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

Summary

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Podcast Summary

Podcast: Big Technology Podcast
Episode: Who Wins if AI Models Commoditize? — With Mistral CEO Arthur Mensch
Date: January 14, 2026
Host: Alex Kantrowitz
Guest: Arthur Mensch, CEO & Co-founder of Mistral

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Episode Overview

This episode explores the evolving landscape of the artificial intelligence industry as leading AI models start to converge in performance, raising the prospect of model commoditization. Alex Kantrowitz and co-host (unidentified) welcome Arthur Mensch, CEO of French AI company Mistral, for an in-depth conversation about where value will accrue as foundational AI models become harder to distinguish, how Mistral approaches open source and service-based strategies, the role of customization for enterprise, geopolitics in AI, and where the technology is making a tangible impact beyond chatbots.

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Key Discussion Points & Insights

1. The Commoditization of Foundational AI Models

[02:47–05:01]

• The major AI model providers such as OpenAI, Google, and Mistral are converging in terms of model capability.
• Mensch explains that “the knowledge you need to actually train a model is fairly short,” so the "IP differentiation gap" is very small and what one lab learns diffuses quickly.
• This leads to commoditization, making it hard for any one company to create defensible moats via the models themselves.

Quote:

“It’s actually not hard to build. You have around 10 labs in the world that know how, access to similar data, follow the same recipes... There’s no IP differentiation gap.” — Arthur Mensch [03:31]

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2. Rethinking the AI Business Model: From Models to Applications & Services

[07:06–10:46]

• OpenAI and others are shifting from racing toward ever-smarter models or AGI (Artificial General Intelligence), to focusing on enterprise applications.
• Mensch criticizes AGI as an oversimplified concept, especially for businesses with diverse, complex needs.
• The next value wave is “downstream applications” and customization for specific enterprise problems.
• AI orchestration (“managed services”) will be essential, moving away from magical thinking to “system thinking.”

Quote:

“AGI is a very simple concept, probably too simple for enterprises. There’s no such thing as one system that is going to be solving all of the problems of the world.” — Arthur Mensch [08:02]

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3. Where Will Value Accrue in an AI-Commoditized World?

[12:19–17:08]

• AI business opportunities break into three areas: consumer products (e.g., chatbots with ads), enhancing existing software (e.g., Microsoft Excel), and enterprise (replatforming software, vertical/industry-specific applications).
• The major value in enterprise comes from either efficiency gains (streamlining processes) or unlocking new technological advancements (e.g., in manufacturing).
• AI will “replatform all enterprise software,” acting as a context engine and generative interface across fragmented data and workflows.

Quote:

“The replatforming of the entire enterprise software stack is the one thing where a lot of value can be created. … Owning the context engine … owning the front ends that are more and more getting generated on demand.” — Arthur Mensch [13:50]

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4. Open Source vs. Closed Source: Customization, Control & Decentralization

[17:08–21:27 & 27:53–31:27]

• Industry faces a fork: sell premium closed models (becoming less defensible) or open models plus enterprise implementation and customization services.
• Mistral’s bet is on open source plus implementation, as companies will demand control (“intelligence as electricity”).
• Open source offers independence, allows enterprises to infuse their proprietary knowledge, and reduces vendor lock-in.
• The performance gap between open and closed models is shrinking, thanks to accessible compute and saturation (limits of available data and compute).

Quote:

“If you treat intelligence as electricity, then you just want to make sure your access … cannot be throttled. That’s also one of the things that open source technology can bring.” — Arthur Mensch [17:51]

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5. How Models Get Better: Specialization & Verticalization

[31:27–35:24]

• Big general-purpose models are reaching saturation; future progress will be in specialized, vertical domains (e.g., pharma, manufacturing, legal) with expert-driven customization.
• Specialized models make more economic sense because they're cheaper to train, serve, and maintain than a single gigantic “God model.”

Quote:

"We are getting to a point where if you pick a domain, you can just make it your superhuman, but we are not going to make it superhuman in every domain at the same time." — Arthur Mensch [33:22]

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6. Geopolitics, Regulatory Advantages, and Global Competition

[35:24–42:07]

• Mistral's European base is attractive to European governments and enterprises that seek strategic autonomy from U.S. Big Tech and want to adhere to local regulatory and data sovereignty constraints.
• Open source, on-premise AI is especially in demand for public sector and defense.
• China’s open source AI efforts (e.g., DeepSeq) are strong; open source fosters rapid innovation through cross-lab collaboration.
• The world will see “multiple centers of excellence” in AI, each serving its own sovereign interests.

