{"id":2823,"date":"2026-02-23T06:54:16","date_gmt":"2026-02-23T06:54:16","guid":{"rendered":"https:\/\/basavapurushottam.com\/?p=2823"},"modified":"2026-02-23T07:26:21","modified_gmt":"2026-02-23T07:26:21","slug":"will-ai-really-steal-your-job-a-160-year-old-paradox-has-some-surprising-answers","status":"publish","type":"post","link":"https:\/\/basavapurushottam.com\/index.php\/2026\/02\/23\/will-ai-really-steal-your-job-a-160-year-old-paradox-has-some-surprising-answers\/","title":{"rendered":"Will AI Really Steal Your Job? A 160-Year-Old Paradox Has Some Surprising Answers"},"content":{"rendered":"\n
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Ever wonder why cheaper, smarter AI might actually end up creating<\/em> way more jobs than it wipes out? And more importantly \u2014 where those new jobs are likely to pop up? Turns out a Victorian-era economist spotted the pattern way back in 1865, and it’s ringing truer than ever in 2025\u20132026.<\/p>\n\n\n\n

Let me take you back to 1865. A sharp young English economist, William Stanley Jevons, noticed something weird about steam engines.<\/p>\n\n\n\n

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(That’s Jevons himself \u2014 looking very serious in his 19th-century beard.)<\/p>\n\n\n\n

Engines were getting way more efficient: they burned less coal to deliver the same punch. Everyone figured England would use less<\/em> coal overall. But nope \u2014 coal use tripled by 1900!<\/p>\n\n\n\n

Why? Cheaper, better engines made coal-powered machines worthwhile in places they’d never been before: tiny factories, brand-new industries, small towns that couldn’t afford the old clunky versions. Efficiency didn’t cut demand \u2014 it exploded<\/em> it.<\/p>\n\n\n\n

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( A couple of classic visuals of Jevons Paradox<\/strong> in action: one showing steam engine efficiency soaring over centuries while coal use kept climbing, and another simple graph using modern hybrid cars to illustrate how lower cost per mile = way more miles driven.)<\/p>\n\n\n\n

Jevons called this the “paradox” \u2014 make something more efficient, and people don’t just do the same amount cheaper; they do a ton more<\/em> of it. Fast-forward to today, and this 160-year-old idea is suddenly the hottest thing in the AI-jobs debate.<\/p>\n\n\n\n

The Big Fear: AI Is Coming for All<\/em>Our Jobs<\/mark><\/strong><\/h2>\n\n\n\n

Let’s be honest \u2014 the worry isn’t crazy.<\/p>\n\n\n\n

The World Economic Forum’s Future of Jobs Report 2025 lays it out plainly: by 2030, about 170 million new jobs get created worldwide… but roughly 92 million existing ones disappear. Net gain: 78 million jobs. Great for the stats sheet, but if you’re one of the 92 million whose role vanishes, that “net positive” feels pretty meaningless.<\/p>\n\n\n\n

The jobs most exposed? The repetitive, predictable ones: data entry, basic bookkeeping, routine customer support, straightforward translations, standard legal doc reviews. AI is already faster and cheaper at those.<\/p>\n\n\n\n

But Here’s Where Jevons Sneaks Back In: Efficiency Usually Multiplies<\/em>Work<\/mark><\/strong><\/h2>\n\n\n\n

When AI slashes the cost of a task, we don’t just keep doing the same volume for less cash. We crank it up \u2014 often dramatically.<\/p>\n\n\n\n

Take radiology. Early on, people panicked: “AI reads X-rays better than humans \u2014 radiologists are toast!” Reality? AI made scans quicker and cheaper, so doctors started ordering way more of them. More scans = more work for radiologists interpreting the tricky cases, deciding next steps, talking to patients.<\/p>\n\n\n\n

Georgetown University’s Jack Karsten put it perfectly: AI isn’t replacing radiologists \u2014 it’s letting them handle more<\/em> work and actually boosting demand for their expertise.<\/p>\n\n\n\n

Translation is another great example. Cheap AI translation didn’t lead companies to fire translators. Instead, they went from localizing products into 5 languages to 40. Total translation work skyrocketed.<\/p>\n\n\n\n

Aaron Levie (CEO of Box) nailed the insight: “When the cost of doing work goes down, the demand for it goes up. And usually there’s far more pent-up demand than we realize.”<\/p>\n\n\n\n

Stanford’s Erik Brynjolfsson adds the history angle: bulldozers, computers, spreadsheets \u2014 every time we’ve made people radically more productive, we’ve just kept hiring more humans.<\/p>\n\n\n\n

The Proof Is Already Here: AI Is Creating Jobs Right Now<\/mark><\/strong><\/h2>\n\n\n\n

This isn’t just theory \u2014 the numbers are rolling in.<\/p>\n\n\n\n

LinkedIn’s 2026 labor market report shows AI has spawned 1.3 million new roles globally in just two years: AI Engineers, Data Annotators, Forward-Deployed Engineers, and more. Plus over 600,000 new data-center jobs to build and run the massive infrastructure AI needs.<\/p>\n\n\n\n

In the US, AI-related job postings jumped 25% year-over-year in early 2025, with median salaries around $157,000. IBM is actually tripling<\/em> entry-level hiring \u2014 thanks to AI.<\/p>\n\n\n\n

PwC’s 2025 Global AI Jobs Barometer (looking at nearly a billion job ads) found workers with AI skills earn over 25% more than peers in the same roles without them. Industries using AI are growing revenue faster<\/em>, not slower.<\/p>\n\n\n\n

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(An infographic showing the myth vs. reality: far more jobs created \u2014 especially in construction and data centers \u2014 than lost, with wages rising fastest in AI-exposed fields.)<\/p>\n\n\n\n

So Where Are These New Jobs Actually Appearing<\/mark>?<\/strong><\/h2>\n\n\n\n

History’s pattern is clear: old jobs fade, but new ones emerge \u2014 usually in bigger numbers.<\/p>\n\n\n\n

Around 1900, 40% of Americans worked on farms. Today it’s under 2%. Employment didn’t collapse; it shifted to manufacturing, services, tech.<\/p>\n\n\n\n

The same is happening now, just faster. Here’s where growth is exploding:<\/p>\n\n\n\n