The Hidden Economics Behind Deep Tech Success with Ramana Nanda | Ep218

Ramana Nanda is a Professor of Entrepreneurial Finance at Imperial College London and the academic lead of the Institute for Deep Tech Entrepreneurship. He has spent his career at the intersection of science, venture capital, and institutional design, previously teaching at Harvard Business School and studying how ideas move from lab bench to balance sheet.

With decades of work across the US, UK, and Europe and deep research into entrepreneurial finance, deep tech, and policy, he offers insights into how capital, universities, and governments either unlock or choke off frontier innovation. In this episode, he discusses why deep tech is fundamentally harder to fund than software, what needs to change in universities and capital markets, and how new institutional models could finally make hard science “venture-shaped” without pretending it’s just another SaaS company.

In Today’s Episode We Discuss:

00:01 – Introduction & Deep Tech Framing

00:49 – Early Life & Career Origins

01:40 – Shift from Engineering to Economics

03:23 – Entrepreneurial Finance Journey

05:02 – Europe vs. U.S. Commercialization Gap

09:11 – Founding Imperial’s Deep Tech Institute

12:54 – AWS and the Cost of Experimentation

16:42 – Deep Tech’s Two Learning Curves

18:27 – Government as Early Customer

20:54 – Proof of Concept vs. Proof of Value

22:07 – Choosing the Right Early Customers

29:18 – Sector Lessons: Energy, Biofuels, Tesla

32:09 – University Incentives & Academic Founders

35:10 – Tech Transfer Models: MIT vs. Imperial

40:11 – Underused Policy Levers

44:26 – AI’s Impact on Science & Labor

47:43 – Future of Scientific Productivity

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