It’s Labor Day, so here’s a post about how human labor is alive and well in the age of AI.
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labor → ’ → AI
I live in San Francisco and hang out with a lot of tech people, both in the AI industry and outside of it.
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I → live → it
And one thing that almost everyone I know here believes is that AI’s main economic effect is to displace humans from their jobs.
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effect → know → jobs
Most people don’t have concrete arguments for why this should be true; it’s just an article of faith.
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it → have → faith
The conventional wisdom is pretty well summed up by the first line of this tweet:
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wisdom → sum → tweet
In fact, AI companies themselves have spent years talking about how their inventions are going to render large swathes of humanity economically obsolete — an odd marketing pitch, perhaps, but one that seemed to reflect their honest expectations.
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that → spend → expectations
This time, though, the public seems to agree.
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public → seem → ?
A recent Ipsos poll found that most Americans expect AI to compete with human workers more than it complements them.
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it → find → them
And Pew finds that this belief has even strengthened in recent years:
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belief → find → years
So basically, most people think AI is a job-killer.
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AI → think → ?
And yet somehow, this job-killer keeps stubbornly refusing to kill jobs.
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killer → keep → jobs
The prime-age employment rate — the single best indicator of how many Americans have jobs — continues to hover near all-time highs:
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Americans → continue → highs
It’s not easy to identify some sort of positive shock that is canceling out the job-killing effects of AI.
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that → ’ → AI
Theoretically speaking, automation can create jobs just as easily as it can destroy them.
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it → speak → them
Here are Acemoglu and Restrepo (2019), explaining the various ways that technology can affect the demand for labor:
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technology → explain → labor
Automation [can be bad] for labor because of a displacement effect—as capital takes over tasks previously performed by labor…
[A]utomation technology also increases productivity, and via this channel, which we call the productivity effect, it contributes to the demand for labor in non-automated tasks…
[T]he displacement effect of automation has [historically] been counterbalanced by technologies that create new tasks in which labor has a comparative advantage.
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labor → take → advantage
Such new tasks generate not only a positive productivity effect, but also a reinstatement effect—they reinstate labor into a broader range of tasks and thus change the task content of production in favor of labor.
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they → generate → labor
The reinstatement effect is the polar opposite of the displacement effect and directly increases the labor share as well as labor demand.
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effect → increase → share
In other words, automation can do three basic things.
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automation → do → things
Yes, it can replace people and take their jobs.
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it → replace → jobs
It can also make them more productive, which can both create jobs and destroy them.1
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which → make → jobs
And, crucially, automation can create new jobs for people to do.
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people → create → jobs
Power looms replaced master weavers, but they created jobs for technicians and engineers to make the power looms work.
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looms → replace → jobs
The internet automated much of the work of travel agents, but created jobs for web designers.
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internet → automate → designers
People who think of AI as a job-killer might not have thought of the second and third of these.
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who → think → these
Or they may have thought of them, but simply assumed they’re not a big deal.
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they → think → them
Anecdotally, a lot of tech people think that AI will keep substituting for more and more tasks until A) productivity increases just increase the demand for AI, and B) there are no new tasks left for humans to do.
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humans → think → AI
AI detractors, meanwhile — like Daron Acemoglu himself — often simply assume that new tasks created by AI will be “bad tasks” like misinformation and cybercrime that hurt the economy instead of helping it.
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that → assume → it
AI might be creating lots of new tasks for humans to do.
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humans → create → tasks
For example, software engineers are writing less and less code themselves.
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engineers → write → code
Instead, they’re spending more and more time telling AI to write code — that represents a productivity improvement.
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that → spend → improvement
But they’re also trying to figure out what code to tell AI to write, making sure AI is writing the kind of code they want, integrating that code into products, and so on.
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they → try → products
This helps explain why in the age of Codex and Claude Code, software developer jobs have been increasing as a percentage of total employment:
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jobs → help → employment
Anecdotally, organizations that thought they could replace lots of their software engineers with AI ended up having to hire many of them back — sometimes at a premium.
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they → think → premium
In fact, this is a story we see throughout the economy.
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we → see → economy
Alex Tabarrok recently reported on a Census Bureau survey about AI that’s been running since 2023.
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that → report → 2023
The Census Bureau calls companies up and asks them A) how AI affected their total employment, and B) how AI affects the tasks that workers do.
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workers → call → that
Most companies reported no change in overall employment, which could just be due to inertia.
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which → report → inertia
But of companies that did report a change, more reported an increase than a decrease!
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more → report → decrease
And here’s the breakdown by sector:
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breakdown → ’ → sector
…and 67 more, not listed.
The Aporia
