TOI AIQ Talks examines India's blueprint for economic scale and technological leadership
Three decades after the 1991 balance-of-payments crisis forced structural economic reforms, India has transformed into the world’s third-largest startup ecosystem, harbouring over 150,000 recognised ventures and a robust digital financial network. However, as global supply chains recalibrate and technological paradigms evolve, the benchmark for national competitiveness is shifting once again. Artificial intelligence (AI) has moved from an auxiliary productivity tool to a core driver of macroeconomic expansion.
Navigating this transition requires evaluating how technological innovation aligns with national scale and public infrastructure. In a detailed discussion on TOI AIQ Talks , host Sonia Singh sits down with Amitabh Kant, former CEO of NITI Aayog and India’s G20 Sherpa, alongside Mayank, Founder, CEO & MD of Adrosonic. The exchange explores how AI integration can support India’s long-term developmental targets. Rather than replicating capital-intensive approaches developed in mature AI markets, the discussion presents a strategic blueprint grounded in accessibility, sustainability, and technological diffusion.
Beyond frontier models and the case for digital public infrastructure
A central theme of contemporary technology strategy is the current concentration of AI development. Presently, the development of many frontier Large Language Models (LLMs) remains concentrated in the United States, relying on a highly centralised hardware and semiconductor supply chain. Rather than pursuing the same large-scale foundation-model approach, the panellists suggest that emerging economies should focus on technological diffusion, domain-specific Small Language Models (SLMs), and application-layer solutions. As compute costs decline and access to processing power becomes increasingly widespread, the strategic advantage shifts towards efficient, lower-bandwidth models designed to operate directly on edge devices.
To enable this at scale, the panel advocates adapting the principles of Digital Public Infrastructure (DPI), which previously brought universal identity and real-time payments to over a billion citizens, to the domain of artificial intelligence. Pointing to this strategic alternative during the podcast, Amitabh Kant stated, "
."
By establishing open reasoning frameworks, standardising public datasets, and offering accessible compute facilities, an open-access AI infrastructure allows developers to construct localised applications across agriculture, healthcare, and education without requiring the level of capital investment associated with large-scale AI infrastructure.
Managing sustainable compute demands and capital mobility
Expanding digital compute capacity introduces substantial physical constraints, particularly regarding power consumption, water usage, and carbon emissions. Addressing themes from his book
, Kant highlighted that data centre expansion must be directly coupled with clean energy transitions, leveraging India’s target of 500 gigawatts of renewable energy capacity by 2030. Co-siting data centres with clean power, utilising advanced cooling technologies, and shifting towards green hydrogen serve both as environmental safeguards and macroeconomic buffers against fossil fuel reliance.
Simultaneously, global talent mobility is undergoing a notable shift. Recent data indicates a steady influx of high-tier technical talent returning to India to establish global capability centres (GCCs) and domestic ventures. Capitalising on this human capital shift requires modernising academic curricula across higher education, expanding curricula beyond foundational coding to include system architecture, prompt logic, and cross-disciplinary problem-solving.
Democratising skill building and building trust at scale
For enterprise adoption to transition from isolated pilot projects to full-scale operational integration, organisational trust and responsible governance remain paramount. Highlighting the practical execution path for business leaders, Adrosonic CEO Mayank emphasised that enterprise readiness hinges on localised model deployment alongside targeted skill development: "
"
Achieving responsible scale demands a balanced regulatory philosophy. While overly restrictive early-stage regulation could limit technological experimentation, establishing clear parameters for data governance, algorithmic accountability, and system security is essential. Internationally coordinated, light-touch governance frameworks can ensure AI systems remain aligned with public interest without restricting enterprise agility.
Looking forward, India's approach to artificial intelligence presents an optimistic model for the global economy. By combining vast data volume, deep technical talent, renewable energy capacity, and open-source infrastructure, the nation is well positioned to demonstrate how advanced technologies can be deployed sustainably and inclusively. As this transformation unfolds, the convergence of public policy, private enterprise, and human ingenuity promises to establish a benchmark for equitable economic growth in the digital age.
