Navigating the Challenges of Deep Tech Startups
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Chapter 1: Understanding Deep Tech
The term "deep tech" signifies both profound technological advancements and intricate problems. While the focus is often on the technology itself, recognizing the underlying issues is essential for startups, requiring a mix of ambition and realism.
Product managers conceptualize in terms of two key areas: solution space and problem space. The "problem space" refers to the array of customer challenges that can be addressed through various technologies, tools, or methods found in the "solution space." This distinction is critical in the context of deep tech.
Deep tech is frequently employed by startups and investors to set apart their products from simpler applications or digital solutions. However, interpretations of the term can vary widely depending on whom you consult. Generally, deep tech products exhibit certain characteristics: they involve a technological breakthrough and necessitate considerable investment and time to reach fruition.
In deep tech, both the problem space and solution space are equally profound. For entrepreneurs and investors alike, understanding the depth of the problem space presents challenges and opportunities that are just as significant as those found in the solution space. Yet, the problem space in deep tech is often murky.
Consider the classic image of entrepreneurs launching their ventures from a garage. This model may not apply in the deep tech arena. While many B2C and B2B SaaS products have emerged from such humble beginnings, the problems they address are typically well-known. From the difficulties of online information retrieval to the convenience of on-demand streaming, the solutions were more straightforward to identify.
In deep tech, particularly in the B2B sector, pinpointing the exact issues can be challenging. For instance, in the realm of computer chips, outsiders may struggle to grasp the intricacies faced by automotive or aerospace manufacturers. Although broad trends, such as chip shortages, are apparent, deciphering their implications—whether companies require more suppliers or need to design their own chips—remains complex.
Because deep tech products often demand customization, determining whether an initial customer validates a product-market fit or if the offering is merely a one-time solution can be difficult.
Getting the Timing Right
Timing is notoriously tricky in early-stage deep tech ventures. Simply having the right technology and awareness of the problem is insufficient. Entrepreneurs must also evaluate how their product aligns with various customers' strategies, consider alternative solutions to the problem, and assess the projected business case.
It's crucial to remember that competition may not always come from companies operating in the same technological domain. Often, competitors may address similar issues through entirely different methods, which could even be non-technological. Identifying these competitors requires an understanding of how customers define their problems internally. This can be particularly challenging in a B2B context.
For instance, Sony's Glasstron, a head-mounted display released in 1996, could have been a precursor to today's metaverse concept—had the timing been right.
Hence, generating a viable deep tech idea (solution + problem) often necessitates entrepreneurs to have privileged access to the problem space, which also applies to their investors when evaluating potential opportunities.
Achieving Product-Market Fit
Successful deep tech startups typically don't require a large customer base. By the Series A or Series B stage, a strong startup can thrive with just 3 to 5 primary customers, provided they generate substantial recurring revenue. Transitioning from one customer to three or more between seed funding and Series A serves as solid evidence of initial product-market fit. However, this is no small feat in the deep tech realm. Industry insiders report that identifying the right contact within a large corporation can take around three months. Furthermore, securing necessary certifications, conducting requirements engineering, negotiating contracts, and deploying the product can extend the timeline by an additional 6 to 9 months.
Closing deals with three significant customers between seed funding and Series A—typically within 18 to 36 months—requires an extraordinary effort, especially since these customers often aren't geographically concentrated.
To encapsulate, for early-stage deep tech entrepreneurs and investors, the keys to success are:
- Accessing the problem space to locate the first customer (and for investors, to evaluate the deal).
- Transitioning from one to three or more customers to establish product-market fit (and for investors, having a network of such customers to assess fit).
Entering the Problem Space
Germany's diverse economy presents a unique opportunity for deep tech startups. Renowned for its "hidden champions" and large corporations, Germany boasts companies that lead in various sectors. For instance, within a 50-kilometer radius of Munich, one can find headquarters for major players in automotive, aerospace, manufacturing, healthcare, insurance, and more. These companies are actively modernizing and investing in research and development, fully aware of the disruptions in their markets and the necessity of technology to maintain their competitive edge.
