Infosys co-founder Kris Gopalakrishnan: ‘The opportunity for India is leveraging AI to reimagine how we use machines’

To reimagine how we use machines means treating them as partners in work that once required human judgment, such as writing code, analysing information, or improving productivity. AI is called horizontal because its effects can spread across many sectors and daily activities. The article describes its immediate impact as the democratisation of intelligent productivity.

Software development shows this shift clearly. AI can write code faster than humans, although people still need to check it for bugs. The same technology can support new systems, data work, model training, and other organisational capabilities. Machines therefore do more than automate one repetitive task; they expand what organisations can attempt.

The article also stresses uncertainty. AI will affect jobs, especially in programming, but it can create new opportunities. Productivity may rise over the medium and long term. However, organisations must understand the downside and keep humans involved where checking, judgment, and responsibility remain necessary.

The article estimates that software development accounts for about 15–20% of total effort in IT-services work. That is the part most directly exposed to automation because AI can write code faster than humans. The figure does not mean that 15–20% of all jobs disappear; it identifies the share of effort where automation is most immediate.

A human remains needed to check generated code for bugs. At the same time, companies will need work around AI, including integrating systems, curating data, and training models. These activities can create demand even as some programming tasks require fewer hours. The technology changes the mix of work rather than simply removing every role.

The wider scale is substantial. India’s IT industry employs six million people, out of a population of roughly 1.4–1.5 billion. Entry-level hiring is already almost zero, while revenues remain steady but may grow only three to four per cent. AI skills will increasingly shape access to jobs.

AI expands IT-services work because organisations cannot benefit from models in isolation. They must connect AI with existing systems, prepare reliable information, train models, and build new systems around newly available capabilities. This creates demand beyond traditional programming and broadens the industry’s role.

The article gives several concrete examples. IT-services companies may integrate AI with older business systems, curate the data used by models, and train those models for organisational needs. They also need people who can write the right prompts. Human involvement remains important because AI-generated code still needs checking for bugs and the technology requires direction.

This mechanism explains why Gopalakrishnan expects opportunity to expand even while some tasks are automated. Software development is only about 15–20% of total effort. New AI-related work can offset pressure on that share. The industry’s future therefore depends on developing capabilities that help clients adopt AI, not only on writing code manually.

When AI performs more programming tasks, companies need fewer beginners for routine coding work. The article says entry-level recruitment is almost zero. It also warns that graduates entering the industry this year or next may struggle unless they have AI skills. This changes the first step into an IT career, even while total industry employment remains large.

The adjustment requires companies to retrain and reconfigure their workforces. Existing employees can learn skills for integrating systems, curating data, training models, and using prompts effectively. Reskilling is economically practical in India because training can happen in a lower-cost economy. A human role also remains necessary to check AI-produced code for bugs.

The transition may last several years. Universities typically lag changing industry needs by three to four years, so recruitment may stay lower while education catches up. Revenues are expected to remain steady, growing around three to four per cent, with profitability still high. The article calls the employment disruption temporary.

Multinational companies are setting up global capability centres in India because the country combines abundant talent with global experience and expertise. India also offers a lower-cost economy, which helps companies retrain employees and operate profitably. These advantages extend beyond routine outsourcing into research and advanced technology work.

The article links this shift to the strength of India’s IT-services model. Indian companies have experienced leadership, deep client relationships, high profitability, and no debt. Their workforce can be reskilled at lower cost. As AI changes the work, multinationals can use India’s talent base for new systems, data curation, model training, and broader research operations.

Research is moving to India, and every multinational company is described as setting up a global capability centre there. The country’s appeal is therefore not only cheap labour. It is the combination of scale, capability, international experience, and cost. Companies that invest in AI skills and research can use India as a central technology base.

India produced many IT-services companies because the model matched its strengths. Companies could use abundant talent, lower costs, remote software development, high-quality workers, and the ability to scale. The industry also grew when venture capital was unavailable, so founders bootstrapped and focused on delivering services rather than funding risky products.

Technology products require a different mechanism. They demand upfront investment, risk capital, and access to a large market. The article says India’s domestic market was not big enough to create large enterprises, so product companies had to go global. Many software-as-a-service companies therefore moved to the United States, where the market valued them more highly.

This is a resource and capital issue, not a lack of capability. Product companies are now emerging, and Indian-origin companies rank among the world’s top banking and logistics products. The services base remains stronger, but better access to capital and global markets can support more product success.

Computing power means the machines and infrastructure used to process information. Data is the information those systems learn from or analyse. Research and development creates new knowledge, tools, and applications. Together, they determine whether a country can build and improve important technologies rather than only consume systems developed elsewhere.

AI illustrates the connection. Models need computing power to run, curated data to learn from, and research and development to improve. In the article, organisations must curate data, train models, and develop new systems. It also notes that research is moving to India because of the country’s abundant talent and global expertise.

The article does not directly explain technological sovereignty. In established usage, that means having enough domestic capability to control critical technology choices and reduce dependence on external providers. India’s IT workforce and growing research role support that possibility, but the article does not provide figures for computing capacity, data ownership, or R&D investment.