References
Department for Promotion of Industry and Internal Trade (DPIIT), Ministry of Commerce and Industry, Government of India. (2025). India's Startup Revolution: Factsheet on DPIIT Recognized Startups. Press Information Bureau. pib.gov.in/FactsheetDetails.aspx?Id=149106 Ministry of Finance, Government of India. (2022). Economic Survey 2021–22: Chapter 08 – Industry and Infrastructure. Union Budget & Economic Survey Portal. indiabudget.gov.in/economicsurvey/ Ministry of New and Renewable Energy, Government of India. (2024). Year End Review 2023 of Ministry of New & Renewable Energy. Press Information Bureau. pib.gov.in/PressReleasePage.aspx?PRID=1992732 Stanford Institute for Human-Centered Artificial Intelligence (HAI). (2024). The AI Index 2024 Annual Report: Human Capital, Talent Concentration, and Skill Penetration . Stanford University. aiindex.stanford.edu/report/
Navigating this transition requires evaluating how technological innovation aligns with national scale and public infrastructure. In a detailed discussion on TOI AIQ Talks , host Sonia Singh sits down with Amitabh Kant, former CEO of NITI Aayog and India’s G20 Sherpa, alongside Mayank, Founder, CEO & MD of Adrosonic. The exchange explores how AI integration can support India’s long-term developmental targets. Rather than replicating capital-intensive approaches developed in mature AI markets, the discussion presents a strategic blueprint grounded in accessibility, sustainability, and technological diffusion.
Beyond frontier models and the case for digital public infrastructure
A central theme of contemporary technology strategy is the current concentration of AI development. Presently, the development of many frontier Large Language Models (LLMs) remains concentrated in the United States, relying on a highly centralised hardware and semiconductor supply chain. Rather than pursuing the same large-scale foundation-model approach, the panellists suggest that emerging economies should focus on technological diffusion, domain-specific Small Language Models (SLMs), and application-layer solutions. As compute costs decline and access to processing power becomes increasingly widespread, the strategic advantage shifts towards efficient, lower-bandwidth models designed to operate directly on edge devices.
To enable this at scale, the panel advocates adapting the principles of Digital Public Infrastructure (DPI), which previously brought universal identity and real-time payments to over a billion citizens, to the domain of artificial intelligence. Pointing to this strategic alternative during the podcast, Amitabh Kant stated, "
."
By establishing open reasoning frameworks, standardising public datasets, and offering accessible compute facilities, an open-access AI infrastructure allows developers to construct localised applications across agriculture, healthcare, and education without requiring the level of capital investment associated with large-scale AI infrastructure.
Managing sustainable compute demands and capital mobility
Expanding digital compute capacity introduces substantial physical constraints, particularly regarding power consumption, water usage, and carbon emissions. Addressing themes from his book
, Kant highlighted that data centre expansion must be directly coupled with clean energy transitions, leveraging India’s target of 500 gigawatts of renewable energy capacity by 2030. Co-siting data centres with clean power, utilising advanced cooling technologies, and shifting towards green hydrogen serve both as environmental safeguards and macroeconomic buffers against fossil fuel reliance.
Simultaneously, global talent mobility is undergoing a notable shift. Recent data indicates a steady influx of high-tier technical talent returning to India to establish global capability centres (GCCs) and domestic ventures. Capitalising on this human capital shift requires modernising academic curricula across higher education, expanding curricula beyond foundational coding to include system architecture, prompt logic, and cross-disciplinary problem-solving.
Democratising skill building and building trust at scale
For enterprise adoption to transition from isolated pilot projects to full-scale operational integration, organisational trust and responsible governance remain paramount. Highlighting the practical execution path for business leaders, Adrosonic CEO Mayank emphasised that enterprise readiness hinges on localised model deployment alongside targeted skill development: "
"
Achieving responsible scale demands a balanced regulatory philosophy. While overly restrictive early-stage regulation could limit technological experimentation, establishing clear parameters for data governance, algorithmic accountability, and system security is essential. Internationally coordinated, light-touch governance frameworks can ensure AI systems remain aligned with public interest without restricting enterprise agility.
Looking forward, India's approach to artificial intelligence presents an optimistic model for the global economy. By combining vast data volume, deep technical talent, renewable energy capacity, and open-source infrastructure, the nation is well positioned to demonstrate how advanced technologies can be deployed sustainably and inclusively. As this transformation unfolds, the convergence of public policy, private enterprise, and human ingenuity promises to establish a benchmark for equitable economic growth in the digital age.
References
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