12 AI Due Diligence Questions Every VC Team Should Ask
A practical question set to pressure-test model quality, product reliability, and cost assumptions before investment.
Focus keyword
ai due diligence questions for vc
Intent: informational
Start with measurable outcomes
Ask which production metrics matter most and how they are measured over time. Teams should connect model performance directly to customer and revenue outcomes.
If impact cannot be measured clearly, claims are difficult to trust.
Test assumptions behind the model
Probe data freshness, label quality, and drift monitoring. Strong teams can explain where the model fails and how fast they recover.
Transparency around failure modes is usually a positive signal.
Validate scaling economics
Ask for expected cost per inference at current and projected load. Unit economics should improve with optimization, not deteriorate with growth.
The best diligence questions connect technical architecture to margin durability.
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