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.Beff (e/acc) explains how thermodynamic computing fits into the current computing stack: "We don't do GPUs. They're called TSUs, thermodynamic sampling units. We think people are gonna put them next to GPUs or whatever their favorite accelerator is." "You don't need to run the whole workload on the TSU,...

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Culture is genetic because behavior is genetic. This beaver never saw a dam in its life. No beavers or anything else ever taught it to build a dam. It wants to build a dam because it is a beaver. Many beavers together build a big dam. That is beaver culture. Humans are not different. Nothing is different. This is what life is. This is how life works. Your body is your mind. A caterpillar wants to build a chrysalis. A bee wants to build a hive. A lion wants to build a pride. You are not special. You are not above your nature. you are INSIDE of it. The thoughts that we think are genetic thoughts. The crimes we commit are genetic crimes. The art we create is genetic art. Just like this beaver, you can give the animal different sticks and it will build a different dam, but it will always build a dam. And you can give humans different "education," but the human will always use it to do what its genes tell it to do. This is the first big answer that you need. This is the biggest piece of the puzzle. This is how to understand people 90% of the way. You just... notice what they do, and get out of the way, and watch them do it. And if they need sticks, you give them sticks. And if you don't like what they do, you have to get away from them. You cannot train dam-building into them or out of them any more than you can with a beaver. A beaver wants to build a dam because it is a beaver. Whatever you see people build, that's what they wanted to build from the sticks they got in the river they were in. Stop pretending you can change it.
0:59

Culture is genetic because behavior is genetic. This beaver never saw a dam in its life. No beavers or anything else ever taught it to build a dam. It wants to build a dam because it is a beaver. Many beavers together build a big dam. That is beaver culture. Humans are not different. Nothing is different. This is what life is. This is how life works. Your body is your mind. A caterpillar wants to build a chrysalis. A bee wants to build a hive. A lion wants to build a pride. You are not special. You are not above your nature. you are INSIDE of it. The thoughts that we think are genetic thoughts. The crimes we commit are genetic crimes. The art we create is genetic art. Just like this beaver, you can give the animal different sticks and it will build a different dam, but it will always build a dam. And you can give humans different "education," but the human will always use it to do what its genes tell it to do. This is the first big answer that you need. This is the biggest piece of the puzzle. This is how to understand people 90% of the way. You just... notice what they do, and get out of the way, and watch them do it. And if they need sticks, you give them sticks. And if you don't like what they do, you have to get away from them. You cannot train dam-building into them or out of them any more than you can with a beaver. A beaver wants to build a dam because it is a beaver. Whatever you see people build, that's what they wanted to build from the sticks they got in the river they were in. Stop pretending you can change it.

hoe_math = PsychoMath

1,189,157 просмотров • 9 месяцев назад

.Reiner Pope explains the difference between a CPU and a GPU. A huge chunk of a CPU die is actually just predicting what code will run next. GPUs strip this out, which is part of why they can fit so much more compute on the chip.
2:49

.Reiner Pope explains the difference between a CPU and a GPU. A huge chunk of a CPU die is actually just predicting what code will run next. GPUs strip this out, which is part of why they can fit so much more compute on the chip.

Dwarkesh Patel

61,491 просмотров • 19 дней назад

Incoming: The Big Ole Chip Flip CPUs > GPUs COATUE CIO of Public Investments Jaimin Rangwalla says AI agents could fundamentally reshape compute demand: “Historically CPUs have done something called serial processing, which is like you just give it a set of instructions and it does one by one by one by one. It was very much a 1 CPU : 8 GPUs. It had gone up to 1 CPU : 16 GPUs, which was all of the math & all of the computation happening at the GPUs. But now the ratio is actually moving from 1 CPU : 4 GPUs."
2:08

Incoming: The Big Ole Chip Flip CPUs > GPUs COATUE CIO of Public Investments Jaimin Rangwalla says AI agents could fundamentally reshape compute demand: “Historically CPUs have done something called serial processing, which is like you just give it a set of instructions and it does one by one by one by one. It was very much a 1 CPU : 8 GPUs. It had gone up to 1 CPU : 16 GPUs, which was all of the math & all of the computation happening at the GPUs. But now the ratio is actually moving from 1 CPU : 4 GPUs."

