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Most conversations about artificial intelligence and jobs revolve around automation—AI taking over tasks that humans currently perform. This perspective mostly looks at the “supply side” of labor: how many workers are replaced by machines that can do the same work more cheaply or efficiently. But there’s another side to the story that often goes overlooked. By changing the underlying problems we need solved, AI has the potential to reduce or even eliminate the demand for certain jobs in the first place.
The usual framework focuses on tasks: which ones can AI do, and how quickly can it do them? That’s the classic automation debate. But there are jobs that exist primarily because of certain problems or risks that society faces—like fires, crime, or household malfunctions. If AI can solve or preempt these problems, the demand for the corresponding labor disappears. This isn’t just about robots taking over tasks; it’s about making some tasks unnecessary.
Firefighting offers a clear example. AI-driven smart homes can use predictive sensors to detect early signs of smoke or faulty wiring and address the issue before a fire breaks out. Automated sprinkler systems or real-time suppression drones can tackle small blazes before they spread. With fewer and smaller fires, the number of traditional firefighting crews—and the massive infrastructure around them—may shrink over time. We’re not talking about AI “replacing” firefighters so much as preventing the fires that justify needing a large firefighting workforce.
Similar dynamics arise in policing. AI-powered surveillance and predictive analytics can help pinpoint high-risk areas and intervene before crime occurs. Social support tools might reduce some root causes of criminal activity, further driving down crime. Again, this doesn’t just replace police officers with drones. Instead, it redefines the nature and scale of crime itself, lowering overall demand for large law enforcement agencies.
Even plumbing—one of the last areas many assume would be automated—can be affected by demand-side changes. Imagine an AI-assisted augmented reality interface that walks homeowners through simpler repairs. Or devices that monitor water pressure and automatically shut off if something goes wrong. That could dramatically reduce emergency calls for professional plumbers. The job isn’t simply getting automated; the problem is being prevented or diminished.
From a labor economics perspective, automation typically looks at the supply of labor and how technology replaces human effort. But these examples show how AI can reduce the demand for labor by eliminating the very reason the job exists. This introduces a concept of “preemptive deflation” of labor demand, where services become less necessary. It also ties to ideas like Baumol’s cost disease (how labor costs balloon in service sectors that don’t benefit from productivity gains) and structural unemployment (shifts that leave entire categories of workers without demand).
History offers parallels. Telephone operators and elevator operators weren’t just replaced by machines that did the same work; phone-dialing and elevator controls became so efficient that entire categories of labor vanished. Similarly, some agricultural jobs disappeared because new technologies prevented many of the problems (like pests or irrigation control) that used to require intensive human labor. When underlying tasks are made unnecessary, the overall market for those jobs evaporates.
If we only focus on how AI might automate existing jobs, we risk underestimating a deeper trend. Over time, certain jobs will fade out not because AI does them directly, but because AI makes them much rarer—maybe even redundant. This complicates employment forecasts, retraining programs, and the way people understand their own roles in society. It’s not just “Will my job be automated?” It’s “Will my job’s purpose even exist in the future?”
AI’s impact on labor markets goes beyond the familiar debate about robots replacing humans. In some sectors, AI won’t just do the work better and faster; it could solve the underlying problems that sustain the demand for that work. As more AI-driven systems anticipate and prevent issues before they happen, we may see labor demand collapse in ways that are both less visible and more profound than simple automation. Understanding this shift is crucial if we want to prepare for the future of work and ensure that people can adapt to a world where many problems we once took for granted no longer exist.