At London Business School, Teaching Fellow Vidur Varma recently had an insightful conversation with David Herbada, a venture partner at Zinc Venture, a leading deep tech venture builder based in London, and a general partner at Fikra Ventures, a Dubai-based AI-focused venture studio. David’s professional activities revolve around deep tech, particularly in healthcare and artificial intelligence. He is also the CEO of Sonosine, a health-tech spin-out from Oxford University, combining his roles as investor, builder, and entrepreneur.
David shared his expertise as a panellist at our recent Private Capital Symposium. This interview delved into the rapidly evolving landscape of deep tech and artificial intelligence. Together, they unpacked what “deep tech” truly means, the transformative potential of AI in healthcare and other sectors, as well as the key challenges and opportunities that are shaping this dynamic field. David’s unique vantage point—straddling venture building, investing, and hands-on entrepreneurship—offers a comprehensive and grounded perspective on the innovation frontier.
What is Deep Tech?
David explained that deep tech goes far beyond the buzzwords around artificial intelligence or software innovation. David clarified the term that often gets thrown around without a clear definition:
“Deep tech refers to technologies developed by companies that do not yet have product-market fit or even a market proof of concept. They exist somewhere between pure research and actual market applications.”
He explains that even in AI, there are foundational models still classified as deep tech because they haven’t fully crossed into commercial viability. These technologies often tackle problems with no existing or good solutions, particularly in healthcare and other broad market segments.
Deep Tech in Healthcare
When asked about specific areas of healthcare deep tech, David highlights drug discovery platforms that use AI and advanced algorithms to develop more efficient pharma products, such as new antibody structures for cancer treatment.
He also discusses his company, Sonosine, which has developed a novel imaging technology based on physics to replace MRI and CT scans. This innovation addresses a critical unmet need for “repeatable, cheap, on-the-spot imaging,” which current methods cannot provide due to cost, availability, patient suitability, or safety limits (e.g., radiation exposure from CT scans).
Why is Deep Tech Gaining Momentum Now?
Several factors have converged to fuel the rise of deep tech:
- The explosion of AI, especially foundational models and their application, has captured massive interest. Early conversations debated whether AI could replace human intelligence, but this has broadened into discussions about ethics and societal impact.
- Advances in defence technology and space technology.
- The urgent need to address energy shortages, a trend that follows a geometric curve of increasing demand observed since humans discovered fire.
Investment Trends in Deep Tech
David notes that deep tech is attracting a growing share of venture capital:
“In 2023, around 20 to 30% of all VC investment was going into deep tech. I think we’ll see those numbers increase because these technologies solve real-life problems.”
He believes this trend will accelerate as deep tech increasingly becomes central to solving real-world problems. While the previous wave of VC capital focused heavily on software and SaaS models, that has since been overtaken by AI-enabled services—some of which may qualify as deep tech. More recently, government and institutional interest has pushed further investment into strategic sectors like pharmaceuticals, biology, defence, quantum computing, nuclear fusion, alternative energy, and smart grid technologies.
According to David, these are all inherently deep tech areas, and their growing geopolitical and economic importance is driving a shift in capital flows toward longer-term, high-impact innovation.
Deep Tech vs. The Ubiquity of AI
David reflects on the longstanding history of AI, with machine learning models existing since the 1950s with languages like Prolog and Lisp. Interestingly, he credits the recent AI surge not to software alone but to hardware breakthroughs, particularly GPUs, which have massively increased computing power, enabling the rise of AI.
He also shares an insightful characterization of large language models (LLMs):
“All papers analysing LLMs have concluded that they are ‘stochastic parrots’ — probabilistic models that generate answers based on learned ways. Humans are also probabilistic parrots, so that’s a fun conclusion.”
Looking ahead, David is particularly interested in “AI at the edge”, involving physical AI applications requiring lower power consumption and the ability to operate in real-world settings without extensive GPU resources. He sees this as the next frontier for AI development.
Challenges Facing Deep Tech
David identifies two major challenges:
- Data availability: Increasing data protections make it harder for companies to share data freely, pushing the field from open-source models toward proprietary, often hybrid models combining LLMs and agents. This raises barriers for new entrants.
- Energy consumption: AI and GPU farms require massive energy resources, prompting investments in sustainable and efficient hardware. David referenced a recent announcement in the UAE about allocating 5 gigawatts of energy capacity to AI data centres and GPU farms—equivalent to five nuclear reactors. This illustrates the massive energy needs of AI today. However, he also notes the promising trajectory toward more efficient computing, such as the use of photonic chips instead of traditional silicon-based ones and gradual but steady progress toward lower energy consumption.
While breakthroughs in these areas remain slow and costly, continuous investment is expected and necessary for sustainability.
Bridging the Builder–Investor Divide
Wearing both the hats of a venture builder at Fikra Ventures and a private investor, David looks for different but complementary ways to unlock the potential of deep tech. As a venture builder, he starts by identifying tangible, real-world problems—particularly in domains like physical security—and then explores how existing technologies, especially in AI and edge computing, can be applied to address them. The focus is on matching need with technical capability, drawing from a pool of deep tech innovations to build purpose-driven ventures from the ground up. In his role as an investor, David seeks out passionate founding teams who are excited about a specific technology. His goal is to support them in the difficult but crucial journey of achieving product–market fit and preparing for scale, fully aware that deep tech ventures often face longer development cycles, greater risk, and slower returns. Yet, for areas like longevity, where the societal implications are vast and the commercial potential immense, he believes the risk is well worth taking—pointing to founders so driven that they leave PhDs behind to launch transformative startups.
Learning to Engage with New Technologies
David highlighted three essential mindsets and actions we must adopt to engage meaningfully with emerging technologies like AI and deep tech:
First, he emphasized that there’s no longer anything stopping us from learning. Thanks to advanced tools like large language models (LLMs), search engines like Perplexity, and other accessible platforms, anyone can now acquire deep knowledge efficiently. What matters is not just acquiring information, but also learning how to trace its sources, question its validity, and apply critical thinking.
Second, he encouraged people to start building. With today’s tools, you can now create products or even businesses using agentic AI tools at very low cost—without needing large amounts of capital or backing. It’s never been easier to experiment, launch, and learn by doing, even as a side hustle.
Third, David urged us to shed our fear of the future. Technological change is inevitable, so we must choose whether to engage with it and shape it ethically—or allow others to shape it for us. Rather than being paralyzed by anxiety, he advocates for proactive and ethical engagement with innovation.
Deep tech, particularly in AI and healthcare, represents a challenging but highly promising frontier. The convergence of advanced research, computing power, and strategic investment is driving innovations that could reshape entire industries. As David Herbada highlights, this is a field where real-world problems meet cutting-edge science — and where patient, visionary investment will be key to turning deep tech’s potential into impactful reality.
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