Who Wins if AI Models Commoditize? — With Mistral CEO Arthur Mensch

0:00What does the AI business look if all the leading models perform the same? which they kind of are. We'll find out with the CEO of Mistraw right after this. Welcome to Big Technology Podcast, a show for cool-headed and nuanced conversation of the tech world and beyond. We have a great show for you today. We're going to talk all about what's happening to the AI business and technology race as some of the leading foundational models start to look the same and how that changes the balance of power in the industry. We're joined by the perfect guest to do it. Arthur Mench is here with us. He is the CEO and co-founder of Mistral.

0:35Arthur, welcome. Thank you. Welcome. Sorry, I should say. I'm happy to be here. Yeah, I'm happy. Yeah, sorry. I'll do it again. I'm happy to be here. And thank you for hosting us. No, it's great to have you. So Mistral is a name that those who are deep in the AI world know very well, but might be new to some of our listeners and viewers. So for folks who are new to Mistral, let me give you a couple of stats. It is Mistral is an AI model builder. does some other things which we're going to get to. It's based in France. Company is valued at$14 billion after starting in April 2023. So a little under three years or two and a half years to make a$14 billion business.

1:15Not bad. There's 500 people at the company. And Arthur, you are leading it after spending some time in the academy and two and a half years at DeepMind. Exactly. We're headquartered in Paris, but we have around the 444 workforce, which is actually in the US. And a lot of our activity is actually here. So that's why I'm spending a lot of time as well. And that's why we are here in New York. All right. Well, great to have you in studio. Let's just go right to what I think is the most pressing issue for AI today. There's been so much talk about how Google at the end of 2025 started to equal OpenAI's models and how OpenAI's models were somewhat on par with others.

2:01And to me, it seems like we're just hitting commoditization of the foundational model much faster than I thought it would be. I thought that there was going to be a race where some companies would leap out further ahead and would take others some time to catch up. But it looks like right now you have lots of model builders with their frontier models exhibiting performance that's so similar is difficult to tell which is the best. So what do you make of that? I would say that inherently, this is a technology that is going to get commoditized. The reason for that is that it's actually not hard to build.

2:37You have around 10 labs in the world that know how to build that technology, that get access to similar data, that follows the same recipes and algorithms, which are very, it's very short, actually. Like the knowledge you need to actually train a model is fairly short. So because it's short, it actually circulates. So there's no IP differentiation gap that you can create. So it's very hard to actually leapfrog and to be way ahead of the competition because there's some diffusion of knowledge that is just making everybody do the same things. And so the question there is therefore, where is the value accruing?

3:11And what kind of business model should you pursue to actually make sure that in the end you're turning profitable? And then the challenge that we see with some of our competitors is that they're investing billions or hundreds of billions into creating assets that are deprecating very fast because those are commodities. And so for us, it has always been, at Mistral, it has always been a question, one of the biggest questions of the industry is that you need to invest enough to actually bring value to enterprises, but you also need to invest reasonably so that you can build unique economics that make sense in a world where the creation of model, which is capital intensive, is actually just bringing you assets that are just in a community competition.

3:56So let's talk a little bit then about this race to build the best possible model. I mean, like you mentioned, it's very expensive. OpenAI is going to put$1.4 trillion into building infrastructure for its models, or at least it says so. If the models are effectively at par, are companies going to say, hey, wait a second, maybe it doesn't make sense for us to invest all this money into building the next evolution of a better model because people can catch up? I mean, strategically, I think it's definitely, there's some cursor to be set. How much do you invest in creating assets that are valuable enough for one company to bring, for one technology company to bring value to an enterprise or to bring value to a consumer?

4:47and at the end of the day, all of these investments will need to be funded by the free cash flow and value creation that is being made downstream. And so the focus that we have as a company, but that I think is the reasonable focus is to be more on the downstream applications and to figure out what is the friction that enterprises are running into and try to lift these frictions. Because at the end of the day, I think one of the major challenges that the industry is facing today is that AI brought a lot of promises. like three, four years ago. But if you ask an enterprise, did you actually make money out of it?

