YouSaid · the spoken record

Steeve Morin

lines on the record: 88
first: 2025-02-24
most recent: 2025-02-24
sittings or episodes: 1
sources: podcast

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“The question is, you know, when, how, like, if there's like the H100 bubble, of course, it will impact NVIDIA. But Black Whale is, I'm probably going to get a lot of flag for this, but I've seen some very worrying numbers about it and varying testimonies about people who operate these things, right? So that ride will stop or at least slow down.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“They have a lot of problems with this chip. So, a lot of people are canceling their orders. These chips are on the frontier of scaling. And so, you know, they were supposed to come out last summer. But that heat dissipation and, you know, matter bending problem used to be called the people who are very privy, Silicon told me, this is what we call a pretty big frucking problem, right? End quote. Probably how to navigate the downslope. Maybe you don't know, but the supply of H-100 was actually smoothed out over the year so that they didn't have a big spike in deliveries and then a quarter less, right? Which pissed a lot of people, mind you, who bought a lot of them. Some of them even haven't received their order.”
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“The highs are very high, but they don't last forever. So probably it's how to navigate the downslope. Blackwell is probably something that keeps him awake at night.”
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“Probably the number one thing I would say is do not resell compute if you can. A lot of AI startups that are building on top of AI are trying to make a margin on top of a very big cake. And ultimately, what they sell is compute. If you look at the dollar of spend, for $10, maybe 98% of it goes to somebody else's margin. So if you do AI as much as you can try to verticalize on the product, but not on the compute. If your business model implies buying a lot of tokens, it's a very hard circle to square to put that into $20, right, a month. So, you know, I always say like, please, you know, look at it from that angle. And if you can try and avoid it.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“So the shift from throughput to how speed my answers to how long it takes for my answer complete to appear. That is probably one of the fundamental, like this year, right? Longer term, I'm very rooting for non-transformer models that will change the compute also landscape. And of course, you know, world models, right? Yes. And or energy-based models.”
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“Today is talent and energy. That's it. The rest, you know, yes, of course, you can buy 500 billion of GPUs. By the way, 90% margin. So if we work on that margin, we can shrink that number probably. So I'm not easily entertained by these numbers. I've seen how the sausage is made way too many times.”
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“My first impression was that I don't buy it. I would say American style, right? You start with the claim and we'll figure it out later. I don't buy it. And ultimately, I'm not sure I cared that much about it. Let's imagine it's true, right? Congratulations. Amazing. But it is more of the same. It is a vertical scaling. And as, you know, my days are spent on efficiency. So I look at these things as being like, all right, this is a bigger, you know, this is an American car of AI. It's big. It consumes a lot of gas. But ultimately, you know, it's not a good car, right? I think there has to be sufficient capital, but at some point, I'm not sure it is really a differentiator. That was prior to deep-sequenced came. That was always, you know, my thesis, but, you know, you need money. You need infrastructure. You need, but what is ultimately the probability the two limiting factors.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“There are very competent. 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 what I read, right? So I am hardly convinced about, you know, everybody was saying that they were dead and boom, they came out with their release and it was insane. So what I know is that I hope they don't have too much money. That's for sure. You want to be clever, right?”
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“No, I don't care. I have zero. This is something I understand the narrative and so on, but I am absolutely not fearful. Let's be successful first and then we'll talk about the politics. So far, but again, I'm not Mistral. I'm not, you know, I'm not building gigawatt detenters and so on. So if you build gigawatt detentors, you run into these problems. But maybe you run into these problems. But the thing is, if you're successful, everything flows from there.”
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“Today, maybe tomorrow I'm not sure they're a bit late in terms of ASIC. There are like A100 level, but they have probably, I would say, one of their unfair advantage is that it's like, you know, when you do exercise in the water, right? It's like, so this is, their state, there are constraints, so they are bound to do better. They can just not buy their way into better compute. So I think it hinders their success. But I think it's short-term to think that way.”
