The potential for deep tech to reshape industries from healthcare to climate science is immense. Yet, harnessing this potential requires more than just breakthroughs in isolated silos; it necessitates a nurturing ecosystem where collaboration and knowledge sharing are paramount.
Deep tech spans several fields, including artificial intelligence (AI), quantum computing, biotechnology, and advanced materials. Each area demands considerable expertise and significant research and development (R&D) investment. Unlike conventional tech companies that might focus on software or applications with quicker market cycles, deep tech ventures typically endure longer development phases and face higher technological risks.
Traditionally, deep tech innovation has often been siloed, with companies and research institutions closely guarding their advancements to secure competitive advantages and protect intellectual property (IP). This approach, while understandable, can hinder the pace of innovation and reduce the potential impact of technological breakthroughs.
The case for collaboration across deep tech
You’ve probably heard the saying “it takes a village,” and this is very much true also when it comes to science and technology advancements. Sharing knowledge, working together, bouncing ideas off each other, and collaborating on projects can achieve far more than one person’s individual effort.
Collaboration fosters the cross-pollination of ideas. When organisations share knowledge, they enable collective problem-solving, leading to more robust and diverse solutions. For instance, partnerships between academia and industry can bridge the gap between theoretical research and practical application, enhancing the relevance and scalability of technological advancements.
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It’s easy to see how involving more subjects amplifies resources — be that time or facilities, funds and community support as well as human contribution. Deep tech ventures in particular often require substantial financial and human resources.
Collaboration leads to resource optimisation by pooling expertise, sharing infrastructure, and reducing redundant efforts. For example, shared research facilities and open innovation platforms can allow multiple entities to leverage advanced equipment and data, reducing costs and duplication.
Working together also offers an additional layer of protection. The high-risk nature of deep tech projects means that failure is a common and necessary aspect of the innovation process, making risk mitigation a priority.
Collaborative ecosystems enable the distribution of risks across multiple stakeholders, making it easier to manage and absorb setbacks. This risk-sharing approach can encourage more bold and ambitious projects, as the potential losses are not borne by a single entity.
Finally, since the ultimate goal of a company is usually to release products and services to customers and clients, collaborative efforts can also facilitate market expansion by combining complementary technologies and expertise. Strategic partnerships can create more comprehensive solutions that address broader market needs, making it easier to enter new markets and attract a diverse customer base.
Open source as a catalyst for deep tech ecosystems
Open source has played a transformative role in traditional software development by fostering collaboration, transparency, and rapid innovation. Applying open-source principles to deep tech can similarly revolutionise the way we approach complex technological challenges.
Open source fosters transparency, enabling researchers and developers to build on each other’s work with confidence. This transparency is crucial in deep tech, where the complexity of technologies requires a clear understanding of underlying principles and methodologies, whilst also encouraging mutual trust in other people’s contributions.
An open-source model also easily encourages a community-driven approach to development. By involving a diverse group of contributors, the ecosystem benefits from varied perspectives and expertise, leading to more innovative and inclusive solutions.
The success of open-source AI frameworks like TensorFlow and PyTorch demonstrates how community involvement can drive rapid advancements and adoption, as members feel empowered to share their point of view and suggestions knowing it will make a difference.
One of the main challenges of developing new tech often comes at the end of the process, when it’s time to step into the outside world and verify how your tech fits in the existing landscape. Because of their collaborative nature, bringing together different experiences and perspectives, open-source initiatives can actually help establish industry standards and promote interoperability between different technologies and platforms.
This standardisation is essential in deep tech, where the integration of various components is often required to build comprehensive systems.
One possible overlooked aspect about open-source collaborations is how they can successfully reduce the barriers to entry for new players in the deep tech space. By providing access to foundational tools and frameworks, it enables startups and smaller research groups to contribute to and benefit from the ecosystem without needing significant upfront investment.
Building a collaborative ecosystem: practical steps
As you may have gathered, there are several reasons to look for collaborations in the deep tech space: but how could you and your company actually start down that path? Here are a few suggestions and advice.
Establish collaborative frameworks
Developing formal frameworks for collaboration is essential. This can include creating consortia, research alliances, and innovation hubs where different stakeholders, including corporations, startups, academic institutions, and government bodies, can collaborate on shared goals. These frameworks should outline clear guidelines for IP sharing, data use, and governance to ensure equitable and effective collaboration.