5:21They will, in general, say no. And the reason for that is that they are not customizing things enough and they are not thinking backward from the problem they want to solve. So they think about the solution, but they don't think about the problem. And so trying to help them to actually go for the right use cases and actually do the right amount of customization so that when it was a team of 20 people actually operating some supply chain workflow, suddenly you can actually operate that with two people. And there's a lot of examples like this. But the challenge that the industry will face is that we need to get enterprises to value fast enough to justify all of the investments that is collectively being made.

6:01That is very interesting because for a long time you would hear these companies focus model, model, model, right? The next, what's GPT-5 was, let's say, when you think about OpenAI, the biggest news. Now they're starting to talk more about how do you take the intelligence that you have and build the applications that work. Just one bit of reporting that I can share a couple of weeks ago. You know, I had this story, this story, basically inside a lunch with Sam Altman and a bunch of newsleaders in New York City. And Altman told him the companies, you know, one of their biggest priorities was building applications for enterprise.

6:40Basically, it's going to be a major priority in 2026. And it's a little bit of a shift in rhetoric from we want to build AGI to we want to build applications for business. So talk about why is that happening? Is that an offshoot of this commoditization issue? Well, I think the issue is, well, first of all, AGI is a very simple concept. So probably too simple for enterprises. There's no such thing as like one system that is going to be solving all of the problems of the world. And so at the end of the day - Not yet, or you just don't believe in that concept at all? It's never going to exist. I mean, you have a wealth of problems, just like you don't have any human that is able to solve every task in the world.

7:19You, of course, need to have some amount of specialization to actually solve problems. And so we are back from magical thinking to system thinking. We need to figure out what is the data that is going to be used to make the model better at a specific task. What is the fallay wheel that we need to set so that we accrue more signal from humans interacting with the system so that eventually the application becomes better and better. And so in real life, enterprises are just complex systems. And you can't solve that with like a single abstraction, which is AGI. And so AGI to a large extent is what we were not able to achieve and which is basically the North Star of I'm just going to make the system better over time.

8:00But because as you've said, it's hard to explain now to investors that the technology you're building is never going to be matched by your competitors, then there's, of course, a shift in the narrative that companies are not building like a North Star single system that is going to be solving all problems, but that will need to go into the weeds of enterprises and solving their actual problems. And I think at Mistral, we've been ahead of time in thinking about this. That's kind of, that set us our story. Our story has been to assume that eventually AI will be more decentralized, that more customization would be needed because we were running into the limits of the amount of data we could accrue and the limits of scaling those.

8:39And because of that, we created the company on that premise, on the fact that we'll bring more customization ability to enterprises. Yeah, and we'll get to the Mistral story in a little bit, but one more question about this. It seemed to me, and I wonder if you think this has been a shift. You were ahead of this for sure, but it seems to me like there's been a shift in the AI industry where the idea was effectively make the models smarter and they'll try to figure out these, they'll be able to figure out these problems on their own. Like for instance, just make it concrete, make the model smarter and it will be able to do a lower level associates job or maybe do data entry for multiple systems and be able to file reports.

9:21And now it seems like there's been a shift from do that to actually build out the infrastructure, that the models are just one component, that the infrastructure is super important and things like orchestration and working through the applications that are built on top of the models is going to be where the value is found. It's interesting. Yes, I think if you look at it from a system perspective, you have two components and we'll always have these two components. The first components are like static definitions of what the workflow should be and how a system should behave. And those static definitions are set by humans that are defining how the system should behave.

10:01And so this corresponds to the manual information that you're using to define the system. And then there's a dynamic component where you're connecting a model to tools and you're giving instruction to the model and the model can go and call the tools itself. And so it can decide on the graph of execution that it's going to follow. And so that part is dynamic. And there's a static part where you're setting up guardrails or you're deciding you have a tree of decision sometimes. And I think it's a bit utopist and irrealistic to think that you can solve everything with a dynamic system without guidance from humans.

