AI will affect some jobs, Nandan Nilekani said in a Moneycontrol interview published February 22, 2025, but he expects only a few jobs to be eliminated entirely. His distinction is between automating tasks and removing whole jobs: many roles, he argued, will retain work even as AI takes over some tasks. He also expects productivity gains and new kinds of work, though he offered no figures or timeline for those outcomes.
What Nilekani said about AI and jobs
Asked whether AI’s effect on jobs would be a problem or an opportunity, Nilekani said some jobs would be affected, but “there’ll be only a few jobs that’ll be totally taken out.” He expects many roles to change rather than disappear: “There’ll be many jobs where a lot of the tasks will be taken out, but not all the tasks, so they still have work to do.”
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That is an important distinction in his answer. He was not saying that AI would leave work unchanged; he was describing jobs as bundles of tasks, some of which could be automated while other responsibilities remain. In his view, this could make people more productive and effective in their jobs: “So it’ll help human beings to become much more productive and effective in their jobs.” He also expects new kinds of work to emerge: “And also, it’s going to create new jobs that we didn’t anticipate earlier.”
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThese are his expectations from the interview, not measured labor-market outcomes. The transcript gives no estimate of how many jobs might be lost or created, how quickly changes could happen, or which sectors would be most affected. It also does not offer a year-specific forecast or a comparison with estimates from other researchers. Readers should treat his comments as a qualitative view, not a quantified prediction.
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Why he expects the outcome to depend on AI’s design
Nilekani’s optimism is qualified: he said he tends to be optimistic, and argued that the way AI is designed will shape who benefits. He contrasted an extractive model, in which one party controls data and captures profits, with an inclusive model in which people share in the benefits. The point is not that AI’s effects are guaranteed to be positive, but that design and distribution choices matter.
That distinction also clarifies what his comments do and do not establish. They describe a possible direction for AI’s benefits; they do not quantify how much value workers or communities would receive, or show that inclusive outcomes will follow automatically. The interview’s contrast is about who benefits from AI, not a comparison of competing forecasts for job losses or job creation.
What he recommends for education
Rather than prescribing a fixed list of technical skills, Nilekani said education should strengthen capabilities that can remain useful as specific skills change. He named empathy, motivating others, leadership, collaboration, first-principles thinking and creativity. His reasoning is that a narrow skill prescription can become outdated: “I would rather go there than say learn skill A, B, or C because skill A, B, or C may not be relevant five years from now,” he said.
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This is his education recommendation, not evidence that the named capabilities are immune to automation or that any particular curriculum will guarantee job security. He did not prescribe a course, a specific technical skill set, or a timetable for education reform. His emphasis is on broad human capabilities and ways of thinking rather than a fixed list of skills to learn.
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The interview’s scope and limits
The conversation also touched on AI in education, agriculture and health, and on how AI relates to digital public infrastructure. Nilekani referred to shortages of teachers, doctors, nurses and other skilled workers as a reason AI could help amplify scarce services. He did not provide workforce figures or demonstrate what those applications would mean for employment. The examples help explain why he sees AI as potentially useful, but they are not evidence that those sectors will gain or lose a particular number of jobs.
The source is a Q&A with Nilekani, not an independent labor-market study. His comments support a distinction between task changes and total job elimination, alongside his expectations of productivity gains and new work. They do not establish the size, timing or distribution of AI-related job losses and gains. The interview was with Moneycontrol Managing Editor Nalin Mehta at an AIMA event in New Delhi on February 21; Moneycontrol published the transcript on February 22, 2025.
Read the full interview
Moneycontrol’s full Q&A with Nandan Nilekani, published February 22, 2025, records his conversation with Managing Editor Nalin Mehta at an AIMA event in New Delhi on February 21.
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Does Nandan Nilekani think AI will eliminate jobs?
He said AI would affect some jobs, but that only a few would be entirely taken out. He expects many jobs to lose some tasks while retaining other work. This is his view in the interview, not a quantified labor-market forecast.
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Did Nilekani estimate how many jobs AI will replace or create?
No. He offered no figures, timeline or sector-by-sector estimate for jobs lost or created. He described expected effects qualitatively.
What skills did Nilekani say education should emphasize?
He named empathy, motivating others, leadership, collaboration, first-principles thinking and creativity. He preferred broad capabilities over a fixed list of technical skills that may become outdated.
Why does Nilekani say AI’s design matters?
He contrasted an extractive model, where one party controls data and captures profits, with an inclusive model in which people share in the benefits. He did not claim that inclusive outcomes will happen automatically.
The Bottom Line
Nilekani’s message is that AI may automate tasks without eliminating most whole jobs, while boosting productivity and creating work that is hard to anticipate today. He presents that as an optimistic expectation, qualified by the choices that shape who benefits—not as a measured forecast of employment outcomes.
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