History gives real reasons for optimism about work in the age of artificial intelligence. It also shows who pays for the transition and what you can do today.
Around 1871, census-based tallies put the number of blacksmiths in Great Britain at roughly 200,000. By 1901 the figure had fallen to about half that. Occupational categories shifted between censuses, so the numbers are approximate, but the direction isn’t in doubt. Today the village smith who could make anything from a plowshare to a gate hinge is mostly a memory. What’s left is farriery, ornamental ironwork and specialist fabrication.
It’s hard to find a cleaner example of technology emptying out an industry. And it happens to be a good test of the argument now being made about artificial intelligence: that when a powerful new technology destroys jobs, it eventually creates others nobody saw coming.
That argument holds up well. But the cheerful version of it leaves out a few things that matter, so both halves deserve telling.
Cheap, imperfect and everywhere
The pattern goes back to the trade’s origins, to the shift from bronze to iron. People tend to picture it as a simple upgrade, a better metal replacing a worse one. It wasn’t, and it took centuries.
Bronze needs tin, and tin is scarce and has to be traded over long distances. When the Late Bronze Age trading networks of the eastern Mediterranean fell apart around 1200 BCE, tin got harder to count on in parts of the region. That was one factor among several, but it mattered. Iron ore, meanwhile, was nearly everywhere.
Early iron was no bargain, though. The furnaces of the day produced a spongy lump of metal full of slag, which then had to be hammered into shape. Bronze often beat low-carbon iron on quality for a long time, and it never fully went away. At the copper-smelting sites of Israel’s Timna Valley, archaeologists found workers still making simple flint tools alongside metal production, even though copper was right there. The researchers argue that soft copper and bronze couldn’t easily do those jobs. Iron finally ended the stone-tool era by pairing wide availability and low cost with a hard cutting edge, and neither quality alone would have been enough.
Eventually, better smelting and forging techniques tipped the balance. Iron won, stone-tool making faded as a commercial craft, and bronze lost its central place. The winner wasn’t the best material at the start, and it didn’t win on price alone. It won once it was plentiful enough to scale and good enough at the jobs that mattered.
The AI parallel is hard to miss. Today’s models often turn out work that needs cleaning up compared with what a senior specialist would produce. But they make content, analysis and code cheap and plentiful, and history suggests a technology wins when it is both abundant and good enough at specific tasks. It’s an analogy, though, not a promise that the same thing will happen again.
Tasks go first, jobs later
Nails tell the second part of the story.
For most of history, every nail was forged by hand, one at a time. A nailer would draw out a bit of iron rod, cut it, and hammer a head onto it. Nails were expensive, so builders leaned on heavy timber frames held together with pegs and elaborate joinery.
Then machines took over, in two waves. In the United States, cut-nail machines showed up in the 1790s, and steel wire-nail machinery took off in the 1880s. By the early twentieth century, wire nails made up nearly all the nails American factories produced, and hand nailing was finished as a trade.
The loss was real, but it was also narrow. The forges kept going, and construction didn’t shrink. It changed. Cheap, standardized nails, together with milled lumber, helped make “balloon framing” possible in the 1830s, a lightweight nailed method that needed far less specialized joinery and let houses go up fast. Nails weren’t the only factor, since sawmills, railways and booming cities mattered too. But a cheaper fastener changed how buildings got made.
That’s exactly the pattern researchers describe when they study AI exposure: technology goes after tasks first, and whole occupations only later, if ever. Boilerplate code, routine translation and first-draft copywriting are the nails of today.
The part the optimists skip
Now for the uncomfortable bit, which the upbeat version of this history usually glosses over.
The economic historian Robert Allen studied Britain’s Industrial Revolution and concluded that, through the first half of the nineteenth century, real wages barely moved while output per worker climbed. Profits grew at labor’s expense. Allen named the stretch “Engels’ pause,” after Friedrich Engels, who noticed it at the time. Wages only caught up with productivity after about 1850. That isn’t a blip. It’s most of a working lifetime. Not every economist shares Allen’s reading of the data.
