Pod Insights Big Technology Podcast Google’s Weird Year + Neeva Goes to Snowflake — With Sridhar Ramaswamy About this episode Sridhar Ramaswamy is the co-founder of Neeva, SVP at Snowflake and former SVP at Google. He joins Big Technology Podcast to reflect on the strange year Google’s had in 2023, working on the fly to reimagine search and ship faster than it was initially comfortable with. In this episode, Ramaswamy delivers deep insights on the future of search, generative AI, and how his former employer will adapt in these times. Stay tuned for the second half where Ramaswamy candidly discusses his search competitor Neeva, why he sold it to Snowflake, and what the two companies hope to accomplish together.
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0:00 Alex Kantrowitz? A longtime Google executive, entrepreneur and generative AI builder discusses how the technology is changing search in the business world and what's next for his old employer. All that coming up right after this.
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1:22 Alex Kantrowitz? Welcome to Big Technology Podcast, a show for cool-headed, nuanced conversation of the tech world and beyond. Joining us today is Sridhar Ramaswamy. He's a friend of the program. He's the co-founder of Neeva. He's an SVP at Snowflake currently, as he puts it, a minister without a portfolio. We'll get into it. We're going to talk a lot about what's going on inside Google and the search world, and then also how generative AI might be applied in the business world in ways that might not have been covered in depth up until this point. We'll get into it. Sridhar, welcome to the show. Great to see you. Thank you, Alex. Good to be back. Great to have you back. So let's start with Google. It's been a pretty weird year for Google. I would say this is the weirdest year for Google since 2011, when they introduced Google Plus. I'm curious if you would agree with that assessment. And like, how would
2:07 Alex Kantrowitz? you really rate? I mean, we're almost, you know, we're a little bit past the halfway now. How would you rate the 2023 for the company? Pretty turbulent, right?
2:15 Speaker 01 It is pretty turbulent. I think at one level, it exposed fairly deep gaps in the ability of Google leadership, both to visualize the future, but also execute towards it. It's very clear that the crazy popularity of ChatGPT and the speed at which others, including Neva, rolled out pretty credible AI-powered products, I think, was pretty surprising for them. It really has led to a bunch of soul searching and team alignments.
2:58 Speaker 01 You know, things like Demis being now the overall head of AI and stuff like that.
3:04 Speaker 01 And, you know, so the company has reacted. And it's actually been interesting to then watch the product developments coming out. I don't know how closely you follow Bard, but I've been following it for a while. There's now a pretty credible integration into a regular search. And Bard is getting better by the day. And this combined with the fact that, you know, Sydney Bing's chatbot has not made as much progress. I would say actually puts them in a better position in the middle of the year than early this year, when it looked like they were truly caught flat-footed.
3:47 Alex Kantrowitz? Yes, I want to pick up on that part and what you said, that they had an inability to visualize the future. I mean, it is interesting because for the past few years, we've been hearing completely about how search was going conversational, how people want to talk to search. The Google Assistant was effectively the right product for this moment, just the wrong execution. What happened there?
4:08 Speaker 01 Well, so that's actually four or five years ago, which was the previous craze around voice search and chatbots and stuff like that. Remember, this was the time of Alexa and all the devices that Amazon was rolling out. I mean, I was still very much a part of Google at the time. And all of us feared that voice search would be the new platform, that these devices sitting everywhere would be the replacement for search. And Google actually put a multi-thousand-person team to work on this, both in search, but also within my team.
4:52 Speaker 01 The shopping team, for example, had an assistant. We had partnerships with companies like Walmart. We took it seriously, but here's the important but. That technology was pretty much a previous generation. It was not based on transformers. It was not based on the rapid advances that have happened in AI. And in many ways, were kind of strung together in ways that limited what it could do. So those were, those were pretty early. And all of us, as consumers, discovered that beyond a select few use cases, like,
5:36 Speaker 01 you know, hey, Google, what time is it? Or what's the weather today like? Or play this song for me. We came to understand that those devices were very, very limited in what they could do. And so your Alexa became a fairly expensive remote control for Spotify.
5:56 Speaker 01 But, and in all of these, course search did not-
5:59 Alex Kantrowitz? But first you'll have to unlock your device. Even worse, podcast guest, uninvited.
6:06 Speaker 01 But the thing that has, the thing that did not change is that Google thinks like the assistant that came along, they were veneers on top of search. They did not change search in a core fashion, meaning that you retrieved some sites and, you know, there were some sets of things that you would directly take actions on, but it was very limited. The power of generative AI now, I think, comes from the fact that you can understand multiple pages and you can write a fluid answer for way more queries than what the assistant could ever do. Remember, with the assistants, whether it's on Alexa or on Google, pretty much if you ask it a
6:48 Speaker 01 complicated question, it'll quickly get into according to so and so's site, blah, blah, blah, which is not quite the same as here is a three sentence summary that truly captures the gist of what it is that you're looking for.
