Funding, Growth, and Innovation Pathways for Deep Tech Startups
Dr. Ramesh Byrapaneni opened the session by positioning deep tech start-ups as a distinct and critical category within the broader innovation ecosystem. Unlike conventional start-ups that iterate on digital platforms, deep tech ventures are grounded in scientific breakthroughs and advanced engineering—often targeting complex, high-impact problems in healthcare, mobility, energy, and materials science. These ventures typically originate from academic or institutional R&D labs and are characterized by long gestation periods, capital intensity, and a multi-disciplinary foundation. Dr. Byrapaneni emphasized that while India has made significant strides in start-up activity overall, its deep tech ecosystem remains relatively nascent, needing targeted support to scale.
Technology Readiness Levels: Mapping the Startup Journey
Using the NASA-adapted Technology Readiness Levels (TRL) framework, Dr. Byrapaneni explained the typical progression of a deep tech start-up—from raw concept to commercial deployment. The journey begins at TRL 0 or 1 with basic research and extends to TRL 9, where technologies become ready for large-scale application. He noted that startup value increases progressively across these stages, particularly after TRL 5, when validation in commercial environments begins. However, moving from TRL 3 to TRL 7 is often the most challenging part of the journey, requiring not just technical development but also market readiness, ecosystem alignment, and patient capital. This journey is non-linear and fraught with risks that conventional digital start-ups may not face.
Understanding Risk: External Barriers Beyond the Lab
The speaker provided a comprehensive overview of the risk landscape for deep tech start-ups, highlighting that risks are not confined to technology alone. While proving the scientific feasibility (technology risk) is critical, equal weight must be given to commercialization risk (whether the market will adopt the solution), scale-up risk (can it be produced reliably at scale), capital intensity risk (how much funding is needed), and IP defensibility (can the innovation be protected). Interestingly, many start-ups fail not because the technology doesn’t work, but because of systemic issues—lack of policy support, capital gaps, or ecosystem fragmentation. Dr. Byrapaneni stressed that deep tech founders and investors alike must prepare for challenges that lie outside the lab, where regulatory, financial, and market conditions can determine success.
AI, Optics, and Engineering in Practice: A Clinical Use Case
Dr. Byrapaneni then illustrated a real-world example from his own entrepreneurial journey, describing how his ventures in radiology and engineering involved integrating AI with optical systems and robotics for high-precision diagnostics. He explained that in cellular imaging, laser targeting works at a depth of just 5 microns, meaning even the slightest deviation alters the reaction at the genetic or cellular level. This micro-level sensitivity necessitates deep integration of hardware, software, and AI. According to him, AI is not optional in healthcare—it must function as an embedded intelligence across devices and platforms. His companies bridged the gap between lab-grade technology and field-level usability, underscoring the need for clinicians and technologists to co-create solutions.
Capital Pathways: From Grants to Equity to Debt
Funding strategies were a key area of focus in the session. Dr. Byrapaneni presented a timeline of capital requirements mapped against start-up maturity. In early stages (TRL 1–3), most funding comes through grants—especially from government consortia and academic bodies. This phase supports ideation, prototyping, and lab validation. Once the start-up enters TRL 4–6, grant funding drops significantly, and equity-based funding from angel investors or venture capitalists takes precedence. In the final scaling phase (TRL 7–9), the model shifts again, with debt instruments becoming more prominent due to reduced technological risk and clearer business models. He warned, however, that investor interest is never guaranteed at any point—even with a working prototype. Risk-averse behavior from investors often stems from uncertainty in regulation, reimbursement, or commercialization timelines.
Early-Stage Saturation: A Symptom of Sector Immaturity
Citing market data, Dr. Byrapaneni pointed out that 80% of Indian deep tech deals happen at the seed stage, with very few start-ups raising Series B or later-stage capital. This imbalance reveals a structural gap in the funding ecosystem—an overemphasis on initial exploration with insufficient support for scale-up and sustained growth. He presented bar charts showing that while the number of deals is high in early rounds, the capital amounts and follow-on investment rounds are disproportionately low. According to him, this emphasizes both the fragility and potential of India’s deep tech sector. There is tremendous early promise, but unless late-stage capital pipelines—such as corporate venture arms, sovereign innovation funds, or strategic partners—are activated, many promising ventures will fail to cross the commercialization chasm.
Stakeholder Collaboration: Crossing the Valley of Death
One of the most powerful visuals in the presentation was a multi-layered ecosystem map illustrating the role of different stakeholders in helping deep tech start-ups cross the ‘valley of death’—the precarious stage between product development and market adoption. Dr. Byrapaneni categorized stakeholders into groups: academia and research institutions, government, facilitators, financial investors, corporates, and storytellers. Each group plays a specific role at different phases—from initial ideation and proof-of-concept to scale-up and global expansion. He emphasized that no single stakeholder can enable success in isolation. Rather, a symphony of support is required, particularly in critical transition zones where ventures either stagnate or surge forward. The model made clear that storytelling, visibility, and narrative-building are just as crucial as technical validation or policy incentives in attracting the right partners.
Dr. Byrapaneni concluded by stating that while India’s startup story has been robust in consumer tech and services, the next leap must be in deep tech. The country needs to transition from a ‘proof-of-concept’ culture to a ‘proof-of-scale’ mindset. For that, institutions must collaborate across boundaries, investors must demonstrate patience and foresight, and the government must incentivize long-gestation innovations. He called on all participants—faculty, researchers, incubators, policymakers, and entrepreneurs—to champion deep tech not just as a domain of science, but as a national imperative. ‘Deep tech,’ he said, ‘is not just about innovation—it’s about bridging the lab and the market, and embedding intelligence across every layer of life.’