In the last decade, digital startups have received all the attention from entrepreneurial students wanting to create the next billion-dollar company. Countless e-commerce websites and social media apps have come into existence in the hopes of making the next unicorn. However, in just the last year, realising that digital-only companies could not sufficiently address grand challenges brought by the pandemic, we have seen a shift of interest towards so-called deep tech.
Deep tech refers to technologies like artificial intelligence, robotics, quantum computing, computational biology and Internet-of-Things (Siegel & Krishnan, 2020). These technologies typically require the careful integration of hardware with software. These technologies are often fresh out of the laboratory of universities and research infrastructures like CERN, NASA, ESO and EMBL (European Association of Research and Technology Organisations, 2017). Still emerging and without immediate market applications, they require long development times and large initial capital investments to be commercialized (Boston Consulting Group & Hello Tomorrow, 2019). Despite the challenges of deep tech, it has become a hot topic across venture capitalists and policymakers as a potential source for future economic growth.
Whenever deep tech is mentioned in the media, advocates never fail to mention
the challenge of acquiring and developing the talent needed to support the growth of these technologies (e.g. DealRoom, 2021; Poh, 2020). While universities already teach the technical skills needed to deal with the complex knowledge needed for deep tech, entrepreneurial universities can do much more in training students to turn these scientific discoveries into potential companies.
Universities can prepare better technology students who are looking to get into research commercialization and business students who want to enter the deep tech space.
I describe three different tensions that students would have to be able to manage in order to be effective in this domain.
Depth versus Breadth
Deep tech requires moving at the interface of different fields to explore whether a technology can be applied to a different domain or whether two technologies can be combined to solve a different problem. For instance, in ATTRACT (attract-eu.com), an EU-funded project aiming to commercialize technologies from research infrastructures, many proposals from astrophysics aim to turn their sensors originally developed for space exploration into imaging devices for healthcare (Wareham et al., 2020).
However, working at the boundary of two fields is easier said than done. With most deep tech too complex to be grasped by a single individual, students would need to have enough fluency to be able to communicate with the experts in the field. At the same time, they need to have enough breadth to be able to interact with peers from other fields. With limited cognitive resources, students need to dig deep into a particular field only up to a particular point, without losing sight of the big picture. PhD students, for instance, are notorious for getting too deep in the weeds. On the other hand, business students are stereotyped for treating technologies as black boxes, focusing only on market application and customer acquisition. To escape these tendencies, universities have to provide holistic training to ensure that students are technically adept at their own fields yet sufficiently open to collaborate with other fields. Universities have many opportunities to address this, including those offered by engaging in programs such as Erasmus+ and Marie Skłodowska-Curie which enables students of different backgrounds to work on a problem, exposing them to different perspectives (Romasanta et al., 2020).
Precision versus Narration
Deep tech companies deal with some of the most sophisticated scientific contraptions, often operating at the narrowest of tolerances. To use these technologies in a meaningful way requires years of training. While these companies would need to collaborate with other knowledge-intensive companies, it is also as important for them to be able to effectively engage with stakeholders who may not be as knowledgeable about the underlying science. People, in the end, are convinced by stories and not by how many decimal points a certain metric has been improved. As such, deep tech companies must be able to craft compelling narratives to build excitement from potential funders. Without revenues or customer numbers, companies aiming to commercialize these technologies require people who can build confidence from investors and create hype on the potential of these technologies.
A perfect example of this was a study I did on the adoption of technologies in the pharmaceutical industry (Romasanta, Van Der Sijde, & De Esch, 2019). In this new technology called fragment-based drug discovery, big pharma companies were quite hesitant in the early years to adopt the approach. However, what ultimately led some companies to adopt them were champions who understood how to sell the technology to their peers and who understood how to navigate the internal politics within their companies. Accordingly, universities can encourage students to work in groups and to discuss the “human” side of the technologies they work with as a way to prepare them for these realities.
Past versus Future
Deep tech is deep in that they require years of development under the radar until they finally emerge and create an impact. This has been the case for instance for RNA technology which nobody heard of before the pandemic but now has created so much buzz that it is now being considered as a tool for all types of diseases. Yet, for this moment to come, people would have needed to continue building momentum on the technology and mobilizing support for its continued development. Scientists would have had to reach their short-term milestones while also building these towards a long-term vision.
A trendy method for this is that of design thinking, which is an iterative methodology to understand the problems faced by users and to create solutions to these problems. Design thinking is pervasive especially in the digital economy where fast feedback loops are necessary in order to find the appropriate market for a certain solution. Creativity is especially crucial for deep tech as their application is still not known. In the ATTRACT project, MSc and MBA students with design thinking knowledge were connected to research teams to help them conceptualize the possible applications of their technologies.
As products and services require increasingly complex and sophisticated technologies, students would need to have these meta-skills that go beyond the material they learn in their courses.
Being able to navigate these spaces of breadth versus depth, precision versus narration and past versus future would be crucial to be effective in this highly competitive landscape. As universities step outside of their traditional roles, they will play an important role in giving deep training to students towards deep tech.
Boston Consulting Group & Hello Tomorrow (2019). The dawn of the deep tech ecosystem.
DealRoom (2021). 2021: The year of Deep Tech.
European Association of Research and Technology Organisations (2017). How to Exploit the Untapped Potential of RTOs ’ Deep-Tech Start-Ups in Europe.
Poh, O. (2020). Attracting talent “still a hurdle for Singapore deep-tech startups.” The Business Times.
Romasanta, A. K. S., van der Sijde, P. C., Smit, M. J., de Esch, I. J. P., Jahnke, W., et al. (2020). Career development in fragment-based drug discovery. Drug Discovery Today: Technologies. https://doi.org/10.1016/j.ddtec.2020.10.001.
Romasanta, A., Van Der Sijde, P., & De Esch, I. (2019). Conforming to Differentiate: The Process of Optimal Distinctiveness in R&D. Academy of Management Proceedings. https://doi.org/10.5465/ambpp.2019.17836abstract.
Siegel, J., & Krishnan, S. (2020). Cultivating Invisible Impact with Deep Technology and Creative Destruction. Journal of Innovation Management, 8(3): 6–19.
Wareham, J., Pujol Priego, L., Romasanta, A., Wareham, Thomas Nordberg, M., & Garcia Tello, P. (2020). Systematizing serendipity for big science infrastructures: the ATTRACT project. https://doi.org/https://attract-eu.com/ATTRACT-Report-Serendipity.pdf.