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“Not sure who is a threat to open AI at the moment. Here's why. You look at the numbers. I mean, we live in a bubble. We follow every new episode, the whatever new model, whatever who said who at, and so on. But, you know, I go to my mother and I ask her, you know, do you know ChatGPT? And she says, yes. And do you know, I don't know, I don't want to dunk on anybody, but do you want to know some other model? And she says, what is it, right? Even Gemini, right? Google, right? So they have a strong brand, they have a strong product, but there's a balance between the product and the models, honestly. So this is Gary from FluidStack, actually, who told me that his mental model in terms of model providers, there'll be like car makers. There's no winner tickle. Everybody will have their own because ultimately also human knowledge is, everybody has everything. So we're converging. But I like that an analogy. Yes, deep seek made a very good way.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“I love it. Constraint is the mother of innovation. Yes, you know, we can trawl a bit about the Singapore, you know, gray market and all of these things. But ultimately, like they had no choice. Here's the thing. If you can buy more, why would you give a damn, right? You can just buy more. So if you are pushed to efficiency, then you will deliver efficiency. These are very, very skilled people. This is the coolest thing to me about AI, honestly, is the geography doesn't matter anymore. You can just do things. You appear out of nowhere, boom, you know, you're on the map. And so I'm very, very glad that they did. I found the reaction very entertaining, to be honest. So, yeah, I mean, constraint is a very good driver of efficiency.”
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“What pushes smaller models are efficiency, roughly speed. You know, less is better. So if we can do with less, then less it is. Simple as this, right? In terms of rag, the key frontier is what we call attention-level search, but this is something we're working on. You have the exclusivity, now I'm putting it out there. It doesn't push, I would say, model sizes. What really pushes model sizes are the efficiency rather than specializing. Meaning that if you can do the same performance with a smaller model that is fine-tuned with rag or whatever, then you'll do it with a smaller because again, less is better.”
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“Depends on how it works, but yes, yes, sometimes it is. But think of it as in it's like a preamble to your question. Knowing the following and the following is a tiny window into the content, please answer my question. And of course, as you talk more and more, it will forget because that window is fixed.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“Please answer my request. And that's it. So that's a bit of a clever trick. It's a bit dirty because, of course, you are limited by the amount of data you can input, right? So there's this problem in which how do you chunk the data that you input?”
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“It's a very Very clever trick. What you do is you represent knowledge into what's called the vector space or latent space. And what you do is through what's called vector search. So imagine you have, let's say, a 3D space that represents all knowledge, all of everything. And let's say a cat sits here, a dog sits close because it's an animal, but it's far from some other property and so on. So what you do is you run the user's request through this same system. It's called an embedding. And that will give you a vector and you will take whatever is closer to you. What's called semantically close. And then it's actually very clever. You actually insert those pieces of text before the request. So it's as if you would say knowing the following and you give the data. Let's say it's law or whatever.”
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“Sometimes it's wasteful to run big models. A lot of times it's actually wasteful to run big models. I think there's going to be a lot of smaller models for efficiency reasons, but there's a but which is you talk to people at DeepMine and they don't even fine-tune anymore because they have such what's called big context window, which is what the model, the data the model you inject, right, at runtime that nowadays they just dump data into it and just say do whatever that data tells you to do instead of fine tuning as we used to do. So if the efficiency gains were not there yet, right? But if the efficiency gains, I would say pass that threshold, we'll just do it at runtime. We'll just have a great model that will just specialize at each request. But that's not for tomorrow, I think.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“One theory is that the smarter model is better at generating output that you would want it to generate, essentially. It's not better in the general sense. It's better at the task at which you were measuring it. This is what it learned to imitate.”
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“Because you don't use the mod the AI model to generate output. You use the machine, you use, you just run the code, right? And you see what it makes and you run all this code and you create data out of it. Whereas if you run LLM and you say to an LLM, all right, generate me two trillion tokens of text, it will do it with its, you know, so you may inject and stuff. So there's a lot of tricks, but ultimately my guts tell me that it feels wrong, right? Because you re-inject data that was there. And so it will deteriorate. There's loss. So yeah, I'm a bit bullish. I'm not sure exactly on what vertical. Code is one. We'll see. Distillation is in some sense a bit like that. You create synthetic data from a bigger model into a smaller one. Probably the most, I would say, mind-blowing thing about distillation is that sometimes the smaller models become better than the bigger models.”
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“I'm a bit split on this. There's a part of me that said that if you reinject data into the system, the system deteriorates. That feels a bit, I would say, intuitive, but if you look at alpha go, for instance, the moment it's ramped up in its skills is when they started generating games, synthetic games, right? So I'm a bit split, but there are some verticals that very much benefit from this code LLMs, for instance. We can run code, right? So this is the pool side thesis.”
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“I think it's fair game, to be honest. I will not shed a tear. It's fair game. If you, there were like some people who tried to ask, I think it was, I don't remember if it was an open AI model. So a diffusion model, image, right? They asked it to generate an image from a Star Wars movie at whatever timestamp. And it came out with the Star Wars movie screenshot. Obviously, it was strained with it. I think it's fair game because there's no free lunch, right? It was trained with data. You had a good ride. Somebody was sneaky and took it, but you took it from the beginning too. So let's just accept it's fair game.”
