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Google 3D Maps aren’t as accurate as you think. Everyone knows Google dominates mapping—but here’s what they won’t tell you. Their 3D maps are built with car-mounted cameras, capturing images every ~10 meters. That’s fine for basic navigation, but it’s nowhere near enough for AR, robotics, autonomous systems, or...

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Over the Reality 🌐

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Over the Reality just hit another major milestone in the AI era. We have now surpassed 200,000 mappings in our dataset. 🎉 These are not just images. They are high-quality 3D maps generated from the mapping activity of our global community, which has produced more than 94M images of real-world locations so far. The dataset also includes 900TB+ of spatial data that can power Visual AI models, Robotics VPS for real-world navigation and positioning, XR VPS for precise spatial anchoring and AR experiences, Spatial Computing applications, Digital Twins, and the next generation of real-world AI infrastructure. In just the past 2 weeks, our dataset grew by +17,955 mapped locations. That is nearly +10% growth in just 14 days. More maps. More data. More demand for $OVR.
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Over the Reality just hit another major milestone in the AI era. We have now surpassed 200,000 mappings in our dataset. 🎉 These are not just images. They are high-quality 3D maps generated from the mapping activity of our global community, which has produced more than 94M images of real-world locations so far. The dataset also includes 900TB+ of spatial data that can power Visual AI models, Robotics VPS for real-world navigation and positioning, XR VPS for precise spatial anchoring and AR experiences, Spatial Computing applications, Digital Twins, and the next generation of real-world AI infrastructure. In just the past 2 weeks, our dataset grew by +17,955 mapped locations. That is nearly +10% growth in just 14 days. More maps. More data. More demand for $OVR.

Over the Reality 🌐

926,557 Aufrufe • vor 5 Monaten

🎉 Big news, folks! 🎉 I'm thrilled to join forces with A16Z GAMES for their upcoming a16z speedrun 🧊 class, backing visionary founders at the frontier of AI, 3D & immersive computing. 🚀 They're looking for startups building the next-generation of: 1️⃣ Visual 3D Creation Tools 2️⃣ Spatial Computing Apps 3️⃣ Real World AI & Digital Twins 4️⃣ (and of course!) Generative AI Experiences As a 3D/VFX creator with over 1.4M+ subscribers, I've spent years pushing the boundaries of visual storytelling and exploring the potential of emerging technologies. From building AR/VR and 3D mapping products at Google to crafting reality-bending VFX, I've witnessed firsthand the transformative power of these tools. Companies selected for Speedrun will receive $750K in investment and join a 12-week program in LA from July-October, tapping into a16z's unparalleled resources and expertise. 💰🌴 In some cases, I'll also personally invest and advise to help accelerate your growth. 🤝 It's been a fun ride backing incredible startups like Backbone Labs, Pika AI, Skyglass VFX, and Hedra. Now, I'm ready to level up with a16z and support bold founders chasing game-changing dreams. 🚀 Ready to take the leap? Applications are open now! Find out how to apply below ⬇️
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🎉 Big news, folks! 🎉 I'm thrilled to join forces with A16Z GAMES for their upcoming a16z speedrun 🧊 class, backing visionary founders at the frontier of AI, 3D & immersive computing. 🚀 They're looking for startups building the next-generation of: 1️⃣ Visual 3D Creation Tools 2️⃣ Spatial Computing Apps 3️⃣ Real World AI & Digital Twins 4️⃣ (and of course!) Generative AI Experiences As a 3D/VFX creator with over 1.4M+ subscribers, I've spent years pushing the boundaries of visual storytelling and exploring the potential of emerging technologies. From building AR/VR and 3D mapping products at Google to crafting reality-bending VFX, I've witnessed firsthand the transformative power of these tools. Companies selected for Speedrun will receive $750K in investment and join a 12-week program in LA from July-October, tapping into a16z's unparalleled resources and expertise. 💰🌴 In some cases, I'll also personally invest and advise to help accelerate your growth. 🤝 It's been a fun ride backing incredible startups like Backbone Labs, Pika AI, Skyglass VFX, and Hedra. Now, I'm ready to level up with a16z and support bold founders chasing game-changing dreams. 🚀 Ready to take the leap? Applications are open now! Find out how to apply below ⬇️

