While the deeptech sector is witnessing a surge in funding, a closer look at the numbers shows a sharp divide in where the money is going. While artificial intelligence and software-led innovations are attracting record investments, other deeptech verticals such as biotech, hardware, and manufacturing technologies are lagging behind, struggling to raise sizable follow-on capital.
According to data from market research firm Tracxn, generative AI startups lead the deeptech pack, having raised $2.84 billion till date since 2020. This is followed by natural language processing ventures at $1.72 billion and AI chatbot companies at $1.5 billion. Other top-funded categories include AI agents and customer service software startups, signalling robust investor interest in enterprise AI solutions and seamless enterprise integration.
However, the disparity becomes evident when looking at the capital flowing into more hardware- or science-heavy sectors. Quantum computing startups, for instance, have attracted just $37.3 million to date, despite their transformative potential. Startups in 3D printing and industrial robotics have seen $27.6 million and $74.9 million in funding, respectively.
The situation is similarly constrained in the biotech domain. Genomics startups have raised $706 million to date, and those working on life sciences platforms and tools have garnered $226 million. Industry experts say that although there is growing interest in early-stage biotech investing, the availability of later-stage capital remains insufficient.
“While there are now more investors interested in identifying and backing early-stage biotech startups than in the past, follow-on funding (Series A and beyond) for biotech and bioscience startups hasn’t fully materialised yet,” said Ashwin Raguraman, co-founder and partner at Bharat Innovation Fund (BIF), which has backed companies like Zumutor Biologics and CreditVidya.
Raguraman attributes this to the specialised expertise required for biotech investing. Unlike AI or SaaS startups, biotech ventures often need domain-specific knowledge and long-term conviction, factors that deter generalist venture capital funds from cutting large cheques.
Ankit Kedia, founder of venture fund Capital-A, echoed similar concerns. “The risks feel unfamiliar, longer gestation, higher capital needs, and less obvious revenue predictability,” he said. Even when the technology is robust, translating scientific innovation into commercial success is not always straightforward, he added.
He pointed to their investment in Leumas, a startup working on micro-batch personal care manufacturing, where the due diligence process extended beyond the core technology to assess the feasibility of its go-to-market strategy. For investors, the lack of clear benchmarks in many deeptech areas makes decision-making more complex.
AI-native SaaS startups, by contrast, are often able to position themselves within familiar business models and metrics, making them more attractive to generalist investors. Venture firms such as Accel and Peak XV have shown a preference for such startups, combining deeptech defensibility with SaaS scalability.
Raguraman believes that biotech’s future depends on building an ecosystem of specialised investors. “Unlike AI startups that can be reframed as ‘AI-native SaaS’ to attract generalist investors, biotech will likely need its own ecosystem of specialised funds,” he said.
To bridge the funding gap, experts are calling for strategic policy support. Raguraman suggests that government capital should be focused where private capital is lacking, especially in scaling mature startups. Instead of dispersing funds across thousands of seed-stage startups, he advocates for targeted R&D grants and incentive programmes for 50-100 companies ready to expand globally.
