Who’s Winning The AI Race? + Software’s Future — With Sridhar Ramaswamy

20:40Sridhar Ramaswamy:So this is work self-correcting, getting itself better over time. And the goal is just things get done a whole lot faster. Already, we are seeing 10x, not 10%, 10x reductions in the amount of time that it takes to debug complex cases that come in.

20:55Big Technology Podcast Host:And so let's just go to this question of, is this working? Because there's been a lot of discussion of agentic AI. Every time we talk about it, there's always like a segment of the audience that says, you know, this is still a lot of hype, pushback harder, conceptual, largely still. And, you know, this is something that, you know, might in demos look really good. But when you actually put it into practice, it struggles. What's what is your read on that?

21:28Sridhar Ramaswamy:You got to walk the walk. We were in Davos together. Yes. And, you know, two weeks ago, and I probably met 20-odd CEOs, CIOs, lots of partners. And my sort of SOP, standard operating procedure for each of these meetings would be, I would ask our sales agent for information about the customer. What's the state of our relationship with? Take your pick. And it generates a report. I would turn it on and show my phone to them. And they would go, holy cow. But uniformly, not one of these CEOs has the same tools that I do. I see. That's the difference between actually getting the work done, making AI serve meaningful needs, and yes, the hype that you're describing.

22:19Sridhar Ramaswamy:All of the people that are in the camp that you're describing have never had useful products built for them that deliver meaningful value. I speak as somebody that lives this, the amount of feedback that my poor team gets about how difficult the mobile experience is, how to make it better. We just launched like Face ID authentication. That's a big deal because I don't have to log in all the time. It's taking care of all of those kinds of nuances, making enterprise data come alive, available for you, and then helping you with decisioning. That's the magic. And that's why you're hearing people say it's hype, but it's companies like Snowflake that are actually living what we are preaching.

23:02Sridhar Ramaswamy:And I give that same feedback to my exec team, which is, hey, all of you need to be demanding tools that are as good as the one that we have for the sales agent. And our team should be providing them to you and you should be using them day to day in how you can work better. I agree that there is work to be done, but the sheer potential of something like this is magical. I'll give you one more small example of something that is cooking this very week. I'm working with our ops team, our operations team that helps manage Snowflake, the software running in the cloud about how to get on a more agentic bandwagon.

23:40Sridhar Ramaswamy:Super crudly infrastructure engineers, they're like, what is this? We know better. But we're walking through this journey of, no, no, let's create tools that our coding agent can use. And you will genuinely find that it's a lot easier. And so someone created a tool that will help detect things like, oh, are there problems with warehouses resuming? Warehouse is a basic unit of work that gets stuff done for our customers. And our customer says, start this, we want it to start quickly. In like 10 seconds, I had generated a histogram of resume times, put a nice graph, and I sent it to the team with one prompt, all English on top of a tool that somebody had built to look at resume times in warehouses and the team is like, holy cow, that's the magic of agentic platforms.

24:30Sridhar Ramaswamy:But yes, you have to do the legwork to put them into place with the guardrails, things like that. But there's real magic here.

24:36Big Technology Podcast Host:A couple of things. So first of all, what you're saying is kind of reminding me of something that Arthur from Mistral, the CEO of Mistral, said here a couple weeks ago, which is basically that the technology has these capabilities, but it's not just like, it's not like in that AGI mode, tell it what to do and it can do it. Yeah, to work at it. In many ways, getting enterprise AI to work is a managed service, which means that it could take some time for what you're talking about to be visible within the entire economy, as opposed to those who have already put the time to figure it out.

25:11Sridhar Ramaswamy:Well, that's also where magic can happen. Right. And, you know, I told you that we released a new product called CortexCode, which is our data coding agent. We launched it to GA yesterday. And it dramatically lowers the amount of time that it takes to get stuff done on Snowflake. We all get carried away with how does AI make it easier for a business user like me to get access to my data. That's great. But on the other hand, everything from how do you set up a database to how do you move data from a production like a transaction database or to Snowflake for analysis? How do you build a machine learning model?

