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Gavin Baker (Gavin Baker) says the disaggregation of inference can extend GPU useful lives from 3-4 years to 10-15. That may single-handedly save private credit and reduce the financing rates for GPUs, which will drive demand and help finance the build-out. "The disaggregation of prefill and inference is going...

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CoreWeave co-founder brannin mcbee reveals the A100 demand through 2029 is a big narrative violation for a market that thought GPUs would only have 2 to 3 years of useful life: "You don't need the latest generation GPU for every workload. You can think about it as a matrix of different sizes of models versus different sizes of GPUs, and it all just gets filled in volumetrically and economically." "That A100 GPU will cost less on a dollar per hour basis than a brand-new GB300 GPU. But the workload fits most efficiently on that A100, and it ultimately comes down to the engineering organizations making those requests." "It's been a pretty big narrative violation for the market that thought that these GPUs would only have two to three years of useful life, and instead we're contracting GPUs out through its ninth year of life. We expect to see that repeated across subsequent SKUs as well." CoreWeave
1:03

CoreWeave co-founder brannin mcbee reveals the A100 demand through 2029 is a big narrative violation for a market that thought GPUs would only have 2 to 3 years of useful life: "You don't need the latest generation GPU for every workload. You can think about it as a matrix of different sizes of models versus different sizes of GPUs, and it all just gets filled in volumetrically and economically." "That A100 GPU will cost less on a dollar per hour basis than a brand-new GB300 GPU. But the workload fits most efficiently on that A100, and it ultimately comes down to the engineering organizations making those requests." "It's been a pretty big narrative violation for the market that thought that these GPUs would only have two to three years of useful life, and instead we're contracting GPUs out through its ninth year of life. We expect to see that repeated across subsequent SKUs as well." CoreWeave

MTS

11,653 views • 16 days ago

Spotted a chip depreciation narrative violation in the wild on the show today CoreWeave founder brannin mcbee: "The A100 SKU. This is a 2020 SKU. We still have clients coming in, asking specifically for that SKU." "It's not like they're coming in asking for Hopper, and it's like, 'Oh, we only have Ampere,' or they're asking for Blackwell, and it's like, 'Oh, we only have Ampere.'" "They are saying, 'No, we want Ampere for workloads,' because that is the most performant platform for their workload." "We signed a contract in the quarter that goes out through 2029. It'll be a 9-year-old chip by the end. And this is a take-or-pay, fixed price contract. And pricing, on A100s, has held solid since early 2025." "One of the big criticisms was, 'Well, it's 2- to 3-year compute, and it's useless after that.' But we've been very consistent that a 6-year depreciable life is accurate for this infrastructure." "And I think that there is a really strong opportunity for it to have material useful life beyond that. And we see it every day." "There isn't one GPU to rule them all. It's just this matrix of different sizes of workloads relative to different sizes of GPUs."
2:19

Spotted a chip depreciation narrative violation in the wild on the show today CoreWeave founder brannin mcbee: "The A100 SKU. This is a 2020 SKU. We still have clients coming in, asking specifically for that SKU." "It's not like they're coming in asking for Hopper, and it's like, 'Oh, we only have Ampere,' or they're asking for Blackwell, and it's like, 'Oh, we only have Ampere.'" "They are saying, 'No, we want Ampere for workloads,' because that is the most performant platform for their workload." "We signed a contract in the quarter that goes out through 2029. It'll be a 9-year-old chip by the end. And this is a take-or-pay, fixed price contract. And pricing, on A100s, has held solid since early 2025." "One of the big criticisms was, 'Well, it's 2- to 3-year compute, and it's useless after that.' But we've been very consistent that a 6-year depreciable life is accurate for this infrastructure." "And I think that there is a really strong opportunity for it to have material useful life beyond that. And we see it every day." "There isn't one GPU to rule them all. It's just this matrix of different sizes of workloads relative to different sizes of GPUs."

TBPN

44,819 views • 17 days ago

Jensen - "We know that every single GPU that Nvidia has in the cloud, across aws and gcp, is rented out. They’re all fully utilized. Demand for gpus is so high that even the spot prices of gpus sold a couple of years ago are going up."
0:52

Jensen - "We know that every single GPU that Nvidia has in the cloud, across aws and gcp, is rented out. They’re all fully utilized. Demand for gpus is so high that even the spot prices of gpus sold a couple of years ago are going up."

