For a business that looks so much at technology, venture capital is often poor in applying it. How should they use AI? Empirical Ventures has already embedded it into their investment process, so we asked Co-founder and General Partner Johnathan Matlock to tell us about what they are doing. We also discussed their approach to deeptech investing.
Amongst other topics, we talk about:
- using AI in an investment process
- how well AI works in niche areas
- keeping the focus on real people
- using a YouTube channel with millions of subscribers
- what a venture scientist is
- the value of execution over ideas
- getting the right founder
- filtering our weak opportunities
Johnathan and Empirical have been thoughtful about their investment process, including using AI, so there's lots of insights into effective investing. Enjoy!
00:50 Johnathan introduces himself
05:00 how angel investing developed into Empirical Ventures
07:45 what Empirical Ventures does
09:00 what is a venture scientist?
16:30 getting the right founder
18:30 talent scouting in pools of PhD talent
22:00 value in execution not ideas
26:00 challenges in getting good feedback
27:30 using AI in investment process
32:00 how well Ai works in niche areas
35:00 keeping the focus on the real people
38:30 how far can the use of AI go in venture capital?
41:30 using a successful Youtube channel
43:00 filtering out weak opportunities
46:00 using YouTube to help existing portfolio companies
50:00 favourite questions
Links
Empirical Ventures website: https://www.empiricalventures.vc/
Subscribe to the EIS Navigator podcast on most services here: https://the-eis-navigator.captivate.fm/listen
Suggested books and media
Clear Thinking: The Art and Science of Making Better Decisions by Shane Parrish
Bio
Johnathan Matlock, Co-founder and General Partner, Empirical Ventures
With a PhD in Organic Chemistry and 15 years at the intersection of science, entrepreneurship, and capital, Johnathan helps build and back technologies that define the next century, focused on Atoms > Bits.
Investor
He is Co-Founder and General Partner at Empirical Ventures, a UK-based DeepTech fund backing entrepreneurial scientists at pre-seed and seed.
-- > £20m AUM | 25 portfolio companies | Top decile TVPI (2022 vintage)
-- > Secured £5m from the British Business Bank as a Regional Angel Programme delivery partner
Portfolio companies have raised £30m in follow-on capital to date, employee 200 people and have an collective enterprise value of £200m (Nov 2025).
Beyond the fund, he has built a 50-company angel portfolio across DeepTech and Life Sciences and made 5 LP commitments to next-generation VC funds.
Operator
Prior to founding Empirical Ventures, he was employee #7 and inventor on the patent behind Ziylo, a University of Bristol spin-out developing a glucose-sensitive insulin, that was acquired by Novo Nordisk for £623m (2018). The largest exit for a University spin-out via acquisition until 2025 (Organox).
In 2024, he co-founded Tru Fit Training UK, a small-group training facility and have currently scaled this business to £250k annual revenue and 140 members (Nov 2025).
He also founded Tru Fit Asset Management in 2024, a commercial property investment business in Bedfordshire that owns 2 x 4,200 sq ft facilities. Built and developed one of those barns with completion in 2025.
Scientist
-- > Published 11 papers, inventor on 4 patents, 5 academic awards
-- > Chartered Chemist (CChem), Royal Society of Chemistry
Driven by a belief that scientific discovery and entrepreneurial drive is the greatest lever for human progress, he now focuses on enabling the next generation of scientist-founders to translate breakthrough research into globally impactful companies.
Disclaimer
Please note this podcast/interview does not constitute a financial promotion and is provided for informational purposes and should not be construed as an invitation or offer to buy or sell any investments. Please be aware that investments into unquoted companies are high risk, long term and illiquid investments. Your capital is at risk. Past performance is not a reliable indicator of future performance. Target returns are not guaranteed and forward looking statements are illustrative only and must not be relied upon. Investors should only invest on the basis of reading the full offer documentation.
