The American Startup Taking On China’s AI Models

Support for the show comes from Enjin. Every year, $1.6 trillion moves through travel. Most of it still runs on technology built decades ago. It works, it's just not intelligent. Enjin spent the last eight years building the foundation that intelligent travel requires. Robust inventory, hotel relationships, pricing intelligence, and payment rails. The unglamorous infrastructure work that simply can't be bought. Today, that foundation runs travel for nearly 40,000 businesses and 2 million travelers, and it's growing 60% a year. Intelligent travel, the future of business travel runs on Enjin. Learn more at enjin.com slash PropG. Welcome to the PropG Markets Founder Series. I'm Ed Elson. There's no shortage of concerns surrounding the AI build-out. Communities are pushing back against data centers. Workers are worried about being replaced. And some fear the technology could pose an existential threat. But one of the most immediate challenges for the U.S. might be the race against China, where open-weight AI models are gaining ground by offering a cheaper, faster alternative to the most powerful proprietary systems. Our next guest is building a company designed to give the U.S. a stronger foothold in that race. The startup recently released its first model, Beam, which it says outperforms other Western open models while being cheaper and faster. As the competition with China intensifies, the company is increasingly seen as a strategically important player in America's AI build-out, and investors are taking notice. Backed by firms including Sequoia, Citigroup, and NVIDIA, the company closed its June funding round at a $25 billion pre-money valuation. So can this new model give American AI a competitive edge against China? And what would it take for the U.S. to win the race in open-weight AI? This is my conversation with Misha Laskin, co-founder and CEO of Reflection. Misha, thank you for joining me. We'll get into open-weights, which is really what I want to focus on in this conversation because it's such an important topic that I think a lot of people don't fully understand. But before we get to that, could you tell us a little bit about your background? I know that you have a background in theoretical physics. You worked at Google. Tell us a little bit about the story behind Open-Weights. Misha Laskin, co-founder and CEO of Reflection. The reason I got interested in physics, it was, you know, I moved to the United States as a kid. We moved to a rural part of Washington State and had a lot of time on my hands and basically developed an interest in physics at the time. It might be, maybe part of it was that, this area was one of the places where the sites for the Manhattan Project, it was the Hanford site for the plutonium, was enriched, so it kind of had a rich history of physics in it. But what I really liked about physics was that it was a fundamental science that had, based on simple principles, that could explain the majority of things that we observe in the world and was at the root of the technologies that we are using every day. So I kind of saw physics as the kind of the fundamental science of the 20th century. The first computer program was brought forth by physicists. It was the ENIAC program at the Institute of Advanced Study. This was during the World War II era when computers were first kind of being used for calculating ballistic missiles coordinates. Then, you know, things like the transistors were invented by theoretical physicists at Bell Labs. Systems like GPS only work because of special relativity. You have to account for the speed of light. In order to have accurate GPS. So it kind of seemed to me that the fundamental science that was at the root of all the technologies we had at our fingertips was physics. And my switch into AI happened when I noticed that something similar was happening in AI where it went from a field that had a lot of, I'd say, diversity, but there's no real simple way to scale intelligence really until deep neural networks were discovered and scaled starting 2020-12 with the ImageNet moment. But when I saw AlphaGo, which was a system that played the game of Go, which is out of the traditional board games like chess and so forth, Go is considered the most complicated one because it has the highest what's called branching factor, meaning that the amount of moves, the amount of different kinds of moves that every particular board position or configuration can afford is just very high. So it's a very difficult, intellectually challenging game for people to play, and a neural network was trained to play it at a superhuman level. And in fact, that system had never stopped learning. You could just pour more compute into it and it would have just gotten better. And what I saw happening there was that a science that I thought was as fundamental as physics was being discovered in real time that would power the majority of technological advances coming into the 21st century. And it kind of seemed like, you know, some big discoveries are made in AI that make it kind of the physics of our time. It's kind of the physics of the 21st century. And that's kind of what got me into AI as a field in around the mid-2010s. So you're working at Google, and then my understanding is that you start Reflection in 2024. Why did you start this company? What did you think was happening in AI? Or what wasn't happening in AI that you could contribute to? The world, when we, it's funny, it's 2024. This is only a bit more than two years ago, but it seems a lot longer on AI timelines. And the field looked very different then. So I'll kind of rewind. What did the field look like in early 2024? GPT-4 had shipped, right? And ChatGPT had taken off. The first Gemini models were shipped, Gemini 1 and 1.5. These are models that my colleagues, co-founder Yanis and I, contributed to, especially on the reinforcement learning side. But at the time, intelligence was really only good at chat. The notion of coding, agents, all this stuff, reasoning, none of that existed, and it didn't work. Like, these models were just good as chatbots. They were not really good at any sort of cognitive work that needed to be done autonomously. And our bet at the time was actually twofold. We saw. the beginnings of an open-source ecosystem. So some open models had started coming out that were pretty good, you know, like the models for Meta and others. And so we thought that those models would give us a good base to build on top of, instead of having to, right, sink in our own large R&D money into pre-training these models. But then the big bet that we made as a company initially was that we thought that the paradigm of reinforcement