SambaNova CEO on Raising $1B at $11B: "It's a Land Grab Right Now"

Rodrigo Liang is the CEO and Co-Founder of SambaNova. The company just announced a first close on a $1B round at an $11B valuation, led by General Atlantic with T. Rowe Price and Capital Group participating. Rodrigo has spent 32 years building chips. We covered the 101 of semiconductors and data centers: what inference is, why it is a different problem than training, and why inference will require orders of magnitude more chips than training. He walks through SambaNova's chip lineup from SN10 to the new SN50, why a 10 kilowatt air-cooled rack changes where AI can be deployed, and why running a trillion parameter model in a single rack matters for agents, latency, and edge. We also covered the era of premium inference, how providers measure revenue per rack, coopetition and traffic routing across Nvidia and AMD, sovereign models, the move back to on-prem, and why tokenmaxxing is the wrong goal. Special thank you to Brex, MongoDB, & AssemblyAI for helping make this RAISE AI Summit mini-series in Paris, France happen. Sourcery covers the people building the future across AI, hardware, and the private and public markets, subscribe for more. Rodrigo Liang: https://x.com/rodrigoliang?s=21 Molly O’Shea: https://x.com/MollySOShea Sourcery: ⁠https://x.com/sourceryy 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊 YouTube: ⁠https://youtu.be/etod19D9IEg⁠ https://youtu.be/etod19D9IEg 𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒 • Brex https://brex.com/sourcery—The modern finance platform, combining the world’s smartest corporate card with integrated expense management, banking, bill pay, & travel. https://brex.com/sourcery • MongoDB https://mongodb.com/ai–Millions of developers and more than 65,200+ customers across industries, including ~75% of the Fortune 100, rely on MongoDB for their most important applications. With integrated capabilities for operational data, search, real-time analytics, & AI-powered data retrieval, MongoDB helps organizations everywhere move faster, innovate more efficiently, & simplify complex architectures. https://mongodb.com/ai • AssemblyAI http://assemblyai.com/sourcery–Millions of developers use AssemblyAI to power their voice ai applications and features. One API gives you access to best-in-class speech-to-text, voice agent, and speech understanding models for both pre-recorded and real-time audio. Granola, ClickUp & HeyGen are scaling with AssemblyAI - get $50 of free credits today at http://AssemblyAI.com/sourcery http://assemblyai.com/sourcery 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Rodrigo Liang , Co-Founder & CEO at SambaNova Systems (00:59) SambaNova’s Series F : $1B raise at an $11 billion valuation (03:00) The Inference problem nobody saw coming (04:52) SambaNova's chip evolution (07:19) Running a trillion-parameter model on a single rack (11:00) Do $100 billion data centers actually make sense? (14:14) What "premium inference" really means (18:28) Speed is about to become AI's biggest price tag (20:43) Starlink, edge computing, and AI reaching every corner of the planet (24:27) Working alongside NVIDIA and rival chipmakers (27:49) How customers actually measure inference performance (32:07) The biggest bottlenecks in AI's global land grab (35:12) Justifying the billion-dollar AI valuations (37:53) Why SambaNova refuses to build its own cloud (41:03) The "AI sovereignty" debate (43:48) Data privacy fears are driving the return to on-prem AI (47:55) How to actually get ROI out of AI spend (51:16) The one question every business should be asking about AI (56:09) The mentors and lessons behind a 32-year career in chips (58:02) Unveiling SambaNova's newest chip, the SN50

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  We just did the first close of a billion dollar fundraise at an eleven billion valuation. I've been in this industry for thirty two years, building high performance trips for a long time. I've never seen the interest in semiconductors higher. Now what you're seeing at scale with Anthropic and with OpenAI and with Gemini, we've got millions and millions of people using it every day. We released SN forty a couple of years ago. It became incredibly popular because instead of a hundred thirty, a hundred forty kilowatt rack of Nvidia GPU, we were outperforming them with a ten kilowatt SN forty rack. Who could take a trillion parameter model and run it in a single rack? Where we take dozens of racks of other people's equipment to run the same model. We're two and a half billion dollar raised in the history of the company. There aren't really that many companies that have raised into the multiple billions. It's all about scaling.

• 0:45Speaker 00

  It's all about who can get to scale faster.

• 0:59Molly O'Shea?

  Rodrigo, welcome to Sorcery.

• 1:01Speaker 00

  Thanks for having me.

• 1:02Molly O'Shea?

  Well, we're here, for context, we're in Paris right now for the Raise Summit. And right now, we're sitting right in front of where the conference is. I don't really actually know what this park is called, but it's next to the Loop.

• 1:14Speaker 00

  Yeah.

• 1:15Molly O'Shea?

  I don't know if you know.

• 1:16Speaker 00

  Yeah.

• 1:16Molly O'Shea?

  You've been here a bunch, right?

• 1:17Speaker 00

  I've been, I've been here, but I'm not sure if I know exactly the name of the park, but right, right in front of the Loop.

• 1:24Molly O'Shea?

  Well, you have some big news. I think this will come out about a week after the news drops, but we'll still make a clip on that. So what is the big news?

• 1:32Speaker 00

  Well, we're super excited. We just did the first close of a billion dollar fundraise at an eleven billion valuation. This is a great show of momentum for the company and great show of support. The, the round was led by General Atlantic with a number of incredible investors that came in. Seligman Ventures, T. Rowe Prize, Capital Group. These are all significant American investors that are coming in. That shows that the company's got momentum, we're driving towards the scale and, and, and a significant amount of capital infusion to help us do that.

• 2:06Molly O'Shea?

  All the energy right now is going into semiconductors. I'm sure this was a very hyped up round in some way or another. Maybe it's been faster than others. What was the process like for you?

• 2:16Speaker 00

  I've been in this industry for thirty two years, both in high-performance chips for a long time. I've never seen the interest in semiconductors higher. All right, and I think it's a realization that chips are the center of this transformation. If you look at what AI is doing in the world and the build outs of the data centers, you can't do it without chips that run and run efficiently. And so with Samba Nova, we're coming in and providing technology that is able to take it to scale, take it to a level of inference scaling that that's just really not that practical to achieve just with traditional GPUs. And so I think the world sees that and the excitement is coming in from some of the top investors in the world.

• 3:00Molly O'Shea?

  So where we are at today is inference. Yeah. Inference has really taken the stage and it's been the next evolution of computing and where everything's going with AI. So for people that don't know Samba Nova, can you walk through the products and how you've evolved them for inference?

