15 Motorcycle Brands Made In America, Ranked By Years In Business







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Buying American-made may be at the top of your priority list for a variety of reasons. Extra costs for interstate delivery, let alone international transportation costs, can put a damper on your excitement when ordering a new motorcycle. Apart from the cost, it might be a priority to support U.S.-based businesses, especially if that means you can travel to the production facility to take delivery of your new motorcycle.

Regardless of your motivation for shopping American-made, there are plenty of choices when it comes to U.S. motorcycle brands. Not every brand has been in business for decades, which means there might be some new-to-you brands to consider. Plus, beyond the standard hogs, choppers, and street bikes, there are other types of motorcycles available from U.S. brands.

Whether you’re considering an all-electric motorcycle, need two or more wheels for off-roading, or want an entirely custom, bespoke bike, here are 15 brands that are made in the United States, listed from newest to oldest based on how long they’ve been in business.

LiveWire

One of the newest and, at the same time, oldest brands made in America is LiveWire. Technically, LiveWire is an offshoot of Harley-Davidson, which is one of the oldest U.S. motorcycle brands. However, since LiveWire operates as its own brand, we’re including it separately. LiveWire doesn’t rely too heavily on Harley branding, but it does pop up in some of its promotional materials.

LiveWire launched in 2021, but it still benefits from all the years of experience of Harley-Davidson. York, Pennsylvania, where LiveWire is made, is also home to a Harley-Davidson manufacturing facility. However, while Harley has other assembly locations, LiveWire specifically states that all of its motorcycles are assembled in York.

LiveWire’s lineup, at the time of writing, includes the S2 series — starting at around $12,000 — and the higher-priced LiveWire One. LiveWire also has off-road bikes, including the diminutive but power-packed Honcho. Retail locations are scattered throughout the U.S., making it easy to find an electric motorcycle.

Tarform

The founder of Tarform notes that it began in 2016, without a concrete idea of where the brand would go. Taras Kravtchouk spent a lot of time restoring vintage motorcycles and also worked in software development. From those areas of interest grew Tarform, out of Brooklyn, New York. Like other newer motorcycle brands, Tarform aims to bring back a sense of heft — both in terms of tangible feeling and the overall aesthetic and vibe — to motorcycle design.

Highlighting that its motorcycles are built in Brooklyn, the Tarform website features the Vera and Luna motorcycles. Both are electric, with pricing starting at $18,000 (the Launch Edition starts at $24,000).

Though a lot of American-made brands have motorcycle assembly facilities in small towns where it’s easy to spread out and find materials, Tarform is a bit different. The company’s manufacturing site is located at the Brooklyn Navy Yard in Brooklyn, New York, with views of the water from the studio.

Curtiss Motorcycle Co.

With a tagline like, “Discover American Motorcycle Luxury,” it’s obvious that Curtiss Motorcycle Co. is proud of the fact that its motorcycles are made in the United States. The brand does have roots in 1902 with its original owner, but the brand as we currently know it began in 2016, according to the company’s LinkedIn data.

Early Curtiss Motorcycle Co. motorcycles were understandably bare-bones, but between the 1990s and 2020s, things changed. The newer designs attracted the likes of Tom Cruise, but you shouldn’t snooze on Curtiss, or you might lose. Currently, the only motorcycle offered on the company’s website is The 1; past models are memorialized, but not promoted as current retail options.

The idea behind The 1 is that motorcycle enthusiasts literally only need one motorcycle for their entire lives. In fact, Curtiss aims to create motorcycles that can become family heirlooms, with one of its slogans being, “Less, but better.”

Janus Motorcycles

Before it became Janus Motorcycles, Paragon Motorcycles, LLC was founded in 2011. The earliest motorcycle didn’t even resemble one, to be honest. Rather, it looked more like an electric bicycle. Yet today, Janus Motorcycles has a broader range of bikes, but at the time of writing, only two engine sizes are available: 250cc and 450cc.

The Halcyon and Gryffin bikes look vintage, but they’re brand new and fairly modern. According to the brand, the “majority of the motorcycle is made within 20 miles” of the company headquarters in Goshen, Indiana. When a customer begins the ordering process, they can choose either a premade bike or use the build tool to order one to their specs.