learning, the same paradigm that created AlphaGo, my co-founder, was one of the key contributors to AlphaGo. So the same paradigm of teaching AI systems to learn through trial and error could be scaled on top of language models and create systems that were not just good at chat, but good at cognitive, agentic work, meaning. I mean, and we actually went in with coding as the entry point, that models that are good at coding, that can build software, that can do other agentic tasks on a computer, legal, finance, and so forth. And, right, we thought that, with building on top of open models, scaling up reinforcement learning would allow us to unlock this kind of capability in these systems. Now, what happened very quickly, actually, in the first year of the company, is that we were able to get, you know, the proof of concept of these systems of reinforcement learning working at small scale on small models. But at the same time, the field had actually discovered how to scale up reinforcement learning. So OpenAI's first models started coming out, the O series of models, that had introduced reasoning into the kind of public market. And so what happened was that reinforcement learning as a problem was solved actually faster than we anticipated. You know, we thought it would take years. It took less than a year, both, you know, within external labs and even within our own experiments. And then we saw another thing happening, which is that the Western open models started significantly lagging behind China, and the Chinese open models started coming out. They were very strong. I think we all kind of remember the DeepSeek V3 moment that happened roughly around the same time as the reasoning models were coming out. And then a whole ecosystem of Chinese models came out that was very strong. And about a year ago, a bit over a year ago, we made the decision that the most impactful thing we can do as a company is to take on the open intelligence mission. Because we saw a company that was doing a lot of work and a couple of things coming that, again, we thought would take a longer time to play out, but then played out a lot faster. One was that we saw the geopolitical tensions coming, that it cannot. The reason open models are so important is because they are kind of the thing that the world. Open source is the thing that the world adopts broadly. When you think about what is the most widely adopted operating system, it's, you know, it's Linux for computers and Android for mobile. These are open. open source operating systems. The internet protocols are open, were originated in America and adopted by the rest of the world. So open technology is the thing that becomes kind of foundational thing that everyone adopts. And with it, it brings in the infrastructure and kind of software of whatever nation is originating, right, the open source software. So we saw this geopolitical tension coming where if it's only, you know, one country that's producing open models, then the rest of the world kind of become dependent on those models and on the infrastructure of that country. And it's extremely important for America as a country to be competitive, and not just competitive, but have the best open source software and models when it comes to AI, and ensure that our allies that the world and domestically, we all build on our own open intelligence. So we saw that coming. Can I just interrupt for a moment? What actually is, what is open source mean? What does it mean to be an open model? When you describe Linux, which is open source? What is the difference between open source and open model? What is the difference between open source versus closed source? The high level distinction, closed, you cannot own, you can only rent, open, you can own entirely, right? So it's kind of like the difference between the rental and the ownership real estate market. When you are renting an apartment, you don't actually own it, right? You are subject to, you know, the whims of your landlord, which could mean price hikes, it could mean, you know, an eviction, it could mean, you know, all sorts of things, but it's very convenient. Right? You just pay rent and someone else manages your property for you. You don't really have to worry about fixing the, you know, when the pipes are leaking or something like that, right? You call, you ask your landlord and they figure it out. On the other hand, in real estate, there's the ownership market, right? Which is you buy a house, but then you also inherit all the problems that come with ownership. And for various reasons, right? It makes sense to buy homes for financial reasons, for control reasons and so forth. And you can think about open source in software and AI as really the same thing. You are either renting software or AI from someone or you're owning it yourself. And so when we think about open source, you know, in AI, we really think about it, you know, we think about it as open models. The model you either, right, you're either renting it from someone or you can put it, you know, I can give it to you on a flash drive, you can own it and you can run it on your own infrastructure and your computers. That is the fundamental difference. My understanding, which I don't think fully aligns with the analogy, is that an element of open source and the open models is that they're free. It's, I mean, when you, when you buy a home and you own a home, you've got to pay money to buy the home. But with open source models, my understanding is anyone can use it. Anyone can build on it, customize it. And that is a sort of a crucial element. Do I have that wrong? No, I think you're right. But it's kind of like, what would be the analogy here? The model may be free, even though some models choose to license. So you can have an open model that someone can own that has a license attached to it, right? And that's, you know, Meta's latest model, Muse, has a license attached to it, commercial license if you're a distributor. Kimmy K3 has a license attached to it if you're a distributor. So you can have open where there's a license attached to it that, you know, that can be monetized. And you know, by the way, even Linux, like the, it has like, it's not just a, it has a license attached to it where, you know, if you make certain modifications to it, those have to be open as well. You know, so it's not like there, there's a whole ecosystem of licenses around open, around open source software and open models. And so it doesn't necessarily mean that open means free. Now, most licenses on open models today are free. And even our model, you know, Beam that we released, it's an Apache 2 license, meaning that it is free. But just because the model is free doesn't mean that everything around