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By 2036, India could have a larger and more internationally visible deep-tech sector—but that outcome depends on whether long-term capital, specialist talent, research-to-market pathways and customer adoption grow alongside the startup count. The available evidence offers examples from satellite data, healthcare technology and AI; it does not justify a confident forecast for every deep-tech field.
It also does not establish Agrani Labs’ identity, technology or founders’ views. The title’s suggestion that all three sets of founders explain the future therefore needs a qualification: this outlook can discuss Pixxel and BrainSight AI, but cannot attribute a forecast to Agrani Labs.
What the numbers say—and what they do not
India’s deep-tech base was already substantial in the estimates available in 2023, but those figures are historical estimates and projections, not a current census or a record of what has since been achieved.
- More than 3,000 Indian deep-tech companies: Nasscom’s 2023 estimate, as reported in a Business Standard interview.
- At least 10,000 deep-tech startups by 2030: a projection expressed in 2023 by Ramkumar Narayanan, then chairperson of Nasscom’s DeepTech Council—not a confirmed count or a forecast of the 2036 total.
- 53% compound annual growth over the preceding ten years: a figure attributed to a Nasscom report in the same 2023 interview. It describes that earlier period; it should not be treated as a growth rate guaranteed to continue.
Narayanan’s central point was that science-led businesses often need a different investment horizon from consumer internet or software-as-a-service companies. He said some could take five or six years, and sometimes eight, to reach a market-ready product; in his account, bringing some science-driven products to market can take five to eight years. That timeline is a reason to assess deep tech differently from businesses that can launch and scale more quickly, not a universal schedule for every startup.
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Business Standard’s 2023 interview with Ramkumar Narayanan also identifies patient capital, domain talent, market access and policy as concerns for the sector. Those are conditions that could shape the next decade; the interview does not establish that they have already been resolved.
What the company examples reveal
These examples show different routes from technical work to practical use. They are illustrations, not a comparative performance ranking or a representative sample of every Indian deep-tech field.
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| Example | Technical area and route to value | What the available source says about the outlook |
|---|---|---|
| Pixxel | Hyperspectral satellite imagery and data analytics, with potential applications in agriculture, mining, the environment and energy. | Founder Awais Ahmed described satellite data as an opportunity he considered achievable within a five-to-ten-year horizon without billions of dollars in initial capital. This is his account of Pixxel’s choice, not a general estimate for space companies. |
| BrainSight AI | Healthcare technology, with a commercialization and expansion ambition described in a 2024 report. | Co-founder Laina Emmanuel argued for collaboration between government and private investors to support early-stage deep tech, and said doctors need incentives to participate in research. These are reported views, not evidence that a particular policy or funding outcome followed. |
| AI outlook discussed by Pratyush Kumar and Rajan Anandan | AI capabilities spanning compute, infrastructure, models and applications, with Kumar describing a use-case- and vertical-oriented approach. | In an August 2026 interview, Anandan said the verdict on India’s position in AI would come eight to ten years later. This is an interview assessment, not a measured forecast. |
| Agrani Labs | Not established in the sources cited here. | The available sources do not reliably establish the company’s identity, technical focus or founders’ views, so no forecast can be attributed to them. |
For Pixxel, Via Satellite’s interview with Awais Ahmed connects satellite data to possible uses across several industries. It offers a founder’s rationale for pursuing that opportunity, rather than proof that those applications will become profitable or widely adopted.
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For BrainSight AI, The Economic Times’ September 2024 report identifies Rimjhim Agrawal and Laina Emmanuel as the company’s founders and reports Emmanuel’s call for stronger government–private investor collaboration. The report describes an ambition, not an independently verified business outcome.
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Why AI is a useful test case, not a proxy for all deep tech
India’s AI position depends on more than the availability of models or promising applications. In an August 2026 conversation, Sarvam AI co-founder Pratyush Kumar and investor Rajan Anandan discussed the roles of compute, capital, talent and infrastructure alongside models and applications. Kumar emphasized the engineering and investment needed across that stack; Anandan placed the time to judge the country’s position at eight to ten years.
The interview makes AI a particularly clear example of the uncertainty in a ten-year outlook: capabilities and applications may advance, while the scale and depth of the supporting ecosystem remain decisive. It does not establish what India’s AI position will be, and its conclusions should not be applied automatically to satellite systems, healthcare technology or other science-led industries. The Indian Express interview, published 26 August 2026, records these as the interviewees’ views.
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What would move the 2036 outlook in either direction
A larger startup count alone would not show that India had built durable deep-tech strength. The more consequential question is whether technical capability can repeatedly turn into products that customers adopt. Several factors could influence that transition:
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- Specialist talent: Companies need people with deep domain knowledge as well as the engineering skills to build and deploy complex systems.
- Routes from research to market: Researchers, companies and potential customers need workable ways to turn technical advances into products and services.
- Infrastructure and policy: Areas such as AI rely on supporting compute and technical infrastructure, while regulation and public-sector choices can affect development and access to markets.
- Real customer use: A technical breakthrough becomes a sustainable business only when its applications solve problems for customers, whether in fields such as agriculture, mining, healthcare or energy.
If these elements improve together, India could have more companies capable of building and commercializing complex technology by 2036, with some serving international markets. If they do not, a rising count of startups could coexist with a thinner record of products reaching customers. These are conditional scenarios drawn from the challenges and examples described above, not predictions that the evidence can settle.
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