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Rocket launch lamp Key Materials Used: Saturn V style model rocket, addressable LED light strip, cotton wool/polyester stuffing, mounting hardware, power supply.

Elle Lookbook

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25,386 次观看 • 1 个月前 •via X (Twitter)

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Here is one of our experimental 3D printed Hadfield liquid rocket engines going through some thrust vector control (TVC) profiles! As we scale to our first light-lift orbital launch vehicle, Tundra, and then our medium-lift reusable launch vehicle, Titan, precise control of the rocket at every phase of flight is critical to mission success. Guiding a rocket to orbit, and eventually back to Earth, demands close coordination across every sub-team. Some of the key challenges we’re tackling: ➡️ Reliable & repeatable TVC actuation: ensuring the engine gimbal responds consistently across every test and flight ➡️ High-frequency control loops: real-time systems that keep the vehicle stable from liftoff to orbital insertion ➡️ Propellant sloshing: accounting for the movement of fluids in tanks and its effect on vehicle dynamics ➡️ Wind shear compensation: countering atmospheric disturbances during ascent ➡️ Structural bending & flex modes: managing how the vehicle’s primary structure responds to aerodynamic and thrust loads ➡️ Precision payload delivery: hitting the exact target orbit, every time Our team at NordSpace has been advancing our propulsion systems on many fronts, as we work to unveil our orbital-scale, pump-fed Hadfield and Garneau rocket engines in the near future — the engines that will carry Canada to orbit for the first time, and unlock sovereign access to space. Check out some of our rocket hardware, meet our engineers, join over 400 attendees and over 40 speakers at the Canadian Space Launch Conference, taking place this May 5th in Ottawa. National Defence Defence Research and Development Canada Canadian Space Agency NSERC / CRSNG

Here is one of our experimental 3D printed Hadfield liquid rocket engines going through some thrust vector control (TVC) profiles! As we scale to our first light-lift orbital launch vehicle, Tundra, and then our medium-lift reusable launch vehicle, Titan, precise control of the rocket at every phase of flight is critical to mission success. Guiding a rocket to orbit, and eventually back to Earth, demands close coordination across every sub-team. Some of the key challenges we’re tackling: ➡️ Reliable & repeatable TVC actuation: ensuring the engine gimbal responds consistently across every test and flight ➡️ High-frequency control loops: real-time systems that keep the vehicle stable from liftoff to orbital insertion ➡️ Propellant sloshing: accounting for the movement of fluids in tanks and its effect on vehicle dynamics ➡️ Wind shear compensation: countering atmospheric disturbances during ascent ➡️ Structural bending & flex modes: managing how the vehicle’s primary structure responds to aerodynamic and thrust loads ➡️ Precision payload delivery: hitting the exact target orbit, every time Our team at NordSpace has been advancing our propulsion systems on many fronts, as we work to unveil our orbital-scale, pump-fed Hadfield and Garneau rocket engines in the near future — the engines that will carry Canada to orbit for the first time, and unlock sovereign access to space. Check out some of our rocket hardware, meet our engineers, join over 400 attendees and over 40 speakers at the Canadian Space Launch Conference, taking place this May 5th in Ottawa. National Defence Defence Research and Development Canada Canadian Space Agency NSERC / CRSNG

NordSpace 🇨🇦

48,970 次观看 • 7 个月前

Infant US startup has split the atom. Including Aalo, US just achieved 4 adv reactor criticalities. Just months after their sodium test loop, Aalo (yes, light in Bengali) reached first criticality with its zero-power X reactor, validating core systems; full-power ops of the 10 MWe sodium-cooled microreactor next year. Contrast is clear: India is pursuing state-led, large-scale reactors for long-term energy security, while the US/West has a laser focus—create early dependency by capturing the near term commercial demand. Likes of DOE Reactor Pilot Programs are ensuring private firms master repeatable reactor design, mfg, fuel, Ops, including building nuclear hardware & supply chain. The goal: rapidly deploy factory-built 10 MWe microreactors for near-term demand, especially data centres. IND already confirmed 🇺🇸Holtec as its key SMR partner.

Infant US startup has split the atom. Including Aalo, US just achieved 4 adv reactor criticalities. Just months after their sodium test loop, Aalo (yes, light in Bengali) reached first criticality with its zero-power X reactor, validating core systems; full-power ops of the 10 MWe sodium-cooled microreactor next year. Contrast is clear: India is pursuing state-led, large-scale reactors for long-term energy security, while the US/West has a laser focus—create early dependency by capturing the near term commercial demand. Likes of DOE Reactor Pilot Programs are ensuring private firms master repeatable reactor design, mfg, fuel, Ops, including building nuclear hardware & supply chain. The goal: rapidly deploy factory-built 10 MWe microreactors for near-term demand, especially data centres. IND already confirmed 🇺🇸Holtec as its key SMR partner.

Roy

17,917 次观看 • 2 个月前

NordSpace is proud to announce another major orbital launch program milestone. Alongside our ongoing orbital hardware development, we have successfully completed the high-fidelity trajectory and flight dynamics model for our Tundra orbital rocket after 2 years of intense design effort, thousands of simulations, and hundreds of physical tests. This high-fidelity model is far more than a simulation milestone or simply selecting the vehicle's capabilities. It is the cornerstone of the entire vehicle design loop, the foundation required for regulatory commercial flight approval, and the only way to confidently predict and guarantee the performance required to achieve orbit. It integrates detailed aerodynamics, structural loads, propulsion constraints, flight mechanics, guidance, navigation, and control, and much more into a single unified framework that will guide every major design decision moving forward. Crossing this milestone gives us the ability to continue to rapidly advance the engineering efforts behind our orbital launch architecture from engines and tanks, to GNC and GSE. Uniquely, we're focused on a lot more than just a light-lift vehicle. Our entire architecture is based upon selecting key technologies and design pathways that result in the most efficient progression from Tundra (500 kg to LEO, 350 kg to SSO) to Titan, our reusable medium lift vehicle (5,000 kg to LEO, 3,500 kg to SSO). Our architecture even allows for the option to extend Tundra without major modifications to a Tundra+ variant, allowing for 1,100 kg to LEO and 850 kg to SSO. It is crucial to balance the thousands of variables and parameters that go into selecting a scalable and flexible architecture which benefits from the flight heritage of decades of rocket development before us, while also ensuring the capability is globally competitive and domestically relevant to Canada. Our Atlantic Spaceport Complex (ASX) in Newfoundland and Labrador will be experiencing significant growth, investment and construction over the coming year as it prepares to host Tundra's launches. Owning and operating our own infrastructure and manufacturing facilities, end-to-end, enables the maximum level of flexibility and efficiency we need to meet Canada's timelines to develop sovereign space launch. Let's launch the north! Transport Canada National Defence Defence Research and Development Canada Canadian Space Agency

NordSpace is proud to announce another major orbital launch program milestone. Alongside our ongoing orbital hardware development, we have successfully completed the high-fidelity trajectory and flight dynamics model for our Tundra orbital rocket after 2 years of intense design effort, thousands of simulations, and hundreds of physical tests. This high-fidelity model is far more than a simulation milestone or simply selecting the vehicle's capabilities. It is the cornerstone of the entire vehicle design loop, the foundation required for regulatory commercial flight approval, and the only way to confidently predict and guarantee the performance required to achieve orbit. It integrates detailed aerodynamics, structural loads, propulsion constraints, flight mechanics, guidance, navigation, and control, and much more into a single unified framework that will guide every major design decision moving forward. Crossing this milestone gives us the ability to continue to rapidly advance the engineering efforts behind our orbital launch architecture from engines and tanks, to GNC and GSE. Uniquely, we're focused on a lot more than just a light-lift vehicle. Our entire architecture is based upon selecting key technologies and design pathways that result in the most efficient progression from Tundra (500 kg to LEO, 350 kg to SSO) to Titan, our reusable medium lift vehicle (5,000 kg to LEO, 3,500 kg to SSO). Our architecture even allows for the option to extend Tundra without major modifications to a Tundra+ variant, allowing for 1,100 kg to LEO and 850 kg to SSO. It is crucial to balance the thousands of variables and parameters that go into selecting a scalable and flexible architecture which benefits from the flight heritage of decades of rocket development before us, while also ensuring the capability is globally competitive and domestically relevant to Canada. Our Atlantic Spaceport Complex (ASX) in Newfoundland and Labrador will be experiencing significant growth, investment and construction over the coming year as it prepares to host Tundra's launches. Owning and operating our own infrastructure and manufacturing facilities, end-to-end, enables the maximum level of flexibility and efficiency we need to meet Canada's timelines to develop sovereign space launch. Let's launch the north! Transport Canada National Defence Defence Research and Development Canada Canadian Space Agency

NordSpace 🇨🇦

27,412 次观看 • 9 个月前

🚨🇨🇳 PENTAGON IN PANIC: CHINA TESTS SHAPE-SHIFTING HYPERSONIC RAMJET ENGINE China has pulled off a major hypersonic breakthrough by ground-testing a variable-geometry Ramjet that reshapes its own internal airflow channel in flight — much like a throat tightening and relaxing — and runs continuously from Mach 1.8 all the way to Mach 6 without leaking superheated gases or needing a heavy rocket booster to get started. 🔸 Chinese engineers solved the decades-old problem of creating reliable airtight seals for moving parts inside variable-geometry Ramjets, a challenge that caused severe gas leaks at extreme heat and speed and led most countries to abandon the design entirely. 🔸 The engine’s combustion chamber throat adjusted itself in just one-third of a second while inhaling gases at 1,650 degrees Celsius, delivering stable performance across a wide speed range that previously required separate boosters and added cost and complexity. 🔸 The graphite seal used in the Chinese breakthrough is the same one the US defense industry desperately needs. The US defense industry, now faces vulnerabilities in its graphite supply chains – material critical for missile nose tips, rocket nozzles, stealth coatings, and nuclear reactor components. 🔸 China produces nearly 80 percent of the world’s graphite, including the high-purity grades needed for aerospace, and restricted exports of the material to the United States starting in late 2024. 🔸 Washington has invoked the Defense Production Act to fund domestic and allied graphite mining while Europe pushes similar efforts under its Critical Raw Materials Act, yet building new supply chains from scratch is expected to take a decade or more. Do you think U.S. engines can catch up with Chinese technology?

🚨🇨🇳 PENTAGON IN PANIC: CHINA TESTS SHAPE-SHIFTING HYPERSONIC RAMJET ENGINE China has pulled off a major hypersonic breakthrough by ground-testing a variable-geometry Ramjet that reshapes its own internal airflow channel in flight — much like a throat tightening and relaxing — and runs continuously from Mach 1.8 all the way to Mach 6 without leaking superheated gases or needing a heavy rocket booster to get started. 🔸 Chinese engineers solved the decades-old problem of creating reliable airtight seals for moving parts inside variable-geometry Ramjets, a challenge that caused severe gas leaks at extreme heat and speed and led most countries to abandon the design entirely. 🔸 The engine’s combustion chamber throat adjusted itself in just one-third of a second while inhaling gases at 1,650 degrees Celsius, delivering stable performance across a wide speed range that previously required separate boosters and added cost and complexity. 🔸 The graphite seal used in the Chinese breakthrough is the same one the US defense industry desperately needs. The US defense industry, now faces vulnerabilities in its graphite supply chains – material critical for missile nose tips, rocket nozzles, stealth coatings, and nuclear reactor components. 🔸 China produces nearly 80 percent of the world’s graphite, including the high-purity grades needed for aerospace, and restricted exports of the material to the United States starting in late 2024. 🔸 Washington has invoked the Defense Production Act to fund domestic and allied graphite mining while Europe pushes similar efforts under its Critical Raw Materials Act, yet building new supply chains from scratch is expected to take a decade or more. Do you think U.S. engines can catch up with Chinese technology?

NewRulesGeopolitics

80,466 次观看 • 2 个月前

I've been recommending incandescent light bulbs. But what about your kitchen? Your bathroom? Incandescents don't fit most of those fixtures. The answer: halogen bulbs. Glen Jeffery, UCL: "In the kitchen I have got a halogen lamp. When you get up in the morning and you're spending that 45 minutes doing stuff — there's a halogen lamp there on at the right time." Halogen is a type of incandescent. Almost identical full spectrum output. Infrared included. The key difference: Halogen bulbs contain halogen gas that redeposits tungsten back onto the filament as it evaporates — longer lifespan, same full spectrum output. The result: longer lifespan than a standard incandescent, higher operating temperature, and slightly whiter light — but still full spectrum with significant infrared output. Like incandescents, halogen bulbs have been phased out and banned across much of Europe and the UK. But they can still be found — Amazon, specialist lighting suppliers, some hardware stores still carry stock. And if you can dim them: "If you just turn the power down — which increases the amount of infrared light — the bulb will last almost forever." Jeffery's team ran a study in a windowless UCL building under harsh LED lighting. They replaced desk lamps with 40W incandescent bulbs — not aimed at eyes, just supplementing the environment. Two weeks later: color perception improved significantly — more than anything they'd seen with long-wavelength LEDs. They removed the bulbs. Came back six days later. The improvement was maintained. A month later — still maintained. Jeffery: "We are suffering from a suppression of our physiology via mitochondria that is just being produced by the built environment." The fix doesn't have to be complicated. Incandescent bulbs where you can. Halogen in the kitchen and bathroom where you can't. Indoor lighting environment sorted.

