I have been following the AI-employment debate with some interest. I have an academic background in economic history and recently finished a software project, LoopLlama, using an AI coding agent. Below I draw on both to offer some ideas that are well-known to economists and historians but underrepresented in popular discourse — and, on balance, more reassuring.

Prologue: they took our jobs!

The fear is intuitive. If AI can do most cognitive work faster and cheaper than humans, employers will use AI instead. Several leaders of major frontier AI labs have claimed that they expect this to happen, and the question is seriously debated among macroeconomists.

And based on a fallacy. Many economists and historians would point out that the fear, at least when expressed simply, rests on the lump of labor fallacy, the assumption that there is a fixed amount of work to be done, so if machines take some of it, humans get less.

Doom averted: what happened last time

Mechanization is the obvious historical parallel. The industrial revolution began automating physical work in the mid-18th century and peaked with assembly-line production in the mid-20th. Like AI, it was a general-purpose technology applicable across industries; like AI, it compressed costs dramatically and enabled previously impossible things. The main difference is that mechanization operated in the physical realm, AI in the cognitive.

Agriculture illustrates the scale of change. Over 50 percent of the US labor force worked in agriculture in 1870; today the figure is under 2 percent. Viewed one way, the mechanization of agriculture — and many other sectors — destroyed most jobs that existed in the past.

Contemporary observers saw catastrophe coming. The Luddites were skilled textile workers who famously destroyed machinery in the 1810s, fearing displacement. David Ricardo, one of the most influential economists from that period, witnessed the effects of mechanization and reversed his earlier position, acknowledging that machinery could permanently harm workers, triggering a debate among classical economists that ran for decades. The parallels to current AI discourse are hard to miss.

Prosperity, not catastrophe. Mechanization produced neither mass unemployment nor impoverishment. By almost any material measure, people living in countries with modern, developed economies are vastly more prosperous than their 19th-century ancestors. As one example among many, US household spending on necessities — food, clothing, housing — fell from roughly 80 percent of budgets in 1900 to under 50 percent today, even as the quantity, quality, and variety of those necessity goods expanded substantially.1

Employment shifted rather than collapsing. Old jobs were genuinely destroyed — those farming jobs simply do not exist. But new jobs emerged to meet an expanded set of wants and ambitions, in sectors like education, healthcare, government, finance, media, and, of course, various engineering and technical fields to create and maintain the ever-growing roster of machinery. Work also became less brutal: fewer injuries, less drudgery, shorter hours. Labor was not a lump: human employment was not fixed; it evolved as we became more mechanized and more prosperous.

Interlude: the case of LoopLlama

Massive boost in productivity. LoopLlama v2 runs to over 11,000 lines of code, built in roughly two months of part-time effort — typically an hour or two per day. More importantly, the AI did not merely speed up work I would have done anyway. I am a software engineer, but not a web developer — a separate domain of expertise, one I had no interest in acquiring. Without an AI coding agent, v2 would not have existed.

And ambition. As an example, consider the application’s cloud backup feature, which came up late in the coding process as a speculative idea for a future v3. Exercising such caution was how I was trained to think as a software engineer, because adding a major feature late in a development cycle is one way software projects fail. But the premise behind that old wisdom — namely, that generating code is costly — had evaporated. Even though the move felt a bit crazy in the moment, we decided to add the feature and the app is much better for it. More generally, cloud backup is just one example among many where the existence of the AI significantly expanded the scope of my ambition for the project.2

Employment did not disappear — it shifted. My old job, writing code, largely evaporated. What replaced it? Planning the architecture and features, directing the AI through implementation, assessing what it produced, identifying gaps and inconsistencies, then planning and directing again. The human effort moved upward, toward judgment about the whole project rather than execution of individual coding tasks.

