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Elon Musk explains Tesla’s new Megapod concept for scaling distributed AI compute: “We’re building a Megapod design that pairs Tesla AI4 computers with x86 systems in a box...essentially digital Optimus in a box Similar to how Megapack packages batteries, a Megapod would combine a large number of AI4 and... x86 systems inside one deployable unit These boxes could be placed anywhere across the country or outside the country allowing us to scale AI compute using distributed electricity production There is power available all around the world, including at our Supercharger sites. We have something like 7 gigawatts of power across the Supercharger network, and that continues to grow We could place Megapods at many of these locations and use that distributed power for AI compute I think that could be quite a big deal” Instead of relying entirely on enormous centralized data centers, Tesla could deploy modular AI-compute infrastructure wherever sufficient power is already availableshow more

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🚨 Tesla just trademarked "Megapod" The filing describes it as modular hardware for AI computing, which has sparked a lot of speculation about what Tesla could be building next. Jeff Lutz 🔋 pointed out that this comes just months after Elon Musk talked about deploying millions of dedicated "Digital Optimus" units at Supercharger locations. If that's the direction Tesla is heading, Superchargers could eventually become more than charging stations. They could also serve as distributed AI hubs powered by Tesla batteries, compute hardware, and potentially even Starlink connectivity. Nothing is confirmed yet, but it's an interesting idea! Most people see a charging network. Tesla may see a future AI infrastructure network. 👀 $TSLA

🚨 Tesla just trademarked "Megapod" The filing describes it as modular hardware for AI computing, which has sparked a lot of speculation about what Tesla could be building next. Jeff Lutz 🔋 pointed out that this comes just months after Elon Musk talked about deploying millions of dedicated "Digital Optimus" units at Supercharger locations. If that's the direction Tesla is heading, Superchargers could eventually become more than charging stations. They could also serve as distributed AI hubs powered by Tesla batteries, compute hardware, and potentially even Starlink connectivity. Nothing is confirmed yet, but it's an interesting idea! Most people see a charging network. Tesla may see a future AI infrastructure network. 👀 $TSLA

Herbert Ong

22,462 views • 3 months ago

The Tesla Megapod trademark might be the most important filing nobody is talking about (Save this). Tesla wants to build a product that is basically a self contained AI computer in a box. You truck it somewhere, plug it into power, and it runs AI workloads. The reason this matters is that the AI industry has a problem because you can't build data centers fast enough. Getting land approved, getting power from the grid, getting zoning cleared, that process takes 3–5 years, and demand is moving much faster than that. US data center construction actually fell in 2025 for the first time in years, because developers kept hitting bureaucratic walls. Tesla's answer to that problem is simple. They already own thousands of Supercharger stations across the country and those stations already have land, power, and grid connections, all the hard stuff is done. The idea is to drop a Megapod at those sites and turn idle charging infrastructure into AI compute, almost overnight. What Jason, Chamath, and Friedberg were all circling around is that this drop a box on a concrete pad approach is already how serious companies are deploying AI compute right now. Vertiv and DUG Technology both ship GPU-packed containers that can go live in 90 days and Tesla would just be bringing that idea to a network it already owns. The opportunity is very real whether Tesla is the one to actually execute on it is still an open question.

The Tesla Megapod trademark might be the most important filing nobody is talking about (Save this). Tesla wants to build a product that is basically a self contained AI computer in a box. You truck it somewhere, plug it into power, and it runs AI workloads. The reason this matters is that the AI industry has a problem because you can't build data centers fast enough. Getting land approved, getting power from the grid, getting zoning cleared, that process takes 3–5 years, and demand is moving much faster than that. US data center construction actually fell in 2025 for the first time in years, because developers kept hitting bureaucratic walls. Tesla's answer to that problem is simple. They already own thousands of Supercharger stations across the country and those stations already have land, power, and grid connections, all the hard stuff is done. The idea is to drop a Megapod at those sites and turn idle charging infrastructure into AI compute, almost overnight. What Jason, Chamath, and Friedberg were all circling around is that this drop a box on a concrete pad approach is already how serious companies are deploying AI compute right now. Vertiv and DUG Technology both ship GPU-packed containers that can go live in 90 days and Tesla would just be bringing that idea to a network it already owns. The opportunity is very real whether Tesla is the one to actually execute on it is still an open question.