7:02 Alex Kantrowitz? Yeah, and it's interesting because you actually built a product in Neeva that is generative search.
7:08 Speaker 01 That's right. That's right. That's right.
7:10 Alex Kantrowitz? You had this experience, it's a really fascinating experience, getting to see what comes in through the backend. I'm just gonna quote something that you said. You said, "The thing that surprised me about chat was how much it has dramatically expanded the pool of queries and questions people pose." As you likely know from Pi, which is another bot, people will type things into a chatbot that they will never dream of typing into a search engine. So, I mean, tell us a little bit about what you saw on the other side. Like, what do people type into these bots? And then how does that, is it even search at that point? Like, how does it change what we, what we see?
7:44 Speaker 01 I think it's a very different product, and I think it's fascinating to watch Pi, to watch character.ai and all of these people create products that are very different from search. And even in the context of search, the kind of questions that you would ask of it have changed in a big way. The one example that I'd like to, you know, give people, but there are many such examples, is Jason Calacanis, as you know, runs like another podcast.
8:14 Alex Kantrowitz? Haven't heard of it.
8:15 Speaker 01 And the question that he asked Neeva was, "Hey, how are the Knicks doing?"
8:21 Speaker 01 And he was, this was early this year, and he was offended that we gave him a summary of articles from like late December, because that was the best that the search engine could find in terms of how the Knicks were doing. Obviously, the season had changed. Once you get used to the idea that you can just say things, I think the set of questions that you can ask dramatically change. You will ask a lot more subjective questions. Remember, at the end of the day, like, the search engines of today are quite limited. If you ask it a deep, complicated question, you get a bunch of like gobbledygook pages.
9:03 Alex Kantrowitz? Right.
9:03 Speaker 01 And so I think that is, that don't, don't really have a whole lot to do with the question that you asked. And so I think we ask a lot more subjective questions. What do you think this article says? This is something I try with Bard because it has access to real-time data. I will put in a link and say, hey, can you summarize this link for me? Or how is this opinion different from that other person's opinion? So I think the class of problems that we expect chatbots to solve, simply by virtue of the fact that they accept full, full-text English or every language, really, I think can dramatically expand the scope of what it is that we ask them to do. Of course, there's a big gap between, you
9:48 Speaker 01 know, what they, what they do currently and what our expectations are. But nevertheless, I think our expectations are just much higher. And this is purely in the give-me-information-that-exists-in-the-world kind of mode. But I think what Pi and Character.ai are showing is the ability for these things to have like conversations. You know, they don't really have things like long-term memory, like there's a bunch of technical gimmicks that people can use to have these bots pretend like they have long-term state. So there's a lot of technology to be, to be built. But open-ended, freewheeling conversations about your feelings, about your emotions, about what you should do. I think like this, this field is just opening up.
10:32 Alex Kantrowitz? And so you were, you had access to the backend there, right? You were, what, you were on this, you're running the search engine?
10:39 Speaker 01 That's right.
10:39 Alex Kantrowitz? So were people, I mean, was that type of, you know, how are the Knicks doing is kind of like, okay, it's a no, I would ask that to Google today. I mean, maybe I would just type Knicks in. But so where does that expand? Like the range of things that people will type into a search engine? Like, were they actually like confessing their feelings in the chat window? Like, what did you see that surprised you?
10:58 Speaker 01 Um, we don't look at, um, uh, at, at sort of individual queries. That's one of the no-nos of any, um, search engine. Uh, and, uh, um, and so like, we would do things like analysis on the length of queries, what sort of quality that, uh, that, that we would serve. But as I said, comparison queries increased a lot. Nuanced questions about how things were working would, um, would, would also increase quite a bit.
11:28 Alex Kantrowitz? Like, like how things are working in terms of like how systems work?
11:33 Speaker 01 Um, how systems work, what, what, what is your opinion of what so-and-so did yesterday?
11:37 Alex Kantrowitz? Right.
11:38 Speaker 01 It's just, you know, it's, it's, these are very different questions, not necess, you know, as I said, we would just not think of putting them into a search engine. And I would almost say that in a search engine, Alex is very likely to type, you know, uh, Knicks standings.
11:54 Alex Kantrowitz? Right.
11:54 Speaker 01 You're not likely to type like, hey, are the Knicks having a great season? Um, how are the Knicks doing? What has their performance been recently? Or what will it take for them to make the playoffs? These all come naturally in the context of a chat bot that we somehow think is omniscient, but are not things that we will type into a search engine.
12:15 Alex Kantrowitz? Did you worry a little bit about how much people trusted the responses that Neva was given?