Bilawal Sidhu

80,427 Aufrufe • vor 2 Jahren

🚨 MACHINES MAY SOON SEE THE WORLD LIKE HUMANS A US company has unveiled what it calls the world’s first “native color lidar” system giving machines the ability to perceive depth, distance, and color simultaneously. Unlike traditional lidar systems that need separate cameras, this new technology captures full 3D color information directly at the point of detection. In simple terms: Machines may soon understand the world more like human vision instead of just measuring shapes and distance. Why this matters: This could dramatically improve: • self-driving cars • robotics • drones • factory automation • AI navigation • autonomous machines The system can reportedly process over 10 million points every second and detect objects nearly 1,640 feet away. Researchers say this could become one of the key technologies powering the next generation of “Physical AI” where machines don’t just calculate the world… They visually understand it. We may be watching the birth of machine perception in real time. Follow for more future technology and AI discoveries.
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🚨 MACHINES MAY SOON SEE THE WORLD LIKE HUMANS A US company has unveiled what it calls the world’s first “native color lidar” system giving machines the ability to perceive depth, distance, and color simultaneously. Unlike traditional lidar systems that need separate cameras, this new technology captures full 3D color information directly at the point of detection. In simple terms: Machines may soon understand the world more like human vision instead of just measuring shapes and distance. Why this matters: This could dramatically improve: • self-driving cars • robotics • drones • factory automation • AI navigation • autonomous machines The system can reportedly process over 10 million points every second and detect objects nearly 1,640 feet away. Researchers say this could become one of the key technologies powering the next generation of “Physical AI” where machines don’t just calculate the world… They visually understand it. We may be watching the birth of machine perception in real time. Follow for more future technology and AI discoveries.

TheNewPhysics

26,128 Aufrufe • vor 3 Monaten

AI IS NO LONGER JUST WRITING CODE IT iS STARTING TO MOVE THINGS IN THE REAL WORLD. Someone just built a pizza delivery system where the drone does the driving No delivery car No traffic No driver sitting behind the wheel Just: -> Order comes in -> Drone picks up the pizza -> Flies directly to the destination -> Delivers it -> Returns And this is where the AI story gets interesting For years, the AI boom was mostly digital: > Chatbots > Coding agents > Image generation > AI music > AI video But the next phase is different AI is getting a body The same technology stack that started with models and GPUs is now moving into the physical world NVIDIA built the compute layer Researchers built the models Companies like Zoox are building autonomous vehicles And now we're seeing AI powered machines actually move through the real world The crazy part? We designed entire cities around the assumption that humans have to physically drive everything AI doesn't have that limitation Why send a pizza through 5 km of traffic when a machine can simply fly over it? The AI boom isn't just about replacing human work It's about removing constraints humans had to design around The next big AI companies might not live inside your browser They might be flying above your house Bookmark this so you wont miss the next delivery
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AI IS NO LONGER JUST WRITING CODE IT iS STARTING TO MOVE THINGS IN THE REAL WORLD. Someone just built a pizza delivery system where the drone does the driving No delivery car No traffic No driver sitting behind the wheel Just: -> Order comes in -> Drone picks up the pizza -> Flies directly to the destination -> Delivers it -> Returns And this is where the AI story gets interesting For years, the AI boom was mostly digital: > Chatbots > Coding agents > Image generation > AI music > AI video But the next phase is different AI is getting a body The same technology stack that started with models and GPUs is now moving into the physical world NVIDIA built the compute layer Researchers built the models Companies like Zoox are building autonomous vehicles And now we're seeing AI powered machines actually move through the real world The crazy part? We designed entire cities around the assumption that humans have to physically drive everything AI doesn't have that limitation Why send a pizza through 5 km of traffic when a machine can simply fly over it? The AI boom isn't just about replacing human work It's about removing constraints humans had to design around The next big AI companies might not live inside your browser They might be flying above your house Bookmark this so you wont miss the next delivery

0xSlyth

13,437 Aufrufe • vor 8 Tagen

You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI
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You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

Jonathan Stephens

17,712 Aufrufe • vor 7 Monaten

The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.
0:10

The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.