Promote open access and open data
Encouraging open access to research publications and datasets can significantly enhance the knowledge-sharing process. Initiatives like open access journals and data repositories allow researchers to disseminate their findings widely, enabling others to build on their work. For example, the Human Genome Project’s open data approach accelerated advancements in genetics and biotechnology.
Develop shared infrastructure
Investing in shared research infrastructure, such as high-performance computing facilities, specialised laboratories, and testing environments, can reduce the costs and barriers associated with deep tech R&D. These facilities can be made available to a broad range of stakeholders, promoting inclusivity and collaboration.
Encourage multidisciplinary teams
Deep tech challenges often require expertise from multiple disciplines. Promoting the formation of multidisciplinary teams can enhance problem-solving capabilities and drive more holistic solutions. Collaborative projects that bring together experts in AI, material science, and biology, for instance, can lead to breakthroughs in areas like advanced robotics or synthetic biology.
Foster a culture of knowledge sharing
Building a nurturing ecosystem requires a cultural shift towards knowledge sharing. Organisations should encourage their teams to participate in industry conferences, workshops, and collaborative projects. Creating incentives for knowledge sharing, such as recognition and rewards for contributions to open-source projects or collaborative research, can further reinforce this culture.
Leverage government and policy support
Governments play a crucial role in fostering collaborative ecosystems. Policymakers can support deep tech innovation by providing funding for collaborative research projects, creating favourable IP laws that balance protection and sharing, and incentivising partnerships between industry and academia.
Case studies: successful collaborative ecosystems
One might now argue that collaboration in deep tech has limited potential when it comes to output, perhaps because they are too focused on commercialising their solutions as products to make a profit. In reality, there are many examples of this approach driving innovation that everybody can benefit from.
For example, think about how the development of the World Wide Web at CERN exemplifies the power of collaboration and open sharing. Initially created to facilitate information sharing among physicists, the open release of the web protocols catalysed global innovation and fundamentally transformed communication and commerce.
For a more recent example, look no further than the growth of AI open-source frameworks. The AI community has greatly benefited from open-source frameworks like TensorFlow, developed by Google, and PyTorch, developed by Meta. These frameworks have enabled researchers and developers worldwide to experiment, innovate, and deploy AI models efficiently, leading to rapid advancements and widespread adoption of AI technologies.
The other side of the spectrum is that of niche areas of science which are the foundations to widespread technologies. Think about cryptography, the core element of modern security solutions and applications: the FHE.org community and annual conferences exemplify successful collaboration in deep tech through its open platform for advancing Fully Homomorphic Encryption (FHE) technology, which allows computations on encrypted data without decryption.
By uniting global researchers, developers, and industry practitioners, FHE.org fosters innovation via shared research, open-source tools, and standardisation of FHE implementation. Its efforts in creating interoperable standards and engaging the cryptography community through workshops and hackathons accelerate FHE development and adoption.
Moreover, FHE.org’s partnerships with industry demonstrate the practical applications of FHE, enhancing data privacy and security across sectors such as healthcare and finance without compromising usability. More recently, cryptography company Zama hosted a 3-days event in Brussels during the EthCC7 conference.
The CoFHE Shop was a dynamic space for the FHE community to meet and expand the conversation beyond blockchain, creating network opportunities between businesses and researchers in this space to share achievements and encourage further developments.
And not to forget the revolutionary impact of the Human Genome Project Starting from 1990, scientists’ commitment to open data sharing accelerated genetic research and enabled a wide range of applications in medicine and biotechnology. By making genomic data freely available, the project set a precedent for large-scale collaborative research in deep tech.
Building a nurturing ecosystem in deep tech hinges on the principle that the collective power of collaboration and knowledge sharing outweighs the benefits of proprietary innovation. By embracing open source, establishing collaborative frameworks, and fostering a culture of inclusivity and transparency, we can unlock the full potential of deep tech to address some of the world’s most pressing challenges.
This collaborative approach not only accelerates innovation but also ensures that the benefits of deep tech advancements are broadly accessible, driving positive societal and economic impact.
Jeremy Bradley oversees day-to-day operations at Zama. He is a cross-functional and highly tactical leader who has worked with a number of organisations to shape strategy, drive communications and partnerships, and lead policy and process. Jeremy’s educational and professional background is multidisciplinary. Apart from working across the non-profit, education, and corporate sectors, Jeremy is the author of two novels (2019 Wishing Shelf Book Award Finalist and 2021 Wishing Shelf Book Award Winner). In 2020, he was named Writer of the Year by the IAOTP, and in 2022 he was named to Business Elite’s 40 Under 40.
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