Intelligence Isn’t Enough: Why Energy & Compute Decide the AGI Race – Eiso Kant

26:08So it's all about de-risking and speed. And you kind of see a common theme. We think about lead time. We do the same, by the way, on our model building site. We think, you know, how quickly can you go from decision to having something online? And how can you make sure the capital that you have to deploy does not have to sit for 12 to 18 months? Because that's when you as a smaller company can all of a sudden scale up, right? Because the big numbers really have more to do with the fact that these decisions need to be made years ahead of time. We can make them months ahead of time. What's the target delivery date for like the first part to come online?

26:39So at the beginning of Q4, end of Q3, so next year, we've got the first compute coming online. and then we're finalizing in Q1 2027 the first 250 megawatts. But already next summer, the next 250 megawatts starts construction. It's a staggered build and this means that we will always have Horizon on essentially three times a year an additional 250 megawatts coming online. Last question on this and then we'll transition over to the more familiar software and AI side of the conversation. The environmental impact, that's obviously a question that the whole data center industry grapples with? How do you guys think about it?

27:19Let's first be honest about this, right? We're using natural gas for the generation of power. And in the world that we are right now, it's a skill that data centers are being built out. The sun doesn't shine all day. The wind doesn't blow all the time. And so renewables only, in combination with the battery capacity that you would need to power a data center if you want renewable only, it's just not economically viable yet. But the combination of like natural gas with renewables, with batteries, we've got a big best being built out on the site. It's a big battery system allows for kind of an optimal point in the middle.

27:56Now, when you talk about natural gas generation, you want to make sure that you have your emissions in check. So what you're adding is SCRs. And so you're an SCR is essentially catalytic reduction. So what you're doing is you are filtering out some of the more harmful particles that come out of natural gas generation. And you do this all within federal standards. You file for air permits that you have that are approved by like at the federal level. And so this is a critical thing to do as a business. And so you're combining those things. So that's on the energy side. The second side of environmental concerns around data centers are usually water.

28:33Right. Water is, depending on where you are, scarce resource. In other places, it's an abundant resource. it can impact like a local community. And so we're very privileged because of where we sit and the sheer scale of land that our partners have. Water is both a resource that is there at the levels that's required for us without actually taking away from like the local community. And this is not the case everywhere else in the United States or in the world. So picking this site was not just about like how large it can scale because it can scale into an incredibly large size, but also can you do so in a way that's responsible.

29:07and community what goes beyond the environmental topic is incredibly important you're building something out even though we are building out in a relatively remote area at the end of the day this would not be possible without the support that we get from the local community so we're in a place called Pecos County which is just some of the best people that I've ever met honestly if you want to go and just walk into a bar somewhere and spend time with some of the greatest people in the United States go to Pecos County because we've been amazed with like how welcoming like the community has been and so this goes hand in hand with how do we invest in you know job programs to bring people there and don't mean to start sounding like a politician matt because i think this is almost when i talk about community it feels like you are but these are you know these are projects that are not one-off right when we talk about this big multi-gigawatt project we've got about eight years worth of construction that we are planning out there and potentially as nuclear you know becomes a viable option in the coming years and we can build out more power potentially in nuclear over there.

30:04This can be construction that happens out there for as long as we can imagine because the space is close to as infinite as you're going to get. And so bringing kind of everyone along has been kind of a critical point for us. We just try to be good actors and try to be transparent about the positives and also the negative externalities and how to mitigate them. Fascinating and very different from the world of RL. But let's precisely switch to RL now. Since we chatted a couple of years ago, you mentioned that the world has changed and everybody was very pre-training focused for the last few years.

30:41And sort of feels like in 2025, the combination of pre-training and RL now has become sort of like the state of the art in what a lot of people do. Walk us through your approach. so when we did this podcast two years ago you know we spoke about our approach of reinforcement learning from code execution feedback. I think at the time I called out we had tens of thousands of these environments where the models would go in they would be given synthetic tasks, explore solutions and then be rewarded on the solutions that we require. And maybe you remind people what that is so you have a whole layer where you have those execution environment that you built.