Quote:

“Artificial intelligence is not a technology that you want to fully delegate to a vendor, especially if it’s a vendor that is from a foreign entity.” — Arthur Mensch [35:59]

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7. Concrete Applications: Beyond the Chatbot

[43:10–47:40]

• Mistral is tackling “end-to-end workflow automation” in industries such as shipping/logistics (e.g., automating cargo dispatch for shipping company CMA CGM) and manufacturing (e.g., automating semiconductor defect analysis for ASML).
• These use cases involve LLMs making operational decisions and interacting with both digital and physical systems, often requiring heavy customization.
• The full power of generative AI in industry comes from integrating perception (e.g., vision) with reasoning and orchestration—going well beyond simple chat interfaces.

Quote:

“The chatbot is a human-machine interface, but it’s not the rest, it’s only that. … The combination of images and logical thinking is what enables us to automate [semiconductor inspection] much faster.” — Arthur Mensch [43:36 & 45:41]

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8. From Promises to Real Impact: Adoption Curve & Bubble Risks

[49:53–57:10]

• Models keep getting incrementally better, but true enterprise value arrives via iterative customization, user feedback, and organizational transformation.
• The process is slow: “It takes some time. You need to learn how to build [these systems], then reorganize … teams are going to change.”
• The current surge of investment in infrastructure may be slightly overhyped, but there is a consensus that “eventually the entire economy is going to run on AI systems.”
• Largest payoffs will require patience—possibly 10–20 years for full economic impact.

Quote:

“Today I would say … we’re maybe over investing a little bit and over committing a little bit. Not Mistral, but some others. … But eventually the entire economy is going to run on AI systems, that’s for sure. But it might take 20 years because it’s actually fairly complex.” — Arthur Mensch [56:32]

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Notable Quotes & Memorable Moments

• “We are back from magical thinking to system thinking.” – Arthur Mensch [08:14]
• “If AI effectively becomes a utility, and you treat intelligence as electricity, then you just want to make sure your access … cannot be throttled.” – Arthur Mensch [17:51]
• “AGI to a large extent is what we were not able to achieve, which is basically the North Star of ‘I’m just going to make the system better over time.’” – Arthur Mensch [08:02]
• “Open source models have caught up … because closed source models run into that wall of pre-training.” – Arthur Mensch [30:10]
• “You need to also help in thinking how the team should perform around the [AI] system itself.” – Arthur Mensch [21:27]
• “[Chatbot] is a human-machine interface, but it’s not the rest, it’s only that.” – Arthur Mensch [43:36]

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Timestamps of Important Segments

• Commoditization of Models: [02:47–05:01]
• Model vs. Application-First Business Models: [07:06–10:46]
• Customization & Managed Services in Enterprise: [12:19–17:08]
• Open Source vs Closed Source / Decentralization: [17:08–21:27]
• Open Source Model Performance & Saturation: [27:53–31:27]
• Specialization and Verticalization of Models: [31:27–35:24]
• Geopolitical Edge of Mistral and Regulatory Needs: [35:24–42:07]
• Industrial and Non-Chatbot AI Applications: [43:10–47:40]
• Enterprise Adoption & “Bubble” Discussion: [49:53–57:10]

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Takeaways

• The era of “superior models” as a moat is fading due to rapid commoditization. The future is in customization, application expertise, open source, and managed services.
• Enterprises will look for control and sovereignty in AI deployment, favoring open source for flexibility and independence—from regulators and foreign vendors.
• The biggest impact of AI will unfold over the long term as it is embedded deeply into industry-specific (often physical) workflows, requiring patient investment and organizational change.
• Multiple global AI “centers of excellence” will serve the geopolitical interests of their regions, with open source as a key enabler of innovation and adaptation.

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For listeners: This episode is a roadmap for understanding not only how the business of AI is shifting, but also where entrepreneurs, enterprises, and governments should focus as model performance converges, and as the next real value lies in tailoring, integrating, and orchestrating AI within real-world systems.

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Transcript

[Big Technology Host]

What does the AI business look if all the leading models perform the same.

[Big Technology Co-Host]

Which they kind of are?

[Big Technology Host]

We'll find out with the CEO of Mistral right after this can AI's most valuable use be in the industrial setting? I've been thinking about this question more and more after visiting IFS's Industrial X Unleashed event in New York City and getting a chance to speak with IFS CEO Mark Muffett. To give a clear example, Muffett told me that IFS is sending Boston Dynamics spot robots out for inspection, bringing that data back to the IFS nerve center, which then, with the assistance of large language models, can assign the right technician to examine areas that need attending. It's a fascinating frontier of the technology and I'm thankful to my partners at IFS for opening my eyes to it. To learn more, go to ifs.com that's.

