Climate CEOs podcast

7 Lessons for Hard-Tech Startups | Vanessa Chan (Time100 List)

2026-09-22
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47:20
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What kills hard-tech startups usually isn’t the technology. It’s everything surrounding it: unit economics, supply chains, customers, workforce, financing, and the long road from demonstration to deployment.

Guest bio:

Dr. Vanessa Chan is an entrepreneur, engineer, angel investor, and educator who works at the intersection of technology, commercialization, and business. She previously served as Chief Commercialization Officer at the U.S. Department of Energy and now leads innovation and entrepreneurship at the University of Pennsylvania’s Engineering program. She was also a partner at McKinsey & Company, where she co-led its innovation practice. TIME named her to its TIME100 Climate list of influential climate leaders in 2024. She holds three patents and, outside of work, runs her own pottery studio.

Seven things you’ll learn in this episode:

  1. Technology readiness isn’t enough: founders also need to systematically reduce market, supply chain, workforce, regulatory, community, and economic risks.
  2. A startup doesn’t need to eliminate every risk before raising capital, but it needs data, explicit assumptions, and a credible plan for driving each major risk down.
  3. Commercialization ultimately comes down to three questions: Do customers want it, will they pay enough for it, and can you deliver it at a cost that lets the value chain make money?
  4. The hardest financing gap in hard tech often sits between development and demonstration, when first-of-a-kind projects remain expensive, but traditional capital wants proven economics.
  5. America’s capital providers may need new structures that spread risk across the first several deployments instead of waiting to finance the eighth or ninth project.
  6. Founders trained as technologists need to replace “technology push” with “market pull,” including talking to customers long before the technology is finished.
  7. Some of the best entrepreneurial training comes from getting comfortable without a rubric: observe problems, ask why, experiment, build networks, and learn how to find answers you were never taught.


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