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A SINGLE TRANSISTOR UNDER THE MICROSCOPE That insane zoomed-in video? It reveals one lonely CPU transistor, the building block of every chip. Transistors are crafted using photolithography, where ultraviolet light etches microscopic patterns into silicon, repeated hundreds to thousands of times to build a full CPU. Only one company... show more

Mario Nawfal

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$Extropic founder Beff (e/acc) explains why his new Z1 chip uses 10,000x fewer transistors than a GPU to run the same AI algorithms: "pbits are not traditional bits that are definitely zero, definitely one. They're oscillating between the two. It's like a light switch dimmer. You ever have it flicker when you're at the edge? It's kind of like that." "It's a fractional bit. I could be like, hey, 20% of the time I'm zero, 80% of the time I'm one. That would be a fractional flip." "The most interesting thing is that you can do all sorts of AI algorithms with about 10,000 times less transistors than if you were to do them digitally." "If you go back to the ancestry of neural networks, they came from something called energy-based models from Hinton." "We can do that specifically with Z1. We have literal Boltzmann machines in the hardware. They're very dense, and they use very few transistors, because each transistor is operating in the regime that would be usually considered janky. It's so low power, it's flipping all the time." Extropic$

Extropic founder Beff (e/acc) explains why his new Z1 chip uses 10,000x fewer transistors than a GPU to run the same AI algorithms: "pbits are not traditional bits that are definitely zero, definitely one. They're oscillating between the two. It's like a light switch dimmer. You ever have it flicker when you're at the edge? It's kind of like that." "It's a fractional bit. I could be like, hey, 20% of the time I'm zero, 80% of the time I'm one. That would be a fractional flip." "The most interesting thing is that you can do all sorts of AI algorithms with about 10,000 times less transistors than if you were to do them digitally." "If you go back to the ancestry of neural networks, they came from something called energy-based models from Hinton." "We can do that specifically with Z1. We have literal Boltzmann machines in the hardware. They're very dense, and they use very few transistors, because each transistor is operating in the regime that would be usually considered janky. It's so low power, it's flipping all the time." Extropic

MTS

22,438 görüntüleme • 1 ay önce

ELON: TESLA'S AI5 CHIP IS THE SILICON VERSION OF “LESS IS MORE” "The AI5 chip will be 40 times better than the AI4 chip – not 40%, 40 times. We have a detailed understanding of the entire software and hardware stack. We are designing the hardware to address all of the pain points in software. We know what the chip needs to do, and we know, just as importantly, what the chip doesn’t need to do. A long list of deletions that are very important. As a result of these deletions, we can actually fit AI5 in a half reticle with a good margin for the traces from the memory to the Tesla chip accelerators, the ARM CPU cores, and the PCI blocks. This is a beautiful chip." Source: Elon Musk, Tesla Podcast, October 22, 2025

ELON: TESLA'S AI5 CHIP IS THE SILICON VERSION OF “LESS IS MORE” "The AI5 chip will be 40 times better than the AI4 chip – not 40%, 40 times. We have a detailed understanding of the entire software and hardware stack. We are designing the hardware to address all of the pain points in software. We know what the chip needs to do, and we know, just as importantly, what the chip doesn’t need to do. A long list of deletions that are very important. As a result of these deletions, we can actually fit AI5 in a half reticle with a good margin for the traces from the memory to the Tesla chip accelerators, the ARM CPU cores, and the PCI blocks. This is a beautiful chip." Source: Elon Musk, Tesla Podcast, October 22, 2025

Mario Nawfal

363,507 görüntüleme • 10 ay önce

It’s easy to overlook this… but everything in AI depends on something almost invisible. Modern transistors are measured in nanometers — where thousands fit across a single human hair. And yet, your phone contains tens of billions of them, packed into a space smaller than your fingernail. The first time you really think about it, it’s striking. Because at its core, a transistor is simple. Just a switch. On or off. 1 or 0. But when billions of these switches flip billions of times per second, they power everything: Computing. Graphics. Artificial intelligence. The contrast is fascinating. The most advanced systems we build today… rely on the simplest action, repeated at extreme scale. So here’s something I’d be curious to hear from you: As we reach atomic limits, where will the next breakthrough in computing come from? #ArtificialIntelligence #Technology #Innovation #Semiconductors #FutureOfAI

