The AI hype cycle is peaking. Seed investors want to see real differentiation and traction.
(A ChatGPT wrapper is not a company — show your data flywheel)
AI seed pitch analysis
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Everything you need to know about getting your pitch deck investor-ready
AI seed investors expect: working product with real users, evidence of data moat or model differentiation, customer retention (proves value isn't novelty), improving model performance over time, and clear unit economics (including inference costs). The bar has risen significantly since 2023.
Show proprietary data pipelines, fine-tuned models with domain-specific benchmarks, customer workflows that create data flywheels, and switching costs. The test: if the foundation model you use improves 10x, does your value proposition survive? If yes, you're differentiated.
Key AI seed metrics: model accuracy vs. alternatives, inference cost per query, customer retention (proves lasting value), data volume and growth rate, and user engagement (daily active usage, not just signups). Show a learning curve — the product should get better with more usage.
AI seed rounds are running $2-5M at $15-30M valuations. The premium reflects high talent and compute costs. Investors want to see capital efficiency though — show you can deliver results without $10M in GPU bills. Use-of-funds should detail compute vs. hiring allocation.
You don't need to be profitable on inference at seed, but you need a clear path to it. Show your cost per query and how it trends as you optimize (smaller models, batching, caching). Investors worry about AI startups that lose money on every transaction with no efficiency plan.
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