I've been recommending incandescent light bulbs. But what about your kitchen? Your bathroom? Incandescents don't fit most of those fixtures. The answer: halogen bulbs. Glen Jeffery, UCL: "In the kitchen I have got a halogen lamp. When you get up in the morning and you're spending that 45 minutes doing stuff — there's a halogen lamp there on at the right time." Halogen is a type of incandescent. Almost identical full spectrum output. Infrared included. The key difference: Halogen bulbs contain halogen gas that redeposits tungsten back onto the filament as it evaporates — longer lifespan, same full spectrum output. The result: longer lifespan than a standard incandescent, higher operating temperature, and slightly whiter light — but still full spectrum with significant infrared output. Like incandescents, halogen bulbs have been phased out and banned across much of Europe and the UK. But they can still be found — Amazon, specialist lighting suppliers, some hardware stores still carry stock. And if you can dim them: "If you just turn the power down — which increases the amount of infrared light — the bulb will last almost forever." Jeffery's team ran a study in a windowless UCL building under harsh LED lighting. They replaced desk lamps with 40W incandescent bulbs — not aimed at eyes, just supplementing the environment. Two weeks later: color perception improved significantly — more than anything they'd seen with long-wavelength LEDs. They removed the bulbs. Came back six days later. The improvement was maintained. A month later — still maintained. Jeffery: "We are suffering from a suppression of our physiology via mitochondria that is just being produced by the built environment." The fix doesn't have to be complicated. Incandescent bulbs where you can. Halogen in the kitchen and bathroom where you can't. Indoor lighting environment sorted.

no.mind

16,042 次观看 • 4 个月前

So the world's best selling car got a significant upgrade. Below I've gathered all that is new with Tesla Model Y refresh ("Juniper"). Do you think it'll become best-seller also in 2025? NEW IN EXTERIOR: • Front light bar (very Cybercab-esque) • New tail light: "Our single, cross-car lamp is the first indirect reflective body panel taillight of its kind." • Front camera in the bumper • New 19” “Interactive” and 20” “Spiral” wheels • drag coefficient 0.22 (instead of 0.23) • about 11lbs lighter • Rear license plate moved to the bumper • Larger Diffusor NEW WITH THE CAR ITSELF: • 3% more range (up to 719km/447mi CLTC) • Peak charging rate (still) at 250kW • updated suspension, wheels and tires for smoother and quieter ride; • Enhanced connectivity (improved car key range, clearer phone calls, faster WiFi connection) NEW IN INTERIOR: • Ventilated seats • Power Recline for the rear seats • 15.4” front touchscreen with smaller bezels • INDICATOR STALK IS BACK ("precision-engineered") • frunk has a drain hole to empty liquids. • has 16 speakers (instead of 14) • 8'' rear touchscreen • Acoustic glass for more quiet cabin Comes in five colors (and some more, like black, in certain markets): • Pearl White Multi-Coat • Glacier Blue [NEW PAINT NOW INTRODUCED] • Stealth Gray • Quicksilver • Ultra Red Tesla also launched a Launch Series Model Y (hat tip to Sawyer Merritt for these), with: • Rear lift gate badge • Puddle light badge • Doorsill plate wordmark • Charging console wordmark • Vegan suede for black interior • All-Wheel Drive comes with Acceleration Boost. All launched in China for now, deliveries begin in March. It seems likely Europe gets it in April-May (not confirmed) and perhaps the same for US.

The Tesla Space

99,292 次观看 • 1 年前

$Elon Musk was asked how he’d manufacture satellites at scale. He described building a factory on the moon. And almost nobody caught what he was laying out. Musk: “The lunar soil is like 20% silicon. So you can mine the silicon on the moon, refine it, and create the solar cells and the radiators on the moon.” He’s not talking about going to the moon. He’s talking about turning it into a production line. Mining silicon from lunar soil. Refining it on the surface. Building solar cells from materials already in the ground. Building radiators from aluminum buried in the dust. No supply ships. No trillion-dollar cargo drops. Extract, refine, build. On site. Musk: “You can make the radiators out of aluminum. There’s plenty of silicon and aluminum on the moon.” The physics backs every word. Lunar regolith is loaded with silicon and aluminum oxide. One-sixth gravity means launching finished products into orbit costs a fraction of the energy. No atmosphere means zero drag, zero weather, zero corrosion. For manufacturing, the moon isn’t just viable. It’s superior to Earth. Musk: “The chips you could send from Earth, ‘cause they’re pretty light.” That’s a complete off-world supply chain in twelve words. Heavy components sourced from lunar materials. Lightweight chips shipped from Earth at minimal cost. The full framework for off-planet manufacturing, solved in a single sentence. This isn’t a theorist speculating. This is the man who already lands orbital rockets on ocean barges. Running supply chain math out loud in real time. And nobody is sitting with what comes next. If the moon becomes a manufacturing base, Earth doesn’t stay at the center of anything. Earth becomes a chip supplier. The planet that invented writing, built every empire, launched the Enlightenment. Reduced to a parts vendor for a lunar factory. Every civilization in history was built on the same bet. Control land, resources, and trade routes on this planet. Every war. Every treaty. Every border. All fought over the same thin crust of rock and water. Musk is the first person engineering around that entire system. The most strategic territory in the solar system won’t be on this planet. It’ll be a quarter million miles above it. Whoever controls lunar manufacturing controls orbital infrastructure. Energy systems. Communication networks. Satellite constellations that blanket the planet below. Not through military force. Through supply chain. The nation that builds factories on the moon doesn’t need to win wars down here. They already hold the highest ground that’s ever existed. And right now, one man’s company is the only operation on Earth with the launch infrastructure to make any of this real. Built over twenty years while the rest of the industry called reusable rocketry impossible. He didn’t build a rocket company. He built the only road off this planet that works. We looked at the moon for ten thousand years and saw something sacred. One man looked up and saw a supply chain. The poets had the moon for millennia. The engineers just took it.$

Elon Musk was asked how he’d manufacture satellites at scale. He described building a factory on the moon. And almost nobody caught what he was laying out. Musk: “The lunar soil is like 20% silicon. So you can mine the silicon on the moon, refine it, and create the solar cells and the radiators on the moon.” He’s not talking about going to the moon. He’s talking about turning it into a production line. Mining silicon from lunar soil. Refining it on the surface. Building solar cells from materials already in the ground. Building radiators from aluminum buried in the dust. No supply ships. No trillion-dollar cargo drops. Extract, refine, build. On site. Musk: “You can make the radiators out of aluminum. There’s plenty of silicon and aluminum on the moon.” The physics backs every word. Lunar regolith is loaded with silicon and aluminum oxide. One-sixth gravity means launching finished products into orbit costs a fraction of the energy. No atmosphere means zero drag, zero weather, zero corrosion. For manufacturing, the moon isn’t just viable. It’s superior to Earth. Musk: “The chips you could send from Earth, ‘cause they’re pretty light.” That’s a complete off-world supply chain in twelve words. Heavy components sourced from lunar materials. Lightweight chips shipped from Earth at minimal cost. The full framework for off-planet manufacturing, solved in a single sentence. This isn’t a theorist speculating. This is the man who already lands orbital rockets on ocean barges. Running supply chain math out loud in real time. And nobody is sitting with what comes next. If the moon becomes a manufacturing base, Earth doesn’t stay at the center of anything. Earth becomes a chip supplier. The planet that invented writing, built every empire, launched the Enlightenment. Reduced to a parts vendor for a lunar factory. Every civilization in history was built on the same bet. Control land, resources, and trade routes on this planet. Every war. Every treaty. Every border. All fought over the same thin crust of rock and water. Musk is the first person engineering around that entire system. The most strategic territory in the solar system won’t be on this planet. It’ll be a quarter million miles above it. Whoever controls lunar manufacturing controls orbital infrastructure. Energy systems. Communication networks. Satellite constellations that blanket the planet below. Not through military force. Through supply chain. The nation that builds factories on the moon doesn’t need to win wars down here. They already hold the highest ground that’s ever existed. And right now, one man’s company is the only operation on Earth with the launch infrastructure to make any of this real. Built over twenty years while the rest of the industry called reusable rocketry impossible. He didn’t build a rocket company. He built the only road off this planet that works. We looked at the moon for ten thousand years and saw something sacred. One man looked up and saw a supply chain. The poets had the moon for millennia. The engineers just took it.

Dustin

73,558 次观看 • 4 个月前

Elon Musk built a second internet above the first one. Nobody asked him to. Thousands of satellites orbit at 550 kilometers. Moving at 25 times the speed of sound. Talking to each other through lasers in the vacuum of space. Musk: “Thousands of satellites providing low latency, high-speed internet throughout the world.” Before Starlink, satellite internet lived at 36,000 kilometers. Geostationary orbit. Signals traveling a tenth of the way to the moon before bouncing back. The lag made it barely functional. Musk dropped the altitude by 98%. One decision rewrote the physics of an entire industry. But the altitude wasn’t the real play. Musk: “There are laser links between the satellites. It forms a laser mesh. The satellites can communicate between each other and provide connectivity even if the cables are cut.” Every internet connection you’ve ever used runs through cables. Fiber optic lines buried in soil. Dragged across ocean floors. Threaded through chokepoints that every military maps before anything else. A single anchor drop can black out a country. An earthquake can sever a continent. The entire digital world hangs from threads in the mud. Musk built a network that doesn’t touch the ground. No cables. No trenches. No ocean floor. No single point of failure. A constellation of machines whispering to each other through light at the edge of the atmosphere. The men who tried before him weren’t fools. Gates backed Teledesic at the height of Microsoft’s power. Motorola built Iridium with the best engineers alive. Both paid someone else to reach orbit. Both went to zero. Musk owned the rocket. SpaceX made launch reusable. Built the satellites in-house. Flew them on its own rockets. Owned every inch of the chain from factory floor to orbit. That isn’t a cost advantage. It’s a moat no one can cross without first building a rocket company from scratch. Starlink passed 10 million subscribers as a side project. Every telecom executive on Earth watched it happen. Not one of them can explain the architecture underneath. They think he built a better satellite company. He built the only network that survives when the ground gives out. And the ground always gives out.

Elon Musk built a second internet above the first one. Nobody asked him to. Thousands of satellites orbit at 550 kilometers. Moving at 25 times the speed of sound. Talking to each other through lasers in the vacuum of space. Musk: “Thousands of satellites providing low latency, high-speed internet throughout the world.” Before Starlink, satellite internet lived at 36,000 kilometers. Geostationary orbit. Signals traveling a tenth of the way to the moon before bouncing back. The lag made it barely functional. Musk dropped the altitude by 98%. One decision rewrote the physics of an entire industry. But the altitude wasn’t the real play. Musk: “There are laser links between the satellites. It forms a laser mesh. The satellites can communicate between each other and provide connectivity even if the cables are cut.” Every internet connection you’ve ever used runs through cables. Fiber optic lines buried in soil. Dragged across ocean floors. Threaded through chokepoints that every military maps before anything else. A single anchor drop can black out a country. An earthquake can sever a continent. The entire digital world hangs from threads in the mud. Musk built a network that doesn’t touch the ground. No cables. No trenches. No ocean floor. No single point of failure. A constellation of machines whispering to each other through light at the edge of the atmosphere. The men who tried before him weren’t fools. Gates backed Teledesic at the height of Microsoft’s power. Motorola built Iridium with the best engineers alive. Both paid someone else to reach orbit. Both went to zero. Musk owned the rocket. SpaceX made launch reusable. Built the satellites in-house. Flew them on its own rockets. Owned every inch of the chain from factory floor to orbit. That isn’t a cost advantage. It’s a moat no one can cross without first building a rocket company from scratch. Starlink passed 10 million subscribers as a side project. Every telecom executive on Earth watched it happen. Not one of them can explain the architecture underneath. They think he built a better satellite company. He built the only network that survives when the ground gives out. And the ground always gives out.

Dustin

96,928 次观看 • 4 个月前

The sun produces more energy in one second than humanity has used in its entire existence. SpaceX thinks the only way to actually use more of it is to leave Earth. Here's why Elon Musk is racing to build data centers in space: 1. Musk frames civilizational progress using the Kardashev scale, a measurement created by a Russian physicist that ranks civilizations by how much energy they can harness. Type one means harnessing a planet's full energy, type two means harnessing a star's energy, and type three means harnessing a galaxy's energy. 2. Right now, humanity registers as essentially nonexistent on this scale. We harness less than a trillionth of the sun's total power output. Musk says we are not even at the level of a micro soul on the scale. 3. The sun makes up 99.86 percent of all mass in the entire solar system. Earth is so small in comparison that it falls into the leftover miscellaneous category alongside everything that is not the sun or Jupiter. 4. Only about half a billionth of the sun's energy even reaches Earth's cross section, and most of that cannot be used because 70 percent of Earth is covered in water, and much of the remaining land is uninhabitable terrain like Antarctica and Siberia. 5. To meaningfully climb the Kardashev scale, humanity has to go to space. Reaching even one millionth of the sun's total energy output would require increasing civilizational energy use by more than a million times current levels. 6. Getting to just 1 percent of the sun's energy would make a civilization vastly more powerful than ours is today. Musk says even reaching that level would represent an extremely advanced civilization. 7. Three core requirements stand between humanity and this goal: mass to orbit capability, a massive amount of solar power, and enough AI chips to actually use that power. 8. Starship solves the mass to orbit problem. It is designed to be the first rocket in history with full and rapid reusability, a breakthrough Musk calls absolutely necessary for making life multi-planetary and for ascending the Kardashev scale at all. 9. Reusability is the same principle behind every successful mode of transport. Cars, planes, boats, and bicycles are all reusable. If airplanes were thrown away after every flight, flying would be far too expensive for anyone to use. 10. Starship is already the largest, heaviest, and most powerful flying object ever built. Version 3 produces more than double the thrust of the Saturn V moon rocket, and version 4 is expected to produce nearly three times that thrust. 11. SpaceX currently delivers between 85 and 90 percent of all mass that reaches orbit from Earth using Falcon 9 and Falcon Heavy. With Starship, the company aims to scale mass to orbit from roughly 2,500 tons a year to millions of tons a year within about three years. 12. The proposed AI satellites are actually simpler to build than Starlink satellites. They mainly need solar cells, radiators, and laser links, without the complex phased array and parabolic antennas that Starlink satellites require. 13. The first version of the SpaceX AI satellite targets 150 kilowatts of peak power and 120 kilowatts of sustained power, roughly matching the output of a single Nvidia GB300 compute rack here on Earth. 14. These satellites will connect to each other through laser links and to the Starlink constellation, which then relays data to the ground. Despite orbiting 600 to 800 kilometers above Earth, the added latency is roughly only 3 milliseconds. 15. Heat management in space is actually easier than on Earth because radiators can simply release heat directly into the vacuum, removing the need for the massive cooling infrastructure required by ground based data centers. 16. SpaceX already operates around 10,000 Starlink satellites and claims to be the only company with real experience safely operating constellations at that scale, which gives them a head start in managing potentially thousands or even up to a million AI satellites. 17. To actually scale chip production to the levels needed, SpaceX is planning what it calls a Terafab, a chip manufacturing facility expected to span roughly 100 million square feet, about ten times the size of the existing Tesla Gigafactory in Texas. 18. The rough timeline targets reaching an annualized rate of 1 gigawatt of space based AI compute by the end of next year, scaling by roughly 10x per year afterward, eventually aiming for a terawatt per year, which is twice the entire current electricity consumption of the United States. 19. To push three orders of magnitude beyond even that terawatt goal, Musk describes building a mass driver on the moon, an electromagnetic rail gun style system that uses the moon's lack of atmosphere and lower gravity to launch satellites into space without needing a rocket at all. 20. Musk frames the long term vision in deeply personal terms. If enough mass and infrastructure eventually moves to the moon, it would become accessible enough that almost anyone who wants to go could go, and potentially even live there permanently. Follow Brad if you want more content on business, mindset & life changing ideas.

