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Anthropic CEO Dario Amodei: AI Progress isn’t magic, it’s just compute, data, and training. "All the cleverness, all the techniques, all of the “we need a new method,” doesn’t matter very much. There are only a few things that matter, and I listed seven of them. One is how...

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Rohan Paul

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75,803 views • 5 months ago •via X (Twitter)

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We asked Sholto Douglas from Anthropic about the costs of RL (Reinforcement Learning) runs. "In Dario Amodei's essay, he said that RL runs cost only $1M back in December." "RL is a more naively parallelizable and scalable than pre-training." "With pre-training, you need everything in one big data center ideally. For RL, in theory, you could scale all over the world."
0:37

We asked Sholto Douglas from Anthropic about the costs of RL (Reinforcement Learning) runs. "In Dario Amodei's essay, he said that RL runs cost only $1M back in December." "RL is a more naively parallelizable and scalable than pre-training." "With pre-training, you need everything in one big data center ideally. For RL, in theory, you could scale all over the world."

TBPN

76,696 views • 1 year ago

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast
1:29

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

AI Evolution

15,565 views • 2 months ago

Dylan Patel of SemiAnalysis says speed is the moat in AI inference, and getting there runs through new compute (Groq/Cerebras), KV-cache offload, and networking optimization (Aria). "Most of the infrastructure is pretty optimized for high throughput, I would say, but more and more people want to pay for speed. It turns out agents are very sequential, task-oriented, and models running in sequential tasks." "Trying something, verifying it, checking if it's right, and you do that in a loop. It ends up being very slow." "And speed is the moat. The faster you go, speed is the moat." "So a lot of the workloads and high-end use cases that people are developing now are all focused on high interactivity, and the infrastructure there is quite different." "So let's break that down into a few components." "One is you can do new compute: you can do Groq or Cerebras-style compute, which is very much focused on high interactivity." "But even with existing GPUs you can do a few things. One, if you're focused heavily on time to first token, you might offload KV cache to storage, keep it cached, move it back into the compute whenever you need it, much faster than recomputing." "And the third thing you can do is you have to optimize the network."
1:12

Dylan Patel of SemiAnalysis says speed is the moat in AI inference, and getting there runs through new compute (Groq/Cerebras), KV-cache offload, and networking optimization (Aria). "Most of the infrastructure is pretty optimized for high throughput, I would say, but more and more people want to pay for speed. It turns out agents are very sequential, task-oriented, and models running in sequential tasks." "Trying something, verifying it, checking if it's right, and you do that in a loop. It ends up being very slow." "And speed is the moat. The faster you go, speed is the moat." "So a lot of the workloads and high-end use cases that people are developing now are all focused on high interactivity, and the infrastructure there is quite different." "So let's break that down into a few components." "One is you can do new compute: you can do Groq or Cerebras-style compute, which is very much focused on high interactivity." "But even with existing GPUs you can do a few things. One, if you're focused heavily on time to first token, you might offload KV cache to storage, keep it cached, move it back into the compute whenever you need it, much faster than recomputing." "And the third thing you can do is you have to optimize the network."

Fireside Alpha

281,474 views • 9 days ago

"Compute is the currency of the future." "Compute is the new OIL." Great, so how do I invest? You can play with the NVIDIA stock, or spin up an AI-Ready Data Center. Challenges of Spinning up AI-Ready Compute: Cost: Even a small cluster of H100 machines is upward to $10M GPU Scarcity: Unallocated or “underutilized” H100, A100, and RTX4090 GPUs are almost non-existent. Very Specific Physical & Technical Requirements: A data center with the right infrastructure is also very rare. There are only 10 in North America that deploy thousands of GPUs, in the open market. Domain Expertise: Teams of experts with the years of experience required to operate, deploy, and manage AI compute at scale are as rare as the GPUs they deploy. Exabits checks all of these boxes and we are the premier Compute Tokenization Platform. We are backed by REAL COMPUTE used by REAL CUSTOMERS.
0:30

