Technologies disrupting the labor market will also bring job opportunity.
in today’s AI debate and you’ll hear dire predictions of massive job losses. The World Bank says over one-third of Philippine jobs are at risk from AI and suggests similar job destruction in Asian middle-income countries like Indonesia and Malaysia.
This view misses the underlying economic reality. AI is going to transform the global economy faster than many expect. But that’s not the same as upending it. History points toward a far more nuanced outcome: efficiency gains tend to unlock suppressed demand for work, not eliminate it.In his YouTube clip “What Everyone Is Getting Wrong About AI and Jobs,” Garry Tan, CEO of Y Combinator, an American technology accelerator and venture capital firm, says AI skeptics should look at a case study from a field once considered ground zero for AI’s looming disruption: radiology. In 2016, Geoffrey Hinton—one of the pioneers of neural networks—famously declared that “people should stop training radiologists now.” Within five years, he predicted, deep learning would outperform humans. Goodbye, radiologists. That was nearly a decade ago. Today, radiologists are not only still employed—their services are in record demand, says Tan. This despite an explosion of AI imaging tools that can detect and classify dozens of conditions faster and often more accurately than humans. Rather than eliminating radiologists, AI made them more productive—and demand expanded to match that new productivity. This dynamic is not new, explains Tan. Economists know it as Jevons Paradox, a 19th-century observation that efficiency gains often increase consumption rather than reduce it.in the 1960s eventually cut shipping costs by more than 90% and global trade exploded. Entire industries—logistics, freight forwarding, port automation—grew from what had been a cost-constrained bottleneck.in the 2010s made infrastructure 10x cheaper. Traditional IT roles didn’t vanish; they evolved. Server admins became cloud architects managing environments at previously unimaginable scale.today has become dramatically more efficient, and instead of dampening demand, it has ignited a global GPU frenzy—pushing Nvidia’s valuation to historic highs.Tan continues: As AI lowers the marginal cost of analysis, drafting, coding, summarizing and decision support, we should expect more—never less—demand for the domains around those tasks. That doesn’t mean jobs remain unchanged. Many roles—especially those defined by repetitive, low-context tasks—will be refactored. Andrej Karpathy, a cofounder of OpenAI, has argued that AI will first transform highly structured jobs: customer support, data entry and high-volume administrative tasks. Yet even there, the likely outcome is not eradication but elevation. Humans become managers of AI workflows, supervisors of fleets of agents and quality-assurance leads rather than mechanical task executors.the AI wave is real and accelerating, emphasizes Tan. Underestimating it is as dangerous today as dismissing the internet was in the late 1990s. Cost curves are falling, capabilities are compounding and competitive gaps are widening.expecting an imminent collapse in labor demand is a misunderstanding of how economies respond to efficiency. AI will reshape the workforce, but through augmentation, new categories and reconfigured value chains—not mass idling of human capital. The companies that win in the next decade won’t be those that wait for permission or clarity. They will be those that recognize latent demand, design for hybrid human-AI workflows, and build the organizational muscle to scale them.
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