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[Big Technology Host]

Welcome to Big Technology Podcast, a show for cool headed and nuanced conversation of.

[Big Technology Co-Host]

The tech world and beyond.

[Big Technology Host]

We have a great show for you today. Going to talk all about what's happening to the AI business and technology race as some of the leading foundational models.

[Big Technology Co-Host]

Start to look the same and how that changes the balance of power in the industry.

[Big Technology Host]

We're joined by the perfect guest to do it. Arthur Mensch is here with us. He is the CEO and co founder of Mistral.

[Big Technology Co-Host]

Arthur, welcome.

[Arthur Mensch]

I'm happy to be here.

[Big Technology Host]

Mistral is a name that those who are deep in the AI world know.

[Big Technology Co-Host]

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 companies valued at $14 billion after starting in April 2023. So little under three years or two and a half years to make a $14 billion business. Not 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.

[Arthur Mensch]

Exactly. We're headquartered in Paris, but we have around the 4, 4, 4 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.

[Big Technology Co-Host]

All right, well, great to have you in studio.

[Big Technology Host]

Let's just go right to what I.

[Big Technology Co-Host]

Think is the most pressing, 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. And 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.

[Big Technology Host]

Frontier models exhibiting performance that's so similar.

[Big Technology Co-Host]

Is difficult to tell which is the best. So what do you make of that?

[Arthur Mensch]

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. You 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 and 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 fairly fast because those are commodities. And so for us, it has always been at Mistral, it has always been one of the biggest question 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 unit economics. That makes 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.

[Big Technology Co-Host]

So 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 and 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.

[Arthur Mensch]

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, to form one technology company, to bring value to an enterprise or to bring value to a consumer? And 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, they 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 try and 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.

[Big Technology Co-Host]

That is very interesting because for a long time you would hear these companies focus, model, model, model, right? The next what's GPT5? 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 weeks ago, you know, I had this story, this story basically inside a lunch with Sam Altman and a bunch of news leaders in New York City. And Altman told him the companies, you know, one of their biggest priorities was building applications for enterprise. Basically, it can 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?

[Arthur Mensch]

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 one system that is going to be solving all of the problems of the world. And so at the end of the.

[Big Technology Co-Host]

Day, or you just don't believe in.

[Arthur Mensch]

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 on the world. You, 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 falley 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 a single abstraction, which is AGI. And so AGI to a large extent is what we were not able to achieve, which is basically the North Star of I'm just going to make the system better over time. But because as you've said, it's hard to explain 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 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 Mistal, we've been ahead of time in thinking about this that set us 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. And because of that, we created the company on that premise, on the fact that we'll bring more customization ability to enterprises.

[Big Technology Co-Host]

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, I'll just make it concrete, make the model smarter and it will be able to do, you know, a lower level associates job or maybe do data entry from multiple systems and be able to file reports. And 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.

[Arthur Mensch]

Yes. I think if you look at it from a system perspective, you have two components and will 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. And 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 decisions sometimes. And I think it's a bit utopist and irrelistic to think that you can solve everything with a dynamic system without guidance from humans. And 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 agent, 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.

[Big Technology Co-Host]

Okay. And so now like, with that established, I'm thinking through like what the businesses, 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 OpenAI 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. So how would you rank the business opportunity in those three buckets?

[Arthur Mensch]

Well, yes, I think on consumer side. On the consumer side, because AI is starting to be, well, is becoming the way you access information. You basically have an ads 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 are 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. And 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 have you 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. Owning 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 my problem and I have 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. You 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. So that's on one side efficiency and the other thing which is the most. So that's, I'd say that's one of the business modalities of 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. And 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, you'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 and less predictable than the efficiency gains that AI is going to produce. But the two things are as important.

[Big Technology Co-Host]

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 a moat forever. And the other way is, you know, the model is actually not the value, it's the know how of 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 that model and put it into action and actually get results. Are those the two choices?

[Arthur Mensch]

Yeah, that's kind of the fork that we see in the industry and.

[Big Technology Co-Host]

Our.

[Arthur Mensch]

View there has been to be on the second one to really the open source 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 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. If 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.

[Big Technology Co-Host]

And if you're using open source, you don't have to worry about like going astray of, I'm just saying like anthropics, you know, user terms. And so then pausing your ability to do what you do. If you use open open source, you can basically run it on your own terms.

[Arthur Mensch]

Yeah, you run it on your own terms. You create the own the redundancy you need, you can serve with higher quality of service, you can make sure that whatever like 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 accrued for decades. The only way in which to turn it into an asset that nobody get access to is to create your own models based on those open source models. And so that's. But it's hard, it'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.

[Big Technology Co-Host]

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.