Molly O’Shea

15,390 просмотров • 27 дней назад

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.
18:01

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.

David Shapiro (L/0)

83,649 просмотров • 7 месяцев назад

Elon Musk on building a self-growing city on the Moon: "You don't necessarily have to go through the moon to get to Mars. We can build a self-growing city on the moon faster than we could do so on Mars, and there's also the potential, if you say you want to scale far beyond what you can do from Earth, is that because the moon has no atmosphere and about 1/6 Earth's gravity, you can use an electromagnetic accelerator, a rail gun or mass driver, basically you don't need to use rockets to do AI data centers into deep space from the moon, you can literally just shoot them like a, like a rail gun type of thing, and and you can manufacture the solar, the solar and the radiators, solar power and radiators on the moon from moon materials that would allow scaling potentially to beyond 1000 terawatts a year, which is a truly staggering number. I think we can do probably do somewhere around one terawatt per year of AI space compute from Earth, but we can do 1000 terawatts or more from the moon."
1:17

Elon Musk on building a self-growing city on the Moon: "You don't necessarily have to go through the moon to get to Mars. We can build a self-growing city on the moon faster than we could do so on Mars, and there's also the potential, if you say you want to scale far beyond what you can do from Earth, is that because the moon has no atmosphere and about 1/6 Earth's gravity, you can use an electromagnetic accelerator, a rail gun or mass driver, basically you don't need to use rockets to do AI data centers into deep space from the moon, you can literally just shoot them like a, like a rail gun type of thing, and and you can manufacture the solar, the solar and the radiators, solar power and radiators on the moon from moon materials that would allow scaling potentially to beyond 1000 terawatts a year, which is a truly staggering number. I think we can do probably do somewhere around one terawatt per year of AI space compute from Earth, but we can do 1000 terawatts or more from the moon."

DogeDesigner

33,023 просмотров • 7 дней назад

Cathie Wood just flagged the sleeper trade inside the AI boom that most people are completely missing. Everyone has been chasing GPUs. Nvidia, the data center buildout, the chip arms race. That trade has been obvious for two years. But OpenAI's CFO Sarah Fryer said something quite different: people are going to be really shocked by how agentic AI activates CPUs. Right now, for every CPU in an AI workload, there are 4 to 5 GPUs. That's the current ratio. Wood thinks that ratio is going to 1 to 1. Think about what that means. AI inference at scale, agents running autonomously, pipelines executing tasks across systems. The compute mix shifts dramatically away from pure GPU dominance. CPUs become a first-class citizen in the AI stack. Cathie called it going "back to the future." Intel has taken off. Flex (formerly Flextronics) is booming. Stocks that were giants in the dot-com bubble are resurging because the underlying demand for their products is real again. The GPU trade made sense at the training stage. You need massive parallel compute to train frontier models. But agentic AI runs differently. Agents are constantly orchestrating, reasoning, calling APIs, executing workflows. That workload looks a lot more like traditional computing. And traditional computing runs on CPUs. If Cathie Wood is right about the ratio collapsing to 1:1, the CPU demand signal embedded in the AI buildout is orders of magnitude larger than the market is currently pricing.
0:49

Cathie Wood just flagged the sleeper trade inside the AI boom that most people are completely missing. Everyone has been chasing GPUs. Nvidia, the data center buildout, the chip arms race. That trade has been obvious for two years. But OpenAI's CFO Sarah Fryer said something quite different: people are going to be really shocked by how agentic AI activates CPUs. Right now, for every CPU in an AI workload, there are 4 to 5 GPUs. That's the current ratio. Wood thinks that ratio is going to 1 to 1. Think about what that means. AI inference at scale, agents running autonomously, pipelines executing tasks across systems. The compute mix shifts dramatically away from pure GPU dominance. CPUs become a first-class citizen in the AI stack. Cathie called it going "back to the future." Intel has taken off. Flex (formerly Flextronics) is booming. Stocks that were giants in the dot-com bubble are resurging because the underlying demand for their products is real again. The GPU trade made sense at the training stage. You need massive parallel compute to train frontier models. But agentic AI runs differently. Agents are constantly orchestrating, reasoning, calling APIs, executing workflows. That workload looks a lot more like traditional computing. And traditional computing runs on CPUs. If Cathie Wood is right about the ratio collapsing to 1:1, the CPU demand signal embedded in the AI buildout is orders of magnitude larger than the market is currently pricing.