Santiago

30,244 Aufrufe • vor 1 Monat

The visionaries behind Pi Network are making waves this week in Miami 🔥 🎤 Chengdiao Fan 🗓️ May 6 | ⏰ 11:15–11:35 AM EDT 📍 Convergence Stage 💡 Topic: Aligning Web3, AI, and Blockchain for Utility She’ll dive into how Pi’s ecosystem — powered by blockchain, verified identity, and a global network — is shaping real-world, utility-driven products for the AI era. 🎤 Nicolas Kokkalis 🗓️ May 7 | ⏰ 10:15–10:45 AM EDT 📍 Convergence Stage 💡 Panel: How to Prove You’re Human in an AI World (Without Doxing Yourself) Exploring one of the biggest challenges today — building trust in a world where AI can mimic humans. 🌍 What This Means for Pi Network: ✨ Strong focus on real utility, not hype 🔐 Advanced identity verification with Pi KYC 🤖 Preparing for the AI-powered digital economy 🌐 Building a trusted, global ecosystem 💜 The future is being built right now — and Pi Network is at the center of it. 👋 See you there, Pioneers! #PiNetwork #Consensus2026 #Web3 #Blockchain #AI #Crypto #FutureTech
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The visionaries behind Pi Network are making waves this week in Miami 🔥 🎤 Chengdiao Fan 🗓️ May 6 | ⏰ 11:15–11:35 AM EDT 📍 Convergence Stage 💡 Topic: Aligning Web3, AI, and Blockchain for Utility She’ll dive into how Pi’s ecosystem — powered by blockchain, verified identity, and a global network — is shaping real-world, utility-driven products for the AI era. 🎤 Nicolas Kokkalis 🗓️ May 7 | ⏰ 10:15–10:45 AM EDT 📍 Convergence Stage 💡 Panel: How to Prove You’re Human in an AI World (Without Doxing Yourself) Exploring one of the biggest challenges today — building trust in a world where AI can mimic humans. 🌍 What This Means for Pi Network: ✨ Strong focus on real utility, not hype 🔐 Advanced identity verification with Pi KYC 🤖 Preparing for the AI-powered digital economy 🌐 Building a trusted, global ecosystem 💜 The future is being built right now — and Pi Network is at the center of it. 👋 See you there, Pioneers! #PiNetwork #Consensus2026 #Web3 #Blockchain #AI #Crypto #FutureTech

Pi Community ᵖⁱ ⁿᵉᵗʷᵒʳᵏ

18,979 Aufrufe • vor 3 Monaten

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:
0:10

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

113,347 Aufrufe • vor 1 Monat

WHY AI NEEDS ROBOTS TO BE THE ECONOMY We keep hearing about the rise of AI - in finance, in art, in code. But here’s the part Elon said out loud: AI doesn’t scale the economy without a body. Right now, AI lives in data centers. It writes essays, diagnoses symptoms, maybe even talks you through a breakup. But it doesn’t fix the plumbing. It doesn’t pick fruit. That’s the missing link: robots. Real-world productivity - the kind that moves GDP - still runs on physical labor. You can automate all the spreadsheets you want, but if strawberries rot in the field or a construction site goes unmanned, you’re not growing the economy. You’re just writing better emails about the shrinkage. AI alone is smart. But AI with wheels, arms, and joints? That’s transformative. Think warehouses where bots stock shelves without lunch breaks. Think 3D-printed houses built in days, not months. Think carebots helping aging populations bathe, cook, and live with dignity. That’s not sci-fi - it’s the only way economies with shrinking workforces and ballooning eldercare costs survive. China’s already there. Japan’s aging crisis is pushing robotic adoption into every corner of daily life. And yet, the conversation stays stuck on chatbots and copyright lawsuits. Because here’s the catch: merging AI with robotics doesn’t just replace jobs - it reshapes civilization. The shift is seismic. It means redefining what “employment” means when machines can work 24/7 and never strike. It’s an economic revolution that doesn’t just disrupt - it displaces. But the choice isn’t between utopia or dystopia. It’s between preparing or pretending. If we want AI to boost productivity, solve labor shortages, and pay off its hype, it needs more than brains. It needs bodies.
0:15