31:17Exactly yeah so what we have done is at the time it was about 10 ,000 or I believe 20 ,000 code bases, like real world repositories that have complex software in them, where we define tasks and we would look for often single shot solutions. So here's a task, model can write some code, it gets tested, it might have additional signal from reward models and others in there, and it will be able to provide feedback. Was the code correct? Was it wrong? Was it rightly styled, et cetera. Over time, that has grown a lot across two axes. One is right now we have over a million of those environments and tens of millions of revisions, putting us in like the 30 million range.

31:56So just two orders of magnitude larger over the last two years. And that's because as you increase the diversity of the type of problems that the models need to learn in, you can increase the capabilities of the models. The second axis is that the models got so capable in their reasoning and longer time horizon capabilities, multi-step like complex planning and execution, that RL moved from kind of single-shot reinforcement learning to agentic RL. So meaning that now we give a much higher-level task, we send in an agent, that agent goes and tries to solve for the problem by using its tools. So editing code, executing commands, updating packages, doing everything that a software developer would do.

32:38And when you see it today in coding agents, it looks even quite natural to how we do things, increasingly more like how we do it. And then it gets rewarded along the way as well. So what we're seeing is that we're entering a world where the reasoning capabilities of models and their longer term horizon planning and execution is now getting so capable that the tasks we provide them are no longer simple tasks like the questions at the end of a textbook. But now it's like the projects that you're made to do in school where you have to do a lot more steps. And so we've continued to scale on those axes.

33:10Today, I believe everyone is scaling amongst those axes. And it's true where two years ago, this was a contrarian opinion that no one was doing. to date is absolutely consensus where we have again found ourselves in a point where we now have again a contrarian opinion on the future. And it's really not by, by trying to be contrarian. It's just that we, I think have had a pretty, we've had a couple of, you know, a long time thinking about this, right? I started thinking about this first in 2016, when I was building sourced and did the first RL with, with LSTMs, with language models. And now the last two and a half years with these larger LLMs.

33:45And our view now has become that the world is going in a direction again that we actually might not fully agree with on RL. So what you're hearing a lot about at the moment is the scaling up of environments with human expert rubrics. So it's not just going into coding where we've got verified rewards. Now we're entering into areas if that's, you know, how to operate on a spreadsheet or if that's how to do a complicated a task as a chemist or, you know, marketing professional, where you use kind of a rubric created by an expert to make it part of what a model can use to judge the answers. And it's highly effective.

34:26And by the way, we do it as well. It is a highly effective way to get models, singular capabilities on singular tasks to push it up. And I think what we'll see in the next couple of years is a lot of model companies ourselves as well. We go into these enterprises, we find high value use cases, we use our agents to get to 60 or 70%, you know, of quality, and then use additional reinforcement learning with, you know, domain experts knowledge to get it to 80 or 90 or potentially even 100%. But the narrative that that's going to scale us to AGI, yeah, we don't agree with. We think this is the right thing to do in the awkward teenage years ahead of AGI.

35:08It's because it allows you to create really economically valuable outcomes for your customers. At the end of the day, we're businesses, right? We need to be able to fund our economic engine towards our mission to Mars, towards reaching human level intelligence and beyond. But the singularly collecting of skills this way by creating environments and experts, we don't think will skill to artificial general intelligence. Our view is that reinforcement learning will have a moment similar like we've seen in language modeling. So what makes language modeling so beautiful? It's the fact that by predicting the next token on the web, you have this general self-supervised objective.

35:48You just can throw the entire internet at it and, of course, continue to filter it into more quality and improve it with synthetic, all the things we spoke about last time. But it forces the model to learn. But the reason that never got us to AGI is because, well, the internet never actually included the data set of the thoughts and actions that created it. It's the final piece of code. It's the final article you write, but not the thoughts that you had and actions that led to it. Not the trial and error. Exactly. And so our view is that there is a generalized objective for reinforcement learning.

