Brand-new adventure motorcycles can be pretty expensive, but there’s a lot you have to look out for if you try to find one used. Adventure bikes are, by their very nature, designed to be ridden off-road on unpaved trails and paths. This means many of them are exposed to all manner of environmental hazards that other bikes don’t usually face. While some of the damage this causes is easy to spot, there are many other things you might not be able to see.
This isn’t just about how the bike was treated, either. Different motorcycle brands and models may react to stressors in different ways. Some have garnered reputations for long-term reliability — the kind of bikes that can take a lickin’ and keep on tickin’ — while others serve as cautionary tales from riders who’ve purchased them only to discover a litany of issues hiding inside the chassis.
Those interested in picking up an adventure bike on the used market may do well to look at platforms like Reddit and other motorcycle forums to see what previous owners have said about them. It might also be worth taking the time to see which motorcycle models owners have warned their fellow riders to steer clear of.
1. Moto Guzzi Stelvio 1200 2008-2012
Moto Guzzi is a fairly boutique Italian manufacturer that has this adventure bike in its catalog: The Moto Guzzi Stelvio. The modern version of this bike runs on a 1,000cc engine, while the larger 2008-2012 model operated on a 1,151cc V-twin engine. This bike had good reviews at launch, but there have been serious complaints about its inability to stand the test of time.
The main culprit appears to be the engine itself. During the 2008-2009 run, Moto Guzzi implemented a Series 1.2 “Quattrovalvole” 8V engine that promised low noise and a smooth riding experience. Unfortunately, it also used a flat-tappet valve train design that was prone to failure and proved to be a massive issue down the line. There are several reports of this issue from owners and mechanics alike. Every motorcycle that used this engine before 2012 is at risk of developing this problem. Some had the engine fail early on, while others took a while for it to develop, and still more never had the issue at all. This has led many to see these bikes as ticking time bombs that might fail at any time.
Moto Guzzi never directly acknowledged this weakness in the engine’s design and never issued a formal recall. Rather, they replaced it with a roller tappet design in later production years and began offering roller tappet conversion kits that customers could purchase to retrofit flat-tappet models. These don’t appear to be available anymore; however, it can be difficult to tell whether a used bike has already been converted without documentation.
2. BMW R1200GS and R1200GS Adventure 2004-2006
BMW isn’t ranked very highly in the Consumer Reports reliability survey overall, but the brand carries a lot of weight in the touring and adventure motorcycle space, and big bikes like the BMW R1200GS and its sibling, the R1200GS Adventure, are sure to catch some attention among enthusiasts. This is another instance where the line, as a whole, is fairly solid, but there is one specific generation that buyers might want to avoid on the used market.
The 2004-2006 BMW R1200GS isn’t a bad motorcycle overall, but it has one issue prospective buyers should be aware of: the brakes. These two models from these specific years utilized a power-assisted, servo-driven integral ABS system. This significantly increases overall stopping power, making it easier for these big, heavy bikes to decelerate. Some riders have even complained that they feel too powerful after coming from a bike with standard brakes, though most people consider it an improvement. It’s an impressive technology, and it’s great when it works, but unfortunately, it can be a bit temperamental.
There have been multiple reports from users stating that these brakes need to be regularly flushed, or else the entire ABS system can fail. This isn’t an issue if the bike has consistently received appropriate maintenance, but it can be difficult to tell on a used motorcycle. What’s more, the BMW Integral ABS System is a touch more complicated than standard systems, as it has four separate circuits that need to be maintained independently. Others have also noted that failure points like faulty servo motors and low-voltage issues can further complicate troubleshooting.
3. KTM 1190 Adventure 2013-2014
KTM is well known for making bikes that excel both on and off-road, so it makes sense that the brand’s adventure models would seem like solid options. There are a couple of years of the KTM 1190 Adventure that you might want to avoid, however. The 2013 and 2014 KTM 1190 Adventure models are capable machines on paper, boasting a V-twin that could generate 150 horsepower and 92 lb-ft of torque as well as an impressive electronic suspension damping system and other cutting-edge electronics. The issue was the airbox.
