The Malthusian Trap

This episode examines India’s AI regulation dilemma through a Startup India lens. It explores why an “innovation-first” approach resonates in a country with small, capital-constrained startups competing globally—and why a “risk-first” approach cannot be ignored in a high-scale, high-diversity society like India.Using concrete examples such as deepfakes in elections and AI-driven credit scoring in digital lending, the discussion looks at how real harms can emerge quickly, how trust can erode at scale, and why regulatory ambiguity can be as damaging as overregulation.The episode also considers whether a workable middle path exists: risk-based regulation, outcome-focused rules, regulatory sandboxes, and phased compliance designed for startups rather than incumbents. It highlights the central execution challenge—state capacity—and why India’s AI governance is likely to evolve incrementally rather than through a single grand framework.This is not a call for faster regulation or looser controls. It is a calm examination of how India might balance experimentation with accountability, and ambition with public trust, as AI systems become more deeply embedded in everyday life.