There is a moment that separates products people use once from products people come back to every day. It is not a feature. It is not a notification. It is the feeling that the product remembers who you are.
I have been thinking about this a lot while building Murror. We spent so much time on acquisition, onboarding funnels, and activation metrics. But the thing that actually moved our retention numbers was something much simpler: continuity.
At the beginning, my reason was very simple: I needed a job and I genuinely liked the product.
I graduated with a Marketing degree, but I never felt like I belonged in agencies or similar environments. It just wasn t for me. At the same time, I didn t have much experience in tech either. So I took a leap of faith and applied for a Customer Support role, almost blindly.
The early days were tough. I had no technical background, no real understanding of how apps were built, and everything felt overwhelming. But the product itself became my motivation. I started from the most basic things: learning simple technical terms, understanding how an app is structured, and slowly exploring how everything works behind the scenes.
China and California both moved to rein in AI companion apps this month and the whole debate hinges on a question most builders never ask: what happens when the user closes your app?
Two weeks ago, on July 15, China rolled out rules specifically aimed at curbing emotional dependence on AI companion bots. Platforms now have to detect distress, intervene in a crisis, cap excessive use, and hand users real control over their data. California, meanwhile, became the first U.S. state to pass legislation regulating companion chatbots, forcing developers to build in actual safety protocols.
What are three things you re grateful for every day? Are they the same, or do they change over time?
For me, the three things I m grateful for most days are:
Having the health to keep working
Having work that I can pursue and grow with
Having a family that cares about me and supports me from behind the scenes
Of course, each day brings different moments, small wins, or reasons to feel grateful. But at the core, it often comes back to the same things: health, work, and family.
Last month I pulled our engagement data expecting to celebrate. Daily active users up. Session length up. Entries per week up. Every growth chart pointed right and up.
Then I read what our most active users were actually writing.
When we first built Murror, our AI would automatically surface emotional patterns after every journal entry. "You've mentioned feeling anxious about work 4 times this week." "Your mood tends to dip on Sunday evenings."
Users loved it in demos. They called it "impressive" and "insightful." Our internal metrics looked great -- pattern detection accuracy was 87%.
Many people have told me that being part of Gen Z comes with advantages we have time, energy, and plenty of opportunities to shape our careers in the AI era. And I do feel lucky to have grown up with technology, to have had early exposure and opportunities to learn and explore it.
But the AI era feels different. The shift is not only new, it s happening at lightning speed. Before I ve even fully adapted to working with AI, we re already seeing waves of layoffs where human roles are being replaced or reshaped by AI systems. And honestly, that creates uncertainty and anxiety not just for me, but for many people around us.
In 2026 a growing share of your customers form their first impression of your product inside a chatbot you can't see and you don't get to write the recommendation.
Distribution used to have a front door you could find. Someone had a problem, they typed it into Google, they scrolled a page of links, and if you'd done your SEO homework or timed a launch right, you were on it. You could see the door. You could optimize the door.
The models got dramatically smarter this year. Your product didn't get more trustworthy and in 2026, those turned out to be two completely different problems.
Here's the line that reframed it for me. Stanford's AI Index this year leans on a phrase from Ethan Mollick the "jagged frontier" to describe how these systems excel and then fall off a cliff with no warning. Their example is almost too good: an AI model can win a gold medal at the International Math Olympiad and still not reliably tell time from a clock face. Genius and incompetence living in the same box, and no obvious seam between them.
