The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 52 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 1 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Mensch: Cloud investments in AI startups resemble round-tripping
“It looks like around flipping. Yes. I don't know about that deal particularly, but it makes sense from both perspectives.”
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: DeepMind's initial Gemini development was too slow before recovering
“Gemini was a bit too slow, and I think they recovered sufficiently well since.”
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: AI application differentiation comes from data and user feedback
“Like general purpose models are a bit undifferentiated, but the differentiation that you need to create for your application comes from the data you put into it, the user feedback that you gather and the intelligence that you have to figure out what the applic…”
Mensch: Compute is no longer the primary bottleneck for text models
“There's obviously compute, but given the amount of data you have we have at hand compute is already running into is no longer the bottleneck. The bottleneck is more the data at that point. You should look at text to text models.”
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: Nvidia captures top AI margins while LLM margins lag traditional software
“NVIDIA is, at that point. The cloud providers are pretty much at cost LLM providers, we're not at cost hopefully, but the margin that, Are known to be lower than the typical software margins.”
Mensch: Foundational AI model margins will not drop to zero
“I don't think there's any way in which the marginal cost and the margin of the most important part of that technology, which is really the foundational layer becomes zero because otherwise there's definitely going to be I guess fairness problem.”
Mensch: AI model training achieved 100x algorithmic improvement over three years
“So if you look at the way we train models from three years ago, and the way we train model today, I think we have probably made something around a hundred times algorithmic improvement. So that's the, that's probably where most of the gains were actually made …”
Mensch: Open-source models provide a shortcut to enterprise software distribution
“A shortcut to distribution is to create demand through open source models.”
Mensch: AI model quality is correlated with compute, not dependent on it
“Capital is equal compute. Then compute is correlated with quality. It's not completely dependent on it.”
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”
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: 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: 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: Fears of AI job replacement are grossly over-exaggerated
“I think they are. I mean, it depends on who you're speaking to. I think job are going to be displaced for sure. Some will be replaced. Some will open up.”
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: 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: Compute scale alone hits a hard ceiling without high-quality data
“Scale isn't the only recipe, the only ingredients to the recipe you need to scale, but you also need to have proper data. Otherwise you reach some data quality limit.”
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: 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.”
Mensch: Developers prefer customizing open-source models over proprietary APIs
“This open source part was, I believe, a good enabler for the community and made people realize that they could build very interesting technology by modifying the models themselves instead of depending on the APIs of a couple of providers.”
Mensch: Current AI fine-tuning approaches are too low-level
“Like the fine tuning aspect that has been like the go-to solution is probably a little too low level from What we should be doing.”
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: Open-source AI shifts market value to platform and customization layers
“It moves the value a little higher than the model itself. It moves the value to the platform and customization part which is really I guess something that we're expecting and it's, it accelerates that process.”
Mensch: European enterprise AI adoption lags US by maximum one year
“So there's some delay compared to the U S market for sure. But it's not, I wouldn't say it's very significant It's one year maximum in terms of delay.”
Mensch: Early-stage companies must stay under founder control to preserve vision
“What is important for a young company like us is to be under, under the control of the founders because there's a lot of things to be invented and the vision it can only be carried by them by us.”
Mensch: Startups cannot operate in both the US and China simultaneously
“We don't operate in China because you can't really operate in US and China without being like a very, very large corporation.”
Mensch: European 23-year-olds match Silicon Valley engineers in four months
“On the talent side we can hire 23, 24 years old people that we can onboard in four months and they operate as well as any software engineer in the Valley.”
Mensch: AI abstraction speed is unmatched in human history
“I think what's happening right now is that probably the speed in, in our elevation toward higher abstraction level is probably occurring at an unmatched rate in history. Though that means that the society adaptation is going to be more challenging.”
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: 7B parameter models run efficiently on smartphones and MacBooks
“And seven B is the size that allows to run Efficiently a model on your Macbook or on your smartphone.”
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: Developers rely on community vouching because evaluating every model is impossible
“People use certain models because they are known to be good. You can't afford to evaluate everything out there. And so having some form of community vouching is super important.”
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: NVIDIA roadmap delivers 30% compute cost reduction every two years
“The cost of compute reduces over time, just based on hardware costs. It reduces around 30% every two years if you follow NVIDIA roadmap. For the same amount of flops.”
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.”
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: Lack of developer tooling limits enterprise open-source AI adoption
“I think they're still lacking some products around like managing correctly load balancing customizing the models because you can do it with DIY solutions, but if you want to make it robust enough and scalable enough, it's actually not easy. And if you want to …”
Mensch: Everyone will adopt core-business generative AI in five years
“So to be not thinking about generative AI as a way to As a way of increasing productivity in a world processing, but rather as a way to change completely the way you operate your core business, which usually involve taking models and customizing them pretty he…”
Mensch: AI spending in customer support has moved to core enterprise budgets
“It's moving into core budget for customer support, for instance, where like areas where the application of AI is pretty obvious. It's definitely moving into core budgets.”
Mensch: Telecom and healthcare AI spending will move to core budgets within a year
“It's also at the experimental stage in my many other functions and for Core applications in the industry in, I guess, telecom the telecom industry and in healthcare, this is still In the playground, but I think it's going to evolve in the next year.”
Mensch: Physical limits on hiring and infrastructure cap startup scaling speed
“You can only hire that fast. You can only scale your infrastructure to manage more GPUs that fast, and you can only raise capital that fast. So there's some acceleration constraints that are pretty hard to fight, and that are pretty much the first principles o…”
Mensch: Silicon Valley leads in senior AI talent while Europe excels in junior talent
“For senior AI scientists you find them more in the Valley than in France for junior AI scientists. It's there's a wealth of talent in France, in Poland, in the UK And that's that I think one of the strengths of the area.”