What is Census's future role in the data stack, as envisioned by its investors (a16z, Sequoia)?

We started working with a16z fairly early. We knew some of the folks over there, so were having conversations with them back in the early days. From their perspective -- and this was echoed by Sequoia as well -- this was 2018 or 2019, and they were seeing the emergence of the modern data stack as a trend. It's interesting because, for me, this is the first time that I've watched a category creation from the front lines. We were starting to write blog posts about the modern data stack in 2019, but in a lot of ways it was a term that was still relatively new. And the reason we were writing blog posts was to try to codify that a little bit as a term that the industry could group around.

It's an interesting flywheel when I think about category creation now. What we saw happen around the phrase "modern data stack," you could kind of see happen around -- the most recent example in my mind is "serverless." And it's happening now with the term "web3." You have this interesting confluence of practitioners, investors, companies in this space that are all talking about this term, trying to define exactly what it is, writing these thought pieces. To a lesser extent, that also happened with "reverse ETL" as a category over the last year, where VCs have come out with their explanation of, "This is where it is. This is where it fits in the map." We've obviously been telling that story as well.

When we were getting started, there wasn't really any category for reverse ETL. We've already discussed this -- when we were talking with customers, they were looking at us like we were crazy. But we were affixing ourselves to the modern data stack, and Andreessen Horowitz and Sequoia were very interested in this emerging modern data stack. Obviously, they had lots of investments in Databricks and Snowflake, and those were growing like gangbusters. To a certain extent, Fivetran was already there, and in 2018 dbt was just starting to get onto people's radar as well. So they were seeing this as an emerging ecosystem with potentially a lot of different products in the space. I don't think they were necessarily looking at it as a Salesforce disruptor in the way that we talked about it previously. But that was our pitch, when we started to say, "Hey, look, you can use the modern data stack for this."

From their perspective, it was an interesting blend where we were both talking about the modern data stack. They're seeing it as a developing trend and growing. They're seeing the adoption of the base component pieces, and now they're writing the speculative: "Where does it go next? What does this enable in terms of use cases?" And we were coming at them from a use case. We were saying, "Hey, this is how one can leverage a modern data stack." Similarly, I think all of the AI companies were giving a very similar pitch: "You need all of the data. We're going to layer on top of the data. We're going to generate insights and that sort of thing." So our shared vision was around the modern data stack. I think they were looking at it from a perspective of: "This trend is happening. We've invested in the base levels of this infrastructure stack. Where is it going to go from here?"

Reverse ETL ended up being one of those categories that they were interested in, but at the time we were pitching them on, "This should be a component on top of the modern data stack," and that wasn't necessarily part of their vision. Now it very clearly is. We unified through that growth of the modern data stack, and we were able to speak the same language there.

The whole concept of category creation is very fascinating. To watch it play out has been an incredible experience. I'm fascinated in that as a trend, how long that lasts, how long you can continue to ride that wave. How do you propagate it?