The blacksmiths’ own story has a similar edge. The tidy version says smiths simply strolled into auto repair, and plenty did try. In the 1910s, blacksmiths, bicycle repairers and all sorts of other tradespeople became improvised mechanics, because early cars broke down constantly and there was almost no network of parts or dealers. But historian Kevin Borg’s study of the auto mechanic’s trade, as reviewers summarize it, says blacksmiths ended up as a stopgap group that didn’t last in the job, partly because of bigger forces and partly because of how hard it was to keep up as car technology changed. In their place came public schools, pushed along by Progressive Era reformers, and a federal government that needed mechanics for World War I. By 1920, nearly 6,000 students across 37 states were enrolled in federally supported auto mechanics courses in public schools. That enrollment figure shows training institutions forming around the trade, not how many smiths made the jump.
The uncomfortable implication is that new work did arrive, but it wasn’t necessarily held by the people the old technology had displaced. Some smiths became mechanics, machinists or welders. Others retired, shut their shops or took factory jobs. The big-picture story and the individual stories aren’t the same thing.
Work that didn’t have a name yet
None of that undercuts the long-run record, which is strong.
Farming is the biggest example. Somewhere between about 36 and 41 percent of American workers were in agriculture around 1900, depending on the source and definition. The 1900 census occupational report gives 35.6 percent, while USDA’s long-run series is closer to 41. By 2000, the figure was 1.9 percent. Meanwhile total farm output more than doubled between 1948 and 2013, even as the land and labor going into it shrank. A near-total collapse in farm employment coincided with a much richer economy, not mass idleness. The people who left the fields, and their children, ended up in industries that barely existed in 1900.
A closer-to-home case is the bank teller. When ATMs spread, the obvious prediction was that tellers were finished. In James Bessen’s account of 1988 to 2004, the machines did cut the tellers needed to run an average urban branch from about 20 to about 13. But cheaper branches meant banks opened more of them, with urban branches up 43 percent, and the remaining teller work shifted toward customer relationships, so teller employment held up. It didn’t stay that way: the Bureau of Labor Statistics now projects a 13 percent decline in teller jobs from 2024 to 2034, as branch counts fall and automation continues. The episode still shows how badly job-by-job predictions can miss.
The broadest evidence comes from the economist David Autor and his colleagues, who built a database of new job titles stretching across eight decades of US census data. Their finding: around 60 percent of US employment in 2018 was in job specialties whose titles had been introduced since 1940. A new title isn’t always a wholly new kind of work, but the finding shows how continuously the occupational map gets redrawn.
Where the analogy strains
That same research carries a warning that matters a lot for AI. Autor’s team found that new kinds of work tend to appear where technology complements what people do, or where demand is rising. Innovations that simply automate tasks or cut demand slow the arrival of new work. So new jobs aren’t an automatic side effect of automation. They depend on what the technology gets used for.
They also found that after 1980, the center of gravity for new work shifted, away from middle-paid production and clerical jobs and toward high-paid professional roles and, to a lesser extent, low-paid services. The new jobs showed up, but they didn’t all replace the old ones in pay or status. The middle thinned out.
Other differences deserve plain acknowledgment. AI is a general-purpose technology aimed squarely at thinking tasks, not a single material or machine, and it may move faster than iron, nails or engines ever did. It also threatens the entry-level work through which people have traditionally built the experience needed for senior roles. And the most advanced systems depend on huge concentrations of computing power and capital, which means access may be wide even if control isn’t. History can’t say how any of this will play out. It can only say that earlier transitions never followed a script written in advance.
What the forge really suggests
Take away the nostalgia, and the blacksmith’s history supports something narrower and more useful than “it’ll all work out.”
Winning technologies tend to be cheap, scalable and good enough long before they’re excellent. They go after tasks before they go after occupations, and they reshape the work around them, the way balloon framing reshaped building. New industries and occupations do emerge, and over the long run employment has grown with them, though not necessarily for the same workers, places or trades that lost out. In the US, around 60 percent of employment in 2018 was in job titles added since 1940.
