity study, less experienced developers saw the largest gains from Copilot: a 35-39% speedup, compared to just 8-16% for senior developers. This is precisely the pattern that preceded the Engels’ Pause in textiles: technology that disproportionately boosts the output of less-skilled workers compresses the skill premium. If a junior engineer with AI tools can produce 80% of what a senior engineer produces, the senior engineer’s bargaining position erodes, even if they’re still more productive in absolute terms. The same dynamic that let Frick tell the Amalgamated workers they were expendable could let a tech company’s CFO reach the same conclusion about a layer of mid-level engineers.

When workers win: the power to say no

But capital doesn’t always capture the gains. In a handful of cases, workers held on and even thrived. These cases look nothing like the textile mills or the Homestead steel plant, and the reasons why are instructive.

Start with surgery. Surgeons are one of the only professions where productivity-enhancing technology (anesthesia, antiseptics, imaging, robotics) consistently made the workers richer rather than poorer. The key difference is that the technology was complementary: better tools made the surgeon more capable without replacing the surgeon’s judgment. The value stayed in the person, not the machine.

But complementary technology alone wasn’t enough. Surgeons also built institutional power that goes far beyond anything a union could achieve. The AMA didn’t just negotiate for better wages. It controlled the pipeline of who could become a surgeon in the first place. In the 1980s and 1990s, the AMA lobbied to restrict the number of foreign-trained doctors entering the US and to limit training slots. In 1997, a consortium of medical organizations including the AMA recommended reducing residency positions by 25%, from roughly 25,000 slots down to 19,000, to prevent what they warned was an impending “physician oversupply.” Congress froze Medicare’s training funding that same year in the Balanced Budget Act, capping the number of residency slots each hospital could fill at 1996 levels. That cap remained essentially unchanged for over 25 years, even as the US population grew by 70 million. Medical school enrollment has risen 52% since 2002, but residency positions haven’t kept pace. The average applicant now submits 73 applications, and each slot receives more than 60. The AMA has spent an average of $18 million per year on lobbying between 1998 and 2020. Surgeons didn’t just unionize. They became the gatekeepers of their own profession.

The Linotype operators pulled off something similar, if more modestly and more temporarily. When Ottmar Mergenthaler patented the Linotype in 1886, a machine that let a single operator set 6,000 characters per hour (many times faster than a hand compositor), the International Typographical Union didn’t resist. Instead, it absorbed the new technology into its sphere of control. The ITU controlled apprenticeships, mandated skill certifications, and insisted on operator-friendly machine features through collective bargaining. Membership surged from about 5,000 in the mid-1880s to over 30,000 by 1900, peaking at 100,000 in the 1960s. The ITU won a 48-hour workweek and a standard wage scale as early as 1897. During the Great Depression, it pioneered the 40-hour workweek across the printing industry, an initiative that eventually became federal law.

For decades, Linotype operators captured a real share of the productivity gains. The machine was complex enough that operating it well was a genuine skill, and the union ensured that complexity translated into wages and job security.

The West Coast longshoremen took perhaps the most calculated gamble of all. Before containerization, loading a ship was backbreaking work controlled entirely by the ILWU through its hiring halls. Eight-man crews were the norm, and the union had fought strike after strike (most famously the 1934 Pacific Coast strike that shut down every port) to maintain that control. Then containers arrived, and the tonnage of cargo moved per hour per worker jumped from 1.5 tons to 37.5 tons. Harry Bridges, the Australian-born president of the ILWU, faced a choice: resist the technology and lose, or negotiate the terms of its arrival.

On October 18, 1960, Bridges signed the Mechanization and Modernization Agreement with the Pacific Maritime Association. The deal was stark: the union would accept containerization without interference. In exchange, fully registered union members got guaranteed employment, shortened work weeks, and early retirement bonuses. By 1966, a $13 million M&M fund had accumulated, and the union paid $1,200 bonuses to all 10,000 full-time longshoremen on the coast. Subsequent contracts raised lump-sum retirement bonuses to $13,000. The surviving longshoremen became, and remain, a well-compensated labor aristocracy earning six figures.

What do surgeons, Linotype operators, and longshoremen have in common? Three things. First, the technology complemented their skills rather than replacing them, at least for a time. Second, they organized and controlled chokepoints: the AMA controlled medical school accreditation, the ITU controlled hiring halls and apprenticeships, the ILWU controlled physical ports. Third, they couldn’t be easily offshored or replaced by cheaper labor elsewhere. You can’t outsource an appendectomy, you couldn’t fax a newspaper page across the ocean in 1920, and you can’t move the Port of Oakland to a cheaper jurisdiction.