"Compute is the currency of the future." "Compute is the new OIL." Great, so how do I invest? You can play with the NVIDIA stock, or spin up an AI-Ready Data Center. Challenges of Spinning up AI-Ready Compute: Cost: Even a small cluster of H100 machines is upward to $10M GPU Scarcity: Unallocated or “underutilized” H100, A100, and RTX4090 GPUs are almost non-existent. Very Specific Physical & Technical Requirements: A data center with the right infrastructure is also very rare. There are only 10 in North America that deploy thousands of GPUs, in the open market. Domain Expertise: Teams of experts with the years of experience required to operate, deploy, and manage AI compute at scale are as rare as the GPUs they deploy. Exabits checks all of these boxes and we are the premier Compute Tokenization Platform. We are backed by REAL COMPUTE used by REAL CUSTOMERS.

Exabits

13,895 views • 2 years ago

Elon Musk explains the Kardashev Scale: "That's the most objective metric that any alien species, say, visiting us would calibrate how much progress we've made as a civilization, and one of the most objective ways to do that is the amount of power that any given civilization has been able to harness, and there was a Russian physicist, actually, who thought about this, and it's, I think, it's a good way to characterize it, which is you can have, you can assess how well a civilization is harnessing the power available on the planet, that's type I. And then type II would be how much of the star's power are you harnessing, and then type III would be how much of the galaxy's power are you harnessing. These are very objective and measurable numbers, so right now we're very low on the Kardashev I scale, like, what proportion of our planet's power are we harnessing, it's a very, very tiny number, and basically we're harnessing almost nothing of our stars' power, so the sun is truly an immense state. We don't even know how to do level III, really. AI will figure it out. One way to appreciate the size of the sun is to think about how heavy is the sun compared to all the rest of the mass in the solar system. So, the sun is about 99.86% of all mass in the solar system. It's everything, and then all the remaining 1.14% most of that is Jupiter, one planet."
2:39

Elon Musk explains the Kardashev Scale: "That's the most objective metric that any alien species, say, visiting us would calibrate how much progress we've made as a civilization, and one of the most objective ways to do that is the amount of power that any given civilization has been able to harness, and there was a Russian physicist, actually, who thought about this, and it's, I think, it's a good way to characterize it, which is you can have, you can assess how well a civilization is harnessing the power available on the planet, that's type I. And then type II would be how much of the star's power are you harnessing, and then type III would be how much of the galaxy's power are you harnessing. These are very objective and measurable numbers, so right now we're very low on the Kardashev I scale, like, what proportion of our planet's power are we harnessing, it's a very, very tiny number, and basically we're harnessing almost nothing of our stars' power, so the sun is truly an immense state. We don't even know how to do level III, really. AI will figure it out. One way to appreciate the size of the sun is to think about how heavy is the sun compared to all the rest of the mass in the solar system. So, the sun is about 99.86% of all mass in the solar system. It's everything, and then all the remaining 1.14% most of that is Jupiter, one planet."

DogeDesigner

53,650 views • 1 month ago

Adaption Labs CEO Sara Hooker says data centers in space are "pretty bonkers", and explains what the current consensus gets wrong: "Most co-located hardware is pretty much for training. I think that's why you care [about Space], otherwise you can distribute. Inference compute, which is where everything's moving towards, you can spread that compute more easily. You can have multiple data centers [on Earth]." "So if you care about space, you probably only care about training compute. I think people underestimate the amount of failures that happen, and you don't want to get your training job interrupted." "The real issue is that GPUs still have failure rates. The 2% of GPUs that are just considered done every year, that's really your cost. It's how quickly you can replace those [in space] and what that looks like."
2:29

Adaption Labs CEO Sara Hooker says data centers in space are "pretty bonkers", and explains what the current consensus gets wrong: "Most co-located hardware is pretty much for training. I think that's why you care [about Space], otherwise you can distribute. Inference compute, which is where everything's moving towards, you can spread that compute more easily. You can have multiple data centers [on Earth]." "So if you care about space, you probably only care about training compute. I think people underestimate the amount of failures that happen, and you don't want to get your training job interrupted." "The real issue is that GPUs still have failure rates. The 2% of GPUs that are just considered done every year, that's really your cost. It's how quickly you can replace those [in space] and what that looks like."