36:21that doesn't require human judges or environments or reward models or anything external, but can generalize over language the same way that we have seen next token prediction generalize. A way of thinking about it is if you have traditionally we've taken the web and we've used synthetic techniques to reformulate it, to improve it, we've moved forward. We've taken the web and generated forward. What if you could generate backwards? So what I mean by that is what if you could reverse engineer the web or decompress the web into the thoughts and actions that created it? Is there such a general objective for RL that can be found?

37:01This is where we've done a lot of our research in the last year and a half. And we have now started seeing a lot of promise towards that being possible. Not yet too ready yet to talk more openly about it because we're still at a stage of the research where we've got a lot of things to prove out. Does it have a name as an approach? Yeah, so we call it reinforcement learning to learn. RL-TEL. Okay. At least the first time I'm probably publicly saying this. And so just to play it back and make sure that it's understandable by everyone. So there's one approach, which is a reinforcement learning where you create environments and reinforcement learning does trial and error, gets rewarded, not rewarded.

37:38And the path to scaling reinforcement learning is to just expand reinforcement learning to all sorts of different tasks. So that's option A. what you're saying is that you're working on an option B, or maybe that's option A, and the first one was option B, but you're working on a different approach where you're going back to internet data, reverse engineer the thoughts that went in reaching that conclusion in the first place. It's a perfect way of framing it. And we don't think they're mutually exclusive, right? We think that you want to have a model learn how to think and reason as early as possible in its training.

38:13And then you want to actually have it learn in the environments to sharpen its skill sets, right? And it's not unlike us, right? Like it's, we'll have learned a lot coming out of university, but then we're put in the job and we're actually doing it and we're learning from experiences. So the learning from experiences, the reinforcement learning from environments, right, and from experience is effectively like a renewable energy. The density of information in those tokens is not as high as like in a physics book is, right? Where a huge amount of density of like within a small amount of tokens and experience it's less, but it's highly valuable.

38:45So our view is that, you know, pre-training and predicting the next token on the web is an incredible bootstrap of understanding language and helping us get to a level of intelligence. Reinforcement learning to learn, RL2L, internally we also refer to as the Bondi techniques, kind of our code name for it, we think will push models to a level of reasoning and thought that will happen far earlier in their training than it does today. And then you have reinforcement learning from code execution feedback and from other verified environments that help learn really what is, you know, to sharpen those skill sets in simulated environments.

39:22And then the fourth stage of training over time increasingly will become learning, continuous learning from real world experiences of these agents. And so those are kind of the four stages that we think training will go to. If you will talk about that fourth stage a little bit, like continuous learning is something that people may have heard about that keeps coming back as one of the next avenues for the whole AI systems to progress? What is it and how does that work currently? And how does that work in the context of Bullseye? Today, there's one thing about foundation models that we have yet to really optimize almost at all, like overall of this time, which is the ability to learn from a single sample of data.

40:01Foundation models today can't do it and we don't yet have a path in them successfully doing so in a way that really improves it. Internally, we call it the hot stove problem. If you're a kid and you touch a hot stove once, you're never touching it again. Single sample and you're good. Foundation models, because of the underlying technology gradient descent, it's such a data-hungry algorithm. It requires so many samples to be able to navigate that higher dimensionality space to a place where it does something more optimal. And so we've got this big hole towards AGI of how can we get a model to learn continuously from a smaller number of experiences like you and I can.

40:40Now, there's a question if that's required to reach AGI. I would actually tend to say that if we take the definition of AGI to be that, you know, foundation models are as capable as you and I to do the vast majority of economically valuable tasks, maybe first behind a laptop and over time embodied in robotics, I would tend to say that it might not be necessary. But it, of course, is an incredible thing to add to intelligence because it will massively make it more compute efficient and will also make it more valuable over time. and what we do have already a path towards is when we've got a large number of users or the real world giving feedback on what an agent is doing, what a model is doing.