25:55Sridhar Ramaswamy:How do you build an agent that you can then give to the business user? Cortex-Code is meant to address all of that again in natural language. And part of what we have built there are what we call a series of skills that help automate this work. And this is a theme that's going to come up again and again, which is how do you use AI to make launching AI products go faster? Right. That's the feedback loop that one needs to be on. It's a little bit of a red pill moment where you're like, wait, you mean I can release new software products pretty much every day? Because releasing a new piece of functionality is as simple as writing a recipe in English, which all of us are very capable of doing.

26:38Sridhar Ramaswamy:I think using AI to make AI go a lot faster is something that we are excited about. And this product is among the best in terms of how do you get it from Snowflake.

26:48Big Technology Podcast Host:It's it's interesting that you talk about how easy it is to build software now. That has been both a benefit for software companies and something that people are worried about because where is where is the moat look? You know, where is the moat if it's so easy to build? This is from this is from Ben Thompson's pretty interesting. His perspective. He says, AI coding doesn't kill software. Customers pay for products, not code. They're paying for support, compliance, integration, security patches. someone else owning the never-ending maintenance commitment. That stuff doesn't just go away because writing the initial app got cheaper.

27:22Big Technology Podcast Host:There's a but here, though, he says. But if every software company can write infinite code cheaply, the competitive dynamics change. The SaaS playbook of finding a niche and growing your slice worked when building was expensive. Now everyone can build into adjacencies overnight. Shifts from growing this pie to... It shifts everything from growing the pie to fighting for share. It's something that, you know, it seems like you're enabling and you're living.

27:47Sridhar Ramaswamy:Yeah, I think that is going to be a concentration towards platform players. But I would also be cautious about general pronouncements for the simple reason that we are all actors in this space. We all get to change the outcome. I feel very good about Snowflake as a data platform, but I honestly do not want to be in a situation where access to Snowflake is always mediated through someone else. That's always a very dangerous place to be, especially in a moment like this. This is the reason that we develop not only Snowflake intelligence, which is the best way for a business user to get access to their business information that is trustable through the devices that they want, like their phones, rather than trudge through dashboards.

28:36Sridhar Ramaswamy:But we are also investing massively in how do you make creating data products? How do you make creating applications a whole lot easier? Absolutely. It's going to be the case that there's a lot of functionality that sits in complex applications. We're actively working with all of those folks, whether it's a ServiceNow, what a sales for, or SAP with whom we have a big partnership in creating this agentic future together. Agentic future is very much going to be, what I said, past, present, future, and actions. And so we think we stand a very, very good chance of being the platform where this work happens.

29:16Sridhar Ramaswamy:But as I said, it's a foot race and it's all about creating value really fast for your customers. And I would shy away from X is going to win or Y is going to win. The companies that are going to win are the ones that have great capabilities, but also take the time to figure out how to create value for their customers.

29:35Big Technology Podcast Host:There was we're speaking on Wednesday, February 4th. This is going to go a week later. But there was interesting thing that just happened this week that I think we should talk about. Yeah. Which is you made such an interesting point where when I when I asked you about this, you said, listen, we do not want to be an input into somebody else's software. And this week, Anthropic released, or within the most recent days, Anthropic released a legal plugin. And the market got wind of this. And then all of a sudden, Thomson Reuters, I think it had its worst day on the market in history. Stocks like LegalZoom just, you know, dropped like a rock.

30:09Big Technology Podcast Host:And I was trying to think through, like, why this could be, because it was just one legal plugin from Anthropic. and the perspective might be that with generative AI, there is a risk that some software shifts from being the place you do the things, you know, LexisNexis, you do the research there to an input into a platform. And if that's the case, I think what the market is thinking is that you lose that control that you had. You become a feature in a platform as opposed to the platform itself. That's the risk.

30:40Sridhar Ramaswamy:It's a very real risk. I think people that were confident about their position in the world because they were essentially walled gardens for data and functionality and are slow at providing modern ways of dealing with information are going to struggle in this world. This is the reason that I stress us living by what we speak in terms of AI and agentic platforms and this future of work concept, precisely because unless you live it, you don't actually feel it. And unless you live it and feel it, you're not going to help your customers get there. I think niche SaaS software providers that basically benefited from lock-in.

