AI social products can grow quickly because conversation creates frequent engagement. The same engagement can also create variable inference and media-generation costs. Investors therefore need a unit-economics model that connects retention and monetization with compute.<\/p>\n
Average revenue per user hides the difference between new, retained and paying users. Model cohorts by acquisition month and track activation, payer conversion and retention over time.<\/p>\n
Subscriptions and credits create revenue, but app-store fees, payment processing, creator revenue share and generation costs reduce contribution margin. Media-heavy products may have very different margins from text-first products.<\/p>\n
Compute cost should be linked to engagement quality. Heavy users may be valuable if they retain and pay, or unprofitable if usage grows faster than monetization.<\/p>\n
Creator-led distribution can reduce paid acquisition cost, but revenue sharing shifts cost into the variable margin. Compare lifetime contribution after creator share rather than treating creator traffic as free.<\/p>\n
Small changes in durable retention can matter more than short-term conversion. AI social products should distinguish novelty-driven usage from recurring relationship behavior.<\/p>\n
Credit systems add accounting complexity. Track purchased credits, redeemed credits, refunds and outstanding balances rather than assuming cash collected equals recognized revenue.<\/p>\n
For each cohort, model acquisition cost, activation, payer conversion, net revenue, generation cost, creator or affiliate share and retained contribution. Then stress-test higher inference prices and lower retention.<\/p>\n