• 3:17Speaker 00

  Yeah, I mean, this, look, with AI, you've got a sophisticated audience. So they know with AI, there was always a training and the inference. There's no point of training a model if you aren't going to inference it, if you're not going to use it, right? And so the example I use with people is, you know, you don't go and invent a search algorithm if you're never going to do search, right? And so saying we're not going to train a model if you aren't going to use it. And now we're in the phase of using these models. We've always used them, we've always inference them, but it was still research to train models better and better. And so when we started the company in 2017, we're very focused on how do we actually lower the cost of training? Right, and back at the time, we're training models for image recognition.

• 4:02Speaker 00

  Can we tell the difference between dogs and cats? And, you know, can we recognize voices? Can we make voices? You know, we're doing all of that research, but really in the end, inference wasn't really a problem yet because the number of people using it were very small, because people are trying to test the model that they trained. Now what you're seeing at scale with Anthropic and with OpenAI and with Gemini, and you're at scale, you've got millions. And millions of people using it every day. And so now you have the problem that some of it was originally focused on, which is around efficiency. How do you actually deploy at scale? So the whole planet can use it without burning up the planet, without kind of running out of data center space, without, you know, blowing up your infrastructure cost.

• 4:44Speaker 00

  Because at scale, the number of chips deployed for inferring will be orders of magnitude greater than whatever you're doing for training.

• 4:52Molly O'Shea?

  And so walk through some of your chips.

• 4:54Speaker 00

  Yeah, so we started a company, so we're on, we've taped out six chips in the last seven years and we'll tape out seven for next year. We're really excited about generation five that's shipping later this year. When we started with SN ten and SN twenty, these are the early chips. They were really focused on training, you know, and they're focused on can we train those models faster with fewer chips, as you know. Some of the largest AI labs are deploying thousands of chips just to train one model. And they were trained for months and months and months, right? And so we're focused on training. As the world moved along, it became very clear that the bigger challenge, the bigger problem was inferring.

• 5:34Speaker 00

  Right, that once you were at scale, how do you actually deploy those same models you train on all of these different, you know, data centers for people to use? And now you can say, you know, I need a gigawatt data center somewhere in West Texas. That's one way to do it. But how do you serve all of the countries, all of the planets, all the different users that are all worldwide? And so, so you need to think about power, you need to think about data center, you need to think about latency. Right, and these are all things that we started taking on. And so by the time we released SN forty a couple of years ago, it, it became incredibly popular. Because instead of a hundred thirty, a hundred forty kilowatt rack of Nvidia GPU, we were outperforming them with a ten kilowatt SN forty rack.

• 6:20Speaker 00

  But in a 10 kilowatt as in 40 rack now suddenly and it was air cooled. You didn't need liquid cooling upgrades. Suddenly every data center that's around the world that you're using traditional CPU, traditional storage for, you could just roll in a somewhat of a rack, air cooled, and you got state of the art inferencing faster than on an Nvidia GPU. Right. And so that became a really, really popular way to deploy, especially if you don't want to put hundreds of racks of Nvidia GPUs. We would collapse the footprint because we could take a true in parameter model and run it in a single rack where it would take dozens of racks of other people's equipment to run the same model. And so, so that's kind of what some of it is really focused and known for is really

• 7:03Speaker 00

  driving premium inference, the largest models that are very low cost, very low power, and delivering ultra high performance, which, which becomes really, really valuable if you're actually starting to deploy these incredible models right at scale.

• 7:19Molly O'Shea?

  How has rack composition changed?

• 7:22Speaker 00

  If you look at for training, one of the challenges that you have is you have to aggregate all of these racks because you need thousands of GPUs together and they have to work in sync, right? As you're training each loop, you're thinking you're working sync. And the problem there was that if any one fails, the whole cluster fails, right? And so you have to put these checkpoints, you have to kind of stop every so often to make sure that you, you store the progress you made up to that point in case the next cycle fails, right? And so there's all of this work and then you need really, and you talk to tech folks all the time, you really, you need really, really high performance networking to connect all those things through. And so your networking equipment becomes really, really expensive.

• 8:07Speaker 00

  You need a lot of memory, you need a lot of software coordination and things like that. As you go into inference, the beauty of inference is it's scale out. And so basically you're adding racks.

• 8:20Speaker 00

  As your users grow, right? And so with Somadova, that minimum quantum is down to one rack. Right? Where if you have other, other service providers, you just run, say, a deep-seek model, which is now one and a half trillion parameters, just to run that, the minimum for some of the other providers might be 10 to 20 racks. And so you're starting to think about, okay, well, if I want to be an inference provider and I want to run these large models, my minimum cluster just to start serving is 20 racks and the cost structure is high, the power needs are high. Somadova, we can actually reduce the minimum down to a single rack. And so now you just grow as your user base grows.

• 9:02Speaker 00

  Significantly more efficient to deploy, significantly more flexible if you're going into environments where you don't have gigawatt. You can go into your data centers here in Paris, downtown Paris. You can find a, an existing data center and deploy five racks, 10 racks there for ultra-low latency where the users are, right? And so this is kind of what's, uh, what's changed in the racks is that, you know, we're, we're very focused on bringing it to broad-based deployment, standard, everything, standard 19-inch rack, standard air cooling, no complicated liquid cooling retrofit in the data center. We're using standard Kubernetes, standard red hat Linux, standard Ethernet at the top for networking. We don't have to use all this kind of really expensive networking equipment that, you

• 9:47Speaker 00

  know, to, to, to gang these things together. And so that allows people to go in to existing data centers, roll this thing in, pull out the old gear and you're up and running with the services, which otherwise might take you nine months to a year, maybe as long as 18 months to build a gigawatt data center, to bring liquid cooling in, to bring all this new power. And sometimes you have to figure out, you know, how to secure new power, new energy and nuclear power plants. And I mean, all those things that people are talking about, it just takes a lot of money, a lot of, a lot of time, where you don't have to do it if you use Somadova equipment.

• 10:19Molly O'Shea?

  That's, that's pretty good.

• 10:20Speaker 00

  Yeah. I mean, it's, and, and, and it's operating at the velocity of inference, which is people want to stand these things up quickly, right? It doesn't take months to inference, because these models have been already trained. Right? Well, that's a OSS model from OpenAI or Mistral moving in France. We talked about Mistral. Or, you know, you've got the DeepSeek and Minimax models, amazing models that are out there. Or the Frontier models or the closed source models that people are offering. It doesn't matter. You deploy this technology, you can bring those models in immediately. And you're up and running with the latest and greatest AI models in the world. And then you can upgrade them as you go along.

• 11:00Molly O'Shea?

  So do you buy the headlines that new data centers should be fifty or a hundred billion dollars?

• 11:06Speaker 00

  Well, I think you're gonna have some data centers like that, because I think there's still going to be large-scale deployments, large-scale access that people want. And I think you're gonna find that the world's gonna be heterogeneous, that there's going to be those large data centers that people go and secure a lot of capacity for some of the things that they wanna do. And I think you're gonna see this new wave of companies that are doing distributed data centers. Right? So these data centers, they're midsize, right? They're midsize, and it's gonna be even more important as you go into this agentic world. Because, you know, in the world of agents, you're not dealing with a single model and a single prompt.

