• Yifei Huang

Rolling in the Deep (Tech)

Deep tech is an emerging segment of venture capital, with over $60 billion invested in 2020 in deep tech companies. It encompasses exciting new technologies such as blockchain, AI, and quantum computing, which has been gaining momentum especially in the last few years. How are deep tech startups different to traditional startups in their operations, and how should VCs treat them so?

Photo by Vishnu Mohanan on Unsplash

Rise of Deep Tech

Technology is becoming a more sophisticated and integral part of society, and I constantly feel that it is a full-time job just to keep track of the latest and greatest advancements! Blockchain, autonomous vehicles, advanced batteries, AI…there’s always a new megatrend that generates hype and excitement.

The term “deep tech” has been used to describe technology innovations that are truly cutting edge, and can have profound impacts on the way businesses, economics and societies operate. Close to $20 billion in deep tech funding have been raised in Europe just in the first nine months of 2021, according to dealroom.co.

From https://stateofeuropeantech.com/chapter/better-ideas-better-companies/article/ideas-gamechangers/

Of all the deep tech trends, blockchain and AI are probably the two that have had the most mainstream coverage, and it seems that every day there’s some news about how these are going to change the world, or have already done so. Recently, I joined a deep tech start-up focused on another deep tech trend, and one that I think has the potential to become a true general purpose technology – quantum computing.

Quantum Computing

Quantum computing sounds like science fiction, and for much of the 20th century, it was. Quantum science has a solid theoretical foundation, but it wasn’t until the last couple of decades that engineers were able to demonstrate quantum computing principles that could be commercialised. As an extremely quick technical overview, whilst classical computers operate using 1’s and 0’s for their calculations, quantum computers use quantum properties in atomic particles such that they can represent both 1’s and 0’s simultaneously. What this ultimately means is that quantum computers can number crunch very effectively for tasks that require brute force or extensive trial and error, dramatically cutting down the time it takes to solve these sorts of problems.

There are a huge number of these brute force problems across many industries. Pharmaceutical companies spend billions every year testing different combinations of compounds to discover new drugs. Logistics companies need to determine optimal routes to save costs and freight time, and route optimisation is a notoriously difficult combinatorial maths problem. All our existing cybersecurity and encryption principles inherently assumes some maths problems are really difficult to solve, so we would need quantum computers to design better and more robust systems and processes.

Market Potential

The market for quantum computing has exploded in recent years, as governments, research institutions and enterprises recognise the impact of its application as well as the huge investments needed to realise these. In 2021, there was over $900m in funding for quantum computing companies, compared to around $500m in 2020. Recently, two leading startups announced (Rigetti) or had (IonQ) their billion-dollar IPOs through SPAC mergers within a week of each other on the NYSE In October 2021, bringing quantum computing closer to the public eye. This was an exciting moment for the industry, as it showed that markets were willing to place a true value on this emerging technology.

From Pitchbook, 2021 data to 11 December.

How large can this value be? McKinsey released research suggesting that the quantum computing market could reach $1 trillion in 2035. Estimates for the current market size vary, though they sit around US$500 million with a CAGR of over 20% for the next 5 years.

Major tech companies including IBM and Google are focused on creating quantum computers that are more powerful than existing super computers, aiming to achieve “quantum supremacy,” which is when a quantum computer can solve a problem that existing classical computers cannot. In fact, Google claimed to have reached that milestone in 2019 through a demonstration, though (of course) IBM disputed the claim and said it had no practical benefit.

Quantum Utility

As the debate continues over how useful existing quantum computers really are, the startup I joined, Quantum Brilliance, takes a different approach. Rather than quantum supremacy, our vision is to achieve “quantum utility,” which is when quantum computers can outperform existing classical laptops and desktops at everyday tasks and applications. This is explained in our recent TechCrunch article https://techcrunch.com/2021/11/11/why-quantum-utility-should-replace-quantum-advantage/.

Source: Quantum Brilliance

Our executives believe that “in the near term, useful quantum computers will massively disrupt supply chains, even entire value chains. Preparing for that impact implies understanding not only the technology, but also its economic impact.” Thus, we want to give the largest group of customers the cheapest and easiest access to quantum computing capabilities so that governments, enterprises and research institutions can start using quantum computing to solve problems sooner across a wider range of needs.

Deep Tech vs Traditional Startups

However, for all its potential, successfully becoming a major player in quantum computing, and in fact in any deep tech industry, has its challenges, some of which are quite different to traditional startups:

  • High barrier to entry - Deep tech requires an enormous amount of R&D investment, both in the form of financial capital, and especially in human capital, since startups need to hire technical staff with the right, and often niche, expertise. For example, over 70% of Quantum Brilliance employees are technical personnel such as software engineers, material scientists, and quantum physicists.

  • Lack of industry standard - Since deep tech requires true cutting-edge innovation, the technology itself may not be developed or standardised. For example, there are a number of quantum technologies aiming to become the de facto hardware and software standard, but currently the landscape is fragmented.

My thesis is that over the next 5 to 10 years, only handful will emerge as true winners. This is because as is common with cutting edge innovation, there are technical unknowns around practical implementations of theories. Each candidate quantum technology has its own benefits and shortcomings, some of which may be fundamentally or commercially insurmountable. Therefore, science and markets will filter out those whose shortcomings can’t be overcome or compromised, leading to a handful of survivors suitable for specific use cases and business models depending on their unique combination of competitive advantages. We have seen this happen (and continue to happen) with wireless technologies in the past few decades (there was more than one type of 3G!), and I expect a similar evolution of quantum technologies.

  • Longer time to commercialisation – Following on from the previous point, a lack of technology maturity means that business models and markets may not be mature either. Commercialisation may take many years as customers need to be convinced that the deep tech solution can in fact deliver what it promises, and can integrate well with existing business models and systems. During a presentation to LBS in 2021, IQ Capital principals Daniel Carew and Georg Glatz outlined how VCs have to adjust their thinking on revenue and commercialisation timeframes – a deep tech startup not achieving revenues until 5 years from now is normal. The figure below from dealcoom.co and European Start Ups illustrates this concept:

From https://dealroom.co/uploaded/2021/04/EUST-Dealroom-Sifted-Deep-Tech-Jan-2021.pdf

  • Different valuation criteria – For many investors, evaluating a startup involves looking at its team, product/market fit, revenue forecasts, cashflow burn or run rate, customer pipeline, and other commercial metrics or projections. For a deep tech startup, using these metrics can be misleading. For example, due to heavy or inconsistent R&D spend, a deep tech startup can be loss making for many years without a single commercial revenue source, yet this does not mean that it is unsuccessful. More relevant criteria for a deep tech startup include IP (e.g. patents), R&D milestones (e.g. proof of concept demonstrations), partnerships with industry leaders, and research grant funding received.

Deep tech is an incredibly exciting segment of venture capital, especially for those who come from a technical background or have specific industry expertise. The promise of new value generated through these emerging and often unproven technologies is what fuels investors’ passion. With deep tech investments having grown 22% year-on-year to over $60 billion in 2020 according to BCG research, it is truly an exciting and proud time to be a “nerd”.

Yifei Huang is an MBA2023 at LBS, and an aspiring venture capitalist in deep tech. He is currently a strategy and finance senior consultant at Quantum Brilliance, a quantum computing startup using diamonds to create useful quantum computers. Prior to LBS, Yifei was in strategy and consulting at both a top TMT consulting firm and a leading telco. Yifei holds a PhD in Electrical Engineering.