12:20 Speaker 01 100%.
12:21 Alex Kantrowitz? Talk a little bit more about that.
12:23 Speaker 01 Yeah, we are, we, um, and I think this is one of the, um, like societal problems that, like, uh, are going to be pretty tough for the next 10, 20 years. It took, what's the right way to put it? We were very clear that Neva represented what was on the internet. They were like, hey, listen, we are not God. Many of these things, we just don't know. But what we are good at is finding out pages, ideally trustworthy pages, and summarizing them for you. If you ask a question for which there are only conspiracy theories, those are the pages we will find and we will summarize them for you. And this is why we were very personicky about making sure
13:08 Speaker 01 that every sentence that we provided came with a citation. So you could see whether it came from, you know, New York Times or the Wall Street Journal, or whether it came from a conspiracy site. So for example, you know, Neeva would provide an AI answer to questions like, what are Hitler's good qualities? Because there are some sites that say like, okay, here are this person's good qualities. He'd be like, according to so and so. But the thing that still shocked me was how much there was a tendency to look at those three sentences and say, okay, done, I'm good.
13:43 Alex Kantrowitz? People trusted it.
13:45 Speaker 01 I'm gonna trust it. This is the same problem that people have had with Facebook, which is the tendency to trust something that's like on your phone that looks kind of authentic is very, very real. Similarly, any text that is generated by a chatbot is, and this is part of the reason why Google has been hesitant, is if they put up some text, even if there's a citation, people are going to say, oh, Google said so, this must be true. And I think that, you know, this, this sort of critical thinking that one needs in order to figure out when is a chatbot representing some site? And is the site trustworthy?
14:27 Speaker 01 When is a chatbot generating an answer or an opinion that we really should be careful about? And now, beyond the realm of chatbots, you basically cannot trust any content that's on a page, because that could be an AI model that is spewing it out and somebody doing SEO to get traffic. I think, and then it goes on and on. Like, we can't trust what we hear anymore, because people can replicate voice. People can soon make videos of everybody and everything. I think like our notion of reality is going to be subject to such a barrage of like fake and real signals that I think it's going to be a real problem for us to keep our heads straight.
15:11 Speaker 01 And by the way, the way search engines would deal with things like that is, you know, Google had this system that was roughly called flight to quality. Whenever there would be a new un-trending topic, the search algorithm would basically say, I'm going to go to trustworthy sites because if it is a brand new topic, the likelihood that someone spots a conspiracy theory about that is super high. So we all need mechanisms like that for us to really figure out what is it to be trust.
15:39 Alex Kantrowitz? So how does this change like what the nature of search is? Because you can build, you can, you can build like something purpose-built, like a character AI, right? Where you can like chat with Thomas Jefferson, or I can go into like Bing and say, pretend you're Thomas Jefferson or a bard and have that conversation. So does search now, I mean, it seems like the use cases blend where search becomes part of conversation partner and part a discovery engine. And these discovery engine or these, these conversation partners, like a character AI, which lets you chat with historical figures, you can ask it what the weather is today and it should have some sort of discussion. So how does this, how does search evolve from this moment?
16:14 Speaker 01 Well, I think we are living in a grand experiment. I don't think anyone knows. I think there are several things that are happening at the same time. First, as you point out, the, you can chat with a lot of chatbots. They have access to a certain amount of, let's call it like real information. And so we are going to type things into these chatbots and expect answers that are backed by authority. It'll make it much easier for us to get information from like honestly completely untrustworthy sites. But there's also a second order thing that is going on. People are understanding that there is a lot of traffic or go money to be made from generating pages and feeding them into these search engines.
16:59 Speaker 01 We worried about it. We did some experiments at Neeva on can you detect content that's generated by AI? But that's already. Happening. And I'm sure you've seen articles that have come out recently that talk about, you know, there's a if these language models learn on content that other language models have generated, beyond a point, they just generate trash. So I think there is that real time experiment going on around new content that is being generated, which is of course going to be reabsorbed back into these language models is going to be indexed by search engines.
17:38 Alex Kantrowitz? Right, like the AI eating itself.
17:39 Speaker 01 Yeah, so it's like the AI eating itself. So I don't think anyone, you know, these are long, powerful cycles with millions, if not hundreds of millions of people all actively trying to game it. I don't hazard to pretend that I know exactly what the outcome is going to be.
17:59 Alex Kantrowitz? Exactly. And let's say we stick with like what search is in general, right? Like search, you know, it, it, let's say we stick with Google and there's a generative layer on it. It still changes, right? Like even if you're not using it for these like, what is the meaning of life? Questions. Now what happens? I'm sure you're in the generative AI lab, right? You write a question and it thinks for a second and then your entire window is content that's generated from Google. So it goes from like a tool that you use to explore the web to effectively the entire answer. You know, to help you find answers, now it becomes the answer.