Milk Road AI

234,596 просмотров • 18 дней назад

Nvidia will pay you to put a $250,000 data center outside your house you don't touch it, you don't manage it, you just collect around $1,000 a month for the electricity and Wi-Fi it uses it looks like an AC unit, it's actually 16 Blackwell GPUs running AI workloads around the clock it's built by Span, a startup Nvidia invested in to make distributed compute a reality the reason Nvidia put money into this is simple they need compute everywhere and building data centers takes years and their solution is to skip the construction entirely, use residential grid capacity and deploy through your neighborhood instead this is much cheaper, faster, and can scale to millions of homes but there is still a question nobody has answered yet, what happens if someone steals it also, technically they could give each homeowner access to a slice of the compute even 2% of 16 Blackwell GPUs is more processing power than most people will ever use bookmark this, it's worth coming back to when you have time
0:57

Nvidia will pay you to put a $250,000 data center outside your house you don't touch it, you don't manage it, you just collect around $1,000 a month for the electricity and Wi-Fi it uses it looks like an AC unit, it's actually 16 Blackwell GPUs running AI workloads around the clock it's built by Span, a startup Nvidia invested in to make distributed compute a reality the reason Nvidia put money into this is simple they need compute everywhere and building data centers takes years and their solution is to skip the construction entirely, use residential grid capacity and deploy through your neighborhood instead this is much cheaper, faster, and can scale to millions of homes but there is still a question nobody has answered yet, what happens if someone steals it also, technically they could give each homeowner access to a slice of the compute even 2% of 16 Blackwell GPUs is more processing power than most people will ever use bookmark this, it's worth coming back to when you have time

Anatoli Kopadze

400,986 просмотров • 13 дней назад

Elon Musk on the SpaceXAI satellites: “We intend for SpaceXAI satellites to allow people to use whatever GPU or TPU they want. NVIDIA GPUs, Google TPUs, Amazon Trainiums, or any other chips can be put on them. We will also offer our own chips, and we want to offer our AI software, in the future. It will be such that you can run anyone’s AI hardware or software on the SpaceXAI satellites.”
0:51

Elon Musk on the SpaceXAI satellites: “We intend for SpaceXAI satellites to allow people to use whatever GPU or TPU they want. NVIDIA GPUs, Google TPUs, Amazon Trainiums, or any other chips can be put on them. We will also offer our own chips, and we want to offer our AI software, in the future. It will be such that you can run anyone’s AI hardware or software on the SpaceXAI satellites.”

NIK

48,256 просмотров • 7 дней назад

ELON MUSK: The only way to reach 1,000 terawatts of AI power is a mass driver on the Moon. "In order to get to 1,000x from a terawatt per year. The only way that we can really achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics and radiators on the moon, maybe you bring the chips from Earth, or you could conceivably make the chips on the moon, and but you need most of the mass to be made on the moon, so you don't have to transport it to the moon from Earth, and then because the moon has no atmosphere and only 1/6 Earth's gravity, you can accelerate the AI satellites into deep space without a rocket, so you can basically shoot them into space using an electromagnetic gun, like a, like a rail gun type. I mean, just, it's basically a linear electric motor, as a way to think about it."
1:06

ELON MUSK: The only way to reach 1,000 terawatts of AI power is a mass driver on the Moon. "In order to get to 1,000x from a terawatt per year. The only way that we can really achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics and radiators on the moon, maybe you bring the chips from Earth, or you could conceivably make the chips on the moon, and but you need most of the mass to be made on the moon, so you don't have to transport it to the moon from Earth, and then because the moon has no atmosphere and only 1/6 Earth's gravity, you can accelerate the AI satellites into deep space without a rocket, so you can basically shoot them into space using an electromagnetic gun, like a, like a rail gun type. I mean, just, it's basically a linear electric motor, as a way to think about it."