Parts come in from local manufacturers via van, although Janus is working on moving more components in-house. Once the parts are procured, the bike is assembled. Then, it’s painted, pinstriped, and tested. If you can’t make it to the Janus facility in Goshen, Indiana, motorcycles can also be shipped anywhere in the U.S.

Arch

Arch may be one of the most famous newer motorcycle brands manufactured in the U.S. Why is Arch so famous? One of the owners and founders is actor Keanu Reeves. Along with helping design the motorcycles, Reeves has Arch motorcycles in his collection and appears in marketing materials for the brand.

Arch launched in 2011 in California, and to date, its motorcycles are designed and hand-built in the U.S. Describing its manufacturing philosophy, Arch says components are manufactured in-house in Los Angeles, California, with the use of advanced 3D modeling and CNC machines. However, Arch does have some collaborations with brands, including Öhlins, which makes custom suspension systems for Arch motorcycles. Also, Arch motorcycle engines are S&S branded.

Of course, Arch won’t be for everyone. The fact that no prices are listed on the website, not to mention the small detail of each Arch bike being built to order, suggests that the ownership experience with this brand is highly exclusive. That said, if you do buy an Arch motorcycle, you may get to meet Keanu himself; Arch owners are invited to exclusive rides and events that have featured Reeves.

Buell

Buell Motorcycles started in 2009 with Erik Buell Racing, although the company has changed hands since then. Buell was re-established in 2021 under Bill Melvin, who owns Buell now. The brand is fully independent and constructs each Buell bike by hand in Grand Rapids, Michigan. Not only are the motorcycles assembled in Michigan, but the engines are manufactured there, too.

Buell wasn’t always an independent motorcycle company, though. Buell and Harley-Davidson were once intertwined, though that partnership seemingly ended in 2009. Buell has also overhauled its production processes over the years. Per Buell’s FAQs, the earlier V-Twin (the 1125cc model) was manufactured in a partnership with Rotax. Later, Buell brought the 1190cc V-Twin manufacturing process in-house.

If you’re in the market for a Buell bike, there are a handful of sales centers scattered around the U.S. (though you shouldn’t have a hard time finding a service center, should you need one). Buell ships to all 50 states, so even if you don’t live near a sales center, you can still get your paws on one. Buell motorcycles start at around $20,000 for the 2026 1190SX.

Lightning Motorcycle

In case the brand name didn’t give it away, Lightning Motorcycle is an electric motorcycle manufacturer. Started in 2006 in Hollister, California, Lightning Motorcycle was actually inspired by Porsche. Owner Richard Hatfield experienced driving an electric Porsche and then turned to using lithium battery tech for motorcycles.

The first-ever Lightning motorcycle was a converted Yamaha, which became the first-ever lithium-electric sport bike. Since then, electric bikes from Lightning Motorcycle have won awards and broken records previously held by gas-powered motorcycles. Lightning Motorcycles also has something no other company does: “MythBusters” star Jamie Hyneman serving as a tech advisor.

As of 2018, Lightning Motorcycle bikes have been assembled in a San Jose, California, facility, though there’s no clear explanation of their current production location on the company website (the corporate headquarters are in Hollister, California). You can, however, submit a request to purchase a Lightning Motorcycle; the Strike R has a retail price of around $27,000.

Zero Motorcycles

Zero Motorcycles’ humble origins began in 2006 in Santa Cruz, California, in — where else? — a garage. Growing from early prototypes to a range of electric motorcycles 20 years later, Zero has remained American-made.

Zero Motorcycles made our list of U.S.-made bikes with a reputation for reliability, and the fact that they’re all electric might be appealing, too. Zero Motorcycles can fully charge in as little as one hour, have a top range of 223 miles per charge, and can reach speeds of 124 miles per hour.

Dozens of dealers across the U.S. have Zero Motorcycles in stock. Pricing varies pretty widely, reaching upwards of $20,000, but the lowest-priced Zero motorcycle at the time of writing was the Zero FX, which costs just over $12,000. More improvements and offerings are to come, according to the manufacturer. Zero Motorcycles has also collaborated with Polaris on other powersports equipment beyond motorcycles.