But there’s a crucial caveat embedded in this hopeful story. The Linotype operators’ victory was temporary. Desktop publishing arrived in the 1980s, and the entire profession collapsed. The ITU lost a quarter of its membership by 1980. The 1962-63 New York City newspaper strike, where Local 6 walked out for 114 days over computerized typesetting, halting seven major dailies and eliminating 5.7 million copies of daily circulation, was a $100 million last stand that only accelerated the transition. By 1986, the ITU merged into the Communications Workers of America. The Government Printing Office shipped off its last letterpress equipment in 2018, marked as hazardous waste.

And Bridges’ M&M deal, for all its fame, wasn’t painless. 42% of ILWU members voted against the 1966 renewal. The less-senior “B” men and casual workers bore the brunt of the cuts with no vote and no protection. Man-hours on the West Coast dropped from 26.7 million in 1966 to 19.7 million by 1970. The Bay Area workforce plunged from 5,000 to around 1,500. The 1971-72 strike, the longest in US maritime history, reflected deep rank-and-file anger. Critics noted the East Coast ILA, which fought containerization through six strikes, retained more crew per job: 18 ILA men did the work that 12 ILWU men performed.

The lesson: even when workers win, they win by accepting a smaller, richer workforce. The gains go to the survivors. Everyone else goes home.

When it flows downstream: the consumer windfall

There’s a third pattern that gets less attention, perhaps because it’s less dramatic than bloody strikes or surgical wealth: sometimes the gains flow past both capital and labor and land squarely on consumers and on entirely new categories of work that didn’t exist before.

Agriculture is the purest example. Farm productivity increased by roughly 40x over two centuries. The result? Food got cheap. Spectacularly, historically cheap. But neither the farmers nor the farm owners were the primary beneficiaries. Ninety percent of the farming workforce simply ceased to exist. The remaining farmers aren’t poor, exactly, but they’re not the ones who got rich. The value accrued to equipment manufacturers (John Deere), seed companies (Monsanto), distributors (Cargill), and grocery retailers. The real winner was every consumer who spends a smaller fraction of their income on food than any previous generation in human history.

The spreadsheet revolution followed a remarkably similar pattern, albeit in white-collar work. And it may be the most instructive parallel for what AI will do to software engineering.

The origin story is now legend: in the spring of 1978, Dan Bricklin sat in a Harvard Business School classroom watching his professor update a financial model on a blackboard ruled into rows and columns. Every time the professor changed a parameter, he had to erase and rewrite entries by hand. Bricklin realized a computer could do this instantly. He and his MIT classmate Bob Frankston built VisiCalc over the winter of 1978-79, launching it on the Apple II in October 1979 at $100.

The impact was immediate. Allen Sneider, an accountant, walked into his local computer store and became VisiCalc’s first registered user. As Bricklin later recalled: if you showed VisiCalc to a programmer, he’d say “Yeah, that’s neat, so what?” But if you showed it to someone who did financial work with real spreadsheets, “he’d start shaking and say, ‘I spent all week doing that.’” Within a year VisiCalc was selling 12,000 copies a month. More than 25% of Apple IIs sold in 1979 were reportedly purchased just to run VisiCalc. Steve Jobs said the program propelled Apple to its success more than any other single event.

Here’s the economic story that matters: VisiCalc and its successors didn’t eliminate accounting. They eliminated a specific kind of accounting job and caused an explosion in a different kind. Since 1980, the US has lost roughly 400,000 bookkeeping and accounting clerk positions. But it has gained about 600,000 accountant and auditor jobs. The bookkeeper who maintained ledgers and performed routine calculations was automated away. The accountant who interpreted numbers, built models, and exercised judgment became more valuable, because when analysis gets cheaper to produce, you produce vastly more of it. Tim Harford, examining the data, concluded it’s hard to argue that accountancy was decimated by the spreadsheet: there are more accountants than ever, merely outsourcing the arithmetic to the machine.

The gains flowed in three directions at once. Companies got leaner finance departments (good for capital). A new, larger class of analyst-accountants earned good wages doing more interesting work (good for adapters, bad for clerks). And businesses of every size gained access to financial modeling that had previously been the province of large corporations with big staffs (great for consumers and small businesses). A task that once required a team of clerks and an adding machine could be done by one person with an Apple II, which meant that the corner hardware store could now do financial projections that used to require a Fortune 500 back office.

