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$AMZN AWS CEO: “We invest on 5-year commitments from customers, so we have great visibility for revenues and when they’ll land.” AWS also recently implied that server investments pay off in under 3 years, so we are looking at 2 years of pure returns after investment is recovered. They...

29,884 просмотров • 9 дней назад •via X (Twitter)

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CoreWeave $CRWV CEO Michael Intrator, on the Big Technology Podcast, providing what I believe is the most compelling rebuttal to the narrative that GPUs only have a 2 to 3-year useful life. CoreWeave's customers are the world's most sophisticated users of GPUs, and they are signing contracts to utilize today's GPUs for the next 5 years, despite Nvidia's new product release cadence resulting in more efficient GPUs every 1-2 years. The use-case for the GPUs might change over time as Nvidia rolls out new products (e.g. older chips are moved from training to inference workloads), but the "usefulness" does not. "The most important tool that I have for understanding what the depreciation curve or the obsolescence curve of compute is not what I think, right? It's not what some historic short (seller) thinks. It's what are the buyers, the most sophisticated companies in the world are willing to pay for today. And when they come to me and they put in a contract for a 5-year deal or 6-year deal, in what world do I not think that they who are the consumers of this understand that there are new, more powerful chips coming out? Of course they do. They understand it, but they also understand what their various use cases are. And they're saying to themselves, "I'm going to buy this because I'm going to need it today. I'm going to need it in 3 years, and I'm going to need it in 5 years"... My opinions around depreciation are informed by the only entities that get to vote in my world, which are the folks that are paying for the compute over time."

Rittenhouse Research

161,828 просмотров • 7 месяцев назад

Dylan Patel believes $NVDA will not use CPO even with Feynman “We’re very bullish on copper and non-CPO optics. We're kinda bearish CPO. $APH is going to perform much better over the next few years than previously expected.” Full quote: “The spending associated with AI chips is currently at or below 10%, but when we move to CPO, networking will grow even further, to 20% or 30%. We are seeing a huge uplift in networking content. CPO is a massive step-function change for the industry, and everyone recognizes it now, but people are getting a little too exuberant. In my view, it is not coming in 2027. It will really arrive toward the end of 2028, with 2029 being the real ramp for scale-up CPO. There have been a lot of manufacturing problems. If we could deploy it today at a good cost, everyone would do it. However, it is really hard. The manufacturing volumes and yields are not there, and the chips are not fully designed for it yet. It is a very complex and difficult technology to ramp, so people are going to stay with copper for as long as they can. That means Rubin is all copper, and Feynman, the next-generation Nvidia GPU after Rubin and Rubin Ultra, is still copper. We are not even shipping Rubin yet. We have a few generations of chips before we get to CPO on the GPU. CPO on switches is coming earlier than on GPUs or AI ASICs. Even without CPO, as cluster sizes get bigger, you need more optics, active electrical cables, and similar components per GPU. We have seen a major shift in the dynamics. On Monday, we released a note for our institutional research subscribers stating that, over the medium term, we are actually very bullish on copper and non-CPO optics, while being slightly bearish on CPO. This is due to delays in downstream chips, such as Feynman not fully utilizing CPO. Copper companies like Amphenol, which make backplane connectors and cables, are going to perform much better over the next few years than previously expected because CPO is being delayed. Ultimately, integrating optics is much more expensive than sending signals electrically. However, electrical signals cannot travel very far without adding repeaters or optics. There is a trade-off continuum, and while CPO will happen, it is currently being pushed out.”