Milk Road AI

16,630 views • 3 months ago

AI compute might be turning into a whole new asset class! Cern Basher makes an interesting point: the AI buildout is getting way too expensive for tech companies to fund everything themselves. That’s where Wall Street comes in. Pension funds, insurers and big investment firms could finance the AI factories, while companies pay to use the compute they produce. Basically, finance AI infrastructure like we already finance airports, power plants and real estate. If that model takes off, it could change how Tesla, SpaceX and the rest of the AI industry fund the next massive wave of compute. $TSLA $SPCX

AI compute might be turning into a whole new asset class! Cern Basher makes an interesting point: the AI buildout is getting way too expensive for tech companies to fund everything themselves. That’s where Wall Street comes in. Pension funds, insurers and big investment firms could finance the AI factories, while companies pay to use the compute they produce. Basically, finance AI infrastructure like we already finance airports, power plants and real estate. If that model takes off, it could change how Tesla, SpaceX and the rest of the AI industry fund the next massive wave of compute. $TSLA $SPCX

Herbert Ong

11,805 views • 1 month ago

Elon Musk explains how data centers in Space could help the US compete with China in AI “Given that Chinese AI companies are doing as well as they are with a relatively small amount of compute, it seems that if they had a lot of compute, there’s a good chance they would be the leaders. The constraints on AI are essentially electricity and AI chips. China is very strong in digital AI, and it has far more electricity than the United States. In fact, China already has more electricity than the United States, Europe and India combined. The US government can prevent US companies from using Chinese models, but it can’t stop the rest of the world from using them. That won’t stop China from becoming the leader in AI. Digital intelligence comes down to how much AI compute, how many chips and how much electricity you have. The current constraint is electricity outside China. In China, the constraint is chips. China is also closer than most people realize to solving the lithography problem, which would enable it to make AI chips in very large volumes. Once we address the power constraint with AI data centers in space, the constraint will once again be chips outside China.”

Elon Musk explains how data centers in Space could help the US compete with China in AI “Given that Chinese AI companies are doing as well as they are with a relatively small amount of compute, it seems that if they had a lot of compute, there’s a good chance they would be the leaders. The constraints on AI are essentially electricity and AI chips. China is very strong in digital AI, and it has far more electricity than the United States. In fact, China already has more electricity than the United States, Europe and India combined. The US government can prevent US companies from using Chinese models, but it can’t stop the rest of the world from using them. That won’t stop China from becoming the leader in AI. Digital intelligence comes down to how much AI compute, how many chips and how much electricity you have. The current constraint is electricity outside China. In China, the constraint is chips. China is also closer than most people realize to solving the lithography problem, which would enable it to make AI chips in very large volumes. Once we address the power constraint with AI data centers in space, the constraint will once again be chips outside China.”

DogeDesigner

246,547 views • 2 months ago

Elon Musk: Now we’re going to look at the computer for the Cybercab. The computer will be somewhat overspecified, but I think there’s a major opportunity here. Similar to Amazon Web Services, if the car is driving for 50 hours a week, that leaves more than 100 hours where the compute is sitting there unused. You could use that idle compute for distributed inference. If you have a fleet of 100 million vehicles, each with roughly a kilowatt of efficient inference compute, that’s about 100 GW of compute. That’s a massive amount of compute. And if it’s already there, you might as well use it. I think that makes a lot of sense. The autonomous future is here.

Elon Musk: Now we’re going to look at the computer for the Cybercab. The computer will be somewhat overspecified, but I think there’s a major opportunity here. Similar to Amazon Web Services, if the car is driving for 50 hours a week, that leaves more than 100 hours where the compute is sitting there unused. You could use that idle compute for distributed inference. If you have a fleet of 100 million vehicles, each with roughly a kilowatt of efficient inference compute, that’s about 100 GW of compute. That’s a massive amount of compute. And if it’s already there, you might as well use it. I think that makes a lot of sense. The autonomous future is here.

Tesla Owners Silicon Valley

58,180 views • 7 days ago

Elon Musk just unveiled SpaceXAI's first AI satellite. • 150 kW peak power / 120 kW sustained compute power • 150 kW solar array using SpaceX-manufactured solar technology • Centralized AI compute payload designed for high-performance AI workloads • 70-meter wingspan when fully deployed • 110 m² deployable liquid radiators to remove waste heat in space • Redundant cooling loops with integrated micrometeoroid shielding • Designed to launch on Starship, enabling the mass-to-orbit needed for large-scale space computing • Uses laser links while avoiding many of the complex communications systems required by Starlink satellites • SpaceX believes future versions can scale far beyond this first design Elon Musk says the path to scaling AI in space requires 3 things: • Massive launch capability (Starship) • Enormous solar power generation • Large radiators to reject heat from AI chips He also suggested that truly large-scale orbital AI could eventually require hundreds of gigawatts to a terawatt of power, implying millions of tons of infrastructure in orbit.