it is free. If you are a customer and, or an enterprise, and you want to run open models, well, you need to get the compute from somewhere. So you need to pay for the compute. You need to get the software for managing that compute and for serving the models, right? Just the technology for serving the models is complex and, and hard to do. So you have to pay for that. You have to, it typically is maybe embedded in some kind of agentic workflow or customization workflow. And so that typically, right, you partner with someone, you pay for that. So you can kind of think about the open model as the engine, but the engine is useless without a car, right? So you have to build, you have to have the car around it. And so open, even while the model itself could be free because the license is permissive, the model in its own right is not that useful. I can give you our open model on a flash drive, but in order to make it useful, you need to have all the stuff around it. Would it make sense then to say that for the open, the open models, and one thing also what would be helpful as a clarification on the difference between open weight and open source, open weight is the new terminology that we are hearing in the AI world. And it's not totally clear what the difference is. But would it be correct to say that for the open model, you need to have all the stuff around it? You're giving away the training for free, but you're not giving away the inference for free. You're saying, here's your model. Here's the underlying model, kind of, as you say, the engine that's free. You can have it and you can mess around with it however you want. But if you want to run that model, and if you actually want to build a business around it, yes, you're going to have to pay someone to run the model, probably us. Is that the right way to frame this? That's correct. So the difference between open source and open weight, it's really, I think, open source is sort of the high level category around, you know, open source software. Open weight is kind of a specific instantiation of it. Because in previous iterations of software, there was, there were no such thing as weights, right? Weights are a specific thing to AI, and in particular to deep learning. So it's a, it's more of a specific instantiation. And there are some people who would argue, I mean, there's kind of open source purists, I would say, who would argue that it's not open source if you don't open source the data and training recipes and so forth. And our position is that, you know, we, we want, like the mission of the company is to build open intelligence and make it widely accessible. And so our goal is to open source, you know, as much as possible, that makes these models easy to consume and widely accessible. So when we launched Beam, we launched it with 40 different partners, we integrated into a number of different open source fine tuning frameworks. We released our own open source agentic harness called MIR. We're, you know, also about to release a tech report that is sort of open sourcing the science effectively, right? Kind of writing up in some detail how it is that these systems were built, because we think it's important for an ecosystem of researchers to understand how the, I mean, how these things are built. This is kind of a fundamental tenet of open source. You want scientists to build on your, your work. And the only way to do that is for them to understand what it is that you did. But it doesn't mean that, for example, in order to get good models, you have to buy a lot of proprietary data and you, in the contracts, you're not allowed to release that data to anyone, right? Because that's, that's some people's business, right? Selling data. So open source doesn't mean open sourcing absolutely everything, but we try to open source as much as possible to, you know, to make these models both useful to the scientific community, the research community, the developer community. But when it comes to then adoption within an enterprise or public sector, you know, context, then indeed the business is charging for the systems around the model that make it useful. And these systems are effectively serving inference to these enterprises, packaging up, you know, agents, you know, funds for those enterprises, fine tuning services for these enterprises, and so forth. We'll be right back. When 1.6 trillion moves through travel every year, it's a shock to remember that most of it still runs on tech from the 80s. I mean, work travel works fine, but it certainly isn't all that intelligent. That's why businesses and travelers book directly through Enjin. Enjin closes the gap between wanting to be somewhere and actually being there. Developers, expense tools, and AI agents all plug into Enjin's API. And as AI spreads, Enjin only becomes more valuable. Every AI agent that wants to book travel needs real supply, real pricing, and real payments. 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We've seen that the AI names that we are so familiar with, specifically open AI and Anthropic, whose models are closed, they have been forced to basically reprice everything. Because all of these open models are bringing the costs down. And that's why I'm interested in this idea of it being free. Because it's seems as though it's not that the entire thing is free, but this one element of it is free, which is resulting in significantly lower prices. I guess just one final question on the difference between open source versus closed. Does open source and open weight, does part of it also mean that anyone can go in and understand the underlying architecture of the model? Does open source mean that anyone can go in and change it on their own time? Whereas something like a model from Anthropic and open AI, you can't see the architecture behind it, and you can't know how to build off of it. Is that a correct distinction or not? That's correct. So, you know, in practice, right, it's not even that we don't know the architecture of, you know, a closed model. You don't even know the size. You don't even, you know, it's like, you, right, we sort of know nothing about it. And, that's in a sense, you know, it's kind of like, again, when we think about the car analogy, it's sort of like, if it's all packaged up and you can't peek under the hood, like the hood is sealed effectively, so you can't peek under the hood, you can't tinker with it, and, you know, right, you can't really understand it, customize it, and so forth. With an open model, you can, right? You can see the architecture. You actually, right, can plug it into either your favorite, open source, fine tuning framework, load it up on your GPUs, and actually, you know, customize it for your needs. So, you