41:22Models and agents to me are effectively the same thing. We can incorporate that feedback in improving the models, and this is really the learning from real world experiences. This is when you've got maybe hundreds of thousands or millions of agent trajectories, tasks that were done, where some feedback was provided that can be by the person who instigated it or can also be by some form of environment or system. And so it's kind of taking it from in our clusters as foundation model companies to where it actually touches the users. And that feedback will come back. And so is that necessary to reach AGI?

41:58Not so sure yet. It's a very honest answer. But will it be valuable in the journey towards it to make our models more capable and more directed to what they want them to be. Like, absolutely. In all of this, you know, I have a bit of an issue. I started to use the term more because it's just more commonly used, you know, AGI. But it treats intelligence, we often treat it as this like singular spot that once we're there, it encompasses everything. But intelligence is so multidimensional, right? You've got people who are incredible creative writers who can create, you know, incredible works of art at Dostoevsky.

42:30On the other hand, you've got incredible mathematicians who can prove theorems. On the other hand, you have incredible people who are amazing at designing a factory. And it is not yet obvious that that is all a singular thing, right? And we see this very clearly with modalities, right? Someone who is not able to see can still be an incredible software developer, but it's unlikely to be able to be an incredible race car driver, right? Or unlikely, it's called impossible to be an incredible race car driver. And so intelligence is so multidimensional that we can get to a point where it's going to be as capable of doing all the knowledge work that we do behind the laptop.

43:10And we're still not going to agree that it's AGI and we still won't have solved every technical challenge. And that's also okay. Like, I think we've got a long road ahead of us to truly get to the incredible, like versatile, like features that we have as humans. But will we get to a point in the next couple of years where economically valuable knowledge work can be done by AI? I think absolutely. You just mentioned something interesting about the model and the agent. being more or less the same thing. So to weave that question into the current debate topics, Carpathia a few days ago said the agents were 10 years away.

43:45So what is your take on where we are with agents as it relates to AGI? And is there an opportunity to build agents without being a foundation model company? I haven't yet listened to the Carpathia interview and I really want to because I don't think you noticed, but I got into this space because of Carpathia. So in 2015, Andre Kapathy wrote an article called The Unreasonable Effectiveness of Recurrent Neural Networks, a blog post about language models. And that singular blog post let me start my company sourced and got me down this obsession for now a decade of like what, you know, language models and can do.

44:21And so I have an immense amount of respect for his opinion. And so while I haven't listened yet to the interview, I can't comment specifically on what he said. But what I can say is this, right? What is today the definition that we use of an agent? It's a model running in a loop inside an environment with access to a set of tools. And it's doing longer trajectory work. And how are we training agents? Agent capabilities as foundation model companies is that we take that agent, the binary, the piece of code that runs this in a loop and has access to, could be a container or could be access to a bunch of tools.

44:55And we train it together with the model. and so this is why you see the most capable coding agents right really coming out of people who are training with the models because and this by the way will likely hold true for lots of domains but when you already have a model that has all of the intelligence and all of the capabilities to do an agentic task right so it doesn't require more intelligence it doesn't need to be pushed further in like reinforcement learning well then the question what's the differentiation in the agent Well, differentiation in the agent is whoever's building it needs to have access to some form of proprietary data, some form of proprietary environment, something that allows that, you know, that loop that the model is running in to be, you know, more advantageous than another competitor.

45:39and there's lots of opportunity there but what i am careful about and we see this in coding right we see this ourselves is if you decide to start a company to build a coding agent where you are not able to improve the model right the agent is not able to train together with the model and foundation model companies like like ourselves are deeply focused on doing so you you don't have that edge and so the cat and mouse game that you're playing becomes a lot more difficult. It's not impossible. I've seen incredible agents being built for specific things because no one can singularly focus on everything, not any foundation model company and including ourselves.

