The session commenced with Shri Mahankali Srinivas Rao welcoming the audience and introducing the theme—India’s evolving relationship with deep technologies. Setting the context, he emphasized the need to move beyond digital start-ups and e-commerce platforms to embrace science-led innovations.
Dr. Nirmalya Bagchi opened the discussion by defining deep tech as innovations rooted in fundamental scientific research, typically spanning domains like AI, quantum computing, biotechnology, aerospace, advanced materials, and robotics. Unlike surface-level innovations, deep tech requires years of investment, patience, and resilience, and is capable of addressing grand societal challenges rather than offering incremental conveniences.
2. India’s Start-Up Paradox and R&D Gaps
In this segment, Dr.Bagchi presented a sobering analysis of India’s R&D ecosystem, pointing out that while India is recognized as a ‘Start-up Nation,’ most ventures focus on low-risk domains like grocery delivery or aggregators. He noted that India invests just .56% of its GDP in R&D, with the private sector contributing very little. Only four Indian companies currently invest ₹1000 crore or more annually in research.
3. The Quadrant Model – Positioning Deep Tech
Using Donald Stokes' Quadrant Model, Dr. Bagchi explained how deep tech is positioned at the intersection of basic scientific research and real-world application—referred to as the Pasteur Quadrant. This quadrant combines the quest for knowledge with a drive for practical impact, making it the most fertile ground for transformative innovation. Unlike Bohr-type pure research or Edison-style tinkering, deep tech demands a dual commitment to rigorous inquiry and scalable solutions. Dr. Bagchi emphasized that India must actively fund and build capacity in this quadrant to stay globally competitive.
4. The Nature of Deep Tech – Risk, Delay, and Vision
Dr. Bagchi, to highlight the high-risk, slow-yield nature of deep tech, remarked that in this space, innovators may not experience the ‘technology feeling’ for years, as tangible outcomes take time to surface. Citing examples like the Kaveri engine project and India’s Air-Independent Propulsion (AIP), he illustrated how such initiatives can absorb significant time and investment without immediate returns. Yet, these efforts are foundational for national capability. Deep tech, he stressed, requires visionary entrepreneurs, patient institutions, and long-term commitment, not quick exits or overnight successes.
5. Deep Tech Across Sectors – A Horizontal Force
Dr. Bagchi explained that deep tech enables progress in advanced software (AI, cybersecurity), energy (green hydrogen, smart grids), healthcare (biomaterials, gene therapy), space tech, mobility (EVs, drones), precision manufacturing, intelligent buildings, agritech, infrastructure (5G/6G), and frontier technologies (quantum computing, neurotech, blockchain). He reinforced the point that deep tech is not limited to labs—it is the underlying fabric of next-generation societal transformation.
6. Six Waves of Deep Tech – Innovation Over Time
Dr. Bagchi illustrated the historical trajectory of deep tech, mapping six waves of scientific innovation: from the Industrial Revolution (water power, cotton mills), to steam and steel, to electricity and chemicals, to aviation and space, then to the internet, and finally the current wave, clean tech, AI, and neurotechnology. He noted a key trend: each successive wave has occurred over a shorter time frame, indicating the compression of innovation cycles. This historical lens emphasized the urgency for India to invest now—or risk falling behind permanently.
8. Accepting Failure as a Natural Part of Innovation
Dr. Bagchi candidly acknowledged that failure is integral to the innovation process, especially in deep tech. He emphasized that cultural fear of failure in India hampers experimentation. Drawing on the earlier examples of the Kaveri engine and AIP, he stated that such projects, even if unsuccessful in immediate terms, build institutional knowledge and technical capability. For India to build a thriving deep tech ecosystem, failures must be embraced. 11. Example: Fast-Money Models vs. Deep Tech Investments
During the discussion, Dr. Nirmalya Bagchi illustrated the contrast between short-term tech applications and deeper, synthetic innovation models through a compelling example. He explained that certain companies might take readily available data, use basic analytical tools, and quickly commercialize services, resulting in fast recovery cycles and immediate revenue generation. Such businesses may thrive briefly in niche markets, particularly in areas where low entry barriers and quick-to-deploy platforms can succeed. However, he cautioned that while the time-to-market is short, these models often lack scalability and depth. In contrast, if a company were to adopt a synthetic, research-intensive approach—such as developing a novel application architecture or proprietary algorithms—the development time and financial risk would be higher, but so would the potential impact and long-term returns. This example reinforced Dr. Bagchi’s broader point: deep tech demands patience and vision, but it is ultimately where lasting market advantage and global leadership reside. He emphasized the need for Indian enterprises to graduate from fast-turnover models to high-payout innovation strategies that can root themselves deeply in the market and scale sustainably.
Dr. Bagchi concluded with a powerful message: the world is not waiting, and India cannot afford to lag behind. As the adoption of technologies accelerates globally, only those who invest ahead of the curve will lead. He called for visionary leadership, patient capital, collaborative ecosystems, and a renewed scientific temperament. His final statement captured the essence of the session