OpenAI CEO Sam Altman Suggests Some Jobs May Lack “Real Work” Value
OpenAI CEO Sam Altman sparked debate earlier this month by suggesting that some jobs eliminated by advancements in artificial intelligence may not have constituted “real work” in the first place.
During a live interview at OpenAI’s DevDay conference on October 12, 2025, Altman responded to a hypothetical scenario involving a farmer from 50 years ago and stated, “The thing about that farmer… [is that] they very likely would look at what you do and I do and say, ‘that’s not real work.’” He further clarified that this perspective made him feel “a little less worried… [but] more worried in some ways,” emphasizing the tangible value of essential professions like farming. This comment comes as AI continues to rapidly evolve, raising concerns about widespread job displacement.
Altman’s statement echoes arguments made a decade ago by anthropologist David Graeber, who popularized the concept of “bullshit jobs” – roles perceived by workers as pointless. Graeber’s 2018 book on the subject resonated with many, but research from 2021, utilizing the European Social Survey and a similar U.S. study, indicated that only approximately 5-20 percent of workers feel their jobs are useless, attributing such feelings more to management issues and work culture than the inherent value of the role itself. Understanding the nature of work and its perceived value is crucial as automation technologies like large language models become more prevalent; learn more about the impact of automation on the workforce from the Brookings Institution.
Altman clarified that AI is likely to eliminate specific *tasks* within jobs, rather than entire roles, particularly those involving automatable processes like compliance checklists and redundant reporting. This suggests a shift in the nature of work, rather than complete obsolescence for many professions. OpenAI officials stated they will continue to monitor the societal impact of their technologies and engage in discussions about responsible AI development.
Sam Altman isn’t known for understatement, but even by his own standards, what he said on stage at OpenAI’s DevDay conference earlier this month was pretty problematic. In a live interview with AI newsletter founder Rowan Cheung, Altman made a sweeping claim that many jobs that vanish in the age of large language models might not have been “real work” in the first place.
Responding to a thought experiment about how a farmer from 50 years ago might view our current reality, Altman said, “The thing about that farmer… [is that] they very likely would look at what you do and I do and say, ‘that’s not real work.’” Altman said this makes him feel “a little less worried… [but] more worried in some ways. If you’re… farming… you’re doing something people really need. This is real work.”
Graeber’s core claim — that entire sectors of the economy are built on box-ticking bureaucracy with no social value — has been cited by everyone from disgruntled office workers to policy think tanks. Altman’s framing felt smug, sure, but it’s not without precedent.
The trouble is, the data hasn’t really backed it up. A 2021 study using the European Social Survey found that only about five percent of people said their jobs felt useless. A similar U.S. study put that number closer to twenty percent. In both cases, the researchers concluded that feelings of pointlessness were more about poor management and work culture than about the job itself. If your boss is a micromanager and your workflow is broken, even valuable work can feel fake. That’s not proof that the role should be automated out of existence.
Where Altman’s comment holds water is in what it hints at, even if it doesn’t spell it out. Most jobs aren’t fake, but many have accumulated layers of automatable junk: compliance checklists, reports nobody reads, emails summarizing meetings that could’ve been Slack threads. That’s the kind of “game-playing” work LLMs are already good at. When Altman says these models will wipe out tasks, not just roles, this is what he likely means. And on that point, he could be right.
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