Elon Musk just unveiled SpaceXAI's first AI satellite. • 150 kW peak power / 120 kW sustained compute power • 150 kW solar array using SpaceX-manufactured solar technology • Centralized AI compute payload designed for high-performance AI workloads • 70-meter wingspan when fully deployed • 110 m² deployable liquid radiators to remove waste heat in space • Redundant cooling loops with integrated micrometeoroid shielding • Designed to launch on Starship, enabling the mass-to-orbit needed for large-scale space computing • Uses laser links while avoiding many of the complex communications systems required by Starlink satellites • SpaceX believes future versions can scale far beyond this first design Elon Musk says the path to scaling AI in space requires 3 things: • Massive launch capability (Starship) • Enormous solar power generation • Large radiators to reject heat from AI chips He also suggested that truly large-scale orbital AI could eventually require hundreds of gigawatts to a terawatt of power, implying millions of tons of infrastructure in orbit.

DogeDesigner

138,388 views • 3 months ago

Think bigger than data centers. Much bigger. Elon Musk just unveiled SpaceXAI's first AI satellite, a spacecraft designed specifically for running AI workloads in orbit. The satellite features 150 kW of solar power, a 70-meter wingspan and massive radiators to dump heat into space, solving one of the biggest challenges in scaling AI. But this is just the beginning. Musk says truly large-scale orbital AI could eventually require hundreds of gigawatts to a terawatt of power, meaning millions of tons of infrastructure in orbit. The long-term vision isn't AI running in data centers. It's AI running in space at a scale that's impossible on Earth. Elon Musk

Think bigger than data centers. Much bigger. Elon Musk just unveiled SpaceXAI's first AI satellite, a spacecraft designed specifically for running AI workloads in orbit. The satellite features 150 kW of solar power, a 70-meter wingspan and massive radiators to dump heat into space, solving one of the biggest challenges in scaling AI. But this is just the beginning. Musk says truly large-scale orbital AI could eventually require hundreds of gigawatts to a terawatt of power, meaning millions of tons of infrastructure in orbit. The long-term vision isn't AI running in data centers. It's AI running in space at a scale that's impossible on Earth. Elon Musk

Tesla Owners Silicon Valley

19,221 views • 3 months ago

Ever wonder how Elon could actually fund something as massive as the $119B Terafab? Cern Basher thinks Jensen Huang may have just given us a clue! NVIDIA is working with Apollo, BlackRock, Blackstone, Goldman and KKR to bring $500B+ of outside capital into AI infrastructure. The idea is pretty simple: investors fund the AI factories, companies rent the compute, and the hardware pays for itself through revenue. Basically, AI compute could start getting financed the way airplanes and power plants are. If that model works, Elon may not need Tesla or SpaceX to write the entire Terafab check themselves. That changes the math quite a bit. $TSLA $SPCX $NVDA

Ever wonder how Elon could actually fund something as massive as the $119B Terafab? Cern Basher thinks Jensen Huang may have just given us a clue! NVIDIA is working with Apollo, BlackRock, Blackstone, Goldman and KKR to bring $500B+ of outside capital into AI infrastructure. The idea is pretty simple: investors fund the AI factories, companies rent the compute, and the hardware pays for itself through revenue. Basically, AI compute could start getting financed the way airplanes and power plants are. If that model works, Elon may not need Tesla or SpaceX to write the entire Terafab check themselves. That changes the math quite a bit. $TSLA $SPCX $NVDA

Herbert Ong

49,754 views • 1 month ago

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

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

Mario Nawfal

1,668,111 views • 10 months ago

What if we could connect all the dark compute across the globe to build the world's biggest AI data center? Most of the compute in the world is dark: phones, laptops, Tesla's, PS5's, TV's. These devices have powerful GPUs but are mostly sitting idle. Today, EXO Labs is announcing the first step in activating all of this dark compute for AI workloads: evML, a distributed computing protocol that enforces honest behavior through hardware security and spot-checks with just 5% performance overhead. Version 0.0.1 of evML is compatible with Apple, Samsung and LG devices. Our preliminary report on evML is now available (link below).

What if we could connect all the dark compute across the globe to build the world's biggest AI data center? Most of the compute in the world is dark: phones, laptops, Tesla's, PS5's, TV's. These devices have powerful GPUs but are mostly sitting idle. Today, EXO Labs is announcing the first step in activating all of this dark compute for AI workloads: evML, a distributed computing protocol that enforces honest behavior through hardware security and spot-checks with just 5% performance overhead. Version 0.0.1 of evML is compatible with Apple, Samsung and LG devices. Our preliminary report on evML is now available (link below).