The AI Investor

69,299 views • 7 months ago

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."
1:33

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 views • 7 months ago

CoreWeave cofounder brannin mcbee says older GPUs and servers still have strong demand, even as new generations ramp up: "Our pricing on Ampere went up during 2025, and our pricing on Hopper stayed within 10% or so of where we started the year." "Why is that happening? I think it's largely driven by inference and the idea that—not every workload needs the latest, greatest GPUs or servers." "There are different types of workloads that need different compute, and the market is efficiently pairing the right GPUs and servers with the workloads that need them." "We see incredibly robust demand all the way back to the Ampere generation, which I believe is six years old at this point."
1:37

CoreWeave cofounder brannin mcbee says older GPUs and servers still have strong demand, even as new generations ramp up: "Our pricing on Ampere went up during 2025, and our pricing on Hopper stayed within 10% or so of where we started the year." "Why is that happening? I think it's largely driven by inference and the idea that—not every workload needs the latest, greatest GPUs or servers." "There are different types of workloads that need different compute, and the market is efficiently pairing the right GPUs and servers with the workloads that need them." "We see incredibly robust demand all the way back to the Ampere generation, which I believe is six years old at this point."

TBPN

31,256 views • 5 months ago

$CRWV CEO says “older generations of GPUs are going to have a longer useful life than anyone anticipated” as CoreWeave signs a fully priced A100 contract through 2029 for hardware first launched in 2020. That means a 2020-era GPU can keep monetizing already-powered facilities that newer systems may not physically fit into while frontier workloads cascade to newer architectures and inference shifts down the stack. $NVDA CUDA strengthens that model by continuously optimizing Ampere, Hopper and Blackwell so the same workloads require less compute over time without forcing a hardware replacement.
0:25

$CRWV CEO says “older generations of GPUs are going to have a longer useful life than anyone anticipated” as CoreWeave signs a fully priced A100 contract through 2029 for hardware first launched in 2020. That means a 2020-era GPU can keep monetizing already-powered facilities that newer systems may not physically fit into while frontier workloads cascade to newer architectures and inference shifts down the stack. $NVDA CUDA strengthens that model by continuously optimizing Ampere, Hopper and Blackwell so the same workloads require less compute over time without forcing a hardware replacement.

Shay Boloor

136,954 views • 15 days ago

$AMZN CEO Andy Jassy describes AI adoption as very barbelled, with most existing enterprise workloads still not using inference though he expects that to change significantly over time. "I think it's actually kind of useful to look at at least our view of what we see in the demand and adoption curve right now, which is we see this adoption curve in AI right now is very barbelled." "There is on one end of the barbell, the AI labs are consuming gobs and gobs of compute. And there are a few runaway successful generative AI applications like Claude Code and ChatGPT." "And on the other end of the barbell are enterprises who are getting real value from AI in cost avoidance and productivity. And these are things like automating customer service or business process automation or fraud." "In the middle of the barbell is all of the current enterprise production workloads, some of which are using inference in a pervasive way, but most of which aren't. And that is going to change very significantly over time." "And that will be, in my opinion, that will be the largest absolute segment, the existing production workloads in the enterprise and new businesses and workloads that startups build too."
1:10

$AMZN CEO Andy Jassy describes AI adoption as very barbelled, with most existing enterprise workloads still not using inference though he expects that to change significantly over time. "I think it's actually kind of useful to look at at least our view of what we see in the demand and adoption curve right now, which is we see this adoption curve in AI right now is very barbelled." "There is on one end of the barbell, the AI labs are consuming gobs and gobs of compute. And there are a few runaway successful generative AI applications like Claude Code and ChatGPT." "And on the other end of the barbell are enterprises who are getting real value from AI in cost avoidance and productivity. And these are things like automating customer service or business process automation or fraud." "In the middle of the barbell is all of the current enterprise production workloads, some of which are using inference in a pervasive way, but most of which aren't. And that is going to change very significantly over time." "And that will be, in my opinion, that will be the largest absolute segment, the existing production workloads in the enterprise and new businesses and workloads that startups build too."

