Machine-learned screening to 500 prospects

This week on The Data Minute, Peter sits down with Ben Orthlieb, Founding Partner at Blue Moon VC, for a look under the hood of a firm that has completely re-engineered the venture capital process using AI.

Ben explains how Blue Moon uses a proprietary tech stack to source over 12,000 teams a year and screen them down to the top 5% based on their probability of graduating to Series B, achieving performance metrics that rival top-tier firms without the massive headcount. He breaks down why the "warm intro" is obsolete, how sending AI-generated dossiers to founders results in a 75% meeting acceptance rate, and why human judgment is still the final decision-maker.

They also discuss the "hollowing middle" of the venture market, why multi-billion dollar funds struggle to innovate their own workflows, and how a small check strategy allows Blue Moon to cooperate, rather than compete, with the biggest names in the industry.

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Chapters
00:00 – Intro: The AI-first VC
01:27 – Blue Moon's thesis: Coverage and Winning
03:11 – How to source 12,000 teams a year without a network
05:40 – "Machine learning instinct": Optimizing for Series B graduation
07:44 – Backtesting the algorithm against top 50 Seed firms
10:12 – The 75% meeting conversion rate (and why cold email works)
12:30 – The "AI Dossier": Showing founders you did the work
14:13 – Finding outliers outside the "Credibility Pool" (The Mercor story)
16:05 – The investment process: Where AI ends and humans begin
18:43 – Does it matter who else is on the cap table?
23:36 – The "Small Check" advantage in winning allocation
25:22 – How to interview for resilience
30:20 – Why personal questions are a competitive advantage
32:31 – Follow-ons, reserves, and systematic secondaries
36:00 – Why haven't big funds copied this strategy yet?
39:54 – The "hollowing middle" of the VC market
44:40 – Why brand is the only defense against noise
49:20 – Do warm intros actually result in better investments?
52:46 – The future of the "Operator-VC" model
56:00 – What LPs really think about an AI-driven fund
59:07 – Outro

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