TBPN

37,463 views • 5 months ago

.Ilya Sutskever on why OpenAI gave up on robotics: "Progress comes from the combination of compute and data. There was no path to data from robotics. I'd say that now it is possible to create a path forward, but one needs to really commit. You could imagine a gradual path of improvement where you build more robots, they do more things, you collect more data and so on. But you need to really love robots and need to be really willing to solve all the physical and logistical problems of dealing with them. It's not the same as software at all."
1:57

.Ilya Sutskever on why OpenAI gave up on robotics: "Progress comes from the combination of compute and data. There was no path to data from robotics. I'd say that now it is possible to create a path forward, but one needs to really commit. You could imagine a gradual path of improvement where you build more robots, they do more things, you collect more data and so on. But you need to really love robots and need to be really willing to solve all the physical and logistical problems of dealing with them. It's not the same as software at all."

Dwarkesh Patel

325,857 views • 2 years ago

The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."
2:40

The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."

TBPN

85,358 views • 6 months ago

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"
2:21

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

Dwarkesh Patel

502,249 views • 8 months ago

We asked Angus about the future of robotics and AI training. “I built an industrial-grade kinematic solver for 6 degrees of freedom motion, with full path planning and joint control." "You can stream AI model outputs into it, or run it like a traditional industrial robot." "The benefit of that is all of that information you can stream and record from those joints… you can use that as the training data." "Frankly, moving a robot with a lever is really crap data for training robots. But this gives you smooth, high-quality motion, without needing two robots.”
2:16

We asked Angus about the future of robotics and AI training. “I built an industrial-grade kinematic solver for 6 degrees of freedom motion, with full path planning and joint control." "You can stream AI model outputs into it, or run it like a traditional industrial robot." "The benefit of that is all of that information you can stream and record from those joints… you can use that as the training data." "Frankly, moving a robot with a lever is really crap data for training robots. But this gives you smooth, high-quality motion, without needing two robots.”

TBPN

18,811 views • 1 year ago

Here it is: The algorithm that toppled the United States government on November 3, 2020. The 2020 election Edison data confirms fluctuations in vote counts—meaning a straight-up algorithm. “Scytl is receiving the election data directly from election officials in at least nine states, meaning there is no projection—this is raw data. When you look at raw data in an election, we would all agree that elections are supposed to be additive processes. So, with each interval report, you would expect to see more votes—not deletions. More votes, not deletions. Now look at this real-time video of the Edison reports. You have in-person votes, then mail-in votes, and look how it pauses—there is oscillation and deletion. This is the algorithm trying to figure out the math problem that is you. That is you. And if I let this video play to completion, you’ll find out it wasn’t just Pennsylvania. We have three counties in North Carolina. We have three counties in Georgia. And guess what— in Georgia, we know for a fact that Scytl contracts directly with the state to get that data. So you should see no oscillation. Instead, what you see is absolute manipulation, and this is all possible because of how wide open the system is.” Ban all voting machines and Pass the Save Act. And arrest Chris Krebs.
4:06

Here it is: The algorithm that toppled the United States government on November 3, 2020. The 2020 election Edison data confirms fluctuations in vote counts—meaning a straight-up algorithm. “Scytl is receiving the election data directly from election officials in at least nine states, meaning there is no projection—this is raw data. When you look at raw data in an election, we would all agree that elections are supposed to be additive processes. So, with each interval report, you would expect to see more votes—not deletions. More votes, not deletions. Now look at this real-time video of the Edison reports. You have in-person votes, then mail-in votes, and look how it pauses—there is oscillation and deletion. This is the algorithm trying to figure out the math problem that is you. That is you. And if I let this video play to completion, you’ll find out it wasn’t just Pennsylvania. We have three counties in North Carolina. We have three counties in Georgia. And guess what— in Georgia, we know for a fact that Scytl contracts directly with the state to get that data. So you should see no oscillation. Instead, what you see is absolute manipulation, and this is all possible because of how wide open the system is.” Ban all voting machines and Pass the Save Act. And arrest Chris Krebs.