It’s easy to overlook this… but everything in AI depends on something almost invisible. Modern transistors are measured in nanometers — where thousands fit across a single human hair. And yet, your phone contains tens of billions of them, packed into a space smaller than your fingernail. The first time you really think about it, it’s striking. Because at its core, a transistor is simple. Just a switch. On or off. 1 or 0. But when billions of these switches flip billions of times per second, they power everything: Computing. Graphics. Artificial intelligence. The contrast is fascinating. The most advanced systems we build today… rely on the simplest action, repeated at extreme scale. So here’s something I’d be curious to hear from you: As we reach atomic limits, where will the next breakthrough in computing come from? #ArtificialIntelligence #Technology #Innovation #Semiconductors #FutureOfAI

Pascal Bornet

103,747 görüntüleme • 5 ay önce

Every serious analyst has called Elon Musk's Terafab unrealistic. The CEO of the company that supplies the machines to build it just said it is an upside opportunity and compared it to the largest chip buildouts in history. That is not a small statement. ASML makes the lithography machines that every advanced semiconductor fab on earth depends on. You cannot print a chip without them. There is no workaround. There is no alternative supplier. If the Terafab gets built, every single machine inside it comes from one company. The interviewer asked the ASML CEO directly about one terawatt of compute power. He did not laugh. He did not hedge. He compared it to the massive DRAM megaprojects in Korea and said new projects at that scale are exactly the kind of upside ASML is positioned for. His exact words: "New projects are opportunity." Then he said something that most people missed. He said ASML is already watching the Terafab very carefully. Tracking when things will happen. At what speed. That is not how you talk about a fantasy project. The mainstream take on the Terafab is that Musk is dreaming. The CEO of the most critical company in the entire chip supply chain just said he is preparing for it. Those are not the same position. (Watch the full 11 minutes Interview at Bloomberg TV on YouTube)

Every serious analyst has called Elon Musk's Terafab unrealistic. The CEO of the company that supplies the machines to build it just said it is an upside opportunity and compared it to the largest chip buildouts in history. That is not a small statement. ASML makes the lithography machines that every advanced semiconductor fab on earth depends on. You cannot print a chip without them. There is no workaround. There is no alternative supplier. If the Terafab gets built, every single machine inside it comes from one company. The interviewer asked the ASML CEO directly about one terawatt of compute power. He did not laugh. He did not hedge. He compared it to the massive DRAM megaprojects in Korea and said new projects at that scale are exactly the kind of upside ASML is positioned for. His exact words: "New projects are opportunity." Then he said something that most people missed. He said ASML is already watching the Terafab very carefully. Tracking when things will happen. At what speed. That is not how you talk about a fantasy project. The mainstream take on the Terafab is that Musk is dreaming. The CEO of the most critical company in the entire chip supply chain just said he is preparing for it. Those are not the same position. (Watch the full 11 minutes Interview at Bloomberg TV on YouTube)

Ihtesham Ali

67,385 görüntüleme • 3 ay önce

$Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.$

Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.

Dustin

160,633 görüntüleme • 5 ay önce

Jane Street engineers, in an internal tech talk on training performance: "GPUs are really slow at doing one floating point operation. It's around a microsecond. CPUs are 1,000 times faster than GPUs." Not a podcast. A talk given inside the firm that trades billions a day, to a room of engineers who already train models. The whole industry sells you the GPU as the fast thing. On a single operation it is a thousand times slower than the CPU sitting in your laptop. The GPU was never fast. It was wide. If your training loop feeds it one thing at a time, you are paying rack prices for the slowest chip in the building.

