Discover more from Data-driven VC
"The Beginning of an Asset Class Revolution" with Mike Arpaia (Moonfire)
DDVC #50: Where venture capital and data intersect. Every week.
👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Thursday I cover hands-on insights into data-driven innovation in venture capital and connect the dots between the latest research, reviews of novel tools and datasets, deep dives into various VC tech stacks, interviews with experts, and the implications for all stakeholders. Follow along to understand how data-driven approaches change the game, why it matters, and what it means for you.
Current subscribers: 12,670, +155 since last week
Brought to you by VESTBERRY - A tool designed specifically for data-driven VCs
Vestberry helps VCs manage and monitor their investment portfolios, providing insights for better, data-driven decisions. But we want to help further! Over the past few months, we have consolidated a list of over 6000 Limited Partners looking to invest in Venture Capital this year. We are sharing it with you to make your fundraising process more efficient. We hope it serves you well!
Data-driven VC Interview Series - Part #2
Following the successful launch of the “Data-driven VC Landscape 2023” and the positive feedback on the first episode of this Q&A series with Pietro Casella from EQT, I’m excited to continue with another high-profile thought leader: Mike Arpaia. Mike joined London-based Moonfire in 2020 as a Partner and is responsible for defining and executing the firm's AI-focused technology strategy.
Thank you for sharing your valuable perspectives below, Mike! 👀
#1 What’s the status of the VC industry in terms of data-driven initiatives and AI?
MA: I think that the VC industry is just starting to embrace data-driven tactics. There are a few firms that have been all-in on this way of working for a while (Moonfire included) and there are increasingly more firms that are starting to make quantitative hires but I really believe that we are just at the beginning of an asset class revolution.
#2 Why should VCs become more data-driven?
MA: We all know that the economics of VC allow for a solo GP or a small firm to make strong returns without any large-scale data infrastructure. But if you're trying to build a financial institution that can outlast any one partner, you need to capture institutional knowledge via technology.
If you want to improve your decision quality at a faster rate, you need to engage with rigorous decision science. Basically - if you want to outperform for a long time, you need to invest in data, science, and technology.
Join 12,600+ thought leaders from VCs like a16z, Accel, Index, Sequoia, and more.
#3 What’s your perspective on buy versus build? Is it “either-or”? Combination?
MA: At Moonfire, I try not to build anything that we could reasonably buy. If you can easily buy it, it's a commodity. We should be focusing our efforts on the things that nobody is building or that nobody can build besides us.
Often this winds up being advanced evaluation models that reflect our internal perspective, larger scale data infrastructure for sourcing and analytics, highly customized investor efficiency tooling, etc.
More specifically, we focus heavily on every part of the venture capital lifecycle - from large-scale data-driven sourcing to learned evaluation models, to highly customized CRM tooling and investor efficiency infrastructure, we are trying to build a fully integrated, highly efficient process. But it doesn't stop there - as many folks will know, we also focus a lot on portfolio construction and financial mathematics as well as tools and products to add value to our portfolio founders.
#4 What are your major challenges or bottlenecks when looking at data-driven initiatives?
MA: I think most VC firms that are hiring data people are struggling to find people who have large-scale, industrial machine learning engineering experience. You get a lot of folks that come from analyst backgrounds or that have come straight from academia.
Building data-driven systems in venture is more of an engineering challenge than a subject matter challenge at this point so I think it's more important to have strong, industrial engineering expertise in the organization. This is the professional demographic of the whole engineering team at Moonfire and I think that it has allowed us to move more quickly than many of our peers.
#5 What would you recommend to a VC firm that just started out to become more data-driven?
MA: Often times the first step that GPs make when they decide that they want to get a bit more quantitative is to hire someone to own this part of the firm. Unfortunately, many people who take these data roles at VC firms see it as an entry point to becoming a VC themselves. I think this creates a scenario where the data people really just want to be investors and thus aren't spending as much of their time, effort, or focus on building large-scale, robust, data-driven infrastructure as they are on trying to network with others, talking to founders, etc.
Personally, I see myself as a computer scientist working on venture capital as my subject matter. I love the VC industry as a subject matter but I wouldn't want to be a full-time investor. I'm a technologist. My whole team is like this too - we are engineers and scientists working in the domain. This focus on technology and research has allowed us to stay vigilant, determined, and committed.
My main bit of advice for any GP looking to hire a data person would be to hire a world-class technologist who loves being a technologist. Be careful about hiring a technologist who wants to also be an investor. IMO you'll wind up with someone who isn't world-class at either.
This is it for today. Hopefully, you enjoyed this short interview-style episode with DDVC thought leader Mike Arpaia.
PS: Don’t forget to download your free list of 6k LPs investing in VC
Thank you for reading. If you liked it, share it with your friends, colleagues, and everyone interested in data-driven innovation. Subscribe below and follow me on LinkedIn or Twitter to never miss data-driven VC updates again.
If you have any suggestions, want me to feature an article, research, your tech stack or list a job, hit me up! I would love to include it in my next edition😎