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Chad Byers
Co-founder & General Partner at Susa Ventures
Chad Byers is a prominent figure in the venture capital industry, based in the San Francisco Bay Area. He is the Co-founder and General Partner at Susa Ventures, a seed-stage venture capital firm he established in January 2013.1
At Susa Ventures, Byers focuses on investments in healthcare, fintech, and supply chain sectors.2 He has led several notable investments, including seed rounds for companies that have since achieved unicorn status, such as Robinhood, Flexport, Andela, Newfront Insurance, Stord, and Mux.12
Byers has received recognition for his work in venture capital:
- He was named to the Forbes 30 Under 30 for Venture Capital in 2015.1
- He appeared on the Forbes Midas Seed List, ranking #14 in May 2022.1
Prior to founding Susa Ventures, Byers worked at various startups in product and marketing roles.2 His background includes:
- A bachelor's degree in Environmental Science from the University of Colorado.2
- Early exposure to entrepreneurship and venture capital, having been raised in Silicon Valley.2
As an investor at Flexport since 2014, Byers has been involved with the company's growth from its early stages.1 Flexport has since reached a valuation of $8 billion.1
Byers is active on social media, maintaining a presence on Twitter (@chadbyers) where he describes himself as investing $1m+ into pre-seed and seed rounds, with a focus on software companies.3
Highlights
I've gone through the product onboarding and it's magical.
- at home blood draw
- fast turnaround of data + review with clinician
- realistic action plan rooted in data with clear follow ups and ways to measure progress
insanely simple, convenient and important.
Healthcare feels like a top 3 place to build over the next decade.
-- $4.5T US spend//20% of GDP --> huge market -- up to 25% of the world's data is healthcare related --> huge data set --> big surface area to build -- not available on the public internet --> not in foundational models --> less big co competition -- multimodal --> AI particularly good at this -- majority of data not used clinically --> latent value that can be captured by new companies
Often the challenge in healthcare is getting distribution.
Distribution --> data --> value/moat