Jane Street engineers, in an internal tech talk on training performance: "GPUs are really slow at doing one floating point operation. It's around a microsecond. CPUs are 1,000 times faster than GPUs." Not a podcast. A talk given inside the firm that trades billions a day, to a room of engineers who already train models. The whole industry sells you the GPU as the fast thing. On a single operation it is a thousand times slower than the CPU sitting in your laptop. The GPU was never fast. It was wide. If your training loop feeds it one thing at a time, you are paying rack prices for the slowest chip in the building.

zostaff

12,502 görüntüleme • 1 ay önce

#WATCH | Bengaluru: Showcasing the silicon wafer, Union Minister Ashwini Vaishnaw says, "This is the wafer in which each die has about 20 to 30 billion transistors... One die is one of those squares you see... This transistor density is used to design the silicon. This is essentially a single chip with a GPU and a CPU. The end product that comes out of this is this kind of module, which becomes an AI computer on the desktop of any person, on the edge, meaning within a camera, within a Wi-Fi router, or within a device on any machine, or any moving car, automobile, train, or aeroplane... That kind of development is underway and reinforced by the talent pipeline we are creating as part of Semicon mission 1.0... We set a target of 85,000 semiconductor-trained people over a period of 10 years. I'm happy to share that, over 4 years, we have trained 67,000 semiconductor engineers. This is now available at 315 universities and colleges, where all semiconductor design-related EDA tools are available. The students are designing chips... The final product is being validated, and it's a strong capability because few universities and countries worldwide have this kind of model." He adds, "Recently, at Davos, when I shared this model with semiconductor industry leaders, they were very happy about it and believe that the 1 million talent gap in the semiconductor industry will largely be filled by talent from India... In the recent budget, the entire IT services industry has undergone various simplifications. Whether it is getting the APS done or getting the safe harbour clause properly defined, all those things are in place. Today, the industry aims to enter India in a big way. Whatever progress we have seen in the last few years will accelerate with the announced reforms..."

#WATCH | Bengaluru: Showcasing the silicon wafer, Union Minister Ashwini Vaishnaw says, "This is the wafer in which each die has about 20 to 30 billion transistors... One die is one of those squares you see... This transistor density is used to design the silicon. This is essentially a single chip with a GPU and a CPU. The end product that comes out of this is this kind of module, which becomes an AI computer on the desktop of any person, on the edge, meaning within a camera, within a Wi-Fi router, or within a device on any machine, or any moving car, automobile, train, or aeroplane... That kind of development is underway and reinforced by the talent pipeline we are creating as part of Semicon mission 1.0... We set a target of 85,000 semiconductor-trained people over a period of 10 years. I'm happy to share that, over 4 years, we have trained 67,000 semiconductor engineers. This is now available at 315 universities and colleges, where all semiconductor design-related EDA tools are available. The students are designing chips... The final product is being validated, and it's a strong capability because few universities and countries worldwide have this kind of model." He adds, "Recently, at Davos, when I shared this model with semiconductor industry leaders, they were very happy about it and believe that the 1 million talent gap in the semiconductor industry will largely be filled by talent from India... In the recent budget, the entire IT services industry has undergone various simplifications. Whether it is getting the APS done or getting the safe harbour clause properly defined, all those things are in place. Today, the industry aims to enter India in a big way. Whatever progress we have seen in the last few years will accelerate with the announced reforms..."

ANI

108,196 görüntüleme • 7 ay önce

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Milk Road AI

11,819 görüntüleme • 3 ay önce

Musk is throwing $25 billion at a single chip factory. TeraFab. Biggest industrial bet anyone's ever made, and the numbers don't work according to pretty much every analyst who's looked at them. Meanwhile one company in a small Dutch town — ASML — still makes the only lithography machines that actually matter. But Musk isn't trying to beat TSMC at their own game. The play is collapsing the whole design-to-packaging loop so Tesla can iterate on silicon 5 to 10 times faster than everyone else. That gap gets wider every cycle. Everyone else is still buying general-purpose chips off the shelf. *** thanks to David Carbutt and team for the edit.

Musk is throwing $25 billion at a single chip factory. TeraFab. Biggest industrial bet anyone's ever made, and the numbers don't work according to pretty much every analyst who's looked at them. Meanwhile one company in a small Dutch town — ASML — still makes the only lithography machines that actually matter. But Musk isn't trying to beat TSMC at their own game. The play is collapsing the whole design-to-packaging loop so Tesla can iterate on silicon 5 to 10 times faster than everyone else. That gap gets wider every cycle. Everyone else is still buying general-purpose chips off the shelf. *** thanks to David Carbutt and team for the edit.

