AI social startups can produce impressive demos quickly, so investors need questions that distinguish product novelty from durable business quality.

1. What does retention look like by cohort?

Ask for new-user, week-one and longer-term retention rather than aggregate activity.

2. What behavior predicts retention?

Look for evidence that memory, creator interaction or other product features drive repeat use.

3. What is cost per retained payer?

Combine acquisition, app-store fees, creator share and generation cost.

4. How does inference cost change with engagement?

Heavy usage can improve revenue or destroy margin depending on monetization.

5. Who owns the identity rights?

Creator likeness, voice and generated media should have documented permissions.

6. How concentrated is creator supply?

A platform dependent on a few personalities may face supply risk.

7. What is the distribution engine?

Separate organic creator distribution from paid acquisition and measure both.

8. What is technically proprietary?

Identify value in memory, orchestration, identity infrastructure, data or distribution rather than assuming the foundation model is the moat.

9. How are safety incidents handled?

Review moderation layers, escalation and auditability.

10. Can the model stack change?

Provider portability reduces dependency and may improve economics.

11. What does the payer mix look like?

Subscriptions, credits and premium media create different revenue quality.

12. What happens if novelty fades?

The strongest answer is a product loop that becomes more useful through identity, memory or creator content.

Pair these questions with our AI social unit-economics framework.

Conclusion

AI social diligence should connect technology to repeat behavior and repeat behavior to contribution margin. That is the path from an impressive demo to a defensible company.