Deep Tech — The Next Frontier of Innovation
The history of humanity is marked by inflection points. The agricultural revolution reinvented the way we produce food, allowing us to settle and grow our population. The industrial revolution transformed our manufacturing processes, bringing widespread availability of material goods. The digital revolution resulted in an explosion of data and connections, amassing an unprecedented level of information and new sources of knowledge. Each revolution had a significant impact on our society, environment, and economy.
Today, we are at the verge of a new inflection point: the deep tech era. Deep tech refers to companies using emerging technologies to develop groundbreaking products. It is embodied by startups and spin outs at the forefront of science and technology. These ventures work in fields such as biomedical imaging, artificial intelligence, and reproductive biotechnology. And they are gaining traction: venture funding in European deep tech ventures quadrupled from 2016 to 2020 and according to Atomico’s State of European Tech 2021, purpose-driven and deep tech innovation represented the fastest growing funding segments, with European deep tech startups raising nearly €20 billion in the first nine months of 2021.
We started Orbem in Munich, Germany in late 2019 at the dawn of the deep tech era. Along the way, we raised €7.2 million in funding; were recognized as one of the most promising deep tech startups in the world by institutions such as the IBM Watson AI XPRIZE, Hello Tomorrow, and the European Institute of Innovation and Technology; and closed 8-digit commercial contracts. In our journey, we learned three realities of building a deep tech startup.
1. Deep tech is hard
No sugarcoating needed: building a deep tech company is extremely hard. Indeed, building any company is difficult — and a deep tech venture especially so. It is particularly challenging because we face engineering risk and market risk.
Engineering risk means there is uncertainty in our product. Unlike established technologies, we are developing a product nobody has built before. Everyday we ask ourselves: can we actually build this? And everyday, we dedicate our engineering skills, coding knowledge, and product development creativity to make sure the answer is: yes, we can.
To tackle engineering risk we think like scientists. We develop hypotheses, collect data, measure outcomes, learn from our experiments — and repeat. Each iteration of this cycle speeds up and de-risks product development. To be successful with this approach, scientific rigor is of utmost importance. As nobel-prize winning physicist Richard Feynman observed: “The first principle is that you must not fool yourself, and you are the easiest person to fool”. To avoid fooling ourselves we run unbiased experiments, objectively evaluate the data, and have the courage to accept the evidence even if it rejects our hypotheses. Every day we challenge the status quo by conducting as many experiments as possible, learning as quickly as possible, and striving for rapid product de-risking.
For instance, we initially believed a system that analyzes one sample at a time would be too slow, expensive, and complicated for the specific applications we are targeting. To test our assumption, we collected data around speed, cost, and ease of use and measured the outcomes. At first, the experiments confirmed our hypothesis and we discarded building such a single sample system. However, market conditions changed, and emerging magnet technologies together with new AI techniques made us rethink our initial decision. In light of new evidence, we pivoted, ran experiments a second time, and this time around rejected our initial hypothesis: the data showed the technology was there to build a single sample system that is fast, accessible, and simple enough to satisfy our customers’ needs. Correspondingly, we then decided to incorporate this system — The Genus Focus — into our product portfolio. Our decision paid off: the Genus Focus resulted in our largest commercial contracts to date. This would not have been possible without a scientific approach to minimizing engineering risk.
Deep tech is hard because we face engineering risk and market risk.
Market risk means we have to put our constantly evolving product in the hands of customers. As a deep tech venture we face the challenge of selling a product based on novel and complex technology. No matter how well we solve our customers’ problems, we need to break down this complexity for them to to adopt and use our product. This becomes even harder considering most deep tech ventures solve problems no one else has solved before. While establishing new markets with novel technology is risky, it also puts deep tech ventures in a unique position to become extremely successful in these markets.
Tackling market risk means achieving product-market fit. We focus on gaining a profound understanding of our customers: their pains and gains, their willingness to innovate, and the value we can deliver. We then take this information to discover what we call product truth: a product that is desirable (it is useful and valuable), technically feasible, and commercially viable.
