Top venture capital firms actively investing in machine learning and AI startups at Series A. Average check sizes, portfolio focus areas, and what they look for in ML startups.
Minimum $1M ARR with clear path to $10M+
Demonstrating 200-300% year-over-year growth
Strong ML/engineering team, 50%+ technical
Logo traction with recognizable brands
Efficient growth, burning less than 2x revenue growth
Clear differentiation in model performance or unique data
AI/ML Fund
Focus Areas:
Foundation models, ML infrastructure, Applied AI
Portfolio:
Anthropic, Hugging Face, Character.AI
Requirements: $2M+ ARR, 3x YoY growth
Visit Website →Technical Founders Fund
Focus Areas:
ML infrastructure, Developer tools, Data platforms
Portfolio:
Databricks, Fastly, DataRobot
Requirements: $1M+ ARR, Strong technical moat
Visit Website →AI/ML Practice
Focus Areas:
Enterprise AI, Healthcare ML, Autonomous systems
Portfolio:
Databricks, Duolingo, Robinhood
Requirements: $1.5M+ ARR, Enterprise traction
Visit Website →Enterprise AI Fund
Focus Areas:
Enterprise ML, Vertical AI, Infrastructure
Portfolio:
Carta, Netskope, TripActions
Requirements: $2M+ ARR, 20+ enterprise customers
Visit Website →AI/ML Investments
Focus Areas:
Foundational AI, ML applications, Infrastructure
Portfolio:
OpenAI, Harvey.ai, Glean
Requirements: $3M+ ARR, Category-defining potential
Visit Website →AI/ML Portfolio
Focus Areas:
Applied ML, Healthcare AI, Climate ML
Portfolio:
Uber, Slack, Stripe (ML features)
Requirements: $1M+ ARR, Technical differentiation
Visit Website →Early Stage AI Fund
Focus Areas:
AI-first companies, ML platforms, Generative AI
Portfolio:
Runway, Perplexity, Adept
Requirements: $2M+ ARR, High growth velocity
Visit Website →AI-Focused Fund III
Focus Areas:
Deep learning, Computer vision, NLP
Portfolio:
Cohere, You.com, Waabi
Requirements: $1M+ ARR, PhD founders preferred
Visit Website →Get matched with ML-focused VCs and prepare your pitch deck