DogeDesigner

79,763 просмотров • 3 дней назад

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 to be amazing for the useful lives of GPU and may single-handedly save private credit. Private credit is in pain from these SaaS loans. But there's a lot of private credit in GPUs too. They were underwriting that to 3-4. The disaggregation of inference means that these GPUs are going to have 10 or 15-year lives. The AI skeptics are like, "Oh, these companies are all cooking their books. The useful life of a GPU is only a year or two. The useful life of a CPU is only four years because the rapid technological change." No. What rapid technological change has done with the disaggregation of prefill and inference is you can put a Cerebras system or Groq LPUs effectively in front of a Hopper or even an Ampere, use that Hopper and Ampere for prefill, and extend the useful life of that GPU until it melts. This is going to be really good for the whole private credit industry. It's gonna help finance the AI build-out. Because if you can start to finance GPUs at 5% or 6% instead of – I think CoreWeave's lowest financing was low sevens – that actually mathematically changes the cost to finance this build-out."
1:51

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 to be amazing for the useful lives of GPU and may single-handedly save private credit. Private credit is in pain from these SaaS loans. But there's a lot of private credit in GPUs too. They were underwriting that to 3-4. The disaggregation of inference means that these GPUs are going to have 10 or 15-year lives. The AI skeptics are like, "Oh, these companies are all cooking their books. The useful life of a GPU is only a year or two. The useful life of a CPU is only four years because the rapid technological change." No. What rapid technological change has done with the disaggregation of prefill and inference is you can put a Cerebras system or Groq LPUs effectively in front of a Hopper or even an Ampere, use that Hopper and Ampere for prefill, and extend the useful life of that GPU until it melts. This is going to be really good for the whole private credit industry. It's gonna help finance the AI build-out. Because if you can start to finance GPUs at 5% or 6% instead of – I think CoreWeave's lowest financing was low sevens – that actually mathematically changes the cost to finance this build-out."

Invest Like the Best

204,793 просмотров • 22 дней назад

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."
1:42

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

486,049 просмотров • 1 год назад

Data is the foundation of any AI training. All of the GPUs in the world can't train an AI model if they don't have data to train it on. Don't forget: Grass is the data layer of AI.
0:40

Data is the foundation of any AI training. All of the GPUs in the world can't train an AI model if they don't have data to train it on. Don't forget: Grass is the data layer of AI.

Grass

211,905 просмотров • 2 лет назад

We asked Tom Schmidt ＞|＜ from Dragonfly ＞|＜ about AI x crypto. "We are more bearish on this category." "A lot of AI crypto is broken down into people trying to do decentralized inference, this GPU marketplace. This is an old idea in crypto." "If you want to train a state-of-the-art model, you don't use 1M consumer-grade GPUs around the world. You want one big co-located A100 cluster." "The areas where we are more excited about are MPC and agent payments, where stablecoins are uniquely positioned to provide microtransactions."
1:38

We asked Tom Schmidt ＞|＜ from Dragonfly ＞|＜ about AI x crypto. "We are more bearish on this category." "A lot of AI crypto is broken down into people trying to do decentralized inference, this GPU marketplace. This is an old idea in crypto." "If you want to train a state-of-the-art model, you don't use 1M consumer-grade GPUs around the world. You want one big co-located A100 cluster." "The areas where we are more excited about are MPC and agent payments, where stablecoins are uniquely positioned to provide microtransactions."

TBPN

40,161 просмотров • 1 год назад

🚨🇷🇼PRESIDENT KAGAME: AID CAN BECOME A LIABILITY ”I don't want to put words in your mouth, but aid could become a liability for a country based on what you're saying.” President Kagame: "That is an understatement. In the long run, it is. It depends on how it is managed, and that has been our position from the beginning. We have told people we appreciate aid—we needed it, and we still need it to an extent. But we need it to build capacities so we don’t need it in the near future. That is our standard. That's what we say, and that's what we do." Source: Paul Kagame
2:20

🚨🇷🇼PRESIDENT KAGAME: AID CAN BECOME A LIABILITY ”I don't want to put words in your mouth, but aid could become a liability for a country based on what you're saying.” President Kagame: "That is an understatement. In the long run, it is. It depends on how it is managed, and that has been our position from the beginning. We have told people we appreciate aid—we needed it, and we still need it to an extent. But we need it to build capacities so we don’t need it in the near future. That is our standard. That's what we say, and that's what we do." Source: Paul Kagame

