Everyone's building AI. Pre-seed investors need to see why your approach wins.
(AI pre-seed rounds are 3x oversubscribed — differentiation is everything)
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Get scored on what pre-seed AI investors evaluate — technical differentiation, data moat potential, team ML expertise, and market timing — then match with AI-focused angels.
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Everything you need to know about getting your pitch deck investor-ready
At pre-seed, AI investors look for: team with genuine ML expertise (papers, experience), a unique data or model advantage (not just GPT wrappers), a clear wedge into a specific market, and evidence of customer pull. You don't need revenue, but you need a credible technical thesis.
AI pre-seed rounds are running $500K-2M at $8-15M valuations in 2025-2026. The premium exists because compute costs are high and AI talent is expensive. Be specific about use of funds: GPU costs, key hires, and customer acquisition.
Three paths to differentiation: proprietary data (unique training data others can't get), domain expertise (deep knowledge in healthcare, legal, finance), or technical innovation (novel architectures, efficient fine-tuning). "We use LLMs" is not a moat.
At pre-seed, most investors accept building on foundation models (GPT-4, Claude, Llama) if you have a clear path to differentiation through fine-tuning, data, or application-layer innovation. Building from scratch requires a compelling reason and significantly more capital.
Ship a focused, working product to 10-50 design partners within 3 months of funding. Prioritize a narrow use case where AI performance measurably exceeds alternatives. Early users who can provide feedback loops for model improvement are worth more than broad launches.
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