Every now and then, a new term is coined that nicely summarizes a concept that is hardly novel. One such recent term is "deep tech". A recent report puts it well: Deep tech innovations are defined as disruptive solutions built around unique, protected or hard-to-reproduce technological or scientific advances. Deep tech companies have a strong research base. They create value by developing new solutions, not only by disrupting business models.
I will focus on deep tech in digital technologies. Deep tech entrepreneurship is about those ventures that have a differentiating technology base that can be used as a barrier to entry. Some of these emerging technologies are presented each summer as a hype cycle by Gartner.
Having worked in deep tech for more than 20 years, here are a few lessons I have learned that every entrepreneur should keep in mind when founding or scaling a company in the digital technology domain.
Lesson one: in deep tech, research and innovation are complementary
As I wrote elsewhere, innovation and research are not similar but orthogonal. Contrary to popular belief, "pure" research does not lead to "applied" research which in turn will necessarily lead to innovation in the market. Rather, innovation and research are best understood in two dimensions - they are independent, orthogonal.
In deep tech entrepreneurship, innovation and research should be complementary. By first focusing on finding what customers want (product/market fit), you can consider technology or research results ("ingredients") and come up with a deep tech innovation that is both relevant for the market and leverages sophisticated technology. To add another perspective, the above can be rephrased as follows: beware of "technology push", which is sometimes a temptation when you are a techie; deep tech entrepreneurship is like any other type of entrepreneurship about first identifying and addressing a business pain. Only then deep tech ingredients can be added to differentiate your product.
Lesson two: data is more critical than technology
I once worked for a major software maker, where I focused on next-generation analytics. We would pitch analytics as enabling data-driven decisions. Better or "informed" decisions would lead to an optimized business, that leads to more customers, which mean more transactions, that would create in turn more data. Closing the loop between analytics and transactions creates a virtuous circle that boosts business.
Similarly, all modern software products are data-driven. Data is directly plugged into your algorithms and improves your technology, and in turn, your product. A better product means more users who will create more data in a virtuous circle. This is the well-known "network effect" that can be extended to "data network effects".
That goes to illustrate the strategic importance of data. From the analytics domain, we know that customer data and operational data can be used to provide business insights. Deep tech goes one step further: data trumps technology infrastructure (cloud infrastructure as a service is a commodity) and algorithmic code (e.g. Github hosts more than 65 million projects). This is counterintuitive so I will say it again: ironically, in deep tech, your "unfair advantage" is data, not technology.
Lesson three: do one thing, but do it well
Science is all about generalizations - e.g. building overarching models from specific observations. Due to the scientific DNA of most the founding teams, a classic pitfall in deep tech entrepreneurship is to build generic products, products with too many features, or a portfolio of too many products.
But entrepreneurs should stay away from peanut butter. A thin layer of investments on various features, products and markets is a weakness -- the sign that you are not quite sure where you are going.
What is needed in entrepreneurship in general and in deep tech entrepreneurship in particular is a clear focus, a big bet on a product that addresses a clearly identified business pain. Vertical markets are a sweet spot for deep tech entrepreneurs, as opposed to generic horizontal platforms, which are costly to develop, maintain and impose to the market.
Lesson four: the name of the game is scaling up
Despite Silicon Valley's dominance in digital, Europe is very well positioned in deep tech. 5 out of the top 10 global computer science universities are European. In the last couple of years, over 1000 deep tech startups have been created in Europe, with on average €1 billion invested annually in deep tech companies in Europe. American tech giants are establishing deep tech R&D centers in Europe.
It is well established that expertise in scaling up is the visible secret of Silicon Valley. Despite some public initiatives, Europe has been slower at understanding the key importance of accelerating scaleups (as opposed to creating startups). A scaleup is a startup that is not early stage anymore; it has reached product/market fit, typically in their domestic market, and is ready to scale commercially beyond the national borders.
But Europe is quickly catching up, and European scaleup accelerators are emerging. A prominent example is the EIT Digital Accelerator. It is a pan European team of hands-on business developers and fundraising experts who support scaleups to quickly acquire customers and raise funds anywhere in Europe (and beyond) in order to dominate their market.
One of the alumni is Munich-based Konux. Founded in 2014, the company operates in the area of the Industrial Internet of Things - providing sensors and learning to predict machine downtime. Konux has rapidly expanded worldwide: it closed a €16 million round of financing this year, signed up Deutsche Bahn as a customer and expanded in Silicon Valley.