AI changes everything about moats

You want to build something that the most general model will not replace you. And oftentimes you build something and you make it really, really good for specific set of users and actually the mode is now in your user feedback. The mode is in whether you listen to them, whether you can rapidly iterate.

I think it's often less about moats and more about stickiness. And what I mean by that is moats are in some sense, typically what folks mean by moats is something that means that a competitor couldn't even enter the market. I think what does exist is stickiness, which I would kind of define as once you have a product experience that you really like, are you excited to keep using that experience.

I think the ceiling is so high that no matter what entrenchment you build, you can be leapfrogged. And I think that this resembles markets that are maybe a little bit different from normal software markets.

Part of what we had to our advantage also was that we had this giant base 30,000 paying customers, hundreds of thousands of active users, millions of their users, billions of data points. So, we had a lot to play with.

Point solutions don't have enough data in the age of AI to be useful. You got to be able to provide the AI with a lot of context about a lot of data so it can do things. It can do joins. It can do correlations.

People are making dashboards on this, and I think that's incredible. I think the products that are doing this, they have a very sharp sense of how well their application is performing, and people don't talk about it, because this is their moat.

People are not going to go and share all of these things, because it makes sense. If you are an email-writing assistant, and you're doing this and you're doing it well, you don't want somebody else to go and build an email-writing assistant and then get you out of business.