🇺🇸RealRobert🇺🇸

18,982 views • 5 months ago

GoogleDeepmind Chief AGI Scientist Shane Legg: AGI by 2028 He’s had the same timelines for 12 years - insane! He gives a log-normal distribution with a mode of 2025. Importantly, while he puts a 50% chance of AGI by 2028, that means there is a 30% chance of AGI in the next three years. How have his timelines been so consistent since 2011? SHANE LEGG: I first formed those beliefs around 2001 after reading Ray Kurzweil's The Age of Spiritual Machines. There were two really important points in his book that I came to believe as true: 1) One is that computational power would grow exponentially for at least a few decades. And that the quantity of data in the world would grow exponentially for a few decades. And when you have exponentially increasing quantities of computation and data, then the value of highly scalable algorithms gets higher and higher. There's a lot of incentive to make a more scalable algorithm to harness all this computing data. So I thought it would be very likely that we'll start to discover scalable algorithms to do this. And then there's a positive feedback between all these things, because if your algorithm gets better at harnessing computing data, then the value of the data and the compute goes up because it can be more effectively used. And that drives more investment in these areas. If your compute performance goes up, then the value of the data goes up because you can utilize more data. So there are positive feedback loops between all these things. 2) And then the second thing was just looking at the trends. If the scalable algorithms were to be discovered, then during the 2020s, it should be possible to start training models on significantly more data than a human would experience in a lifetime. And I figured that that would be a time where big things would start to happen that would eventually unlock AGI. And I think we're now at that first part. I think we can start training models now with the scale of the data that is beyond what a human can experience in a lifetime. So I think this is the first unlocking step. DWARKESH: If we're in 2029 and it hasn't happened yet, if there was a problem that caused it, what would be the most likely reason for that? SHANE LEGG: I don't know. At the moment, it looks to me like all the problems are likely solvable with a number of years of research.
3:34

GoogleDeepmind Chief AGI Scientist Shane Legg: AGI by 2028 He’s had the same timelines for 12 years - insane! He gives a log-normal distribution with a mode of 2025. Importantly, while he puts a 50% chance of AGI by 2028, that means there is a 30% chance of AGI in the next three years. How have his timelines been so consistent since 2011? SHANE LEGG: I first formed those beliefs around 2001 after reading Ray Kurzweil's The Age of Spiritual Machines. There were two really important points in his book that I came to believe as true: 1) One is that computational power would grow exponentially for at least a few decades. And that the quantity of data in the world would grow exponentially for a few decades. And when you have exponentially increasing quantities of computation and data, then the value of highly scalable algorithms gets higher and higher. There's a lot of incentive to make a more scalable algorithm to harness all this computing data. So I thought it would be very likely that we'll start to discover scalable algorithms to do this. And then there's a positive feedback between all these things, because if your algorithm gets better at harnessing computing data, then the value of the data and the compute goes up because it can be more effectively used. And that drives more investment in these areas. If your compute performance goes up, then the value of the data goes up because you can utilize more data. So there are positive feedback loops between all these things. 2) And then the second thing was just looking at the trends. If the scalable algorithms were to be discovered, then during the 2020s, it should be possible to start training models on significantly more data than a human would experience in a lifetime. And I figured that that would be a time where big things would start to happen that would eventually unlock AGI. And I think we're now at that first part. I think we can start training models now with the scale of the data that is beyond what a human can experience in a lifetime. So I think this is the first unlocking step. DWARKESH: If we're in 2029 and it hasn't happened yet, if there was a problem that caused it, what would be the most likely reason for that? SHANE LEGG: I don't know. At the moment, it looks to me like all the problems are likely solvable with a number of years of research.