The sun produces more energy in one second than humanity has used in its entire existence. SpaceX thinks the only way to actually use more of it is to leave Earth. Here's why Elon Musk is racing to build data centers in space: 1. Musk frames civilizational progress using the Kardashev scale, a measurement created by a Russian physicist that ranks civilizations by how much energy they can harness. Type one means harnessing a planet's full energy, type two means harnessing a star's energy, and type three means harnessing a galaxy's energy. 2. Right now, humanity registers as essentially nonexistent on this scale. We harness less than a trillionth of the sun's total power output. Musk says we are not even at the level of a micro soul on the scale. 3. The sun makes up 99.86 percent of all mass in the entire solar system. Earth is so small in comparison that it falls into the leftover miscellaneous category alongside everything that is not the sun or Jupiter. 4. Only about half a billionth of the sun's energy even reaches Earth's cross section, and most of that cannot be used because 70 percent of Earth is covered in water, and much of the remaining land is uninhabitable terrain like Antarctica and Siberia. 5. To meaningfully climb the Kardashev scale, humanity has to go to space. Reaching even one millionth of the sun's total energy output would require increasing civilizational energy use by more than a million times current levels. 6. Getting to just 1 percent of the sun's energy would make a civilization vastly more powerful than ours is today. Musk says even reaching that level would represent an extremely advanced civilization. 7. Three core requirements stand between humanity and this goal: mass to orbit capability, a massive amount of solar power, and enough AI chips to actually use that power. 8. Starship solves the mass to orbit problem. It is designed to be the first rocket in history with full and rapid reusability, a breakthrough Musk calls absolutely necessary for making life multi-planetary and for ascending the Kardashev scale at all. 9. Reusability is the same principle behind every successful mode of transport. Cars, planes, boats, and bicycles are all reusable. If airplanes were thrown away after every flight, flying would be far too expensive for anyone to use. 10. Starship is already the largest, heaviest, and most powerful flying object ever built. Version 3 produces more than double the thrust of the Saturn V moon rocket, and version 4 is expected to produce nearly three times that thrust. 11. SpaceX currently delivers between 85 and 90 percent of all mass that reaches orbit from Earth using Falcon 9 and Falcon Heavy. With Starship, the company aims to scale mass to orbit from roughly 2,500 tons a year to millions of tons a year within about three years. 12. The proposed AI satellites are actually simpler to build than Starlink satellites. They mainly need solar cells, radiators, and laser links, without the complex phased array and parabolic antennas that Starlink satellites require. 13. The first version of the SpaceX AI satellite targets 150 kilowatts of peak power and 120 kilowatts of sustained power, roughly matching the output of a single Nvidia GB300 compute rack here on Earth. 14. These satellites will connect to each other through laser links and to the Starlink constellation, which then relays data to the ground. Despite orbiting 600 to 800 kilometers above Earth, the added latency is roughly only 3 milliseconds. 15. Heat management in space is actually easier than on Earth because radiators can simply release heat directly into the vacuum, removing the need for the massive cooling infrastructure required by ground based data centers. 16. SpaceX already operates around 10,000 Starlink satellites and claims to be the only company with real experience safely operating constellations at that scale, which gives them a head start in managing potentially thousands or even up to a million AI satellites. 17. To actually scale chip production to the levels needed, SpaceX is planning what it calls a Terafab, a chip manufacturing facility expected to span roughly 100 million square feet, about ten times the size of the existing Tesla Gigafactory in Texas. 18. The rough timeline targets reaching an annualized rate of 1 gigawatt of space based AI compute by the end of next year, scaling by roughly 10x per year afterward, eventually aiming for a terawatt per year, which is twice the entire current electricity consumption of the United States. 19. To push three orders of magnitude beyond even that terawatt goal, Musk describes building a mass driver on the moon, an electromagnetic rail gun style system that uses the moon's lack of atmosphere and lower gravity to launch satellites into space without needing a rocket at all. 20. Musk frames the long term vision in deeply personal terms. If enough mass and infrastructure eventually moves to the moon, it would become accessible enough that almost anyone who wants to go could go, and potentially even live there permanently. Follow Brad if you want more content on business, mindset & life changing ideas.

Brad

12,955 次观看 • 2 个月前

$Everyone talks about "𝗔𝗜 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮," but Sarvam AI just walked onto the stage at the India AI Impact Summit 2026 and showed the world what "𝗔𝗜 𝗯𝘆 𝗜𝗻𝗱𝗶𝗮" actually looks like. This is sovereign compute. 𝗧𝗵𝗲 𝗟𝗮𝘂𝗻𝗰𝗵: They didn't just launch one thing; they dropped an entire ecosystem tailored for 1.4 billion people. 𝗧𝗵𝗲 𝗛𝗲𝗮𝘃𝘆𝘄𝗲𝗶𝗴𝗵𝘁: Sarvam 105B & 30B 🧠 They unveiled two massive sovereign Large Language Models (LLMs) trained from scratch. 𝗦𝗮𝗿𝘃𝗮𝗺 𝟭𝟬𝟱𝗕:This is the beast. It’s a 105-billion parameter model that reportedly outperforms DeepSeek R1 on reasoning tasks and rivals global giants like Gemini Flash in efficiency. 𝗦𝗮𝗿𝘃𝗮𝗺 𝟯𝟬𝗕:The efficiency king, designed to run cost-effectively while handling complex Indic language reasoning. These aren't just translated models. They understand the nuance of 22 Indian languages, code-mixing (Hinglish, Tanglish), and cultural context that Western models often miss. Sarvam Kaze (Hardware!) 🕶️ This was the surprise "One More Thing" moment. ▶️They unveiled Sarvam Kaze, India’s first AI-powered smart glasses. ▶️PM Modi was the first person to demo them at the summit. ▶️They capture what you see and hear, processing it with their multimodal AI to give real-time intelligence. Launching May 2026. 𝗦𝗮𝗿𝘃𝗮𝗺 𝗔𝘂𝗱𝗶𝗼 & 𝗦𝗮𝗺𝘃𝗮𝗮𝗱 🗣️ An audio-first model that doesn't do "speech-to-text-to-LLM." It just hears and understands audio directly. It handles Indian accents, background noise, and interruptions flawlessly. 𝗛𝗼𝘄 𝗱𝗶𝗱 𝗮 𝘀𝘁𝗮𝗿𝘁𝘂𝗽 𝗮𝗰𝗵𝗶𝗲𝘃𝗲 𝘁𝗵𝗶𝘀? Building a 100B+ model isn't just about code; it's a logistics war. ▶️They secured 4,096 NVIDIA H100 GPUs (via Yotta Data Services). This is serious, nation-state level compute power. ▶️They trained on a massive 16 Trillion token dataset. Crucially, 2 Trillion of those were high-quality Indic tokens data that simply doesn't exist in the training sets of GPT-4 or Claude. ▶️They used a Mixture-of-Experts (MoE) architecture. This allows the model to be huge (smart) but only activate a fraction of parameters for each token (fast/cheap). ▶️They are a key part of the IndiaAI Mission, receiving subsidies and support to build "Sovereign AI" so India's data stays in India. 𝗪𝗵𝗼 𝗯𝘂𝗶𝗹𝘁 𝘁𝗵𝗶𝘀? 𝗣𝗿𝗮𝘁𝘆𝘂𝘀𝗵 𝗞𝘂𝗺𝗮𝗿 (𝗖𝗼-𝗳𝗼𝘂𝗻𝗱𝗲𝗿):The research heavyweight. Ex-IBM/Microsoft Research and IIT Bombay/Madras alum. He’s the one ensuring the models aren't just "big" but mathematically sound and efficient. 𝗩𝗶𝘃𝗲𝗸 𝗥𝗮𝗴𝗵𝗮𝘃𝗮𝗻 (𝗖𝗼-𝗳𝗼𝘂𝗻𝗱𝗲𝗿):The scale architect. He spent years with UIDAI (Aadhaar). He knows how to build systems that don't just work for a few thousand users, but for a billion people. For the last 3 years, the question was "𝗖𝗮𝗻 𝗜𝗻𝗱𝗶𝗮 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹?" Sarvam just answered: "Yes, and we can put it in hardware too." We are witnessing the shift from India being the "Back Office of the World" to the "Brain Office of the World." Col AJ🇮🇳 Major Sammer Pal Toorr (Infantry Combat Veteran) Navroop Singh Colonel Mayank Chaubey TheGlobalDecoder #SarvamAI #IndiaAIImpactSummit2026$

Everyone talks about "𝗔𝗜 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮," but Sarvam AI just walked onto the stage at the India AI Impact Summit 2026 and showed the world what "𝗔𝗜 𝗯𝘆 𝗜𝗻𝗱𝗶𝗮" actually looks like. This is sovereign compute. 𝗧𝗵𝗲 𝗟𝗮𝘂𝗻𝗰𝗵: They didn't just launch one thing; they dropped an entire ecosystem tailored for 1.4 billion people. 𝗧𝗵𝗲 𝗛𝗲𝗮𝘃𝘆𝘄𝗲𝗶𝗴𝗵𝘁: Sarvam 105B & 30B 🧠 They unveiled two massive sovereign Large Language Models (LLMs) trained from scratch. 𝗦𝗮𝗿𝘃𝗮𝗺 𝟭𝟬𝟱𝗕:This is the beast. It’s a 105-billion parameter model that reportedly outperforms DeepSeek R1 on reasoning tasks and rivals global giants like Gemini Flash in efficiency. 𝗦𝗮𝗿𝘃𝗮𝗺 𝟯𝟬𝗕:The efficiency king, designed to run cost-effectively while handling complex Indic language reasoning. These aren't just translated models. They understand the nuance of 22 Indian languages, code-mixing (Hinglish, Tanglish), and cultural context that Western models often miss. Sarvam Kaze (Hardware!) 🕶️ This was the surprise "One More Thing" moment. ▶️They unveiled Sarvam Kaze, India’s first AI-powered smart glasses. ▶️PM Modi was the first person to demo them at the summit. ▶️They capture what you see and hear, processing it with their multimodal AI to give real-time intelligence. Launching May 2026. 𝗦𝗮𝗿𝘃𝗮𝗺 𝗔𝘂𝗱𝗶𝗼 & 𝗦𝗮𝗺𝘃𝗮𝗮𝗱 🗣️ An audio-first model that doesn't do "speech-to-text-to-LLM." It just hears and understands audio directly. It handles Indian accents, background noise, and interruptions flawlessly. 𝗛𝗼𝘄 𝗱𝗶𝗱 𝗮 𝘀𝘁𝗮𝗿𝘁𝘂𝗽 𝗮𝗰𝗵𝗶𝗲𝘃𝗲 𝘁𝗵𝗶𝘀? Building a 100B+ model isn't just about code; it's a logistics war. ▶️They secured 4,096 NVIDIA H100 GPUs (via Yotta Data Services). This is serious, nation-state level compute power. ▶️They trained on a massive 16 Trillion token dataset. Crucially, 2 Trillion of those were high-quality Indic tokens data that simply doesn't exist in the training sets of GPT-4 or Claude. ▶️They used a Mixture-of-Experts (MoE) architecture. This allows the model to be huge (smart) but only activate a fraction of parameters for each token (fast/cheap). ▶️They are a key part of the IndiaAI Mission, receiving subsidies and support to build "Sovereign AI" so India's data stays in India. 𝗪𝗵𝗼 𝗯𝘂𝗶𝗹𝘁 𝘁𝗵𝗶𝘀? 𝗣𝗿𝗮𝘁𝘆𝘂𝘀𝗵 𝗞𝘂𝗺𝗮𝗿 (𝗖𝗼-𝗳𝗼𝘂𝗻𝗱𝗲𝗿):The research heavyweight. Ex-IBM/Microsoft Research and IIT Bombay/Madras alum. He’s the one ensuring the models aren't just "big" but mathematically sound and efficient. 𝗩𝗶𝘃𝗲𝗸 𝗥𝗮𝗴𝗵𝗮𝘃𝗮𝗻 (𝗖𝗼-𝗳𝗼𝘂𝗻𝗱𝗲𝗿):The scale architect. He spent years with UIDAI (Aadhaar). He knows how to build systems that don't just work for a few thousand users, but for a billion people. For the last 3 years, the question was "𝗖𝗮𝗻 𝗜𝗻𝗱𝗶𝗮 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹?" Sarvam just answered: "Yes, and we can put it in hardware too." We are witnessing the shift from India being the "Back Office of the World" to the "Brain Office of the World." Col AJ🇮🇳 Major Sammer Pal Toorr (Infantry Combat Veteran) Navroop Singh Colonel Mayank Chaubey TheGlobalDecoder #SarvamAI #IndiaAIImpactSummit2026