• 11:46Speaker 00

  Right? If I go to ChatGPT, you know, we're here in France, and what should I do if I have an extra day in France? Right? You can talk to ChatGPT, it'll generate an attention rate for you. That's between me and the model, and it produces the result. In the world of agents, these agents are orchestrating within themselves without us. My prompt starts in the beginning, ten agents are all intercommunicating, and each of them taking some amount of time. And so if you actually have a lead time or a response time, let's say, two seconds, which for one user to one model is not very long, right? For our eyes, it takes as long as two seconds to read the output, right? And so that's okay. In the world of agents, where you have, say, twenty agents orchestrating with each other, if each of them takes two seconds to respond, now they've got forty seconds.

• 12:30Speaker 00

  The end user did an initial prompt here, like.

• 12:36Speaker 00

  Please move some money from my bank account, my Bank of America bank account over to PayPal and then give me a report of all my expenses over the last, you know, six months. Okay, that's my prompt. Then the orchestration of security and balances and all of that has to happen. And the output, if each of those 20 agents took two seconds, that's 40 seconds, you've already given up on that prompt. Right, so the response time by the end the user's expectation is say one to two seconds. Divide that by 20, it's less than a second. It's 0.1 seconds per. And so your response time is going to be really, really important. And that's why latency matters. And so you're now seeing us coming in and saying, look, we're going to deploy the hardware where the users are

• 13:20Speaker 00

  in large metropolitan cities, right? Because that's where business is being run. And so latency is really important. So we're going to deploy that. Well, unfortunately, in those large metropolitan cities, you don't have those gigawatt data centers. There's no space for Manhattan in Paris, right? Where we're going to find space to drop a, well, what'd you say, how much money you just say?

• 13:43Molly O'Shea?

  50 to 100 billion.

• 13:45Speaker 00

  I mean, where are you going to find even the space to build that? You're going to find some space in some place to build that. But in terms of ultra-low latency in the big cities, you're going to have to find smaller quantums, right, smaller spaces that allow you to deploy what you need for that, you know, for the users there that require that really, really low latency. And thinking banking, healthcare, like there are many use cases where your latency is really, really important and you're just not going to want to wait.

• 14:14Molly O'Shea?

  On the topic of inference, what is premium inference?

• 14:17Speaker 00

  The way we define premium is ultimately the highest value use cases. And there's two dimensions. It's basically on one dimension is the size of the model because it's about accuracy. Okay, and so years ago, we used to talk about hallucinations, right? You talk to Jack UPT and come, what it was this.

• 14:34Molly O'Shea?

  That was a fun time. I think we like, we should probably look back. Like hallucinations was a fun time.

• 14:40Speaker 00

  It does less of it now. It does less of it, right? These models are getting pretty good and does less of it. And yet accuracy is still incredibly important, right? And so why, why are these models going bigger and bigger? It's not that people want to spend hundreds of millions of dollars to train. Right? I mean, they're fighting for that bit of accuracy because you you look at a model like a Claude, Anthropic. Right? You look at that model. Why did it become the most popular code generation? A model when software developers go and type, it generates really good code. And you take a different model, sometimes not as much. And this is where also the open source, the minimax model, you know, that's out there as an open source model became very popular.

• 15:25Speaker 00

  Incredibly accurate when generating our code, right? And so, so these models still are being valued significantly for the output they generate. Because if you can trust it to produce good output, you don't have to invest as much human energy to go double check it. Right? If you have to go double check it, then certainly, you're certainly you're starting to invest more time. So on one dimension is premium is how accurate the model is. And today, that's proportional to the size of the model. Right? And so you have models that are very, you know, Lama eight B, for example, is a eight billion parameter model, very small. By seventy B, pretty small, used to be seventy B used to be the big, it's tiny today, right?

• 16:07Speaker 00

  When you had chat GPT and GPT five had five trillion, right? The new models are heading towards ten trillion. Even the open source models are already one to two, two trillion parameter models. And so now you're starting to see these models getting very big because people are looking for accuracy, right? And they wanna, they wanna, they wanna model that handles a broad range of things, but handles it correctly. And so we're very focused on making sure that we handle the largest models well. And then the second dimension of premium is learn it fast. Right? For the the reasons I just described about agents that, you know, you don't wanna take a long time. We live in an impatient world. Yeah. Right? You and I, I mean, a few seconds, we're starting to tap the phone, something happened, right?

• 16:49Speaker 00

  And so, so if the service behind it is agentic, you need to actually run really, really fast. There's not time for you to actually wait. And so that combination of running really big models, as you know, they don't run fast. Or you look at services like. Rock, cerebals that run fast, you can only run the small models. Right? So how do you find the ones that run really fast on the big models? And that's where some of our strength is, you know, that we take the biggest models and run them in the original precision. We don't, you know, we don't quantize, you know, quantizing is, you know, you chop half the, you know, weights off. So, so we don't chop the model down. We just run original precision, full precision, running faster than anybody else.

• 17:29Molly O'Shea?

  This episode is brought to you by Brex, my favorite. You become what you spend on, and I refuse to spend my time on work that shouldn't exist. Expense reports, receipt chasing and manual closes. The companies building what's next from Versel, OpenAI, Anthropic, Granola and Deepgram. All made the same call. They all run on Brex. Brex is the intelligent finance platform that combines cards, expenses and banking into a single stack. With agentic finance built in, AI agents that handle expenses automatically. Enforce policy before spend happens and close your books in minutes. That's why sorcery runs on Brex, so I can spend time on building and not busy work.

• 18:14Molly O'Shea?

  It's time to get Brex AF. Learn more at brex.com/sorcery. That's B R E X dot com slash S O U R C E R Y. Bye. At what point do you think speed is going to bifurcate the market much more into different pricing units? I know that token costs and everything is a really big topic right now, but for consumers, we do expect speed. But like, should we be getting enterprise quality speed?

• 18:46Speaker 00

  Well, I think you're going to find that people pay for it. And it's always been true. If you look at the internet, you know, people paid. Initially, they would pay a premium for the upper end of the packages for faster internet. You know, and then some people will still remain on the basic, right? And same cellphone, but here, here, I'll say this, look. When, um, when 5G showed up, nobody's signing up for 2G. Right? If your phone, if your cellphone is not transferring very fast, you're getting very frustrated, right? So I think on the curve is over time. As, as fast inference is broadly available, who's going to want slow?