AI Notkilleveryoneism Memes ⏸️

74,490 views • 2 years ago

I'll let Noam Brown (OpenAI research) answer Yann's “told you so” again. There is nothing to add. There is no wall, there is no end in sight! "Now, we are in a world, like I said earlier, where the amount of compute that's going into pretraining for things large language models is very, very high. But the inference costs are very low. And there was a reasonable concern among various people that we were going to start seeing diminishing returns from AI progress because the costs and the amount of data that you need for pretraining would become so astronomical. And I think the really important takeaway from o1 is that that wall doesn't actually exist, that we can actually push this a lot further. Because, now, we can scale up inference compute. And there's so much room to scale up inference compute."
0:46

I'll let Noam Brown (OpenAI research) answer Yann's “told you so” again. There is nothing to add. There is no wall, there is no end in sight! "Now, we are in a world, like I said earlier, where the amount of compute that's going into pretraining for things large language models is very, very high. But the inference costs are very low. And there was a reasonable concern among various people that we were going to start seeing diminishing returns from AI progress because the costs and the amount of data that you need for pretraining would become so astronomical. And I think the really important takeaway from o1 is that that wall doesn't actually exist, that we can actually push this a lot further. Because, now, we can scale up inference compute. And there's so much room to scale up inference compute."

Chubby♨️

132,808 views • 1 year ago

Mark Zuckerberg says he is not an AI researcher -- so here is the one number he obsesses over to win the superintelligence race: "I'm not an AI researcher. So it's not like I'm in there telling them what research ideas to do." "The things that I can do as CEO of the company are: I can make sure that we have the very best people and talent density. I can make sure that we have by far the highest compute per researcher, and that we do whatever it takes to go build out all that capacity." "We're building this Prometheus cluster, which is going to be the first gigawatt-plus single cluster, a contiguous cluster for training, coming online next year." "We're building a cluster in Louisiana that is going to be 5 gigawatt, and we're building several more clusters like that that are just going to be multiple gigawatts." "We're talking about many hundreds of billions of dollars of capital. So you both need to have a good business model that can support it, and you need to believe in it." The tell: the richest lab in the world is not optimizing headcount. It is optimizing compute-per-researcher, and betting hundreds of billions that whoever owns the biggest contiguous cluster owns the frontier. Talent gets you into the room. Watts decide who leaves with the model.
1:06

Mark Zuckerberg says he is not an AI researcher -- so here is the one number he obsesses over to win the superintelligence race: "I'm not an AI researcher. So it's not like I'm in there telling them what research ideas to do." "The things that I can do as CEO of the company are: I can make sure that we have the very best people and talent density. I can make sure that we have by far the highest compute per researcher, and that we do whatever it takes to go build out all that capacity." "We're building this Prometheus cluster, which is going to be the first gigawatt-plus single cluster, a contiguous cluster for training, coming online next year." "We're building a cluster in Louisiana that is going to be 5 gigawatt, and we're building several more clusters like that that are just going to be multiple gigawatts." "We're talking about many hundreds of billions of dollars of capital. So you both need to have a good business model that can support it, and you need to believe in it." The tell: the richest lab in the world is not optimizing headcount. It is optimizing compute-per-researcher, and betting hundreds of billions that whoever owns the biggest contiguous cluster owns the frontier. Talent gets you into the room. Watts decide who leaves with the model.