Fireside Alpha

29,242 views • 27 days ago

Cerebras just had the biggest IPO of the year. Founder Andrew Feldman says the 3 most important things he had to convince investors of while doing the roadshow were that demand for inference is going to 1,000,000x, the GPU isn't the only way to do compute, and that the CUDA moat is overstated. What he said: "Jensen said some time ago on Brad Gerstner's podcast that the demand for inference will grow by a 1,000,000x, and nobody believed him. And at the same time, you saw Sam Altman displaying real vision and going out and trying to lock up huge amounts of compute, memory, data centers, and power, because he saw it too." "[We tried] to share what that means — what exponential demand means. And that we're still so early, and yet the demand for AI compute is overwhelming." "The other thing is that there are lots of ways to do this. The GPU isn't the only way. You've got TPUs, Trainium, and us. There are lots of different ways to build a solution here." "And finally — the notion that CUDA is this grand lock-in is overplayed. Gemini 3, which is an excellent model, was trained on TPUs with no CUDA. The Anthropic models were trained on Trainium with no CUDA. Some of the best models, some of the most interesting things are being done without CUDA. And that lock-in might be overplayed." $CBRS
1:46

Cerebras just had the biggest IPO of the year. Founder Andrew Feldman says the 3 most important things he had to convince investors of while doing the roadshow were that demand for inference is going to 1,000,000x, the GPU isn't the only way to do compute, and that the CUDA moat is overstated. What he said: "Jensen said some time ago on Brad Gerstner's podcast that the demand for inference will grow by a 1,000,000x, and nobody believed him. And at the same time, you saw Sam Altman displaying real vision and going out and trying to lock up huge amounts of compute, memory, data centers, and power, because he saw it too." "[We tried] to share what that means — what exponential demand means. And that we're still so early, and yet the demand for AI compute is overwhelming." "The other thing is that there are lots of ways to do this. The GPU isn't the only way. You've got TPUs, Trainium, and us. There are lots of different ways to build a solution here." "And finally — the notion that CUDA is this grand lock-in is overplayed. Gemini 3, which is an excellent model, was trained on TPUs with no CUDA. The Anthropic models were trained on Trainium with no CUDA. Some of the best models, some of the most interesting things are being done without CUDA. And that lock-in might be overplayed." $CBRS

TBPN

36,474 views • 3 months ago

Speaking of spot on. Gavin Gavin Baker was asked in early Jan'25 his prediction for best performing asset class of the year (as @jason references in latest The All-In Podcast): "I think the companies that make high bandwidth memory. Going back to "we're going to run out of compute," it's actually a pretty shocking stat: high bandwidth memory is a bigger part of NVIDIA's COGS on GPUs than Taiwan Semi is. And today there's two companies that can make it, SK Hynix and Micron. We'll see if Samsung gets their act together. But HBM, it's in NVIDIA GPUs, AMD GPUs, Amazon Trainiums. And particularly in a world of test-time compute and inference being so important, high bandwidth memory is arguably more important than it ever was. And it's been sold out for the last two years. So high bandwidth memory would be my pick."
0:52

Speaking of spot on. Gavin Gavin Baker was asked in early Jan'25 his prediction for best performing asset class of the year (as @jason references in latest The All-In Podcast): "I think the companies that make high bandwidth memory. Going back to "we're going to run out of compute," it's actually a pretty shocking stat: high bandwidth memory is a bigger part of NVIDIA's COGS on GPUs than Taiwan Semi is. And today there's two companies that can make it, SK Hynix and Micron. We'll see if Samsung gets their act together. But HBM, it's in NVIDIA GPUs, AMD GPUs, Amazon Trainiums. And particularly in a world of test-time compute and inference being so important, high bandwidth memory is arguably more important than it ever was. And it's been sold out for the last two years. So high bandwidth memory would be my pick."

Fireside Alpha

66,227 views • 2 months ago

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.”
1:33

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.”

The All-In Podcast

304,297 views • 9 months ago

Marc Andreessen says AI is hyper democratizing. “Is the future of the world going to be one or a small number of companies, or for that matter, governments or super AIs that kind of own and control everything?” “Or is this a technology that's going to diffuse out and be in everybody's hands?” “This is the time in which AI is actually hyper democratizing. It has spread to more individuals, both in the country and around the world in the shortest period of time of any new technology I think in history.” “The best AIs in the world are in the consumer products.” “This technology is going to be in everybody's hands. And everybody is going to be able to use it to optimize the things that they do, have it be a thought partner, assistant for building companies.” Marc Andreessen 🇺🇸
1:34

