Last quarter, we built a feature at Murror that our engineering team jokingly called "the empty room." After a user finishes a journal entry, the AI doesn't immediately respond. It waits. For 30 seconds, the screen shows nothing but the user's own words and a gentle prompt: "Sit with what you just wrote."
No analysis. No reframe. No pattern recognition. Just silence.
When your AI does the work, you can't charge per seat anymore but the fashionable fix, charging per outcome, only works if your outcome is something you can count. Here's how I think about pricing when the thing you sell is subjective.
There's a pricing question sitting under every AI product right now, and most makers are answering it by copying whoever's biggest in their category. That's how you end up mispriced.
When we first built Murror's reflection AI, we optimized for insight. Every journal entry got a thoughtful, confident analysis. Pattern recognition, emotional connections, suggestions for growth.
Models shipped million-token windows this year. Then the research showed they quietly get worse the more you feed them and that flips how you build.
There's a move almost every maker makes the first time they build something on top of an AI model, and I made it too. The window is huge now, so you fill it. All the docs. The whole conversation history. Every tool you might conceivably need. The reasoning goes: the model is smart, more information can only help it, so give it everything and let it sort things out.
High-risk got pushed to December 2027 and the whole timeline read as "delayed." Article 50 didn't move. It applied on 2 August, it catches you if an EU user sees your output, and the ceiling is 15m or 3% of worldwide turnover.
Two and a half weeks ago a rule started applying to most people reading this, and the coverage around it said the opposite.
AI apps convert trials 52% better and churn annual subscriptions 30% faster. The flattering metric lands in week one. The honest one lands in month twelve and by then you've already spent money on the gap.
Here's a pairing I can't stop thinking about, and both halves come from the same dataset.
Scroll the leaderboard right now and you can predict the next launch before you see it. It's an AI coworker. It lives in your Slack. It answers for you, drafts for you, books for you, follows up for you. The pitch is always the same: do more, automatically, so you don't have to.
I've been building long enough to feel the pull of that pitch. "Indispensable" is a comforting word. It sounds like a moat. It fundraises well.
When someone journals on Murror, our AI doesn't just process text. It reads emotional weight. It picks up on patterns the user might not see yet -- the way their language shifts when they talk about work versus family, the recurring themes they circle back to every few weeks, the gradual change in tone that might signal something deeper.
We built this because it makes the product better. The AI can ask more relevant questions, create more meaningful reflections, and know when to give space versus when to gently prompt.
Most product teams track acquisition, activation, retention. The usual funnel. We track all of that at Murror too.
But there's one metric we started paying attention to that changed how we think about growth entirely: how often users talk about themselves differently after using the product.