• 19:25Speaker 00

  And so it's going to not only be the premium today where kind of the the people who need it, cogeneration people, real time banking, real time healthcare. There's a number of industries where speed matters because it's their livelihood. They're going to pay up for it. That's kind of the the premium service that we're offering. But over time, what you're going to find is that all of us are going to want. Like, I I don't see a use case where the average population, either consumer or enterprise, is going to say, actually, I prefer this low. That wasn't the case on the internet. That's not the case on cellular, you know, on on mobile data, right? It's it's never been the case. Let me pay more for this low. Right? Or even let me pay a little bit less for this low.

• 20:09Speaker 00

  But most people over time are going to say, no, I want the fastest. Right? And so as the cost of delivering fast goes down, you're going to see most people switch over to the fast. And so I feel like, you know, the the premium inference, which is large models, which equals the most accurate. Most accurate models and fast ultimately steady state is what everybody's going to want. Right? Is whether today we can offer a little enough cost that everybody can afford it, right? And so until then, you're going to see people very quickly moving over if they have a need for that fast inference and have a need for that accuracy, which is, I think, a large part of the enterprise.

• 20:43Molly O'Shea?

  There's also a couple other macro themes that are happening that are just going to explode data and usage across the world.

• 20:50Speaker 00

  Yeah.

• 20:50Molly O'Shea?

  One of them is Starlink. Like, I don't know if you've been flying around on planes that have Starlink, but even having access to that in remote areas.

• 20:58Speaker 00

  Yeah.

• 20:59Molly O'Shea?

  It just increases the amount of work you can do and work for, you know, like edge cases. Another part of that is edge computing. I think, do you you guys deploy in like, um, like edge remote areas as well?

• 21:13Speaker 00

  Well, what happens here is, again, this is tying into your question. Is is a fifty billion dollar data center ubiquitous? It's it's going to be hard to say that when you have economies that can't afford a fifty billion dollar data center, right? And you're going to see across the world. If you believe, if you believe that AI is going to be a technology as pervasive as as internet, which I do, right? This is something that everybody on the planet should have access to. Right? And so mobile service, internet, AI, you're gonna, everybody on this planet should have access to it. So if that's the case, then it's not gonna be equally deployed. Because in some countries, you can't afford to put a hundred billion dollar gigawatt data center somewhere. And then let tens of hundreds of millions of people come in and use it.

• 21:57Speaker 00

  Or you you might be in a in a place where you don't have that and you need something significantly smaller. And today, because of somewhat of a technology being as little as ten kilowatts per rack, we can put them inside shipping containers. Right? So we build out these data centers, clusters of ten, twenty racks, inside the shipping containers that you see on these ships, right? And then you deploy them in these edge data center use cases in a much, much more cost efficient, power efficient footprint than having to build out this liquid cool gigawatt data center with its own nuclear power plant, right? And you can, and then you can put, you know, a startling connection to it. You know, you can bring the internet in, you can actually have solar farms next to it, you know, and then you can actually power that.

• 22:40Speaker 00

  So there are lots of different ways that people are creating these data center clusters that allow their communities, right? Whether that's in regions or in industries or, you know, in in in and sectors, to be able to get access to the best models.

• 22:57Molly O'Shea?

  Yeah, one exactly, like, one particular company I was thinking of is Armada. I don't know if you know Armada, but Armada, they deploy modular data centers on the edge. And so that, I mean, those are, it's not just in like remote communities, but it's it's for critical industries. Critical industries that don't have that real time data that they've had before. So it's like oil and gas, it's mining, it's, you know, oil rigs out in the water, all that kind of stuff. And even for like military and and defense and that kind of thing.

• 23:25Speaker 00

  Yeah, no, you look, Armada's and Armada's a partner of ours, they've been a partner for a few years now, Dan's a good friend and.

• 23:32Molly O'Shea?

  Oh, yeah.

• 23:32Speaker 00

  And yeah, yeah, we've, they've got our racks and the same concept that if you can actually take, instead of having to put a hundred kilowatt Nvidia rack in there, you can put a ten kilowatt someone over a rack and generate more tokens. Right? That becomes very valuable because you can actually fit a lot more output in a smaller amount of space, a small amount of power. Right? And then deploy it into regions where you don't have, you know, the traditional data center available. You are in remote areas where you want AI to actually manage your operation out there. You know, oil rigs and things like that is an example, right? Uh, and, and, and, uh, and so it, uh, it's a incredible opportunity to actually get, uh,

• 24:14Speaker 00

  uh, this type of technology deployed in different vehicles in different ways. Because again, uh, access to them will be quite varied, right? It won't be just in that very large hundred billion dollar data center.

• 24:27Molly O'Shea?

  Talking back into the data center and the racks. You're now working pretty much with other chip companies in a way that like you weren't before. So how has that evolved? Is it, is it weird? Do you think that's gonna last?

• 24:42Speaker 00

  The world of co-opetition, you've got, you know, partners, you know, that, uh, competing in certain areas and collaborating in other areas. Look, the, the, the core tech industry is incredibly small. But who was building tech? But if you really look at it, how many people are truly building and deploying chips? How many people are truly building, deploying systems and building, deploying racks? And the building and deploying data centers is actually not that many, right? If you really look at in the, in the construct of like, you know, the entire economy, right? And so, uh, so we do collaborate, but here's what, what, what I'll say. In the end, we're all in service of customers. Right? And so customers are coming and telling us, look, we have these Nvidia racks. We have Zeon racks. We have AMD racks.

• 25:25Speaker 00

  We have other chips, you know, and how can you actually get my total business operating more efficiently? Right? And now that the world's going to inference. I'll, I'll give you this example. If you look at any service, uh, provider, like inference cloud provider, they've got Nvidia back sitting there. And this do all sorts of things, right? And let's say a thousand racks. You're doing training some models from some customers. You're influencing models for other customers. You might be doing HPC, right? Some sort of, you, you, you may be doing some biology, some physics. Who knows, right? Games, you know, you're gaming or you're doing all sorts of different things. And you are then, you bought those racks and you're trying to monetize. So someone else comes in and so we, we, we are focused on inference.

• 26:10Speaker 00

  Right? And we know that. When you're inferencing these models, we run it significantly faster at a fraction of the cost. Right? And so if I go into that data center, the inference has arrived and you look at the percentage, seventy, eighty percent of those racks are running inference. And so why would you run inference on those racks? When you can run it at a fraction of the cost, at a higher performance on someone over. And so route that traffic to someone over frees up all these racks for you to resell to all these other things that people want anyway.

• 26:41Molly O'Shea?

  Hmm.

• 26:42Speaker 00

  Right? And so that the economy starts getting much better because now without buying more hardware, they generate more revenue. They actually run the most popular models in a much more efficient rack, at a much lower OpEx and a much lower CapEx. And they're generating faster tokens, which then you can charge more, right? And so your premium service is able to charge more for faster. And so you're making more money. And in general, they're just lifting your margins on the same hardware infrastructure. And so, so that's usually kind of what the customers are asking us to do is how do you get our business actually generating better margins? Because for them to sustain themselves, as you know, today inference services, they're not making enough margin. You're generating lots of revenue, but you're not generating enough margin.

