Infosys Chairman Nandan Nilekani Predicts 170 Million New AI Jobs
In a significant forecast for the global technology landscape, Infosys Co-founder and Chairman Nandan Nilekani declared that Artificial Intelligence is “rewriting the grammar of software development.”
Speaking at the company’s Investor AI Day 2026 on February 17, Nandan emphasized that traditional coding will no longer be the central goal for tech professionals, signaling a “root-and-branch” overhaul of the industry.
The End of the “Coding-First” Era
Nandan observed that while previous technology shifts like mobile and cloud were additive, AI is structural.
“Talent will have to deal with a world where writing code is not the goal—it’s making AI work,” he stated.
He noted that AI adoption is occurring in just a couple of years, whereas the internet took a decade to reach a billion users.
This unprecedented speed is forcing enterprises to rethink their entire operating models and customer journeys.
Infosys Chairman Advises on Vanishing Roles vs. Emerging Careers
While the transition poses a challenge, Nandan provided a balanced outlook on the job market.
He identified four legacy roles that are expected to shrink as AI automates repetitive tasks:
- Front-end web developers
- QA testers
- IT support specialists
- Traditional blockchain roles
However, he predicted that AI will create 170 million new jobs globally, far outpacing the 92 million it may displace.
High-demand roles in this new era include AI engineers, forward deployment engineers, forensic analysts, AI leads, and data annotators.
Infosys Chairman Prioritises Solving the “Legacy” Challenge
A major focus of Nandan’s address was the “tech debt” accumulated by large firms, who often spend 60% to 80% of their IT budgets on maintenance.
He argued that AI finally provides the tools to modernize these trillion-dollar legacy systems “fast and economically.”
However, he warned that the real risk lies in execution, not the technology itself, calling for a shift from “buying software” to “building AI solutions.”
Avoiding “AI Slop”
Nandan cautioned against superficial AI use, or “false productivity,” which can lead to “AI slop”—unreliable or unhelpful outputs.
He stressed that successful implementation requires first-principles thinking, rigorous quality gates, and a complete retraining of the workforce to handle non-deterministic systems.
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