WHY AI NEEDS ROBOTS TO BE THE ECONOMY We keep hearing about the rise of AI - in finance, in art, in code. But here’s the part Elon said out loud: AI doesn’t scale the economy without a body. Right now, AI lives in data centers. It writes essays, diagnoses symptoms, maybe even talks you through a breakup. But it doesn’t fix the plumbing. It doesn’t pick fruit. That’s the missing link: robots. Real-world productivity - the kind that moves GDP - still runs on physical labor. You can automate all the spreadsheets you want, but if strawberries rot in the field or a construction site goes unmanned, you’re not growing the economy. You’re just writing better emails about the shrinkage. AI alone is smart. But AI with wheels, arms, and joints? That’s transformative. Think warehouses where bots stock shelves without lunch breaks. Think 3D-printed houses built in days, not months. Think carebots helping aging populations bathe, cook, and live with dignity. That’s not sci-fi - it’s the only way economies with shrinking workforces and ballooning eldercare costs survive. China’s already there. Japan’s aging crisis is pushing robotic adoption into every corner of daily life. And yet, the conversation stays stuck on chatbots and copyright lawsuits. Because here’s the catch: merging AI with robotics doesn’t just replace jobs - it reshapes civilization. The shift is seismic. It means redefining what “employment” means when machines can work 24/7 and never strike. It’s an economic revolution that doesn’t just disrupt - it displaces. But the choice isn’t between utopia or dystopia. It’s between preparing or pretending. If we want AI to boost productivity, solve labor shortages, and pay off its hype, it needs more than brains. It needs bodies.

