Successful deep tech ventures bring together multiple talents (including scientists, engineers, and entrepreneurs) to solve a problem. Often, they develop brand-new technologies because no existing technology fully solves the problem at hand. In some instances, though, success depends on developing new applications for established technologies.
Successful deep tech ventures tend to have four complementary attributes:
- They are problem oriented. They focus on solving large and fundamental issues, as is clear from the fact that 97% of deep tech ventures contribute to at least one of the UN’s sustainable development goals.
- They operate at the convergence of technologies. For example, 96% of deep tech ventures use at least two technologies, and 66% use more than one advanced technology. About 70% of deep tech ventures own patents in their technologies.
- They mostly develop physical products, rather than software. In fact, 83% of deep tech ventures are engaged in building a physical product. They are shifting the innovation equation from bits to bits and atoms, bringing the power of data and computation to the physical world.
- They are at the center of a deep ecosystem. Some 1,500 universities and research labs are involved in deep tech, and deep tech ventures received some 1,500 grants from governments in 2018 alone.
Despite representing a small minority of startups, deep tech ventures have an outsize impact because they attack large-scale issues and because their work is both futuristic and practical. Deep tech ventures reside in what Donald Stokes termed “Pasteur’s Quadrant,” combining a quest for fundamental understanding with applied research.
POWERING THE GREAT WAVE: THE DEEP TECH APPROACH
Successful deep tech ventures rely on a threefold approach:
- They use problem orientation to identify opportunities and to navigate and master complexity.
- The convergences of approaches and of technologies power innovation, broaden the option space, and solve problems for which solutions have not previously been available.
- The design-build-test-learn cycle (DBTL) de-risks and speeds product development and time to commercialization.
Deep tech ventures take shape across four moments of truth that occur in parallel, each posing a critical question
- Frame the Paradigm. Could reality be different?
- Forge the Theory. Is there a way to develop a solution?
- Take the First Step. Can we build the solution today?
- Change the Reality. What must happen for the solution to become the new normal?
Each of these four general questions requires restatement in the context of the specific venture under consideration to determine whether solving the problem through the deep tech approach is probable, possible, real, and profitable.
The challenge in dealing with the four moments of truth is that all of them must be addressed early on, more or less at the same time. The relevance of the question associated with each moment of truth will vary over time but addressing all of them is essential to de-risking the endeavor by anticipating difficulties and adapting strategy and execution as needed.
FOUR CHALLENGES FOR DEEP TECH
Despite its potential, deep tech must overcome multiple challenges in order to reach its full potential. Four challenges in particular stand out, with implications not only for deep tech ventures but also for all participants in the ecosystem:
- The need for reimagination
- The need to push science boundaries
- Difficulties in scaling up
- Difficulties in accessing funding
For deep tech ventures, which are often based on applying a technological breakthrough to solve a problem, finding the right business framework can be a major challenge. Many struggle to identify a compelling value proposition through a clear reimagination of value chains and business models.
Governments, universities, and startups can all work to push the boundaries of science and to translate technological capabilities into business applications.
For all the harm it has done, the COVID-19 pandemic has also shined a spotlight on deep tech’s ability to solve a human problem of historic proportions—and to do it speedily, efficiently, and at relatively low cost. In its essence, the coming great wave brings a new approach to innovation by seeking to solve fundamental and complex problems.
Deep tech draws together three approaches (advanced science, engineering, and design) to master problem complexity and three technology domains (matter and energy, computation and motion, and sense and motion) to leverage their combined solving potential. Powering and accelerating this new innovation paradigm are the DBTL cycle and continuous learning.
The world faces other big problems, too, starting with climate change. Deep tech’s potential for disruption is unprecedented, and the breadth of problems it could address remains for us to uncover.