You’re Not Posting On Platforms Anymore - You’re Operating Inside Them
We still talk about “platforms” like they’re neutral public squares. They’re not. They’re privately run environments with rules, tolls, and physics that shape what’s seen, shared, and sold. Recommender systems sit between you and your audience; native features keep attention in-app; and policy shifts can quietly throttle links or reprice access to data and distribution. The bigger picture: we’re sinking into vertically integrated ecosystems where content, commerce, and community are enclosed-less web, more walled garden. What’s actually changing isn’t abstract. We’re seeing more incentives to keep activity on-platform (in-app video, shops, messaging), tighter control over third-party data pipes, and ongoing tweaks that reward native behaviors over outbound clicks.
What this means for creators and brands is operational, not philosophical. Treat each environment as its own market with distinct inputs (format, cadence, signals) and outputs (reach, watch time, saves). Optimize inside the walls-design for in-feed completion, shares, and conversations-while building leverage outside them via owned channels, first-party data, and portable audience graphs. Worth noting for brands: the deeper you integrate into in-app checkout and customer service, the more you trade control for convenience; hedge with email/SMS, community hubs you manage, and content archives that are easy to redeploy. Build “change tolerance” into the plan: diversify channels, maintain creative variants that can be retuned as signals shift, and set measurement that doesn’t rely on a single platform’s analytics. The key takeaway here: stop assuming public utility; assume vendor relationship. Negotiate accordingly-budget for volatility, document brand safety guardrails, and use platform-native objectives when you must, but keep a path to your own endpoints. The bigger picture isn’t doom; it’s discipline. Operate effectively inside the ecosystem-and keep investing in the parts of your business that exist after the algorithm changes.