Marc Andreessen says AI is hyper democratizing. “Is the future of the world going to be one or a small number of companies, or for that matter, governments or super AIs that kind of own and control everything?” “Or is this a technology that's going to diffuse out and be in everybody's hands?” “This is the time in which AI is actually hyper democratizing. It has spread to more individuals, both in the country and around the world in the shortest period of time of any new technology I think in history.” “The best AIs in the world are in the consumer products.” “This technology is going to be in everybody's hands. And everybody is going to be able to use it to optimize the things that they do, have it be a thought partner, assistant for building companies.” Marc Andreessen 🇺🇸

a16z

79,935 views • 10 months ago

Gavin Baker: “I think Anthropic is worth $3 trillion today.” Gavin laid out the case on the most recent All-In Podcast: – They will end 2026 with over $100B in revenue – They will likely end 2028 with $200-$300B in revenue – At that scale, Anthropic will be very profitable because compute will be inference-dominated – Anthropic reportedly has 85% gross margins on inference – The market has already absorbed Anthropic’s valuation, it will just shift from private to public @jason: “I'm sorry, did you say Anthropic is worth $3 trillion?” Gavin: “Yeah, I think that is roughly where it would probably trade as a public company.” Travis Kalanick: “Wow. Oh man. Holy sh*t.” Gavin: “They're going to end this year with well over $100 billion (in revenue). So what's the '28 number? Is it $200 billion? Is it $300 billion?” “It's probably not going to trade at 10 times that number, and it will be very profitable at that scale because it'll be inference-dominated and people are reporting they have 85% gross margins on inference.” “But in terms of the market absorbing this, like the market's already absorbed it. It's just shifting from private to public.” “And so in the scale of global capital markets, these seem like really big numbers, but you're just moving from the private markets to the public markets, which are even bigger.”
0:55

Gavin Baker: “I think Anthropic is worth $3 trillion today.” Gavin laid out the case on the most recent All-In Podcast: – They will end 2026 with over $100B in revenue – They will likely end 2028 with $200-$300B in revenue – At that scale, Anthropic will be very profitable because compute will be inference-dominated – Anthropic reportedly has 85% gross margins on inference – The market has already absorbed Anthropic’s valuation, it will just shift from private to public @jason: “I'm sorry, did you say Anthropic is worth $3 trillion?” Gavin: “Yeah, I think that is roughly where it would probably trade as a public company.” Travis Kalanick: “Wow. Oh man. Holy sh*t.” Gavin: “They're going to end this year with well over $100 billion (in revenue). So what's the '28 number? Is it $200 billion? Is it $300 billion?” “It's probably not going to trade at 10 times that number, and it will be very profitable at that scale because it'll be inference-dominated and people are reporting they have 85% gross margins on inference.” “But in terms of the market absorbing this, like the market's already absorbed it. It's just shifting from private to public.” “And so in the scale of global capital markets, these seem like really big numbers, but you're just moving from the private markets to the public markets, which are even bigger.”

The All-In Podcast

85,013 views • 2 months ago

IN NEWS: Baseten raises a $150M series D round. Tuhin Srivastava (Founder & CEO, Baseten) on the future of inference: “I think the token price goes down and inference should get cheaper over time. And that really just means there is going to be more inference.” “Every time we lower prices or optimize models to make it cheaper, four months later customers are spending more anyway.” “Inference prices will go down, but if the world is run by AI in 10 years, there is going to be a lot of inference. It better be cheap.”
0:52

IN NEWS: Baseten raises a $150M series D round. Tuhin Srivastava (Founder & CEO, Baseten) on the future of inference: “I think the token price goes down and inference should get cheaper over time. And that really just means there is going to be more inference.” “Every time we lower prices or optimize models to make it cheaper, four months later customers are spending more anyway.” “Inference prices will go down, but if the world is run by AI in 10 years, there is going to be a lot of inference. It better be cheap.”

TBPN

17,057 views • 11 months ago

The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.
0:50

The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.

Fireside Alpha

38,370 views • 2 months ago

Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.
1:01

Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.

Karl Mehta

44,425 views • 1 month ago

The Way Forward & A New Chapter For AI powered by GamerHash AI & $GHX. Welcome to the future of bleeding edge connectivity and cloud infrastructure ⚡ GamerHash AI is the answer to the billion dollar question on how to offset the GPU scarcity, enable exponential growth of AI through utilizing consumer-level GPUs and establishing a powerful sharing economy by sourcing a global network of gamers. The synergy of our GPU #DePIN and blockchain for #AI #inference is the technology of the future that already works today. See you in the network!
1:01

The Way Forward & A New Chapter For AI powered by GamerHash AI & $GHX. Welcome to the future of bleeding edge connectivity and cloud infrastructure ⚡ GamerHash AI is the answer to the billion dollar question on how to offset the GPU scarcity, enable exponential growth of AI through utilizing consumer-level GPUs and establishing a powerful sharing economy by sourcing a global network of gamers. The synergy of our GPU #DePIN and blockchain for #AI #inference is the technology of the future that already works today. See you in the network!