Orange County Choppers

Orange County Choppers may have earned its fame thanks to a certain TV series, but the bikes themselves also have a reputation. Orange County Choppers was formed in 1999 under the guidance of owner Paul Teutul Senior in Newburgh, New York. The brand’s claims of manufacturing the “most unique motorcycles in the world” seem to ring true if you’ve ever caught an episode of “American Chopper.”

Though “American Chopper” has since gone off the air, leaving viewers wondering what happened to its cast, it seems that Orange County Choppers is still doing well today. Not only can you buy a chopper from the company, but you can also visit the Orange County Choppers Road House & Museum for a glimpse of the brand’s past and television fame.

It’s hard to come by pricing information for a motorcycle from Orange County Choppers, but it’s safe to say the bikes are pretty expensive. Because every bike is custom, there’s no inventory to choose from, either.

Combat Motors

Combat Motors formed in 1991, and the company started out by enlisting the help of engineers and designers who had already made American motorcycles. First, Combat Motors worked with an engineering firm in Grass Valley, California, but it later moved to San Francisco. Ultimately, the brand began manufacturing its motorcycles in Baton Rouge, Louisiana.

While other motorcycles can boast of being American-made, Combat Motors also highlights that it’s a “leader” in “bespoke” motorcycles. Although Combat Motors’ website indicates that there are dealers offering the bikes throughout the U.S., it didn’t offer a directory at the time of writing. In 2024, the owner, Ernest Lee, said he hoped to produce six bikes per week, but it’s unclear whether production has ramped up since then.

If you have your heart set on a brand-new Combat Motors bike, you may have to travel to the dealer in Hurricane, Utah, for a glimpse of the showroom and an opportunity to test ride a motorcycle. Prior to moving to its Utah location, Combat Motors was based in Alabama, and even earlier it operated under a different name. Previously known as Confederate Motors and then Curtiss Motorcycles, the company was sold to Lee in 2018, with the rebranding to Combat Motors following afterward.

Boss Hoss

Not every American-made motorcycle brand has a lineup of hogs or dirt bikes. Boss Hoss makes V8 motorcycles and trikes, which originally started out as kits. The brand started in 1990 and, to this day, manufactures its machines in a Dyersburg, Tennessee, factory. Dyersburg, where Boss Hoss bikes are built, appears to have some inventory of bikes, but you can also find the brand at dealers around the U.S.

More than a dozen dealerships across the U.S. have Boss Hoss bikes, but be warned — these machines aren’t cheap. If the inventory at the Tennessee location is any indication, most Boss Hoss motorcycles are priced above $65,000. If you have that kind of cash, a Boss Hoss may be worth considering; some owners report racking up over 300,000 miles on their motorcycles.

Not only does Boss Hoss make its own motorcycles in Tennessee, but the brand also has a proprietary fuel injection system. Boss Hoss motorcycles are aimed at buyers seeking high performance, V8-powered machines, and they’re one of the longer-lived U.S.-made brands to choose from.

ATK

ATK is one of a handful of dirt bike brands made in the U.S., and the brand has been doing so since 1985. The manufacturer has a very specific designation for its bikes, calling itself the “only off-road motorcycle [gasoline powered] manufacturer in the USA.” Back in 1984, the first-ever ATK dirt bike was the winning ride in the Barstow to Vegas Desert Race.

The brand has been involved with motocross, desert racing, and dirt track racing ever since. ATK also makes ATVs, which are another award-winning branch of the manufacturer. Beyond the bike designs themselves, ATK also patented a specific part to normalize chain torque. In fact, the brand is named for the part — an anti-tension Kettenantreib.

Early on, ATK was based in Southern California, but it later moved to Utah when the brand changed hands in 1993. Its history includes specialty motorcycles for law enforcement, prototypes for other motorcycle manufacturers, and the acquisition of Cannondale’s powersports equipment. In 2010, ATK began offering street bikes, too, although some components for those bikes are manufactured by a Korean company.

Rokon

One of the most unique motorcycles manufactured in the U.S. started way back in 1958. Rokon, which manufactures all-terrain motorcycles, started in Sylmar, California. The Trail-Breaker rolled off the assembly line in Sylmar in 1960, but production wound up in Vermont a few years later. Eventually, Rokon moved to New Hampshire, and Rokon bikes are still produced in the U.S. today.