[Arthur Mensch]

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. Like 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.

[Big Technology Co-Host]

You know what's stunning to me, it's we're three years past ChatGPT, 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 ChatGPT 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. 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 to do that sort of orchestration or implementation with the current intelligence, you need a service like a managed service. So 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 that's interesting.

[Arthur Mensch]

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. So you need to also help in thinking how the team should perform around the system itself. 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, these 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. And 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.

[Big Technology Co-Host]

You know, I started our conversation by calling you a model builder and I kind of paused on it and I said and some other things that we're going to get into it later. And here we are being. Basically what I'm hearing from you is at Mistral, obviously proud model builder, but it seems like without the services, without being able to sit with a 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?

[Arthur Mensch]

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. And 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. And 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 runs into physical problems. So that's, that's the, that's the flywheel that, that we have set up by having the science team and the, and the business team actually sit together.

[Big Technology Co-Host]

Okay.

[Big Technology Host]

We're here with Arthur Mensch.

[Big Technology Co-Host]

He is the CEO of Mistral, also co founder.

[Big Technology Host]

When we come back after the break.

[Big Technology Co-Host]

We are going to talk about open source, the open source movement versus closed source. Remember, deep Seq and open source was supposed to surpass closed source. Well, has it? We'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.

[Big Technology Host]

And we're back here on Big Technology.

[Big Technology Co-Host]

Podcast with Arthur Mensch. He's the CEO of Mistral.

[Big Technology Host]

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[Big Technology Co-Host]

Open Source over the past Year I.

[Big Technology Host]

Remember reading about Deep SEQ doing reporting.

[Big Technology Co-Host]

On Deep Seq in January and the overriding theme was it was such a.

[Big Technology Host]

Leap forward for open source that soon the closed models models like OpenAI's GPT.

[Big Technology Co-Host]

And Anthropics Claude, and maybe Google's Gemini would be surpassed by open source because open source, the open source community was working together and building on each other's innovations, where the closed source community was kind of going at it on their own.

[Big Technology Host]

We just had this moment.

[Big Technology Co-Host]

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 being, you know, living up to that expectation from the beginning of the year. So am I missing something or am.

[Big Technology Host]

I reading it wrong or what do you think?

[Big Technology Co-Host]

If something has held back open source, what has it been?

[Arthur Mensch]

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. So I guess it's up to anyone to guess what the gap is going to be next year. But effectively this gap has been shrinkening 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 the Power 26 flops facilities over the course of a few months.

[Big Technology Co-Host]

That's a measure of compute.

[Arthur Mensch]

That's a measure of compute and that's a measure of compute times. So you, you need to. Yeah, 10 to 26 flops is 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 ahead kind of run into that wall of pre training. So what that means is that this is only going to continue shrinking. And if we look at like the latest open release we did, which is devstyle2, which is a coding model, well, it's performing I think around the performance of Entropic around two or three months ago. So yeah, I think the gap is shrinkening. And again, I think the question is probably not posed in the right way that way because it's also offering very two different distinct value propositions. Because on one side this is well managed and you will depend on the provider itself. On the other side, well, it takes a little more effort because you will need to own it more, you will need to learn about how to customize it, you will need to use the right tools for doing so, you will need to maintain its deployment if you choose to deploy it on your own facilities. But at the end this is creating the leverage you need against closed source providers. So the two categories are effectively different. But if you look at the pure performance side, they are definitely converging.

[Big Technology Co-Host]

You mentioned that there's a saturation effect. So without getting too technical, are the models sort of done with getting better? Like put it this way, are AI models going to continue to get better given the fact that they all seem to be hitting saturation?

[Arthur Mensch]

They will get better in more and more specific domains. In that I think we really collectively made them very clever and able to reason about long context and able to call multiple tools, etc. But if you go and want to effectively put them into production in a bank or in a manufacturing company? Well, the models need to learn about all of the knowledge that is contained into the companies themselves. And so what it effectively means is that for very precise directions, let's say I want to make my model extremely good at discovering materials or extremely good at designing planes. I will need to go and sweat it a little bit and get the right reward signal and get the right experts and ask them to make my model specifically good in that very precise direction. And so we are definitely not done doing that because what we are all racing for is the right environment and the right signal provider for specific capabilities. But the broad horizontal reasoning capabilities are still going to improve them. But nobody is going to improve them in a way that is creating strong, that is creating a strong gap versus its competitors. So the strong gap is actually in working with vertical experts that know exactly how they design a plane and that actually explain to the model how to do it. And you have a wealth of directions that you can take because you can do it in physics, you can do it in chemistry, pharmaceutical, in biology. And so to me, the most exciting part of what's going to happen in the next two years is that explosion of very precise directions in which the model are going to get better. And for us, the opportunity is to have the right platform for enabling those kind of verticalization, whether with enterprises or, or you have like AI startups actually that are working on very verticalized capabilities and we're happy to help them as well. So that's my view of where the field is going to go. We have been about horizontal intelligence growing and things getting clever, more and more clever. And the next two years is going to be about taking model and making them extremely good at a certain skill set. And that's actually more exciting because we are getting to a point where if you pick a domain, you can just make it your superhuman, but we are not going to make it superhuman in every domain at the same time.