Engineering and market risks are highly correlated in deep tech ventures. To de-risk the product by evidencing technical feasibility we must achieve product-market fit — and to achieve product-market fit we must demonstrate technical feasibility. Breaking this cycle is one of the most difficult challenges a deep tech startup faces. Many of the principles that make startups successful, such as beginning with a cheap MVP and then rapidly iterating are much harder to implement in deep tech startups: iterations cost time and money, sometimes even many months and millions of dollars. Moreover, because these risks are correlated, it is more difficult to access the funding necessary for achieving product-market fit.
Despite these difficulties, we are well on track to de-risk our technology, achieve product-market fit, and admittedly, have done a good job in securing financing. One of the main reasons we have nearly crossed the innovation valley of death is that we quickly adopted the second learning of building a deep tech startup.
2. Deep tech requires expertise depth and experience breadth
Talent is everything in deep tech. Our diverse, multidisciplinary team combines 15+ nationalities, rich cultural backgrounds, and varied fields of experience. Besides our internationality, our team stands out because it combines deep domain expertise with broad perspectives.
Eleven out of our first nineteen team members completed a PhD in fields such as biomedical imaging, artificial intelligence, reproductive biotechnology, and neuroscience. We spent years studying the interactions of light with matter, developing computational techniques to process high dimensional datasets, and understanding the basic building blocks of life, learning, and cognition. We push the boundaries of science and technology, and imagine new frontiers to translate scientific knowledge into valuable technology products.
Deep tech requires deep knowledge and broad talent.
While specialized domain expertise is necessary for success in deep tech, it is not sufficient. In fact, most domain experts lack experience in crucial areas of company building, such as operations, marketing, HR, product management, and infrastructure development. That is why we combined domain experts with phenomenal all-rounders. With experience breadth, we connect the dots in new ways, and imagine new frontiers to ensure groundbreaking technology achieves product-market fit.
Depth of expertise and breadth of experience renders Orbem a unique place to work. We are data scientists from Austria and product managers from the USA. We are radiofrequency engineers from Guatemala and business developers from Germany. We are software developers from India and office managers from South Africa. We are one of a kind and we perform as a team.
Recruiting a diverse, high-performing team is only the first step, followed by retainment and empowerment. We recruit talent by defining inspiring roles, retain team members by creating a great environment for people to work, and inspire each other to do our best by offering meaningful work with impactful applications.
As we reflected on the first two key realities of deep tech, we couldn’t help but ask the question: if deep tech is so hard and it requires such a unique team constellation, is it really worth it? That brings us to our final learning.
3. Deep tech will change the world
Yes — deep tech is hard. It is difficult to minimize technical risk, market risk, and secure funding. Yes — deep tech requires unique skill sets. It is challenging to recruit, retain, and inspire the best talent. Yet, deep tech offers something most ventures can’t: the opportunity to change the world.
Our investors at The Venture Collective and Possible Ventures understand deep tech’s unique opportunity to change the world: they are placing big bets on game-changing technologies. For example, MarvelFusion is building the first commercially viable fusion plant to enable humankind to access clean energy, Life Biosciences is developing innovative therapies to transform how we treat diseases, and Yuri is enabling experiments in space to help find cures to the world’s deadliest diseases or discover advanced materials.
At Orbem, we are seizing the opportunity to build a sustainable and healthy future. We are finding new ways to sustainably feed the world, accelerate the transition to a green economy, and transform disease detection. We are convinced deep technology is necessary to make a significant positive impact and work every day to shed light on the world’s toughest challenges.
Improving on the agricultural revolution, deep tech can reinvent the way we grow food to be more resilient to climate change. Rethinking the industrial revolution, deep tech can help us produce the materials we need in a sustainable way. Building on the digital revolution, deep tech can help us find patterns in data to transform disease detection.
Deep tech is hard, it requires depth of expertise and breadth of experience, and ultimately, deep tech will change the world.