These two model years reportedly had a notorious issue with airbox seals that allowed dust to get through. This is extremely problematic, as it can cause dust to enter the throttle bodies and even the engine itself, ultimately leading to much bigger problems if it isn’t taken care of. This is particularly bad when you consider that it’s an adventure bike and is therefore designed to handle dusty trails. There are several reports from bike owners who opened the airbox and found a fine layer of dust inside. Others weren’t so fortunate and didn’t find out about the problem until dirt had already made its way into the bike’s engine and destroyed a cylinder.
There are aftermarket filters that can be used to replace the stock airbox filter, but this is only effective if it’s done before dust has gotten inside the bike. Purchasing a used model is risky, as it can be hard to say how much damage has already been done.
4. Harley-Davidson Pan America 2021-2022
Harley-Davidson might be best known for its cruisers, but the brand has also branched out over the years, producing several other types of motorcycles. Only recently did the company make its first foray into adventure riding with the Harley-Davidson Pan America, though riders reported a few issues with the original 2021-2022 models.
It’s pretty common for a brand that branches out into a new vehicle style to have growing pains, but it seems these bikes had serious struggles. According to owners, the biggest problems with the 2021 and 2022 Harley-Davidson Pan America had to do with the bikes’ electronics and software. This wasn’t isolated to a single system, either. It seems that just about everything was buggy. Users have reported issues with the Adaptive Ride Height feature not functioning properly and locking into incorrect seat heights, as well as problems with the adaptive headlights, throttle-by-wire, ride modes, and even the horn. Two of the main sources of these issues appear to be voltage dips from the battery, which many consider to be underpowered, and power drainage issues related to the starter clutch. This has reportedly caused error codes and often locks out the bike’s digital interface.
While the electrical issues appear to be the most common complaints, riders have also experienced some mechanical problems. Burnt radiator hoses, failing fuel pumps, and outright engine failures have all been reported in the few years since the model launched. This has largely led to a nosedive in the cost of these bikes on the used market, but even at these lower prices, many riders still say that the electrical and mechanical problems mean the bike simply isn’t worth the headache.
5. Ducati Multistrada 1000DS 2003-2006
Most of the time, when people think about Ducati, the company’s sport bikes are the first models that come to mind. It’s true that many of the best Ducati motorcycles ever made were built for the track, but the Ducati Multistrada 1000DS was a crossover adventure bike that blended the company’s trademark style with versatile off-road capability. It was produced only from 2003 to 2006 and won many fans during that time with its unique design and dual-spark 1000cc air-cooled, two-valve L-twin Desmo engine. That said, many riders have reported a crucial design flaw that came years later: The fuel tank would swell and warp.
The problem was that the plastic fuel tank didn’t react well with modern ethanol-blended gasoline. This led to swelling, which could cause a number of issues, such as the shell rising on its mountings, interference with steering, and fuel leaking through the fuel pump mount. Another issue is that once the tank expands enough to become tight, it can be difficult to remove and even more difficult to reinstall. Many owners replaced the tank with an OEM model, though this doesn’t really fix the problem, as the new tank may also develop swelling. The problem was so prevalent that Ducati was hit with a class action lawsuit, which made it so that “U.S. residents who on September 16, 2011, owned any 2003-2011 Ducati Monster, Multistrada, SportClassic, Streetfighter, Superbike or Hypermotard motorcycle manufactured with a plastic fuel tank” would receive an extended warranty that would cover these repairs, but only for a few years. These warranties are now all out of date.
How we listed these motorcycles
Motorcycle fans will always have different preferences, and some will like certain models despite their flaws, while others will claim they’re best avoided. That said, in making this list, we strove to choose bikes with multiple reports of specific model years and prevalent issues that made them a risky buy on the used market.
We combed through dozens of adventure motorcycle forums, searching for bikes that had garnered reputations for design or performance flaws. We checked multiple sources for each to verify that these weren’t isolated incidents but were repeatedly mentioned by multiple owners across multiple platforms. This serves as a strong indicator that a flaw isn’t simply a one-time fluke, but rather something inherently wrong with the design across the entire model year. We paid particular attention to complaints that would be easy for a seller to conceal and that a used buyer might not know about until after they’d made the purchase.
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 Perspectivesarticle. 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.
AI-generated variety keeps expanding. New AI-produced goods compete for every dollar that might otherwise land on a human-made product or service.
Human experiences carry opportunity costs. Time spent at a live concert is time not spent consuming some potentially superior AI-generated alternative.
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.
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.
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