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“Go from one state to the other. And that actually makes sense. Like, if you try and pick this AirPod case, I'm not going to go round trip around the blog to get it, right? I just get it. And in my brain, it's wired to just do the thing. If I go and, you know, talk to myself out loud, put the hand down, move to the left and whatever, that feels very inefficient. So probably this will be something that changes. And in the case of LLMs, there's good work also on what's called diffusion-based LLMs, which means like instead of thinking, you know, what's called autoreagressively, that means you get a new token, you re-inject, and you redo, et cetera, they think more like what we do, which is in patches, right? Imagine a paragraph of text and words appear until it's done.”
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“It's hard too. He's no bullshit, right? So he explained to me how it worked and I was blown away. But it makes a lot of sense. We are creeped out because the machine talks back to us. But it's not a new thing, right? It used to, you know, this is not new technology when it came out. Like when it exploded, it wasn't new technology. But suddenly it was talking back. And that freaked us out. And we got crazy on it, right? But language is one form of communication, but it is ultimately a very narrow window into the world. We use it to describe the world arguably with some loss, right? And so the JPA approach is, long story short, is that you have essentially two things you want to do and you try and minimize the energy to do them. And from this understanding emerges, physics emerges and etc because you're trying to minimize the amount of energy to”
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“Jan's thesis, which is the word model. As in LLMs are at that end, what we need is something that understands the world fundamentally, and this is itssis, it's called. I'm very bullish on this, but it's very frontier”
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“I think until somebody does it, DeepSeek was a good wake up call, right? Suddenly efficiency is in. That's number one. And number two is until there's a new architecture that comes out and changes the game. So in the case of LLMs, for instance, you have these what's called non-transformer models that changes fundamentally the compute requirements. So that might be a frontier that completely obsoletes the transformers. And if the transformers are the, I would say the building block by which current model work, right? So the way they work is that for each token or syllable, if you will, the model will look at everything behind it. So you can see that as you add more text, you have more work to do. So there are these new architectures that do not require this, that might change these things and probably shift the amount of compute needed to do training or to do inference. And then there's the new thing, which is...”
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“Which is this is not scaling, and at some point you need to look at the problem in the face and do something better, right? So of course we push and push and push because there's capital still, but I'm more of these two approaches. I think you can do more with less.”
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“There's like a brute force approach to this. It is a very American approach, more and more. But the thing is, you look at, for instance, the XAI cluster. Not a hundred thousand GPUs, it is four times 25,000. You're starting to see because InfiniBand and in the case Rocky, which is, anyways, the technology they used to bridge their GPUs together, you have upper bound, right? At some point, you're fighting physics. So you can push, it's like trying to get to the speed of light. As you approach it, the amount of energy you need is a lot higher and a lot higher and it grows and grows. There's two, I would say, counter to that would be that number one is we still scale, but there's a lot of waste and excess, you know, spending on the engineering side, which is the deep-seek approach, right? Very successful at that, mind you. They said, yeah, if we do this and this differently, then we get multiple sometimes, right? So virtually you increase your compute capacity because you're more efficient. And the other approach is Jan's Lecan's approach.”
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“Yeah, even Microsoft, they bought when they were running NVIDIA, they bought NVIDIA at some outrageous margins. I talked to a lot of people that built data centers and I tell them, you know, mine do these people buy tens of thousands of GPUs. And I ask them, hey, do you get at least a discount or something? And they're like, no. The only thing we get is the supply. So, I mean, ultimately, if you don't own your compute, you're starting with, you know, something at your ankle. Definitely. And so this is why I like to think in this triangle, product data compute. And you can see where everybody sits and their weaknesses and their strengths.”
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“Amazon, they don't have products, they have Amazon, right? They have AWS, but they don't have actual products. Google has like Android, Google Docs, whatever. They have everything. They can sprinkle everywhere. This is the sleeping giant in my mind. If they're not busy doing a reorg, they might.”
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“I mean, they're still going after training. So there's still this frontier. Probably it's why also NVIDIA is the better buy right now. Because on the NVIDIA side, if you do the training, it's incremental. If you have bought 1,000 NVIDIA GPUs and you buy 1,000 new NVIDIA GPUs that gives you 2,000 GPUs, right? But if you buy 1,000 and 1,000 AND, that gives you twice a thousand, right? It's a bit different. So they're still going after training, definitely. And they're very pragmatic in doing so. I mean, they have the CapEx to spend. They're not making their money out of it, probably. The only one, by the way, that owns their compute are Google. There's like this triangle of, I would say, of wind that I, this is my mental model, mind you. You have the products, the data, and the compute. Who has all three? And you get everything flows from there.”