This landscape makes Germany an ideal entry point into the problem space of deep tech. An entrepreneur focused on computer chips would be in close proximity to numerous industry leaders, enabling them to discover and comprehend the pertinent challenges, dynamics of customer business cases, and secure their initial customers.
Investing in early-stage deep tech can be particularly challenging for traditional venture capitalists. Nonetheless, the success of established deep tech VCs demonstrates the capabilities of their teams.
Given the prevalence of German companies at the forefront of their markets, any entrepreneur who develops a chip solution suitable for them is likely to have a product that can compete on the global stage. In summary, Germany is an excellent environment for early-stage deep tech startups.
Strategizing Early-Stage Deep Tech Investment
Despite the exciting prospects, early-stage deep tech investment poses unique challenges. Investors must focus on:
- Accurately assessing timing.
- Identifying genuine product-market fit (beyond one-off solutions).
- Assisting companies in transitioning from one customer to three or more by the Series A stage.
These tasks may not align seamlessly with the typical structure of early-stage VCs. Given the fragmented nature of deep tech—where one area can vastly differ from another—determining the right timing requires deep insight across various sectors. This complexity is daunting for a conventional VC team and necessitates a robust advisor network for deeper engagement than is customary.
Nonetheless, there are successful early-stage deep tech VCs. The aforementioned considerations highlight the intricate nature of investing in this field. But is there a more suitable structure for addressing the specific challenges of early-stage deep tech investment? At Spacewalk, we believe we have found a viable solution.
Creating a Deep Tech VC with R&D Capabilities
In 2013, we founded Motius R&D to address the rapid pace of technological advancements. We observed that new technologies emerge frequently, and companies struggle to keep up systematically. This led us to conclude that a dedicated entity was needed to stay abreast of relevant emerging technologies and leverage them to develop new products or resolve market challenges.
The challenge was to establish a mechanism for remaining consistently close to emerging technologies. Thus, we adopted a "fluid structure" for Motius R&D, which comprises a talent pool of approximately 900 individuals alongside a core team of about 120. The idea is that this talent pool would rotate every two to three years, ensuring ongoing exposure to cutting-edge technology. This approach has proven effective, with Motius R&D collaborating with major corporations like BMW, Siemens, Microsoft, Intel, and Allianz across areas such as robotics, autonomous driving, AI, insurance technology, AR/VR, and light electric vehicles.
Recently, we launched our own VC fund, Spacewalk. Motius R&D provides Spacewalk with distinct advantages in early-stage deep tech investment:
- Anticipatory market insights spanning 18 to 24 months, derived from our technological research and market analysis.
- Validation of product-market fit for early-stage technologies, which mitigates deal-flow risks.
- Access to a distribution network with well-regarded customers.
The synergy between Motius R&D and Spacewalk aims to address many obstacles associated with early-stage deep tech investments. By partnering with other VCs specializing in later-stage investments, we aspire to cultivate an ecosystem where deep tech companies can thrive beyond the early stages.
Examining "Deep Technology" in Deep Tech
Having established that "deep" pertains to "deep problems," we must also consider the "deep technology" aspect of deep tech. Numerous groundbreaking and potentially transformative technologies remain far from achieving product-market fit. This raises intriguing questions: Should a VC with a decade-long track record invest in technologies that are still ten years away from market readiness? Should we abandon the typical ten-year duration for VCs and transition all early-stage funds into evergreen structures? Or should we rely on governmental and academic institutions for funding instead?
At Spacewalk, we can leverage Motius R&D to navigate this landscape. If we encounter a promising technology that is still distant from market viability, we can further develop it within Motius R&D through co-research initiatives with industry leaders until it matures enough to secure its first customer and subsequently expand to three or more.
In conclusion, not every deep tech concept should evolve into a deep tech startup.
In this video, Michelle Lee discusses common pitfalls faced by deep tech startups, providing insights on navigating challenges in the industry.
Antoine Gourévitch explores the concept of deep tech and its potential to shape the future, shedding light on its implications for innovation and investment.