Will AI take your job? Infosys cofounder Kris Gopalakrishnan has the answer - India Today

TEXT START: AI is changing the workplace, but does that mean it will take your job?

The Dissection

The text converts a narrow observation into systemic reassurance. AI can lower service costs, expand demand, and leave parts of a profession intact. The radiology example proves only that task automation can trigger demand expansion during a transition. It does not prove that AI will create more economically necessary human labor across cognitive work.

The article also changes the question. “Will AI take your job?” becomes “Will AI create some new opportunities somewhere?” Those are not equivalent. Aggregate opportunity does not preserve the existing worker, wage, career ladder, or mass-consumption circuit.

The Core Fallacy

It mistakes demand elasticity for employment preservation.

When AI makes a service cheaper, demand may rise enough to offset displaced labor in that specific market. But under the Discontinuity Thesis, the decisive issue is whether AI eventually dominates the cognitive tasks that make human labor economically necessary. If AI lowers the cost of both production and coordination across sectors, expanded output can be served by increasingly small human teams—or by no human team in the ordinary production loop.

Radiology is therefore a lag case, not a rebuttal. Hinton’s example shows that his timing and job-boundary assumptions were early and incomplete. It does not defeat P1, P2, or P3. The system can order more scans while requiring fewer humans per scan. Volume growth can delay labor displacement; it cannot guarantee permanent human participation.

Hidden Assumptions

• Demand will expand indefinitely enough to absorb displaced workers.
• New opportunities will be numerous, accessible, and comparable to the jobs destroyed.
• AI will automate isolated tasks but leave surrounding human responsibilities economically necessary.
• New roles created by AI will not themselves become targets for automation.
• The relevant metric is total jobs rather than wages, bargaining power, job quality, or ownership of productive systems.
• A profession surviving in altered form means the individual worker survives economically.
• Physical, legal, and institutional lag will function as a permanent defense.

These assumptions turn a temporary transition pattern into a law of history. That is the article’s load-bearing error.

Social Function

Primary classification: copium and ideological anesthetic, with a partial truth inside it.

The partial truth is that automation can expand markets and create transitional niches. The anesthetic is presenting those niches as proof that the mass employment-to-wage-to-consumption circuit remains intact. The article does not address ownership, distribution, competitive automation, or the point at which most humans lose access to economically necessary labor.

It is also prestige signaling: an industry founder invokes a famous AI pioneer’s corrected forecast to make a comforting conclusion appear technically validated. Correcting an early prediction about one profession is not evidence that the broader employment system is safe.

The Verdict

This is a transition-management narrative dressed as a forecast. It correctly identifies that AI may increase demand for some services while destroying specific tasks. It fails at the decisive scale: more output is not more human necessity. Under DT mechanics, AI can create opportunities during the approach to obsolescence while still severing the mass labor circuit. The article mistakes the ambulance’s continued workload for proof that the hospital is not burning.