46:17But we have a phrase at poolside, which is over time, everything collapses into the models. And I think increasingly that's what we're seeing. Frameworks or agents or products that were two years ago, you know, around today are, have, you know, either gone or sometimes their UIs have collapsed. So you used to see these products with lots of bells and whistles and lots of things behind the scenes and you would see lots of UI options. And today it's just like a screen that says agent and you talk to it and it goes off and does the work. And so I think you have to ask yourself the question, like where do you sit?

46:53Like what's your unfair advantage that you have? And if a foundation model company decides to focus on it, will that advantage still sustain or will it fall away because they're able to train the models to be further and more capable in combination with their agent and with yours. Yeah, it's funny how we went from talking about thin wrappers two years ago, three years ago, to thick wrappers last year. And it feels like in 2025 going into 2026, we're starting to think about agents that are really thin wrappers. Again, it's a constant evolution. I think we're all ignoring something, right? And maybe it's the wrong way of putting it.

47:30I think we have a hard time holding the point of view that models will reach the same level of intelligence and capabilities that the world's most capable people in every field have. And when you take a step back and don't take the next 12 month view, but just take the next, I think it's in the next 36 months for knowledge work. Maybe it's the next, someone else might think it's the next five years. I think few people today would argue that it's not going to happen in the next decade. And when you're building a business, you're not building it for the next five years. You're building it for the next 20 or 30.

48:00You're building a company that can sustain and be at the level of success, at least particularly when it's venture-backed businesses. In that world, what does your business look like if AI is as capable as the most capable person in the field? And that means certain things still exist. Like my Uber Eats app still exists because I want to look at my food and et cetera. But a whole bunch of vertical software or vertical agents are probably gone. And finding out where you sit in that world, I think, is critical. I think we have a tendency, all of us and myself included, to get so caught up in what's right in front of us that we don't take a step back and come back to the big things.

48:38And I think the big things really here are is that we are going to reach those levels of capabilities. And it rewrites the world really like that pre and post electricity moment far more than we, I think, have even factored in today. I think not even in the financial markets or not even in the way that we operate our businesses. It's truly living on an exponential to a point that I don't think we have ever seen before. Everything we've created till now was because of intelligence, but now intelligence itself is something we will be able to create and scale. And I think it becomes a very different world afterwards.

49:11So what do you tell people that argue that AI progress is actually plateauing? Frankly, the same thing. I've had this question for two and a half years now since starting Poolside. This is not the first podcast where, you know, there was a, are we hitting a wall? we are I think at a point where we are continuing to see with every new generation of chips an ability to make the model larger and it's important that this links to every new generation of chips because it is not a world where you can throw infinite dollars at scaling it's that's a false narrative and you can think of it very easily yourself because if I take the size of a model which is still effectively the biggest determinant of how much compute it takes to train it because of the limitations on the networking and because of the limitations on the flops on those chips it is not that I can linearly add more GPUs and train increasingly a more larger model if I do so the time it takes becomes exponentially longer so if tomorrow someone said I wanted to train a 300 trillion parameter model completely out of the realm of anything anyone's doing today it just wouldn't be possible.

50:24No matter how much money you have, no matter how many chips that you put up, it would become exponentially more expensive and longer to train that. And this is because of the current hardware limitations. But every two years, and now the chip cycle is even getting less, we have an incredible new chip and an incredible new networking stack that is improving, that all of a sudden makes the next generation of size model possible. Now, I don't think this will scale infinitely. I look at intelligence as compression. And at some point, you can compress further, or at least not to a point where it's worth it that it's diminishing returns.

51:00But we still have an ability to do that. So on one hand, we have a free lunch. As new generations of chips come out, we can build and train larger models. And it continues to show that it improves the model capabilities. On the other hand, and I think far more interesting as we're now getting into the world of scaling reinforcement learning, we're able to train models for longer duration with more data. Because on one end, with the size axis, and the other thing, we have the duration axis, how much data we're showing the model. And every single year, we've also become multiples more efficient with the data we had to make models become more capable in the lesser compute budget.