The Sacred Scroll

22,333 次观看 • 7 个月前

🚨Trump just announced that NASA will be a national intelligence and security agency going forward, not just a civilian space effort. In light of that, we’ve compiled the most mind-bending facts about the American Space Program including its past involvement with Mind-Matter Experiments, UFOs, Classified Science & Pagan and Polytheistic Mythology & Rituals (Full Episode in Reply). This Gets Wild!🚨 We Cover: 1. Buzz Aldrin bringing a Scottish Rite Freemasonry Flag to The Moon; NASA mission patches depicting Egyptian gods & Masonic symbols. Occult rituals performed before launches. 2. The fact that J. Alan Hynek, Operation Bluebook’s Chief Astronomer, running the OFFICIAL Air Force UFO investigation program in the 50’s/60’s, was approached by none other than Neil Armstrong to create a new UFO research outfit (according to his Hynek’s son, Paul, - this was on Chris Ramsay's excellent pod Area52) 3. Jack Parsons, founder of JPL and American Rocketry, invoking Babalon and singing Aleister Crowley’s version of the “Hymn to Pan” while launching rockets. He believed he was in touch with non-human entities helping him get off the ground. He even told his wife he met a “tall white” alien woman in the desert. 4. Wernher von Braun, SS officer turned NASA chief, builds Saturn V but also has a background steeped in Nazi occultism. His space mentor Herman Oberth regularly speculated about UFOs and believed non-human intelligence helped establish the German Space Program. Von Braun goes onto to work closely with Walt Disney. 5. Soviet cosmists believed rocket equations came as transmissions from “ethereal beings”. Tsiolovsky and Fedorov believed in immortality, time travel and colonization of the stars. 6. Astronauts like Edgar Mitchell ran mind-matter experiments in space; experiments which claimed support from Von Braun himself. 7. SkunkWorks directors studied levitation, consciousness, and UFOs behind closed doors. 8. NASA Mission Controllers claiming to get divine downloads, engage in “protocols” to help rockets launch and engage in TIME TRAVEL. The space age was never just about physics. It was a fusion of mysticism, secret orders, gnostic ideas and contact with the unknown woven, into our ascent to the stars. Full Video Below! Enjoy this episode of American Alchemy

🚨Trump just announced that NASA will be a national intelligence and security agency going forward, not just a civilian space effort. In light of that, we’ve compiled the most mind-bending facts about the American Space Program including its past involvement with Mind-Matter Experiments, UFOs, Classified Science & Pagan and Polytheistic Mythology & Rituals (Full Episode in Reply). This Gets Wild!🚨 We Cover: 1. Buzz Aldrin bringing a Scottish Rite Freemasonry Flag to The Moon; NASA mission patches depicting Egyptian gods & Masonic symbols. Occult rituals performed before launches. 2. The fact that J. Alan Hynek, Operation Bluebook’s Chief Astronomer, running the OFFICIAL Air Force UFO investigation program in the 50’s/60’s, was approached by none other than Neil Armstrong to create a new UFO research outfit (according to his Hynek’s son, Paul, - this was on Chris Ramsay's excellent pod Area52) 3. Jack Parsons, founder of JPL and American Rocketry, invoking Babalon and singing Aleister Crowley’s version of the “Hymn to Pan” while launching rockets. He believed he was in touch with non-human entities helping him get off the ground. He even told his wife he met a “tall white” alien woman in the desert. 4. Wernher von Braun, SS officer turned NASA chief, builds Saturn V but also has a background steeped in Nazi occultism. His space mentor Herman Oberth regularly speculated about UFOs and believed non-human intelligence helped establish the German Space Program. Von Braun goes onto to work closely with Walt Disney. 5. Soviet cosmists believed rocket equations came as transmissions from “ethereal beings”. Tsiolovsky and Fedorov believed in immortality, time travel and colonization of the stars. 6. Astronauts like Edgar Mitchell ran mind-matter experiments in space; experiments which claimed support from Von Braun himself. 7. SkunkWorks directors studied levitation, consciousness, and UFOs behind closed doors. 8. NASA Mission Controllers claiming to get divine downloads, engage in “protocols” to help rockets launch and engage in TIME TRAVEL. The space age was never just about physics. It was a fusion of mysticism, secret orders, gnostic ideas and contact with the unknown woven, into our ascent to the stars. Full Video Below! Enjoy this episode of American Alchemy

Jesse Michels

157,157 次观看 • 1 年前

Patch 0.12.3 - "Blades of Ezochi" is now released! Patch 0.12.3 is a minor update patch that includes 3 new classes, some rules updates and a lot of changes to classes. Earthcleaving and Searing Lotus style are two elemental style sword classes, one of which is an anti-tank and the other a tank. Another paladin has also been added with gun paladin. A lot of classes have seen some key abilities redesigned or changed. Some of these such as backlash were too easy to use and lacked counterplay, while other such as defensive flexibility were resourceless defenses that warped the game around having them or not having them. Some other less used abilities have gotten some number buffs to make them less clunky to use. For the basic rules, precise attacks now have pinpoint by default to make them a better option against dodgy targets who often would still have enough guard to negate most of the damage. Block now works against damage in general, giving more of a reason to take it over simply dodging everything and making failed save damage have some counterplay. As always, thank you for your support. Rules Precise attacks now gain Pinpoint. Some abilities do not gain it. Added a Push basic action. Block reaction now works vs damage in general and not just attacks. Updated rules for abilities to state all abilities as general rule require you to have line of sight to it. Races Ryujin Dragon Breath now has the Safe keyword. Classes NEW: Gun Paladin, a ranged support/striker hybrid. Earthcleaving Style, an anti-tank earth melee striker. Searing Lotus Style, a fire style tank Abjurer Mana Shield slightly reworked Adventurer Steel Aura now has the Circuit and Rage keywords. Secret Art now also resets your Circuit keyword when used. Power Jump now has the Circuit keyword. Aeromaster Razor Hurricane now deals Wind damage. Alchemist Stone Grinding now also works on shielding effects. Amanita Spore Burst now deals Poison damage. Assassin Study Target can now apply Death Mark with only 2 AP spent. Battlemage Battle Cast now has the Circuit keyword. Battlemaster Can now be accessed through adventurer. Bloodbinder Blood Drain now deals Dark damage. Now has the Safe keyword. Transfusion and Life sacrifice no longer trigger effects on yourself that would trigger from taking damage. Carpenter Hammer and Nail now has the Rage keyword. Felling Strike mana cost reduced from 2→1. Chronofighter Past Self slightly reworked Attacks from the past reworked Secret Art reworked and renamed. Chronomancer Haste mana cost increased from 1→2 and given the circuit keyword. Colossus Can now be accessed throguh adventurer. Two Sword Crush now has the Rage keyword. Double Slam now gives you the option to make only 1 attack even when wielding two 2 handed weapons. Mighty Leap range increased from 20→40ft. Culinarian Herbal Stock now keeps the bonus for the entire node. Curse Knight Backlash is now Offstep rather than Sure Hit Devil's Blow wording updated to prevent confusion on the full pierce. Cutthroat Can now be entered through Culinarian. Deadeye Disarming Shot mana cost reduced from 3→2. Death Knight Call of the Grave AP cost reduced from 3→2. Dragoon Can be Jiangshi instead of the jumping expertise now. Duelist Deflect Missiles renamed to Deflect Everything, now works vs all attacks. Electromancer Zap no longer gains Sure Hit against targets valid for your superconductor. Forked Lightning now applies Focus x 2 static. All electromancer abilities now scale their static of Focus instead of Power. Electromaster Static Field now scales static of Focus instead of Power. Exalted Blade Divine Storm clarified. Execavator Cave Raider now has the Circuit keyword. Executor Holy Orders now can only be used before you attack and the free movement only occurs if the target moves away from you while in your melee range. Farmer Scarecrow's Gaze now has the Circuit keyword. Fencer Precise Thrust updated to not disable updated dueling weapon. Fighter Slam now lets you spend a mana to ignore the power requirement. Power Strike reworked. Defensive Flexibility reworked. Geomancer Earthquake now states it only hits people on the ground. Gravitect Black Hole now allows for a save. Graviturge Localized Collapse RP spend now fully avoids it. Gunfu Gun Martial Arts now lets your unarmed attacks benefit from your pistol material bonuses. Highlander Aftershock now has the Rage keyword. Can now be accessed through adventurer. Hydromancer Aqua Drill now deals Water damage. Now has the Safe keyword. Crashing Foam Wave now has the Active keyword. Lancer Can now be accessed through Adventurer and Cavalier. Mage Arcane Barrier now has to be in reaction to damage taken and no longer prevents true damage. Mycomancer Poison Spore damage reduced from Power + 5 → Power + 2. Necromancer Soulreap now also instantly kills the enemy on killing blow. Corpse Explosion now deals Dark damage. Pyromancer Incendiary Ray mana cost increased from 1→2. Now applies Burning on damage. Pyromaster Hellfire only applies when you inflict Burning on your own turn. Sage Lengthened Incantation capped to 2 additional AP, now applies to Scattering AoE attacks again. Can now be entered through Mage Knight. Selective Casting now has the Circuit keyword. Scout Can now be accessed through Rogue. Shield Paladin Holy Weapon light emitting part now also works on ranged weapons. Spearmaster Updated Gae Bolg wording. Spellblade Spellstrike wording updated. Sky Talon Style Heavenfall Descent now has the Rage keyword. Keywords Added an Opener keyword for abilities who must be used as the first action in a turn. Added a Circuit keyword for some abilities to give them a one time discount on their mana use. Added a Safe keyword for attacks that do not provoke AoO's Items NEW: New mods for thrown weapons. New mods added for poison materials. New ghimslag armor mod. New mod for shields. Some cloaks now gain a higher benefit if made with a higher tier base material. Bags now gain a benefit from higher materials and have a mod. Carts now gain a benefit from higher materials. Shields no longer gain bonuses to evasion or guard from material, only to block/dodge. (Does not affect existing ones) Elemental Weapon Alloy crafting point costs reduced from 15→10. Dueling weapon ability no longer works if you make a Heavy attack with it using AP. Added a mod that regains the ability for 1 heavy. Scythes Reworked Long Haft. Added a Focused Spin B mod. Armor Anti-poison silver mod removed. Chain Links reduced from 8→4. Flexible armor values increased. Gourmet Cooking Stock old wording updated. Greenhouse labor cost fixed to be consistent with other values. Perfect Balance mod now also works with precise attacks.

Patch 0.12.3 - "Blades of Ezochi" is now released! Patch 0.12.3 is a minor update patch that includes 3 new classes, some rules updates and a lot of changes to classes. Earthcleaving and Searing Lotus style are two elemental style sword classes, one of which is an anti-tank and the other a tank. Another paladin has also been added with gun paladin. A lot of classes have seen some key abilities redesigned or changed. Some of these such as backlash were too easy to use and lacked counterplay, while other such as defensive flexibility were resourceless defenses that warped the game around having them or not having them. Some other less used abilities have gotten some number buffs to make them less clunky to use. For the basic rules, precise attacks now have pinpoint by default to make them a better option against dodgy targets who often would still have enough guard to negate most of the damage. Block now works against damage in general, giving more of a reason to take it over simply dodging everything and making failed save damage have some counterplay. As always, thank you for your support. Rules Precise attacks now gain Pinpoint. Some abilities do not gain it. Added a Push basic action. Block reaction now works vs damage in general and not just attacks. Updated rules for abilities to state all abilities as general rule require you to have line of sight to it. Races Ryujin Dragon Breath now has the Safe keyword. Classes NEW: Gun Paladin, a ranged support/striker hybrid. Earthcleaving Style, an anti-tank earth melee striker. Searing Lotus Style, a fire style tank Abjurer Mana Shield slightly reworked Adventurer Steel Aura now has the Circuit and Rage keywords. Secret Art now also resets your Circuit keyword when used. Power Jump now has the Circuit keyword. Aeromaster Razor Hurricane now deals Wind damage. Alchemist Stone Grinding now also works on shielding effects. Amanita Spore Burst now deals Poison damage. Assassin Study Target can now apply Death Mark with only 2 AP spent. Battlemage Battle Cast now has the Circuit keyword. Battlemaster Can now be accessed through adventurer. Bloodbinder Blood Drain now deals Dark damage. Now has the Safe keyword. Transfusion and Life sacrifice no longer trigger effects on yourself that would trigger from taking damage. Carpenter Hammer and Nail now has the Rage keyword. Felling Strike mana cost reduced from 2→1. Chronofighter Past Self slightly reworked Attacks from the past reworked Secret Art reworked and renamed. Chronomancer Haste mana cost increased from 1→2 and given the circuit keyword. Colossus Can now be accessed throguh adventurer. Two Sword Crush now has the Rage keyword. Double Slam now gives you the option to make only 1 attack even when wielding two 2 handed weapons. Mighty Leap range increased from 20→40ft. Culinarian Herbal Stock now keeps the bonus for the entire node. Curse Knight Backlash is now Offstep rather than Sure Hit Devil's Blow wording updated to prevent confusion on the full pierce. Cutthroat Can now be entered through Culinarian. Deadeye Disarming Shot mana cost reduced from 3→2. Death Knight Call of the Grave AP cost reduced from 3→2. Dragoon Can be Jiangshi instead of the jumping expertise now. Duelist Deflect Missiles renamed to Deflect Everything, now works vs all attacks. Electromancer Zap no longer gains Sure Hit against targets valid for your superconductor. Forked Lightning now applies Focus x 2 static. All electromancer abilities now scale their static of Focus instead of Power. Electromaster Static Field now scales static of Focus instead of Power. Exalted Blade Divine Storm clarified. Execavator Cave Raider now has the Circuit keyword. Executor Holy Orders now can only be used before you attack and the free movement only occurs if the target moves away from you while in your melee range. Farmer Scarecrow's Gaze now has the Circuit keyword. Fencer Precise Thrust updated to not disable updated dueling weapon. Fighter Slam now lets you spend a mana to ignore the power requirement. Power Strike reworked. Defensive Flexibility reworked. Geomancer Earthquake now states it only hits people on the ground. Gravitect Black Hole now allows for a save. Graviturge Localized Collapse RP spend now fully avoids it. Gunfu Gun Martial Arts now lets your unarmed attacks benefit from your pistol material bonuses. Highlander Aftershock now has the Rage keyword. Can now be accessed through adventurer. Hydromancer Aqua Drill now deals Water damage. Now has the Safe keyword. Crashing Foam Wave now has the Active keyword. Lancer Can now be accessed through Adventurer and Cavalier. Mage Arcane Barrier now has to be in reaction to damage taken and no longer prevents true damage. Mycomancer Poison Spore damage reduced from Power + 5 → Power + 2. Necromancer Soulreap now also instantly kills the enemy on killing blow. Corpse Explosion now deals Dark damage. Pyromancer Incendiary Ray mana cost increased from 1→2. Now applies Burning on damage. Pyromaster Hellfire only applies when you inflict Burning on your own turn. Sage Lengthened Incantation capped to 2 additional AP, now applies to Scattering AoE attacks again. Can now be entered through Mage Knight. Selective Casting now has the Circuit keyword. Scout Can now be accessed through Rogue. Shield Paladin Holy Weapon light emitting part now also works on ranged weapons. Spearmaster Updated Gae Bolg wording. Spellblade Spellstrike wording updated. Sky Talon Style Heavenfall Descent now has the Rage keyword. Keywords Added an Opener keyword for abilities who must be used as the first action in a turn. Added a Circuit keyword for some abilities to give them a one time discount on their mana use. Added a Safe keyword for attacks that do not provoke AoO's Items NEW: New mods for thrown weapons. New mods added for poison materials. New ghimslag armor mod. New mod for shields. Some cloaks now gain a higher benefit if made with a higher tier base material. Bags now gain a benefit from higher materials and have a mod. Carts now gain a benefit from higher materials. Shields no longer gain bonuses to evasion or guard from material, only to block/dodge. (Does not affect existing ones) Elemental Weapon Alloy crafting point costs reduced from 15→10. Dueling weapon ability no longer works if you make a Heavy attack with it using AP. Added a mod that regains the ability for 1 heavy. Scythes Reworked Long Haft. Added a Focused Spin B mod. Armor Anti-poison silver mod removed. Chain Links reduced from 8→4. Flexible armor values increased. Gourmet Cooking Stock old wording updated. Greenhouse labor cost fixed to be consistent with other values. Perfect Balance mod now also works with precise attacks.

