Speaker unverified: uncertainty

Transcript context

…How would you say the search paradigm you you've been in search for many, many years. How do you how would you say the search paradigm you know as as these models came out, what was in your mind about what does this enable for search? What? What? What has been the big difference between the two eras? You know, post LLM and pre LLM? Yeah, I would say, you know one of the things that being in search for a long time, search used to be short. It used to be, you probably don't remember back this far, but you used to type in two or three words to search and then it was longer. And then as there were other modalities of information being pushed to you, they kind of went shorter. Again, I think with large language models, what large language models do when they get a long query is they think, what is the set of queries that's going to help me answer this question? And then they fire off a set of queries and they're all quite long. If you watch Claude or Grok, they, they'll actually tell you in their tool calls. I know not everybody looks into them, but of course I do. If you look at them, they, they're alone. Sometimes you know, they're like 8 words and stuff. And I don't know if you can remember the last time you typed in eight words into a keyword search box, but definitely you'll notice in a large language model, it's almost like a full sentence or sometimes 2 sentences is good because you want to actually describe almost the essay that you want given back to you. So that is that how it has, you know, evolved. To contrast your approach, I think this is like very interesting and maybe the answer ultimately will be both. But when I think about a company like Exa and then your product, in some ways they're similar in that I think they're both kind of designed for AI users, right? The, the Exa paradigm is like you can write a whole paragraph and it's all very sort of semantically oriented, very embedding based. And but, but I've heard, I think I even spoke to will about the idea that, you know, nobody's going to type in a paragraph long query, but the, your AI can, you know, it has time to do that. And then you're taking kind of a different angle on the same thing saying, well, keyword and you can maybe tell us a little bit more about like how to think about how best to use a keyword based search. But it's, it's not semantic. It's it's not doing things like, you know, finding synonyms or, you know, doing like higher abstraction level embedding type matching. But the agent can, as you've said, kind of fire off dozens of these potentially to try to really cast a wide net. How do you think about the the kind of compare and contrast of those approaches? Do you think it will in the end, like all be using one of each at the same time? Or if if one paradigm wins out over the other, like why do you think one will win? What? What are the kind of, you know, drivers that would make one a better bet long term than the other?…

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