Technology futurist David Shrier helped launch four spinout companies from Massachusetts Institute of Technology (MIT), including, most recently, AI-powered edtech company Esme Learning. He also created online offerings in fintech, blockchain and cybersecurity that extended reach in over 150 countries for Saïd Business School, University of Oxford.
He has just been appointed Professor of Practice (Artificial Intelligence and Innovation) at Imperial College Business School where he will lead the creation of new ventures fuelled by artificial intelligence. He holds a secondary appointment as Associate Fellow with Saïd Business School, University of Oxford and also advises governments and government-affiliated entities across the US, UK and EU. His latest book, Augmenting Your Career: How to Win at Work in the Age of AI, will be published by Little Brown in spring 2021.
You have launched four spinout companies from MIT – what were they and what was the elevator pitch for each?
I helped put together four companies – I founded or co-founded three and was vice-chairman of a fourth.
● Esme Learning – transformational digital learning through collective intelligence
● Riff Analytics – solving the problem of effective collaboration at a distance
● Distilled Identity – digital identity for the world
● Endor – Google for prediction.
What is it about MIT that makes it unparalleled at commercialising its research? Does it give spinout creation the kind of attention and priority that other universities simply don’t?
Firstly, there is the history. MIT is responsible for pioneering a number of the practices now commonplace in university commercialisation – even teaching Stanford how to set up its tech licensing office back in the day. Secondly, they attract a tremendous critical mass of innovative thinkers, so there is selection bias towards entrepreneurship. Thirdly, they make it easy for faculty to spin out multiple companies while retaining their research focus, encouraging innovative academics to work there.
What are you most looking forward to in your new role at Imperial College Business School?
Some really exciting cross-campus collaborations we are building across the faculties of business and engineering to bring together research excellence with commercial application for real-world impact.
You specialise in AI-based entrepreneurship – without giving away any insider secrets, what fields of AI innovation are most exciting to you at the moment?
Building AI systems that work together with people, rather than replacing them. I talk a great deal about this in my new book, Augmenting Your Career: How to Win at Work in the Age of AI, which comes out this spring.
When seeking investment for a spinout, how important is the reputation and track record of your lead scientist or innovator?
It’s pretty important from the perspective of the kind of deal you can secure with the investor. If you’re an unknown, you run the risk of getting a much less favourable deal.
Angel investors or venture capitalist?
Seriously, you will likely need to take many different forms of capital if you are building a growth enterprise. It’s pretty limited to only talk about angels versus venture capitalists. There are government grants, venture debt, strategic funders, crowdfunding, token sales, and even (dare I say) revenue funding.
We bootstrapped Esme Learning with first customer revenue from a large corporate prior to raising our first outside investment. I like recommending to entrepreneurs that they get revenue early and often because it encourages product-market-fit discipline.
What’s a realistic timeframe for getting a spinout up and running? And what kind of things can throw unexpected delays in your path?
Two to 20 years of university research. Then… beyond that timeframe depends on the nature of the business. Deep tech like hardware, biotech, materials, strong AI, could take 12 to 20 years to commercial scale, whereas software could get there in three to five years after spinout.
Any other tips for UK universities wanting to up their spinout game?
Get customers. Prove that people actually want to adopt your technology and are willing to pay for it. Remember that people buy solutions to problems, not technologies.
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