can, you know, you can take it apart. You can understand it. And fundamentally, what that means is that you move from a world where there are maybe, across the closed labs, maybe 5,000 researchers who understand these systems and are working on them. A minority of them are safety researchers. So, let's say out of the 5,000, maybe 500 are working on the safety of a small community, right? And this is such foundational technology that is so powerful that it's impossible for such a small community of researchers to really understand everything that there is to understand about these models and how to make them safe, how to align them, and so forth. With an open model ecosystem, you're inviting a much larger group of researchers, engineers, developers, right? Anyone who takes an interest can take a look and contribute to these things. And that is fundamentally, you know, how science is done, right? Science, right, it's not alchemy. It's science is fundamentally open. That's the way cybersecurity works. Like, the whole field of penetration testing is based on the notion that you have access to, you know, to software that you can actually see and understand. And actually, the most hardened software in the world, the safest software in the world, tends to be open source software. Because it's been, there's this saying that with enough eyeballs, all bugs become shallow. And that's why open source software is so ubiquitous and robust, because it's had hundreds of thousands of developer eyeballs on it looking at, you know, various sorts of vulnerabilities. And while they might not patch all of them, open source software is definitely more, tends to be much more hardened and robust than closed software. The thing that it's, you know, quite different from, though, is the traditional business of intellectual property, where if you create something that is extremely valuable, usually the number one priority of the business is to put patents on it and make sure that it is extremely closed, such that they can build gates around it and then monetize that thing. And so that's why the open model business, for me, I'm fascinated by how this business is going to work. Because I look at a company like Anthropic and OpenAI, who are, you know, number one and number two in the world of LLMs, but they are still losing billions of dollars a year. And, you know, for them, I think a big question around their business model is like, how can they raise prices? How can they keep prices up? And the open model ecosystem is bringing prices down. But I guess for a company like yours, and something I wonder about with DeepSeek, with Quen, with all of the other Chinese open models, is how is that business model actually going to work? Because my understanding is that to build a model and to train it, that's the most expensive part of the whole thing. And that's the part that is technically being given away. So how will the business model work over the long term? For most foundational technologies that we're aware of, and even in the software technology era, they started off as closed. But if the technology is important and foundational, then it's going to be closed. And if the technology is important and foundational enough, it was followed by an open version of it that then took up the majority of consumption. And this, right, operating system started closed. Operating system started closed. And, you know, Linux came, you know, after. So, right, closed could be, you know, MS-DOS and Windows, right? Microsoft's closed operating system, built a great business around it. Open source version of it, Linux came after. And now over 90%, of the world's servers, I think the number is even higher, over 95% run on, you know, some variant of an open source operating system, most often Linux. The same thing is true for Android, right? The first really mobile OS came from Apple, was closed. It was followed by an open source version, Android, that now the majority of the world's phones actually run on Android. The internet is an open protocol startup. It's an open protocol. It was built basically within, you know, government and universities. So, this is actually, like, what's happening here is a continuation of a very natural thing. And I think that the way we probably need to reposition our thinking about AI is that the models are a commodity like electricity or water, right? When we think about electricity and water, we don't think about, oh, how does, you know, my energy company, sustain its business, you know, it needs to be making 90% margin, right? That's not, we think about energy and water as commodities, that there is some margin, right, that these companies sustain that's actually low, but it's a, you know, massive service that's provided as fundamental infrastructure to the entire country, right, entire world. So, I think what's happening in terms of margin compression on the model side is a healthy thing because intelligence should be viewed like, you know, as foundational fundamentals, electricity, water, it's a commodity. Now, the inference, you know, margins, even on open models, are actually quite high. And the reason for that is because compute is scarce. Now, the reason is compute is scarce, which is, you know, GPUs effectively, but CPUs as well. Compute is scarce because the amount of, you know, intelligence you can pack sort of per watt, let's say, keeps growing, right? So, the amount, these models get smarter, faster than our ability to stand up compute. And you're, and when you're in the intelligence market, that's kind of like the market of artificial labor. And it's a very kind of positive sum market in the sense that how much, you know, artificial labor would I throw at really difficult problems like drug discovery or, you know, cyber defense, right? Um, you'd probably throw as much as possible at it in order to get the best cyber defensive systems, um, in order to discover new drugs, um, right? And, and I, and what I think will continue happening is that compute for the foreseeable future, I mean, I don't know how long this, uh, you know, how long that lasts, but for the foreseeable future, intelligence is going to outpace our ability to stand up compute because compute is, you're standing up stuff in the physical world. Um, and so the amount of intelligence you'll get per watt is going to keep increasing. Um, and so you want to drive intelligence, the cost of intelligence down as much as possible so you can use it more, right? There's like this classic Jevons paradox kind of thing. So all this to say that intelligence is a, is, should not be seen like a premium product, like an Apple, you know, like iPhone or something. Intelligence is more like electricity and water, and it should