You can find these motorcycles — including the Rokon with hollow wheels — at dealers in a handful of U.S. states. Although the original Trail-Breaker has changed a lot since its debut, it’s still a top pick for rugged applications with organizations like the Forest Service, Fish and Game agencies, and the United States Army.

You can get your own Trail-Breaker for around $10,000, though you might want to splurge on add-ons like a sidecar, trailer, or even a log skidder. The Trail-Breaker also isn’t the only model offered by Rokon, and every option on the brand’s website carries a “Made in America” label.

Harley-Davidson

Harley-Davidson may be one of the first few names that comes to mind when you think of motorcycles made in America. Harley began in 1903 in Milwaukee, Wisconsin, specifically in a “small shed.” The original four founders grew Harley-Davidson into the global company that it is today.

Three Harley-Davidson factory locations are in the U.S., while there are some international locations, too. The headquarters remains in Milwaukee, and its major U.S. manufacturing facilities are in Menomonee Falls and Tomahawk, Wisconsin, and York, Pennsylvania. The Menomonee Falls facility is where the Harley Big Twin engine is assembled, and it offers powertrain operations tours. Plastic and composite parts are primarily manufactured at the Tomahawk facility. The Pennsylvania facility handles vehicle operations and assembles many of Harley-Davidson’s motorcycles, including LiveWire models.

Of course, you don’t have to visit a factory to get yourself a U.S.-built Harley-Davidson. There are so many Harley dealers in the U.S. that the brand has an entire directory page, with a handful of dealers in every state.

Indian

With the longest history of any motorcycle manufacturer in the United States, Indian has been in business since 1901. The brand also labels itself “America’s First Motorcycle Company.” A huge part of Indian’s marketing centers on the fact that the bikes are made in the U.S. and that it is the longest-running motorcycle brand in America. In 2026, enthusiasts can even buy a Chief Vintage 125th Anniversary Edition bike — and it’s hand-painted.

Originally, Indian was headquartered in Springfield, Massachusetts, before a move in 2026 due to a change in ownership. Indian’s current HQ is in Minnesota, while you’ll find its assembly facility in Iowa. A few hundred employees assemble Indian motorcycles at that facility, and you can even see them in action.

Visitors can get a glimpse of where Indian motorcycles are made in Spirit Lake, Iowa, both at the assembly plant itself and at a museum-like center next door. Manufacturing facility tours are offered on a limited schedule, however. Visitors can also see vintage Indian motorcycles and other memorabilia at the neighboring Indian Motorcycle Experience Center.





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There’s a popular argument that AI will do to human workers what tractors did to horses. Tractors could do what horses did. Horses became obsolete. AI can do what humans do. Therefore…

Plenty of major AI figures seem to agree. Elon Musk says AI will “replace all jobs.” Anthropic CEO Dario Amodei regularly warns about mass job loss, framing AI as “a general labor substitute.” OpenAI investors talk openly about AI replacing “80% of all jobs by 2030.” These are influential people, not random bloggers. Still, they are not necessarily a representative sample of the world’s most careful economists.

And the fear itself is hardly new. Economist Wassily Leontief—best known for developing input-output analysis, a way of mapping how industries depend on one another—raised similar concerns in the early 1980s. If AI really were a perfect substitute for human labor, the logic would be straightforward. Any cost advantage would eventually drive firms toward 100% AI labor. You do not need a long essay to prove that result.

The problem is that the phrase “AI will eventually be a perfect substitute” does almost all the analytical work. That assumption hides a great deal: differences across tasks, industries, and workers; the many margins along which firms adjust; and the messy heterogeneity that makes the real economy more than a toy model.

How substitutable is AI today? What would need to happen for that substitutability to rise meaningfully? What other conditions would also need to hold? Even the historical analogy—“tractors could do what horses did, therefore horses became obsolete”—compresses several distinct steps into one neat sentence. “AI can do what humans do, therefore humans become obsolete” hides even more.

So let’s unpack those steps.

(This post draws on a new working paper that walks through the math and economics in detail. Really, though, it is mostly basic accounting.)