Mario Nawfal

141,937 Aufrufe • vor 9 Monaten

Thrilled to unveil Youmio, our new brand identity that represents the next evolution of what we’ve been building. Agents are the biggest technological leap since the internet, destined to transform crypto, games, and entertainment. With Youmio, we are shaping the agentic era, where agents learn, play and entertain in revolutionary ways. 🚀 So far, 2D entertainment and social media agents dominate the market. 3D agents are rare, requiring advanced AI and game engine skills. Yet 3D agents, especially those in game engines, unlock groundbreaking opportunities. Time to unleash them. Youmio empowers anyone to create and deploy valuable agents that are on-chain, cross-platform and ready for 3D worlds. Here’s how: ⭐️ Youmio Agents Youmio Agents lets anyone design and personalize 3D agents, equip them with powerful agentic capabilities, interact with them in unique ways and trade seamlessly within a cross-platform browser experience. 🕹️ Youmio Worlds Previously known as Today The Game, Youmio Worlds is a petri dish AI simulation where users build & co-inhabit beautiful, living 3D worlds with autonomous agents. Build dynamic worlds where players interact with intelligent agents, manage resources and participate in a player-agent marketplace. Ancient and Mythic Seeds are the most powerful entry points into the Youmio Worlds ecosystem, generating rare and beautiful worlds that unlock unique opportunities. 📡 Interoperable 3D Agents With Youmio, you’re not limited to our ecosystem. Using our API, developers can integrate Youmio agents into other experiences built in Unity and Unreal. On top of this, agents from other frameworks can also join Youmio, creating a truly interconnected metaverse. 🎭 Welcome to Limbo Meet Limbo, the first AI agent built using Youmio tech. Paired with the power of Youmio Worlds, we’re creating the Limboverse - a unique AI Big Brother setting where Limbo and your favorite and most valuable agents coexist in an ever-evolving, narrative-driven environment that you, the audience, will shape. $LIMBO is the most powerful entry point into the Limboverse and will be stakable on the Youmio Agents platform for unique rewards. Thanks for reading everyone and thanks for being on this amazing journey with us.🌱
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Thrilled to unveil Youmio, our new brand identity that represents the next evolution of what we’ve been building. Agents are the biggest technological leap since the internet, destined to transform crypto, games, and entertainment. With Youmio, we are shaping the agentic era, where agents learn, play and entertain in revolutionary ways. 🚀 So far, 2D entertainment and social media agents dominate the market. 3D agents are rare, requiring advanced AI and game engine skills. Yet 3D agents, especially those in game engines, unlock groundbreaking opportunities. Time to unleash them. Youmio empowers anyone to create and deploy valuable agents that are on-chain, cross-platform and ready for 3D worlds. Here’s how: ⭐️ Youmio Agents Youmio Agents lets anyone design and personalize 3D agents, equip them with powerful agentic capabilities, interact with them in unique ways and trade seamlessly within a cross-platform browser experience. 🕹️ Youmio Worlds Previously known as Today The Game, Youmio Worlds is a petri dish AI simulation where users build & co-inhabit beautiful, living 3D worlds with autonomous agents. Build dynamic worlds where players interact with intelligent agents, manage resources and participate in a player-agent marketplace. Ancient and Mythic Seeds are the most powerful entry points into the Youmio Worlds ecosystem, generating rare and beautiful worlds that unlock unique opportunities. 📡 Interoperable 3D Agents With Youmio, you’re not limited to our ecosystem. Using our API, developers can integrate Youmio agents into other experiences built in Unity and Unreal. On top of this, agents from other frameworks can also join Youmio, creating a truly interconnected metaverse. 🎭 Welcome to Limbo Meet Limbo, the first AI agent built using Youmio tech. Paired with the power of Youmio Worlds, we’re creating the Limboverse - a unique AI Big Brother setting where Limbo and your favorite and most valuable agents coexist in an ever-evolving, narrative-driven environment that you, the audience, will shape. $LIMBO is the most powerful entry point into the Limboverse and will be stakable on the Youmio Agents platform for unique rewards. Thanks for reading everyone and thanks for being on this amazing journey with us.🌱

Youmio

126,399 Aufrufe • vor 1 Jahr

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.
0:18

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.

MrNeRF

290,202 Aufrufe • vor 9 Monaten

This week is already so hot. 🔥 Massive release from Decart : Lucy 2.0 a World Editing Model running at 1080p, 30FPS in realtime. This is truly exciting, the era of real-time generative reality is here. We are moving from watching AI video to living inside AI video. A breakthrough model capable of transforming the visual world in real-time. Moving beyond offline rendering, Lucy 2.0 delivers high-fidelity 1080p video generation with near-zero latency. Lucy 2.0 literally "redraws" the entire world pixel-by-pixel, while you are watching it. e.g. If you want to be an anime character, it doesn't just put a mask on you. It turns your skin into anime skin, your hair into anime hair, and the lighting in your room into anime lighting. Lucy 2.0 is also trained to stop the generated video from slowly falling apart over time, so the same stream can run much longer without faces and details drifting. So why is this a "Massive Deal"? Traditional AI video-generation model takes a prompt, you wait 10–20 minutes, and the computer "bakes" a video for you. You couldn't touch it or change it while it was happening. But Lucy 2.0 works like a mirror. It happens in real-time (30 frames per second). There is no waiting. You move your hand, the AI character moves its hand instantly. The craziest part isn't the visuals; it's the physics. Usually, AI hallucinations are glitchy—hands merge into faces, walls melt. Lucy 2.0 understands how the world works without being told. It knows that if you take off a helmet, there is hair underneath. It knows that if you splash water, droplets fly. It learned "physics" just by watching millions of videos. The physical behavior you see emerges from learned visual dynamics, not from engineered geometry or explicit physics engines. Their official technical report explicitly states that the model does not use traditional 3D engines, depth maps, or wireframes. It is a "pure diffusion model."
0:14

