The fundamental focus of deep tech is to pioneer new solutions that solve society’s biggest issues – this applies to everything from chronic disease, climate change, clean energy and food production (Medium). This kind of enabling power is certainly very profound and has the potential to bring about real change in the world. Take for instance these examples of key deep technologies that have huge potential to change the world, whilst also covering a vast swathe of technological areas (Medium):
● Artificial intelligence and machine learning
● Big data
● Language processing
● Vision and speech algorithms
● Advanced material science
● Photonics and electronics
● Quantum computing
These technological areas go to prove that deep tech ventures tend to innovate in areas of pivotal importance – life sciences, agriculture and clean energy generation. To succeed in deep tech entrepreneurship, companies must focus on the core principle, which is problem-solving. In fact, 97% of deep tech ventures today contribute to the UN Sustainable Development Goals (BCG). This is one of the major factors setting deep tech apart from other startups, as their heavy reliance on technological innovation can be revolutionary as far as influencing positive impacts on the world around us. In other words, deep tech harnesses cutting-edge technologies to create tangible societal shifts, and it has never been more relevant (Medium).
A successful deep tech venture often combines science, engineering and design, with science providing the theory that underpins the solution (Jumpstart). Engineering ensures the solution’s technical feasibility (Jumpstart). The design ensures that the basic idea of a deep tech firm comes through. With this in mind, understanding why deep tech needs to solve a purpose becomes clearer: the founding principles behind it are areas that can constantly evolve, allowing us to constantly re-think how these technologies can be used – technologies that are innately intertwined with our immediate needs and daily life.
In a way, deep tech can be seen as a new industrial revolution. Even though mobile technologies and IOT are only the beginning of this new revolution, it’s very clear to see that they have dramatically changed the ways in which we work and live our everyday lives. However, seizing opportunities to use science and technology in problem-solving has certainly been a struggle throughout history, and has posed itself as a barrier time and time again. So for deep tech to reach its full potential, there is a pressing need to continuously improve and upscale the way we implement our technological innovations.
That said, we must not confuse deep tech with technologies that are focused on enduser services. Deep tech startup models circle around some sort of real innovative technology, solving intractable problems in the real world. In this way, they rarely sit in one sector alone and are often at play in AI, life sciences, agriculture, aerospace, chemistry, industry, and clean energy (WolvesSummit). The characteristics that set deep tech apart from other forms of a startup is that they often rely on large investments over a longer-term, coupled with vast amounts of research. Their commercial success often takes longer as they deal with disruptive technologies that might take longer to achieve real market adoption. Due to this, Deep Tech ventures are often surrounded by extensive IP moats, and when they get to market, this makes it very tough for competitors to replicate what they’ve done, thus making them entirely unique in the way they solve problems.
This underpins their very nature – rather than just being based on innovative business models, they solve problems through meaningful scientific or technological developments (BBVA). They solve problems and serve purposes through garnering investment that allows them to bring their solutions to market, usually being founded on a scientific discovery or meaningful engineering innovation (BBVA).
Take the company DeepMind as an example. DeepMind is an AI company that created a neural network that imitates the way the human brain works and learns how to play video games. It also created a neuronal Turing machine that is capable of accessing external memory (DeepMind). In 2014, Google bought DeepMind for more than $500m (Techcrunch) and in 2016, its program Alpha Go beat the world champion of the ancient Chinese board game Go (the first time that AI beat the player) (The Atlantic). Its new version, Alpha Go Zero, is capable of learning on its own, without any human interventions (DeepMind).
This kind of technology has massive implications for the real world that can advance society as a whole. Demis Hassabis, the co-founder of DeepMind, said that AlphaGo Zero was so powerful because it was “no longer constrained by the limits of human knowledge.” (Scientific American) Being able to train AI without datasets derived from human experts has significant implications for the development of AI with superhuman skills because expert data is often expensive, unreliable, or simply unavailable (Yahoo Finance). According to Hasasbis, AlphaGo is able to solve a purpose in this way as its algorithms are likely to be of the most benefit to domains that require an intelligent search through an enormous space of possibilities, such as protein folding (see AlphaFold) or accurately simulating chemical reactions (The Economist).
From this, we can conclude that deep tech is able to solve a wider purpose as it draws together three key 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. 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 (BCG).