10:37And what has happened in the industry in the last three years is that effectively the dynamic part has grown because models can think for longer, because they can call multiple tools, because they can code. But the static part remains extremely important. And even if the dynamic part grows, then the static part allows you to create systems that are even better and more interesting. And you can solve problems that you were not able to solve before. So the combination of these static systems, which you can call orchestration if you want, and the dynamic systems that you can call agents is going to stay super important because the two things are moving up together so that we can tackle problems that are more and more complex.

11:14Okay. And so now, like, with that established, I'm thinking through, like, what the business is. Let's say the model has commoditized. So what are the businesses going to be in AI? It will be, I imagine, some form of consumer products, like chatbots where you could put open AI in that bucket. There will be a business where you could make your existing products better. Like for instance, maybe chatting with Microsoft Excel. That could be one way that current companies can make their products better. But then there is this other big bucket, which we've talked about a little bit already, which is the enterprise side of things.

11:50So how would you rank the business opportunity in those three buckets? Well, yes, I think on the consumer side, because AI is starting to be, well, it's becoming the way you access information. You basically have an ad business to be built and that's pretty clearly going to be built. It's not the focus of our company. And then if you look at the enterprise side, we're basically replatforming all enterprise software. So enterprise is about having the right, in enterprises you have people, you have data, and then you have processes. Historically, there was a fragmentation of the tools to run multiple processes, multiple data systems, multiple system of records.

12:29And there was a fragmentation in teams that were not able to access all information at the same time. And essentially what AI allows you to do in an enterprise is to start with a unified data or even you can start with fragmented data sources because the AI is able to navigate them. Then you put an AI on top that is building the right amount of intelligence, understanding what's going on in the enterprise. And then the AI system is able to somewhat generate the interfaces that is useful for every human to actually work. And so that part, that re-platforming of the entire enterprise software stack is the one thing where a lot of value can be created in the enterprise.

13:09Owning the context engine, so the system that is constantly running, that is looking at what's happening and figuring out, creating documentation for what's happening. Owning the front end as well that are more and more getting generated on demand. So let's say I'm a lawyer, I want to fix one of my problems and I have a very specific review to make. I just bring my documents and then the system actually evolve in showing me the right widgets and show me the right information I need. So the generative interfaces on top of a context engine that is constantly updating its representation of what's happening in the enterprise on top of system of records that are essentially going to be just pure databases.

13:54You don't need everything that was sitting on top before. This is where this is going. And that replatforming is going to be, I think it's going to take a decade because it takes a while to get enterprises to adopt these things. But there's just immense value to be created because suddenly you can reorganize your company around the fact that for many of the processes where you had a lot of people, you can actually run those very much faster. That's on one side, efficiency. And the other thing, which is the most, so I'd say that's one of the business models in the enterprise. The second one in the enterprise is about working with enterprises to help them take their really proprietary data, the assets being produced by their machines, if it's in the manufacturing industry, for instance, and turning that into intelligence that nobody else can reproduce.

14:42And so making models specifically good at a certain kind of physics when we're working with a company doing planes, for instance, or when we're working with ASML, making models that are specifically good at operating their machines, that's huge value because suddenly you're not building efficiency within the company, but you're effectively unlocking technological progress that was locked by the absence of AI. So that unlock that the new systems are providing, that's immense amount of growth. It's actually harder to measure because the first one is shorter term. You can look at what a company will look like in five years because you've reduced certain parts of the company.

15:25You've reoriented other people to be creating growth. You can create models of that. On the technological side, I think it's a little harder because we know there are things like nuclear fusion or sharper engraving of semiconductors, for instance. These are things where we are starting to run into physical constraints. And artificial intelligence can actually help to lift those physical constraints. And so the acceleration of technological progress is, I think, where most of the value creation will be. It will take a little bit of time. And it will be less measurable, less predictable than the efficiency gains that AI is going to produce.

