Nathan Labenz: preference

Transcript context

…Models like QAN are incredibly powerful, and if they'd like to start with models like that and then fine-tune it, you can get very capable models. But you also have other alternatives. So it really depends on the internal policies of these customers to decide which route to go. I'd say It's such a competitive space that I don't think there is one model that dominates it all, whether that is in proprietary world or open source world. So there are a lot of choices out there. Gotcha. So on these partnerships, we've got announcements recently of partnerships with Anthropic and also with Google for the Gemini models. I believe there's also one, although I think it was not so recently announced with OpenAI. I didn't see, I didn't catch anything with respect to XAI and Grok. Providing all the latest and greatest stuff to customers is at the heart of that strategy. But tell me more about kind of some of the nuances of the partnership. Is there a XAI relationship? If not, why not? And does it have anything to do with them putting women in bikinis all over the place? And then I definitely want to get into how are we bringing these models to data? Because that is a bit of a narrative violation relative to what you typically hear is, We can't use that because we'd have to send the data to them and we're not comfortable with that. So I'm very interested in unpacking how you are reversing that and bringing the models to the data on the Snowflake platform. Yeah, absolutely. Actually, let me start there because that's incredibly important for us. When we started the journey two and a half years ago or so, we heard loud and clear that our customers do not want to move their data out of the Snowflake security boundary. Instead, AI needs to come next to data, and that gives them a lot of advantages. You can just respect all of the security that you've established. You respect a lot of the governance on the data so that you're not replicating this data. The attack vectors shrink in terms of securing all of this information. So what we have done is, thanks to our relationships, we've built, we are bringing, we're bringing essentially inference to run inside the Snowflake security boundary. So that's accomplished through these partnerships, through the connections, as well as a lot of the legal guarantees around the data. So essentially these models become sub-processors. There is no state that's saved in any of these models. So that's super helpful for our customers who are very sensitive, many of them in regulated industries. So when they're using any of these models, they know that the data still stays inside the Snowflake security boundary.…

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