• 27:26Speaker 00

  And in order for them to sustain, they gotta be more profitable. And this is all the investors, you, you, you, you mentioned a couple of investors you're talking to. They're all thinking about, well, how do I sustain this? Well, you sustain it by having every service provider make more money, right? If they're making more money, they can continue to invest. And what we do is we generate more margins by giving you better inference service at a much lower cost.

• 27:49Molly O'Shea?

  How are customers measuring that difference? And how are they measuring the routing performance?

• 27:55Speaker 00

  Well, the rounds already happened today, right? So I'll start there. So if you look at, you know, a large data center, let's just say you're running open source models, closed source models, you're running some HPC. What, when a request comes in, you are already routing those models to certain racks, right? And so you could, these, these racks are running, you know, Entropic or these racks are running Minimax and DeepSeek. And so so that routing is already coming in. You say, hey, I wanna, you know, I I wanna run my service over to do a prompt router over to a minimax model. It will already go over.

• 28:31Molly O'Shea?

  It's automatic. Is there like software in there? Like, how does that work?

• 28:34Speaker 00

  Well, there's all, I mean, the software that's on top. And so if you look at kind of what these these API services are. And this is one of the beauties of what what the OpenAI's and Anthropic's really set up is they all set up these open standard interfaces. And so there's these API calls that you will run, and these are standard API calls, and some of we actually matched that as well. And so that when you prompt, actually, it will look for a particular API to a particular model learning on a particular IP address. Right? And so once you actually have that, then it's a standard interface. So when you're deploying your racks at a NeoCloud or hyperscale cloud, you just have the same API interfaces, and then you can leverage kind of what's out in the open community for routing.

• 29:17Speaker 00

  Right? And so you were already doing that before. So if I go and and I'm a customers, a customer of GPUs, for example. Right? The GPUs have A100s, H100s, B200s, B200s, even the different versions of it. Those are being routed to, right? Because, you know, if I pay for the newest chip, I don't want to be routed to a no chip. Right? And so so same thing with now you can just stand up other chips. You can stand up AMD chips and some other chips and other chips next to it. And they're all just already part of that ecosystem for routing. Right? And so so that's kind of the routing. But most service providers, and I think when before before we started, we were talking about

• 29:59Speaker 00

  KPIs and economics and how do you measure, right? How do you measure? And this is the way we we see most providers measuring. They purchase per rack, they operate per rack, and so they want to generate revenue per rack. And the revenue is generated per token. Right? If I put a rack of hardware, I'm just seeing how many tokens I might generally get in a particular model, and that model has a price per token. Right, multiply that by thirty days per month, twenty-four hours per day, number of tokens per second, and you can figure out how much money that rack is generating, and you look at how much it's costing you to operate. So that's as simple as that, right? And so that's what we focus on.

• 30:41Speaker 00

  We're very focused on making sure that when you deploy a rack of someone over here, generate great margins relative to the model. And you can change the model, because different models have different pricing per token, but you're still generating a significant number of tokens per month, so that you're making profit on that rack that you actually spent and operating on a per month basis.

• 31:02Molly O'Shea?

  Yeah, to your point, I did wonder this, because with some of the conversations I've been having, a common theme is there are no standard definitions for anything.

• 31:13Speaker 00

  Yeah.

• 31:13Molly O'Shea?

  And you're like, well, it's very clear with hardware. This is how you do it. And I was like, oh, well, that is the clearest definition.

• 31:19Speaker 00

  Yeah.

• 31:19Molly O'Shea?

  It does get a little murky when you go into like, how do you determine what an AI agent is?

• 31:24Speaker 00

  Yeah.

• 31:24Molly O'Shea?

  What does that even mean? What does a jailbreak mean? We just talked about this with Dylan Field, and it's just so interesting. And even way back when, it was like, maybe a couple of months ago, I interviewed Theresa Carlson. She's CEO of the General Catalyst Institute in DC. She used to be CEO of AWS for public, the public sector. And so she worked with government, and she was the first one to get AWS actually like a CIA contract. It was crazy, crazy story. She said the biggest difference for getting cloud into the public sphere to start selling to government was to have a standard definition.

• 32:01Speaker 00

  Yeah.

• 32:01Molly O'Shea?

  And then if you had that standard definition, they were like off to the races.

• 32:05Speaker 00

  Yeah.

• 32:05Molly O'Shea?

  Cloud really started to get adopted.

• 32:07Speaker 00

  Yeah.

• 32:07Molly O'Shea?

  So I guess between all the lines of all these, you know, different topics that we've been covering, from the chips to the data centers to power edge and also tokens and the costs there, what are the biggest bottlenecks that you're seeing, and what are the biggest challenges there?

• 32:25Speaker 00

  Well, I think, look, it's a land grab right now, right? From inference providers, well, that's at the hyperscale level. The frontier lab level, NeoCloud level, right? Or Sovereign Clouds? It's a land grab. You know, you can come to France, right? There's gonna be the hyperscalers coming in and trying to compete there. You've got all the model makers trying to compete here. There are even regional cloud players trying to compete in this space. Regional meaning not just France, but Europe, right? And so you're NeoClouds. And so you're gonna have all the competition. And so it's all about scaling. It's all about who can get to scale faster, right? Because as we've seen over history, the large players globally or regionally

• 33:09Speaker 00

  end up having this enduring lasting impact in the market, right? And so, so people are investing a lot to go and grab the users, yet grab the customers. Because usually once you're in, once you're kind of using, say, Microsoft or Google Gemini, you're pretty much kind of in that ecosystem for a while, right? And so you see that investment happening aggressively. And so for, for Somnovo, what we're really focused on is providing people with that edge and advantage. You know, what you want is you want to be able to come in and secure as many users quickly without having to deploy the traditional amount of capital that you otherwise would. People forget, as much as Nvidia costs, it's commodity.

• 33:51Molly O'Shea?

  Yeah.

• 33:51Speaker 00

  Right? Because what you offer is the same as what your neighbor offers. And your differentiation is, I can save you a little bit of money because maybe I got a discount from Nvidia. Right? Or maybe I got a subsidy from the government on my data center space, right? But in the end, your cost advantage is very limited, right? And so what Somnovo does is we come in and we offer differentiated service with premium token, premium inference, fast inference, the largest models, and allow you to remix them with existing infrastructure and now give you the flexibility to create different services, right? Now I can provide a sovereign service. I can provide my own national model on Somnovo infrastructure running faster.

