Shawn Wang: belief

Transcript context

…I do, given that they can't use Snowflake. If the variant type were exposed in a way that allowed more people to use it, and by the way, I'm just sort of zeroing in on Snowflake in this case. Redshift has something called Super, which is fairly similar. Clickhouse is also working on something similar, and that might actually be the thing that lets more people use it. DuckDB does not. It has a struct type, which is dynamically constructed, but it has all the downsides of traditional structured data types. For example, if you infer a bunch of rows with the struct type, and then you present the n plus first row, and it doesn't have the same schema as the first n rows, then you need to change the schema for all the preceding rows, which is the main problem that the variant type solves. It's possible that on the extreme end, there's something specific to what Honeycomb does that wouldn't directly map to the variant type. And I don't know enough about Honeycomb, and I think they're a fantastic company, so I don't mean to pick on them or anything, but I would just imagine that if one were starting the next Honeycomb, and the variant type were available in a way that they could consume, it might accelerate them dramatically or even be the terminal solution. I think being so early in single store also taught you, among all these engineering lessons, you also learned a lot of business lessons that you took with you into Impira. And Impira, that was your first, maybe, I don't know if it's your exact first experience, but your first AI company. Yeah, it was. Tell the story. There's a bunch of things I learned and a bunch of things I didn't learn. The idea behind Impira originally was I saw when AlexNet came out that you were suddenly able to do things with data that you could never do before. And I think I was way too early into this observation. When I started Impira, the idea was what if we make using unstructured data as easy as it is to use structured data? And maybe ML models are the glue that enables that. And I think deep learning presented the opportunity to do that because you could just kind of throw data at the problem. Now in practice, it turns out that pre-LLMs, I think the models were not powerful enough. And more importantly, people didn't have the ability to capture enough data to make them work well enough for a lot of use cases. So it was tough. However, that was the original idea. And I think some of the things I learned were how to work with really great companies. We worked with a number of top financial services companies. We worked with public enterprises. And there's a lot of nuance and sophistication that goes into making that successful. I'll tell you the things I didn't learn though, which I learned the hard way. So one of them is when I was the VP of engineering, I would go into sales meetings and the customer would be super excited to talk to me. And I was like, oh my god, I must be the best salesperson ever. And after I finished the meeting, the sales people would just be like, yeah, okay, you know what, it looks like the technical POC succeeded and we're going to deal with some stuff. It might take some time, but they'll probably be a customer. And then I didn't do anything. And a few weeks later or a few months later, they were a customer.…

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