There seems to be broad consensus that AI will eliminate many jobs, but some experts aren’t clear on whether it will create any new ones.
Geoffrey Hinton, often called the “Godfather of AI” for his pioneering work on neural networks, has raised concerns about the economic disruption that artificial intelligence may bring—and unlike many tech optimists, he’s not convinced that history will repeat itself. While economists have long pointed to previous technological revolutions as evidence that displaced workers eventually find new opportunities, Hinton suggests that the AI revolution may be fundamentally different.
Drawing on historical parallels, Hinton noted how past technological shifts created alternative pathways for workers. “If you got unemployed because you used to dig ditches during previous tech shifts, now you have to do something else,” he observed. “You could get a job in a call center.” But then came his stark warning: “Now those jobs are all gonna go. It’s not clear where those people go.”
The AI pioneer directly challenged the prevailing economic wisdom that has comforted policymakers and business leaders for decades. “Some economists say these big changes always create new jobs,” Hinton acknowledged. “It’s not clear to me that this will.” His skepticism isn’t merely academic—he believes major corporations are already positioning themselves for this future: “I think the big companies are betting on it causing massive job replacement by AI, ’cause that’s where the big money’s gonna be.”
Hinton’s comments come at a critical juncture for the AI industry. Customer service roles, once seen as a safe harbor for workers displaced from other sectors, are now among the most vulnerable to AI automation. Companies like Klarna have already reported using AI chatbots to handle work equivalent to hundreds of customer service agents. Meanwhile, OpenAI’s recent advancements in voice AI and Google’s customer service AI demonstrations suggest that call centers—historically a major employer—may face unprecedented disruption.
The implications extend far beyond customer service. If Hinton is correct that AI-driven automation won’t generate sufficient new employment opportunities, policymakers may need to consider more radical interventions than traditional retraining programs. Universal basic income, significantly shortened work weeks, or other restructuring of how society distributes resources and meaningful work could move from fringe ideas to urgent necessities. What makes Hinton’s warning particularly noteworthy is that it comes not from a critic of AI, but from one of the technology’s chief architects—someone who understands both its capabilities and its trajectory better than almost anyone alive.