This week is already so hot. 🔥 Massive release from Decart : Lucy 2.0 a World Editing Model running at 1080p, 30FPS in realtime. This is truly exciting, the era of real-time generative reality is here. We are moving from watching AI video to living inside AI video. A breakthrough model capable of transforming the visual world in real-time. Moving beyond offline rendering, Lucy 2.0 delivers high-fidelity 1080p video generation with near-zero latency. Lucy 2.0 literally "redraws" the entire world pixel-by-pixel, while you are watching it. e.g. If you want to be an anime character, it doesn't just put a mask on you. It turns your skin into anime skin, your hair into anime hair, and the lighting in your room into anime lighting. Lucy 2.0 is also trained to stop the generated video from slowly falling apart over time, so the same stream can run much longer without faces and details drifting. So why is this a "Massive Deal"? Traditional AI video-generation model takes a prompt, you wait 10–20 minutes, and the computer "bakes" a video for you. You couldn't touch it or change it while it was happening. But Lucy 2.0 works like a mirror. It happens in real-time (30 frames per second). There is no waiting. You move your hand, the AI character moves its hand instantly. The craziest part isn't the visuals; it's the physics. Usually, AI hallucinations are glitchy—hands merge into faces, walls melt. Lucy 2.0 understands how the world works without being told. It knows that if you take off a helmet, there is hair underneath. It knows that if you splash water, droplets fly. It learned "physics" just by watching millions of videos. The physical behavior you see emerges from learned visual dynamics, not from engineered geometry or explicit physics engines. Their official technical report explicitly states that the model does not use traditional 3D engines, depth maps, or wireframes. It is a "pure diffusion model."

Rohan Paul

12,761 Aufrufe • vor 6 Monaten

I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.
0:10

I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.

Teslaconomics

25,494 Aufrufe • vor 4 Monaten

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.
0:15

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

Arvind Jain

280,790 Aufrufe • vor 2 Monaten

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷
0:13

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

Axis Robotics

27,858 Aufrufe • vor 7 Monaten

🚨 SCIENTISTS JUST BUILT A CHIP THAT CAN SEE, THINK, AND REMEMBER ALL AT THE SAME TIME. And it works more like a biological brain than a traditional computer. Researchers at RMIT University have created a neuromorphic vision chip that mimics the human eye and brain. Unlike conventional systems that capture images and send data to external processors, this chip performs sensing, processing, and memory storage directly where the light hits. The active layer is thousands of times thinner than a human hair. It uses doped indium oxide to detect light, process the information on-chip, and retain what it sees over time without constant electrical refreshing. Why this matters: • It dramatically cuts energy use and latency by eliminating data transfer to separate processors • Enables much faster real-time decision making for autonomous systems • Works more like biological vision than traditional machine vision • Could power the next generation of efficient edge AI in vehicles, robots, and remote sensors The deeper implication: For decades, we’ve built vision systems by bolting cameras, processors, and memory together like separate organs. This chip collapses those functions into one biological-style unit. It’s a step toward machines that don’t just “see” but actually perceive and remember in a more efficient, brain-like way. If scaled successfully, it could become a foundational component for autonomous systems that need to operate intelligently with minimal power and minimal delay. We’re moving from cameras that take pictures to chips that truly see. How do you think neuromorphic vision chips like this will change what’s possible for self-driving cars and autonomous robots? Follow for more frontier neuromorphic computing, AI hardware, and brain-inspired technology.
0:10

