The Wisdom Wall

“The countries that control compute will control AI, and you cannot have compute without energy.”

“That's how the hyperscalers feel. So of course they're going to be spending like drunken sailors because the alternative is that they're completely locked out of their business. So it's not a purely economical framework that they're using. It's a, do we get to maintain our leadership?”

“Going off and saying, I'm gonna build my own AI chip to compete with NVIDIA, It's a little bit like saying, you know, that Google search is pretty nice. Let's go replicate it. It's insane. Like the level of optimization, the level of design and engineering that goes into that, um, you're not going to be able to…”

“There's also, um, this situation where the margin on HBM is so high, That no one wants to actually increase the supply, because then the margin goes down.”

“So if you are the incumbent, you have an advantage because people are designing their models for your hardware. It doesn't even matter if there's a better architecture out there. It's not going to run well, so it's not a better architecture.”

“AI doesn't work the way SAS does. In SAS, you have a bunch of engineers who go out and build a product, and the quality of that product is determined based on what those engineers did. That's not the case in AI. In AI, I can improve the quality of my product by running two instances of the prompt, and then picking the…”

“There's two kinds of risk. There's, um, mistakes of commission, where you do something that's a mistake, and then there's mistakes of omission, where you don't do something and it's a mistake. And the United States is terrified of making mistakes of omission. When you are in a massive growth economy, missing out is…”

“Um, you could have a model that is 10 times smarter than OpenAI's model. And if you have 10 times the compute, OpenAI's model's gonna be better.”

“the most valuable thing in the economy is labor. And now we're going to be able to add more labor to the economy by producing more compute and better AI. That has never happened in the history of the economy before.”

“right now the biggest problem I see in AI is if you see a good engineer, one that you would have hired before, they can go out and they can raise 10, twenty, hundred million, a billion dollars, and then rather than contributing to one of the other AI startups, they go create their own. Which means that you have…”

“If we lower, um, what we charge 50%, people are gonna buy twice as much. They're spending as much as they're making because whatever they spend increases the quality of the output.”

“And when you are growing faster than exponential, there's no amount of profit that you can make that matters. What matters is getting a toehold in the market and becoming relevant.”

“You could have an LLM generate synthetic data, and when it generates the synthetic data, the data is better. You then train on that synthetic data.”

“One of the reasons that these LLMs struggle to multiply large numbers is because, um, multiplying is not linear. These LLMs could do anything linear without, you know, needing to think, but just like on a piece of paper how you need to write out all those intermediate steps, these LLMs need that intermediate space in,…”

“If you're competing, it means that you haven't found an unsolved customer problem, because if you're competing, someone else has already solved the problem.”

“Why would they try and smuggle in GPUs when all they'd have to do is log into any cloud provider and rent GPUs? This is like the biggest gaping hole in the whole Um, way that export control is done. You can literally log in. You can swipe credit card, whatever, and just like pay and get GPUs to use.”

“It's also a little bit fruitless because, you know, someone could just like rent a server anywhere and then log into us from there. Right? And then there's nothing we can check. So I, I don't know that IP addresses are really the right way to do it anyway. I think we need something more sophisticated, but I mean, yeah,…”

“And the formula is actually pretty simple. You're not allowed to make net money. You're allowed to spend more money in China. But the moment that you start to become profitable or anywhere near profitable, all of a sudden there's a thumb on the scale.”

“Linux has switching costs, and I think what we've discovered is LLMs have no switching costs whatsoever.”

“So better models create more demand for inference, more demand for inference. Then has people going, I should train a better model, and the cycle continues.”

“If you're not a member of the mag seven, You're not going to be able to get anywhere near the valuation. And so what do you do to stay there? You spend. And it's worth it because the stock value stays up because you're in the top seven or 10.”

“What is the number one thing that, um, a high margin correlates to in CPG? It's the speed at which the ingredient acts on you. So that dopamine cycle, how quickly something occurs, determines your brand affinity. And so, um, when something, uh, has a very quick response, you associate to that brand, and then you accrue…”

“the more inference you have, as, as mentioned before, the, the more you need to train the model to optimize for the inference. And the more training you have, um, the more inference you want to deploy to optimize for the cost of that training, to amortize the cost of the training. There's a virtuous cycle between the…”

“Compute is the easiest knob because it just keeps getting better and better and better every year. And if I write enough, uh, you know, if I write a check for enough money and I'm willing to wait a little while, I'm going to get more compute. It's the most predictable part of the pipeline.”

“the real reason why you need higher margins is volatility, because if you have a razor thin margin and the market moves, you may not be able to raise more money, you may not be able to get a loan, and so what a margin does is it gives you stability and staying power in the market. On the other hand, um, what it does is…”

“they're misunderstood because, um, the assumption is that all of the, all of the data is the same quality.”

“Like, just tell me what the, the tokens per dollar is, and tell me what the tokens per watt is. Nothing else really matters.”

“I think saying that you're, you're gonna hire B players because you've gotten large enough is, is laziness and an excuse. And it, it's a lack of creativity in your business model and how you're going, the algorithm of how you're going to scale.”

“If you are a company right now, it, it feels like, okay, well, it's harder to poach, but what does that do? It suppresses wages. It's harder to hire someone. They're less likely to move. There's less competition. It suppresses wages.”

“The reason that the writing from LLMs is terrible is because it's predictable.”

“What most people don't realize is that assumes that the data quality is uniform. If the data quality is better, then you can actually get away with training on fewer tokens.”

“There needs to be some sort of Automated response of like, if you do this, we will respond. If you subsidize this industry, we will automatically subsidize the equivalent industry. Just automatic. So don't do it because there's no benefit to you.”

“Is AI the next internet? And I'm like, absolutely not, because the internet is an information age technology. It's about duplicating data with high fidelity and distributing it. It's what telephone does. It's what internet does. It's what the printing press did. They're all the same technology, just much different…”

“And just like you have software compatibility, you have prompt compatibility.”

“in terms of whether or not the, the economy is overheated, I think one of the best predictors of that is, is the economy getting in the way of the success of the companies. If it's not getting in the way, then I don't think it's overheated.”

“Enterprises make these long-term deals, and they stick with whatever their deal they made a year ago.”

“I think over time, we're gonna realize that LLMs are the telescope of the mind. That right now, they're making us feel really, really small. But in a hundred years, we're gonna realize that intelligence is more vast than we could have ever imagined, and we're gonna think that's beautiful.”

“if, if your job is to get better at multiplying numbers, and I tell you that I want you to be able to do it with fewer steps, more intuitively, for you to be able to multiply three-digit numbers versus two-digit, you need 10 x the data, and you need 10 x the examples, right? And so as you get better on the intuitive…”

“Compute has been more of a, uh, less of a bottleneck and more of a sort of a, you know, soft neck or something, right? Where, when you provide even more compute, you can sort of overpower the lack of data, the lack of improvement in algorithms. So it's not a hard bottleneck. It's a soft bottleneck.”

“As a founder, the only type of happiness you get is this third type, which is future happiness. The other two, the common ones are, um, the present is happy, right? And the, the other one is you went through some real crappy stuff, but the memories are, make you happy, right? So there's past, there's present, and…”

“Model is, um, it's a piece of machinery. It's an engine, but what is the car? What is the experience?”

“And now the defense has to be automated, because there's no way to keep up with automated attackers.”