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“Probably not a lot of people are accustomed to what it entails to hunt production. So that inference is production and production is hard. Somebody has to wake up at night. And I used to be that guy, right? I don't want to do it again. So production is hard. Thankfully, we have a lot of software nowadays to do that a lot better, but there's not a lot of reuse because the AI field at least is not really accustomed to that yet. It's changing, but you know, the discount. Today are not the same, they're going to the right direction, but they're not there exactly yet. So probably that would be the number one thing. That is only, you know, training code running only for WordPass, right? This is not what it is”
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“I used to think the market was efficient. So probably I would go today, at least I would go with NVIDIA still because the supply. But, you know, if we play our cards right, we ship our stuff. Hopefully I will come back and tell you to buy AMD as much as you can or 10 store and if they go public or whoever else. These chips are amazing, by the way.”
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“It's like it's the better one, and that's it, right? And imagine if you had to care about these things. That would be insane.”
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“Out, but if you look at all of these things, there are tremendous amounts of, you know, we talk to companies who have chips are coming with almost 300 gigabytes of memory on it, right? So that is a model like one chip per model. This is the best thing you want if you run 70Bs, right? Which is what I would say not the state of the art, but this is the regular stuff people will use for serving. So if you look top to bottom and you know what you're going to build with them, then it's a lot better to do the efficiency gains because four times is a big deal, right? And mind you, these chips are 30% cheaper than NVIDIA's. It's like a no-brainer. But if you go brought them up and say, I'm going to rent them out, people will not rent them. Simple. So that's why I think it's a good way to attack it from the software because ultimately, do you really care about that your MacBook, let's say, is an M2 or an M3?”
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“Because I can give you actual numbers. If you run eight H100, you can put two 70B models on them because of the RAM, right? That's number one. Number two is if you go from one GPU to two, you don't get twice the performance. Maybe you get 10% better performance. Yeah, that's the dirty secret nobody talks about. I'm talking inference, right? So you go from, let's say, 100 to 110 by doubling the amount of GPUs. That is insane. So you rather have two by one than one by two, right? So with one machine of eight H100, you kind of run two 70Bs model if you do 4 GPUs and 4 GPUs, right? That's number one. If you run on AMD, well, there's enough memory inside the GPU to run one model per card. So you get AGPUs, A times the throughput. Whether on the other end, you get AGPUs, two, maybe two and a half times the throughput. So that is a 4x right there. Just by virtue of this. So that is the compute.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“Because the cost of buying. It's always the same, right? You have to spend six months of engineering to switch to TPUs. And mind you, TPUs do training. They're the only ones. With Trainium now, but AMD can do training, but it's so, so. But in terms of maturity, by far the most mature software and compute is TPUs, and then it's NVIDIA, right? The buy-in is so high that people are like, we'll see, right? I'm not on Google Cloud. I have to, you know, sign up. Oh my God, right? These are tremendous chips. These are tremendous assets. Now, in terms of the risk, I think if you want to do it, you have to do it top to bottom. You have to start with whatever it is you're going to build and then permeate downwards into the infrastructure. Take, for example, Microsoft with OpenAI. They just bought all of AMD's supply and they run ChatGPT on it. That's it. And that puts them in the green.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“This is actually a great question. I think that if you are doing it bottom up in FRA to applications, you will lose because nobody will care. As they don't today, right? If you look at TPUs, they're available. They're great. Nobody cares.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“Oh, yeah, yeah, yeah. Not agreements, but we work with them to support their chips. But the thing is, as I would say, a user myself of our tech is that if it's free for me to switch or to choose whichever provider I want in terms of compute, right, AMD, NVIDIA, whatever, then I can take whatever is best today and I can take whatever is best tomorrow and I can run both. I can run three different platforms at the same time. I don't care. I only run what is good at the moment. And that unlocks, to me, a very cool thing, which is incremental improvement. If you are 30% better, I'll switch to you.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“It means that you can freely switch to compute freely, right? You just say, hey, now it's AMD, boom, it runs. You just say, oh, it's 10 storant, boom, it runs, right?”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“Because this is what we do, this is our promise. Our thesis is that if the buy-in is zero, you know, you completely unlock that value.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“Absolutely. The right approach to me is making the buying zero. If the buy in is zero, you don't worry about this. You just buy whatever is best today.