GamerHash AI

1,866,164 views • 1 year ago

Bessent: "These 2008, 2009, 2010 financial rules were too tight. They hamstrung the American financial system. It was time for a change. We're gonna be safe, smart, and sound in terms of our deregulation. But we have to take the financial system out of this straitjacket. This substantial increase in private credit which is outside of the regulated banking system -- that tells me that the regulated system is too constrained."
0:50

Bessent: "These 2008, 2009, 2010 financial rules were too tight. They hamstrung the American financial system. It was time for a change. We're gonna be safe, smart, and sound in terms of our deregulation. But we have to take the financial system out of this straitjacket. This substantial increase in private credit which is outside of the regulated banking system -- that tells me that the regulated system is too constrained."

Aaron Rupar

706,619 views • 8 months ago

we achieved some progress in the implementation of state inference inside octra , first we managed to implement continuous reasoning without clearing the circle memory, second we completed a pure resource model under which validators can verify and certify the execution flow of a set of useful work (in this case this is ML inference with text generation by input tokens fully onchain), a few months ago the answers of the test model were one-word and substantially limited, in the current version (which will be available for everyone) the reasoning flow is automatically extended, it will be possible to upload various models into circles or create tools for their use making them public or only private (full demo will be opensource as soon as we finish, it will be possible to deploy anything in 2-3 clicks) live use case, the economic model also converges here - wallet pays for inference in native coins, validators (with GPU support can maintain availability of their resources to quickly process such private calls, while the execution core remains private through HFHE)
1:11

we achieved some progress in the implementation of state inference inside octra , first we managed to implement continuous reasoning without clearing the circle memory, second we completed a pure resource model under which validators can verify and certify the execution flow of a set of useful work (in this case this is ML inference with text generation by input tokens fully onchain), a few months ago the answers of the test model were one-word and substantially limited, in the current version (which will be available for everyone) the reasoning flow is automatically extended, it will be possible to upload various models into circles or create tools for their use making them public or only private (full demo will be opensource as soon as we finish, it will be possible to deploy anything in 2-3 clicks) live use case, the economic model also converges here - wallet pays for inference in native coins, validators (with GPU support can maintain availability of their resources to quickly process such private calls, while the execution core remains private through HFHE)

λ

11,296 views • 24 days ago

NVIDIA just convinced the biggest credit firms on earth to bet $500 billion that Michael Burry is wrong The bet is over ONE number: how long does a GPU live? Jensen thinks it's longer than most and a new asset class w/ a secondary market The hyperscalers write off GPUs over 5 to 6 years (Amazon uses 5, Meta 5.5, Google 6) On the other hand, Burry's famous bear thesis says the real life is 2 to 3 years becuase the newer models make the older models effectively worthless. If true, this would mean the industry is overstating profits by roughly $176 billion from 2026 to 2028 Here's why this one number matters so much. On every $100B of GPU capex: 1. 3 year life = $33B a year of depreciation expense 2. 6 year life = $17B a year 3. 8 year life with resale value = $10B a year The gap between Burry's world and Jensen's world is $20B+ of profit per year on every $100B spent, and the industry now spends well over $500B a year! So who's right and is this new $500B AI infra fund a risk on the market? Let's look at what the market is paying for it right now: A100s are 6 year old chips, fully depreciated, worth roughly zero on the books, and their rental price is UP about 25% this year (see the chart I reposted below) A supposedly dead asset from Burry has been climbing all year Azure ran V100s for 7.5 years before retiring them, Google still runs 7 to 8 year old TPUs in production. Old GPUs don't seem to die, they get demoted from training to inference and then continue earning cash That's what Jensen showed the credit desks, and it changes everything, because an asset with real resale value can be financed more similar to something like an aircraft Airlines lease planes instead of buying them because lenders trust the residual value. If GPUs work the same way, customers don't need equity to buy chips anymore, credit does it, and the pool of NVIDIA buyers expands massively It looks like Jensen is going to be right about this (which makes sense, he is the King of GPUs) and $500 billion of the smartest credit money on earth too his side too This is extremely bullish for NVIDIA of course, but also the hyperscalers and even neoclouds who are the main buyers/sellers of GPUs. The longer their assets retain value, the more they will buy and resell If you want to see how I'm investing in this market you can track my entire portfolio and research in real-time, alongside 4 other analysts, inside Milk Road PRO. It's currently just $1 to try it out (insane price just to check it out) and you can learn more and join here: Good luck out there!
1:59

