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Commercializing deep tech startups: A practical guide for founders and investors

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BEIJING, CHINA - MAY 26: A researcher deals with a wafer arrayed with carbon nanotubes (CNT) at a laboratory on May 26, 2020 in Beijing, China. (Photo by VCG/VCG via Getty Images)
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Vin Lingathoti

Contributor

Vin Lingathoti is a partner at Cambridge Innovation Capital, where he focuses on technology investments. A software engineer by background, Vin has spent more than a decade in Silicon Valley working with tech companies. Before joining CIC, he led venture investments and acquisitions for Cisco Systems in London and San Jose.

As a deep tech investor, I have often noticed that deep tech startups go through a different evolution cycle than a typical B2B or B2C company.

Accordingly, the challenges they face along the way are different — commercialization tends to be more complex and founders are often required to approach it differently.

Deep tech companies are usually built around a novel technology that offers significant advances over existing solutions in the market; often they create new markets that don’t yet exist. Taking these technologies from “lab to market” requires substantial capital carrying a much higher degree of risk than an average venture investment.

Typically, the underlying intellectual property (IP) of a deep tech company is unique and hard to recreate, resulting in a significant competitive advantage.

High risk, high reward

Since most deep tech companies are built around a fundamentally new and unproven technology, they carry higher risk. Typically, the tech has been tested in a lab or a research center and the early results are therefore often derived in a controlled environment. As a result, while building a product, founders are likely to encounter technical challenges along the way and won’t be able to eliminate the technology risk until later in the process.

By comparison, if a company is building a marketplace for used cars, for example, the technology risk is almost zero. Deep tech companies have the capability to create new markets with little competition and can replace existing technologies while fundamentally transforming an industry.

Microsoft, Nvidia, ARM, Intel and Google were all deep tech startups in the beginning. These companies will almost always require higher capital, carry higher risk and have longer time to return on investment.

However, if successful, they could deliver outsized returns over an average venture investment.

Technology-first approach

An obvious, but fundamental difference with deep tech companies is their technology-first approach. Typically, the founder has developed a novel technology or IP as part of their Ph.D. thesis or postdoc work and is in search for a real-world problem it can solve. Most startups, in general, pick an existing problem in a market they know well and develop a product that solves for that problem and they have a clear sense of the problem they need to solve.

Deep tech entrepreneurs take the opposite approach and as a result they often suffer from SISP (a solution in search of a problem), as Y Combinator calls it. Founders need to be aware of this and must be willing to pivot and repivot based on market and customer feedback. Investors should be prepared for this before backing the company and support the founders as they navigate through the challenges of building a successful deep tech company.

Things to consider when scaling a deep tech startup

  • Find the right investors:  Deep tech as an investment sector has recently gained popularity among many VCs and it seems as if everyone wants to get on the deep tech bandwagon. Driven by the success of companies such as Darktrace, DeepMind and Graphcore, most VCs these days are keen to offer premium valuations to get in on the deep tech hype although few understand the risks. The majority of VCs are often surprised by the amount of complexity involved in building a successful deep tech company. I encourage founders to carefully choose their investors, especially in the earlier stages. While it is often tempting to take term sheets from a VC who offers the highest valuation, founders must be wary of investors who lack experience in the sector.
  • Know your strengths/weaknesses: Founders need to be honest with themselves about their strengths and weaknesses. While it applies to any entrepreneur, founders of deep tech companies need to be particularly vigilant of this. Often these founders with Ph.D.s and postdocs find it hard to accept their weaknesses, especially in nontechnical areas such as marketing, sales, HR, etc. However, building a company requires a broader set of skills than becoming a world-class expert in one field. Founders need to be self-aware about their blind spots and realize that depth doesn’t substitute for breadth. Know your weaknesses and proactively seek help in areas that you lack experience.
  • Be precise in your communication: Founders need to pay careful attention to how they communicate with investors and customers. Since the product often involves a complex technical problem, they must be aware that the average audience is not as knowledgeable as the founder. VCs, even those who have a Ph.D., have depth in an area but not necessarily in the one you are solving for, and the customers less so. As the founder (and/or CEO), it is your job to carefully break it down and explain it to your audience in layman terms. More than two-thirds of founders I speak to are not good at this. Don’t assume it’s the audience’s job to understand you or that they are too stupid to see how genius your product is. It is your job as the founder — be precise and clear in your communication.
  • Success in the lab doesn’t equate to success in the market: This is probably one of the big surprises that most deep tech founders encounter after they start the company. While it might be true that the founder is a highly published author with hundreds of peer reviews, it has no relevance in the market. It is easy to assume that success in the lab and research environment will translate into success in the market — it is often not the case. Trying to force your product onto the market because it is regarded as superior among your fellow scientists is often not going to work. Founders need to understand the market drivers and build the product accordingly — lack of knowledge about the market dynamics or ignorance of it will hurt you. The only exception to this is when there is no existing market and you are creating a fundamentally new market. Failing to understand the market and relying on successes in the lab as an indication of validation could be fatal to deep tech startups.
  • The customer is king: While the product you are building might be solving for a complex issue, it still needs to solve for a customer problem and engage them. While there is a lot of advice out there to not ask the customer what they want, I think it is particularly important for deep tech companies to pay close attention to the customer and listen to their needs. If you ignore the customer, deep tech companies are particularly prone to build a technologically sophisticated and superior product that no one will use.

Why deep tech companies fail

There are several reasons why deep tech companies might fail. The most common reason is their inability to find product-market fit before they run out of cash. As discussed above, by starting with solution/technology that is in search of a real-world problem, deep tech companies face more challenges in finding viable product-market fit.

Founders often overestimate the hypothetical market, which in reality turns out to be much smaller given the complexity of the solution. It’s a common pitfall that most founders overlook: “Very complex” doesn’t equate to “very big market opportunity.”

Another issue is timing and market maturity — often some technologies are just too early to the market. Despite the best efforts, the market might not be ready for a technology even if it is solving a very important problem. Think of the clean tech wave in the 2000s — the market just wasn’t ready due to multiple underlying factors but is now seeing a revival.

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