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“Apple just came and said, Hey, we're going to buy 100,000 of them. So you want to buy 10,000? You feel like the big shot, right? Yeah, but go back to the queue because there's Apple before you, right? So they have to have very high commitments. You cannot be incrementally better. It's very hard, right? And also very hard. I can give you one metric if you want. I know for a fact that being seven times better and take whatever metric you want, whether it's PEN, whether it's whatever is not enough to get people to switch. People will choose nothing over something. So this is a very hard market to enter into because you cannot also compete of incremental gains. It's very hard, right? So you have to convince a lot of people. Maybe you can go the Middle East route in which they sprinkle everything and they evaluate everything. That's not very sustainable, I would say, strategy.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“It's actually both. The buy in is very high. So to make it worth it, you have to buy a lot. And if you buy a lot, this is, you know, what we talk to all of them. They always have the same questions. And it's completely understandable. They say, this is great.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“Have a lot of incentives to buy into that ecosystem. So, I need to buy a lot of them. So if your AMD, that is already a problem. But then Microsoft comes along and buys it all, makes, by the way, OpenAI, or at least on the inference side, puts OpenAI in the green because of the efficiency gains.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“So, all I would say chip makers have a GTM problem. All of them, whether it's Google, whether it's AMD, whether it's 10 storing. The problem is that I would say probably two fundamental problems. The number one is if you're maintaining multiple stacks today is very, very, very hard. So you don't. So let's say I buy AMD. I want to buy AMD, right? That means I'm going to abandon NVIDIA. Oh, crap, you know, I have a six-year amortization plan on that. Oh, man, what do I do? So do I need to support both stacks? Unclear. Maybe until AMD tells me, hey, you know, you have, I don't know, let's say 1,000 NVIDIA GPUs, you're about to buy 100,000 of AMD. I mean, come on, right? And I'm like, okay, that is, you know, makes it worth my while, right? But that is ultimately the fundamental problem is that the steps are very high, right? I need to.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“So, this is where I think the market is going in. Of course, there's the ability problem. There is, you know, if you piss off Jensen, you might need to kiss the ring to get back in line, right? But, I mean, ultimately, I don't see this as being sustainable.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“They have a protocol nim. Sort of does that. The thing with NVIDIA is that they spend a lot of energy making you care about stuff you shouldn't care about. And they were very successful. Like who gives a shit about CUDA? I'm sorry, but I don't want to care about that, right? I want to do my stuff. And Nvidia got me into saying, hey, you should care about this because there's nothing else on the market. Well, that's not true. But ultimately, this is the GPU I have in my machine. So, you know, off I go. If tomorrow that changes, why would I pay 90% margin on my compute? That's insane. This is why I believe it ultimately goes through the software. Because the software, like if this is my entry point to the ecosystem. So if the software abstracts away those idiosyncrasies as they do on CPUs, right? Then the providers will compete on specs and not on fake modes or circumstantial modes.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“Depends on the supply. I think that there is a shot that they don't. Because here's the thing, you know, even if we take same amount of, let's imagine we have a new chip from Amazon, right? That is the same amount. Oh, wait, we do. It's called Ranium. You know, why would I pay 90% margin of NVIDIA if I can freely change to Tranium? My old production is runs on AWS anyways. Like if you run on the cloud and you're running on NVIDIA, you're getting, you know. squeezed out of your money, right? So if you're on production on dedicated chips, of course, so maybe through commoditization, but hey, I'm on AWS, I can just click and boom, it runs on AWS's chips. Who cares, right? I just run my model like I did two minutes ago.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“Means you get maybe not SRAM level performance, but you get a lot faster performance in terms of compute. So, and if you translate that to LLMs, let's say you get much, much higher tokens per second. In a single stream, which is exactly what you want when you go into reasoning. You want your model to maybe think, let's say, for like half a second, and then boom, you don't want to wait 50 seconds and context switch to some other thing, which is the problem everybody has today, mind you. So, yeah, I think inference will be pushed to compute landscape will be pushed to change because of these two constraints. I know I'm working on it.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source
“Frontier after that, which is called compute in memory. There's two companies that are on that market. One is called Rain, Rain.ai. Sam Altman is one of the investors. There's no surprise. The other one is called Fractile. So this is an ex frontier. And the idea is that instead of like transferring the data between external memory and the CPU and do the compute there, you actually bring the CPU to the memory and you do everything. It's crazy stuff. But it's coming. Maybe not this year, but.”
2025-02-24 · The Twenty Minute VC · 20VC: Why Google Will Win the AI Arms Race & OpenAI Will Not | NVIDIA vs AMD: Who Wins and Why | The Future of Inference vs Training | The Economics of Compute & Why To Win You Must Have Product, Data & Compute with Steeve Morin @ ZML · IDENTIFIED FROM THE TRANSCRIPT · source