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In the last two weeks, I covered why now is the best time to start leveraging AI for investing and how I would start today if I’d need to build a tech stack for a VC firm all over again - based on 8 years of experience of transforming Earlybird VC.
Last episode, I asked a question:
89% of readers want to see the most prominently used tools by 500+ firms from our VC Tool Finder platform - so here we go.
Today, I’ll share:
What the most common VC tool stack looks like across firms, ranging from solo/micro GPs to early-stage, growth, and multi-stage funds in our unique dataset from VC Tool Finder. If you want to compare your stack to firms similar to yourself in terms of fund size, team size, AUM, etc. - click here.
Recap of my personal tool stack today, including a few words on how I use MCP and workflow automation
Links to other dozens of tool stacks from firms such as as a16z, Hoxton, Point Nine, Ben’s Bites Fund, Craft Ventures, Hustle Fund, 201 Ventures, Chapter One, Awesome People Ventures, AirAngels, Susa Ventures, Ganas Ventures, Forward Deployed VC, Davidovs Venture Collective, Footwork, 81 Collection, Untapped Capital, and many more
Let’s jump in!
1. Most frequently used tools across 500+ firms from VC Tool Finder
Our unique data shows that VC tool stacks follow a rough 80/20 logic: Very few tools (20 or so out of 600+ that we track) like Affinity, Carta, Crunchbase, Harmonic, Notion, and Superhuman are used by a big majority (more than 80%) of all investment firms.
However, the majority of tools are are only used by small subsets of investors. Oftentimes, these investor groups share clear characteristics like fund size, geography or industry focus. For example, firms investing in Europe tend to rely on Dealroom as a data source or funds with larger fund sizes leverage more data sources that are oftentimes redundant and more expensive, such as Pitchbook or CB Insights.
What’s interesting, however, is that the group of “early adopters” who use tools like Granola, WhisprFlow, ArcBrowser, and others cannot be segmented based on characteristics. Said differently, there’s no pattern for early adopters as they come from all fund sizes, all AUM groups, all geographies, all industry focuses, etc.
