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Welding robots. The original use case for industrial automation—first deployed in the 1960s at GM. 60 years later, they dominate automotive body shops. Hundreds of robots in perfect sync, laying thousands of spot welds per vehicle. But step outside high-volume production and they're rare. Most fab shops still weld... show more

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78,283 просмотров • 10 месяцев назад •via X (Twitter)

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Elon Musk: "We’re in the final stages of completion of Optimus 3, which is really going to be by far the most advanced robot in the world, nothing's even close. In fact, I haven’t even sent any demos of robots that are as good as Optimus 3, frankly. We’ll start production on Optimus 3 this summer, but very slow at first. And then probably reach high-volume production around summer next year. We’ll have Optimus 4 design complete by next year like, we will try to release a new robot design every year, an improved robot design every year."

Elon Musk: "We’re in the final stages of completion of Optimus 3, which is really going to be by far the most advanced robot in the world, nothing's even close. In fact, I haven’t even sent any demos of robots that are as good as Optimus 3, frankly. We’ll start production on Optimus 3 this summer, but very slow at first. And then probably reach high-volume production around summer next year. We’ll have Optimus 4 design complete by next year like, we will try to release a new robot design every year, an improved robot design every year."

Mars University

201,416 просмотров • 3 месяцев назад

Elon Musk says every new chip fab takes five years from start to volume. Every AI company on Earth needed more chips by 2030. OpenAI. Anthropic. Google. Meta. Each one had built the budget. The chips didn't exist yet. Capacity through 2030 had already been booked at every major fab. "I ask TSMC or Samsung, okay, what's the timeframe to get to volume production?" Break ground. Climb the yield curve. Reach volume. Each stage took at least a year, and the curve refused to flatten. Yield meant percent of wafers that ship. Yield meant survival. Then Musk dropped the actual number. "That, from start to finish, is a five-year period." Musk gave the constraint a name: **the five-year fab clock**. Five years. No shortcuts. Musk, who had prepaid TSMC and Samsung through 2030, knew the clock. His own AI5 chip targets production in 2026's second quarter. A new fab needed to break ground, install ASML tools, train tens of thousands of operators, run pilot wafers, debug each process step, and ramp from one wafer per day to fifty thousand. TSMC Taiwan. Samsung Korea. TSMC Arizona. Samsung Texas. All booked. Every shovel of dirt accounted for. After Musk drew the clock, every 2027 AI promise became unfulfillable. Compute would have to wait for cement to dry. Musk, on the suppliers who were trying: "They're going balls to the wall, as fast as they can. It's still not fast enough." What ceiling on your roadmap is bound by physical constraints nobody is naming? P.S. If you want to stop overthinking, control chaos, and navigate any decision with the clarity... I made a full playbook breaking down the timeless decision-making mental models used by history's greatest thinkers. Comment "models" and follow GeniusThinking so I can DM you a copy. If you're new here, GeniusThinking is a gallery for the greatest minds in economics, psychology, and history. Follow along for more similar content. — Elon Musk ( Elon Musk ), CEO of Tesla and SpaceX, on Dwarkesh Patel's ( Dwarkesh Patel ) podcast

Elon Musk says every new chip fab takes five years from start to volume. Every AI company on Earth needed more chips by 2030. OpenAI. Anthropic. Google. Meta. Each one had built the budget. The chips didn't exist yet. Capacity through 2030 had already been booked at every major fab. "I ask TSMC or Samsung, okay, what's the timeframe to get to volume production?" Break ground. Climb the yield curve. Reach volume. Each stage took at least a year, and the curve refused to flatten. Yield meant percent of wafers that ship. Yield meant survival. Then Musk dropped the actual number. "That, from start to finish, is a five-year period." Musk gave the constraint a name: the five-year fab clock. Five years. No shortcuts. Musk, who had prepaid TSMC and Samsung through 2030, knew the clock. His own AI5 chip targets production in 2026's second quarter. A new fab needed to break ground, install ASML tools, train tens of thousands of operators, run pilot wafers, debug each process step, and ramp from one wafer per day to fifty thousand. TSMC Taiwan. Samsung Korea. TSMC Arizona. Samsung Texas. All booked. Every shovel of dirt accounted for. After Musk drew the clock, every 2027 AI promise became unfulfillable. Compute would have to wait for cement to dry. Musk, on the suppliers who were trying: "They're going balls to the wall, as fast as they can. It's still not fast enough." What ceiling on your roadmap is bound by physical constraints nobody is naming? P.S. If you want to stop overthinking, control chaos, and navigate any decision with the clarity... I made a full playbook breaking down the timeless decision-making mental models used by history's greatest thinkers. Comment "models" and follow GeniusThinking so I can DM you a copy. If you're new here, GeniusThinking is a gallery for the greatest minds in economics, psychology, and history. Follow along for more similar content. — Elon Musk ( Elon Musk ), CEO of Tesla and SpaceX, on Dwarkesh Patel's ( Dwarkesh Patel ) podcast

