Founder Playbook · Sub Club by RevenueCat
9 tactics from Phil Carter
The AI Growth Playbook for Subscription Apps
Watch the full episode“AI has created this window of opportunity for consumer app developers to create these magical experiences that can hook users and grab their attention and ideally convert them into trials or even directly paid subscriptions within the first session and sometimes even within the first 30 to 60 seconds.”
Automagical first session: AI lets you deliver a magical onboarding in under 60 seconds
Over 80% of trial starts now happen on day zero — and in some categories nearly 90%. Phil Carter argues AI has opened a brief window where apps can deliver a truly magical first experience that converts users before they leave. The Tolen app demonstrates this: a dynamic onboarding quiz, AI-matched companion personality, and immediate voice chat make it feel like talking to an old friend within the first minute. In a world where vibe-coded copycat apps flood the stores, personality and craftsmanship in that first 60 seconds is the new competitive moat.
“With AI you're not just sending users into a few buckets — you can actually create almost an in of one experience for every individual user based off of their unique responses to these questions.”
Hyperpersonalization: AI turns onboarding quiz data into an N-of-one product experience
Where apps once bucketed users into 3-5 personas, AI now enables genuinely individual experiences. Runna adapts training plans to each runner's race goals and weekly performance; Ladder personalizes progressive overload weights workout by workout. Phil Carter frames this as the key competitive moat in crowded categories: users who feel the product is speaking directly to them are more likely to retain and to pay. He also notes a secondary benefit — onboarding quiz data collected for personalization is invaluable for optimizing paid marketing spend in web-to-app flows.
“The bottleneck is the capacity of a human brain to absorb the most valuable parts of your product experience. As your product gets more bloated and complex sure absolute value goes up but the noise and complexity also goes up which means your value to noise ratio may drastically decline.”
Value-to-noise ratio: the human brain is the bottleneck — prune features to let the best ones breathe
Shipping fast is table stakes in the AI era, but Phil Carter warns that unchecked feature growth destroys the value-to-noise ratio. Adding features competes for the same cognitive bandwidth users have to absorb what makes the product valuable. His prescription: continuously analyze feature engagement and its correlation with long-term subscriber retention, then ruthlessly prune anything not contributing — keeping hero features loud and clear rather than buried under clutter.
“If possible find a way to build an extrinsic trigger into your target users workflow that reminds them when they should be using it. Eventually as the user goes through enough loops and builds a habit it becomes an intrinsic trigger and they don't need the reminder anymore.”
Use extrinsic triggers to build habits before intrinsic ones take hold
Nir Eyal's Hooked framework starts every habit loop with an extrinsic trigger. Carter gives two modern AI-era examples: Granola sends a calendar-linked desktop notification before meetings (helpful in its own right), and Whisper Flow places a subtle widget at the bottom of the screen that trains users to reach for voice-to-text. The goal is to run enough loops that the trigger internalizes. Not every product can build an extrinsic trigger into the workflow — but if possible, the habit formation speed advantage is significant.
“More and more AI products are using hybrid monetization models where you've got a subscription but then you also have to purchase additional AI credits. That leads to an opportunity around incentivizing referral programs where now if I invite a friend or colleague I get additional AI credits.”
PLG loops and AEO are the cheapest acquisition as paid channels get more crowded and expensive
As Meta and Google CPMs rise, product-led growth loops become the highest-leverage acquisition channel. Carter identifies four loop types: personal viral (invite friends), social viral (shareables like Tolen's personality infographic), financial viral (AI credits for referrals), and content-driven (SEO and AEO from crawlable output). AI apps have a structural advantage — their outputs are inherently shareable. He also highlights Reddit as an early AEO tactic: products with a Reddit community show up more often in LLM prompt responses.
“When they launched their new AI powered background feature — not only did they see subscriber conversion rates in the product go up significantly but they also found that by leading with those AI features in ad concepts on Meta and TikTok they got much better performance especially when they put it within the first 6 seconds of the ad.”
AI features in ads unlock new markets — PhotoRoom cracked Mexico, Brazil, and Indonesia this way
PhotoRoom had failed to achieve sustainable paid UA in emerging markets because conversion rates were too low for unit economics to work even at low CPMs. Launching an AI-powered background removal feature changed everything: leading with the AI demo in the first 6 seconds caught attention, drove higher CTR, lifted subscription conversion, and fundamentally unlocked new geographies. Carter's lesson: AI features are marketing hooks as much as product features — the wow-moment that drives organic sharing also dramatically improves paid ad performance.
“We saw a 75% increase in LTV per user through the implementation of this multi-step paywall along with some other pricing and packaging optimizations which has pretty fundamentally altered the full potential size of this business.”
Multi-step paywall drove a 75% LTV lift — freemium is chess while hard paywall is checkers
Carter's biggest win of the year was helping a client shift from a hard paywall to a multi-step paywall: 'this product is free and always will be free, but try the best version for 7 days.' The result was a 75% LTV increase per user. He cautions this is harder to execute than a hard paywall (checkers vs chess) and can backfire — another client saw 50%+ subscriber conversion drop when they tried it. The freemium switch works when strong organic acquisition exists to fill the larger top-of-funnel it requires.
“It used to be the marginal cost of serving a user subscriber for most consumer apps was close to zero. But now with these AI powered products and features that's no longer true because you do have the underlying compute costs to support the AI features.”
AI apps need tiered pricing — compute costs eliminate the zero-marginal-cost software model
The economics of subscription apps changed fundamentally with AI. Duolingo Max, Perplexity Pro, Claude Pro — the pattern is consistent: a free or basic tier without full AI access, and a premium tier for the complete AI-powered experience. Carter argues this structure is necessary for AI apps to protect margins, and that consumers are increasingly receptive because they understand AI compute costs after watching ChatGPT charge $20/month. Bootstrapped and early-stage founders must be especially conscious of this — unlimited AI features at a flat monthly price is often unprofitable.
“If you give away 30 days of free AI feature usage and you have whales racking up massive compute costs without paying you a dime and then they churn — that's not a sustainable business.”
Shorten free trials for AI apps — whales consuming free compute without converting are not sustainable
RevenueCat data shows most apps trending toward shorter trials despite 14-30 day trials outperforming on conversion. Carter believes AI apps are the primary driver — unlimited AI feature access for 30 days is economically untenable at scale. He suspects if you segmented the data by AI vs non-AI apps, the trend would be even more pronounced. Pair shorter trials with strong paywall placement to convert users before they burn through free AI compute budget.