It seems SA was too bullish on the new GW added per year in the next few years?

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Saw a lot of "Brad Gerstner Sold All of His CRWV" headlines going around yesterday.. If you actually watch the entire conversation, he comes off to me as pretty positive.. "I don't own any CoreWeave today.. Over the course of the last 3 months we took our exposure down not because of anything specific to CoreWeave but.. because.. AI is overshot. We moved 10% of our book two months ago into Meta.. We had to go somewhere to get that capital.. Let me say a few things about CoreWeave. Number one, the team that's running CoreWeave, one of the best in the business. 90% of the stuff that I read out there is misinformation with respect to CoreWeave.. There's probably opportunity today in CoreWeave in both the equity and the debt. I'm sure my team is back at the shop working away on that and somebody will make the pitch to me.. I think Mike Intrator who runs that business is one of the best. I think their capital stack is good. I think their partnerships like they have with NVIDIA are fantastic. They're going to be one of the new AI hyperscalers that make it to the other side... They went public, I think, at $30 bucks a share. I don't know. The stocks at $60 a share today. It's still a double off the IPO, which occurred sometime in the last two years, right? Not terrible." $CRWV

Just finished listening to Jensen Huang with Brad Gerstner on the BG2 podcast Probably one of the best things you can spend 90 minutes of your time listening to this weekend. Highly recommend. Some of my takeaways from the conversation 1. The demand for compute will continue to be exponential. He once again cited inference as the biggest reason. Reasoning takes a lot more compute than simply spitting out answers from stored memory of knowledge 2. As more compute is used, the work and answer quality will also be better. This will lead to higher demand for that compute, leading to the exponential cycle that Jensen is referencing 3. The harness and tool usage have been the two biggest unlocks in AI over the last year. AI used to simply be a brain of knowledge. Now it has a skeleton around it that allows it to be useful. It is learning how to use existing tools to do better work without having to recreate tools from scratch each time. 4. The cost of intelligence will fall fairly quickly in the coming years. This will be driven by a combination of more compute coming online and better efficiency of models. No different than Moore's Law. Jensen still believes that the consumption of the intelligence (volume) will more than make up for the decline in pricing 5. The original GPT-1 cost $20M to train. If you train GPT-1 today, that number would be closer to $100. That is how fast the cost of intelligence is coming down. 6. The offset to this has been usage of the models and human ambition to build even better models. Despite cost coming down, usage of models has gone up 1M (x) times since GPT-1. 7. Jensen disagrees that work hours are going to come down. The more you learn, the more you want to do. Human ambitions have always existed to achieve and earn more. The bottleneck was the opportunity to do it. 8. Robotics is entering its ChatGPT moment. Similar to what Elon did with cars, every physical aspect of the world will soon be digitized with software and compute. Every massive corporation will have to reinvent itself as a technology company with AI 9. Safety is a core component of accelerating AI technology. When you think of a car, you don't just think of the engine. The airbags and braking systems are components of this car. Similarly, regulation and safety are core components of AI 10. NVIDIA's share of capex with the Neocloud customers is roughly 80%. With the cloud service providers, it is closer to 40%. Both of these numbers are growing every single year. 11. Even without AI, the semiconductor industry was already on its path to becoming the largest industry in the world. AI is the "X factor" that has accelerated that. 12. Moore's law is over. Before you could wait a year for your transistor cost to become cheaper. Today, if you wait a year, you will pay 2x as much 13. Analyst consensus estimates have been revised nearly 200% higher for future revenue and free cash flows vs. this time last year. Despite that, the stock is only +20% 14. Analysts questioned the need for general-purpose chips vs. custom chips. The reason that Jensen remains focused on general purpose is because it is fungible. Fungible = lower risk, which matters a lot when you are putting hundreds of billions of dollars to work 15. Global compute infrastructure spend was roughly $1T/yr. That old world was the retrieval way of doing computation. Jensen sees this number closer to $10T/yr in the near term, with roughly half of that spent on intelligence capex

Never seen anything grow as fast as @Grok @Bot! And there is no free tier, which means people are deriving real value in excess of what they pay. As the saying goes, you get what you pay for.