[Big Technology Co-Host]

Okay, but then on that note, earlier in our conversation you said that you're not going to have a model that can do everything. But if that training gets done in certain verticals, why not?

[Arthur Mensch]

Well, we are also getting to a point where the verticals that you choose do not really transfer to the others. So there's no point in making a model that is good at very precise biology and very precise physics, because the transfer between those things actually pretty unclear. The problem is that if you actually want your model to be able to solve every problem at the same time, you're making it very big, very expensive and very costly to serve.

[Big Technology Co-Host]

So specialized models is really. You're going to specialize one for bio, one for chemistry, one for like this particular physics problem.

[Arthur Mensch]

Well, it actually makes more, makes more sense because if you want to run it at scale, if you want it to run on the background, if you want it to run day and night thinking about specific problems, well, you want it to be as small as possible because the cost of a model is actually proportional to its size. And if you inflate the size by making the model great at multiple modes, well, you're actually not very efficient if you want to deploy it and use it as much as possible. So if you look at the Economies of it. It does make sense to make specialized model in certain directions.

[Big Technology Co-Host]

Let me ask you a little bit about the Mistral competitive area. I think that we're here in the US I'll just tell you what people in the US say and let you address it because it's worth talking about. I think there is a feeling among some, not all, but some, that, you know, Mistral has been set up in Europe to effectively take advantage of regulatory capture because US Companies have a hard time competing in Europe and therefore Mistral will be there to like pick up all the AI business. What do you think about that argument?

[Arthur Mensch]

Well, you know, we've built our technology so that we could serve companies and states that wanted to have enough control. Artificial intelligence is not a technology that you want to fully delegate to a vendor, especially if it's a vendor that is from a foreign entity. And that was true before it was true for data. It's going to be all the more true for artificial intelligence for multiple reasons, but one of them is the fact that if you're depending on an external vendor, your commercial balance is effectively increasing and you're importing services and that becomes a problem long term if you're importing too much digital services, for instance. So that's one thing. And then sovereignty and this kind of topic is also very important for defense. As if you're an independent country, you want to have independent defense systems. And if you want to have independent defense systems, you will need them to, you will need your own independent artificial intelligence because this is making it into the defense systems.

[Big Technology Co-Host]

So it's really working for you. This pitch being like, we are not an American company, we're based in Europe. We'll be able to help you build, whether it's something with like important data protection or national security, like defense.

[Arthur Mensch]

Well, it's a technological differentiation we've built. So because we can build on the edge, because we can deploy wherever our customers wants us to deploy, we effectively can die. And the system is going to still be up, which actually matters for many, many industries. And the more critical it gets, the more it matters. And so what that also means is that we can serve the US Customers. We can serve US customers that want to depend less on certain providers. We can serve banks that want to have more customization, more control, that are more regulated. It also means we can serve, we can of course serve the European industry, where historically that's where we were based. You sell next door when you start your company, and that's what we did. But we also serve Asian countries and Asian countries, they have similar problems. They want to have a technology that they can rely on even if we were to die. They want to have a technology that they can customize to their own cultural needs. And so that has been driving our business for sure. That aspect, that technological differentiation that we've built around control open source, like a technology built on open source models and around customization.

[Big Technology Co-Host]

And do you have European governments coming to you and being like, we just don't trust Google or Anthropic and we'd prefer not to build on them?

[Arthur Mensch]

Well, we have European governments actually coming to us because they want to build the technology and they want to serve their citizens. They want to increase the efficiency of their public sector. And we happen to have a good proposition for them, which is, which is deployable on their premises where we can go send forward deployment people to help them get to value. And it turns out we're European as well. So it's actually pretty good for, for European country, for European countries to invest in European technology because the investment they're making, the revenue that they are creating for us is a revenue that we reinvest in Europe and we're effectively creating an ecosystem around us. So that investment of the flow of revenue from European countries to European technology provider is something that is very beneficial. And to be honest, in the US that has been working for the last 80 years and I think in Europe we haven't been doing it enough for sure.

