The India Paradox: AI Anxiety Rises Even as Energy Jobs Surge

Niels Bohr once remarked that prediction is difficult, especially about the future—a line that captures today’s debate on artificial intelligence. AI has become a global amplifier of hopes and fears, and the fear that dominates public imagination is that machines will take away human jobs. Yet the evidence, especially from the energy sector, tells a very different story.

India sits squarely inside this contradiction. Public discourse warns persistently of AI-driven unemployment, while India’s energy economy—particularly electricity—has been adding jobs at one of the fastest rates in the world. The World Energy Employment 2025 report by the International Energy Agency (IEA) presents a reality sharply at odds with the popular narrative. The global energy workforce now stands at 76 million people, growing 2.2 per cent in 2024—almost twice the global employment rate. More than five million new energy jobs have been created since 2019, even as AI adoption has accelerated across industries. Electricity has become the single largest employer in the global energy system, outpacing oil, gas and coal. And India is among the countries showing the steepest rise.

AI anxiety is rising—but so is demand for skilled work. The myth is loud; the data is louder.

The belief that AI will steal jobs endures because it offers a simple storyline: machines get smarter, humans get sidelined. But the electricity sector—arguably the backbone of the modern world—offers a counter-example too substantial to ignore. Since 2019, electricity has become the fastest-growing source of energy employment worldwide. Solar alone employs more people today than any energy technology in history. Transmission lines, grid expansion, battery installations, nuclear maintenance and renewable manufacturing have all generated millions of jobs.

For India, this shift is happening at exceptional speed. The country recorded nearly 6% growth in energy employment in 2024—among the highest in major economies—driven by rapid renewable expansion and rising electricity demand. Solar parks across Rajasthan and Gujarat, new transmission corridors, EV charging networks and emerging battery gigafactories are all labour-intensive undertakings.

And none of these jobs can be automated away. Automating a call centre is one thing; automating the installation of a 400-kilovolt transmission line is another.

Electricity systems are physical and improvisational. They demand judgement under uncertainty, risk-taking and hands-on skill. AI can guide decisions, but it cannot hold a spanner or secure a safety harness. This is why electricity-related employment has grown across nearly every region—and why fears of AI-induced mass unemployment do not match real-world trends.

Across the electricity sector, a clear pattern emerges: AI multiplies human work by enabling infrastructure to grow faster, operate more reliably and expand in complexity.

Sensors embedded in power plants, turbines and substations generate continuous data. AI models analyse this data to predict failures, but prediction is not repair. Humans still diagnose problems, replace components and restore service. AI simply makes their work faster and more effective.

AI-enhanced grid analytics forecast demand spikes, detect faults and improve load management. But human operators still rebalance flows, dispatch crews and upgrade equipment. Even permitting and compliance are accelerating through AI tools, allowing projects to begin sooner—leading not to fewer jobs but to more physical work happening on tighter timelines.

Training is changing too. Virtual reality tools now prepare linemen, nuclear trainees and maintenance crews for hazardous tasks. Learning is quicker and safer, yet real construction and repair remain human.

The economic principle underneath all this is simple: when efficiency rises, sectors expand. And when sectors expand, labour demand grows.

Predictive tools reduce breakdowns, so companies deploy more infrastructure—and need more technicians. The IEA dataset shows no evidence that AI has reduced electricity-sector jobs. What it does show is a global labour shortage: too few electricians, solar installers, grid technicians, battery specialists and engineers who understand both electrical systems and data analytics.

India’s energy transition is among the most ambitious in the world. With over 500 GW of installed power capacity—more than half from non-fossil sources—the country is expanding rapidly. Every new gigawatt requires design teams, manufacturing workers, construction crews, inspectors and engineers. India’s challenge is not the disappearance of work; it is the shortage of workers trained to do it.

The IEA report is clear: India’s grid is becoming more digital, but the workforce is not keeping pace. Predictive maintenance tools require technicians who can interpret diagnostic dashboards. AI-assisted control rooms require engineers fluent in circuits and code. Solar and wind installations require teams skilled in electrical standards, safety protocols and digital monitoring. This skill mismatch is becoming one of India’s biggest economic constraints.

Coal-dependent regions illustrate both risk and opportunity. As India moves toward cleaner energy, technicians, machinists, fitters and operators can shift into roles in grid operations, storage, distribution and manufacturing—but only with structured reskilling. This demands curriculum reform, regional training ecosystems and far greater investment in vocational education.

Countries that embrace AI as a force multiplier will train better, build faster and grow stronger. Those that cling to fear will misread the era entirely.

India’s choice is clear. The myth of job-stealing AI has run its course. The evidence points decisively elsewhere: work is not disappearing; work is evolving. And the nations that prepare their workers for this evolution will shape the next century.

Nishant Sahdev is a theoretical physicist at the University of North Carolina at Chapel Hill and the author of the forthcoming book Last Equation Before Silence.