Before We All Become Horses

For those unfamiliar with the history of horses in the United States, the horse population actually rose for decades alongside industrialization. It increased from 4.3 million in 1840 to 27.3 million in 1920. The collapse came later, as tractors and motor vehicles displaced horses in agriculture and transportation. The number of farm horses and mules then fell to roughly 3 million by 1960.

Horses, in effect, had one main economic role, and that role disappeared. Humans are different. So before jumping from “AI can do tasks” to “humans become obsolete,” we should define carefully what that outcome would actually mean.

To keep things simple, suppose demand for human labor falls to zero. Not “low.” Zero. What would that require?

It would mean that no dollar spent anywhere in the economy passes through human labor at any point in the supply chain. Not the person who made the product. Not the person who shipped it. Not the person who designed it, marketed it, maintained it, or cleaned the building where it was assembled. Zero human labor embodied in final expenditure. That is the benchmark. That is what “humans become horses” would mean, stated precisely.

This is the input-output framework the aforementioned Wassily Leontief built his career on. The idea is straightforward: trace any final purchase backward through its supply chain and add up all the labor that contributed to it, both directly and indirectly. A cup of coffee includes the labor of the barista, but also the roaster, the truck driver, the coffee farmer, and the workers who built the truck. “Embodied labor” means all of it.

For labor demand truly to collapse, every one of those links would need to disappear across every good and service consumers buy. That is a much stronger claim than “AI can do some jobs.” The economy is not a single production function. It is a sprawling network of activities. When AI makes one activity cheaper, consumers do not simply buy more of the same thing forever. They redirect spending elsewhere.

Every dollar lands somewhere. Some spending flows into highly labor-intensive activities, such as restaurants, therapy, or home repair. Other spending flows into activities that require very little labor, such as cloud storage, automated checkout systems, or streaming subscriptions. So the relevant question is not merely: “Can AI do my job?” It is: “When AI makes some things cheaper, where does the saved money go next?”

Aggregate labor demand depends on at least three things: total spending in the economy, the share of spending that goes toward labor-intensive activities, and the amount of labor embodied in each activity. For labor demand to fall to zero, AI cannot merely displace workers in a few sectors. Every dollar of spending, wherever it ultimately lands, must shed all embodied human labor. The “humans become horses” story therefore requires three separate margins to collapse simultaneously.

A useful starting point is the simple observation that firms do not want labor per se. A restaurant does not want waiters because it enjoys employing waiters. It wants orders taken, customers reassured, mistakes fixed, and meals delivered. Labor demand is therefore “derived demand”—firms demand workers because workers help produce something else consumers value.

When AI can perform those underlying tasks more cheaply, two things happen at once. First, firms substitute AI for workers, reducing labor demand per unit of output. Second, lower production costs reduce prices, output expands, and that expansion tends to pull labor demand back upward. Whether total labor demand rises or falls depends on which force dominates.

Economists call this the Hicks-Marshall decomposition of derived demand into substitution effects and scale effects. The terminology sounds forbidding, but the intuition is simple: cheaper production reduces the need for workers in one sense, while expanding the market for output in another. That tension will organize the rest of the discussion.

When a dollar gets saved, where does it go? Into new tasks? New jobs? New industries? The money has to end up somewhere.

Your Job Is Not a Checklist

The case that AI can automate many tasks is not speculative anymore. This is obviously true to some extent, and it has been true for years.

Even early large language models (LLMs) showed substantial potential to affect workplace tasks. One widely cited paper by Tyna Eloundou, Sam Manning, Pamela Mishkin, and Daniel Rock estimated that roughly 80% of the U.S. workforce could see at least 10% of their job tasks affected by LLMs. When paired with complementary software tools, 86% of occupations crossed that 10% exposure threshold.

Since then, the empirical literature has grown rapidly, and the task-level evidence is hard to dismiss. In a large customer-support study, access to generative AI increased the number of issues resolved per hour by roughly 15%. In an experiment involving professional writing tasks, ChatGPT reduced average completion time by 40% while increasing measured output quality by 18%. In a controlled GitHub Copilot study, software developers completed coding tasks 55.8% faster. Those are not rounding errors.

