Machine learning drives 75% of VC investment decisions in 2025.
Gartner says that less than 5% of venture capital investment decisions utilized data science and machine learning, although that number is expected to reach 75% by 2025.
The shift is fundamental. VCs use ML to source deals. Founders build ML-first products. Traditional metrics no longer apply.
This guide lists 30 active ML investors. Their thesis, check sizes, and what they actually fund.
Track your ML pitch deck
ML model trained. Benchmarks beat SOTA. Enterprise pilots converting.
Yet investors ghost your emails.
Stop sending static PDFs. Track engagement instead. Know which technical slides get studied.
Ellty shows you exactly how investors interact with your deck.
Top machine learning investors
1. Andreessen Horowitz (a16z)
$1.5B dedicated AI/ML fund launched in 2024.
Investment focus: ML infrastructure, foundation models, applied ML
Investment range: Pre-seed to growth, flexible sizing
Notable investments: Databricks, Scale AI, Character.AI, ML platforms
Contact: a16z.com
2. Sequoia Capital
Leading ML investments since before the hype.
Investment focus: ML across all verticals, infrastructure to applications
Investment range: Series A typical entry, seed to growth
Notable investments: OpenAI, Nvidia (early), Harvey, ML infrastructure
Contact: sequoiacap.com
3. Alumni Ventures
Most active ML investor by deal count in 2025.
Investment focus: Follow-on ML rounds, diversified approach
Investment range: $200K-$2M typical, syndicate model
Notable investments: 94+ ML deals in past 12 months
Contact: av.vc
4. Khosla Ventures
Early believer in deep learning commercialization.
Investment focus: ML moonshots, technical breakthroughs
Investment range: Seed to growth, patient capital
Notable investments: OpenAI seed, ML research companies
Contact: khoslaventures.com
5. Google Ventures (GV)
Alphabet's venture arm leveraging internal ML expertise.
Investment focus: Applied ML, ML tools, enterprise ML
Investment range: Pre-seed to Series E
Notable investments: ML startups across verticals
Contact: gv.com
6. Data Collective (DCVC)
Deep tech specialist focused on ML since 2011.
Investment focus: ML infrastructure, applied ML in regulated industries
Investment range: Seed to Series B, $3M-$15M
Notable investments: ML in healthcare, agriculture, defense
Contact: dcvc.com
7. M12 (Microsoft Ventures)
Microsoft's strategic ML investment arm.
Investment focus: Enterprise ML, ML developer tools, B2B ML
Investment range: Series A-C, $2M-$20M
Notable investments: nEYE Systems, ML infrastructure companies
Contact: m12.vc
8. Two Sigma Ventures
Quant hedge fund's venture arm.
Investment focus: ML-first companies, data infrastructure
Investment range: Seed to Series B
Notable investments: ML companies leveraging proprietary data
Contact: twosigmaventures.com
9. Amplify Partners
Early-stage fund for technical founders.
Investment focus: ML infrastructure, developer tools, data platforms
Investment range: Seed to Series A, $1M-$10M
Notable investments: Technical ML infrastructure companies
Contact: amplifypartners.com
10. Radical Ventures
ML-specialist fund by AI pioneers.
Investment focus: Deep learning, ML research commercialization
Investment range: Seed to Series B
Notable investments: Cohere, vector database companies
Contact: radical.vc
11. SV Angel
Super angel fund active in ML.
Investment focus: ML applications, consumer ML
Investment range: $100K-$1M seed checks
Notable investments: 41+ ML investments recently
Contact: svangel.com
12. Felicis Ventures
Growth investor embracing ML transformation.
Investment focus: ML-enabled businesses, vertical ML
Investment range: Series A to growth
Notable investments: 35+ ML deals in past year
Contact: felicis.com
13. Village Global
Network-driven fund backed by tech titans.
Investment focus: Early-stage ML, ambitious ML projects
Investment range: Seed, $100K-$3M
Notable investments: ML startups with technical moats
Contact: villageglobal.vc
14. Sierra Ventures
Early-stage firm with dedicated ML practice.
Investment focus: Enterprise ML, ML automation
Investment range: Series A-B, $5M-$15M
Notable investments: Pantomath, ML operations platforms
Contact: sierraventures.com
15. Antler
Global accelerator with ML focus.
Investment focus: Pre-seed ML globally
Investment range: $100K-$500K initial
Notable investments: 100+ ML companies
Contact: antler.co
16. Bessemer Venture Partners
Century-old firm investing in ML transformation.
Investment focus: Cloud ML, vertical ML solutions
Investment range: Series A to growth
Notable investments: ML SaaS companies
Contact: bvp.com
17. Index Ventures
Transatlantic firm with strong ML portfolio.
Investment focus: ML platforms, European ML leaders
Investment range: Seed to growth
Notable investments: ML infrastructure companies
Contact: indexventures.com
18. Lightspeed Venture Partners
Multi-stage global investor in ML.
Investment focus: Consumer ML, enterprise ML platforms
Investment range: Seed to growth
Notable investments: ML companies globally
Contact: lsvp.com
19. NEA
Large fund with dedicated ML team.
