AI Isn't Too Expensive — We're Just Pricing It Wrong
summary
Argues current AI billing is broken and proposes outcome-based pricing with predictive cost estimates built from real workload telemetry. Gives numbers: startups spend nearly 70% of growth capital on AI compute, typical GPU requests fall 70–80% short of actual needs after assessment, and 90% reuse of standard models reduces unique compute. Describes using aggregated run logs and public forum complaints to train an AI that predicts per-task cost and warns commodity GPU marketplaces will collapse under a race to the bottom.
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