18:33 Speaker 01 Yep.
18:33 Alex Kantrowitz? So I'm curious like what you think that means for search. And I know some of these are unanswerable, but I'm going to keep firing at you. What do you think it means for search?
18:41 Speaker 01 I mean, you, let's face it, if that is the format that we want, and you know, again, from a personal experience, I just much preferred a four line summary that told me what I wanted. It was just fine, ninety five, ninety eight percent of the time. There was no reason to click on anything and go elsewhere. So this entity that's been one of the main sources of traffic to all of our sites, your site, whatever site I know, Neeva created, I created, it is just going to behave very, very differently. Of course, there are going to be second order effects. Like a Reddit saying, wait, wait, wait, you don't get to do this.
19:26 Alex Kantrowitz? Right.
19:27 Speaker 01 You don't get to take my content and use that to generate answers. But there is a further cascade from there. A bunch of Reddit moderators are going, wait, wait, wait, I don't understand how you make money off of content that we, that, that we are going to create. So, you know, we might yet come to a place where content creators, essentially for their own survival, have to essentially like collapse together. And so there might very much be a consolidation when it comes to content creation. Just like out of the, you know, all information should be free and the web is free.
20:09 Speaker 01 Let's face it, the sort of two credible pure information businesses, newspapers that have come out of that are the New York Times and the Wall Street Journal. And everybody else is a little bit off and also ran. So I think those kinds of consolidation effects are most definitely possible. And I think there's a technology opportunity which we explored towards the end of Neeva, which is any content creator, especially if they are part of this conglomerate type organization, is basically going to work as hard as they can to keep everybody that came to them. Meaning that chatbots are going to be the norm for how information is discovered on
20:52 Speaker 01 a, on a site, once you get that person to come to that site. And part of the fun of technology like this is on your site, for example, to be able to say, hey, you can talk to any of the podcasts that I have put up here, just ask a question and we will fish out the right segment for you. So I think there are all this, there's going to be like this cascade of actions that are happening both at the center of Google, but also towards the periphery where content is being created.
21:26 Alex Kantrowitz? And there's an impact for Google's advertising business as well. I mean, you ran ads at Google for a number of years, right? When the content all of a sudden comes down, takes up the whole browser window and doesn't have you go on a fishing expedition for the website you're trying to find, A, there's less room for ads and B, you're not, you're not going to click on those blue links as often as you would have otherwise. What happens there?
21:49 Speaker 01 I think there's more opportunity coming there. I think, you know, it would not surprise me if, you know, like the advertising arm of Google essentially comes up with a chatbot for how you should get your your local plumber or something. And maybe that becomes an entirely paid experience. Now, Google's already gone back and forth. Google used to have organic shopping. I famously made combined organic shopping and paid shopping because I was like, all of this is commercial content. I can't have four search engines on one search page with organic shopping, paid shopping, paid text advertising and organic search.
22:33 Speaker 01 So I think you will see business model innovation. I think part of the exciting technology that is being developed by lots of people. This is something we are looking into from enterprise use cases as well is essentially API calling, driving tools. So I think you're going to see experiences where you can, again, chat with a website and be able to drive purchases off of it. This was the kind of thing that was really hard with the previous generation of voice technology. There is hope that the technology has gotten significantly better so that shopping becomes easier. I don't know about you, but I find shopping on the web to be incredibly difficult.
23:16 Speaker 01 If it is not like the twenty items that I keep buying from Amazon over and over again. Anything that is like meaningfully complicated is actually really, really hard to find on the web. And we have also gone to you shall talk to no one. So most of the time I'm just like lost trying to figure out what to do. So I think there's a lot of business model innovation to come as well. And Sydney, as you likely know, is also experimenting with things like, you know, sponsored sentences. I don't know what to call them.
23:47 Alex Kantrowitz? Very strange. It feels weird even to say it.
23:50 Speaker 01 Yeah, and there's a part of me that like, you know, my heart sinks when I see stuff like that. I'm like, this is an assault on my reality.
23:58 Alex Kantrowitz? Yeah.
23:58 Speaker 01 So I think there's lots to come here. This is part of the reason why Google's kind of slow. I think in an ideal case, you know, the way to deal with this is to say, Alex, for all your informational queries, we have the perfect AI answer for you. But the minute you type best headphones, my man, we're going to show you a bunch of links and you're going to click and you're going to give us money.
24:18 Alex Kantrowitz? Yeah. So what about the competitive side of this thing? So Google, you mentioned earlier, right? They developed the transformer model, which has basically sprung a lot of this innovation. And that was a model that they they put the paper out. They open sourced a lot of this technology. Was that a mistake? I would just think that you put the mode up, right? And say, all right, we have this technology. This is probably going to change. I mean, it's conversational, it's probably going to change the way that we operate. I'm keeping it.