The agriculture and spreadsheet stories share a crucial feature: the technology didn’t just make existing work faster. It made entirely new things possible. Cheap food enabled urbanization, industrialization, and every economic transformation that followed. Cheap financial analysis enabled startups, small-business growth, and the explosion of financial services. The gains weren’t captured by the original workers or even the original employers. They were captured by the next economy that the productivity made possible.

So which story is AI writing for software engineers?

Every one of these seven historical cases contains a plausible version of what’s coming for the software industry. The honest answer is that multiple patterns will play out simultaneously, and which one dominates depends on a few key variables.

Is AI a loom, or a scalpel? The most important question is whether AI is substitutive (replacing the worker’s judgment, like the power loom replaced the weaver’s skill) or complementary (amplifying the worker’s judgment, like imaging technology amplified the surgeon’s). Today we’re firmly in the complementary phase. 46% of developers say they don’t fully trust AI outputs, and the biggest reported challenge is inaccurate suggestions that require experienced human review. But the textile workers of 1780 also had a few decades where new machines complemented their skills before making them irrelevant. The trajectory matters more than the current state.

Do engineers have a chokepoint? Every group of workers that captured productivity gains controlled something scarce. Surgeons controlled credentialing through the AMA’s $18-million-a-year lobbying machine. Linotype operators controlled hiring halls and apprenticeships through the ITU. Longshoremen controlled physical ports that couldn’t be moved or duplicated. Software engineers control… what, exactly? They have no licensing body, no union, no physical infrastructure that creates a bottleneck. Their leverage has always come from skill scarcity alone. And AI is specifically designed to reduce skill scarcity. In the Microsoft study, it was the least skilled developers who gained the most.

Will the spreadsheet pattern hold? The most optimistic model is the VisiCalc story: AI eliminates routine coding (the bookkeeper role) while creating an explosion in the total amount of software being built (the accountant role). In this world, fewer people write boilerplate, but far more people design systems, and the net effect is more engineering jobs, not fewer, just different ones. The early data is suggestive: the biggest AI productivity gains show up in routine tasks like implementing an HTTP server, not in the complex architectural and debugging work that defines senior engineering. If that pattern holds, we get the accountant’s middle path: transformation rather than elimination.

Or will it be agriculture? The scarier model is farming: AI makes building software so accessible that the value migrates entirely away from the people who write code and toward the platforms they write it on (Anthropic, OpenAI, cloud providers), the companies that deploy it, and the end users who consume it. Individual engineers become like individual farmers, necessary but not where the money concentrates. This is arguably already happening: the largest gains from software productivity over the past decade have gone to platform companies (AWS, Shopify, Stripe), not to the median developer.

Who goes home? Even in the best-case scenarios (the M&M Agreement, the Linotype era, the spreadsheet transformation), some workers lose. Harry Bridges saved the “A” men and let the casuals absorb the blow. The ITU’s 100,000 members dwindled to a merged remnant. The 400,000 bookkeeping clerks lost their jobs even as 600,000 new accountant positions were created, but they weren’t the same people. Every productivity revolution creates a line between those who adapt and those who are displaced. The question is never “will anyone lose?” It’s “how many, and how fast?”

Here’s what I think is actually most likely: several of these patterns at once. A smaller number of senior engineers who deeply understand systems, architecture, and AI tools will capture significant gains, like the longshoremen who stayed on the dock after the M&M Agreement, or the accountants who mastered Excel, or the surgeons whose scalpels kept getting sharper. A larger number of engineers doing routine work will face real pressure, like the hand compositors after the Linotype, or the cottage weavers after the power loom, or the bookkeeping clerks after VisiCalc. And the biggest winners of all may be neither group, but the companies and consumers who benefit from a world where building software is dramatically cheaper, just as the biggest winners of agricultural productivity weren’t farmers or farm owners, but everyone who eats.

The historical precedent that should haunt us isn’t the worst case (textiles, where capital captured everything for 60 years) or the best case (surgery, where workers controlled the gate). It’s the most common case: a long, uneven transition where capital moves faster than labor, where the gains are real but unevenly distributed, and where the workers who adapt thrive while the rest discover that “more productive” and “better off” aren’t the same thing.

The productivity is coming whether anyone wants it or not. The question, as always, is who gets the money when the machine gets faster.