Start an AI Unit-Economics and Subsidy-Runway Advisory

1. 1

   Center the burn-now-monetize-later pattern

   The core problem is quantifiable: agentic queries cost roughly 0.15 to 0.30 dollars each in inference, free-tier heavy users run dozens daily, and analysts estimate only about 6 to 12 months of subsidy runway before pricing must tighten. This burn-now pattern recurs in any AI product where usage cost scales non-linearly with feature sophistication. Build your advisory around this specific, measurable dynamic, because it is concrete enough to model and urgent enough to sell.

2. 2

   Build the unit-economics diagnostic

   Offer a diagnostic that models a client's real per-user and per-query inference costs against their pricing and usage distribution, exposing where heavy users make the free tier structurally unprofitable. This math is what founders avoid and most need. Sell the diagnostic first, because seeing the true burn and runway is what motivates a client to act.

3. 3

   Model the subsidy runway

   Project how long the client can sustain current free-tier economics given burn rate, funding, and usage growth, producing a clear runway before pricing walls must tighten. A dated runway turns a vague worry into a plan. Deliver a concrete timeline, because founders manage against deadlines, not abstractions.

4. 4

   Design sustainable pricing

   Help clients design the path off unsustainable subsidy: usage-based tiers, caps on the most expensive agentic actions, premium pricing for heavy features, and a graceful transition that keeps users. The goal is surviving the pricing-wall moment without collapsing growth. Design the transition, because the value is not just the diagnosis but a workable route to sustainable economics.

5. 5

   Generalize across subsidized AI products

   The same non-linear-cost, subsidy-runway dynamic applies to any VC-subsidized AI consumer product, so productize your models and sell across the category, not just browsers. Benchmarking against the pattern is a repeatable service. Be honest that you advise on hard trade-offs with no painless answer; there is no income promise, and your value is helping clients confront the math early enough to act on it.

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Best for: Finance-and-pricing strategists who understand AI inference cost structures

Not for: Anyone who wants clients comfortable ignoring their economics, or who only diagnoses without designing sustainable pricing. The value is confronting the burn math before the subsidy window closes.