Robby Stein

AI doesn't just replace existing search behavior; it expands the total volume of queries by enabling users to fulfill deeper curiosity.

"AI is expansionary. There's actually just more and more questions being asked and curiosity that can be fulfilled now with AI."

Effective AI strategy involves specializing models for specific domains (like information retrieval) rather than just general-purpose chat.

"We wanted to be the best at informational needs, that's what's Google's all about, and so how does it find information? How does it know if information is right? How does it check its work? These are..."

The interface for steering AI is shifting from complex prompt engineering and fine-tuning to natural language instructions and tool-use.

"It used to be even just months back that you had to do a lot of work to get the AI to do the thing you're trying to get it to do... increasingly, you can just use language. Almost if you were to write..."

Product-market fit is evidenced by a retention curve that flattens over time rather than decaying to zero.

"You might look at something like a J-curve. This is the retention, the percentage of people still using the product day seven, day 30, day 90, and does it flatten or do people just drip out of there?"

Clear, descriptive naming is often superior to 'clever' or abstract branding because it reduces cognitive load for the user.

"AI Mode as the name is such a good example of clarity. What is this? This is AI Mode... we could call it something random, but then what is that? And now you're working against yourself."

Teams must recognize when a product has reached the plateau of its S-curve to shift resources toward new growth drivers.

"You get to these points of just diminishing marginal return in every system where it feels like you could put 50 people on this project, it's just not going to dramatically move the needle... You then..."

Successful products adopt successful industry formats (like Feeds or Stories) and adapt them to their specific user expectations.

"Not every great thing is going to be invented by you... At the end of the day, you're just robbing your user base of the opportunity to have a better product if you're not making the best possible pro..."

Deeply understanding the 'causation'—the specific reason a user 'hires' a product—is the foundation of building successful features.

"I think a lot about this job to be done framework... you have to really be a student of causation. Why is someone using this product? What are they doing with it and what are they trying to get done w..."

Identifying a common psychological barrier across user feedback can lead to the creation of major new features like 'Close Friends'.

"When we asked people to understand people like, 'Why aren't you posting to your story? What's preventing you from doing it?'... It was like this commonality was audience problem. Someone had an issue..."

A staged rollout—from a small internal team to trusted testers to a 'Labs' environment—allows for iterative improvement based on real data.

"We basically got, I mean, this was probably five to 10 people worth of people originally... We had that for a while, and then we got to a point where it was feeling good, the trusted testers were liki..."

Early-stage success often requires extreme scrappiness and immediate action to secure high-leverage partnerships.

"My co-founder and I just basically got the contact of Scooter Braun... we literally went immediately to the airport. I just remember just basically going straight to the airport, flying to LA meeting..."