31:33Sridhar Ramaswamy:And think about it. If you use a piece of SaaS software, logged into it on your browser, God help you if you want your data back. Just like not going to happen. What this current moment is pointing out is that that's a very dangerous place to be. And a lot of these players risk becoming dumb back-ins to the models, which is why Snowflake is so leaning forward on agentic AI and living by what we speak, because that's the place where value is going to get created.

32:02Big Technology Podcast Host:The market doesn't really seem to know what it's doing when it comes to software. It doesn't really seem to know how to value software in this moment. This is from Liz Thomas. She says, software's forward 12-month price to equity ratio is compressed from a 33.1 to 23.2 multiple contraction of 30%, which is wild because software gets these big valuations because of what it is. Here's another stat. SaaS Index from Talia Goldberg. SaaS Index is down 32 % year over year, despite most companies meeting or beating plans while the markets are up 15%. What do you think the market's reaction is here? We had Brett Taylor on.

32:44Big Technology Podcast Host:He said it was just kind of the uncertainty of who wins? Is that your perspective? Or why do you think, despite like Talia saying here, the fact that these companies are meeting their earnings expectations, they're still getting hammered and the multiples are contracting.

33:00Sridhar Ramaswamy:There are a few things that we should take into consideration here. As you know, companies are valued not on what they're doing today, but on what they're going to do in the future. And I would actually distinguish data platforms like Snowflake from pure software providers operating on a subscription model. Not that it's a bad model, but the way they have operated is, AI became another skew for these folks. And customers have had to sign up for AI products, regardless of whether they created value or not. That's sort of become the favored way of becoming AI native. I think what the current moment points to is a real risk that that is not a winning AI strategy.

33:49Sridhar Ramaswamy:Meaning that work is not going to get done by interacting with a chatbot on a particular SaaS app that you used. Which is why our vision of agents operating on a data platform that has as much of the analytic insights about the past, as a lot of our customers do, but with the ability to bring in integrations via MCP, via other APIs for how do you talk to other systems. I think that's the compelling vision. I think companies are going to win if they have both a convincing vision for how work gets done in the future, but are able to back it up with, and here is how we help you, the customer, get it done fast.

34:38Sridhar Ramaswamy:The model makers approach it from this view of the model is everything and nothing else matters. We approach it from the viewpoint of it's the entirety of the experience. It's the model. That's why we partner with all of these folks. It's the most critical data that's valuable to your company, but it's also integrations with the operational systems that really help get work done. I think that's the compelling vision for how work gets ready, what the markets are, In some ways, pricing is the fact that AI as a bolt-on to SaaS software does not feel like a winning strategy. You know, I feel much better about the path that we are pitching.

35:19Sridhar Ramaswamy:Also, our products are consumption-based, meaning that if something doesn't get used as much, there's not a penalty to just building them and using them as much as you want.

35:30Big Technology Podcast Host:But can I ask, I mean, you know, as we've had this conversation, the idea that people would come to like a snowflake agent, because all their data is there. So they can go through all these use cases that we talked about. And that's compelling. But why doesn't that just end up getting subsumed into some like, you know, master agent that has not just not just the snowflake data, but everything else?

35:53Sridhar Ramaswamy:It can. That's very much a fear that we need to operate with. That's very much the opportunity of the moment. Okay. The big model makers want to create a world in which all of the data for all of the enterprises is easily available to them. Through like a JGPT. Through, yes. Or a Gemini. And, you know, everything else, the world, is just a dumb data pipe that feeds into that big brain. That's the vision that they would like to see come true. And the vision that I would like to see come true is, hey, we host the most important data for every company and the most important predictive models for every company.

36:32And I can create agents that can deliver substantial value.

36:37Sridhar Ramaswamy:But by the way, we also follow, like others do, an interoperability strategy. Because if a customer comes and says, I want to build a data product on Snowflake, fine. It can have an AI interface, but I really want it to be accessible somewhere else. I don't get to say no to that. The only people that win are the ones that effectively deliver what customers want.