• 34:35Speaker 00

  And so for that, now you can blend in premium pricing. And so you can go and capture more users because you offer something different. You can generate more revenue because you're premium. And you're actually deploying greater capacity at a lower cost, right? And so that land grab that's happening, you've got to find a way to actually do efficiently because the capital, you know, the capital cost today is getting more and more expensive, right? And so now that everybody's kind of, you know, building out data centers. So you have to use that capital really, really efficiently and secure users, secure companies as quickly as you can.

• 35:12Molly O'Shea?

  I mean, you, I feel like you've raised capital pretty efficiently. What do you make of all these other companies that are raising ridiculous amounts? Like, how do you justify that?

• 35:21Speaker 00

  I don't know. Well, look, I mean, well, one, so, so we're two and a half billion dollars raised in the history of the company. And there aren't really that many companies that raised into the multiple billions, right? If you count, if you really count them, right? There's a lot of money that's kind of been thrown around, but in the end, if you're able to raise that much, it means that there's something about your technology that makes investors comfortable with the fact that you are going to be a long-term player, right? You don't, investors are incredibly astute in this space, right? They don't put their money in if they don't think you're going to last. And so that's one. I think, two, if you look at why the biggest, you know, the biggest players, the kind of the most credible names are able to raise that

• 36:04Speaker 00

  is because there is a race to, you know, land grab. There is a race to be the dominant player. And in the end, I, I don't think, I think as, as much as these data centers and service providers are heterogeneous, right, they're using different chips, Nvidia and other chips, it's not going to be a hundred different chips, right? It might be two or three, maybe three or four, right? That's as heterogeneous as AI infrastructure is going to get. It's not going to be thousands of different versions because there's diminishing returns, right? You take someone over that offers premium, why do I need another, you know, and, and I don't want, you know, incrementally, you know, more variety in the data center, just, just, you know, for that reason, because you have inefficiency there.

• 36:49Speaker 00

  But bringing in providers that offer significantly different services from what they have. Initially is going to allow you to blend it. And so I think you're going to see that, you know, being able to actually take technologies that are different, deploy them and offer different services will start becoming the way that these service providers are differentiating in the marketplace. Right? Because today, if you look at NeoCloud, what's NeoCloud A and NeoCloud B? What's the difference? And we struggle, right? And we struggle, well, there's a black hole there, there's a black hole here. They run, you know, I mean, similar price. What is really the difference, right? And so I think you you're going to see these inference providers coming in and say, oh, I offer ultra-low latency agents. I offer very low latency in metropolitan areas.

• 37:32Speaker 00

  I offer it for banking for most secure and data private influencing. I've, you know, so so now you're kind of adding this layer of technology capability that allows these service providers to hone in on something that's differentiated, allow them to charge these premium services and allow them to actually average down the cost instead of being completely dependent on just one provider.

• 37:53Molly O'Shea?

  How important do you think it is to have cloud as a product?

• 37:56Speaker 00

  It's really important. I think, you know, being able to give people very low entry cost, you know, developers being able to access for free, being able to kind of get in is really, really important. Some of we do that, we do that through our partners, right? So some of you, for the most part, many of the chip companies have chosen to go build their own cloud and compete with the AWSs of the world. We have chosen not to do that. What we decided that, you know, we want to do is focus our our energy on creating technology that we can ship. Right? So we ship racks. What we've done is we've created a broad range of partners that are building the NeoCloud services, right? And so last month, we announced this great partnership with Vista Equity and Cambium

• 38:40Speaker 00

  on this new NeoCloud vector core compute, you know, VC2. And what they're doing there is, you know, so they they're deploying these ultra-low latency data centers because for them, you know, for them, using some of the technology opens up these markets that was was not possible before. And so we now are able to then offer cloud services through that partnership and actually. Bringing some of the biggest model providers into that ecosystem. Because they are able to actually support all the data center, the energy and the, you know, the facilities that, you know, service providers should do. And then we can actually then just ship the infrastructure that they need, right?

• 39:22Speaker 00

  And so that type of collaboration creates velocity, creates capital efficiency, right? Because that's their business to actually go and build the capital, raise the capital to build data centers. And then we can actually continue to focus on shipping racks, shipping racks, shipping chips, shipping software.

• 39:38Molly O'Shea?

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• 40:19Molly O'Shea?

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• 41:03Molly O'Shea?

  On the topic of sovereignty, this is, this is becoming, you know, a hot topic again because of Alex Carr on CNBC the other week, really talking about sovereignty and owning your data, your products, everything in the pipeline, Berkeley.

• 41:20Speaker 00

  Yeah.

• 41:20Molly O'Shea?

  And so i- in terms of your stance, we're in Europe, right? So like, how important is it for Europe to own their own chips?

• 41:27Speaker 00

  It's been a big part of our business, incredible, you know, part of our business. I think owning their own chips, I think every, every, every company wants to buy their own infrastructure in order to be able to actually run privately. What's more important is running a service and running models where their private data went into it. Right? That's what they're trying to protect, you know? And so if you look at, you know, whether that's sovereignty at a national level or sovereignty at a corporate level. Right? I don't want my data trained into a model and have that model shipped worldwide. But I can't imagine if your bank account information starts showing up in ChatGPT in some other place in the world without your permission, right? And so, so that's what people are thinking about.

• 42:09Speaker 00

  How do we protect our information in a way that it doesn't accidentally become part of just, you know, the models, right? And so in order to do that, what a lot of, you know, uh, countries are doing is they're saying, look, we don't want to base off of a global model or an American model. You know, we want to base it off of our own national model. And so countries have started doing this work and you see this in Japan and Korea announced the same thing. You see in other parts of the world where they're investing a significant, significant amount of money to actually train from scratch, train their own national model for use cases in the government, for their own citizens, et cetera, et cetera, right? And so, so it's not derived from an American model is, um, homegrown.

• 42:53Speaker 00

  And then somewhat of we're helping them in that, where we're helping them in, uh, part of the training process and the inference process of those models. And so you're, you're going to see this globally that there's going to be more and more emphasis around the fact that we should have as part of the mix, right? As part of this heterogeneous AI computing, uh, where you're going to use. Anthropic and OpenAI because they're great models. You're gonna use some open source models out there. But you're also going to have models that are, um, at the very least, privately fine tuned and owned by that country, right, through a partner? Or, you know, companies, right, banks and, you know, certain, you know, tech companies, creating their own models because they have IP that they don't want disclosed,

• 43:38Speaker 00

  and they want their own user base using those models. And so I think you're gonna see that become more and more, um, prevalent. And for that, you need your own infrastructure.

• 43:48Molly O'Shea?

  Yeah, the point being, competition wise, you don't want to be giving your data up, one for a security measure on PII or anything like that. But two, you don't want your competitors, this happened with Anthropic and Figma, like that was just an insane account to have someone internally and the company itself pretty much copy the product.

