The Ledger
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Randle: Mistral AI has fallen very far behind on model development
“Like Mr. All itself has fallen very far behind on, on the actual model side, and they've done a very good job becoming this inference platform and the FDE model and all these things, but they're outside of China.”
Morin: Written-off narratives about Mistral AI's demise are baseless FUD
“They are very competent. So I don't know. I think it's easy to spread FUD. There's a lot of FUD going around, especially about regulation and everything. But here's the thing. I look around me and I don't see You know, what I read. I am hardly convinced about,…”
Bret Taylor: Companies needing standard models should simply fine-tune Llama or Mistral
“You know, I think that in that market probably if you need a model like that, you should download llama. That's the answer. It's like, you don't need much of a cheat sheet on that, you know, and or maybe Mr. All, but pick one of the open source models that are…”
Wang: Most serious enterprises will adopt on-premise AI models
“And this is actually why I think there's a very there's a very big sort of opportunity for whether it's open source models or the llama models or the mistral models or whatnot, basically these models that can go on prem and that enterprises can take. And then …”
Srinivas: Competing with OpenAI on base models is a terrible arena
“If there are companies that are working on Foundation model competitor to OpenAI. It is definitely like one of the worst arenas to be part of. Almost like I think there are five men standing today sort of thing. Google, and Anthropic, Meta Mistral, and you can…”
Mensch: European VCs could not structure Mistral's large pre-revenue Series A
“European funds were unstructured to do the kind of deal that we were proposing. So we didn't even have a lot of conversation cause they just couldn't get their head around the investment that needed to be made as well as we were a pre-revenue company.”
Mensch: Founders should not start new foundational AI model companies today
“Is it too late? I wouldn't recommend going into the foundational layer business. I know Sam didn't recommend to do that one year ago, and we did, and it seemed to have so far changed a few things. So I wouldn't be I think it would be arrogant for me to say tha…”
Mensch: Mistral could not operate solely from Europe without US presence
“We could not operate only from Europe, and that we needed to go to the US very quickly.”
Mensch: Vertical AI models will be built by application makers
“And actually these vertical models are not going to be out there. They're going to be built by the application makers because the only way you can Make a low latency model that is super good at a specific task is to get rid of the general purpose aspect becaus…”
Mensch: AI models will become merely starting points for developers
“And so I think that's the end state is models are effectively going to be A starting point for any AI application developer. They need to be surrounded by tools, by lifecycle management platform basically, and that's the one thing that we started to build.”
Mensch: A team of five is faster than fifty unless uncoupled
“A team of five is faster than a team of 50. Except if you organize the team of 50 to be 10 teams of five that are sufficiently uncoupled.”
Mensch: Mistral does not need to scale compute at competitor rates
“So, I mean, we're growing our compute like every companies. We are convinced that we don't need to grow at the same rate because there's a lot of barriers that are not compute related that are appearing on the way and that we're already seeing”
Lebrun: Startups with billions in funding can match OpenAI's capabilities
“I think a new startup with enough talent and enough money, and when I say enough money, I'm talking about billions, just to be clear, then I'm sure you can do as, at least as well as OpenAI.”
Jain: Only China and France have produced AI models outside the US
“The only Country in the world, you know, that has produced models outside of US is China. And then of course, maybe a little bit in, like, you know, France with Mistral.”
Midha: Investment thesis for Mistral AI is a fully independent European stack
“Independence at scale, at every part of the AI infrastructure stack, like land, PowerShell in Europe, that's sovereign, it's local. Compute infrastructure, that's local. And models that are trained locally, by the way, fully open, so they can be deployed and c…”
Taneja: Mistral fell behind due to compute constraints, but has caught up
“And I think they were a compute constraint and capital constraints, so they fell behind, but I think they, I think they've caught up.”
Taneja: Meta isn't an enterprise company; Mistral leads Western open-source AI
“It's not meta. They're not an enterprise company. It really is in the West. It's really missed, missed trial today.”
Sovereign AI models will not become the largest general-purpose players
“Maybe wins in a scoped part of the market. Like, I could see why, for example there would be a lot of benefits to having Mistral be an expert in European law that might have nuances from other kinds of law, and they've just invested far more in having the best…”
Lemkin: AI startups under $100M revenue should accept $20B acquisition offers
“If you're at less than a hundred million in revenue and someone wants to buy your team for twenty billion today, I say take it.”
Mensch: Significant efficiency gains remain possible for given AI model sizes
“I believe there is. I believe we can make models that are much better for a certain size.”
Mensch: The price per unit of AI intelligence will definitely decrease
“The price around the model, the dollar per intelligence unit, let's say, is definitely going to reduce.”
Mensch: Foundational AI model market retains high defensibility
“There's a few barriers that are pretty hard to face that you need to you need to accrue sufficient, well, to raise sufficient capital to have enough compute and be relevant. You need to have people that knows how to train models, which is still a scarce resour…”
Mensch: Pre-Mistral 7B models lacked utility for real applications
“So there was already seven B models before, but they weren't good enough to do interesting applications.”
Mensch: Mistral operates on 1.5k GPUs, a fraction of competitors' compute
“We are still bottlenecked by compute for sure, but that's because we don't have many of it. We have 1.5 k 800, which is a few percent of our competitors.”
Mensch: Developer freedom is the best path to ubiquitous AI
“Bringing freedom to developers and AI application makers is I think the best way in, in distributing generative AI as widely as possible, which is our objective as a company, making AI ubiquitous bringing frontier AI into everyone's head.”
Cannon-Brookes: Anthropic is one of Atlassian's largest AI models
“We use Anthropic a lot. It's one of our biggest models. We use, you know, multiple models which I think is what most good SaaS vendors are doing within their customers' choices, right? Like we use a lot of Gemini, a lot of Anthropic. We have a whole bunch of L…”
Mignot: Index Ventures holds investments in LLM providers Cohere and Mistral
“We're in Cohere, and in and we have a seed investments in Mistral.”
Mensch: Mistral started go-to-market when it had nothing to sell
“We did start the go to market motion at the time where we had absolutely nothing to sell.”
Mensch: Mistral AI operates with a team of just 25 people
“Although the team is only 25 people, so that's actually not super challenging.”
Mensch: Mistral targets model efficiency to reach top performance per compute cost
“And so that's why we continued of targeting very efficient models with the Mixtral HX seven B and more recently, Mixtral HX 22 B ensuring that for a certain costs and for a certain size, we were reaching the top performance of the market.”
Mensch: Mistral was surprised by Cohere's recent strong model releases
“We were surprised by cohere recently. They did came up with new good models. And I think that was a surprise for us.”
Mensch: Exposing AI researchers to business teams improves model performance
“Ensuring that the science team has some relatively direct exposure to the product and to the business team is actually important to make them understand what, where the model is failing and how it could be improved significantly.”
Mensch: Open-source distribution creates critical trust and brand equity in AI
“Brand is important because trust is important in that domain and open source brings trust in terms that Provide some trusted brand.”
Mensch: Mistral will remain an open-source leader while monetizing commercial models
“Like we still intend to be a leader in the open source part and to have some unique assets that we can license. And to have some unique platform that developers can use.”
Benioff: Salesforce Has Invested in Anthropic, Mistral, and Cohere
“We also use a lot of other models. We change that on a regular basis, and we've invested in a lot of model companies, including Anthropic and Mistral and Cohere and many Many of them.”
Mensch: Mistral focus is enabling developers to easily fine-tune models
“It's a very hard job to make a specialized model. So it's actually very tied to the way you create a pre-trained model. And so bringing the tools that allow to do it in a foolproof way. So allowing developers to create a customized model that are performing ve…”