be a, it should be a commodity. The whole system around intelligence is expensive. Um, they'll have margin, but the margins today on inference are actually very healthy for both closed and open model inference. On the inference though, because, and so I think the utility point is really interesting. I think Sam Altman has said that he thinks that there's a world where that might happen with open AI. It doesn't seem to be the direction that they're actually headed in terms of what they're pursuing as a business. But the idea of AI as a utility is really interesting and agreed on inference margins are very healthy. But I do think that we're also, when we say that, leaving out this question of the training, which seems to be a very important and significant expense. But on utilities, generally speaking, utilities are in some way subsidized by federal, state, local governments in order to make it accessible forever and electricity, water, etc. Do you see that as the world or the AI economy, where, you know, perhaps because training is very expensive, that there will be some level of subsidization, similar to what we see with utilities? Perhaps in some future. I think right now the market is kind of too early to tell, right? Because kind of, I guess what I'm describing is kind of the desirable end state for AI is that it's, it's a utility and, like, like internet, right? The internet is effectively a utility, right? It's a, it's a commodity that is affordable to most, to most people. And there are a lot of projects in terms of how do you get Wi-Fi to work in rural areas as well. So it's unclear to me whether AI goes down the path of electricity, or it goes down the path of, you know, the internet. But nonetheless, whether, you know, one is government subsidized or not, both are sort of seen as, they're seen as large scale commodity services. And a lot of very successful high margin companies are built on top of them, right? A lot of companies with high margins are built, built on top of, of the internet, even though, you know, access to an internet in its, in its own right is fairly affordable. These open models are so interesting, because DeepSeat comes along. And I mean, we all remember what happened in the market, suddenly, everyone panics, and you see all of the AI, US AI stocks going down. But it seems like what happened, what is happening in China, which is why you have emerged as this strategically important company for the US. And correct me if I'm wrong on this. But it seems as though China is using vast amounts of government subsidies to create these models, which they then distribute to the world for free, thus changing the entire complexion of the AI model market, bringing down prices significantly. But ultimately, from my understanding, the government, the CCP seems to be paying for it. At least when it comes to DeepSeat. I mean, it seems like that is, that is what is happening here. But is that wrong? I mean, to what extent is, is, is Chinese government involved in funding and promoting these open models? I probably don't know, you know, much more than, you know, you do on the subject of the CCP's involvement. But I would agree with you that it's very hard to explain the, you know, Chinese open model ecosystem without deep support from the government in some way. Right. It's just hard to explain the, you know, these companies were around for actually some time. It took a few years for them to get to the point where they actually started charging, you know, meaningful money for their products. And even so, you're right, there's like, you know, a large investment that needs to be put into it. I think there's a big difference between, you know, China, you know, the business models that these companies have in China versus Western labs and distribution. Distributors in China, it kind of, I mean, from the outside looking in, it looks in a sense like a captive market in the sense that while there are open models, you know, in China, I don't really see too many distributors of those open models in China outside of the companies that are building them. Right. Like DeepSeek has their API and Kimmy has their API and, you know, GLM or Zipu, ZAI have, have their API. And it seems to me, again, from the outside looking in, that they are actually, their business models are not dissimilar from closed models in the West, but in China, right? But in the domestic market in China. To clarify, you're saying that they operate as closed models within China, but outside of China, they're open? Is that what you mean? At least that is my perception from the outside looking in, because I'm not, I'm not seeing like, you know, distributors popping up in China, right, that are serving, you know, DeepSeek, outside of DeepSeek, for example, right? So something is like interesting in the market structure there, where, you know, because they're, I think that there isn't much trust in consuming from Chinese APIs in the West. And so for that, but, you know, China is a big market. And so I suspect that, you know, the way in which like their businesses operate within China is, you know, not too dissimilar from a closed API, effectively. And then you kind of give away the model, you know, for free, you know, for free, you know, for free, you know, for free, you know, for free, you know, for free, you know, for free. And so I think that's a good thing. And I think that's a good thing. Well, because you can't really monetize it anyways, because people abroad don't really want to use a Chinese API. And I do think it has geopolitical advantages and as a sort of, you know, one could think about it as a sort of like economic, right, incentive to, to drive down the margins of your competitors abroad. Right. So, so that there's kind of something interesting happening there in the West. In the West, it's, it's similar, you know, but different in a sense that it's a competitive market. When you release an open model, there's an ecosystem of distributors that, right, can basically go and serve that model. But you do get a few kind of advantages, an open model builder that we're seeing come to fruition. One is that by building the model, you're, you've also built frontier state of the art kind of stack, a stack for serving the model, for building agents around it, fine tuning and so forth. And you are kind of like six months ahead of your competition in terms of your technology development, because you're, by the time your model is released, right, the technology for supporting it was actually built out well in advance. So you have a technological lead. And the other thing that you have is because you are a, you know, a strategic company to the nation and to the West is, you know, the, the trust