EXO Labs

137,990 views • 1 year ago

ELON MUSK: Kardashev-Scale civilization will need Moon Bases and Mass Drivers. To reach a Kardashev-scale future for AI, humanity need space-based infrastructure including Moon bases, mass drivers, and large-scale solar power systems. Future AI may require energy at a scale far beyond what Earth can easily support. Solar-powered AI satellites launched from Earth could potentially generate terawatt-level power, but would require around 10 million tons of payload to orbit every year. That level of infrastructure could produce around twice the annual electricity use of the United States for AI power. The idea is not limited by physics; the real challenge is building at a massive enough scale. Lunar infrastructure could unlock petawatt-scale solar-powered AI, making Earth’s current economy look small by comparison.

ELON MUSK: Kardashev-Scale civilization will need Moon Bases and Mass Drivers. To reach a Kardashev-scale future for AI, humanity need space-based infrastructure including Moon bases, mass drivers, and large-scale solar power systems. Future AI may require energy at a scale far beyond what Earth can easily support. Solar-powered AI satellites launched from Earth could potentially generate terawatt-level power, but would require around 10 million tons of payload to orbit every year. That level of infrastructure could produce around twice the annual electricity use of the United States for AI power. The idea is not limited by physics; the real challenge is building at a massive enough scale. Lunar infrastructure could unlock petawatt-scale solar-powered AI, making Earth’s current economy look small by comparison.

DogeDesigner

17,792 views • 4 months ago

ELON: AI CLUSTERS WILL BECOME A NORMAL THING EVERY COUNTRY HAS Compute is becoming a new currency. While building a "frontier model" today is a high-stakes gauntlet that requires a level of technical skill only a few companies possess, he predicts that AI compute clusters are about to become as essential to a nation as a power grid or a military. By 2026, "AI Sovereignty" will be the only thing keeping nations from becoming digital colonies of Silicon Valley or Beijing: “Probably all countries will have their own AI clusters over time. It is currently very difficult to actually build an AI cluster and have it run... because if you are training a frontier model, then you need a massive amount of compute and a level of technical skill that only a few companies possess. But over time, I think every country will have AI compute clusters. It is just going to be a normal thing that every country has.” Source: vitrupo Elon Musk

ELON: AI CLUSTERS WILL BECOME A NORMAL THING EVERY COUNTRY HAS Compute is becoming a new currency. While building a "frontier model" today is a high-stakes gauntlet that requires a level of technical skill only a few companies possess, he predicts that AI compute clusters are about to become as essential to a nation as a power grid or a military. By 2026, "AI Sovereignty" will be the only thing keeping nations from becoming digital colonies of Silicon Valley or Beijing: “Probably all countries will have their own AI clusters over time. It is currently very difficult to actually build an AI cluster and have it run... because if you are training a frontier model, then you need a massive amount of compute and a level of technical skill that only a few companies possess. But over time, I think every country will have AI compute clusters. It is just going to be a normal thing that every country has.” Source: vitrupo Elon Musk

Mario Nawfal

20,502 views • 8 months ago

AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.

AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.

Cristiano R. Amon

15,783 views • 3 months ago

🚨 Elon Musk's reported Terafab project could be bigger than most people realize. 👀 Jo Bhakdi says the planned Tesla-SpaceX facility could reach 100 million square feet, about 10 times larger than Giga Texas, with a goal of producing around 1 terawatt of AI chips per year. He also highlighted Elon’s vision for AI in space, with plans to rapidly increase compute power in orbit over the next few years. If these plans become reality, SpaceX and Tesla may not just be building products. They could be building the infrastructure that powers the future of AI. $TSLA $SPCX

🚨 Elon Musk's reported Terafab project could be bigger than most people realize. 👀 Jo Bhakdi says the planned Tesla-SpaceX facility could reach 100 million square feet, about 10 times larger than Giga Texas, with a goal of producing around 1 terawatt of AI chips per year. He also highlighted Elon’s vision for AI in space, with plans to rapidly increase compute power in orbit over the next few years. If these plans become reality, SpaceX and Tesla may not just be building products. They could be building the infrastructure that powers the future of AI. $TSLA $SPCX

Herbert Ong

23,227 views • 3 months ago

If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.

If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.