Karl Mehta

255,587 views • 1 month ago

The CEO of the world's largest asset manager just said something that should reframe how every investor thinks about the AI trade. Larry Fink, managing $11.5 trillion at BlackRock, stood at the Milken Institute Global Conference and said four words that matter, "We just don't have enough compute." "The United States is short power. We're short compute. We're short chips. And there's going to be shortages in all three and memory, four things. I actually believe a new asset class will be buying futures of compute." Think about what that means. Fink is predicting that compute becomes a tradable commodity like oil, like grain, like natural gas where investors buy forward contracts on future capacity because the shortage is so structural and so predictable that a derivatives market will emerge to price it. That is not a minor observation from a finance executive but rather the chairman of the most powerful capital allocator on the planet telling you that compute scarcity is a multi-year, investable megatrend. The data backs him up completely. Data centers will consume 70% of all memory chips produced globally in 2026. Advanced HBM production from Samsung, SK Hynix, and Micron is sold out through 2026 and into 2027 and a single AI server consumes 10-20x more memory than a conventional workload server. DRAM supply growth is running at just 16% annually while AI infrastructure demand is growing at 80%+. The chip crunch, the power crunch, and the compute crunch are not temporary dislocations, they are structural, and they will get worse before they get better. Fink also said something the bears keep getting wrong: "There is not an AI bubble. There is the opposite. We have supply shortages. Demand is growing much faster than anyone has ever anticipated." This is why the Milk Road Pro portfolio is built the way it is, long the companies producing and supplying the constrained resources: chips, memory, compute infrastructure, and power. Check out Milk Road Pro, link below to access our full thesis and plays.
0:23

The CEO of the world's largest asset manager just said something that should reframe how every investor thinks about the AI trade. Larry Fink, managing $11.5 trillion at BlackRock, stood at the Milken Institute Global Conference and said four words that matter, "We just don't have enough compute." "The United States is short power. We're short compute. We're short chips. And there's going to be shortages in all three and memory, four things. I actually believe a new asset class will be buying futures of compute." Think about what that means. Fink is predicting that compute becomes a tradable commodity like oil, like grain, like natural gas where investors buy forward contracts on future capacity because the shortage is so structural and so predictable that a derivatives market will emerge to price it. That is not a minor observation from a finance executive but rather the chairman of the most powerful capital allocator on the planet telling you that compute scarcity is a multi-year, investable megatrend. The data backs him up completely. Data centers will consume 70% of all memory chips produced globally in 2026. Advanced HBM production from Samsung, SK Hynix, and Micron is sold out through 2026 and into 2027 and a single AI server consumes 10-20x more memory than a conventional workload server. DRAM supply growth is running at just 16% annually while AI infrastructure demand is growing at 80%+. The chip crunch, the power crunch, and the compute crunch are not temporary dislocations, they are structural, and they will get worse before they get better. Fink also said something the bears keep getting wrong: "There is not an AI bubble. There is the opposite. We have supply shortages. Demand is growing much faster than anyone has ever anticipated." This is why the Milk Road Pro portfolio is built the way it is, long the companies producing and supplying the constrained resources: chips, memory, compute infrastructure, and power. Check out Milk Road Pro, link below to access our full thesis and plays.

Milk Road AI

419,283 views • 2 months ago

This is the best take I have heard around the existing AI landscape today Fantastic explanation around the constraints at OpenAI and Anthropic, focus on enterprise business models, and the doomerism narrative in AI From former AI czar David Sacks "Even though Anthropic's revenue has followed this exponential graph very predictably, it cannot do that ever. The reason is that as you hit new levels of scale, you encounter new problems. You are simply going to run out of compute, electricity, data centers, and infrastructure. There are limits to the physical world you are going to hit. And there is already evidence that Anthropic is hitting some of those limits."
4:27

This is the best take I have heard around the existing AI landscape today Fantastic explanation around the constraints at OpenAI and Anthropic, focus on enterprise business models, and the doomerism narrative in AI From former AI czar David Sacks "Even though Anthropic's revenue has followed this exponential graph very predictably, it cannot do that ever. The reason is that as you hit new levels of scale, you encounter new problems. You are simply going to run out of compute, electricity, data centers, and infrastructure. There are limits to the physical world you are going to hit. And there is already evidence that Anthropic is hitting some of those limits."