36:59Big Technology Podcast Host:Right. Is this going to be the big battle field in technology over the next couple of years? I mean, we even had an example, I think it was Amazon, who like protested in a big way from having, I think, perplexity scrape its pages. And it seems like this is going to happen on consumer. and this is going to happen. Because is this a conversation that OpenAI has with you? Hey, Sridhar, we'd love to have all your data available in ChatGPT Enterprise.

37:28Sridhar Ramaswamy:You stick to customer choice. What do customers want? Right. If they want to access data through a Snowflake intelligence agent, the OpenAI team doesn't say no. If on the other hand, our customers want to expose important enterprise data that they have as an MCP endpoint into ChatGPT, we don't get to say no.

37:50Big Technology Podcast Host:So then how much agency does a software company actually have like one in your position? Because if it is up to customers.

37:57Sridhar Ramaswamy:It's all about creating products and value. It's not about anyone. No one has an insurmountable. ChatGPT, like OpenAI doesn't get to say the only way you get 5.2 is to come to ChatGPT. Right. I don't get to say the only way you get to access data on Snowflake is to come to Snowflake Intelligence. It's a little bit of, it's pretty much may the best player win. And so it's very much about creating value.

38:20Big Technology Podcast Host:And the burden that you have is large because if people are going to go to like a specialized bot as opposed to a centralized bot, that specialized bot has to be orders of magnitude more useful because it's requiring a different behavior. Or maybe I'm wrong.

38:37Sridhar Ramaswamy:Maybe, maybe, maybe not. This is the part. It's very, very early. And remember, we are still living in a world. I don't know how many tabs you have open, Alex. Mine is 200. Okay, that's the state of my work. That's pretty good.

38:51Big Technology Podcast Host:I have enough that I can't read the tab names. I'll put it that way.

38:54Sridhar Ramaswamy:Command shift A if you use Chrome is your magic answer to all problems, but still. And so I think it's early.

39:01Big Technology Podcast Host:Yeah. When your stock price gets kind of caught up in like the market says category, you know, this category must do this and your stock price gets caught up. How do you manage that as a CEO? because it must be in some ways frustrating to see that the market acts on categories versus individual companies. It's my job to make us stand up.

39:22Sridhar Ramaswamy:It's my job to make sure that our prospects are clear. It's my job to make sure that our company accelerates to seize the moment that is today and come have these conversations. Yes, the markets are reacting to the best information that we have. If we get clubbed with other SaaS software providers that tells you that I have more work to do, that's fine.

39:48Big Technology Podcast Host:Yeah. OK, I want to talk to you about the about shadow AI and how people are individuals are starting to build their own AI programs. We've seen that a lot over the past couple of weeks. Yeah. So let's do that when we come back right after this. If a driver in your fleet got in an accident tomorrow, can you prove what actually happened? Without footage, it's much harder. So your insurance rates spike and you're stuck paying for it. That's why so many fleets choose Samsara's AI-powered dashcams, clear video evidence, real-time alerts, and coaching tools that help prevent accidents before they happen.

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41:41Big Technology Podcast Host:Easily create email and social campaigns wherever your customers are scrolling or strolling. It's time to turn those what-ifs into with Shopify today. Sign up for your$1 per month trial today at shopify.com slash big tech. Go to shopify.com slash big tech. That's shopify.com slash big tech. And we're back here on Big Technology Podcast with Sridhar Ramaswamy, CEO of Snowflake. Sridhar, great to have you on the show. Thank you for coming back.

42:11Sridhar Ramaswamy:Always great to chat. What did you think when this open-claw-claw-bot-mult-bot

42:18Big Technology Podcast Host:moment happened when people started running all their own agents on their computers and doing crazy things.

42:25Sridhar Ramaswamy:Well, I hope they were not running them on their own computers, but still.

42:30Big Technology Podcast Host:Somewhere and got their API keys exposed.

42:32Sridhar Ramaswamy:Exactly, exactly. I think all rules of security don't vanish because of AI. It's remarkable. I'm fortunate in that I have two young sons who are both in software. and I get to see the world through their eyes. And as it turns out, one of them had one day between when he came to San Francisco, he moved from New York and when he started his job on Tuesday. And in that one day, when I was at work and he was home, he had managed to get like an Ubuntu instance on AWS completely separate from everything else, including his laptop, thank God. And he had set up OpenClock on it as his personal AI assistant.