that it builds with enterprises, public sector, sovereign entities, is, is very high as well. And fundamentally where we see the markets for consumption of open models, like the biggest markets for consuming them are, you know, going to be in enterprise, you know, like the big banks, public sector, sovereign entities, and those like the sell there, like, whereas like selling to a startup might be a very technical sale, like, Hey, just go pick up my product and, you know, you know, try it. Selling to those entities is like, yes, you have to win on the technical stuff and you have to show, um, really strong technical differentiation. But you also right there, they're looking at you as, are you going to be the strategic partner that helps me transform my organization with, uh, with open models and the open model builders, um, have a lot of kind of, you know, trust from that perspective. As being the right kind of strategic partner to transform the enterprise. We'll be right back. Support for the show comes from engine when 1.6 trillion moves through travel every year. It's a shock to remember that most of it still runs on tech from the eighties. That's why businesses and travelers put correctly through. Engine closes the gap between wanting to be somewhere and actually being there. Developers, expense tools, and AI agents all plug into engines API. And as AI spreads engine only becomes more valuable. With 40,000 businesses relying on engine and growing fast, they're powering travel for the masses with partnerships like Navon and the Tampa Bay Buccaneers. That's the kind of foundation that takes years to build. Eight years in engine's case, and it can't be bought. The future of business travel runs on engine. Visit engine.com slash prop G. You mentioned that there might be some sort of geopolitical strategy in Chinese, in the Chinese open market. And that seems, I mean, if it's the case that the ecosystem is, somewhat closed in China and then not closed outside of China. That, I think, fully aligns with what I think we are in agreement on, which is that it seems like the proliferation of the Chinese openweight models, which they have really doubled down on and they've invested significant resources into, was partly, actually, I think almost entirely, a mission to, as you say, erode the margins of the existing AI businesses in America and upend the AI market. My co-host on Profiteering Markets, Scott Galloway, calls it AI dumping. And basically, China goes in and they say, we're going to inject a huge amount of supply into the economy, and that's going to kind of screw up the businesses that have really gotten ahead in America. And that's what I think is going to happen. That seems to be true. At least that's what I think is going on. So I guess the question is, you know, your company Reflection is seen as the response to that. You've had significant investment from some of the most important firms in the world, from NVIDIA as well. And Jensen Huang has specifically stated his support for open models. He thinks it's really important. You've also sold your model to government infrastructure groups. I believe the Pentagon is using Reflection's models. There is this idea that Reflection is going to be America's response to China. My question, how will this get back at China? Like, how will this change the market and put America in front again? What is strategically important about Reflection, given things go right? First, I actually disagree with the, you know, premise of AI dumping and it's somehow, you know, being negative for the Western markets. I think. But yeah, I kind of agree as well. I'm sort of framing it. Yeah. The fact that there are great open models out there, period, doesn't matter where they come from, is a gift to us all, right? So the fact that this happened in China or wherever, but it happened in China, and there are great open models in the world that people can build on, is a gift. So they gave us a gift, right? Like, so it's whatever their intentions were, they gave us a gift. And how do we see that? Our best startups that, you know, started kind of getting tall poppied by some of the, you know, closed labs. And again, it's not anything bad or negative at closed labs. It's more that they have businesses to build. So of course, if coding is very lucrative, they're going to go build a vertical product around it. Why not? Like, they have to build businesses. But, right, you want a competitive ecosystem where the startups that are also building coding agents can build businesses too. And what did those startups do? Well, they actually started moving to Chinese open models, building on top of them, customizing them for their workloads, and standing up on their own two feet. So this is a gift, right, that China gave to actually American startups. And if you were to take away open source, you know, models altogether because of some, you know, geopolitical worries, it would actually hurt the American innovation ecosystem. So I think the first thing that we need to do is to start building open models. And I think that's a thing we have to think about is that, like, actually, this is kind of, from a builder, developer, innovation perspective, these things are gifts. And it's great. Like, we could be in a world where there was no good open models, and that'd be a much more worrisome, darker world to be in. Yeah. And the other thing is that, again, there's AI is one of those, it's not kind of a fixed pie zero something. When you drive the cost of something, in this instance, like the demand goes up. It's a sort of Jevons-Payne thing. It's a sort of, you know, it's a sort of, you know, paradox sort of thing. So when you take AI and you, so far, every time you've kind of driven down the costs, the demand has gone up for them, right, for these intelligence systems. And right now, obviously, enterprise is trying to get very cost conscious, not because, like, look, they put in, an enterprise would happily put in billions of dollars into AI if it yielded some multiple of that, right, in value back to them. But it just takes some time for that to manifest. And it is the case that, to date, AI has been quite expensive. So, again, I think that, on the first point, that the existence of great open models is a net positive. Now, why, like, now, when we think about it from a geopolitical and strategic perspective for America, it's very important that America is, you know, the leader in open model building and that there's an ecosystem that's distributed across the world. And the reason for that is that, both domestically and abroad, so domestically, our Fortune 500 enterprise is very, very expensive. And so, again, I think that, on the first point, that the existence