[Big Technology Co-Host]

Speaking of open source companies, there are efforts that have some links to geography. What do you think about China's open source effort? Because obviously they've made a lot of noise. It seems like things are going quite well there.

[Arthur Mensch]

Yeah, I mean, China is very strong on artificial intelligence. We were the first actually to release open source models and they realized it was a good strategy and they've proved to be very strong actually. And so we've been not sure if we're competing. Because the good thing about open source, it's not really competition. You build on top of one other, right.

[Big Technology Co-Host]

You see everything they have out there and you learn what works.

[Arthur Mensch]

Well, yeah, and the same is true, the reverse is true. Like we released the first sparse mixture of experts back at the beginning of 2024 and they built on top and they released deep seq3 and then deep.

[Big Technology Co-Host]

Seq was built on top of that.

[Arthur Mensch]

Well, it's the same architecture and we released everything that was needed to rebuild this kind of architecture. And the same is true. I mean, everything that companies that are investing on open source are Releasing are things that other open source companies are reusing and actually it's kind of the purpose those R and D is just much more efficient if you share your findings across different labs. And so it's been very effective in China. They share knowledge across the different labs. It's been pretty inefficient here in the US because there's actually no, there's like US incorporated companies are not investing on open source and we've taken the lead on just being the west open source provider and I think it's going to be very much needed to have a Western open source provider.

[Big Technology Co-Host]

What do you think China's strategy is? And do you think that there's like in the US there's often this kind of very large conversation about the need to stay ahead of China. Do you think there's a risk if China runs away with this?

[Arthur Mensch]

I think China is very strong. It's vertically integrated. They have strong engineers, they have compute, they have energy, they have everything they need to compete. Europe also has everything it needs to compete. I don't think we'll be in a setting where anyone is going to have one artificial intelligence ahead of the others. And if you look at the world in its entirety, every large enough sovereign entity which is a big economy is going to want some form of autonomy in its usage of AI and its deployment of AI. So that does justify the emergence of multiple centers of excellence. I would say one of them which is in Europe, which is led by us, one other which is more in Nangzhou in China and then you have a bunch of companies here in the west coast.

[Big Technology Co-Host]

Why do you think it's in China's strategic interest to develop these open source models? Because they don't have a similar business as you do. Right. They're not like going out globally and becoming implementers.

[Arthur Mensch]

They have a big business in China for sure. The companies that are building open source models in China are actually cloud providers in general you have a bunch of startups but. But you also have Alibaba which is the cloud provider.

[Big Technology Co-Host]

Right.

[Arthur Mensch]

And so they have this vertical integration that allows them to create value there internally. So in China, but also in the markets where they are operating and growing. So in Asia for instance, which for us is a, is a place where we tend to compete with them not in China itself, but in the rest of Asia. So does make sense to for them to compete internally and then their best way of accessing the US market is by just giving the things for free. And so it does make sense. It's A very natural thing to do to build a business in China which is protected than to export the thing for zero.

[Big Technology Co-Host]

Not bad.

[Arthur Mensch]

I would do the same if I were in their shoes.

[Big Technology Co-Host]

Right.

[Big Technology Host]

All right, I want to talk a.

[Big Technology Co-Host]

Little bit before we leave about the practical applications of this technology that you're building. It's interesting. You were talking a little bit about AI being used for physics, AI being used in other research applications, AI being used for defense. None of this sounds like a chatbot. So talk a little bit about the applications that you are working on and whether we're going to see AI move beyond the chatbot.

[Arthur Mensch]

I mean, the chatbot is oftentimes the interface because artificial intelligence is a generative. AI allows you to interact with machines in a human way. So say chatbot is a human machine interface, but it's not the rest, it's only that. Now if you look at the, at the actual applications that are strongly exciting for us, you have two things. You have the things that are really on the end to end. Workflow automation that effectively changes the way your business is fully run. So examples are like cargo dispatching. When we work with cmshm, which is a shipping company, and we help them dispatch all of their containers when the ship comes into the port. And they need to dispatch everything. They need to contact like hundreds of people, they need to contact the harbor, they need to contact the regulators. They need to actually 20 software differently. And so that takes like, I mean, I think a few hundred people to do it. And by working together around how to automate those things, suddenly you can save 80%.

[Big Technology Co-Host]

So the LLM is making those communications.

[Big Technology Host]

And also deciding, not just making the.

[Big Technology Co-Host]

Call, but deciding who gets what it decides.

[Arthur Mensch]

And it wires the things and, and you measure whether it's doing the right thing. And if it doesn't, then you improve the system.

[Big Technology Co-Host]

How's it doing?