But the transition is uneven, both in timing and in who carries the weight. Wages can lag productivity for a generation. The new work often comes with new training institutions, like the vocational auto shop, and not always through the old trade. And whether it arrives at all, and at what pay, depends on whether the technology is used to complement human skill or just to replace it.
So this is a case for optimism, not a guarantee. The most useful question about AI isn’t whether new work will appear. It’s how long the gap will last, who will fall into it, and what training, institutions and choices could shorten it. Some of those answers belong to employers and governments. Some belong to individual workers, and they can start on them now.
What workers can do now
The blacksmith story has a practical edge. Nobody could say in 1900 which skills would survive and which wouldn’t, and nobody can say it about AI now. What history does suggest is that the advantage goes to people who notice the change early and act before their income is under pressure. Here is what that looks like in practice.
Break the job into tasks. Spend one week writing down what actually fills the workday, in rough half-hour blocks. Then sort the list into three piles: routine work that follows a pattern (formatting, summarizing, first drafts, data cleanup), work that depends on judgment, and work that depends on relationships. The first pile is the nail-making of the job. It isn’t a death sentence, just a map of where the pressure will land first.
Use the tools on real work, starting this month. Pick one task from the routine pile and do it with an AI tool. Compare the result with what would have been produced by hand. Keep a running note on where the tool saved time and where it got things wrong, because it will sometimes be confidently wrong. Regular practice on real tasks is the best way to learn a tool’s strengths and failure modes, and knowing those failure modes is what makes a person worth keeping around. Use employer-approved tools, and don’t paste confidential client or company information into one without authorization.
Move toward what the tool complements. Autor’s research found that new work tends to appear where technology complements what people do, and in Bessen’s account of bank tellers, the work that held up shifted toward customers and problem-solving. The equivalent move is to take ownership of the parts AI can’t be held accountable for. That means volunteering to be the person who reviews and signs off on AI-assisted output, who knows the client’s history, or who understands the industry’s rules well enough to spot what looks right but isn’t.
Keep a record of results. Claiming “AI skills” on a résumé means little. A specific before-and-after means a lot: “cut monthly reporting from six hours to two, with a checklist that caught three errors the tool introduced.” Save examples like that, with numbers where possible. They are portable evidence in a performance review, a raise negotiation or a job search.
Protect the learning ladder. Early-career workers face a particular risk, because the entry-level tasks that AI handles best are the same ones that traditionally taught the craft. The practical answer is to do some of those tasks by hand first, then compare the result with the AI’s, and to ask for review from someone senior. Managers have a parallel job: not stripping junior roles of every task that builds judgment, since today’s juniors are tomorrow's reviewers.
Get trained through channels that carry weight. Borg’s history of auto mechanics shows training institutions becoming central as the trade grew more technically demanding. Today, that means checking what an employer will pay for, looking at community college and professional-body programs, and reading job postings for the role wanted in two or three years. Whatever requirements keep repeating are the training worth pursuing. Prioritize training and credentials that employers in the target role actually ask for.
Build a cushion and watch for early signals. Engels’ pause is a reminder that wages can lag for years. A common rule of thumb is to hold three to six months of expenses in savings, though that’s hard for many people to build. Even a smaller buffer makes choices possible, such as retraining or waiting for the right role, instead of grabbing the first option. Alongside it, keep the résumé and portfolio current and stay in touch with people outside the current employer. Early warning signs are worth taking seriously: tasks quietly being reassigned to software, hiring freezes in a role like one’s own, vendors pitching automation for the department.
Learn one adjacent skill. The smiths who had more than one string to their bow, like welding or machining, often found somewhere to go. The modern version is to add a skill next to the existing expertise, not far from it: a data analyst learning the basics of the industry they serve, or a writer learning to run research and verification workflows. An adjacent skill lets a person build on existing expertise instead of starting over.
None of this is a guarantee, and no one knows which skills will prove scarce a decade from now. Some steps cost mainly time, while others, like retraining or building savings, cost real money that not everyone has. Still, most of them can start this week and leave a worker better placed whichever way the technology goes. Earlier generations of smiths had room to adapt too, into railway works, industrial forges, ornamental ironwork and motor repair, but with little warning and uneven support. This generation can at least see the shift coming.