Angel's Sword RPG Official

16,548 次观看 • 6 个月前

$ASTI Ascent Solar Technologies Space and Drone Solar Panels The "Going to Zero" or Mispriced Space/Drone Solar Play Intro and comparison to $RKLB and $RDW panels Let’s get the ugly stuff out of the way first. $ASTI is a distressed penny stock with a ~$5M-$10M market cap. • They burn millions in cash. • 2024 Revenue: ~$40k. 2025 Revenue (YTD): ~$60k. • They generate less revenue than a single Tesla Model Y. • They have diluted shareholders relentlessly. $ASTI just raised $2M in December with the potential of $3.5M more via warrants while being a ~$5M mcap "company". Yikes. To most, this is "uninvestable trash." Stay away. Full stop. So why did I buy ~5% of the float? IF the technology works and IF they execute then I believe this is a massive market pricing dislocation about to inflect. They have been grinding for years and may finally be hitting an inflection point. $RKLB Rocketlab is the king of space solar and they are my second largest position overall, but here is why $ASTI might be a very high risk but asymmetric bet in Space & Defense right now. 1. The Tech Pivot: Flexible CIGS vs. The World Ascent started in 2005 but pivoted 2 years ago from consumer to pure-play Space & Defense. They have sunk ~$250M and 20 years of R&D into proprietary CIGS (Copper-Indium-Gallium-Selenide) thin-film technology while building out fully domestic and vertically integrated manufacturing capabilities. The Physics: • Thickness: 0.03 mm (Thinner than paper). • Flexibility: Wraps around drones/satellites; rolls up like a poster. • Durability: "Self-Healing" capabilities against space radiation. Can take a bullet or micrometeoroid and keep working. Can handle shocks/vibration. Does not shatter. The Metric that Matters: Specific Power (W/kg) (aka energy to weight ratio) In space, mass means cost and difficult decision decisions. • Rocket Lab ($RKLB) / Spectrolab: ~150 W/kg (System level). • Ascent Solar ($ASTI): ~1,960 W/kg (Module level). $ASTI is roughly 10x lighter for the same power output potential (mass-wise). This frees up design limitations and cost. 2. The Competition: $RKLB & $RDW Rocket Lab (SolAero) & Redwire (iROSA): • Tech: Rigid Crystal Cells (Multi-junction) embedded in a fabric mesh. • Pros: Extreme Efficiency (~30%+). Perfect for limited surface area. • Cons: Heavy, Brittle, Expensive ($3k-$10k per Watt). Manufacturing multi-junction cells (SolAero) involves slowly growing crystals in a vacuum chamber. With radiation the panels degrade and loose efficiency over time which will limit the satellite lifespan. • Use Case: James Webb Telescope, Flagship missions. Ascent Solar (ASTI): • Tech: Flexible Thin-Film on Plastic. • Pros: Ultra-light, Durable, Cheap ($500-$1k per Watt). Manufacturing CIGS is roughly similar to printing newspapers (roll-to-roll). The panels are radiation degradation resistant and will outlive the satellite • Cons: Lower Efficiency (~17.5%). Requires 2x surface area. • Use Case: Mega-Constellations (Starlink/Amazon Leo), Small/Low cost satellites, Drones, Deformable surfaces. The lower efficiency is not an ASTI failing. It is the inherent physics trade-off of not using glass/rigid silicone. The downside however is increased atmospheric drag with very larger/massive panel sheets. Because ASTI modules are ~50% less efficient than rigid panels, they require ~2x the physical surface area to generate the same amount of power. In GEO (High Orbit): Drag doesn't matter. Weight savings are king. A massive solar array allows for more sensors and longer project lifespan. ASTI is highly competitive here. In LEO (Low Orbit): Atmospheric drag is real. A massive solar array acts like a large parachute, causing the satellite to de-orbit faster unless it burns more fuel to stay up. At LEO, smaller satellites are a better fit for ASTI. 3. Durability & Radiation "Self-Healing" Radiation Hardness This is ASTI's "Ace in the Hole" for physics. The Problem: In space, high-energy protons (radiation) smash into solar cells, creating atomic "defects" that trap electrons. Over time, this kills the panel's power output (degradation). The CIGS Advantage: CIGS (Copper-Indium-Gallium-Selenide) material has a unique property where heat (annealing) allows the atomic structure to relax and "heal" these defects. Self-Healing: Because CIGS heals at relatively low temperatures (often achieved just by the sun heating the panel), it suffers significantly less degradation than traditional Silicon or even some GaAs panels over long missions in high-radiation belts (like MEO or GEO). Lifespan: While a rigid GaAs panel might lose 15-20% of its power over 15 years (enough to kill a satellite), CIGS panels heal and can maintain a flatter power curve, potentially outlasting the satellite itself in high-radiation orbits. 4. Brittleness & Flexibility ASTI (CIGS on Polyimide): Flexible. You can roll it like a poster. It can take a bullet or micrometeoroid and the hole will just be a dead spot; the rest of the panel keeps working. It does not shatter. Redwire (ROSA) & Rocket Lab (SolAero): Brittle Cells on a Flex Blanket. $RDW's ROSA (Roll-Out Solar Array) typically uses rigid multi-junction cells (made by SolAero/Rocket Lab or Spectrolab) mounted on a flexible mesh fabric. The Risk: If you bend the cells too far, they crack. They rely on the mesh backing for flexibility, but the active generating material is still a brittle crystal wafer. Much heavier, more expensive, and less durable than $ASTI's option 5. The Inflection Point (Why Now?) After years of silent struggle, late 2025 has seen an explosion of activity. Recent Agreements (Nov/Dec 2025): NovaSpark: Hydrogen-powered military drones. $ASTI panels generate power in the field → NovaSpark creates hydrogen fuel. CisLunar Industries: Integrating ASTI solar with power conversion hardware for deep space longevity. Defiant Space: A strategic alliance to act as the "door opener" for classified DoD/NATO programs. More headlines: Ascent Solar Technologies Provides Leading Space Company with Thin-Film PV modules for Spacecraft Power Generation Testing in Cislunar Space December 03, 2025 08:00 ET Ascent Solar Technologies Delivers Thin-Film PV for Saltwater Environment Durability and Space-Based Power Beaming Testing October 14, 2025 08:00 ET Ascent Solar Enters Teaming Agreement with Emtel Energy USA to Advance Thin-Film PV Energy Storage Capabilities September 16, 2025 08:00 ET Ascent Solar Technologies Signs MOU with Star Catcher Industries to Improve Power Capabilities for Thin-Film Solar Technology in Space August 28, 2025 08:00 ET Ascent Solar Technologies Establishes Rapid Thin-Film PV Delivery Process to Provide Customized Space Solar Products Ahead of Schedule on Mission Enabling Timelines August 07, 2025 08:00 ET The Pipeline (From Aug Corporate Presentation) 18 new NDA's signed in 2025. They are field testing with 3 major players: • Company A: Mega-constellation (+2,500 satellites). • Company B: Space Defense (Explicitly mentioned "Golden Dome"). • Company C: Satellite Manufacturer (30-200 unit scale). Management: New board members include a former founding member of SpaceX and a retired Air Force General and Deputy Assistant Secretary for Contracting (acquisitions expert). The company started in 2005 based out of Colorado, but two years ago pivoted to Space & Defense and away from consumer applications. Made in USA: Defense contracts heavily favor domestic supply chains. ASTI manufactures in Colorado. This is a huge moat against cheap Chinese solar. In their Q3 report they note that their market has seen sudden recent acceleration. The space solar industry is currently only capable of 8 to 12 MW per year of production meanwhile the demand is growing to over 100 MW per year. 6. The Risk (The Sword of Damocles) ⚠️ This is critical. $ASTI just raised ~$2M in December. Attached to that raise are ~2 Million Warrants with a strike price of $1.70. These are exercisable immediately. If the stock rips to $3.00, warrant holders exercise at $1.70 and dump on the market for a risk-free 76% profit. This creates a massive "sell wall" and potential 40% dilution of the float. Summary: This is a binary bet. • Bear Case: They run out of cash in 6 months, dilution spirals, stock goes to $0. • Bull Case: They land one of the "Company A/B/C" contracts. Revenue jumps from $60k to projected $20M+ in 2026. The stock reprices from a "bankrupt penny stock" to a "critical defense/space supplier." I have gradually accumulated ~5% of the float. I am ready for it to go to zero. But if the space economy demands "Cheap, Light, and Durable," $ASTI is the only public pure-play. Disclaimer: This is a very high-risk microcap. Do your own due diligence. Not financial advice.