[Arthur Mensch]

So it's, it's working. It's live actually in certain agencies. So. So that's very like. It has a. To me, it's very exciting because it has a physical footprint, it takes decisions in a safe way, and it's effectively bringing a very large efficiency gain to a company. Now another example which is more on the growth side are things that we do with asml. We are working with them on vision.

[Big Technology Co-Host]

Systems and talk a little bit about what ASML is for those that don't know.

[Arthur Mensch]

So ASML is a company that is doing computational lithography and scanning and their role is to build those big machines that are effectively engraving the wafers that are then used as the chips in Nvidia, for instance.

[Big Technology Co-Host]

Right. So they're like key industrial component of these semiconductor manufacturing.

[Arthur Mensch]

They provide the machines for semifinals and.

[Big Technology Co-Host]

Something so specialized you would think, how's generative AI going to help them?

[Arthur Mensch]

Well, generative AI is generally the generative AI models are predictive AI models. And one good thing they have is that they can see and reason about what they see. And so one of the things that SML needs to reason about are the images coming out of their scanners that are verifying whether there are errors in the engraving of the chips. And it's actually fairly complex because there's some logical thinking to be done. And the combination of images and logical thinking is what enables us to actually automate those things much faster, which means that the throughput down the line of fabs is going to increase. And so in that setting, customization is key because the kind of input that is coming in is nowhere to be found elsewhere. HTML is the only one who has access to these images. And so we find a physical problem that is effectively a bottleneck in a manufacturing process and we go and we train models that are effectively solving it. And this is going to occur in many, many different places. And generative AI is needed there because you need a model that can reason about images. And so the reasoning capabilities are critical. But customizing those reasoning models for a specific problem with a specific kind of input is the one thing that is the unlock there.

[Big Technology Co-Host]

Yeah, the industrial applications of generative AI to me have been super surprising and interesting. Like there has been technology, for instance, computer vision technology that can take a look at a piece of machinery or an output and be like, that's not good. Or actually that's what we need. Right. But there hasn't been this nerve center that information can be channeled to and then sort of have a decision made up about it and then communicate it to somebody in the field. And that's what this stuff is enabling, is that full line of technical work is starting to be able to be done by this technology.

[Arthur Mensch]

Yeah, basically what you need is, are models that can perceive multiple kind of information. And oftentimes in manufacturing, information is visual. So having very strong visual models is super useful. And then based on those vision models on these inputs, you can make choices and you can rely on the LLMs themselves to orchestrate calling an agent or going into the next step of the workflow, or actually calling a tool or writing something in a database. And that having dynamic agents that are able to see what's happening in the factory that are able to see what's happening in the process and that can take the next step, whether it's actually an automatic step or a call on agent step so that they validate a decision is where a lot of the value can be created. And that's going to reorganize manufacturing. You know, manufacturing had to reorganize itself multiple times. When we invented the steam engine, we had to rebuild the entire factories around like a central steam machine because that was the energy provider. And so what's going to happen, I think in the next 10 years is that all of the manufacturing processes will be rebuilt around LLM orchestrators. And it's super interesting because you have physical problems to solve the system as physical footprint. So there's some safety issue that you need to solve. Just the complexity of the system itself is huge. And so that's a fascinating problem for engineers like us.

[Big Technology Co-Host]

Let me see if I'm getting this right. Okay. So I think what we're starting to see is the seeds of this stuff starting to be able to really have an impact in business. We had, we just did an episode with a reporter who was reporting on how some lawyers are really able to use this to sift through documents better. Is it perfect? No. We heard it in the comments. Not perfect, but it's. It has, it's showing potential. Same same thing in the industry and maybe also in. In other areas that, that you touch on, but still feels nascent. So what's going to get it from like where it is today to something that's like, you know, effective in a way that like we really see the impact in the economy. Is it just like time and patience on customization or is it improving improvement of models or.

[Arthur Mensch]

I think models are getting better, which helps. Whenever you have a stronger model, you can trust that it's going to reason for a longer period of time and that it's not going to fail. It's going to fail less. But then the thing that needs to be embraced is iterations. You're never going to be able to build systems that work out of the box in a single shot. And the one thing that we try to convey to our customers is that they need to build a prototype that's going to work 80% of the time. But then how do they get from 80% to 99% while they can move the thing into production? And the way to get it is to actually get feedback from users if the system is not working, if the AI software you've built is not working. It means that you need more data and signal. And that's something that is quite different from the way we used to build software, because when the software was not working before, you basically went back to coding and you would fix the problem. But because we are building organic systems, so systems that imitate humans, the way to make them better is to give them feedback and then to retrain the system. So that will take the seeds that you mentioned, that will make them actual valuable things. That's, that's going to work. And you mentioned lawyers. I think it's one of the area where it's very knowledge intensive. You have very little physical footprint. And so it's a natural. It's a lot of this. And so it's the easiest one. It's the easiest thing to do. It's not, it's not easy at all. It's not done yet. There's still a lot of subtleties to fix to make models great at, at lawyering. But if you go into the physical world, then it gets even more complex. So we'll see applications on the knowledge world go faster into production than the one on the physical world. But arguably the one on the physical world would be more transformative.

