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 30 venture capital funds investing in machine learning startups. Find the right investor with $22B in combined assets under management.
The machine learning venture capital ecosystem has reached unprecedented scale, with 30 specialized funds managing $22B 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 $6Bacross 513 funding rounds, with the average fund size reaching$230M. 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 30 active VC funds specializing in machine learning investments, managing a combined $22B 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 $11M in Series A funding, with typical ownership ranging from 18-34%. 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 $24M, reflecting the sector's ability to scale efficiently once product-market fit is achieved.
Top-performing machine learning VCs have generated 5x average returns average returnsover the past decade, with the best funds creating 17 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 82% 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 $6B in 2026across 513 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: 4/12/2026 | Data aggregated from 30 VC funds, 513 deals, and 50 successful exits |About our methodology