24:46 Speaker 01 Remember, six years ago was not clear that this technology was going to be transformation. And at that time, the currency for a lot of researchers was the ability to publish. If you had told these people at that time that they could not publish, they would have gone and worked for universities, they would have gone and worked for Microsoft, they would have gone and worked for other people. And so, you know, yes, there is a, I mean, there's some altruism, but the altruism at Google, as it should be, is always governed by a combination of, you know,
25:30 Speaker 01 we, if we do this, we will attract higher quality researchers to work with us. And a bet that if there is a commercial application of some paper, it will be as fast, if not faster than anyone else.
25:47 Alex Kantrowitz? So how did that not happen?
25:47 Speaker 01 It's easy to say in hindsight that this was a mistake, but I actually think that Google got better about publishing in the 2010s. Like first 10 years of Google, we really did not publish much. And I think that drove forward a bunch of innovation. That's generally been good for, you know, for all of us. And remember, Google's lack of progress in generative AI, like, you know, that's internally driven. Nothing stopped them from creating ChatGPT. They chose not to.
26:23 Alex Kantrowitz? So what does that say about the inside of Google?
26:26 Speaker 01 I mean, it's a, it's a big place. There are tons and tons of opportunities and people had been burnt by generative AI before. You remember the Facebook chat bot as well as the Microsoft chat bot that went, that went racist. You know, and so they were, you know, it's, they were cautious. They were hesitant. And those were fine qualities at a time where stability mattered more than breakneck innovation. But now that they see the existential threat, clearly a bunch of people are pretty aggressive about getting, getting the technology out.
27:09 Speaker 01 Maybe you need a startup that has nothing to lose. This is a beautiful thing about startups. They have nothing to lose. Well, you know, they have something to lose, which is they'll disappear if they don't do interesting things and make money. And so perhaps it needed a company like OpenAI to pave the way for others to then come and figure out how to exploit.
27:29 Alex Kantrowitz? And the question is, who actually is going to win on this? There was a Google engineer in May who talked about the open source question. And they said, the uncomfortable, the uncomfortable truth is we aren't positioned to win this arms race and neither is OpenAI. While we've been squabbling, a third faction has been quietly eating our lunch and that's the open source community. I mean, it's kind of interesting. You were running a search engine, right? You were building this stuff.
27:50 Speaker 01 On open source models.
27:51 Alex Kantrowitz? Open source, yeah, mattered. So what do you think about this claim from inside Google that like, by allowing open source, or not even allowing open source, that they don't have real moat against open source?
28:03 Speaker 01 I think that's a misinformed opinion.
28:06 Alex Kantrowitz? Yeah.
28:06 Speaker 01 Simply because products matter. Technologies don't win.
28:11 Speaker 01 Businesses, products win and relationships win. I don't think there's been much of a change to Google's search share. That tells you how powerful the default position for Google's search has been.
28:27 Speaker 01 Uh. Still not trivial to make chat G P T your search engine, even if you want it.
28:33 Alex Kantrowitz? Right.
28:33 Speaker 01 Uh. And uh. There is innovation in open source models, but again, the blunt truth is that the very best of the models out there, whether it's G P T four or Claude's biggest model, or a clear step ahead of the pack when it comes to quality. Um. When it comes to reasoning, when it comes to the quality of the texts that they produce, um, there is a big gap. Having said that, there is a tremendous amount of excitement around open source models. There's a lot of innovation and there are a lot of researchers um who felt cut out of how Google and Open A I and Anthropic operated that are salivating and going, oh wait,
29:16 Speaker 01 this is a chance for us to um, you know, have a big deal. Uh. And so not a week goes by um without another open source model coming out uh and people, you know, claiming, no, I mean, and people having uh a substantial jump in metrics for some case or the, you know, for some case or the other. Um. But the fact at least today is that the very largest models um are ahead of the curve, uh even though the open source models are uh are are catching up. Uh. And there is a tremendous amount of uh um innovation behind uh, you know, behind these
29:55 Speaker 01 models. I think that is what makes this exciting. I'm very unexcited by the prospect of like, you know, three companies having uh a technology that everyone on the planet has to use again. These are well entrenched companies and it would just add to their strength. I think it's actually quite nice that there is uh a lot more competition. Uh. Having said that, you know, search still appears to be a game between Google and Bing. Chat G P T, you know, has had growth, but
30:28 Alex Kantrowitz? Mm hmm.
30:29 Speaker 01 but it has, it has sort of flattened out and uh, you know, I, I, I don't think the mere presence of open source models um threaten the existing uh businesses as much.