It shifted because AI has limits. An AI coding agent is like a genius new hire who has not just RTFM but memorized it: encyclopedic knowledge of APIs, frameworks, and coding patterns; work ethic out the wazoo; and rarely stumped by a well-specified task. But like talented junior engineers, AIs tend toward myopia. Given a bug or design problem, the AI sometimes offered a fix that resolved the immediate issue while quietly introducing trouble elsewhere. Had I passively accepted every short-term fix the AI proposed — in the style of vibe coding, where you simply tell a computer what you want and it happens — the result would have been much less coherent. The success of v2 derived from human-AI collaboration, not one type of intelligence or the other.

Doom reconsidered: this time might actually be different

AI limits might be temporary. AI is different from a loom or an assembly line in one critical respect: it is a cognitive technology running on computers, which means it can, in principle, be turned on itself. We already see this happening in limited ways, with frontier AI labs reporting, or at least predicting, that AI is accelerating progress toward better AI.3 If that loop continues, the limits I observed — difficulty with larger or open-ended tasks, myopia regarding downstream consequences, failures when weighing tradeoffs among project goals, and inability to generate outside-the-box solutions in those situations — those limits might represent a snapshot rather than a ceiling.

No escape valve. Extrapolate the trend and the reassuring analogy to mechanization breaks down. As mechanization proceeded in the 19th and 20th centuries, employment shifted toward office and professional jobs. But the combination of increasingly capable AIs for cognitive work and robots for physical work leaves no obvious area where human labor holds a structural advantage.

Doom inverted: the worst case might be great

The doom scenario leads to contraction. Assume the most extreme version: AIs and machines handle virtually all work, and mass unemployment follows. But that scenario also implies huge gains in productivity, an economy generating goods and services with a small fraction of the labor previously required. That translates into vastly greater income, at least in the aggregate. But who among the jobless will purchase this river of goods and services?

Door #1: the problem of distribution. Labor in a market economy serves two functions. It is an input: labor makes and does things. It is also a mechanism to distribute income: wages give workers the money to buy what the economy produces. Employment bundles the two functions, but extreme automation severs the bundle: no employment, no wages, no demand, no reason to engage the capable AIs and machines in the first place. The gears grind to a halt. Down this branch of the thought experiment lies one extreme outcome: supreme technological capability amid system-wide impoverishment.

Door #2: seizing the means of distribution. Down this branch lies broad prosperity that mimics or even dwarfs what occurred during mechanization: shorter work weeks, material abundance without drudgery, labor as vocation rather than necessity — each of us crafting our own LoopLlamas. For that to happen, something else must replace wages as the means by which income reaches people. Examples include broad capital ownership, profit-sharing, a universal basic income (UBI), or public ownership of productive capacity.

The pessimist’s objection: deep inequality. Very few regular folks find talk of UBI and broadly shared AI prosperity reassuring. The default stance I observe in the US is pessimistic: those who control the technology will resist redistribution, accumulate more wealth and power, and leave the rest of humanity to suffer.

Politics happens. That pessimism assumes economics settles the outcome on its own. But elites factionalize and compete; at times populations organize and resist. In the US, concentrated economic power has provoked exactly the discontent the pessimist describes, sometimes followed by redistributive reforms: trust-busting and the income tax in the Progressive Era, labor law and Social Security in the New Deal, civil rights and anti-poverty legislation in the Great Society. Whether that happens at all, and if so how — early or late, smoothly or catastrophically — is the relevant question, not whether advanced AI and machines can produce enough wealth to make us all prosperous.

  1. See the BLS report, 100 Years of U.S. Consumer Spending, notably Chart 1, page 3. ↩

  2. I often wonder about the impact that our current AI technology would have had on the largest projects of my academic career, such as Historical Statistics of the United States, IPUMS, and my dissertation on the history of US state taxation. In every case, I think the multiplier would have been large, substantially increasing what my teams and I could have accomplished. The political scientist Andy Hall writes frequently on such matters and encourages academics to figure out how to use AI to scale up the ambition of their research agendas and, more directly in political science itself, to scale up our ambitions for democracy itself. ↩

  3. See Dario Amodei for predictions. In various interviews, employees at Anthropic report that most of the code in Claude Code is now written by the AI model, with human management and judgment now as the gating factor on the rate of progress — similar to the description of my role in LoopLlama v2. ↩

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