Farzad 🇺🇸 🇮🇷

264,597 görüntüleme • 5 ay önce

Merry Christmas everybody! On Christmas Eve thousands of Dutch people gather all over the Netherlands ar the local war cemeteries and light candles on the graves of the fallen. A tradition that started decades ago at the Canadian War Cemetery at Holten and has since spread all over the country. I’m proud to have participated with my family at the CWGC cemetery in Uden where the fierce wind made it a big challenge to light the candles.

Merry Christmas everybody! On Christmas Eve thousands of Dutch people gather all over the Netherlands ar the local war cemeteries and light candles on the graves of the fallen. A tradition that started decades ago at the Canadian War Cemetery at Holten and has since spread all over the country. I’m proud to have participated with my family at the CWGC cemetery in Uden where the fierce wind made it a big challenge to light the candles.

The Battlefield Explorer

33,000 görüntüleme • 8 ay önce

EUV machines are the most complicated tools humans make. Their supply chain has over 10,000 individual suppliers, and any one of them not scaling fast enough can bottleneck the entire AI industry. An EUV tool fires lasers at a tiny tin droplet three times in precise sequence, blasting it hard enough to emit EUV light. That light bounces off 18 multilayer mirrors onto the wafer. Meanwhile, the two platforms inside the machine - one holding the stencil, one holding the chip - are flying back and forth at 9Gs in opposite directions. The successive passes have to land on top of each other to within 3 nanometers. If any part of this is off, yield goes to zero. Take just one component. The mirrors are mostly supplied by Carl Zeiss, who have probably fewer than a thousand people working on them. In turn, Carl Zeiss rely on machines from Switzerland to deposit each of the layers, and use a coating process co-developed with a different German company. None of these companies have woken up. They’re gradually increasing production, but nowhere near the levels necessary for what the labs want by the end of the decade. Dylan Patel predicts production can't scale beyond about 100 EUV machines per year by 2030, no matter how much money gets thrown at the problem. In the medium term this is the key bottleneck on scaling.

EUV machines are the most complicated tools humans make. Their supply chain has over 10,000 individual suppliers, and any one of them not scaling fast enough can bottleneck the entire AI industry. An EUV tool fires lasers at a tiny tin droplet three times in precise sequence, blasting it hard enough to emit EUV light. That light bounces off 18 multilayer mirrors onto the wafer. Meanwhile, the two platforms inside the machine - one holding the stencil, one holding the chip - are flying back and forth at 9Gs in opposite directions. The successive passes have to land on top of each other to within 3 nanometers. If any part of this is off, yield goes to zero. Take just one component. The mirrors are mostly supplied by Carl Zeiss, who have probably fewer than a thousand people working on them. In turn, Carl Zeiss rely on machines from Switzerland to deposit each of the layers, and use a coating process co-developed with a different German company. None of these companies have woken up. They’re gradually increasing production, but nowhere near the levels necessary for what the labs want by the end of the decade. Dylan Patel predicts production can't scale beyond about 100 EUV machines per year by 2030, no matter how much money gets thrown at the problem. In the medium term this is the key bottleneck on scaling.

Dwarkesh Patel

154,381 görüntüleme • 6 ay önce

Extremely telling clip of how the US sees its "allies", which I am sure will go down well with the Dutch... This is Eric Schmidt (former CEO of Google and currently on an advisory board for the Department of Defense) congratulating Alan F. Estevez (U.S. Under Secretary of Industry and Security), telling him the "genius of your strategy [to ban semiconductors to China] is that you found a monopoly that exists in one company in the world that we had control over, which is ASML" ASML is a Dutch company...

Extremely telling clip of how the US sees its "allies", which I am sure will go down well with the Dutch... This is Eric Schmidt (former CEO of Google and currently on an advisory board for the Department of Defense) congratulating Alan F. Estevez (U.S. Under Secretary of Industry and Security), telling him the "genius of your strategy [to ban semiconductors to China] is that you found a monopoly that exists in one company in the world that we had control over, which is ASML" ASML is a Dutch company...

