Writing code will no longer be the goal as AI reshapes tech jobs, says Infosys' Nandan Nilekani

Infosys cofounder and chairman Nandan Nilekani said the rapid pace of AI development has created a widening gap between the capability of the technology and the ability of enterprises to deploy it.

• AI will shift IT jobs from coding to making AI systems work
• Infosys' Nilekani says AI demands new skills and operating models
• Modernising legacy systems is key for effective AI adoption

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Writing code will no longer be the objective for software technology professionals as artificial intelligence (AI) fundamentally changes how software is built and deployed, Infosys co-founder and chairman Nandan Nilekani said, flagging a major talent and operating model transition for the IT industry.

Speaking at Infosys’ Investor Day on February 17, Nilekani said the AI transition is unlike previous technology shifts, as it represents a “root-and-branch” change in how businesses operate rather than a new layer of technology.

Unlike earlier transitions such as mobile or cloud, where enterprises could add a front end or lift and shift applications, AI requires rethinking customer journeys, business processes and organisational structures.

“This is a huge challenge for talent,” Nilekani said, adding that “Talent will have to deal with the world where writing code will not be the goal, it will be actually making AI work. So, the jobs will change and operating model (too).”

The comments assume significant as it comes at a time when investors are pressing large IT firms for answers on how generative AI will translate into sustainable revenue growth, margins, and competitive differentiation.

The focus on AI has gained urgency amid a sharp selloff in IT stocks globally and in India over the past few sessions.

Cognizant, HCLTech, Wipro, and many major IT companies have all underscored a similar shift, telling investors that AI-led platforms and productivity gains are redefining their business models.

Nilekani said enterprises will need new skills focused on AI engineering, agent orchestration, and managing non-deterministic systems, where the same prompt can generate different outcomes each time.

This marks a shift from the deterministic world of traditional software, where outputs were predictable and repeatable.

The transition, he said, also forces companies to confront long-deferred issues such as legacy systems and technical debt. Many large enterprises continue to spend a majority of their IT budgets maintaining decades-old systems, limiting their ability to adopt AI effectively.

Nilekani said modernising these systems can no longer be postponed if companies want to benefit from AI at scale.

At the same time, the rapid pace of AI development has created a widening gap between the capability of the technology and the ability of enterprises to deploy it.

“The technology is far ahead of its deployment. Model performance is going up, but progress in implementing is not really there because implementing this is hard stuff. Fundamentally, it’s about organisational change, business change, retraining your people, changing your data so it’s no longer in silos,” Nilekani added.