Mario Nawfal

80,963 просмотров • 1 год назад

.Scott Wu to Joe Lonsdale on why Cognition uses AI in job interviews: "One or two years ago, people had this mentality of 'How do we make sure people aren't using AI while we're interviewing them?' And I think that has totally flipped. Honestly, I think that was wrong... If you're asking the question of like, how do we evaluate people on exactly the thing that AI can already do, that seems kind of the wrong. Our interview process has always been... you can use as much AI as you want to use. We're going to give you a few hours. Just build your whole own product surface. A lot of these are projects that, frankly, is like, if you were trying to do this by hand, you would not be able to get done in a few hours. So you kind of have to use AI for this. But in reality, what we actually want to test is, in addition to how familiar you are with these tools, what we actually want to test is, what do you think is the right thing to build? Or how do you make these product decisions? How do you make these trade-offs? How do you decide or collect information about what you should be doing?"
0:58

.Scott Wu to Joe Lonsdale on why Cognition uses AI in job interviews: "One or two years ago, people had this mentality of 'How do we make sure people aren't using AI while we're interviewing them?' And I think that has totally flipped. Honestly, I think that was wrong... If you're asking the question of like, how do we evaluate people on exactly the thing that AI can already do, that seems kind of the wrong. Our interview process has always been... you can use as much AI as you want to use. We're going to give you a few hours. Just build your whole own product surface. A lot of these are projects that, frankly, is like, if you were trying to do this by hand, you would not be able to get done in a few hours. So you kind of have to use AI for this. But in reality, what we actually want to test is, in addition to how familiar you are with these tools, what we actually want to test is, what do you think is the right thing to build? Or how do you make these product decisions? How do you make these trade-offs? How do you decide or collect information about what you should be doing?"

American Optimist

13,182 просмотров • 2 месяцев назад

This is cool, Elon Musk says SpaceX will allow their customers to use any GPU or TPU they want in SpaceX AI Satellites. "We do intend with our SpaceX AI Satellites to allow people to put whatever GPU or TPU they want. So Nvidia GPUS can be put on it, Google TPUs can be put on it, Amazon Trainium, or any other chips people want to put on. It will be such that you can run anyone’s AI, hardware or software on the SpaceX AI Satellites." An open platform design is so smart.
0:44

This is cool, Elon Musk says SpaceX will allow their customers to use any GPU or TPU they want in SpaceX AI Satellites. "We do intend with our SpaceX AI Satellites to allow people to put whatever GPU or TPU they want. So Nvidia GPUS can be put on it, Google TPUs can be put on it, Amazon Trainium, or any other chips people want to put on. It will be such that you can run anyone’s AI, hardware or software on the SpaceX AI Satellites." An open platform design is so smart.

Nic Cruz Patane

115,102 просмотров • 6 дней назад

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.
4:50

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.

Santiago

384,482 просмотров • 3 лет назад

An Awakening Is Happening In America. Black Resident From Harlem New York Says The People Of Harlem Are Done With The Democrat Party “You are public servants. You serve at our pleasure. We buy your houses. We buy your cars, we pay for you and your wife's vacations, and the people are serving notice on the government of the United States. We are serving notice on the state of New York. We are serving notice on the mayor of this city. We are serving notice on the city council that if you do not do your job, you will no longer have a job. I live in Harlem and I'm telling you the there is we are living in the middle of a crack epidemic big nightmare. People need help. It is a violent circle of oppression when you create the situation. And then when we go out to protest the situation, you in turn exact more violence upon us. We are saying no more.”
1:03

An Awakening Is Happening In America. Black Resident From Harlem New York Says The People Of Harlem Are Done With The Democrat Party “You are public servants. You serve at our pleasure. We buy your houses. We buy your cars, we pay for you and your wife's vacations, and the people are serving notice on the government of the United States. We are serving notice on the state of New York. We are serving notice on the mayor of this city. We are serving notice on the city council that if you do not do your job, you will no longer have a job. I live in Harlem and I'm telling you the there is we are living in the middle of a crack epidemic big nightmare. People need help. It is a violent circle of oppression when you create the situation. And then when we go out to protest the situation, you in turn exact more violence upon us. We are saying no more.”

Wall Street Apes

455,623 просмотров • 2 лет назад