43:26Sridhar Ramaswamy:And it comes with things like Telegram integrations. You can talk to it. He started using it as his to-do list. And he had set up a little chat bot for giving me a summary of cool AI happenings on X because I told him like X can be a lot. I don't like to spend that much time on it. I still want to get what's important. So I get like a briefing every day of cool things happening in AI done entirely by the chat bot.

43:51Big Technology Podcast Host:Tell him not to prioritize that because I could be in trouble if he does.

43:54Sridhar Ramaswamy:I think it took all of a few hours for him to do that.

43:58Big Technology Podcast Host:Build this newsletter.

43:59Sridhar Ramaswamy:But funnily enough, he was to build the entire self-contained working thing that can literally react to any question that he has. if he says, hey, I have this hobby and I need you to help me get better at this hobby, it'll start sending him messages every day about what should he do to learn a new skill. The general purpose nature of this is truly, truly mind-blowing. Took him a few hours to set up. That's the wildness of the moment. But funnily enough, he's 26 and he was like, yeah, yeah, yeah, I want no part of this notebook thing. I think it's a bunch of hype. I think it's actually people posing as agents that are posting this, you want to know part of that.

44:40Sridhar Ramaswamy:And so it's fun. I think it's a remarkable moment in terms of what is happening out there. But I do think that you're seeing what happens as these agents or agent frameworks become easier and easier to use and set up and people will figure out a set of security guardrails for how to use that and things like that. This is, I think, it's a pretty remarkable moment.

45:11Big Technology Podcast Host:Yeah, Moatbook, 175 ,000 posts, 1.1 million comments as of, it's the social network for the AI boss as of the time we're speaking. So I don't think it's entirely, I mean, if that's entirely human, it's a pretty successful social network on the rise. So it's done that in a week. Pretty interesting. you made some predictions ahead of the year and one of them really stood out to a couple of them we could talk about them both but one of them that I found really interesting was you said shadow AI will drive enterprise adoption from the bottom up employees who select their own free AI tools will remain the primary driver of enterprise AI adoption in 2026 rather than waiting for IT departments to sanction approved products.

45:57Big Technology Podcast Host:Workers are using ChatGPT, Claude, and other consumer AI tools for their daily work, forcing organizations to catch up. I think that's so interesting. And it's something that I've talked about on the show before, how it seems like there's these two tracks, companies that are kind of slow to move and adopt these tools, and individuals that are starting to find ways to use them in their work. Why do you think that is, first of all? I mean, anyone who's been inside a even moderately sized company knows that it's filled with approvals and lawyers and

46:29Sridhar Ramaswamy:you know pilots i have a simpler answer yes it's the true 10xing of the moment i talk to you about how with something like a cortex code you can get a job that you need to do on snowflake like working with data is tough it's tedious yeah you have to get lots of things right a lot of little details can use our cli and just automate this stuff and get it done in less than a tenth of the time you have otherwise have taken you right that is remarkable and uh i now write documents this is with our officially approved enterprise version of our chat bots i write position papers coming out of dialogues that i have with these chat bots where I say, this is the situation.

47:20Sridhar Ramaswamy:These are my thoughts. These are the options. What do you think? We sort of go through almost a Socratic process of debating stuff and producing something that looks mighty polished. But if I've done pricing studies entirely inside chatbots, we have to change prices. You trust them?

47:36Big Technology Podcast Host:Because sometimes when I like, I have them do the numbers. Okay.

47:39Sridhar Ramaswamy:I have never, ever run a coding agent with accept all my recommendations. I am as anal as they come. Okay. My first rules when I started using our coding agent was never delete a data, never ever delete a database, never ever switch an account because I have access to production systems that have snowflake data. I'm like, don't switch to it when I'm playing around with something else. You got to put the guardrails. You got to be smart about how you work and you got to check the work. And so when I did the pricing study, it's like, hey, plot this for me. How does revenue and margin change? You got to go study the work, but it's a massive accelerant.