$ASTI Ascent Solar Technologies Space and Drone Solar Panels The "Going to Zero" or Mispriced Space/Drone Solar Play Intro and comparison to $RKLB and $RDW panels Let’s get the ugly stuff out of the way first. $ASTI is a distressed penny stock with a ~$5M-$10M market cap. • They burn millions in cash. • 2024 Revenue: ~$40k. 2025 Revenue (YTD): ~$60k. • They generate less revenue than a single Tesla Model Y. • They have diluted shareholders relentlessly. $ASTI just raised $2M in December with the potential of $3.5M more via warrants while being a ~$5M mcap "company". Yikes. To most, this is "uninvestable trash." Stay away. Full stop. So why did I buy ~5% of the float? IF the technology works and IF they execute then I believe this is a massive market pricing dislocation about to inflect. They have been grinding for years and may finally be hitting an inflection point. $RKLB Rocketlab is the king of space solar and they are my second largest position overall, but here is why $ASTI might be a very high risk but asymmetric bet in Space & Defense right now. 1. The Tech Pivot: Flexible CIGS vs. The World Ascent started in 2005 but pivoted 2 years ago from consumer to pure-play Space & Defense. They have sunk ~$250M and 20 years of R&D into proprietary CIGS (Copper-Indium-Gallium-Selenide) thin-film technology while building out fully domestic and vertically integrated manufacturing capabilities. The Physics: • Thickness: 0.03 mm (Thinner than paper). • Flexibility: Wraps around drones/satellites; rolls up like a poster. • Durability: "Self-Healing" capabilities against space radiation. Can take a bullet or micrometeoroid and keep working. Can handle shocks/vibration. Does not shatter. The Metric that Matters: Specific Power (W/kg) (aka energy to weight ratio) In space, mass means cost and difficult decision decisions. • Rocket Lab ($RKLB) / Spectrolab: ~150 W/kg (System level). • Ascent Solar ($ASTI): ~1,960 W/kg (Module level). $ASTI is roughly 10x lighter for the same power output potential (mass-wise). This frees up design limitations and cost. 2. The Competition: $RKLB & $RDW Rocket Lab (SolAero) & Redwire (iROSA): • Tech: Rigid Crystal Cells (Multi-junction) embedded in a fabric mesh. • Pros: Extreme Efficiency (~30%+). Perfect for limited surface area. • Cons: Heavy, Brittle, Expensive ($3k-$10k per Watt). Manufacturing multi-junction cells (SolAero) involves slowly growing crystals in a vacuum chamber. With radiation the panels degrade and loose efficiency over time which will limit the satellite lifespan. • Use Case: James Webb Telescope, Flagship missions. Ascent Solar (ASTI): • Tech: Flexible Thin-Film on Plastic. • Pros: Ultra-light, Durable, Cheap ($500-$1k per Watt). Manufacturing CIGS is roughly similar to printing newspapers (roll-to-roll). The panels are radiation degradation resistant and will outlive the satellite • Cons: Lower Efficiency (~17.5%). Requires 2x surface area. • Use Case: Mega-Constellations (Starlink/Amazon Leo), Small/Low cost satellites, Drones, Deformable surfaces. The lower efficiency is not an ASTI failing. It is the inherent physics trade-off of not using glass/rigid silicone. The downside however is increased atmospheric drag with very larger/massive panel sheets. Because ASTI modules are ~50% less efficient than rigid panels, they require ~2x the physical surface area to generate the same amount of power. In GEO (High Orbit): Drag doesn't matter. Weight savings are king. A massive solar array allows for more sensors and longer project lifespan. ASTI is highly competitive here. In LEO (Low Orbit): Atmospheric drag is real. A massive solar array acts like a large parachute, causing the satellite to de-orbit faster unless it burns more fuel to stay up. At LEO, smaller satellites are a better fit for ASTI. 3. Durability & Radiation "Self-Healing" Radiation Hardness This is ASTI's "Ace in the Hole" for physics. The Problem: In space, high-energy protons (radiation) smash into solar cells, creating atomic "defects" that trap electrons. Over time, this kills the panel's power output (degradation). The CIGS Advantage: CIGS (Copper-Indium-Gallium-Selenide) material has a unique property where heat (annealing) allows the atomic structure to relax and "heal" these defects. Self-Healing: Because CIGS heals at relatively low temperatures (often achieved just by the sun heating the panel), it suffers significantly less degradation than traditional Silicon or even some GaAs panels over long missions in high-radiation belts (like MEO or GEO). Lifespan: While a rigid GaAs panel might lose 15-20% of its power over 15 years (enough to kill a satellite), CIGS panels heal and can maintain a flatter power curve, potentially outlasting the satellite itself in high-radiation orbits. 4. Brittleness & Flexibility ASTI (CIGS on Polyimide): Flexible. You can roll it like a poster. It can take a bullet or micrometeoroid and the hole will just be a dead spot; the rest of the panel keeps working. It does not shatter. Redwire (ROSA) & Rocket Lab (SolAero): Brittle Cells on a Flex Blanket. $RDW's ROSA (Roll-Out Solar Array) typically uses rigid multi-junction cells (made by SolAero/Rocket Lab or Spectrolab) mounted on a flexible mesh fabric. The Risk: If you bend the cells too far, they crack. They rely on the mesh backing for flexibility, but the active generating material is still a brittle crystal wafer. Much heavier, more expensive, and less durable than $ASTI's option 5. The Inflection Point (Why Now?) After years of silent struggle, late 2025 has seen an explosion of activity. Recent Agreements (Nov/Dec 2025): NovaSpark: Hydrogen-powered military drones. $ASTI panels generate power in the field → NovaSpark creates hydrogen fuel. CisLunar Industries: Integrating ASTI solar with power conversion hardware for deep space longevity. Defiant Space: A strategic alliance to act as the "door opener" for classified DoD/NATO programs. More headlines: Ascent Solar Technologies Provides Leading Space Company with Thin-Film PV modules for Spacecraft Power Generation Testing in Cislunar Space December 03, 2025 08:00 ET Ascent Solar Technologies Delivers Thin-Film PV for Saltwater Environment Durability and Space-Based Power Beaming Testing October 14, 2025 08:00 ET Ascent Solar Enters Teaming Agreement with Emtel Energy USA to Advance Thin-Film PV Energy Storage Capabilities September 16, 2025 08:00 ET Ascent Solar Technologies Signs MOU with Star Catcher Industries to Improve Power Capabilities for Thin-Film Solar Technology in Space August 28, 2025 08:00 ET Ascent Solar Technologies Establishes Rapid Thin-Film PV Delivery Process to Provide Customized Space Solar Products Ahead of Schedule on Mission Enabling Timelines August 07, 2025 08:00 ET The Pipeline (From Aug Corporate Presentation) 18 new NDA's signed in 2025. They are field testing with 3 major players: • Company A: Mega-constellation (+2,500 satellites). • Company B: Space Defense (Explicitly mentioned "Golden Dome"). • Company C: Satellite Manufacturer (30-200 unit scale). Management: New board members include a former founding member of SpaceX and a retired Air Force General and Deputy Assistant Secretary for Contracting (acquisitions expert). The company started in 2005 based out of Colorado, but two years ago pivoted to Space & Defense and away from consumer applications. Made in USA: Defense contracts heavily favor domestic supply chains. ASTI manufactures in Colorado. This is a huge moat against cheap Chinese solar. In their Q3 report they note that their market has seen sudden recent acceleration. The space solar industry is currently only capable of 8 to 12 MW per year of production meanwhile the demand is growing to over 100 MW per year. 6. The Risk (The Sword of Damocles) ⚠️ This is critical. $ASTI just raised ~$2M in December. Attached to that raise are ~2 Million Warrants with a strike price of $1.70. These are exercisable immediately. If the stock rips to $3.00, warrant holders exercise at $1.70 and dump on the market for a risk-free 76% profit. This creates a massive "sell wall" and potential 40% dilution of the float. Summary: This is a binary bet. • Bear Case: They run out of cash in 6 months, dilution spirals, stock goes to $0. • Bull Case: They land one of the "Company A/B/C" contracts. Revenue jumps from $60k to projected $20M+ in 2026. The stock reprices from a "bankrupt penny stock" to a "critical defense/space supplier." I have gradually accumulated ~5% of the float. I am ready for it to go to zero. But if the space economy demands "Cheap, Light, and Durable," $ASTI is the only public pure-play. Disclaimer: This is a very high-risk microcap. Do your own due diligence. Not financial advice.

YeahDave

208,571 次观看 • 9 个月前

$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).

$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).

TheValueist

76,267 次观看 • 9 个月前

Quick chat with dylan ツ (Dylan Bristot, GTM @ $NBIS). Also on YouTube (link in first comment) for those who prefer to watch/listen there. Timestamps 00:00 – Dylan's role at Nebius and Nebius Token Factory 01:48 – Dylan's investing philosophy and portfolio approach 05:07 – How working in AI infrastructure influences his investing 08:29 – Training vs. inference and why inference demand could explode 13:22 – Enterprise AI adoption: from POCs to production 18:02 – Open-source vs. closed/frontier models 24:44 – The economics of open vs. closed AI models 29:27 – Where the next AI infrastructure bottlenecks could emerge 31:22 – Dylan's AI Bottlenecks project and approach to stock selection 34:06 – Closing thoughts Key Insights (AI Summary, so you don't have to copy paste and prompt for exactly that ;D) “I seem to like areas where the demand really looks kind of secular, but the supply is genuinely hard to create.” → Implication: The most attractive AI trades may sit in physical bottlenecks where supply cannot quickly respond to demand. “The bottleneck is who has the pricing power and kind of what might get commoditized and where the concentrate might move next.” → Implication: Value capture across the AI stack will keep shifting as individual layers become scarce or commoditized. “Training creates the intelligence and then the inference actually monetizes and distributes.” → Implication: Training and inference are complementary, rather than one ultimately replacing the other. “One user action can become dozens or hundreds of model calls, tools calls, and like verification steps, retries.” → Implication: Agentic AI can drive token consumption far faster than user growth alone would suggest. “The best infra for making any model and the best infra for serving a billion interactions are not necessarily the same.” → Implication: Training and inference could increasingly require different hardware and infrastructure architectures. “The Frontier Labs might be incentivized to run more and more of the inference of these models for internal research instead of providing it to external people.” → Implication: The most capable models and their compute could increasingly be used internally to accelerate frontier research rather than monetized externally. “Enterprise AI adoption is actually much further along than a lot of people kind of think. But probably less mature than the headlines suggest.” → Implication: Enterprise demand is real, but deployment maturity still has significant room to improve. “The POC problem might be solved for a lot of companies, but the production problem isn’t yet.” → Implication: The enterprise bottleneck is shifting from proving AI works to deploying it reliably, securely and economically at scale. “They feel like it’s time for them to actually not only integrate AI, but build some sort of moat out of the AI.” → Implication: Enterprises increasingly want proprietary AI systems built around their own data rather than simply consuming generic models. “The more autonomous the software becomes, the more infra discipline you need underneath it.” → Implication: Agents increase the importance of inference cost, reliability and infrastructure optimization. “Maybe I have fifteen different versions of very different LLMs, fine tuned on fifteen different kinds of tasks that I’m operating across my business, instead of having a one model fits all.” → Implication: Enterprise AI could evolve toward many specialized models rather than one frontier model handling every workload. “I don’t necessarily think it’s open versus closed. That might be the wrong framing.” → Implication: Open and closed models can coexist because they optimize for different customer needs. “Historically the problem was that that control came with a massive operational tax.” → Implication: Better inference infrastructure can make open models materially more competitive by removing the complexity traditionally associated with running them. “I don’t think open needs to beat the best closed model on every single benchmark. It just basically needs to be good enough for the workload of the given customer while offering a much better combination of control, cost, and deployment flexibility.” → Implication: For production AI, workload-specific economics may matter more than having the absolute smartest model. “Maybe actually the bulk of tokens generated in the future might come from open models.” → Implication: Frontier intelligence could remain dominated by closed labs even while open models capture most production inference volume. “I could really imagine frontier intelligence being really concentrated while most of the production inference becomes super fragmented.” → Implication: AI could consolidate at the intelligence layer while fragmenting heavily at the inference layer across models, GPUs, providers and regions. “I don’t think that necessarily means the margins of open source will be much worse than the ones of closed source.” → Implication: Optimization can potentially make open-model inference highly profitable despite lower pricing. “I think now we’re probably in the middle of phase two... everything feeding the accelerator.” → Implication: The AI trade is broadening beyond GPUs toward networking, packaging, data centers, electrical equipment and power. “It’s no longer about the megawatts, about energized megawatts.” → Implication: Available power on paper matters less than how quickly that power can actually be delivered to operating AI infrastructure. “It’s increasingly about utilisation and conversion now and like how efficiently you convert expensive infra into actual useful AI work.” → Implication: Infrastructure efficiency and utilization become increasingly important as the absolute amount of deployed AI infrastructure grows. “The market tends to really notice demand before it notices what demand breaks.” → Implication: Second-order bottlenecks may offer some of the most interesting opportunities in the next phase of the AI buildout. “The interesting question now is which part of the mine breaks next?” → Implication: Finding the next constraint in the AI supply chain may matter more than simply identifying continued AI demand.

Quick chat with dylan ツ (Dylan Bristot, GTM @ $NBIS). Also on YouTube (link in first comment) for those who prefer to watch/listen there. Timestamps 00:00 – Dylan's role at Nebius and Nebius Token Factory 01:48 – Dylan's investing philosophy and portfolio approach 05:07 – How working in AI infrastructure influences his investing 08:29 – Training vs. inference and why inference demand could explode 13:22 – Enterprise AI adoption: from POCs to production 18:02 – Open-source vs. closed/frontier models 24:44 – The economics of open vs. closed AI models 29:27 – Where the next AI infrastructure bottlenecks could emerge 31:22 – Dylan's AI Bottlenecks project and approach to stock selection 34:06 – Closing thoughts Key Insights (AI Summary, so you don't have to copy paste and prompt for exactly that ;D) “I seem to like areas where the demand really looks kind of secular, but the supply is genuinely hard to create.” → Implication: The most attractive AI trades may sit in physical bottlenecks where supply cannot quickly respond to demand. “The bottleneck is who has the pricing power and kind of what might get commoditized and where the concentrate might move next.” → Implication: Value capture across the AI stack will keep shifting as individual layers become scarce or commoditized. “Training creates the intelligence and then the inference actually monetizes and distributes.” → Implication: Training and inference are complementary, rather than one ultimately replacing the other. “One user action can become dozens or hundreds of model calls, tools calls, and like verification steps, retries.” → Implication: Agentic AI can drive token consumption far faster than user growth alone would suggest. “The best infra for making any model and the best infra for serving a billion interactions are not necessarily the same.” → Implication: Training and inference could increasingly require different hardware and infrastructure architectures. “The Frontier Labs might be incentivized to run more and more of the inference of these models for internal research instead of providing it to external people.” → Implication: The most capable models and their compute could increasingly be used internally to accelerate frontier research rather than monetized externally. “Enterprise AI adoption is actually much further along than a lot of people kind of think. But probably less mature than the headlines suggest.” → Implication: Enterprise demand is real, but deployment maturity still has significant room to improve. “The POC problem might be solved for a lot of companies, but the production problem isn’t yet.” → Implication: The enterprise bottleneck is shifting from proving AI works to deploying it reliably, securely and economically at scale. “They feel like it’s time for them to actually not only integrate AI, but build some sort of moat out of the AI.” → Implication: Enterprises increasingly want proprietary AI systems built around their own data rather than simply consuming generic models. “The more autonomous the software becomes, the more infra discipline you need underneath it.” → Implication: Agents increase the importance of inference cost, reliability and infrastructure optimization. “Maybe I have fifteen different versions of very different LLMs, fine tuned on fifteen different kinds of tasks that I’m operating across my business, instead of having a one model fits all.” → Implication: Enterprise AI could evolve toward many specialized models rather than one frontier model handling every workload. “I don’t necessarily think it’s open versus closed. That might be the wrong framing.” → Implication: Open and closed models can coexist because they optimize for different customer needs. “Historically the problem was that that control came with a massive operational tax.” → Implication: Better inference infrastructure can make open models materially more competitive by removing the complexity traditionally associated with running them. “I don’t think open needs to beat the best closed model on every single benchmark. It just basically needs to be good enough for the workload of the given customer while offering a much better combination of control, cost, and deployment flexibility.” → Implication: For production AI, workload-specific economics may matter more than having the absolute smartest model. “Maybe actually the bulk of tokens generated in the future might come from open models.” → Implication: Frontier intelligence could remain dominated by closed labs even while open models capture most production inference volume. “I could really imagine frontier intelligence being really concentrated while most of the production inference becomes super fragmented.” → Implication: AI could consolidate at the intelligence layer while fragmenting heavily at the inference layer across models, GPUs, providers and regions. “I don’t think that necessarily means the margins of open source will be much worse than the ones of closed source.” → Implication: Optimization can potentially make open-model inference highly profitable despite lower pricing. “I think now we’re probably in the middle of phase two... everything feeding the accelerator.” → Implication: The AI trade is broadening beyond GPUs toward networking, packaging, data centers, electrical equipment and power. “It’s no longer about the megawatts, about energized megawatts.” → Implication: Available power on paper matters less than how quickly that power can actually be delivered to operating AI infrastructure. “It’s increasingly about utilisation and conversion now and like how efficiently you convert expensive infra into actual useful AI work.” → Implication: Infrastructure efficiency and utilization become increasingly important as the absolute amount of deployed AI infrastructure grows. “The market tends to really notice demand before it notices what demand breaks.” → Implication: Second-order bottlenecks may offer some of the most interesting opportunities in the next phase of the AI buildout. “The interesting question now is which part of the mine breaks next?” → Implication: Finding the next constraint in the AI supply chain may matter more than simply identifying continued AI demand.