Jensen Huang on BG2Pod Nvidia used to be a GPU company, then became the largest and fastest growing networking company; now becoming one of the largest CPU companies, "surely the fastest growing one." What Nvidia sells into one gigawatt went from about $18B to about $20B to now $40B plus per gigawatt, and he expects it to keep going up. Six years ago Nvidia was single digits of world capex; now about 40% with the CSPs and about 80% outside them (on-prem and neo clouds). Both halves doubling every year, so the share is still growing.

Brad Gerstner predicts an Anthropic IPO could draw more than $500 billion in demand and says whether it prices at $1.5 trillion or $2 trillion is irrelevant “I suspect if they choose to come public, there’s going to be tremendous demand based on everything I know about the company. “In the SpaceX IPO, I think it was a $100 billion offering. You had, I think, about $350 billion of demand for that offering, and the stock has done incredibly well coming out of that. “I suspect here you would have over $500 billion in demand for this offering. “Where it prices, I don’t really care. Is it at $1.5 trillion? Is it at $1.75 trillion? Is it at $2 trillion? Those things are irrelevant if you’re going to own the stock over the next year or two.”

"People say we're going to have more time. I completely disagree." New capabilities unlock more opportunities, and people will be able to pursue them. From Jensen Huang on the @BG2Pod

Too aggressive re Anthropic. Base case of $250 B ‘27 exit run rate (same size as MSFT & Meta) seems wild but achievable. $450 B (size of Apple & Google) seems too aggressive given compute & regulatory constraints. Margins also likely lumpy as big compute comes online. 🧐📈

I've been thinking about this convo on the @theallinpod in mid-August a lot over the past 24 hours. Everyone's trying to get a finer point on the Anthropic ARR (meaning RUN RATE, not recurring), but it's conspicuous that @GavinSBaker who's commentary is always thought provoking, felt the need to create such a high bar for Ant ($400-$500B ARR exiting 2027). Then @altcap quickly realized that creating this kind of bar when the biz was clearly slowing in August (see @tryramp data) could create a real expectations problem. Now it seems like the street has lowered expectations for 2027 exit rates of $200-$250B based on the idea that maybe Ant could come out at 10x that? Candidly even getting to $200B ARR exiting 2027 seems challenging given the magnitude of competition and let's call it measured investment in AI from enterprises, but I guess we'll see what their next models bring.

NVIDIA CEO @JensenHuang tells @altcap on @BG2Pod that people misunderstand Nvidia's demand. About a third is classical computing growing 30 to 40% a year with no AI labs. A quarter to a third is five or six frontier labs, not two. And a third is startups renting Nvidia like next-gen AWS. In my view, treating Nvidia as a two-lab capex story is a real mistake. "People misunderstand what's going on. There are basically three large segments. The first is baseline computing. Data processing, molecular dynamics, physics, simulation, computer graphics, classical machine learning, fraud detection, recommender systems. Classical computing, not AI, not deep learning, not agentic systems. Call it a third of our business. And that business is growing call it 30%, 40% per year. In the absence of any AI labs, Nvidia is not zero. We're quite a large company. The second part is the AI labs. People see two, but we work with every one of them. Meta Muse is doing fantastic. It's vastly Nvidia. Grok didn't exist three years ago. We didn't run Gemini before, we run Gemini now. Starting this year, we started working with Anthropic. People see a couple of labs, but I see more than that. I see five, six labs. And if you told me that represents not quite one-third of our business, let's say a quarter of our compute, fantastic. And another third, this is the part that's really great. Because we're at the beginning of a new computing platform shift, a vast number of new startups are coming out. Thousands of them. And what do they do? They rent NVIDIA compute. So a third of our business is kind of cloud computing for the next generation. It's kind of like AWS for today. And that represents about a third of our business altogether. My sense is that we're in the early innings of the buildout of this new computing architecture. And it's a real big mistake to recognize just a couple of companies and think that they were funding the infrastructure.”

Banger dropping Thursday.🔥@BG2Pod

Wow! First @MichaelDell and now @altcap. My daughter is going to benefit from the compounding on this for the next 15+ years. Amazing! Thank you both!