Daniel Romero

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David Sacks Explains How AI Will Go 1,000,000x in Four Years "I would say the rate of progress is exponential right now on at least three key dimensions." 1) The models "So number one is the algorithms themselves. The models are improving at a rate of, I don't know, 3-4x a year." "They're not just getting faster and better, but qualitatively they're different." "Remember, we started with pure LLM chatbots." "Then we went to reasoning models." "We didn't even get to the agents part of it yet, but that's the next big leap after reasoning models." "We're just starting to scratch the surface there." 2) The chips "Then you've got the chips." "Depending on how you measure it, each generation of chips is probably 3-4x better than the last." "It's not just the individual chips that are getting better, they're figuring out how to network them together." "Like with NVL72, it's like a rack system to create much better performance at the datacenter level." 3) The compute "And that would be the third area where you're seeing basically exponential progress." "Just look at the number of GPUs that are being deployed in datacenters." "So when Elon first started training Grok, I think they had maybe 100K GPUs. Now they're up to 300K. They're on their way to a million. Same thing with OpenAI's data center, Stargate." "And within a couple years they'll be at, I don't know, 5M GPUs, 10M GPUs? How Sacks gets to 1,000,000x: "The algorithms, the chips, and the datacenters are all improving or scaling at a rate of 3-4x a year." "That's 10x every two years." "Where people don't understand exponential progress is that if you're getting better at 10x every two years, that doesn't mean you'll be at 20x in four years." "It means you'll be at a 100x." "So you multiply those things together: the algorithms, the chips, and the raw compute that's available." 100x models 🧠 x 100x chips 💾 x 100x compute ⚡️ = 1,000,000x AI 🤖 "You're talking about 1,000,000x increase." "Some of which will be captured in price reductions, some of it will be in the performance ceiling, and then some of it will just be in the overall amount of AI compute that's available to the economy." "But the impact of this thing is gonna be absolutely massive." "And I think people still don't even appreciate that fact because they don't understand exponential progress."

The All-In Podcast

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Jeff Bezos on how to build a business strategy “I very frequently get the question: ‘What’s going to change in the next 10 years?” And that is an interesting question… But I almost never get the question: ‘What’s not going to change in the next 10 years?’ And I submit to you that that second question is actually the more important of the two.” Jeff argues: “You can build a business strategy around the things that are stable in time. In our retail business, we know that customers want low prices, and I know that’s going to be true 10 years from now. They want fast delivery. They want vast selection. It’s impossible to imagine a future 10 years from now where a customer comes up and says, ‘Jeff I love Amazon, I just wish the prices were a little higher.’ Or, ‘I love Amazon, I just wish you’d deliver a little slower.’ Impossible. And so we know the energy we put into these things today will still be paying dividends for our customers 10 years from now.” He gives AWS as another example. It’s impossible to imagine AWS customers asking for a less reliable or more expensive service. ”When you have something that you know is true, even over the long term, you can afford to put a lot of energy into it… The big ideas in business are often very obvious, but it’s very hard to maintain a firm grasp of the obvious at all times. But if you can do that and continue to spin up those flywheels and put energy into those things, over time, you build a better service for your customers on the things that genuinely matter to them.” Video source: Amazon Web Services (2012)

Startup Archive

61,894 просмотров • 1 год назад

David Friedberg: Michael Burry’s Datacenter Math is Wrong “I actually think Michael Burberry's got this wrong.” “What Michael Burry is saying is that all of these hyperscalers have extended their depreciation schedule or the useful life of their data centers by roughly 2x, which cuts the operating costs in half when they report it in earnings. And so it's making their earnings inflate.” “So he's claiming they're cooking the books. Google first made this change in Q1 of 2021, where they said the servers are now going from 3 to 4 years. Separately in 2021, Google took networking equipment from 3 to 5 years. And then in 2023, they took it from 5 to 6 years.” “And so this is a result of this effort where they went in and did an analysis. So what happened?” “What happened in the data centers is that the data centers transitioned from being primarily data storage and data transfer systems, where you would use hard drives and RAM and memory to store data and then transmit it back out, to being data processing centers because of the AI boom.” “So as AI became more important in the data center, more of the dollars that are going into data centers were allocated towards chips from data storage, which initially was hard drives.” “And then suddenly, when you put these processors in to process the data to do AI, the majority of the spend and the majority of the energy is going towards the processors.” “I made some calls and I checked around with some other friends, and everyone says the same thing: that these 7-8 year old TPUs and GPUs that are sitting in the data centers are still being used and they're being used at 100% utilization.” “So that actually justifies and validates the depreciation schedule being much longer versus shorter.”

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