Explore active machine learning venture capital investors by stage, thesis, and check sizes. This directory highlights fund sizes, recent filings, and sector focus to help you prioritize investor outreach and improve match quality.
Complete database of 26 venture capital funds investing in machine learning startups. Find the right investor with $19B in combined assets under management.
The machine learning venture capital ecosystem has reached unprecedented scale, with 26 specialized funds managing $19B in assets.Investment activity has shown 39% growth year-over-year, reflecting strong investor confidence in the sector's long-term prospects.
In 2026, machine learning startups attracted $12Bacross 598 funding rounds, with the average fund size reaching$331M. This represents a maturing ecosystem where specialized funds are increasingly focusing on vertical-specific expertise.
Investing in revolutionary machine learning companies that are transforming industries through innovative technology and scalable business models.
Investing in revolutionary machine learning companies that are transforming industries through innovative technology and scalable business models.
Investing in revolutionary machine learning companies that are transforming industries through innovative technology and scalable business models.
Investing in revolutionary machine learning companies that are transforming industries through innovative technology and scalable business models.
Investing in revolutionary machine learning companies that are transforming industries through innovative technology and scalable business models.
Investing in revolutionary machine learning companies that are transforming industries through innovative technology and scalable business models.
Investing in revolutionary machine learning companies that are transforming industries through innovative technology and scalable business models.
Investing in revolutionary machine learning companies that are transforming industries through innovative technology and scalable business models.
Investing in revolutionary machine learning companies that are transforming industries through innovative technology and scalable business models.
Investing in revolutionary machine learning companies that are transforming industries through innovative technology and scalable business models.
There are 26 active VC funds specializing in machine learning investments, managing a combined $19B in assets under management. This represents one of the largest concentrations of specialized capital in the venture ecosystem, with funds ranging from $50M micro-funds to $2B+ growth-stage vehicles. The sector has attracted significant institutional capital due to its39% growth trajectory and strong exit potential.
Machine Learning startups raise an average of $9M in Series A funding, with typical ownership ranging from 16-29%. This is above the cross-industry average due to the capital-intensive nature of many machine learning business models and longer development cycles. Series B rounds average $31M, reflecting the sector's ability to scale efficiently once product-market fit is achieved.
Top-performing machine learning VCs have generated 3x average returns average returnsover the past decade, with the best funds creating 8 unicorn companies. Leading funds like Machine Learning Ventures A have demonstrated consistent performance through multiple market cycles, combining deep domain expertise with extensive portfolio support. Success rates for Series A investments reach 78% among top-quartile funds.
The typical machine learning funding process takes 4-6 months, from initial pitch to signed term sheet. This includes 2-4 weeks for initial screening, 4-8 weeks for due diligence, and 2-4 weeks for final negotiations and documentation. Machine Learning startups often require longer diligence periods due to technical complexity and regulatory considerations, but experienced sector-focused VCs can move faster due to their domain expertise.
Top machine learning VCs prioritize technical differentiation, large addressable markets, and experienced teamswith deep domain knowledge. They look for startups that can demonstrate clear competitive moats, scalable business models, and strong early customer traction. Regulatory compliance, intellectual property position, and go-to-market strategy are particularly important in machine learning. The best VCs also value founders who can articulate long-term vision and have the technical depth to execute complex roadmaps.
Yes, machine learning VCs deployed $12B in 2026across 598 transactions, showing continued strong appetite for quality deals. While overall VC activity has normalized from peak 2021 levels, machine learning remains a priority sector for most institutional investors. Hot subsectors include AI-powered machine learning, Next-gen machine learning platforms, Enterprise machine learning solutions, which are seeing particularly strong investor interest and premium valuations.
Last updated: 5/27/2026 | Data aggregated from 26 VC funds, 598 deals, and 21 successful exits |About our methodology