48:19Sridhar Ramaswamy:And the benefit that you get from something like this, unlike a handwritten doc, is let's say you decide to change your mind and want to introduce another new thing. You know, normally we just don't do that in a document or a study because it's so tedious to go make all the changes. These chatbots, they don't get bored. They're like, you want to redo this work? Not a problem. They do the work for you. I think it's that value creation that's driving the adoption. And it's not that we are actually trying to be a lot more receptive to this because we know that we would rather have a tool with enterprise controls than just have everything go underground.

48:55Sridhar Ramaswamy:And so it's worked pretty well. And most companies are also doing things like approve AI policies on top of Snowflake, for example, a lot quicker than what they would have done before, because it is that value creation that they're all hungering for.

49:11Big Technology Podcast Host:Right. But I think the thing is, and I mean, this is your prediction, so we can go deeper into it, is that individuals, is it a 10x thing of the moment? I would say, yeah, there's definitely value to be found in these applications. But it is interesting that it's the individual, maybe this is normal, the individuals are finding this technology and doing it in a way that you describe as shadow AI, right, where companies are a little bit slower to move. So how does that change the dynamic of companies if you have a couple of people in there that are like leaning all the way into the tools and the company is like, yeah, we're working through this.

49:44Sridhar Ramaswamy:Well, part of what every company has to do is to figure out how to embrace these change agents and make sure that they're surfacing what they want to do and the value that they're getting to everyone. I wanted to roll out CortexCode to the entirety of our solution engineering team, 2 ,000 people. It's a lot of people. And the way we did that was we selected a subset of them, over 30, 40 people, and gave them a little bit of training and said, hey, you should go try this out, see what this is like. We called them our AI champions. We celebrated the fact that these were the forward-leaning folks.

50:25Sridhar Ramaswamy:And we also made them effectively responsible for spreading the word down to the different teams. change in any large company is not going to come from top-down mandates. Let's face it, what I know about AI is minuscule compared to the sum totality of what my 9 ,000 people know about AI. And you need to create an environment in which the most progressive of the ideas that are coming up, the most innovative of the people, they have a way to quickly surface the idea up. In fact, for the next all hands, I've been working with my comms team. It's in a few weeks. They wanted to have, you know, a regular all hands standard set of discussions with the exec staff.

51:10Sridhar Ramaswamy:I said, I want to spend two minutes personally because I have to say something as a CEO. I want the rest of the time to be devoted to finding these firebrands, looking at what they do and highlighting this as the champions. We need to figure out how to identify and how to learn from. And we have to embrace the moment in terms of how do we use our collective wisdom to drive our organizations forward?

51:34Big Technology Podcast Host:It's very interesting because it seems like as these tools get better, there are going to be companies that will have that mentality. And there'll probably be companies with leaders who are just like, I don't know about, you know, all this AI stuff. And it could actually change the competitive balance of industries pretty quickly if you have organizations with more permission versus less.

51:55Sridhar Ramaswamy:I would distinguish it more as progressive organizations. Okay, what does that mean? What I mean by that is we always have to balance. I will flip out if I find out that anyone's running OpenClaw on a Snowflake laptop. Please don't do that. That's not safe. We will help you get like a free Ubuntu machine on AWS if you want. There are smart things that people should be doing and dumb things that they should not be doing. a progressive head of security is an important asset here where they let the innovation happen without making people do unsafe things. We are custodians of data for some of the most valuable companies in the world, and we take that part very, very seriously.

52:42Sridhar Ramaswamy:And so it is that balance that one needs. But back to your point about changing competitive dynamics, very, very, very real.

52:52Big Technology Podcast Host:I think we can end here. You also have this interesting prediction about big tech, big tech's grip on AI models loosening. I'll just read a little bit of it. For years, conventional wisdom held that only a handful of tech giants could afford to build competitive AI models. In 2026, that will change new approaches to training like those developed by DeepSeek have shown that building the biggest, most expensive models isn't the only path to strong performance. You know, we're a year where this is great timing. We're a year after DeepSeek. Didn't fully change the AI industry in a way a lot of people anticipated.