of great open models is a net positive. And so, again, I think that, on the first point, that the existence of great open models is is that our domestic enterprises are, do not have the same competitive edge, let's say, as the startups that are able to, you know, assume the risk and don't care about it, right? So, we need to provide good solutions for, you know, you can kind of think about enterprises who, like, instead of renting their intelligence, they want to own it or they want to do something, they want to do a bit of both. You have to provide them a good solution. And so, where we are at the market is that our, you know, largest enterprises are public sector agencies. are operating at a competitive disadvantage to Chinese companies and also to other companies within our own ecosystem who are willing to take the, right, the sort of risk around it. So, that's, it's extremely important to have something that's competitive to Chinese models to support our own companies and agencies. And then abroad, it's really important that our allies build on American technology. And this is kind of where the geopolitical part comes in, is that the world of abroad will adopt whatever the best stack is, right? Whatever the best technology stack, they'll adopt it. There's a reason why Linux and Android came from America and not, you know, from China. America has been traditionally really good at building the best open source technology in the world. This is the first time where, for something so major, it is not being originated in America. And what that means that the world abroad is building on Chinese technology. What that will lead to is that, you know, open models are, they're kind of, you can think about it as Trojan horses for the infrastructure that they bring. Because, again, it's just the engine. You need the car around it. And what's the car? Well, it's the chips, right? So, now, like when, you know, Chinese companies go and sell open models into the Middle East or other Asian countries or, you know, Brazil or what have you, they will now also start selling chips, right, and their data centers. And what that means is that other governments, allied nations, will start getting locked into Chinese infrastructure. And that is really the risk to the United States from a national security perspective. And so, it's just important for us, both domestically and internationally, in order to have, you know, for our national security as a country, to have the best stack on a merit that, you know, our open models are the best, our chips are the best, and the world chooses to build on our technology. So, it's kind of nuanced. It's like Chinese models, it's like incredible. It's a gift to the world that they exist. But for our national security, we need to have a great offering that our allies can adopt. When you say the Chinese gave us a gift, I'm like, would they ever really give America a gift? And I think what you're pointing to is that there are sort of two theories behind why they would do this. One of the theories is it's not a gift at all, and they're trying to erode the economics of the AI market in the U.S. It sounds like you don't fully agree with that. And in your view, maybe the analogy would be something like the Belt and Road Initiative, where they're going out, building, making other Western societies incredibly dependent on their own technology. And as soon as you have the upper hand in that way, maybe similar to the way that we have a huge dependence on their critical minerals, which has obviously become a huge problem, then that's a lot of leverage that they can use later down the line. That's correct. Right. So, I'm going to assume that's what is happening here. I guess the next question is, why wouldn't the U.S. just ban Chinese models? I've been sort of waiting for that to happen. Like, you know, why not just ban DeepSeek? Why are we letting them flood our market with their open models? I guess it's useful. But I don't know. I'm just a little surprised, given this administration's positioning on China, why we have been kind of open to them, to using their models. as a country is in its ability to foster very diverse competition and innovation across this country. And openness enables you to do that. So I actually think that these, what actually happened, right, is that when DeepSeek V3 came out, there was a market scare and, you know, some stocks went down. But the reality is that even like it was an irrational reaction. Those stocks rebounded and started doing very well because of demand for various things like compute went up because, you know, now you have an open model out there. Do you have more buyers for compute, right? You have now anyone who, again, right, it's an ownership market and ownership means that I get the model and I put it on my own compute. So by nature of having a good open model, there's just gonna be more demand for, you know, diverse demand for compute. Also, you know, the like startups and the various, you know, ones that are doing like more scientific pursuits will just be able to explore a much wider set of ideas than any one company could. This is just kind of the way, right, the innovation economy works. And it's extremely important that we preserve that. Now, right, so it's kind of, I think that there's a simple answer that I think would be sort of nice for us to believe that like China is bad, therefore, like, you know, open models from China is bad, but it's actually more complicated. It's actually, no, open models from China, like very good, but we also need to have our own, like it's really good for the ecosystem, but we also need to have, you know, our own great open models. Like these are the two superpowers, right, of the world, right? And America is the predominant superpower. And so it's kind of like you're building a foundational technology that it's like, you know, like if electricity existed in one place, but not in another, right? You have to be competitive. You have to ensure that the world builds on top of your stuff, you know, for your national security. And while they kind of seem at odds, both things can be true, that it's great that Chinese open, models are out there, but it's also something that is a matter of national security for this country. Do you think anthropic and open AI will at any point switch to being open? Do you think that, I mean, I just, if this is the direction we're headed in, where, you know, if we're basically giving the engines out for free, and the idea being that if you give the engines out for free, then that's going to spur a lot more activity and a lot more growth and a lot more innovation, which will result in a more prosperous and more productive ecosystem where the