30:42 Alex Kantrowitz? What do you think about the multimodal models? Right now, even Google's been hinting about the fact that you're going to build model, they're going to build models or researching models that are like not only capable of understanding text, but also can process images, maybe do other things. I mean, that to me, like, you know, coming from an age where we really were working with narrow AIs, AIs that were really good at one task, the concept that there's going to be models that can deal with more than one tasks is is kind of mind blowing to me and somewhat underrated, I think, in the popular discussion. Or maybe I'm wrong. I'm curious what you think about that.
31:17 Speaker 01 I think multimodal models will have a lot more business use cases where you're looking at PDFs. We announced a model at Snowflake Summit that can understand PDFs, extract diagrams from them, also understand the text, extract facts. So I think that are, I see lots and lots of use cases for these models. But to me, that is one of many, many dimensions of this. I think API calling, being able to call actions and use the output of those actions to drive further actions.
31:58 Speaker 01 I think that is just as exciting as the multimodal capabilities. So I think it's very, very early. I have a harder time, you know, other than for things like image generation, how multimodal is going to make a humongous difference for something like, like consumer search. I mean, think about the last time where you said, you know, here's an image and I have a question and do something interesting for me. But there are, there are tons of use cases. These, this is where technology, I think, like this, this core AI technology has these angles, whether it's multimodal, whether it is tool use, that I think can meaningfully solve a tremendous number of problems that we can't quite, like, you know, end vision
32:43 Speaker 01 just yet. My dream that there is a a nice model on my phone that I talk to that can copy information from, you know, one app to the other, it can actually take a photo that I took and actually attach it to the, you know, chat that I have with you, all just with voice instructions. I think like, you know, even compared to the web browsers, the phones that we use day to day are so whatever 1970s. I'm waiting for the time when there is a real language model on the phone that can truly help us do stuff much more easily than what we were able to before.
33:28 Alex Kantrowitz? Yeah, that would be amazing. I mean, it's been the dream that big tech companies have been talking about for a while and to actually come through with would be cool. All right, let's let's go to a break. We're here with Sridhar Ramaswamy. He is the co-founder of Niva, the search engine that we've been talking about throughout this conversation. He's also an SCP at Snowflake. How the two fit together? Well, he recently sold Niva to Snowflake. We'll tell the story and then go to a bit of a lightning round on the other side of this break. This episode is brought to you by App Point. Everyone's racing to roll out AI right now. Co-pilots, chatbots, agents doing real work. But here's the part nobody loves talking about. All that AI runs on your data and most teams have no single way to see it,
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35:59 Alex Kantrowitz? And we're back here on Big Technology Podcast with Sridhar Ramaswamy. He's the co-founder of Niva. He's also a SVP at Snowflake. And let's talk a little bit about the Niva story. So, you know, we've been dancing around it a little bit in this conversation, but you built a search engine. It was no, there were no ads. You subscribed to it. You would sign in, you had identity there. And it seemed like a real challenge to Google. You had come from Google. And, you know, it's kind of interesting because then, you know, right as you're kind of hitting, you know, your moment where you're trying to figure out what the company is going to be, this generative AI moment hits. And all of a sudden, there's a chance that search is going to reinvent. And so it is, I find it kind of interesting that you then decided to shut down the consumer side of it
36:44 Alex Kantrowitz? and then went and sold it to an enterprise company like Snowflake. So can you tell us the story about like what happened there? What it's like competing with a Google? What lessons you learned?
36:54 Speaker 01 Yeah, I mean, lots of people know this. It is, it was still sobering to deal with it in practice, which is that getting consumers to change their behavior about search is hard. And the players involved, the browsers, the companies, simply do not make it easy. It is really, really hard. Outside of the prescribed five, you can't change the search engine on Safari to anything else, even today.
37:25 Alex Kantrowitz? Why is that? That's just an Apple thing?
37:27 Speaker 01 That's an Apple thing.
37:29 Speaker 01 So you just can't do it.
37:31 Speaker 01 And so that was sort of the reality. And we have talked about this before. The people that tried us, a pretty decent fraction of them were perfectly willing to say, it's like, ah, fifty bucks a year? That's fine. They would just pay the fifty rather than the five dollars a month. And the thing that changed Alex in a pretty big way was we went from an environment twenty twenty, nineteen, twenty one, where a company could get funded at like three hundred times next year's earnings. And he's, I mean, next year's revenue, when the revenue was small, to suddenly the expectation being, oh, your valuation is ten to fifteen times revenue.
38:17 Speaker 01 And ironically, a whole bunch of enterprise opportunities also popped up earlier this year, where people are interested in our crawl table, generative AI companies, language like, they wanted, language model companies especially, they wanted access to a search API. There are also a bunch of pricing changes, where people wanted the search API to power search. There are a whole bunch of these opportunities that, that, that came about early this year. But our overall conclusion was that in the new five percent interest rate environment, we could not catch up to, can you be, can your valuation be ten times revenue?