• 44:11Speaker 00

  Yeah.

• 44:11Molly O'Shea?

  And so that then disaggregates the types of people that feel comfortable training on that. We, we recently had Harvey AI on. Their legal model and so what they see their biggest competition as Anthropic. Everybody is on prem. There's like now a shift back to on prem. It's like the hot thing.

• 44:29Speaker 00

  Yeah.

• 44:29Molly O'Shea?

  You know, we always have these like, you know, fun cyclical, uh, patterns and behaviors of, okay, like, let's unbundle everything, let's bundle it back up again. Now we're like unbundling again. How do you think that effect will change prices and the service for customers?

• 44:47Speaker 00

  Well, repatriation of infrastructure into on prem is definitely happening, right? You saw this big shift, everybody's got a cloud, you know, everything to the cloud. And, and 20 years later, you still have companies just starting the migration to the cloud, right? And I was talking to a CIO recently about Big Bank and they were never gonna go to a cloud and say, see, I knew all along. I knew all along that, you know, the answer is on prem. I was like, it's cyclical, right? And so, uh, a 20 year cycle, wait long enough, it'll come back, you know? And so you're seeing that today, uh, for exactly the reasons you're talking about, right? That people want data privacy, data security. Uh, but here's the other thing. If you think about the global 2000, right, most of the largest companies in the world, um, what, in the end, what do they have?

• 45:33Speaker 00

  What is there kind of differentiate? Why do I get to charge a certain amount of money to my customers for service A, B, or C? Right? It's usually tied to some IP that I have. I know something, I have data, I have access to, you know, I have, you know, these supply chain relationships. It's some information, right? That allows you to operate your business that competes in the market. Right? If you actually transfer all of those, you know, services, that differentiation to all using the same exact model. That's in the community. Where does the differentiation come? Right? And so what most companies start to realize, if you just fast forward, it's not 10 years away. It's two years away. Right?

• 46:15Speaker 00

  If you fast forward and say, hey, I'm going to use all the commodity models.

• 46:22Speaker 00

  How do I get to charge more? Right? And so what you find yourself is in a place of low margin, you know, for all these enterprises that historically had been able to enjoy much better margins. And so what we see is the world is starting to, enterprise world is starting to come in and say, okay, how is AI going to impact me? You hear less and less about, it's about saving money. Right? The companies do want to save money and AI could save you some money, like I could help reduce the cost of X, Y, Z. Some, I can reduce some paperwork, some operational things. But where you're really starting to have people start to think about is how do I differentiate? Because you are going to have to do top line improvements, generating new businesses, generating new services, generating things that others don't have.

• 47:04Speaker 00

  Hard to do if you're using the exact same model that everybody else is using. Right? And so now they're starting to come in and say, okay, well, I need to train my own model. I have all this data, I know my customers better than anybody else, I can customize that better than others can because they don't have the data that I have. I've worked in this industry for 40 years, I know my customers, let me train my own and I don't want to give that to a model maker. Right? And so you're starting to see people come in and yes, the operational cost improvement that AI can deliver is starting to show in certain places. The more people starting to figure out how do I create a better A better experience, a better use case, so I can actually charge new services into the market.

• 47:47Speaker 00

  In order to actually generate better revenues for my company. Instead of just saving money, when saving money is ultimately gonna actually erode your top line.

• 47:55Molly O'Shea?

  I was really thinking about this from your standpoint too, because you deliver such a more cost effective and like. Higher performance product versus. The other chips that are out there, but like right now, like for companies that are out there, like they've been told. And I know the token maxing thing is a whole thing and now people are throttling that back a bit. But like, the guidance on everything is like, throw all the money you can at AI. And so at what point does that proceed? And then you start to get more economical about how you're throwing money at AI.

• 48:27Speaker 00

  Yeah, well, I think throwing money at AI isn't really the goal, right? I think the goal is really, is how do we actually let AI generate more? Business benefit to you, right? So what I would say is, throw as much of your cost at AI and see if you can bring it down. Right, so not spend more is take what you're spending and dump it into AI, see if you can actually generate more efficient cost. And on the other side is, take what you're doing, throw it to AI and see if it opens up new services that you didn't have before that generate more value to you. Right, and so, uh, so I think that that's gonna how how I think the world's gonna operate.

• 49:07Speaker 00

  Before you know exactly how you do it, people are just trying to encourage company, encourage your company to actually use more to discover. Right, to discover where can I save money, where can I generate new services, right? And a great example that I see in Google does, you know, in this transformation, what what they emphasized was, everything becomes AI first. Right, because you don't know what we don't know, right? It's kind of like early days of internet, if we say, what's the, what is the ROI for email? Well, show me, you know, I mean, there are companies that say, prove me ROI before you use it.

• 49:41Molly O'Shea?

  Yeah.

• 49:43Speaker 00

  You know, am I typing an email, should I try to figure out what the ROI of this email is before? You know, it's really hard to do, so, so what what some companies are doing is say, look, we don't know yet, right? Just. Go AI first. Because then, two, three steps down the line, you start inventing things that the world's never seen. Right? So that's incredible way of approaching a new technology from a very development-first, research-first type of company, where they're creating, inventing, right? And great things come out of that. Now, the danger of that's what you said, right? If you aren't paying attention, it becomes tokenized. They're just like, hey, just spend, right?

• 50:26Speaker 00

  Well, that's not really the goal of just spend on AI models is, how do you actually transform the business as efficiently as possible? And so what we see more companies doing now is saying, look, these are the things that we do. How do we actually use AI to actually make it more efficient, faster, simpler, cheaper? Or these are the things that we sell, services that we sell. How do we broaden our offerings into the market by letting AI expand it? Right? And so those two are much more systematic ways that we see companies actually using AI. And then what comes out of that is a calculation of, well, how many tokens does it cost, etc., etc.? But that's not, the goal isn't to maximize token usage. The goal is to actually maximize services that you offer.

• 51:07Speaker 00

  And then the net result is, can I actually do that more efficiently than, you know, I was doing it in the old way of doing business?

• 51:16Molly O'Shea?

  I know, we covered so much. And I'm so curious to see what else you have in that brain of yours. What are the biggest questions you think people should be asking today that they aren't?

• 51:27Speaker 00

  This world's changing very, very fast. And the first thing that people need to think about is, how are you going to differentiate?

• 51:35Speaker 00

  Right? And you've got, what is it? Eight billion people on this planet today. And you've got, I don't know how many, tens of thousands of hundreds of thousands of businesses out there, right? And the fear that AI is going to actually remove jobs, I think that's, you know, I don't think that's really the natural course. I think what you'll see is people are going to use AI to create more services and compete in the market more. And if you aren't doing it, right? Then you may run the risk of, you know, losing ground in your market. But most companies are trying to use AI to compete. And so for the first question that people should be thinking about is, how do you differentiate? Right? Just using AI doesn't differentiate. Right, but how do you differentiate?