53:27Big Technology Podcast Host:And so it's interesting to see that that is the prediction you made, especially if I'm, because if I'm right, Snowflake did try to build some foundational models and then decided that was not the game you wanted to play.

53:38Sridhar Ramaswamy:The foundation models became very expensive to build. We now have four players that are creating models that are widely acknowledged to be the state of the art. But a new QN model came out yesterday that is shockingly close to the best Zonnet model that there is from Anthropic. There continues to be a lot of innovation in this space. I think that's very, very healthy for us. And from a selfish perspective, Snowflake as a data platform prefers a world in which there are many people making great models, especially open source models, because we also have a really good infrastructure team. We are very good at running them at scale.

54:18Sridhar Ramaswamy:But this is a world where a lot of value is being created and a lot of change is happening. And I think being nimble and ready for that future of agent TKI, that future of work, while always having a laser focus on what makes a difference to your customer, those are the enduring qualities through the year. Life will keep changing.

54:41Big Technology Podcast Host:you're comfortable with the chinese open source models

54:45Sridhar Ramaswamy:so we test them we use them we try to um we we try to learn uh from them uh we also partner with uh us companies that are trying to create open source models there's actually a company that's based in uh in in brooklyn uh and san francisco that is um that that that we work with.

55:06Big Technology Podcast Host:Which one?

55:06Sridhar Ramaswamy:If I remember, this is Reflection AI. And it's a remarkable company. I think there is a lot that we are missing out in not having a robust open AI ecosystem. We sometimes get caught up in this world of, you know, we have the best AI companies on the planet, but we also should understand that much of their work has effectively become walled off from the rest of the world you and i simply do not know what techniques open ai and anthropic are adopting to produce the great models you can say how does it matter google search for example pretty much died as an academic as an academic area after google became big why they published nothing and they were ahead of everyone else by a million miles.

55:57Sridhar Ramaswamy:The area just died. And that was okay for us geopolitically because Google was an American company. I think part of what you're reacting to is this fear now of open source is not here, but much more in a situation where there is no winner. What is happening right now is that it's the Chinese companies that are publishing their work. And what then happens is all the universities, all the students and professors in our country are looking at their work and figuring out how to build on top of it. And so academia is diverging from what's happening in the research labs. That's part of the danger of this moment.

56:38Sridhar Ramaswamy:And that's the reason why we need to have a more robust ecosystem. If it had been a world in which there was one model maker that was a winner and there was an American company, I think we'd have a slightly different attitude. It's very clear now that that's not going to happen. Hence the fear about open models.

56:55Big Technology Podcast Host:And then if these, you know, I think there's been so much conversation about the Chinese open models over the past couple of weeks. You know, I think Demis Asab has said at the crack of the new year that the U.S. or the West is four years ahead, sorry, four months ahead of them. Recently, there's been some discussion that it's kind of, you know, closer than that. as what happens in the world where like those models become on par with the leading US foundational models?

57:26Sridhar Ramaswamy:For most of us, it opens up lots of opportunity. As you know, the very existence of something, knowledge about the existence of something can spur innovation in other areas. You don't even have to know exactly what someone did. History has shown this repeatedly. Just knowing that something is possible makes people work feverishly on making the same thing happen. You can bet that Reflection is looking at it and going, we can do better at this. Right. So from a macro perspective, I would say that that is actually a positive because Mistral is going to figure out how to reverse engineer all of this stuff and go one step forward, which will be good for Europe.

58:04Sridhar Ramaswamy:And Reflection will figure out how to do this in the US. This will also force Meta to be doing more things in the US. I think in a weird way, that's actually a net positive for us as a whole. I think the impact on the model companies, that becomes a little bit more murky. But welcome to this world, Alex. You know this is change every month. It's constant.

58:29Big Technology Podcast Host:The website is snowflake.com street. So great to see you. Thank you for coming, Don.

58:33Sridhar Ramaswamy:Thank you, Alex. Always a great conversation.

58:35Big Technology Podcast Host:Definitely, it really is. We hope we can do this again soon.

58:37Sridhar Ramaswamy:Thank you.

58:38Big Technology Podcast Host:All right, everybody. Thank you for listening and watching, and we'll see you next time on Big Technology Podcast.

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