Are Deep tech the next Big Thing?

The Reis telephone receiver mechanism (from Wikimedia Commons, public domain)

As investors in innovation, VCs are always searching for the next exponential technology that would have a massive global impact. Previously based in North America, as an investor I have witnessed something quite remarkable over the past three years: entrepreneurs and VCs have stepped back and examined the wider technology landscape. First with hardware renaissance and more recently with deep technologies.

Although there are many definitions and categorizations by deep tech, I mean startups focusing on solving hard technical problems and addressing to dynamic or emerging markets that have the potential to provide strong growth.

Typically, I would include (not exhaustively as it’s always changing): 3D printing, aeronautics/space, artificial intelligence, autonomous vehicles, augmented/virtual reality, big data / machine learning, blockchain, computer vision, conversational UI, cyber security, drones, industry 4.0, (industrial) internet of things, neuroscience, robotics and smart city.

Deep tech are at the edge of several deep tech areas emerging from (applied) R&D. Most of the time it includes software and one or more hardware technologies or scientific fields of research. Deep tech entrepreneurs are business driven both with their head in the clouds and their feet on the ground. They address to promising markets with a strong vision and scalable business models.

As illustrated in the Dentist Office Software Story by Fred Wilson, software alone is a commodity.

Deep tech for example, can make the difference between a company providing a chatbot with a bad user experience and a startup delivering an innovative platform that can help to develop really smart UI conversational experience thanks to world-changing technology (see Maluuba’s article here).

How are they different from the digital space?

Even if they cover various areas at different stages of maturity, deep tech have common specificities:

  • Technology barrier as a competitive advantage. An intrinsic advantage due to proprietary IP or know-how that is difficult to acquire or replicate. Of course it increases the level of risks but this unfair advantage could trigger quicker exits.
  • Founding teams often composed of engineers and PhDs only. Entrepreneurial experience and market knowledge are crucial in deep tech. As a result, VCs would need to be significantly more involved at the early stage and would help to complete the team.
  • Higher capital requirements. Deep tech usually require more capital, especially at early stages, when the risk is higher. Lean startup methodology cannot be implemented as it is in deep tech. It implies that a fund dedicated to this space should be configured accordingly.
  • Longer tech maturity phase and market lead times. Deep tech startups usually need a lot more time in order to bring their products to market (often emerging). It is especially true when hardware is involved. Metrics are also very different depending on the space but there are a lot of similarities with go-to-market strategies.
  • Inherent technology and product-market fit risks in addition to market uptake and execution risks. Sometimes a technology that performs perfectly well at the prototypal stage does not work at an industrial scale. Furthermore deep startups tend naturally to focus too much on their technology/product and not enough on their customers’ problems/needs.

Is deep tech trend the next step of the data revolution?

Recently, we have also experienced an innovation slowdown in the digital sector especially in traditional VC spaces like mobile, social and web. Most of the late majority adopters have already began the digital transition and it seems that we have reached the mainstream level of maturity of this technological wave.

Even if large industries such as financial services, healthcare, manufacturing or utilities are still at the early stages in their adoption of digital tech, there’s clearly a commoditization of the industry and not so much room for more promising new apps, market places or social networks.

Despite the fear of a tech bubble in the U.S., 2015 was a record year in deep tech VC investment and 2016 would still be a significant leap according to CB Insights. Furthermore, a dozen deep tech companies presented at YCombinator’s W16 Demo Day, ranging from cancer detection devices to farming robots or supersonic aircraft.

Last September, it was also the first time that the Demo China Summits chose deep tech as the main theme instead of mobile. The deep tech trend is also to be noted in Europe and I was agreeably surprised to discover the level of quality of the European and French deep tech startups since I have moved back.

Is deep tech investment really new?

Not really, traditional VC investment sectors like electronics/semiconductor, cleantech/industrial, telecom or life sciences are similar to deep tech in terms of investment approach. Some of them have a long history of high returns and great successes and are still growing quickly (especially life sciences) while some spaces are no longer active or have never really taken off.

All the more, mature deep tech like artificial intelligence (AI) have been in the radar of VCs for a couple of years. AI-driven products are already deployed, improving the performance of recommendation systems, ad display or financial trading for example. By the way, France is a good example of AI ecosystem (see Chris O’Brien’s article here).

Why it’s a good time now to launch a startup and invest?

First, deep core technologies have progressed significantly, exited from scientific labs and are now mature enough to be industrialized and implemented quickly into innovative solutions.

Second, most of deep tech are data-driven and the digital wave has created data assets (massive amounts of data), platforms (hardware and software) and infrastructures (computing, data collection, storage, etc.) that are fundamentals to develop and deploy products. Hardware is also benefiting now from decreased development costs, shorter time to market and new recurring revenue business models.

Third, the tech space and recently more traditional industries are now eager to acquire startups to speed up their innovation. With the end of the digital innovation wave, it’s now time to find the next Apple, Google or Microsoft.

Conclusion

Actually, the current digital wave of innovation (mobile, social and web) is currently declining and it will be more difficult to build and invest in promising ventures in the coming years. Deep tech are now mature enough to provide exit opportunities that are aligned with a VC fund life cycle.

Most of deep tech startups are software driven and considering that hardware is just a nutshell, this innovation wave will be able to provide scalable business models. As deep tech investment is significantly more difficult and risky than in the digital space, it implies different approaches, knowledge and skills for VCs.

The quality of the local ecosystem is crucial and the good news is that Europe has some advantages to foster such environment. Entrepreneurs, it’s now a good time to consider launching or joining deep tech startups!