The AI-First VC | Ben Orthlieb, Founding Partner, Blue Moon VC

Dec 18, 2025

Ben Orthlieb, Founding Partner of Blue Moon VC and a former LinkedIn corporate developer, dives into how his firm is revolutionizing venture capital through AI. He shares how Blue Moon sources over 12,000 teams annually, using machine learning to narrow the field to the top 5%. Discover why the traditional 'warm intro' is now obsolete and how AI-generated dossiers achieved a remarkable 75% meeting acceptance rate. Ben also discusses the importance of human judgment in decision-making and the challenges faced by mid-sized funds in a competitive landscape.

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INSIGHT

Coverage And Winning Are The Core Metrics

Blue Moon optimizes for coverage and winning by using AI to see far more seed-stage teams than traditional VCs.
Their tech-first approach concentrates time on founders who are likeliest to graduate to Series B, improving decision quality.

INSIGHT

Massive Top-Of-Funnel With ML Filtering

Blue Moon captures roughly 12,000 North American B2B seed teams per year using 20+ data sources and techniques.
They then use unsupervised ML to reduce that funnel to ~500 candidates for deeper evaluation.

INSIGHT

Optimize For Series B Graduation

Their algorithm is trained to predict which founders will progress from seed to Series B, making Series B graduation the optimization target.
Backtests show the screening alone matches top 50 seed investor graduation rates, concentrating winners efficiently.

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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 He breaks down why the "warm intro" is obsolete, how sending AI-generated dossiers to founders results in a 75% meeting acceptance rate, and why human judgment is still the final decision-maker.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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