[Big Technology Co-Host]

That brings us to robotics. So let's end here. People have been talking about how we could see an explosion in robotics because of LLMs or the advancements in world models, but it still seems far off. I mean, they had the, this demo, what was it? The Neo. The Neo humanoid robot, where there's like a person controlling it, teleoperating it, kind of weird, they might be in your house. So we haven't seen progress in robotics, you know, start to move as fast as we've seen it in the software side, in the large language model side. So when does that come, if it ever does?

[Arthur Mensch]

I think in robotics you have the combination of two things that needs to work. Hardware platforms that needs to be. You need to have the right actuators with the right haptic signals that needs to be built at scale with good economics. And this is starting to be true. And we are not the one working on it, but the industry has made a lot of progress on that domain. Then the other thing is that you need to be able to have control system that are sufficiently intelligent to be deployed on those robots. And so that's where actually we come in, in that again, you need to have custom models because the problem is the model needs to be customized to the platform, whether it's a humanoid robotic or whether it's something on wheel or Whether it's a flying drone. And it needs to be customized to the mission because the mission is going to bring different kind of images, the kind of actions that can be taken are going to vary across the mission. Maybe the guardrails are different. And so that adaptation to the world and to the wealth of data that the hardware platform that is being deployed is bringing does require the right platform and the right training platform. And so our bet in robotics and what we've been doing with multiple companies in defense in particular, is to build that platform that allows to train models fit to purpose, that can then be deployed on the edge potentially. Because in robotics, strategically in robotics, I believe we'll see deployment of such systems first in areas where you don't want to send humans. So firefighting, I think is a very good example. So when the risk and benefit, the risk of deploying the system is way under the benefit of deploying the system. It's going to be the case in manufacturing as well. Because there are places where you just want the factory to be dark. And I think that's where most of the lot of the value will be created. I would say midterm and then maybe long term you have things that are sitting in your house. But it's a bit dangerous to have some pretty strong thing out there. And so the same way we've been waiting for self driving car for the last 15 years, we'll be probably waiting for humanoid robotics in house for meaningful time. And before that, what we'll see is at scale deployment in manufacturing. And that will take the right software platform. And that's the software platform that we're building.

[Big Technology Co-Host]

Okay, all right. Really the last one, we've talked a lot about AI in business. Some businesses have gotten a lot out of it, some have not clearly potential, but also just like a shit ton of investment. What do you think about the bubble question? Are we in a bubble right now?

[Arthur Mensch]

Well, we're in a setting where we need a lot of infrastructure, so we need to invest and that's what we do in Europe for instance. But then the viscosity of adoption in enterprise is slow, is high in that it takes time to understand how to build the software. It takes some building. You can't buy off the shelf solutions and then trust that you're going to make immense progress in your productivity. That has been the disappointment that a lot of enterprises went through in the last two years. So there's some building to be done. You need to maybe buy the primitives, buy a certain number of factorized functions but then you need to bring your own knowledge onto it. So it takes some time. You need to learn how to build and then you need to learn how to reorganize and that takes even longer because the teams are going to change. You need less management because you need less infrastructure to circulate information, because AI allows information to circulate faster. You need, certain functions are going to disappear, certain functions are going to grow. So there's just a lot of work to be done on reorganizing things and it will take years. And so the question is the infrastructure investment that are being made today, are they going to create long term value in two years, in five years or in 10 years? And that does define whether some people are losing money or making money. That's the, that's, that's the problem. And we don't really know. So many people are over investing. Maybe people are under investing. Some people will certainly lose money. Some people will certainly like miss opportunities as well. But today I would say my view is that we're maybe over investing a little bit and over committing a little bit. Not Mistral, but some others because we see how complex it is to actually create value in enterprises. But eventually we'll get there. Eventually the entire economy is going to run on AI systems, that's for sure. But it might take 20 years because it's actually fairly complex.

[Big Technology Co-Host]

All right, the website is Mistral AI. Our guest has been Arthur Mensch, the CEO of Mistral. Arthur, thank you so much for coming in. Really appreciate being here.

[Arthur Mensch]

Thank you you for hosting me. You bet.

[Big Technology Co-Host]

All right everybody, thank you for listening and watching and we will see you next time on big technology podcast.

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