returns will eventually be seen in the future. If that's the direction we're headed, and it seems like maybe we are, especially if China's just deciding to do that, why would you not use cheaper open weight models? I mean, does that not mean that anthropic and open AI are going to have to get with the program? Like, what are they going to do? Yeah, I think they're going to do just fine. These are incredible, companies that, you know, have scaled at an unprecedented rate and keep discovering new breakthroughs in AI. So, you know, I think that you can be in a world where, I mean, it's a positive some world where it's a this and that. There'll be open model companies and kind of builders on top of open models and distributors that are successful, right? And there will be closed model companies that are successful, and you have a rental and ownership market. And just because ownership is starting to, you know, grow in its, kind of in its demand, doesn't mean that everyone will then like own a house, right? There'll be still people who are renting, still people who are owning. I think that, I mean, open AI has open sourced models in the past. I think that when something is not the core strategy of the company, you tend to not pay as much attention to it. And you tend to open source, you know, maybe like a lesser version of the thing that you're actually kind of monetizing. And that's kind of what we've seen from, open source launches from, you know, Google has done a lot for the open source community with the Gemma launches, but it's a very different model class than the Gemini models, similar when an open AI launch GPT-OSS. So, right, it's very different from the class of actual internal models they were sitting on. So I don't see that strategy necessarily changing for them. Because I think that they'll, they have so much opportunity ahead of them just chipping away at their kind of current business that I don't think they might. But I think they're also going to do just fine. Reflection has been in the news recently. Reportedly, NVIDIA was in talks to acquire your company or at least deepen their relationship, deepen their investment. NVIDIA is already one of Reflection's largest shareholders. If you can speak to that, that would be great. But more generally, what's next for Reflection? What is the plan? What's the plan over the next few years? You know, the goal for the company is to continue building great open models, deploy them into customer segments, into enterprise, public sector sovereign, and build a generational company around this. And we hope that we're not kind of the only ones that, we hope to see an ecosystem of open model builders come out of the States this year, next year, that rivals, you know, that is comparable, right, in quality to the Chinese market. I think the thing that makes China so powerful in its, you know, open model build-out is that it's not just one company. It's a handful of companies, and it's a whole ecosystem. And the fact that one keeps leapfrogging the other ensures that kind of as a composite, there's always, like, great models coming out from there. So we want, you know, Reflection to be a standalone leader, right, in this ecosystem. But we want to see a whole ecosystem of such companies emerge in the future. So we want to see a whole ecosystem of such companies emerge in the future. Right, in the West. We don't really comment on, you know, on media speculation. But, right, the plan for the company is to continue building great models and, you know, and basically distribute them into enterprise and public sector and sovereign accounts. For anyone listening to this who is thinking about entrepreneurship, thinking about AI, you know, you've built an incredibly valuable company, $25 billion, that was the most recent round. You've worked at Google. I mean, you have a lot of expertise in this area. Any advice for any younger people listening to this, thinking about entrepreneurship and maybe starting a company themselves? The biggest learning that I've had, and it's pretty straightforward, is to just have a deep sense of what it is that you're interested in. And it's okay if you don't know what that thing is now. And there's no rush to jump into, you know, building companies. Like, there's no. Just building a company for the sake of building a company, there's no rush to jump into, you know, building companies. Building a company is not a good reason. Building a company for, I think, for a financial reason. There are plenty of way better, you know, less risky ways to make money. So it has to come from, you know, something that you're deeply interested in, that you're, you know, that you're going to commit a lot of time to and see through, you know, even when things are tough. And so for me, like, the journey of getting to this company was preceded by studying physics for a long time. For years, developing an interest in science there, discovering this interest in AI as, you know, in the mid-2010s, as, you know, deep neural networks were starting to work, going in and working at the frontier, really understanding the frontier of something, right? And so understanding what is the problem set, finding a, you know, a business partner that I trust fully and, you know, that I was. very kind of excited to build something with because we already had worked together very closely on projects before that. So, right, I would say that this, if someone is considering starting a company, I'd advise it not to be rushed in the sense that, oh, I need to start a company for the sake of starting it. It should just come from a place of deep interest and commitment and you'll kind of, people should, will feel, when it's right. You can't kind of, you know, we all have our own BS detectors and maybe there's a quote by Feynman that the easiest person to fool is yourself. And so I think that maybe the advice is not to, not to fool yourself into building a company, but only do it if it's coming from a place of deep interest in that space and idea. Misha Laskin is the co-founder and CEO of Reflection. Misha, we appreciate your time. Ed, thanks for having me. This episode was produced by Alison Weiss and engineered by Benjamin Spencer. Our research associates are Dan Shalon, Kristen O'Donoghue, and our senior producer is Claire Miller. Thank you for listening to the Profiting Markets Founder Series. We'll see you next month with another founder story. Travel tech from the 80s got us this far, but it's about time someone rebuilt it for the modern era. That's what Enjin has done over the past eight years, building strong hotel partnerships, proprietary supply, real-time rate shopping, and payment rails. 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