Investment focus: Healthcare ML, enterprise ML
Investment range: Series A+, up to $100M
Notable investments: ML drug discovery, clinical ML
Contact: nea.com
20. Greylock Partners
Early investor in ML infrastructure.
Investment focus: ML platforms, developer tools
Investment range: Seed to Series B
Notable investments: ML infrastructure leaders
Contact: greylock.com
21. SignalFire
Data-driven VC using ML for sourcing.
Investment focus: ML applications, ML-first startups
Investment range: Seed to Series B
Notable investments: Portfolio selected by ML models
Contact: signalfire.com
22. Accel
Global VC with ML expertise.
Investment focus: ML applications, European ML
Investment range: Seed to growth
Notable investments: ML companies worldwide
Contact: accel.com
23. General Catalyst
Cross-stage investor in ML transformation.
Investment focus: Applied ML, healthcare ML
Investment range: Seed to growth
Notable investments: ML across sectors
Contact: generalcatalyst.com
24. Coatue Management
Crossover investor with ML focus.
Investment focus: Late-stage ML, proven ML companies
Investment range: Series C+, $50M+
Notable investments: ML unicorns
Contact: coatue.com
25. Insight Partners
Growth investor in ML scale-ups.
Investment focus: B2B ML, ML SaaS
Investment range: Series B+, $10M-$100M+
Notable investments: ML enterprise companies
Contact: insightpartners.com
26. Y Combinator
Leading accelerator with ML focus.
Investment focus: Early-stage ML across verticals
Investment range: $500K for 7%
Notable investments: Hundreds of ML startups
Contact: ycombinator.com
27. Homebrew
Seed fund by former Google/Twitter execs.
Investment focus: ML products, applied ML
Investment range: Seed, $500K-$2M
Notable investments: 15+ ML companies recently
Contact: homebrew.co
28. Floodgate
Seed specialist backing ML pioneers.
Investment focus: ML breakthroughs, contrarian ML bets
Investment range: Pre-seed to seed
Notable investments: ML companies before inflection
Contact: floodgate.com
29. Air Street Capital
London-based ML specialist.
Investment focus: ML infrastructure, European ML
Investment range: Seed to Series A
Notable investments: ML research commercialization
Contact: airstreet.com
30. Hyperplane Venture Capital
Dedicated ML/data fund.
Investment focus: ML infrastructure, data platforms
Investment range: Seed to Series A
Notable investments: Technical ML companies
Contact: hyperplane.vc
How to approach ML investors
What Sets You Apart
Every pitch claims revolutionary ML. Investors see through buzzwords.
Show real differentiation:
- Novel architecture or approach
- Proprietary training data
- 10x performance on specific task
- Domain expertise others lack
Metrics That Matter
Forget vanity metrics. ML investors track:
Model performance: F1 scores, precision/recall, inference latency
Unit economics: Cost per prediction, GPU utilization
Data advantage: Collection rate, labeling efficiency
Customer metrics: Retention, expansion, NPS
Due Diligence Preparation
ML investors go deep. Prepare for:
- Architecture review sessions
- Benchmark reproduction
- Data pipeline audits
- Team technical interviews
Have your Weights & Biases logs ready. Document your experiments. Version your models.
Alternative funding sources
Cloud Credits
AWS Activate: Up to $100K credits
Google Cloud: $200K for ML startups
Azure: $150K plus TPU access
Oracle: $500K for select startups
Research Grants
NSF: $2M for ML research
DARPA: ML defense applications
NIH: ML in healthcare
European Research Council: €2.5M grants
ML-Specific Programs
NVIDIA Inception: Hardware access plus funding
Intel Ignite: Accelerator plus investment
IBM Ventures: Strategic ML partnerships
Success stories
Infrastructure Wins
Databricks: $10B round in 2024. Dominated ML operations.
Scale AI: $14.3B valuation. Labeled data for everyone.
Weights & Biases: ML experiment tracking. $200M Series C.
Application Layer
Harvey: Legal ML. Series B at $715M valuation.
Jasper: Marketing ML. $125M Series A despite GPT competition.
Ironclad: Contract ML. Survived by going vertical.
Why ML Startups Fail
No moat beyond model. OpenAI releases update. Game over.
Underestimated compute costs. Burned runway on training.
Academic approach. Published everything. Competitors copied.
How Ellty helps ML startups
ML pitch decks differ. Technical depth matters. Architecture diagrams get scrutinized.
Ellty data shows:
- Model architecture slides: 5x longer view time
- Benchmark slides: Most forwarded internally
- Team ML credentials: Second most viewed
Track what resonates. Iterate based on data.
ML founders using Ellty report:
- 4x response rate vs. PDFs
- Know which VCs understand ML
- Identify technical vs. business investors
FAQs
Q: Pre-trained or train from scratch?
Most Series A companies fine-tune. Training from scratch needs $10M+.
Q: Open source our model?
Only if distribution strategy. Most VCs prefer proprietary.
Q: How much runway for ML startup?
24 months minimum. Compute costs kill faster.
Q: Patent ML algorithms?
Rarely worth it. Execution and data matter more.
Q: MLOps stack requirements?
Production-ready from day one. Investors check latency.