Daniel Koss

49,970 次观看 • 21 天前

Elon Musk just revealed why Tesla almost died before it started. The best suppliers in the world refused to take them seriously. Musk: “The top suppliers would not work with us, or we would get like their D team.” Not the B team. The D team. The cold logic of a supplier is simple. You assign your best engineers to Toyota and Ford. Guaranteed volume. Guaranteed payment. Companies that aren’t going bankrupt. Musk: “Which team are you going to assign to the startup that everyone says is going bankrupt? You’re going to assign the interns and rejects.” So Tesla tried outsourcing battery production to a company in Thailand that made barbecue grills. Their cells came from Japan, went to Thailand, then to England. Musk: “The supply chain was so long that you’d only find out that it didn’t work 5 months later.” Five months of dead capital. Five months of scrap inventory. Five months of building something broken before anyone knew it was broken. Musk: “There’s a massive amount of work going from prototype to production.” When you’re inventing something that has never existed, you need a fast feedback loop between the factory floor and the engineers. Find the problem today. Fix it tomorrow. Test it next week. A factory across the world destroys that loop entirely. Musk: “You cannot create a production line that never existed that’s super far away from where the engineers are.” Tesla moved everything back to headquarters. Not for better margins. Not for control. For survival. Musk: “It was vertically integrate or die.” Most founders think vertical integration is a strategic choice. Something you do when you’re big enough to afford it. Musk learned it’s the opposite. You vertically integrate precisely because you can’t afford not to. When nobody believes in you, the market will crush you with other people’s incompetence. He never forgot that lesson. He applied it to everything. SpaceX builds its own engines, rockets, and launch infrastructure. Tesla builds its own chips, batteries, and factories. xAI built its own supercomputer rather than waiting for someone else’s capacity. Starlink owns the satellites, the ground stations, and the terminals. Every company. Every layer. Owned end to end. Most people see a collection of separate ventures. What Musk built is a single vertically integrated stack running from raw materials to orbital infrastructure to artificial intelligence. That stack is now the most strategically significant private asset on earth. The geopolitical AI race isn’t just about who has the best model. It’s about who controls the full infrastructure underneath it. The chips. The power. The rockets. The satellites. The data. Countries without that infrastructure will run their AI on someone else’s hardware. Through someone else’s satellites. On someone else’s terms. The D team lesson from a battery factory in Thailand became a blueprint for technological sovereignty. The market won’t wait five months for you to discover your supply chain doesn’t work. The D team doesn’t care if your company lives or dies. Your engineers do. And right now, Musk’s engineers own the entire stack.

Elon Musk just revealed why Tesla almost died before it started. The best suppliers in the world refused to take them seriously. Musk: “The top suppliers would not work with us, or we would get like their D team.” Not the B team. The D team. The cold logic of a supplier is simple. You assign your best engineers to Toyota and Ford. Guaranteed volume. Guaranteed payment. Companies that aren’t going bankrupt. Musk: “Which team are you going to assign to the startup that everyone says is going bankrupt? You’re going to assign the interns and rejects.” So Tesla tried outsourcing battery production to a company in Thailand that made barbecue grills. Their cells came from Japan, went to Thailand, then to England. Musk: “The supply chain was so long that you’d only find out that it didn’t work 5 months later.” Five months of dead capital. Five months of scrap inventory. Five months of building something broken before anyone knew it was broken. Musk: “There’s a massive amount of work going from prototype to production.” When you’re inventing something that has never existed, you need a fast feedback loop between the factory floor and the engineers. Find the problem today. Fix it tomorrow. Test it next week. A factory across the world destroys that loop entirely. Musk: “You cannot create a production line that never existed that’s super far away from where the engineers are.” Tesla moved everything back to headquarters. Not for better margins. Not for control. For survival. Musk: “It was vertically integrate or die.” Most founders think vertical integration is a strategic choice. Something you do when you’re big enough to afford it. Musk learned it’s the opposite. You vertically integrate precisely because you can’t afford not to. When nobody believes in you, the market will crush you with other people’s incompetence. He never forgot that lesson. He applied it to everything. SpaceX builds its own engines, rockets, and launch infrastructure. Tesla builds its own chips, batteries, and factories. xAI built its own supercomputer rather than waiting for someone else’s capacity. Starlink owns the satellites, the ground stations, and the terminals. Every company. Every layer. Owned end to end. Most people see a collection of separate ventures. What Musk built is a single vertically integrated stack running from raw materials to orbital infrastructure to artificial intelligence. That stack is now the most strategically significant private asset on earth. The geopolitical AI race isn’t just about who has the best model. It’s about who controls the full infrastructure underneath it. The chips. The power. The rockets. The satellites. The data. Countries without that infrastructure will run their AI on someone else’s hardware. Through someone else’s satellites. On someone else’s terms. The D team lesson from a battery factory in Thailand became a blueprint for technological sovereignty. The market won’t wait five months for you to discover your supply chain doesn’t work. The D team doesn’t care if your company lives or dies. Your engineers do. And right now, Musk’s engineers own the entire stack.

Dustin

103,021 次观看 • 6 个月前

In a newly released technical update, SpaceX's leadership team, which includes communications manager Dan Huot, Director of Satellite Engineering Ian Dahl, and CEO Elon Musk, detailed a highly ambitious infrastructure roadmap to design, manufacture, and operate specialized artificial intelligence computing satellites at scale. Positioned as a major strategic pillar to dramatically elevate civilizational energy and processing capacity on the Kardashev scale, this strategy moves past traditional communications architectures into massive orbital server arrays. Here is the complete breakdown of the core technologies and timelines driving this space-based intelligence revolution: 🛰️ AI1 satellite power and compute capacity Ian Dahl and Elon Musk introduced the baseline performance targets for the first-generation AI1 satellite, explaining how its custom hardware is engineered to operate like an orbital data center server rack. Ian Dahl noted that their direct operational experience with xAI guided them to target a 150-kilowatt peak power capacity. To manage active machine learning workloads continuously, Elon Musk explained that the satellite is optimized to maintain a sustained average compute power envelope of 120 kilowatts, which directly mirrors the real-world performance of a terrestrial NVIDIA server rack. The official presentation slides outline several key operational metrics for this payload configuration: ⚡ The custom architecture delivers a 150 kW peak compute payload. 🔋 The system maintains a 120 kW sustained average compute payload under active workloads. ⚖️ The hardware achieves a highly optimized power-to-weight density of 70 kW per ton. 🔄 The layout features a completely interchangeable compute provider design. "We thought that the right place to start is around the 150 kilowatt peak power level. But as we look at the workloads with our experience with xAI, we see that we can support about 120 kilowatts of average compute. The 150 kilowatt peak power level roughly matches what, say, an NVIDIA GV300 rack would do. A more reasonable operating envelope would be around 120 kilowatts average power, but it can peak up to 150. So it is basically thinking about it as a rack of compute in space." --- 📐 AI1 satellite dimensions and thermal efficiency specs Elon Musk detailed the physical layout of the AI1 satellite, highlighting the massive dimensions required to accommodate its immense power and cooling hardware. He shared specific design criteria, explaining that the engineering relies on a custom 150 kW solar array paired with a high-capacity deployable liquid radiator thermal management system. The technical specifications of this vehicle layout include: 📏 The structural frame features a massive 70-meter wingspan. ↕️ The vehicle spans a total deployed height of 20 meters. ☀️ The onboard solar array delivers an efficiency of 250 W/m² using technology manufactured in Bastrop, Texas. 🌡️ The thermal system utilizes a 110 m² deployable liquid radiator to cleanly dump waste heat. 🔄 The cooling architecture incorporates redundant pumping loops for mission safety. 🛡️ The exterior contains integrated micrometeoroid shielding to protect the fluid lines. 🧭 The double-sided radiators achieve a dissipation rate of 1400 watts per square meter while remaining oriented knife-edge to the sun. "The assumptions here are 250 watts per square meter for the solar array and about 1400 watts per square meter for the radiators. The radiators are double-sided, radiating on both sides, and they're oriented knife-edge to the sun. They have about a 70-meter wingspan, so these are fairly large." --- 🧩 Simplified design architecture built on Starlink V3 tech Elon Musk explained that despite the satellite's imposing size, its internal architecture is fundamentally much simpler than a standard Starlink satellite. Because it lacks heavy phased array and parabolic communications antennas, the entire vehicle layout is completely streamlined around a few essential structural modules: 🎛️ The hardware framework is arranged around a centralized compute module. ☀️ Large deployable solar arrays extend outward to capture orbital energy. 🌡️ A deployable liquid-radiator thermal management system controls active operational temperatures. 🔄 The engineering team heavily leverages the component evolution and manufacturing experience gained from developing the Starlink V3 vehicle platform. "The AI satellite is actually much simpler than a Starlink satellite. A Starlink satellite has gigantic phased array antennas, parabolic antennas, and a lot of laser links, making it much more complicated. An AI satellite is essentially a lot of solar cells, a radiator, and you still need some laser links, but you don't have all of the super complex antennas that you have on a Starlink satellite. A lot of this is technology we've already made for the Starlink V3 satellites." --- 🔌 Interchangeable compute reference designs and high connectivity Elon Musk outlined a modular hardware approach for the satellite's payload, allowing it to house a variety of industry-standard processing units depending on client requirements. This interchangeable compute rack is supported by a high-bandwidth connectivity loop that links separate orbital units together or transmits data directly back to Earth. The core network parameters include: 🧠 Reference designs are fully established to seamlessly accommodate NVIDIA Reuben chips. 💾 The system architecture is built to support alternative setups using NVIDIA GB300 chips. 💻 Custom hardware layouts are explicitly designed to integrate Google TPUs. 🌐 The onboard communications setup delivers roughly 1 terabit of laser link connectivity. ⏱️ The network closes the communication loop directly with the main Starlink constellation at an ultra-low latency of only 3 milliseconds. "Our current reference design is for NVIDIA Reuben chips, or it could be either GB300 or Reuben chips. We'll also have a reference design for TPUs. Essentially, you can put up any existing chips into orbit. There would also be probably something on the order of a terabit of laser link connectivity from the satellite. Then you can connect these racks of compute to each other by the laser links or directly to the Starlink constellations. Light travels 300 kilometers per millisecond, so that's about three milliseconds away." --- 🏭 The "gigasat" AI satellite and solar production hub in Bastrop, Texas Dan Huot highlighted that the primary production hub for this entire hardware ecosystem is anchored at their sprawling complex in Bastrop, Texas, officially designated as the Gigasat factory. Elon Musk verified that construction is already actively underway on the solar manufacturing facility to feed the project's supply line, with plans moving forward to construct the adjacent AI satellite assembly lines. The physical footprint and timeline of this manufacturing hub are defined by the following benchmarks: 🗺️ The company has over 1,000 acres of land currently owned or under contract for the site. 🏢 The manufacturing complex boasts a massive structural building potential exceeding 11 million square feet. ⚙️ The facility will vertically integrate production to manufacture solar ingots, wafers, solar cells, and completed AI satellites. 📅 Both the solar and AI satellite production lines are targeted to be operational at a viable volume by the end of next year. "We're going to be building a lot of satellites and we're going to be building them here in Bastrop. We already have the solar manufacturing facility under construction, and then we will be building out the AI sat production building soon. We expect to have the AI sat production, the solar production, and all of that operating at some reasonable volume by the end of next year." --- 🏢 The 100-million-square-foot "terafab" chip factory Elon Musk revealed a massive, long-term scaling strategy to build an immense chip manufacturing facility dubbed the "terafab" to completely bypass global semiconductor volume constraints. This manufacturing infrastructure is designed to transition the company into next-generation industrial scaling by producing highly specialized computing components at an unprecedented volume. The scale of this infrastructure project is defined by several extraordinary engineering and production benchmarks: 🏭 The colossal factory is projected to span approximately 100 million square feet, making it ten times larger than the current Tesla Gigafactory Texas. ⚡ The facility is structurally engineered to achieve a massive manufacturing output of 1 terawatt per year once fully operational. 📦 This unprecedented physical footprint provides the capacity required to manufacture 1 billion full-reticle equivalent chips annually. 🔌 Each individual chip manufactured by the facility is designed to run at a power capacity of 1 kilowatt. 🇺🇸 The total scaled output of the facility represents an energy footprint that is exactly double the current annual electricity consumption of the entire United States. "In order to get to the next order of magnitude, you need a gigantic chip factory. To give you a sense of scale here, we expect that the terafab is going to be around 100 million square feet, which is 10 times the size of the Tesla Gigafactory Texas. From a logic die standpoint, that's like having a billion chips per year with a kilowatt per reticle, scaling to a terawatt per year. That is twice the current electricity consumption of the United States." --- 📶 Next-generation high-volume Starlink terminals Dan Huot and Elon Musk introduced their next-generation Starlink user terminals, which have been redesigned specifically to achieve massive manufacturing throughput. Elon Musk pointed out that these newer models will be produced in vastly higher volumes than current hardware designs to fulfill their long-term global deployment targets: 📈 The upgraded user hardware is manufactured at a much higher volume capacity than existing units. 🌍 The company's ultimate target is to successfully deploy a few hundred million of these next-generation terminals worldwide. "In fact, these are the new Starlink terminals, which we made in much higher volume than the current terminals. Ultimately, we think there's probably going to be a few hundred million Starlink terminals out there." --- 📈 Aspirational timeline for orbital AI compute scaling Elon Musk laid out an ambitious, multi-year execution timeline detailing how the company plans to progressively scale space-based processing power. The roadmap targets an initial run-rate by the end of next year and sets an aggressive pace to increase total operational capacity sequentially through a structured, multi-phase timeline: 1️⃣ The initial target aims to hit an annualized run-rate of 1 gigawatt of space AI compute by the end of next year. 2️⃣ The capacity scales to an annualized rate of 10 gigawatts within the next two and a half years. 3️⃣ The operational envelope expands to reach 100 gigawatts in three and a half years. 4️⃣ The long-term deployment plan scales directly to a full terawatt capacity per year using the output of the terafab. "The goal is to get to roughly an annualized rate of a gigawatt per year by the end of next year in terms of space AI compute. Then aspirationally, we want to scale that by an order of magnitude per year. In two and a half years, hitting an annualized rate of 10 gigawatts a year in space, and in three and a half years, maybe a hundred gigawatts, going beyond that with the terafab to scale to a terawatt per year." --- 🌕 Ultimate scaling via lunar production and mass drivers Elon Musk explained that scaling three orders of magnitude past a single terawatt forces a transition completely off-planet to avoid the logistical penalty of Earth's deep gravity well. The vision relies on establishing manufacturing infrastructure directly on the moon to leverage localized resource loops and zero-atmosphere physics: 🌙 The company plans to establish localized raw production lines on the moon to fabricate solar panels, photovoltaics, and radiators from lunar materials. ⚡ Manufacturing components locally avoids the massive fuel and mass penalties of transporting heavy structural materials from Earth. 🧲 Because the moon has no atmosphere and only one-sixth of Earth's gravity, the facility will utilize an electromagnetic mass driver to launch completed satellites. 🚀 Operating essentially as a linear electric motor rail gun, this mechanism will shoot fully assembled AI satellites straight into deep space without relying on chemical rockets. "The only way that we can really see that you can achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics, solar panels, and radiators on the moon. Because the moon has no atmosphere and only one-sixth Earth's gravity, you can accelerate the AI satellites into deep space without a rocket. You can basically shoot them into space using an electromagnetic gun, like a rail gun type—it's basically a linear electric motor."