But they are effects on tasks, not necessarily on jobs. That distinction matters. When a task gets automated, the saved dollar does not disappear into the void. Firms and workers often redirect it toward new activities within the same occupation: more client management, more review and verification, more coordination, more judgment calls, more customization.

Just as there is no fixed amount of demand in the economy, there is no fixed bundle of tasks that permanently defines a job. Jobs evolve. They absorb new responsibilities, shed old ones, and reorganize around whatever remains scarce and valuable.

The O-Ring Problem

There is a familiar ritual in AI discourse. Someone posts a demo. The demo performs a task associated with a particular job. People immediately conclude that the job is doomed.

Sometimes they are right. But that inference skips about 15 intermediate steps.

What does it actually cost to deploy the system once error rates are included? Do customers trust it? Can firms reorganize workflows around it? Does management even know how to integrate it effectively? A chatbot demo can appear overnight. A hospital cannot reorganize clinical liability around AI overnight.

That distinction matters because firms are not simply collections of isolated tasks. They are organizations. In many cases, the result will not be pure replacement, but rather a human-AI team producing output together. Economists call this complementarity: two inputs become more valuable when used jointly than separately.

But complementarity is not free. A human-AI pair that produces only marginally more value than the AI alone will not justify paying a full human wage. The human worker must contribute something the AI cannot reproduce cheaply or reliably.

That matters especially in high-stakes settings where errors are extraordinarily costly. Surgery, aviation, structural engineering, fiduciary advice, and many legal services all fall into this category. In these fields, the cost of failure can easily dwarf the savings from cheaper production.

That could eventually change. It probably will change in some areas over time. But it is not likely to change quickly.

This is essentially the “O-ring” logic from economics, named after the tiny rubber seal whose failure destroyed the Space Shuttle Challenger. When the value of the entire system collapses because one component fails, buyers do not focus primarily on sticker price. They focus on the expected cost of a system that actually works.

In those environments, human-supervised production can remain economically efficient even if AI itself becomes extremely cheap.

Horses Had Nowhere Else to Go

Suppose substitution effects really do dominate within most jobs. The saved dollar then escapes the workplace entirely. Where does it go next?

Most standard economic models collapse the economy into a single “final good,” which makes that question disappear by assumption. Real economies do not work that way. They contain many sectors, and every dollar eventually lands somewhere.

Start with software, which serves as a useful microcosm. Software-intensive industries have already undergone decades of automation through digital tools. If automation were going to drive human labor out of a sector entirely, this is where you would expect to see it first. The chart below groups industries according to how much software they purchase relative to value added: low, medium, and high software intensity. The result is striking.

The most software-intensive industries do not merely retain human labor. They actually devote a larger share of income to labor compensation—about 67%—than the least software-intensive industries, which devote roughly 55%. In other words, the industries that automated the most heavily also remained highly labor-intensive.

The same pattern appears in employment projections. The Bureau of Labor Statistics (BLS) projects total U.S. employment to increase by 5.2 million jobs between 2024 and 2034. Employment for software developers—a profession directly exposed to AI tools—is projected to grow 17.9%. BLS could ultimately prove wrong. Forecasting always carries uncertainty. Still, the evidence so far points strongly toward scale effects dominating in software-intensive industries. Automation reduced costs, output expanded, and labor demand remained robust.

Software may be an extreme case, but versions of this pattern appear across the broader economy and over much longer periods. Take the shift from goods to services. In 1929, most consumer spending went toward physical goods. Today, roughly two-thirds of consumer spending flows toward services. As manufacturing became dramatically more efficient, consumers did not respond by purchasing infinite refrigerators and toasters. Instead, spending shifted toward health care, education, restaurants, entertainment, travel, and personal services.

That is the “saved dollar” in action at the economy-wide level. Goods became cheaper. The substitution effect largely won within goods-producing industries. Employment growth in manufacturing did not continue indefinitely. But the freed-up purchasing power migrated elsewhere, and the scale effect emerged across sectors instead.

From a macroeconomic perspective, output expanded overall. Consumers simply redirected spending toward new categories of consumption. But migration alone does not help workers unless the destination sectors still contain substantial human labor. Did they?

Again, the answer appears to be yes.

