Artificial Intelligence as a Productivity Shock: A Techno-Economic Analysis of India's AI Governance Framework and Inclusive-Growth Strategy

This paper has argued that India's AI strategy is best read as a coherent, if still incomplete, economic wager: that a labour-abundant, capital-scarce economy stands to gain disproportionately from AI deployed as a labour-augmenting general-purpose technology layered onto pre-existing digital public infrastructure, rather than treating the automation anxieties calibrated to high-income labour markets as directly applicable to India's own. The evidence assembled here -the World Bank's cross-country automation and productivity-enhancement estimates, the statistically confirmed AI-preparedness gap documented in Section 5.3, the measured returns from Odisha's agricultural-advisory programme and Telangana's weather-forecasting intervention, and the scale-up of subsidised public compute -lends empirical support to this wager without settling it conclusively. The Seven Sutras governance framework and the IndiaAI Mission's seven pillars together constitute the institutional scaffolding for this bet, but three areas identified in Section 8 will determine whether the aggregate, population-scale gains documented in this paper translate into genuinely inclusive growth: closing the measurement gap around informal-sector task exposure, resolving the energy and capital constraints on compute expansion, and building enforcement capacity commensurate with the guidelines' stated principles before the voluntary governance model is tested by a significant AI-related harm. For policymakers, the priority implied by this analysis is not to slow AI deployment but to accelerate investment in exactly the complementary factors -human capital, energy infrastructure, and regulatory enforcement capacity -that general-purpose-technology theory identifies as the actual determinants of whether a productivity shock of this kind is broadly shared or narrowly captured, as India works toward the Viksit Bharat 2047 horizon.

Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. Aghion, P., Jones, B. F., & Jones, C. I. (2017). Artificial intelligence and economic growth (NBER Working Paper No. 23928). National Bureau of Economic Research. Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. Bresnahan, T. F., & Trajtenberg, M. (1995). General purpose technologies: 'Engines of growth'? Journal of Econometrics, 65(1), 83–108. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company. Business Standard. (2026, May 6). India's GCC revenue nears $100 billion as AI drives sector growth. Cazzaniga, M., Jaumotte, F., Li, L., Melina, G., Panton, A. J., Pizzinelli, C., Rockall, E., & Tavares, M. M. (2024). Gen-AI: Artificial intelligence and the future of work (IMF Staff Discussion Note SDN/2024/001). International Monetary Fund. IMPRI. (2025). Developing IndiaAI's dream: Understanding the IndiaAI Compute pillar's GPU infrastructure growth. Impact and Policy Research Institute. India AI Rulebook. (2026). AI governance guidelines 2025. Ministry of Electronics and Information Technology. (2025, November 5). India AI governance guidelines: Enabling safe and trusted AI innovation. Government of India. NASSCOM. (2026). Technology sector in India: Strategic review 2026. National Association of Software and Service Companies.