🚨 SCIENTISTS JUST BUILT A CHIP THAT CAN SEE, THINK, AND REMEMBER ALL AT THE SAME TIME. And it works more like a biological brain than a traditional computer. Researchers at RMIT University have created a neuromorphic vision chip that mimics the human eye and brain. Unlike conventional systems that capture images and send data to external processors, this chip performs sensing, processing, and memory storage directly where the light hits. The active layer is thousands of times thinner than a human hair. It uses doped indium oxide to detect light, process the information on-chip, and retain what it sees over time without constant electrical refreshing. Why this matters: • It dramatically cuts energy use and latency by eliminating data transfer to separate processors • Enables much faster real-time decision making for autonomous systems • Works more like biological vision than traditional machine vision • Could power the next generation of efficient edge AI in vehicles, robots, and remote sensors The deeper implication: For decades, we’ve built vision systems by bolting cameras, processors, and memory together like separate organs. This chip collapses those functions into one biological-style unit. It’s a step toward machines that don’t just “see” but actually perceive and remember in a more efficient, brain-like way. If scaled successfully, it could become a foundational component for autonomous systems that need to operate intelligently with minimal power and minimal delay. We’re moving from cameras that take pictures to chips that truly see. How do you think neuromorphic vision chips like this will change what’s possible for self-driving cars and autonomous robots? Follow for more frontier neuromorphic computing, AI hardware, and brain-inspired technology.

TheNewPhysics

23,196 Aufrufe • vor 2 Monaten

Just weeks ago I considered this to be a map of Superchargers. The foundation that made EV’s a going concern for the first time. But it’s not. These dots represent fully permitted and built power stations in an era where every AI player is trying to figure out how to get power for compute. This is the largest electrical power moat I’ve ever seen that can fully supply current and future charging needs while collecting and storing cheaper power during off-peak to supply an AI revolution. Elon doesn’t care who knows anymore. 2nd place is… crumbs. AWS was built from excess server space needed during peak demand. Now it’s bigger and more valuable than the core Amazon retail business. Tesla is about to drop a hardware smackdown right in the face of software engineers. AI prototypes are easy. Scaling is hard. If only Tesla had a factory pouring out millions of chips each year that are underutilized 95% of the time. Each with their own battery storage. Imagine that. Tesla could call them ‘cars’. And that power moat? It’s all fully upgradable. It’s primed to be tripled. Only a handful of Tesla charging stations currently have solar and/or Megapacks. For 14 years Tesla has permitted and built the gas stations of the future around the world. Everyone said it would fail. All of them were wrong. And now Tesla is in a process of creating a possible 10x value stamp on those stations that should have never been built. It makes you wonder, When a vehicle now leaves the Tesla factory; how do we even begin to calculate the economic production that it will generate during its lifetime? It’s a Robotaxi, energy storage, and a fully sealed/cooled compute processor. They may as well start shitting gold out of a tailpipe.
0:15

Just weeks ago I considered this to be a map of Superchargers. The foundation that made EV’s a going concern for the first time. But it’s not. These dots represent fully permitted and built power stations in an era where every AI player is trying to figure out how to get power for compute. This is the largest electrical power moat I’ve ever seen that can fully supply current and future charging needs while collecting and storing cheaper power during off-peak to supply an AI revolution. Elon doesn’t care who knows anymore. 2nd place is… crumbs. AWS was built from excess server space needed during peak demand. Now it’s bigger and more valuable than the core Amazon retail business. Tesla is about to drop a hardware smackdown right in the face of software engineers. AI prototypes are easy. Scaling is hard. If only Tesla had a factory pouring out millions of chips each year that are underutilized 95% of the time. Each with their own battery storage. Imagine that. Tesla could call them ‘cars’. And that power moat? It’s all fully upgradable. It’s primed to be tripled. Only a handful of Tesla charging stations currently have solar and/or Megapacks. For 14 years Tesla has permitted and built the gas stations of the future around the world. Everyone said it would fail. All of them were wrong. And now Tesla is in a process of creating a possible 10x value stamp on those stations that should have never been built. It makes you wonder, When a vehicle now leaves the Tesla factory; how do we even begin to calculate the economic production that it will generate during its lifetime? It’s a Robotaxi, energy storage, and a fully sealed/cooled compute processor. They may as well start shitting gold out of a tailpipe.

No Safe Words

56,727 Aufrufe • vor 1 Monat