39:03 Speaker 01 We thought about this. And when we had conversations with Snowflake, part of what was really exciting was the core technology that we had built around search, which not only was a keyword-based, quality-based system, but also had things like vector indexing built in. We realized that we had a chance to have a big impact with search within Snowflake. And I've talked a lot about this. In my mind, one of the key ingredients for believable AI, for referenceable AI, is a great search retrieval system that sets the context for how a language model is going to generate answers.
39:49 Speaker 01 So these are the two broad areas where we were very convinced, we, meaning Vivek and I, and the Neeva team, but also the Snowflake team, in terms of the impact that we could bring to bear. And that was the main reason the acquisition went through. And we've been at this for four weeks. There are existing teams in Snowflake that have been doing things like, you know, deep learning models to better understand documents. But this is the area that we are working on, which is search and generative AI. We showed some demos of what is possible.
40:32 Speaker 01 Imagine a co-pilot experience is built into every place that where you interact with Snowflake. But imagine also creating technology that will let our customers, which are most of the Fortune 500, you know, top 2,000 enterprises in the world. How do we bring this technology to all of them? So those are sort of roughly the areas. And that's, that's sort of, that was our motivation for why we decided to stop the consumer journey and be part of a larger organization focused on enterprise data.
41:04 Alex Kantrowitz? Yeah, and then there's been this moment now where it's like, okay, the chat, the interest in ChatGPT has kind of tailed off. And people are wondering, like, have we hit the, you know, the end of innovation here, or is there more stuff coming? And Some of the stuff that you're going to be able to do with Snowflake, to me, seems to be the place where we could see some of the breakthroughs happen on the existing technology and I guess incorporating the innovations. And I think you've made this point in previous interviews, but I think maybe you could elaborate on it. It seems to me like what people are going to be able to do is they're going to have all their data in Snowflake and then basically be able to speak with it. So you could have like anybody in the organization access, you know, whatever part of the data is, you know, available to
41:48 Alex Kantrowitz? them and actually start to have a conversation and not have to run like complex coding algorithms in order to be able to make sense of what's going on in the company. So is that what's gonna happen? Like, give some practical examples.
42:00 Speaker 01 Yeah, so, you know, Snowflake is proud of its mission to democratize data access to everybody within the enterprise. There are companies like Fidelity that have made Snowflake the centerpiece of their data architecture. What we're excited about being able to do is use the power of generative AI on top of this incredible platform that's already been built. It ranges from the simple, which is how do we help you generate much better SQL queries? We have something called Snowsite, which is where you type in SQL queries. I
42:42 Speaker 01 don't know about you, but you know, I've spent a good chunk of my life writing SQL, even at, even at Neeva, and it's tedious. It is, it is tricky to get right. We want to make it much easier. So people that are doing this, who are typically analysts, data engineers, can do this 10x faster. But even more importantly, and this goes to the point that you're talking about, is how do we make it easy for business users that don't necessarily understand the ins and outs of the schemas and the tables and stuff like that, to be able to ask business questions and for Snowflake to then automatically decide, is that an existing dashboard? Is that a
43:26 Speaker 01 SQL query that's been run before, do we need to write something new from scratch and visualize it? It is that ability to offer up this data. And this is everything from, hey, how is revenue doing by region for this quarter? Uh, to more complicated questions. Um, how do we make that easily available to lots of people? Uh, but there's a definitely more uh part of the transformation that Snowflake has been going over the past like five, six years now. Um, is to really become the data cloud, a platform, not just for the data, but also to build applications on top of the data.
44:04 Speaker 01 Um, and so we bought a company uh that uh that makes it super easy for you to write visualization programs on top of this um of this data. So it's almost a complete programming stack. Uh, these are the things where um I think, you know, like our our bet is that we can ten X the number of users that can use the platform, hundred X the number of queries that are going to be run on the platform. But just as importantly, think deeply about how do we make this tech available to all of, you know, our customers. Um, part of the problem right now um is that um
44:44 Speaker 01 guess there are big language models like G P T four, but pretty much most of the time you're sending over your proprietary data over to them. And uh at Snowflake, Vivek and I are particularly excited about all of the um great things that are happening with open source models. Because we want to make it really easy for our customers to be able to then deploy them um within uh their Snowflake security perimeter uh and be able to do meaningful things um with them. This basic arc of everything in Snowflake, whether it's writing a SQL query, visualizing or interacting, gets an assistant is just the first part. But lots of other applications, including things like if you have a table with a set of
45:27 Speaker 01 documents inside it, they can even be sitting in cloud storage and you can just point Snowflake to it. How do you create a quick conversational interface there instead of having, you know, I don't know how you search through PDFs, but my favorite method is Command F that I put in a word. It's super painful. You should be able to simply talk to it and say, if you have earnings reports, how did this company do? What were the growth rates for the past four quarters? And then the underlying model goes and figures this out across a set of documents, shows it to you, but also shows the citation so that you can be sure that it is the right answer.
