Found an AI-native startup and run it month by month: build the product, set your pricing and model tier, spend on growth, and hire — and learn burn, runway, and SaaS unit economics by living them.
Learn how an AI-native software startup really works — burn and runway, subscription unit economics, why every free user costs inference money, and growth vs. default-alive — by founding one and living the consequences.
Inference on all your active users outweighs revenue — your free tier is bleeding money. Convert more, price higher, or drop to a cheaper tier.
A startup's burn rate is how much cash it loses each month; its runway is how many months of that burn its bank balance can cover before it runs out. Founders live or die by these two numbers, because a company that can't reach profitability or raise more money before the runway ends simply stops. Software-as-a-service (SaaS) startups are judged on unit economics — the per-customer maths. Monthly recurring revenue (MRR) is the predictable subscription income; churn is the share of customers who cancel each month; and the ratio of customer lifetime value (LTV) to the cost of acquiring a customer (CAC) tells you whether growth actually pays for itself. For an AI product there's a twist: every user consumes inference, so compute is a real cost of goods sold — even free users burn money. That forces a constant trade-off between growing fast and staying "default alive" (profitable enough to survive without new funding). Raising money buys runway but dilutes ownership. Sustainable startups balance growth, margins, and runway rather than chasing users at any cost.
You found an AI startup and run it month by month — building the product, setting price and model tier, spending on growth, and hiring — while burn, runway, MRR, and churn respond to your choices. Living the consequences turns abstract unit economics into decisions you feel: overspend on growth and the runway vanishes; ignore inference cost and margins quietly bleed.