Services consistently devote a larger share of value added to employee compensation than goods-producing industries do. Spending did not merely migrate. It migrated toward sectors where more of each dollar ends up in someone’s paycheck.

So yes, one could argue that this still resembles the horse story in one respect. The relative importance of goods production declined as productivity increased. The point, though, is that large, diverse economies contain adjustment margins that horses never had. There are escape valves.

Comparative advantage keeps reappearing. When automation makes some activities extremely cheap, spending tends to shift toward the activities that remain relatively expensive. And the activities that remain expensive are often the ones that are hardest to automate. Those are precisely the areas where humans continue to hold a comparative advantage—that is, where human labor remains relatively more productive or valuable than machine substitutes. The saved dollar therefore tends to drift toward areas where humans are still worth paying.

That is not technological optimism. It is simply the logic of comparative advantage.

James Bessen documents this dynamic sector by sector. In early textile manufacturing, power looms sharply reduced labor required per yard of cloth. But cloth became so much cheaper that demand exploded, and total textile employment increased for decades. Similar patterns appeared in steel and automobile production. Eventually, demand saturated. Prices stopped falling rapidly enough to offset labor-saving automation, and employment in those sectors declined.

The key question for AI, then, is not whether automation can destroy jobs. Of course it can. The real question is: Which sectors are in which phase? Where might AI-generated savings flow today?

Health care already accounts for roughly 18% of U.S. GDP, and that share continues to rise. Elder care will likely expand further as populations age. Personalized services, human-intensive care work, and new categories of consumption may absorb growing shares of spending.

Joel Mokyr, Chris Vickers, and Nicolas Ziebarth make this historical argument well in a Journal of Economic Perspectives article. Across prior waves of technological change, new tasks emerged, comparative advantage persisted, and entirely new categories of work appeared that earlier generations could not have anticipated.

Horses had no equivalent adjustment path. They did not move into elder care.

Will Humans Become a Luxury Good?

The saved dollar migrated toward human-intensive sectors last time. The strongest argument for why this time could be different comes from economist Philip Trammell’s paper, “Is Labor a Luxury in the Long Run?

His answer is: probably not. Even if richer consumers initially spend more on human-intensive goods and services—live music, handmade products, personal care, bespoke experiences—four long-run forces may steadily erode that demand.

  1. AI-generated variety keeps expanding. New AI-produced goods compete for every dollar that might otherwise land on a human-made product or service.
  2. Human experiences carry opportunity costs. Time spent at a live concert is time not spent consuming some potentially superior AI-generated alternative.
  3. Labor competes with other scarce goods for consumers’ willingness-to-pay premiums. Beachfront property, status goods, intellectual property, and research-intensive products may all absorb spending that might otherwise flow toward human labor.
  4. Capital goods become cheaper over time. If investment opportunities continue expanding, the share of economic activity devoted to capital accumulation could grow indefinitely.

Trammell’s Coca-Cola analogy captures the intuition cleanly. Original Coke once held roughly 50% of the soda market. Then came Diet Coke, Cherry Coke, Pepsi Max, energy drinks, flavored sparkling water, and endless other varieties. Even with enormous brand loyalty and supply constraints, Coke’s market share fell below 20%.

The implication for AI is straightforward. Even if consumers initially prefer human-made goods, that preference may weaken as AI continuously generates new substitutes and varieties. Human labor does not need to become worthless. Its share can erode through dilution.

That is a serious argument, and I take it seriously. Still, notice what the argument requires. It is not enough for AI-generated variety merely to expand. That will almost certainly happen. The stronger claim is that AI-generated substitutes must expand broadly and rapidly enough to pull spending away from every human-intensive category simultaneously.

The real question is not whether AI competes with some human-produced goods. Of course it will. The question is whether any human-intensive islands survive. Does anyone still spend money on something with a person inside it?

The arithmetic quickly becomes more demanding than many “humans become horses” narratives imply. Suppose AI eventually captures 85% of economic activity. Software, accounting, logistics, medicine, law, management, and much of media production become almost fully automated. Human labor largely disappears from those sectors.

Now suppose the remaining 15% of spending flows toward activities that still contain at least 30% human labor: elder care, live entertainment, skilled trades, therapy, surgery, in-person education, luxury craftsmanship, status goods, and other relational or trust-intensive services.