16:01But the two things are as important. Okay, so let me see if I can sort of game this out here a little bit. So if that is going to be the key driver of value in the AI world, there's two ways to do it. One is to build a model that's better than everybody else and sell it for a premium. But we've already talked about the fact that like, that doesn't seem like it's going to be emote forever. And the other way is, you know, the model is actually not the value, it's the know how and the implementation side of things. So you can make the model open source, but then provide a service to businesses to be able to figure out how to take it.

16:33take that model and put it into action and actually get results. Are those the two choices? Yeah, that's kind of the fork that we see in the industry. And our view there has been to be on the second one. The open source and implementation side. Which brings customization, but it also brings decentralization. In that, if you assume that the entire economy is going to run on AI systems, well, enterprises will just want to make sure that nobody can turn off their systems. So it's the same way if you have a factory, you connect it to the grid, you want to make sure that nobody's going to turn off the grid because they don't like you.

17:13If AI effectively becomes a community, which is what's happening, and if you treat intelligence as electricity, then you just want to make sure that your access to intelligence cannot be throttled. And so that's also one of the things that open source technology can bring. If you're using open source, you don't have to worry about going astray of, I'm just saying, anthropics user terms. And so then pausing your ability to do what you do. If you use open source, you can basically run it on your own terms. Yeah, you run it on your own terms. You create the redundancy you need. You can serve with higher quality of service.

17:49You can make sure that whatever the geopolitical situation may be, you can still run the systems if you want. And then, so that's really on the IT side. So if I'm a CIO, I really look at open source as a way to create leverage and independence. But more on the scientific side, it's also the only way in which you can create systems that are effectively using the folklore knowledge of your employees. That's the knowledge that you've recruited for decades. The only way in which to turn it into an asset that nobody gets access to is to create your own models based on those open source models. And so that's, but it's hard.

18:25It's hard to actually build those. And so that's where you need the right tools, you need the right expertise. And that's like the complement business model to building open source models. But even the closed source model providers, companies like Anthropic, will say they'll be able to customize their models with your data. You don't believe that? They will say that, but then they will put some guardrails on top of it. So you're basically trusting that their engineers are going to give you enough access to the depth of the system. And can you trust that for eternity? I'm not sure. So the issue there is as much a question of control as a question of customization.

19:04Like a vendor is going to try to lock you in. So if you get access and if you build on top of open source models, like our open source models or anyone, you're basically less locked into the vendor. And this is a technology which is so important that you don't want to be locked into a single vendor. So that's also the opportunity we bring. You know what's stunning to me? uh it's we're three years past chat chp t which basically brought this into a lot of people's consciousness although i think big technology listeners would have known about it a little bit beforehand especially since we were interviewing the people that thought this stuff was sentient before chat chp t came out but that's a conversation for another time but but what we're basically saying today i'm going to sum up two of the main points that you've made uh one is that today's ai models can't do it all themselves they need orchestration and the second big point that you made is uh to do that sort of orchestration or implementation with the current intelligence you need a service like a managed service so i it is interesting to me that like we've gone from like this perspective of you know maybe working towards a god model that could do it all to the fact that you know this this may be the most powerful technology that we've seen come through in our lifetimes however when you actually want to use it you kind of need it becomes a managed service in a way.

20:22That's interesting. This is true. I don't think it's the first time that we observe it in history. It's a new technology. It's a new platform. And so the knowledge on how to use it is actually still pretty scarce. So there aren't that many people that can build systems that are performing at scale, that can run at scale reliably, that can actually solve an actual issue. And so when working with enterprises, you always need to have some services on top because of the complexity of implementation, even with fairly well-understood technology like databases. But for artificial intelligence, it's even more necessary in that it requires to transform businesses.

21:00So you need to also help in thinking how the team should perform around the system itself. And it does require to customize things. So you need data scientists that know how to leverage data and turn it into intelligence. And today, this is still a pretty scarce resource. I would say I do expect the part of the software in those deployments to increase. So the amount of the way customization occurs today with fine-tuning, reinforcement learning, this kind of things, this is going to be abstracted away from the enterprise buyer because it's too complex. And they actually should just worry about having adaptive systems that are learning from experience and from deployment with people instead of thinking about, should I use fine-tuning or should I use reinforcement learning to actually put that knowledge into my models?

