Matt Clifford, Cofounder of Entrepreneur First on Investing in Deep Tech the Lean Startup Way
This is the first episode of SOSV’s new podcast ‘Deep Tech: From Lab to Market’ where Founders and Investors share how ‘deep tech’ innovation can go from lab to market. It is available on Apple Podcast, Google Podcast, Spotify, and other platforms.
Matt Clifford is the co-founder and CEO of Entrepreneur First. EF is a unique ‘talent investor’ that runs programs that helps scientific and business talent get together to create ambitious startups. SOSV is a strong believer in their model and already funded several EF graduates such as Unbox Robotics (logistics robots) and Rebartek (robots for construction) via our hardware accelerator program HAX.
Here are some of the takeaways of the conversation:
EF Creates Silicon Valley Bubbles
Alice and Matt realized that talented and ambitious people were often hesitant to start companies because they were lacking a good team, a good project or a supportive environment.
The minority of people that do decide they want to start companies don’t know the right people to start with.
Matt had studied in Cambridge (UK) then at MIT and, at the time, the contrast between the two ecosystems was striking, so he resolved to create a ‘Silicon Valley bubble’ to support entrepreneurs.
To shortcut the process of ecosystem building, we [tried] to create a little pocket of Silicon Valley culture and norms in every city that we operate.
By finding talented individuals and helping them try and form teams rapidly, the EF programs is applying the ‘Lean Startup’ methodology to team formation.
The focus on deep tech came organically, as such companies enjoy an inherent competitive advantage and can have a disproportionate impact.
Focus on Large Markets with Predictable Winners
EF is almost always working with companies that have no traction at the point where they decide to invest. Their approach is to focus on large markets where you can have predictable winners.
Those markets typically have three important characteristics:
- An Unambiguous Problem
Often one that seems to be solvable for the first time thanks to recent technical developments or discoveries. For example, autonomous vehicles. On the opposite, a service like Instagram would be a bet on a specific behavior. - A Significant Barrier to Entry
This limits competition, the talent and capital flows to the most promising teams, and the outcome is much less of a lottery. - (Somewhat) Objective Standards to Evaluate Solutions
Something customers care about unambiguously. The best product on that dimension will win.
Having an ambitiously large target market is also crucial as both talent and capital are more attracted to ambition then they are to perceived likelihood of success.
Don’t be afraid of ambition: it actually helps you
Productivity Is the Key Metric Of a Good Team
To determine if a team is a good match, the best metric is their productivity, rather than the friendship of their founders. Of course, they need to be productive toward meaningful goals.
The main mistake people make there is to evaluate the quality of the friendship rather than the productivity of the working relationship. If you’re not making each other more productive, it’s not a good team.
Community-Driven Due Diligence
In addition to the knowledge within the EF team and various advisors, the community of founders itself is also a good filter to figure out the technical viability of a project.
We rely to a great extent on the cohort to filter. Because the cohort themselves, they have both the right incentives and the right skills and capabilities to figure out who’s legitimate and what’s possible and what’s not.
New Venture Capital Playbooks
Deep Tech startups don’t have the same funding trajectory as SaaS or marketplaces (which typically need millions or more for user acquisition). The valuations, capital needs, timing and risk differ with each startup.
Investors need to get comfortable with learning the new capital value chain for these companies.
Eventually, those building the new playbooks are likely to be rewarded.
Conclusion
EF’s approach is quite unique around the world, and has been tried and tested already in multiple locations. We look forward to its continued success!