51:40It's kind of mind-blowing to see where even if I think about our pre-training at the beginning of the year, or we have a new run that's going on at the moment versus now, You can really see incredible improvements because you're refining the data better. You're creating it cleaner. You're creating a better curriculum. I know there's a long-winded answer to your question, but I think it's important to understand that it is not an infinite dollar. You can just keep throwing intelligence. It's kind of bounded by physics of every chip generation. As reinforcement learning is becoming increasingly more scaling axis, we are able to improve models within those generations far more.

52:14but we have not yet found a generalized infinitely scalable you know dollars we can throw at reinforcement learning either and so both on increasing model size and on increasing data from rl there's still limitations and if those limitations didn't exist pulsa wouldn't have been able to catch up right i think this is a really important part because we we're now at a point where we're starting like we should definitely spend some time with the models after this but They've gotten really darn good. We're now scaling up compute to make it to the frontier. That wouldn't have been possible if it was just a world of dollars.

52:47But it is definitely a place where, yeah, I think right now I don't see any limitations. I see more than limitations. I see opportunities in some of the research we're doing in reinforcement learning to learn and others that might completely open up new scaling access that can go even further. And then every generation of chip that comes out will see a stepwise function that goes up in our industry. You're very focused on software development. How do you think about the beta lesson? Is that something that you guys worry about? If you go back to our first podcast, or we put a post on our website on day zero of the company, we always set the path for AGR runs through software.

53:26It is not software. And we laid out this three-step master plan, which was step one, assist people in coding. It's kind of a very early day. Step two, allow people, anyone to build software. I think the world is clearly there right now. And then step three, generalize to all domains. And we're now really in that step three moment. And so already our models today have become generally capable across the board. But because we had kept our blinders on for those first couple of years on really software development capabilities as a proxy for intelligence, it allowed us to go really far. It allowed us to push the things that mattered.

53:58And our view, what was really missing was pushing reasoning. Like improving knowledge, you can improve knowledge outside the model. You can give it access to the right sources of information. but improving this kind of complex reasoning is what was missing. So we're kind of converged on the same point. So today I use our models to, the other day I had to write a sci-fi book while I was reading it, which was a lot of fun, right? And my brother uses it in his growth marketing job and prefers it over using other models. And so we're already in that domain now, but software development is still, with our deep focus on enterprise and government, often the most highest value, like value or highest driver of cost and also impact of knowledge work in a lot of businesses.

54:42It's not the only one, but it's a big one. So it's been an amazing place to enter into organizations with. And now we're starting to increasingly go beyond that. So we had a big announcement this week with a company called Red Panda, we're big fans of, and we just integrated their 300 plus enterprise data connectors inside it. And the demo that we were showing at the New York Stock Exchange was one of an AML process and finance that our agents are end to end together with these data connectors like doing. And so we're starting to open up much more broader outside of software development and come back to what we've always said, like we want to be able to power all of knowledge work in the world.

55:17For the last part of this conversation, let's go back down in the weeds and talk about some of the stuff that you just mentioned. I think I and people may be curious about the current reality of Poolside, both from a model standpoint, product standpoint, and commercial. So starting with a model, so you have three products on your website. You have Malibu, you have Point, and then you have Assistant. What's the current status of those products and what do they do with products slash models? Yeah, so if you go back two and a half years ago, one of the first things was Point, which was like the code completion models.

55:58Today, they're table stakes. We have them, but it's not where the intelligence sits. So our Malibu was our first big family of models. And so within the Malibu models, they were really oriented towards originally to be very capable coding assistants. Now they've become incredibly capable coding agents. And now they've also become incredible knowledge work agents. And so within that, those models are right now for their size and weight class, best in class. They have become incredibly capable of coding, but they're not yet at what I would define as the frontier. So frontier today is OpenAI, Anthropic, Google, and XAI.