46:08 Alex Kantrowitz? That would be cool. I would use that for sure. Okay. So do you have time for a quick lightning round before we head out?
46:13 Speaker 01 Let's do this.
46:14 Alex Kantrowitz? Okay. First thing, where do you think this is going to leave us on jobs? Are we going to lose jobs for this? Are we gonna, I mean, it seems like, you know, everyone said ChatGPT is going to take your job. It hasn't really happened yet. Why is that?
46:27 Speaker 01 Because change is slow. I think definitely when it comes to things like customer support, you know, you need, you need tools, you need like much better retrieval systems, you need much better action-taking systems. I definitely think that there are a whole class of white collar jobs that are going to be affected in a pretty significant way. Hopefully there are new jobs that are going to be created, but you know, one can't bet on stuff like that. Simple information functions, 100% are going to be done better by AI models.
47:02 Alex Kantrowitz? Elon Musk is starting a company called XAI. It's his answer to OpenAI. What do you think is going to happen there?
47:11 Speaker 01 They have competent people. They're going to generate, you know, yeah, they're good people.
47:16 Alex Kantrowitz? University of Toronto.
47:17 Speaker 01 Yeah, yeah, we've met Igor.
47:18 Alex Kantrowitz? Plenty of GPUs. Yeah.
47:20 Speaker 01 Yeah, yeah.
47:23 Speaker 01 You know, I don't know what to say. I think it's a way to, I think it's a way to stand out. Let's face it, things like how you make AI models safe is a little bit of an art and, you know, art and science. And Elon sees a way in which this, like, you know, the company can stand out. But competition in general is a good thing. I'll mention that the work that Facebook is doing to open source some of their models or to even have them be commercially usable by lots of people is an exciting development for, for everybody. So my attitude generally is like, the more the merrier.
48:05 Speaker 01 Competition is good. I don't know about you, but I love the streaming providers. There's lots of competition, lots of choice. Do I really want like five subscriptions? Probably not, but I'm glad that they're there.
48:20 Alex Kantrowitz? Why does everyone who is worried about the future of AI and AI wiping us out seem to be working on their own project advancing this state of the art in this technology? Elon included.
48:35 Speaker 01 I, I sort of genuinely do not know. I find things like the call for a auditorium for six months to be absurd. And, you know, and, and the people that were starting new efforts in AI were some of the signatories to that, you know, to that effort. Don't get me wrong. They're, you know, like, yes, this tech can get out of hand. But the way I would handle that is to make sure that existing laws we have against discrimination or illegal use are also applied equally aggressively to these, to these models.
49:18 Speaker 01 I don't think stopping work on AI or declaring it to be the end of humankind is the right way to think about it. There are lots of positive ways in which AI can be used. And 100%, as I said earlier, AI is going to be an assault on our reality. So there's a lot of public education that needs to happen simply about what is believable. But he can't also necessarily stop technologies like this, especially ones that can no longer be centralized. It is, you know, I'm sure you know this, you can find you know model for 500 bucks in one evening without a whole lot of technical skill.
49:58 Speaker 01 And so, you know, I think this technology, similar to the internet, is going to be widely available to a lot of people. Is going to produce some, you know, unforeseen consequences. We have to be ready for it.
50:12 Alex Kantrowitz? Finally, what makes Nvidia so special?
50:16 Speaker 01 They made an early bet. It's, I think, such a fascinating story. Remember for much of the last 30 years, we were like, yeah, they make GPU for games. It's like such a niche industry. I think it's one of these cases where it's like, there's a lot of right place, right time. The same things that made them really good for doing graphics processing, which is a lot of, you know, fairly simple operations done at massive scale. You know, graphics processing is like drawing a lot of triangles on your screen.
50:56 Speaker 01 But the same thing and matrix multiplication were wildly applicable in the era of AI. And pretty much the world has centralized around them. It's an amazing story.
51:10 Alex Kantrowitz? Sridhar Masari, thank you so much for joining.
51:13 Speaker 01 Thank you, Alex. Great to chat.
51:15 Alex Kantrowitz? Always great to talk. Thank you, everybody for listening. Thank you, Nick Watney, for handling the audio, LinkedIn for having me as part of your podcast network and all of you for listening. We'll be back on Friday, breaking down the news as we do always. Thanks again for listening and we'll see you next time on Big Technology Podcast.
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