Patrick Stuart-Constant & Pablo Sánchez - $16M AI CEOs: How to Make $1.3M/Month With AI Apps

Look for apps that look like short-term viral hits but tap a broad, lasting interest (e.g. celebrity lookalike curiosity). Buy them after the viral peak while revenue is falling and sellers don't know how to grow them. Judge on data: can we scale it profitably long-term and 20x revenue, making multiples of the acquisition price. Then run the studio's UA machine plus dozens of A/B tests a month per app to lift ARPU (the LTV/CAC loop).

Combine a top-down market view (structure, willingness to pay, growth) with a bottom-up test: run ads for apps that don't exist yet or that are acquisition targets. Ads are highly standardized so results compare against a large internal benchmark. Then check the target's own ARPU, retention and marketing data.

A/B tested removing the end-of-onboarding paywall so users reach the 'moment of maximal intrigue' (their lookalike result) as fast as possible. Works because the app already had ~10 optimized conversion placements downstream (on seeing the result, on sharing, on reopening). Advice: keep the onboarding paywall (hard or trial) unless there is a clearly better moment later in the app to ask.

Don't treat a losing test as permanently settled: results depend on the surrounding flow, which keeps changing. Periodically retest old assumptions, especially high-lift ones, in the current configuration. The Celebs paywall-removal test failed several times before winning.

Dozens of A/B tests a month per app (~100/month across the portfolio). Focus early on the top of the funnel (first screens, adding/removing onboarding screens), then test all around the app once an app has ~1M users/month. Each test starts at small scale and is expanded if promising. Of ~20 tests: 10 neutral, 5 bad, 3 promising, 2 winners.

Every A/B test gets a stated reason (hypothesis), result and learning in a backlog, never 'it feels better'. Learnings are generalized into strategies (e.g. 'remove onboarding friction when down-funnel conversion is strong') with the 5-7 concrete changes that achieved it, so an agent or new hire can find the equivalent in other apps.

All ads fully AI-made for 2+ years, including screen-recording styles: ~4,000 video ads, hundreds of playable ads and thousands of statics per month. 6-person ads team mostly generates new ideas and prepares material; agentic video editing via Poolday once an angle is nailed; human video editors for one-offs, new concepts and taste; others build playable mini-game ads. Scaling volume required more diverse ideas, so the creative team grew 3x in 6 months.

Treat creative as an infinite search space: spread tests across many new angles, concepts and formats; only when one works, make variations and go deeper there. Don't copy the market leader (they are the leader in most niches). Brute-forcing is impossible, so combine taste, data and process.

AI surfaces videos (TikToks etc.) from completely different industries; creative analysts review them and, when relevant, turn them into ad ideas for the studio's apps, rather than copying the niche leader.

Ad story continues into the funnel: users arriving from an ad get a long question flow (work situation, tools, where they chat, pain points, goals) with answers reflected back and a summary at the end, then 'Get Sam for $1' promo week, then $10/week, straight to Stripe checkout. Answers are actually used to personalize the agent. Direct website visitors skip most questions because they weren't warmed up by an ad. Funnel length and screens are A/B tested.

Instead of an empty chat, show opinionated buttons (helpful tasks, calendar, mailbox, 'let's get to know each other') that send a prepared message and have Sam ask e.g. for a voice note about the user. Goal: learn where they work, who matters, their goals. Next step: prepare ~10 quick-win use cases (birthday reminders, daily price checks) before asking for sensitive access like email.