"Ai is the new electricity" - Andrew Ng, AI pioneer
Shift in global energy employment
The energy workforce reached 76 million in 2024. While AI is often feared as a job destroyer, this sector adds millions of roles, proving technology and human work coexist. It now surpasses fossil fuels in employment worldwide. So if AI consumes more energy, it should end up creating more energy jobs, goes to positive logic.
Energy transition
India is central to this shift with its renewable goals. Reaching 500 gigawatts of capacity creates massive demand for technicians. This transition is physical- and labour-intensive, making it resilient to digital automation in the long run. The assumption is that automation doesn't hurt this logic!
Limits of automation
AI cannot completely maintain reactors or install transmission lines, at least not so far. These tasks require manual skills and judgment. Construction and maintenance remain human domains that software simply cannot replace, regardless of how advanced digital models become.
How AI scales human work
AI models analyze data to predict failures. Since prediction is not repair, human crews must still fix faults. By increasing efficiency, ai helps systems scale, necessitating more hires to manage the resulting growth.
Bridging the skills gap
The challenge is a shortage of workers who understand both electrical and digital systems. India must modernize training to integrate ai. Success belongs to technicians who understand physical machinery while navigating digital dashboards.
Summary
The energy sector is creating millions of roles, defying the narrative of ai driven unemployment. India's renewable transition demands a workforce blending digital literacy with physical expertise. The bottleneck for growth is a shortage of trained talent rather than a lack of work.
Food for thought
Why is public debate focused on job loss when the real crisis is a global skills shortage?
AI concept to learn: AI Energy Needs
Artificial
intelligence is deeply physical, not virtual. Training and running AI
models require vast amounts of electricity for computation and
significant water for cooling data centres. As AI scales, energy demand
rises sharply, stressing power grids and local resources. Efficiency
gains in chips help, but cannot offset growth alone. Sustainable AI
depends on renewable power, smarter data-centre design, water-efficient
cooling, and coordinated energy-infrastructure planning.
[The Billion Hopes Research Team shares the latest AI updates for learning and awareness. Various sources are used. All copyrights acknowledged. This is not a professional, financial, personal or medical advice. Please consult domain experts before making decisions. Feedback welcome!]
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