• 52:16Speaker 00

  Because people want, people want to see services they have never seen before. Right, the World Cup is going on today and you look at kind of some of the players that are just the most popular players out there. Right, two things I'll say, one, we can get robots out there running around and playing games and, you know, and I will bet you that the human interest in watching machines play machines is gonna be far lower than what human, humans are able to do, right? I mean, we can get computers to play video games for us and there are services where people are staring 12 hours a day watching other people play video games. Right, and so, why? Because, you know, we, we want to see kind of what, you know, the, what humans can do and there, there, there's that piece of it.

• 53:01Speaker 00

  And so I think you're gonna see, one, that I think, you know, the people are gonna look for services that, you know, that they've never seen before. You know, something that's new, something they feel connected to, and there's a huge opportunity for people to do that. But you gotta start now, right? Instead of kind of worrying about, oh, you know, is, is AI gonna take away some of the jobs that we have today? And my answer is, I hope so. Some of these menial tasks, nobody wants to do.

• 53:27Molly O'Shea?

  Yeah.

• 53:27Speaker 00

  Right, I hope it does, right? Starting with, I hope I don't have to do taxes. Like, you know, think about some of these things that just, you know, you don't wanna do, but it should free up ourselves to create, to invent, to do things that other people haven't seen. And now, if you're, uh, uh, the, the goalie for, for Cape Verde, you go from, what, you know, what is it, 50,000 followers to like now 16 million followers overnight? Because, you know, he does something that people didn't ever expect him to do. Right, and so I think you're gonna see, one is that. I think for enterprises, I think you need to think about, how do I scale? Right, how do I scale? Because, um, the hints that you're seeing today with, um,

• 54:09Speaker 00

  Energy constraints, data center constraints, chip availability constraints, cost constraints. All of those things are only getting exacerbated, right? The world needs to, needs your service, but they need your service at scale. And so what is the ecosystem? What is the structure that allows you to kind of fast forward to say, okay, well, I'm gonna do this in a way that I can actually see a path for me at scale to compete in the global stage, which, which not everybody's thinking about. Because a lot of people are just thinking, well, how can you sprinkle a little AI to kind of save some money and get the board off my back? That could be one thing, but you know, look, there are companies, when the internet came in, a lot of companies who said, you know, it's not gonna last.

• 54:53Speaker 00

  Yeah, take a Circuit City, you know, so your audience may know, you know, or a Blockbuster Video, right? Yeah, I mean, if you remember at the time, Netflix was shipping DVDs to your home. And people are saying, you mean, you're taking a DVD that everybody rents for $2.99 and you swap it for a service, a $5 service for unlimited videos? You're gonna destroy your business. Fast forward. It's one of the top businesses in the world, right? And so if you think about kind of the embracing of the technology and think, okay, well, what can I do that I couldn't do before? And if I get fast and if I get differentiated, I'm able to attack the market in a way that I couldn't.

• 55:36Speaker 00

  And then fast forward two years, I'll be the dominant player, right? And that's kind of what these folks are, need to be thinking about with scaling. Is how I offer something at scale that dominates. And all the other players are slow to transform, slow to adapt. I think they're gonna have a hard time competing because as you see, capacity, supply, all those things are hard to get. And just like in Formula One, if you start your last position versus pole position, boy, it's hard to catch up, right? It's hard to catch up in a race where everybody's running really fast.

• 56:06Molly O'Shea?

  Damn, that was a great answer.

• 56:09Molly O'Shea?

  So I guess on that note, as we close out, you started the company in 2017. You've scaled it up to $11 billion. This is a question I like to ask on performance. One of our sponsors is Brex and they're all about spending is where we're moving faster. But on performance, I like to take it a more personal way. So for you, as you've scaled up the company throughout your career, I believe performance kind of comes down to who you surround yourself with, who you're mentored by, who you're surrounded by, like inspired. Who are those people for you?

• 56:40Speaker 00

  What I've learned in 32 years in the chip business is you gotta be resilient, you gotta be, stay in it, it's never a straight line, right? Business at scale is gonna come with the highest highs and sometimes the lowest lows and you've gotta fight through all of it. It's never a straight line and Jensen talks about this with Nvidia and all the different things that that company went through. And you know, you see it with some of the largest companies. But being very systematic about what is it you're about? What are you trying to do? You can't control what the world wants at any given point in time. You can't control what the economy does. You can't even control what the politics do, right?

• 57:24Speaker 00

  What you can control is your conviction around what you're building and being resilient and staying with it, right? Because great businesses don't just show up overnight. You have to keep at it, keep at it, keep at it. And this is what I'm proud of, that we're surrounded ourselves with great people that are incredibly hardworking, but more than that incredibly resilient, right? And they just tackle the next challenge, the next surprise, the next thing that's gotta be done with a level of consistency. And you keep going at it and then you wake up one day and you build something great.

• 58:00Molly O'Shea?

  Amazing. Before we close out, we have to see the chip.

• 58:04Speaker 00

  Yeah, here it is. This is the latest the latest Sama Nova chip. This is the SN50. You know, it's something we announced earlier this year, but we're now actually producing some really incredible results on this. And this aggregated inference that we talked about with Nvidia chips with Sama Nova already used with Zions is just the most efficient way of actually deploying inference at scale. And we're really excited to have this out there. It's gonna it's gonna change the world. And we're able to create very large clusters for hyperscale deployments down to very, very energy efficient deployments for edge data centers. And so it's something that I think is gonna is gonna change the inference landscape.

• 58:49Speaker 00

  And I think a lot of people are going to use this for their premium service.

• 58:53Molly O'Shea?

  Amazing. Thank you so much, Rodrigo.

• 58:55Speaker 00

  Great. Thanks for having us.

• 58:56Molly O'Shea?

  Awesome. Huge thank you to the entire Raze team for an incredible event. And thank you to Brex, MongoDB, and Assembly AI for making this trip and series possible. If you enjoyed this conversation, you're gonna love the rest of the Raze series with Tony Kim from BlackRock, Scott Wu from Cognition, Andrew Feldman from Cerebras, Rodrigo Yang from Sama Nova, Michael Hurston from Lumentum, CJ Desai from MongoDB, and many, many more like our hot takes that we did at a secret location that you can find on X, YouTube, and Instagram. Subscribe to Sorcery on YouTube for more conversations with the people shaping AI and join the free newsletter.

• 59:36Molly O'Shea?

  You can also do paid at sorcery.vc for weekly insights on AI, robotics, enterprise software, consumer, semiconductors. Did I say AI? AI again. And everything that's coming next, like funding announcements and all big things in tech. Thank you. Bye.