17 Series A AI & Machine Learning Investors in San Francisco — 16 Currently Deploying
Updated
Funds (17)
Caffeinated offers founders pursuing their life's work an alternative to the hype cycle through long-term thinking, supporting category-defining companies across all sectors that w…
Operator Collective is an early-stage B2B venture firm that connects founders to exceptional enterprise operators, believing companies succeed because of the people who build them.
Lead and co-lead Series A and Seed rounds in companies with early signs of product-market fit, focusing on consumer technology and consumerization of enterprise technology.
Accelerating AI-native founders at the earliest stages. Lead early rounds in companies and founders building the future, providing that first check before others think you're onto …
Partner with bold technical founders from inception, before product or first check, backing AI-native infrastructure, security, apps, and models to build the autonomous enterprise.
Amplify invests in technical founders building the next generation of applications, models, tools, and infrastructure. For almost 15 years they have been the first investor and par…
NFX identifies and backs founders building companies with network effects. The fund emphasizes software development, founder support through content/tools, and emerging technologie…
Early stage B2B-focused venture firm specializing in building enterprise leaders through disruptive B2B AI software investments in enterprise infrastructure, cybersecurity, and rel…
The product-market fit partner for technical enterprise founders, turning product-market fit from improbable to inevitable through proprietary playbook and methodology.
Investing in deeptech and foundational technologies that remove structural constraints to sustainable scale in food, agriculture, and planetary health, with AI as core infrastructu…
Back visionary and relentless founders building the future using technology and science. Belief in AGI for good, decentralized systems, personalized well-being, quiet electric tran…
Partner at inception with technically brilliant founders who see past impossible to inevitable. Invest in technical breakthroughs becoming world-changing companies, particularly in…
Invest in technical founders building software that will improve millions of consumers' lives. Back bold founders with a vision worth fighting for.
First believers in AI Native founders, providing zero to one capital and hands-on support for builders of AI Native applications, infrastructure, and developer tools.
First institutional investor for early-stage AI founders, with 70% of investments beginning at inception. People-first approach to building breakthrough companies.
First-check investor (pre-seed, seed, series A) deeply focused on AI-first companies. Partners selectively, cares deeply, and strives for excellence in reshaping industries through…
Piva Capital invests in people, companies, and breakthrough technologies at the inflection of scientific breakthrough and mass market impact to usher in a new industrial era across…
Which of these Series A AI & Machine Learning funds actually fits your startup?
DryPowder's AI matching ranks all 17 by thesis fit, check size, and deployment status.
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Frequently asked questions
How many series a ai & machine learning investors are actively deploying right now?
DryPowder tracks 17 series a ai & machine learning funds. Of these, 16 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 series a ai & machine learning VCs write?
Among 17 series a ai & machine learning funds on DryPowder, check sizes run from $250K–$15M. 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 series a ai & machine learning VC?
A warm intro is still the fastest path. Most series a 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.
How long does a Series A process typically take?
Series A processes average 4-6 months. Unlike seed, you will navigate formal partner meetings, IC presentations, data room reviews, and reference calls. First meeting to term sheet alone takes 6-10 weeks at most firms. Running a compressed, competitive process where you approach all target investors in the same short window tends to produce faster decisions and better terms than a sequential approach.
How many series a 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 16 series a 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 17 series a 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.
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