Tip
Casey Winters talks about consumer subscription businesses: “You need higher user-based retention than B2B SaaS businesses with more unpredictable users. We’re talking annual retention that needs to be north of 60, perhaps even 70%. You look at who’s actually been able to do that at scale, and it’s a really small list. Netflix in the U.S., Amazon Prime, Spotify, Duolingo. Just look at Blue Apron. The company raised $300 million in an IPO that valued it at $2 billion. It’s worth $50 million today.”
Turns out AI consumer subscription strategy works the same way.
Your team wants to launch an AI writing assistant with a freemium model—free tier, $10/month pro tier, paid acquisition to drive signups. Finance models it out: 30% free-to-paid conversion, 50% annual retention, $15 CAC. Looks profitable at scale. Board loves it. “Grammarly makes it work. We can too.”
You’ve seen this math before. In 2015, every meal kit startup ran the same model. Blue Apron IPO’d at $2 billion valuation with beautiful cohort economics. Two years later, worth $50 million. The math breaks: 50% annual retention means you lose half your users every year. After 3 years, you’ve churned 87.5% of your original cohort. You’re constantly rebuilding the user base. Meanwhile, paid acquisition gets more expensive as you exhaust the best targets.
Younger product leaders see Grammarly and Duolingo and think “consumer subscription works.” They model the happy path. You know better. You’ve watched enough consumer subscription businesses over two decades to know the survivors (Netflix, Spotify, Amazon Prime, Duolingo) all have one thing in common: either massive economies of scale spending billions on content, or genuine network effects that make the product better with usage. Grammarly has data network effects—writing corrections improve with usage data. Duolingo has the same.
Your AI writing assistant just using GPT-4 API? No network effect. No economies of scale. Same problem every other consumer AI subscription will hit: retention math doesn’t work. You’ll model out exactly when you run out of money. It’s that predictable.
This judgment—knowing which consumer subscription models have structural problems—comes from watching the full lifecycle repeatedly. You saw meal kits burn billions learning that lesson. You saw fitness apps, meditation apps, recipe apps all hit the same wall. The pattern is clear: without network effects or massive scale advantages, consumer subscriptions churn themselves to death. That pattern recognition lets you pivot the strategy before raising millions on broken unit economics.
Context
Casey Winters led growth at consumer-facing companies (Pinterest, Grubhub, Eventbrite) and watched the consumer subscription wave from 2014-2020. He saw Blue Apron go from $2B valuation to $50M, watched countless subscription apps fail, and identified the pattern: only companies with network effects (Duolingo’s data effects, Beek’s creator distribution) or massive scale (Netflix, Amazon, Spotify) survive the retention math.
For experienced executives evaluating AI consumer product strategy, this pattern recognition is critical—you’ve watched enough consumer subscription businesses fail over decades to recognize the structural problems before millions get burned. That comes from seeing the full arc repeatedly.