What the FoaK is Deep Tech VC

During my first week at work, I was asked to review the deck for a certain energy storage startup, the founder of which was expected to visit us later during the day. Very diligently, I started taking notes and preparing questions for this Ivy League PhD founder with over a decade of experience in the industry. It might be useful to add here that I was 23 and had a cumulative 15 months of odd internships for work experience at that point. Anyway, four slides into the deck I realize that this company is essentially trying to defy all practical thermodynamic principles to achieve ideal states that were only talked about in my engineering textbooks - a technology so cutting edge that even Google Scholar would only have a handful of papers on it. Naturally, I sat like a starstruck fanboy throughout the meeting letting my colleagues do the talking while I learned how Deep Tech Venture Capital actually works (just kidding, I still haven’t figured it out lol).

I was born in late 1999 towards the tail end of the dot com bubble and for the better part of my life, I had associated venture capital with asset-light, fast-growing, capital-efficient startups, many of which inadvertently happened to be software and internet companies. To think that this investment class will be deployed towards accelerating risky, sluggish, and capital-intensive FoaK (First-of-a-Kind) technologies with long gestation periods and no proven successful exits, seems pretty counterintuitive prima facie. But that hasn’t always been the case.

Interestingly, the origin of venture capital, dating back to the post-World War 2 era, can be traced to the founding of the American Research and Development Corporation (ARDC) in 1946 - a fund that is credited with the first major venture capital success when it invested $70,000 in Digital Equipment Corporation (DEC) - one of the world's first and largest computer manufacturing companies. DEC went on to go public and provided ARDC with a return of over $350 million, showcasing the potential of VC investments. This almost 500x return on investment drew more institutions into the space increasing the availability of high-risk capital and fueling the growth of cutting-edge technologies in transistors, processors, biotechnology, etc. These technologies lay down the groundwork for the next generation of startups - with computers and the internet now making their way into households, a host of internet companies proliferated and founders and investors made millions as these companies hit public markets. The excitement fizzled out when the dot com bubble popped but VCs soon reconfigured their strategies. While some went back to the first principles and chased the CleanTech 1.0 wagon (which also met an unfortunate end soon after, failed by macros), others held conviction in tech and, with previously burnt fingers, shifted focus on lean, fast-moving startups driven by a demand-first approach. Software and the internet facilitated low-cost building and testing, faster iterations, and rapid growth. The growth of mobile phones and increased internet access provided stronger tailwinds to such companies and the rest is history - and very much the present too.

There are two key takeaways from this super-condensed one-paragraph history of venture capital -

1. This trade has always been about seeking out those promising, high-growth opportunities that could potentially become the next big thing in technology, business, or other industries
2. Deep technology/deep science/hardware or whatever you’d like to call it is very much venture investable

The nature of venture capital involves taking calculated risks in exchange for potentially high returns on investment. While the risk-reward tradeoff principle always holds, the idea is to trim down risk as much as possible by taking hints from the broader macro signals and building out an investment thesis based on those signals. Perhaps years of playing by the playbook have led most investors to exclude Deep Tech startups from their thesis because the cookie-cutter approach simply doesn't create a case for such startups. But if one is playing by the playbook or if a playbook even exists in the first place, odds are that money has been made and you’re investing in nothing but a sophisticated Ponzi scheme. Interestingly Deep Tech’s share of total venture capital investments stood at 20% as of 2023. While this is up from around 10% in 2013, there's clearly a lot of ground to cover (and maybe a lot of money to be made?)

If you couldn’t tell by now, I’m a big-time Deep Tech proponent and before I go on explaining why, here’s my understanding of Deep Tech - these are startups emerging from extensive scientific research and development, pioneering revolutionary technologies, and establishing businesses centered around intellectual property. This IP is usually integrated with some form of a tangible asset to create a highly differentiated product catering to industries such as energy, food, healthcare, space exploration, chemicals, manufacturing, etc. Simply put, these startups are built around a fundamental scientific breakthrough instead of a business model innovation.

I find Deep Tech exciting for the following reasons:

• High potential for disruption: Deep Tech startups often develop groundbreaking technologies or scientific advancements that have the potential to disrupt entire industries and fundamentally change the way we live, work, and interact with technology. Think category creators such as photovoltaics, lithium-ion batteries, precision fermentation products, advanced semiconductors, etc. that have disrupted existing markets and birthed new industries. While the pace of disruption may vary and is subject to a multitude of factors, these innovations, at scale, can have a massive market.
• Long-term competitive advantage: Deep Tech innovations typically involve significant research and development efforts, leading to intellectual property and proprietary technology that can create strong barriers to entry for competitors. These barriers, coupled with a strong IP strategy, are resilient over time, being grounded in fundamental scientific innovation rather than business model innovation. Examples can include novel catalyst materials, advanced nanotechnology, unique propulsion systems for spacecraft, etc.
• Addressing complex challenges: Deep Tech startups often focus on addressing complex societal challenges or industry-specific problems through innovative solutions. These are challenges that conventional technologies struggle to address. Industrial decarbonization, food security, the need for alternate materials, etc. are some of the key challenges that only novel core technologies can address. Given how pressing and urgent some of these challenges are, there already is a strong demand for such solutions.
• Attracting top talent: Deep Tech startups tend to attract top talent, including scientists, engineers, and researchers, who are passionate about solving challenging problems and pushing the boundaries of what is possible. VCs go where the founders go and with increasing avenues for translational research opening up through university incubators and government grants, more and more Deep Tech startups are envisioning commercialization right from the lab stage and are learning to build for the market.
• Software aids hardware: In recent years, advances in the software space have also enabled faster iteration cycles in hardware development, reducing the overall cost and time of research and development. With these software-aided solutions, the lab-to-market duration has been cut down significantly and startups have also become more cash efficient. Software-enabled simulation, additive manufacturing, AI for new material discovery synthetic biology, etc. are making Deep Tech an exciting proposition.

Just like any other class of innovative startups, Deep Tech comes with its own set of risks too. These startups are typically heavy on COGS, tend to have lower margins, and therefore lower free cash flow for organic growth. Because of the involvement of tangible assets, the scaling and iteration cycles are slower and growth is typically proportional to the CAPEX addition. These startups go all the way from the lab to commercialization, going through multiple valleys of death, and given how high-risk Deep Tech is conventionally perceived as, financing options are limited. Even from an investor’s perspective, evaluating Deep Tech requires a different set of skills as technical due diligence can no longer be an ancillary checkbox to tick and most of the conventionally looked-for KPIs do not fit. However, as the broader financing ecosystem warms up to Deep Tech and more and more successful exits enchant more capital into the space, a lot of these risks can be mitigated.