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Big Technology Podcast · 4 min · Jan 16, 2026

Model commoditization shifts value downstream

Arthur Mensch · CEO & Co-founder, Mistral

Arthur Mensch argues that training know-how spreads quickly, making durable model leads unlikely.

The episode’s best minutes

Full analysis
  1. 1:39Model commoditization shifts value downstream4 min
  2. 15:55Open models give enterprises control over critical AI4 min
  3. 39:57AI can automate complex shipping and factory workflows6 min
  4. 5 more moments in the full episode
“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?”
Arthur Menschat 3:00 · Big Technology Podcast
3:00

Takeaways

  1. Around ten labs share similar data, recipes and training know-how.
  2. Model assets depreciate quickly as competitors catch up.
  3. The durable opportunity is solving enterprise problems downstream.

About this segment

Arthur Mensch argues that training know-how spreads quickly, making durable model leads unlikely. He says investment must be balanced against downstream enterprise value and profitability.

Arthur Mensch

Arthur Mensch is CEO and co-founder of Mistral.

Co-founded and leads Paris-based Mistral AI as CEO; named to Time's 100 Most Influential People in AI.

  • Alex Kantrowitz, Host · Founder, Big Technology

    Former BuzzFeed News senior tech reporter and author of Always Day One.

From “Who Wins if AI Models Commoditize? — With Mistral CEO Arthur Mensch”, Jan 16, 2026. Watch the full episode

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Answers “How do AI companies make money?” See all 42 answers

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 1:44

    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 frontier models exhibiting performance that's so similar is difficult to tell which is the best.

  2. 2:21

    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. 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.

  3. 2:47

    So because it's short, it actually circulates. So there's no IP. E-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?

  4. 3:13

    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.

  5. 3:52

    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. 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 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

  6. 4:49

    that is being made downstream. And so the focus that we have as a company, but that I think is a 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 trying and help them to actually go for the right use cases and actually do the right amount of customization so that

  7. 5:37

    But 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.