Tip
Casey Winters talks about data network effects: “Leveraging product usage data to make the product value stronger and stronger over time. Especially with Facebook and Apple’s platform changes, your product being able to generate its own data versus just relying on the big platforms to do all the work for you—that’s a real edge.”
Turns out AI product strategy works the same way.
Your growth team wants to spend heavily on Meta ads and App Store optimization. They’re building the entire acquisition engine on Facebook’s targeting data and Apple’s search algorithms. The performance looks great—CAC is reasonable, attribution is working, dashboards are green.
You’ve watched this movie before. In 2018, you saw companies like Zynga and other Facebook-dependent apps get crushed when Facebook changed its algorithm and cut organic reach by 50%. You watched iOS 14.5’s ATT framework blow up attribution models overnight in 2021. Companies that relied entirely on platform data saw their unit economics crater within a quarter.
Younger growth leaders haven’t lived through enough platform changes to recognize the pattern. They see “Meta ads are working today” and double down. You know better. You’ve seen Facebook change News Feed algorithms 4 times. You’ve seen Google search updates destroy SEO strategies overnight. You’ve seen Apple privacy changes wreck mobile attribution. Every platform eventually closes or monetizes the data advantage.
The companies that survived those transitions (Pinterest, Spotify, Netflix) all had one thing: their own data moats. Usage data that made the product better independent of platform access. Personalized recommendations from user behavior. Targeting models built on first-party engagement data.
That recognition—knowing platform dependencies eventually blow up—comes from watching enough platform shifts over two decades. You can see the risk in the growth dashboards: 80% of acquisition coming from two platforms, zero differentiation in targeting without platform data. That judgment lets you invest in building your own data advantages before the platforms change the rules again.
Context
Casey Winters led growth at Pinterest (built personalized results from product usage data) and Eventbrite (built targeting data for advertising from event attendance patterns). For experienced executives managing AI growth strategy, this isn’t theoretical—you’ve lived through Facebook algorithm changes, iOS 14.5 attribution breakage, Google search updates that killed SEO strategies.
That pattern recognition about platform dependency risk comes from watching enough platform shifts over decades destroy companies that didn’t control their own data.