NVIDIA just convinced the biggest credit firms on earth to bet $500 billion that Michael Burry is wrong The bet is over ONE number: how long does a GPU live? Jensen thinks it's longer than most and a new asset class w/ a secondary market The hyperscalers write off GPUs over 5 to 6 years (Amazon uses 5, Meta 5.5, Google 6) On the other hand, Burry's famous bear thesis says the real life is 2 to 3 years becuase the newer models make the older models effectively worthless. If true, this would mean the industry is overstating profits by roughly $176 billion from 2026 to 2028 Here's why this one number matters so much. On every $100B of GPU capex: 1. 3 year life = $33B a year of depreciation expense 2. 6 year life = $17B a year 3. 8 year life with resale value = $10B a year The gap between Burry's world and Jensen's world is $20B+ of profit per year on every $100B spent, and the industry now spends well over $500B a year! So who's right and is this new $500B AI infra fund a risk on the market? Let's look at what the market is paying for it right now: A100s are 6 year old chips, fully depreciated, worth roughly zero on the books, and their rental price is UP about 25% this year (see the chart I reposted below) A supposedly dead asset from Burry has been climbing all year Azure ran V100s for 7.5 years before retiring them, Google still runs 7 to 8 year old TPUs in production. Old GPUs don't seem to die, they get demoted from training to inference and then continue earning cash That's what Jensen showed the credit desks, and it changes everything, because an asset with real resale value can be financed more similar to something like an aircraft Airlines lease planes instead of buying them because lenders trust the residual value. If GPUs work the same way, customers don't need equity to buy chips anymore, credit does it, and the pool of NVIDIA buyers expands massively It looks like Jensen is going to be right about this (which makes sense, he is the King of GPUs) and $500 billion of the smartest credit money on earth too his side too This is extremely bullish for NVIDIA of course, but also the hyperscalers and even neoclouds who are the main buyers/sellers of GPUs. The longer their assets retain value, the more they will buy and resell If you want to see how I'm investing in this market you can track my entire portfolio and research in real-time, alongside 4 other analysts, inside Milk Road PRO. It's currently just $1 to try it out (insane price just to check it out) and you can learn more and join here: Good luck out there!

Kyle Reidhead | Milk Road

268,534 views • 20 days ago

Gavin Baker: "if you're a foundation model company and you do not have unique data and internet scale distribution, you are the fastest depreciating asset in human history - I think most of these companies are zeros, and there's like 10 of them" this is him explaining why the 100 largest buildings in the world will be data centers, why there are only three places left in America you can legally build one, and what he says is setting up the first real memory cycle since 1996 "in our lifetime there will be data centers that are bigger than Manhattan" "the two rate limiting factors for AI are no longer compute and GPUs - it's power, finding places to plug the GPUs in" "there's only three places in the United States you can put one of these - it's got to be near multiple nuclear reactors that are unregulated within 50 miles of each other" "if Apple doubles the memory in the iPhone and Google does the same for Android at the same time, you could have the first real memory cycle since 1996 - and in 1996 Micron was a 50 bagger" "the only hope most of these companies have is for Amazon to acquire them the way Microsoft did with Inflection" bookmark & watch the full conversation ↓
11:48

Gavin Baker: "if you're a foundation model company and you do not have unique data and internet scale distribution, you are the fastest depreciating asset in human history - I think most of these companies are zeros, and there's like 10 of them" this is him explaining why the 100 largest buildings in the world will be data centers, why there are only three places left in America you can legally build one, and what he says is setting up the first real memory cycle since 1996 "in our lifetime there will be data centers that are bigger than Manhattan" "the two rate limiting factors for AI are no longer compute and GPUs - it's power, finding places to plug the GPUs in" "there's only three places in the United States you can put one of these - it's got to be near multiple nuclear reactors that are unregulated within 50 miles of each other" "if Apple doubles the memory in the iPhone and Google does the same for Android at the same time, you could have the first real memory cycle since 1996 - and in 1996 Micron was a 50 bagger" "the only hope most of these companies have is for Amazon to acquire them the way Microsoft did with Inflection" bookmark & watch the full conversation ↓

Сarm1ne

274,201 views • 28 days ago