56:31And that's why we have all of this compute coming online to scale up those model sizes. That's our upcoming family of models, Laguna, where we'll have a small, a medium, and a large. So small is finishing training in a couple of weeks. Medium actually starts training this week. And large starts training as our 41 ,000 GB300s come online. And you'll have benchmarks for those. Do you have benchmarks? We do have benchmarks, yeah. So if you think about our benchmarks today, we primarily search on them privately with the organizations. We'll get the commercial part that we work with. but if you think about the Malibu agent as a coding agent for instance right now it sits at a level of like SWE bench verified for instance where Gemini 2.5 Pro was when it came out and so it's a much smaller model but it's really pushed those capabilities because of our work in reinforcement learning and so we should do a demo after I think we're actually doing a demo in New York publicly because we've only done it privately with enterprises in a couple of weeks at the AI engineer summit and so if anybody's interested they can come and see it there and I think that will be recorded as well.

57:31It's an interesting thought from a go-to-market standpoint because you guys have been both public but kind of stealth in a way for the last couple of years and we're in a world of just like massive noise and like everybody's like struggling for attention. How do you think about that tension working with enterprises on the one hand but like on the other hand, like everybody's, you know, all developers want those products to be in their hands. So our view has been quite simple actually is that we will make models publicly available when we are clearly best on a very valuable axis, right? And I think that's important.

58:07It's not just about having a hype on Twitter for a couple of weeks and having something. You need to bring something in that can scale and that is valuable to the world. So before, and this gets to your commercial part of the question, we weren't at the frontier. Now we are getting increasingly closer and closer and that's also opened up our market. So before we were at the frontier, we picked a market where no one else could go, which was the defense industrial base and government. Because one of the things that we're willing to do, kind of from the enterprise DNA we have, is take our entire model weights and the full stack all the way with the agents, anywhere the customer needs it to be.

58:44So we today operate on workstations that go into SCIFs. We operate on servers that are on-prem or even as far as air-gapped. We operate in commercial cloud like AWS and private VPC environments. We also operate in more commercial, just cloud regular environments. And then on GovCloud, Top Secret Cloud, kind of the places where the weights have to travel to the customer. And we did this because it wasn't just about the model. It's because we knew that as the world was moving towards agents doing increasingly more work in the enterprise, it was going to become incredibly important to build out everything around the model.

59:22So today we can rattle off a very long list of enterprise features that we've built over time so that these agents and models can actually operate in complex regulated environments. And so that goes everything from a data access layer towards the managing of assigning of agents across role-based access control, deep integrations with all of the kind of active directory-like systems that people have, all of the monitoring, audit logging that associates with observability, go on and on. and we battle tested that in the defense industry because they're extremely large organizations. They're highly complex, they're highly regulated and they often need segregated deployments for different missions that they're on.

1:00:03And so we've been scaling that up in that industry. Now as the models have gotten to a point where we say, okay, now we feel that we can compete, we're going to wider enterprise. And so you'll see us increasingly more showing up in kind of the large enterprise names amongst financial services, industrial, like technology companies. And we treat our business as kind of two business models. On one hand, we want to make our models publicly available and allow anyone to use them. But we want to do so when we say, hey, we have something here that sets us apart. And that's the scaling up of computers necessary for that.

1:00:34So Laguna will be the... Yeah, the Laguna family will be public. And then we have the part of the business where we go very value add. And here we started with product. So we started with a coding assistant that sits in VS Code and IntelliJ now also sits outside of it. Then we started with kind of web interfaces that you expect to be able to chat and use the models, you know, connected with data sources. But we found something over and over again, which was that a lot of these enterprises and organizations, they have incredibly high valuable problems that can be solved with AI today. Hell, in many cases, with AI capabilities of two years ago or a year ago.

1:01:12But they're not successfully doing so. And a lot of that has to do with the gap between what the intent of the project is versus being able to bring it all together. The right data source is the right context engineering, often additional reinforcement learning that's needed. And here we started building up a very strong forward deployed motion. At Poolside, we have former Palantirians who came over and really kind of instilled some of that DNA. The DNA of what it means to find a high value problem and come in with high agency and help a customer solve that. You even have like FDR, you call them FDR, right?

