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Diving in to Deep Tech
“One of the major problems faced by Deep Tech companies like ours is the amount of time it can take to transition from prototype to commercial product, especially when entering a market that doesn’t exist yet. The wind energy industry shares our vision for the future, but we also need key players to be part of a collaborative effort and be on the journey with us so we can make sure the end product is validated and fulfils their needs.”
Diving in to Deep Tech
Chris Cieslak, CEO of BladeBUG who are developing advanced robots for the inspection and repair of wind turbine blades.
Many of the world’s most pressing problems — from addressing climate change to developing sustainable food and water systems and improving human health and wellbeing — depend critically on the successful commercialisation of fundamental science and engineering innovations that are often referred to collectively as “Deep Tech”.
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nlike software or digital innovations, Deep Tech innovations require years of developing, testing, iterating, perfecting and sometimes pivoting to bring to market. They provide solutions to some of the world’s most pressing problems which means that, if successful, the payback and societal impact could be huge. But, due to their pioneering nature, they also embody inevitable risk in both the tech and its market which requires committed and patient investment. Not easy to come by in today’s fast-paced and resultsdriven society.
As a STEM university, Imperial has Deep Tech very much at the heart of its entrepreneurial ecosystem. By enabling access to facilities and support through initiatives such as those hosted by the Imperial Enterprise Lab, Imperial White City Incubator and Imperial College Advanced Hackspace (ICAH), there has been an increasing number of Deep Tech startups founded by students as well as academics. According to Oscar Ces, Head of Department of Chemistry and Co-director of the ICAH, the increase in student Deep Tech startups is a relatively recent shift. “If
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you go back 15 or 20 years most student startups were fintech or digi-led,” he says. “Because we just didn’t have the infrastructure to facilitate Deep Tech ventures for students. But now there’s almost no barrier to a student spinning out a company in life sciences, material sciences or biotech and as a result we’ve seen an explosion in Deep Tech startups.” With the expanding number of Deep Tech startups there has been a growing realisation that these companies need tailored support to get them to the point where investors are ready to commit the
right amount of capital for the right amount of time. The runway along which these ventures develop is lengthy – ten to fifteen years minimum– and this requires investors who understand the nature of Deep Tech and, above all, are able to wait until these endeavours to come to fruition. Deep Tech requires a different approach to entrepreneurship that incorporates the complexities of scaling up this kind of venture from a laboratory prototype to feasible product. Current approaches such as the Lean Startup Model work to a relatively linear and simple Issue 4 / 2021–22
