Physicist Nishant Sahdev argues that the speed of scientific discovery has become a strategic asset—and that artificial intelligence is rapidly becoming the “super-accelerator” that will decide which nations lead and which become dependent. Writing from Chapel Hill, he points to a recent move by the US Department of Energy: the launch of 26 “science and technology challenges of national importance” under the “Genesis Mission,” an effort to compress discovery timelines from years into weeks by embedding AI into the full scientific workflow.
Genesis is not just an “AI policy” or a push for more chatbots. Its deeper bet is systemic: treat science like an integrated machine that can be sped up through computation, automation, shared data infrastructure, and tight feedback loops. In that vision, AI systems help design experiments, optimize complex systems like energy grids, accelerate materials discovery, and rapidly iterate on results—explicitly targeting a 20x–100x acceleration in parts of the discovery process. The author’s warning is blunt: historically, countries competed on who discovered more; now they will compete on who discovers faster, and laggards may lose not just prestige but sovereignty.
However, India remains constrained by a fragmented research ecosystem—split across ministries, councils, autonomous institutes, public labs, and universities with inconsistent standards and slow, risk-averse funding cycles. India’s public R&D is spending around 0.65–0.7% of GDP, far below China and the U.S., the issue isn’t only how much money is spent, but how strategically concentrated it is—especially around high-performance computing and mission-oriented discovery systems.
A major theme is “scientific sovereignty” in the AI era. If discovery increasingly emerges from models trained on national datasets—and if those models, compute resources, and governance frameworks are controlled elsewhere—then countries risk becoming providers of raw inputs (talent, data, clinical populations) while others own the “discovery engines.” The author highlights “autonomous labs” as a disruptive frontier: robotic, sensor-rich experimental systems that can run, adjust, and repeat experiments with minimal human involvement. But many Indian institutions are not yet “machine-legible,” lacking interoperable data, reproducibility standards, reliable instrumentation uptime, and real-time pipelines needed for autonomy.
AI acceleration is linked to energy capacity: supercomputers and large-scale AI demand enormous reliable power, and the U.S. is explicitly coupling its AI-science strategy to energy planning. India, too often treats AI strategy and energy strategy separately—an approach that could cap ambitions as demand rises. He ends with three urgent questions: whether India should build a national AI-science platform linking institutions like Indian Institute of Science and the Indian Institutes of Technology; how it will protect sovereignty when AI systems drive discovery; and whether its funding and education systems are ready for machine-accelerated science.