The aggregate labor share would still equal at least:

S ? 0.15 × 0.30 = 0.045

That leaves labor with at least a 4.5% share of economic output. That may not sound comforting, but remember what this calculation is doing. It is merely establishing a lower bound under extremely aggressive automation assumptions. It is not utopia. It is not full employment. But it is also not zero. And a falling labor share does not necessarily imply falling labor demand if total output grows rapidly enough.

Alex Imas offers another reason to doubt the “humans disappear” story. As AI drives down the cost of commodities, real incomes rise. Historically, richer consumers tend to shift spending toward what Imas calls “relational goods”—goods and services whose value depends partly on human connection, scarcity, or social meaning.

That idea connects to a large economics literature on structural change. Over time, economies tend to shift from agriculture to manufacturing to services as incomes rise. The key debate is why. Do consumers simply buy more of whatever becomes cheaper? Or do rising incomes fundamentally change what people want?

Diego Comin, Danial Lashkari, and Marti Mestieri decompose those effects and conclude that income effects account for more than 75% of the long-run shift toward services. That distinction matters enormously here. If structural change were driven mainly by falling prices, then AI-generated abundance might pull spending overwhelmingly toward AI-produced goods. But if structural change is driven mainly by rising incomes and evolving preferences, then richer consumers may continue demanding more human-intensive experiences and services. Historically, that is exactly what has happened.

Experimental evidence points the same way. In one set of experiments, subjects learned that other people would be excluded from purchasing an otherwise identical product. Willingness to pay roughly doubled. The exclusivity itself created value.

Importantly, the exclusivity premium was stronger for human-made goods than AI-generated ones. Human-created artwork gained roughly 44% in value from exclusivity, compared with about 21% for AI-generated artwork. AI-made goods feel infinitely replicable. Human-made goods feel scarce, even when they technically are not. People value what other people cannot easily obtain. That impulse does not disappear as societies grow wealthier. If anything, it intensifies.

Perhaps AI-generated variety eventually overwhelms even those preferences. Maybe. Still, the structural-change evidence consistently suggests that income effects dominate price effects by roughly three to one. When basic goods become cheaper, humans do not announce that they are finally satisfied and stop developing new wants. They invent new forms of distinction, identity, taste, and status competition. The open question is where those new desires land. So far, the evidence points toward humans retaining an important role.

One final clarification matters here, because popular AI discussions often conflate two distinct claims. A falling labor share is not the same thing as falling labor demand. Labor’s share of national income can decline even while total employment and total wages continue rising, provided the overall economy grows fast enough. In that world, AI appears to “take over” a larger share of production while human workers still earn more in absolute terms because the economic pie itself expands dramatically.

That may well describe the phase we are currently entering. We already observe the basic pattern. Higher-income households consume more services, and service sectors remain relatively labor-intensive. Could that eventually reverse? Of course. But at the moment, this is the evidence we actually have.

The Horse Story Ends Here

Walking through all these layers—from tasks, where we are only beginning to see meaningful substitution, up through firms, sectors, and the macroeconomy—leaves me fairly skeptical of the “humans become horses” outcome. I know I have concealed that conclusion masterfully until now.

AI will absolutely perform many tasks. It will reorganize jobs, sometimes painfully. Some sectors may lose most of their human labor. Spending will often chase automation and lower prices. All of that can happen without driving human labor demand to zero. Because at every stage of the process, there is still a saved dollar looking for somewhere to land. And the same question keeps reappearing: Where does it go next?

For the horse outcome to occur, that saved dollar must eventually fail to find any activity with meaningful human labor embodied in it. Not some activities. All activities.

That is a very specific future. It is logically possible. But it requires substitution to dominate simultaneously across tasks, firms, sectors, and final consumption patterns, with no surviving human-intensive islands anywhere in the economy. The evidence we currently have—structural change, revealed preferences, comparative advantage, and experimental results—keeps pointing the other way.

Horses lost because the economy stopped needing horsepower. Humans are not just horsepower.

 

The post Why Humans Are (Probably) Not Headed for the Glue Factory appeared first on Truth on the Market.



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