21:49And the work that we are doing is to try and abstract away from lower-level routines that data scientists understand to higher-level systems that business owners can actually use. And so it's going to occur, and we're working on it, but the service part is still going to be quite important. And today, the combination of the two things is the fastest way to value if you're an enterprise. So we've been combining the two. You know, I started our conversation by calling you a model builder and I kind of paused on it and I said some other things that we're going to get into it later. And here we are.

22:27Basically, what I'm hearing from you is that Mastral, obviously proud model builder, but it seems like without the services, without being able to sit with the business and showing them how to use it, just would be an incomplete puzzle. So do you consider yourself, like, is the most important thing you do building the models? Or is the most important thing you do the service? Or are you primarily a model builder or primarily service provider? I mean, we are there to help our customers get to value. So service. We're here to, but to get to value, they need to have great models. And to get to value, they need to have the right tools to train the models.

23:04And so the best way to train, to create those tools is effectively to train the best models. So the two things are extremely linked together. We create models that are very easy to customize. We create models with tools that we then export to our customers so that they can use them. And we help our customers train their own models. So you can't go and sell to an enterprise that you're going to help them create very custom systems. If you can't show to the world that you're effectively the leader in open source technology. And so that's the two parts are equally important. The first is enabling the other.

23:38and there's effectively a flywheel there because we make our choices when it comes to the model design in a way that is enabling the various customers we have. One example is that we've put a lot of emphasis on having models that are great at physics because we work with manufacturing companies that run into physical problems. So that's the flywheel that we have set up by having the science team and the business team actually sit together. Okay. We're here with Arthur Mench. She is the CEO of Mistral, also co-founder. When we come back after the break, we are going to talk about open source, the open source movement versus closed source.

24:19Remember, DeepSeek and open source was supposed to surpass closed source. Well, has it? I'll also talk about the geopolitics and regulation and whether that's going to give this company a leg up and then maybe get into some more practical examples because we should talk about how the technology is being used on the ground. We'll be back right after this. And we're back here on Big Technology Podcast with Arthur Mench. He's the CEO of Mistral. Arthur, I want to ask you about the progression of open source over the past year. I remember reading about DeepSeek, doing reporting on DeepSeek in January, and the overriding theme was it was such a leap forward for open source that soon the close models, models like OpenAIS GPT and Anthropics Cloud and maybe Google's Gemini would be surpassed by open source because the open source community was working together.

25:17and building on each other's innovations where the closed source community was kind of going at it on their own. We just had this moment, we talked in the beginning of the show about how maybe Gemini commoditized OpenAI's GPT models, but that conversation was not being had about open source living up to that expectation from the beginning of the year. So am I missing something or am I reading it wrong? Or what do you think? If something has held back open source, what has it been? Well, if you look at the trends in 2024, I'd say there might have been like a six-month gap. If you look at the trend in 2025, I think the gap is more around three months.

25:59So I guess it's up to anyone to guess what the gap is going to be next year. But effectively, this gap has been shrinking quite significantly. the reason for that is that basically you have a saturation effect when you pre-train models around 10 to the power 26 flops the reason for that is that there's only that much data you can find to compress when you pre-train models and so effectively labs that maybe started a little behind created enough compute capacity to train models at this kind of scale And efficiency has also increased. And so what it means is that today, everybody has access to 10 to 26 flops facilities over the course of a few months.

26:49And that's a measure of compute. That's a measure of compute. And that's a measure of compute times. So you need to, yeah, 10 to 26 flops, something that any lab today can achieve in a couple of months. And because of that, the saturation effect means that open source models have caught up because closed source models that were started had kind of run into that wall of retraining. So what that means is that this is only going to continue shrinking. And if we look at the latest open release we did, which is DevStyle 2, which is a coding model, well, it's performing, I think, around the performance of Entropic around two or three months ago.