5 AI & Machine Learning Investors in London — 5 Currently Deploying

Early-stage venture firm backing category leaders in Software, AI, Fintech and Consumer Internet across Europe and North America through rigorous investment, hands-on partnership, …

Build world-changing businesses with Europe's best founders over two decades. Support founders as complete people, not just CEOs, through deliberate well-being and business buildin…

Investing to architect the digital economy by disrupting traditional sectors so more people can benefit, driven by conviction in profit and progress through principle-driven invest…

Partner with exceptional people to help them realize their full potential across pre-seed, seed, growth, and public market stages through long-term founder relationships.

Back trailblazers solving hard problems in large markets through deep tech venture capital across Intelligence (AI, computing, quantum), Human (health, medicine, wellness), and Pla…

How many ai & machine learning investors are actively deploying right now?

DryPowder tracks 5 ai & machine learning funds. Of these, 5 are currently deploying capital based on recent fund closes, portfolio activity, and partner signals. Use DryPowder's fund matching to see which ones fit your specific startup.

What check size do ai & machine learning VCs write?

Among 5 ai & machine learning funds on DryPowder, check sizes run from $100K–$20M. Ranges vary by fund size, vintage, and whether the fund leads rounds. Each fund profile shows check size detail and lead preference.

How do I get a meeting with a ai & machine learning VC?

A warm intro is still the fastest path. Most ai & machine learning VCs on DryPowder do accept cold outreach or direct applications, but response rates vary widely by fund. Before reaching out, use DryPowder's AI matching to get a fit score and specific talking points. Founders who lead with thesis-aligned framing get significantly better response rates than those sending the same deck to every fund on a list.

What do ai & machine learning VCs look for beyond a good deck?

Most ai & machine learning investors evaluate three things before reading your deck in detail: your background in the space, whether they have seen the problem from multiple portfolio companies, and whether anyone they trust vouches for you. After that, the deck needs to show market size, a clear problem-solution fit specific to ai & machine learning, and early evidence of customer demand. Thesis alignment matters more in ai & machine learning than in generalist investing because these funds see a high volume of pitches in the same space.

How many ai & machine learning investors should I be talking to at once?

Most fundraisers recommend a parallel process: identify 20-40 target investors but approach your highest-fit funds first. Of the 5 ai & machine learning funds currently deploying, not all will match your specific stage and sector. Use fund matching to find your top 10-15, approach those first, collect feedback, then go wider. Going to every fund simultaneously without qualifying first burns bridges and dilutes the urgency that helps close a round.

How is DryPowder different from building a VC list on LinkedIn or Crunchbase?

The fund list you build on LinkedIn or Crunchbase is roughly the same list every other founder is building. DryPowder adds three things a manual search cannot: deployment signals (which funds have recently closed new vehicles and are actively writing checks), AI thesis matching (which of the 5 ai & machine learning funds actually fits your specific startup, ranked by fit), and verified partner contact details. The goal is 15 targeted, thesis-aligned pitches instead of 150 generic ones.