Arnaud Bertrand

363,916 görüntüleme • 2 yıl önce

VIDEO | India AI Summit 2026: AAP MP Raghav Chadha says, "In the 21st century, the levers of power will essentially be controlled by those who have access to AI, GPUs, compute capability and data centres. That was the key element of my argument, how India needs to build its own compute capability. Today, we are in an AI ecosystem where there is, in some sense, a monopoly. Design is controlled by one company based in the US, while nearly 90 per cent of chip manufacturing and fabrication is controlled by a company based in Taiwan. Then there are export controls, where certain countries, particularly the US, have defined these chips, GPUs and the entire AI architecture as strategic assets and imposed controls on their export." #PTIAtAIImpactSummit #IndiaAIImpactSummit2026 #AIImpactSummit2026 (Full video available on

VIDEO | India AI Summit 2026: AAP MP Raghav Chadha says, "In the 21st century, the levers of power will essentially be controlled by those who have access to AI, GPUs, compute capability and data centres. That was the key element of my argument, how India needs to build its own compute capability. Today, we are in an AI ecosystem where there is, in some sense, a monopoly. Design is controlled by one company based in the US, while nearly 90 per cent of chip manufacturing and fabrication is controlled by a company based in Taiwan. Then there are export controls, where certain countries, particularly the US, have defined these chips, GPUs and the entire AI architecture as strategic assets and imposed controls on their export." #PTIAtAIImpactSummit #IndiaAIImpactSummit2026 #AIImpactSummit2026 (Full video available on

Press Trust of India

47,600 görüntüleme • 7 ay önce

Here it is: The “glitch” — a phenomenon that mysteriously favors Democrats by default during elections. The Los Angeles Times CONFIRMS a ballot dump of tens of thousands cast for only the communist freedom fighter Karen Bass and the socialist Nithya Raman, but not a single one for Spencer Pratt. And now they’re trying to talk their way out of it. Funny, these glitches have yet to favor a Republican in history.

Here it is: The “glitch” — a phenomenon that mysteriously favors Democrats by default during elections. The Los Angeles Times CONFIRMS a ballot dump of tens of thousands cast for only the communist freedom fighter Karen Bass and the socialist Nithya Raman, but not a single one for Spencer Pratt. And now they’re trying to talk their way out of it. Funny, these glitches have yet to favor a Republican in history.

🇺🇸RealRobert🇺🇸

27,808 görüntüleme • 2 ay önce

These two Belgians were tangled and unable to move. This was one of the times a news reporter came with us to Skid Row. Where was the owner? Passed out on drugs in a tent. These dogs were removed by officials only after the news contacted them.

These two Belgians were tangled and unable to move. This was one of the times a news reporter came with us to Skid Row. Where was the owner? Passed out on drugs in a tent. These dogs were removed by officials only after the news contacted them.

JoeyTuccio

53,520 görüntüleme • 7 ay önce

Discovered Materials' Akash Ramdas argues the next chip scaling frontier isn't smaller transistors, it's how many you can stack on top of each other: HBM is already 12 high DRAM going to 16 high "We're starting to do this thing where we're taking a logic chip and then putting memory on top of it. That's where materials can make a difference, because now you're actually thermally limited. The thing that's preventing your scaling is no longer how small is your transistor, but how many transistors can you put on top of each other?" "The high bandwidth memory you'd have seen is a stack of dynamic random access memory. It's a 12-high dynamic random access memory stack right now. One of the roadmaps is to make this even higher, 16-high, and this is how you deal with memory bottlenecks in inference or training." "The next stage of that is putting that HBM on top of a logic die. Both these technologies are going to be partially enabled by new materials."

Discovered Materials' Akash Ramdas argues the next chip scaling frontier isn't smaller transistors, it's how many you can stack on top of each other: HBM is already 12 high DRAM going to 16 high "We're starting to do this thing where we're taking a logic chip and then putting memory on top of it. That's where materials can make a difference, because now you're actually thermally limited. The thing that's preventing your scaling is no longer how small is your transistor, but how many transistors can you put on top of each other?" "The high bandwidth memory you'd have seen is a stack of dynamic random access memory. It's a 12-high dynamic random access memory stack right now. One of the roadmaps is to make this even higher, 16-high, and this is how you deal with memory bottlenecks in inference or training." "The next stage of that is putting that HBM on top of a logic die. Both these technologies are going to be partially enabled by new materials."

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