Satya Nadella

2,026,666 views • 1 year ago

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

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

Exabits

13,896 views • 2 years ago

In the 20th century, power depended on OIL and STEEL. In the 21st century, power depends on AI, CHIPS and COMPUTE CAPABILITY. At AI Summit 2026 India, I spoke about why the GPU challenge is not just a ‘supply chain issue’ but a ‘sovereignty issue’. In the AI century, power will rest with those who control chips, compute capacity and data centres. Today, design monopolies, manufacturing concentration and export controls determine who gets access. India controls none of these critical levers. We have the talent. We have the ambition. We have the intent. We have the capital. But talent without compute is aspiration. And compute without sovereignty is dependence. The role of policymakers is not to slow AI down; it is to remove bottlenecks, diversify access, set clear national targets, and build sovereign AI infrastructure in India. This is not partisan. It is strategic. #IndiaAIImpactSummit2026 #AI #ResponsibleAI #ArtificialIntelligence #BharatMandapam

In the 20th century, power depended on OIL and STEEL. In the 21st century, power depends on AI, CHIPS and COMPUTE CAPABILITY. At AI Summit 2026 India, I spoke about why the GPU challenge is not just a ‘supply chain issue’ but a ‘sovereignty issue’. In the AI century, power will rest with those who control chips, compute capacity and data centres. Today, design monopolies, manufacturing concentration and export controls determine who gets access. India controls none of these critical levers. We have the talent. We have the ambition. We have the intent. We have the capital. But talent without compute is aspiration. And compute without sovereignty is dependence. The role of policymakers is not to slow AI down; it is to remove bottlenecks, diversify access, set clear national targets, and build sovereign AI infrastructure in India. This is not partisan. It is strategic. #IndiaAIImpactSummit2026 #AI #ResponsibleAI #ArtificialIntelligence #BharatMandapam

Raghav Chadha

70,153 views • 7 months ago

Tillman says Bitcoin proves decentralized networks work at scale, and AI compute should follow "The thing from a safety perspective in AI, we're focused on the wrong thing. How powerful the model gets is one aspect. Where the compute is stored is the most important aspect." "If you decentralize the compute across the entire nation like we do our grid system, like we do electricity poles, and put a compute box on each electric pole that's owned as a utility across the country. Then guess what? There isn't this big, bad, scary monster that has a huge amount of computers in an underground bunker that can essentially rule us with an iron fist." "And that's what I think the Bitcoin standard shows. If you're going to take anything from Bitcoin and what it's done: it's the largest network that's ever been built by humans, and it was built organically on natural law principles of supply and demand."

Tillman says Bitcoin proves decentralized networks work at scale, and AI compute should follow "The thing from a safety perspective in AI, we're focused on the wrong thing. How powerful the model gets is one aspect. Where the compute is stored is the most important aspect." "If you decentralize the compute across the entire nation like we do our grid system, like we do electricity poles, and put a compute box on each electric pole that's owned as a utility across the country. Then guess what? There isn't this big, bad, scary monster that has a huge amount of computers in an underground bunker that can essentially rule us with an iron fist." "And that's what I think the Bitcoin standard shows. If you're going to take anything from Bitcoin and what it's done: it's the largest network that's ever been built by humans, and it was built organically on natural law principles of supply and demand."

The Wolf Of All Streets

25,011 views • 22 hours ago

The cheapest place to put AI will be space. Elon Musk says solar panels in space produce 5 times more power than on the ground. No atmosphere, no clouds, no day-night cycle. No batteries needed. And critically, no permits. Scaling energy on Earth means fighting land use regulations, environmental reviews, and grid interconnection queues that take years. Space has none of that. The math gets wild fast. SpaceX could launch hundreds of gigawatts of AI compute into orbit annually. That's more compute than currently exists on Earth. Ten thousand Starship launches per year. One launch every hour. And you only need 20 to 30 ships to sustain that cadence. Earth gets about half a billionth of the Sun's total energy output. A millionth of the Sun's energy is 100,000 times all of civilization's current electricity production. The AI race is not a software race. It is a power plant race. And software people are about to get a very hard lesson in hardware.

The cheapest place to put AI will be space. Elon Musk says solar panels in space produce 5 times more power than on the ground. No atmosphere, no clouds, no day-night cycle. No batteries needed. And critically, no permits. Scaling energy on Earth means fighting land use regulations, environmental reviews, and grid interconnection queues that take years. Space has none of that. The math gets wild fast. SpaceX could launch hundreds of gigawatts of AI compute into orbit annually. That's more compute than currently exists on Earth. Ten thousand Starship launches per year. One launch every hour. And you only need 20 to 30 ships to sustain that cadence. Earth gets about half a billionth of the Sun's total energy output. A millionth of the Sun's energy is 100,000 times all of civilization's current electricity production. The AI race is not a software race. It is a power plant race. And software people are about to get a very hard lesson in hardware.

Shruti

11,700 views • 3 months ago