Boring_Business

236,380 views • 3 months ago

Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire
2:24

Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire

KanekoaTheGreat

39,709 views • 1 month ago

HRT, one of the largest quantitative trading firms in the world, just revealed the scale of their AI operation. "two of the biggest ingredients you need to build deep learning models are compute and data. HRT has both in incredible orders of magnitude." "we have vast quantities of data, on the order of petabytes, just in terms of raw tick data we've written down since the beginning of electronic trading." "we have a very high compute-per-researcher ratio." then the line that puts it in perspective: "there probably aren't too many places on Earth you can do cutting-edge deep learning research at the level we do it, just because of how few places have access to as much compute." "it's basically the hyperscalers of the world and then we're in the next tranche." a trading firm putting itself in the same compute tier as Google, Microsoft, and Meta. not for chatbots, for trading.
2:08

HRT, one of the largest quantitative trading firms in the world, just revealed the scale of their AI operation. "two of the biggest ingredients you need to build deep learning models are compute and data. HRT has both in incredible orders of magnitude." "we have vast quantities of data, on the order of petabytes, just in terms of raw tick data we've written down since the beginning of electronic trading." "we have a very high compute-per-researcher ratio." then the line that puts it in perspective: "there probably aren't too many places on Earth you can do cutting-edge deep learning research at the level we do it, just because of how few places have access to as much compute." "it's basically the hyperscalers of the world and then we're in the next tranche." a trading firm putting itself in the same compute tier as Google, Microsoft, and Meta. not for chatbots, for trading.

Goshawk Trades

14,548 views • 2 months ago

ELON: AI WILL HARNESS THE POWER OF THE SUN AND GALAXY FOR COMPUTE "I think we'll see intelligence continue to scale all the way up to where most of the power of the sun is harnessed for compute and then ultimately most of the power of the galaxy, you know, sort of cloud Kardashev two, Kardashev three scale compute. So I guess once you think of artificial intelligence not as a destination that you reach, but really as part of the overall escalation of intelligence that we are aware of." Source: Jon Hernandez
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ELON: AI WILL HARNESS THE POWER OF THE SUN AND GALAXY FOR COMPUTE "I think we'll see intelligence continue to scale all the way up to where most of the power of the sun is harnessed for compute and then ultimately most of the power of the galaxy, you know, sort of cloud Kardashev two, Kardashev three scale compute. So I guess once you think of artificial intelligence not as a destination that you reach, but really as part of the overall escalation of intelligence that we are aware of." Source: Jon Hernandez

Mario Nawfal

1,667,204 views • 8 months ago

Elon Musk about the meaning of life and the trap of wishful thinking : “When I was a kid, I was wondering what's the meaning of life? Like, why are we here? What's it all about? And I came to the conclusion that what really matters is trying to understand the right questions to ask. And the more that we can increase the scope and scale of human consciousness, the better we are able to ask these questions. And so I think that there are certain things that are necessary to ensure that the future is good. One of the biggest mistakes people generally make, and I'm guilty of it too, is wishful thinking. You know, like, you want something to be true even if it isn't true. And so you ignore the things that you ignore the real truth because of what you want to be true. This is a very difficult trap to avoid.”
0:55

Elon Musk about the meaning of life and the trap of wishful thinking : “When I was a kid, I was wondering what's the meaning of life? Like, why are we here? What's it all about? And I came to the conclusion that what really matters is trying to understand the right questions to ask. And the more that we can increase the scope and scale of human consciousness, the better we are able to ask these questions. And so I think that there are certain things that are necessary to ensure that the future is good. One of the biggest mistakes people generally make, and I'm guilty of it too, is wishful thinking. You know, like, you want something to be true even if it isn't true. And so you ignore the things that you ignore the real truth because of what you want to be true. This is a very difficult trap to avoid.”

Eva Fo𝕏 🦊 Claudius Nero's Legion

75,023 views • 8 months ago