In a newly released technical update, SpaceX's leadership team, which includes communications manager Dan Huot, Director of Satellite Engineering Ian Dahl, and CEO Elon Musk, detailed a highly ambitious infrastructure roadmap to design, manufacture, and operate specialized artificial intelligence computing satellites at scale. Positioned as a major strategic pillar to dramatically elevate civilizational energy and processing capacity on the Kardashev scale, this strategy moves past traditional communications architectures into massive orbital server arrays. Here is the complete breakdown of the core technologies and timelines driving this space-based intelligence revolution: 🛰️ AI1 satellite power and compute capacity Ian Dahl and Elon Musk introduced the baseline performance targets for the first-generation AI1 satellite, explaining how its custom hardware is engineered to operate like an orbital data center server rack. Ian Dahl noted that their direct operational experience with xAI guided them to target a 150-kilowatt peak power capacity. To manage active machine learning workloads continuously, Elon Musk explained that the satellite is optimized to maintain a sustained average compute power envelope of 120 kilowatts, which directly mirrors the real-world performance of a terrestrial NVIDIA server rack. The official presentation slides outline several key operational metrics for this payload configuration: ⚡ The custom architecture delivers a 150 kW peak compute payload. 🔋 The system maintains a 120 kW sustained average compute payload under active workloads. ⚖️ The hardware achieves a highly optimized power-to-weight density of 70 kW per ton. 🔄 The layout features a completely interchangeable compute provider design. "We thought that the right place to start is around the 150 kilowatt peak power level. But as we look at the workloads with our experience with xAI, we see that we can support about 120 kilowatts of average compute. The 150 kilowatt peak power level roughly matches what, say, an NVIDIA GV300 rack would do. A more reasonable operating envelope would be around 120 kilowatts average power, but it can peak up to 150. So it is basically thinking about it as a rack of compute in space." --- 📐 AI1 satellite dimensions and thermal efficiency specs Elon Musk detailed the physical layout of the AI1 satellite, highlighting the massive dimensions required to accommodate its immense power and cooling hardware. He shared specific design criteria, explaining that the engineering relies on a custom 150 kW solar array paired with a high-capacity deployable liquid radiator thermal management system. The technical specifications of this vehicle layout include: 📏 The structural frame features a massive 70-meter wingspan. ↕️ The vehicle spans a total deployed height of 20 meters. ☀️ The onboard solar array delivers an efficiency of 250 W/m² using technology manufactured in Bastrop, Texas. 🌡️ The thermal system utilizes a 110 m² deployable liquid radiator to cleanly dump waste heat. 🔄 The cooling architecture incorporates redundant pumping loops for mission safety. 🛡️ The exterior contains integrated micrometeoroid shielding to protect the fluid lines. 🧭 The double-sided radiators achieve a dissipation rate of 1400 watts per square meter while remaining oriented knife-edge to the sun. "The assumptions here are 250 watts per square meter for the solar array and about 1400 watts per square meter for the radiators. The radiators are double-sided, radiating on both sides, and they're oriented knife-edge to the sun. They have about a 70-meter wingspan, so these are fairly large." --- 🧩 Simplified design architecture built on Starlink V3 tech Elon Musk explained that despite the satellite's imposing size, its internal architecture is fundamentally much simpler than a standard Starlink satellite. Because it lacks heavy phased array and parabolic communications antennas, the entire vehicle layout is completely streamlined around a few essential structural modules: 🎛️ The hardware framework is arranged around a centralized compute module. ☀️ Large deployable solar arrays extend outward to capture orbital energy. 🌡️ A deployable liquid-radiator thermal management system controls active operational temperatures. 🔄 The engineering team heavily leverages the component evolution and manufacturing experience gained from developing the Starlink V3 vehicle platform. "The AI satellite is actually much simpler than a Starlink satellite. A Starlink satellite has gigantic phased array antennas, parabolic antennas, and a lot of laser links, making it much more complicated. An AI satellite is essentially a lot of solar cells, a radiator, and you still need some laser links, but you don't have all of the super complex antennas that you have on a Starlink satellite. A lot of this is technology we've already made for the Starlink V3 satellites." --- 🔌 Interchangeable compute reference designs and high connectivity Elon Musk outlined a modular hardware approach for the satellite's payload, allowing it to house a variety of industry-standard processing units depending on client requirements. This interchangeable compute rack is supported by a high-bandwidth connectivity loop that links separate orbital units together or transmits data directly back to Earth. The core network parameters include: 🧠 Reference designs are fully established to seamlessly accommodate NVIDIA Reuben chips. 💾 The system architecture is built to support alternative setups using NVIDIA GB300 chips. 💻 Custom hardware layouts are explicitly designed to integrate Google TPUs. 🌐 The onboard communications setup delivers roughly 1 terabit of laser link connectivity. ⏱️ The network closes the communication loop directly with the main Starlink constellation at an ultra-low latency of only 3 milliseconds. "Our current reference design is for NVIDIA Reuben chips, or it could be either GB300 or Reuben chips. We'll also have a reference design for TPUs. Essentially, you can put up any existing chips into orbit. There would also be probably something on the order of a terabit of laser link connectivity from the satellite. Then you can connect these racks of compute to each other by the laser links or directly to the Starlink constellations. Light travels 300 kilometers per millisecond, so that's about three milliseconds away." --- 🏭 The "gigasat" AI satellite and solar production hub in Bastrop, Texas Dan Huot highlighted that the primary production hub for this entire hardware ecosystem is anchored at their sprawling complex in Bastrop, Texas, officially designated as the Gigasat factory. Elon Musk verified that construction is already actively underway on the solar manufacturing facility to feed the project's supply line, with plans moving forward to construct the adjacent AI satellite assembly lines. The physical footprint and timeline of this manufacturing hub are defined by the following benchmarks: 🗺️ The company has over 1,000 acres of land currently owned or under contract for the site. 🏢 The manufacturing complex boasts a massive structural building potential exceeding 11 million square feet. ⚙️ The facility will vertically integrate production to manufacture solar ingots, wafers, solar cells, and completed AI satellites. 📅 Both the solar and AI satellite production lines are targeted to be operational at a viable volume by the end of next year. "We're going to be building a lot of satellites and we're going to be building them here in Bastrop. We already have the solar manufacturing facility under construction, and then we will be building out the AI sat production building soon. We expect to have the AI sat production, the solar production, and all of that operating at some reasonable volume by the end of next year." --- 🏢 The 100-million-square-foot "terafab" chip factory Elon Musk revealed a massive, long-term scaling strategy to build an immense chip manufacturing facility dubbed the "terafab" to completely bypass global semiconductor volume constraints. This manufacturing infrastructure is designed to transition the company into next-generation industrial scaling by producing highly specialized computing components at an unprecedented volume. The scale of this infrastructure project is defined by several extraordinary engineering and production benchmarks: 🏭 The colossal factory is projected to span approximately 100 million square feet, making it ten times larger than the current Tesla Gigafactory Texas. ⚡ The facility is structurally engineered to achieve a massive manufacturing output of 1 terawatt per year once fully operational. 📦 This unprecedented physical footprint provides the capacity required to manufacture 1 billion full-reticle equivalent chips annually. 🔌 Each individual chip manufactured by the facility is designed to run at a power capacity of 1 kilowatt. 🇺🇸 The total scaled output of the facility represents an energy footprint that is exactly double the current annual electricity consumption of the entire United States. "In order to get to the next order of magnitude, you need a gigantic chip factory. To give you a sense of scale here, we expect that the terafab is going to be around 100 million square feet, which is 10 times the size of the Tesla Gigafactory Texas. From a logic die standpoint, that's like having a billion chips per year with a kilowatt per reticle, scaling to a terawatt per year. That is twice the current electricity consumption of the United States." --- 📶 Next-generation high-volume Starlink terminals Dan Huot and Elon Musk introduced their next-generation Starlink user terminals, which have been redesigned specifically to achieve massive manufacturing throughput. Elon Musk pointed out that these newer models will be produced in vastly higher volumes than current hardware designs to fulfill their long-term global deployment targets: 📈 The upgraded user hardware is manufactured at a much higher volume capacity than existing units. 🌍 The company's ultimate target is to successfully deploy a few hundred million of these next-generation terminals worldwide. "In fact, these are the new Starlink terminals, which we made in much higher volume than the current terminals. Ultimately, we think there's probably going to be a few hundred million Starlink terminals out there." --- 📈 Aspirational timeline for orbital AI compute scaling Elon Musk laid out an ambitious, multi-year execution timeline detailing how the company plans to progressively scale space-based processing power. The roadmap targets an initial run-rate by the end of next year and sets an aggressive pace to increase total operational capacity sequentially through a structured, multi-phase timeline: 1️⃣ The initial target aims to hit an annualized run-rate of 1 gigawatt of space AI compute by the end of next year. 2️⃣ The capacity scales to an annualized rate of 10 gigawatts within the next two and a half years. 3️⃣ The operational envelope expands to reach 100 gigawatts in three and a half years. 4️⃣ The long-term deployment plan scales directly to a full terawatt capacity per year using the output of the terafab. "The goal is to get to roughly an annualized rate of a gigawatt per year by the end of next year in terms of space AI compute. Then aspirationally, we want to scale that by an order of magnitude per year. In two and a half years, hitting an annualized rate of 10 gigawatts a year in space, and in three and a half years, maybe a hundred gigawatts, going beyond that with the terafab to scale to a terawatt per year." --- 🌕 Ultimate scaling via lunar production and mass drivers Elon Musk explained that scaling three orders of magnitude past a single terawatt forces a transition completely off-planet to avoid the logistical penalty of Earth's deep gravity well. The vision relies on establishing manufacturing infrastructure directly on the moon to leverage localized resource loops and zero-atmosphere physics: 🌙 The company plans to establish localized raw production lines on the moon to fabricate solar panels, photovoltaics, and radiators from lunar materials. ⚡ Manufacturing components locally avoids the massive fuel and mass penalties of transporting heavy structural materials from Earth. 🧲 Because the moon has no atmosphere and only one-sixth of Earth's gravity, the facility will utilize an electromagnetic mass driver to launch completed satellites. 🚀 Operating essentially as a linear electric motor rail gun, this mechanism will shoot fully assembled AI satellites straight into deep space without relying on chemical rockets. "The only way that we can really see that you can achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics, solar panels, and radiators on the moon. Because the moon has no atmosphere and only one-sixth Earth's gravity, you can accelerate the AI satellites into deep space without a rocket. You can basically shoot them into space using an electromagnetic gun, like a rail gun type—it's basically a linear electric motor."

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22,203 次观看 • 3 个月前