1:01:47It's not FD is for chumps, like you have for deployed research engineers. Engineers, yeah. And look, what we found is that there is a gap between the skill set of doing traditional forward deployed engineering, right? Where you're focused on a high value problem and using kind of piping data sources together and building interfaces on top, which is, by the way, an incredible thing. I have a lot of respect for everyone who has done this. The research engineering side is interesting because what you're looking for is the people in that who also have the experience and the natural tendency to work very well with models and with agents and can work on additional reinforcement learning if it's necessary.

1:02:26And so we're taking these highly capable research engineers and we're putting them inside our customers. And that's for us something you're going to see us scale up increasingly more. It's why we're going to be opening an office in the UK actually next week. We're going to be opening an office here in New York and really kind of scaling up that motion of forward deployed research engineering. On that topic of geography, you guys started mostly in Europe, I believe, but it feels like over the last couple of years, you've recentered the company in large part towards the US. Is that a fair sentiment?

1:02:55And if so, what drove it? Yeah, so we've always been an American company. We've been incorporated, had quite a lot of US since day zero. And I think at any given moment, the balance of people would have always sat somewhere between 40-60 or 60-40 one way or another, like throughout the life of the company. So building it as a global company was critical for us. But the decision we made early on, and I think we spoke about it on the podcast two years ago, is to hire researchers outside of the Bay Area and particularly originally very centered on Europe. And still today, if we look at where researchers for pool sites sit, they sit predominantly 95 % outside of Silicon Valley.

1:03:30And this offered an incredible opportunity for us. It offered the opportunity to find highly capable, like highly motivated people who were not in the echo chamber that is the Bay Area. It's one of the most valuable echo chambers in the world. I personally love it. I spent a fair amount of time there. But you've seen and probably realized that there's a bunch of things we've done that were quite contrarian to the belief of that echo chamber. And I think that was partially unlocked by the fact that we sat outside of it and continue to do so. So we hire all across the United States. We hire all across Europe.

1:04:03We're hiring increasingly more in Asia as well as we're scaling up. And so, but we have and continue to try to avoid as much of an echo chamber as possible. Because I think the world, we're very early in all of this still. And the path towards AGI is one that deserves to be built by opinions and people that aren't all the same. And we have just found that there is no shortage of incredible talent in the world. It just might not be as obvious on a CV or a LinkedIn as it is in the Bay Area. So zooming out the next 12 to 18 months, what happens at Poolside? What can we expect? Models reach the frontier.

1:04:43That's right now. I think we see a straight line towards this. You'll see the scaling up of physical infrastructure to both empower training even more larger and capable models, but also serving them to the world. You'll see us have forward deployed research engineers and increasingly larger number of enterprises globally. You'll see us just continue to work towards the mission, right? I mean, for us, we never want to lose sight of the fact that we are building this company and the economic engine associated with it because we think that the world that lives after this is one where a lot of abundance can be created.

1:05:18Abundance through scientific progress that's going to happen, but also abundance through the fact that costing of goods and services ultimately are dependent on the cost of what it takes to create them. And as we shift intelligence to compute, we can find ourselves in a world where we can drive that cost down. And if you look back at the last 100 years, there's no moment that I think any of us rather live than today, right? And so that's the core mission. It always has been. And you'll see more of that. And yeah, looking forward to probably being here in 12 months to tell you about what happened.

1:05:52Looking forward to it. ISO, great to catch up. Thank you so much. Congrats on everything that you guys have accomplished in the last couple of years. Excited to see the data center and the new models. And thank you for spending time with us. Appreciate it. Thank you, Matt. Hi, it's Matt Turk again. Thanks for listening to this episode of the MAD podcast. If you enjoyed it, we'd be very grateful if you would consider subscribing if you haven't already or leaving a positive review or comment on whichever platform you're watching this or listening to this episode from. This really helps us build a podcast and get great guests.

1:06:26Thanks and see you at the next episode.