GeniusThinking

93,319 просмотров • 4 месяцев назад

Elon Musk reveals Tesla is building a 30,000-robot academy where humanoids learn from each other. Cars were easy. Tesla had ten million on the road, beaming back driving data every second. But humanoid robots? There weren't ten million Optimi yet. There weren't ten. Robotics had run data-starved for decades. Tesla decided to fix it. You couldn't train a humanoid that had never been deployed. So Musk built a school for them instead. "We can have at least 10,000 Optimus robots, maybe 20-30,000, that are doing self-play and testing different tasks." Tesla called it the Optimus Academy. Picture a warehouse the size of a chip fab. Thirty thousand humanoid robots inside. Picking things up. Folding clothes. Walking. Tripping. Catching themselves. Failing in ways no human roboticist had thought to script. Each watching the others, learning what the human body shouldn't have made look easy. Every move generated a data point. Every failure generated a sample. Every robot taught every other robot. In simulation, Tesla could spin up a million robots overnight. But simulated physics lied about friction, slip, and drift. Real physics didn't. Cars learned from drivers. Optimi learned from each other. Each generation made the next one cheaper, faster, smarter. By the tenth generation, no human would recognize the curriculum. Recursive learning at electromechanical scale. Musk, on closing the loop: "You use the tens of thousands of robots in the real world to close the simulation to reality gap." Whoever opened the academy first owned the species. P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Grab a free copy here: If you're new here, follow Genius Thinking for content on the greatest minds in economics, psychology, and history. — Elon Musk ( Elon Musk ), CEO of Tesla and SpaceX, on Dwarkesh Patel's ( Dwarkesh Patel ) podcast

Elon Musk reveals Tesla is building a 30,000-robot academy where humanoids learn from each other. Cars were easy. Tesla had ten million on the road, beaming back driving data every second. But humanoid robots? There weren't ten million Optimi yet. There weren't ten. Robotics had run data-starved for decades. Tesla decided to fix it. You couldn't train a humanoid that had never been deployed. So Musk built a school for them instead. "We can have at least 10,000 Optimus robots, maybe 20-30,000, that are doing self-play and testing different tasks." Tesla called it the Optimus Academy. Picture a warehouse the size of a chip fab. Thirty thousand humanoid robots inside. Picking things up. Folding clothes. Walking. Tripping. Catching themselves. Failing in ways no human roboticist had thought to script. Each watching the others, learning what the human body shouldn't have made look easy. Every move generated a data point. Every failure generated a sample. Every robot taught every other robot. In simulation, Tesla could spin up a million robots overnight. But simulated physics lied about friction, slip, and drift. Real physics didn't. Cars learned from drivers. Optimi learned from each other. Each generation made the next one cheaper, faster, smarter. By the tenth generation, no human would recognize the curriculum. Recursive learning at electromechanical scale. Musk, on closing the loop: "You use the tens of thousands of robots in the real world to close the simulation to reality gap." Whoever opened the academy first owned the species. P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Grab a free copy here: If you're new here, follow Genius Thinking for content on the greatest minds in economics, psychology, and history. — Elon Musk ( Elon Musk ), CEO of Tesla and SpaceX, on Dwarkesh Patel's ( Dwarkesh Patel ) podcast

Genius Thinking

136,481 просмотров • 4 месяцев назад

Elon Musk: Most engineering is done with people who don’t have PhDs. You don’t need a PhD for that — but you do need competent personnel. Right now, Tesla is pedal to the metal, going as fast as possible to get AI5 chip design into production, reach full scale, and secure every chip fab we possibly can. If everything goes at maximum speed, AI5 will reach production around the second quarter of next year, and then AI6 would hopefully follow less than a year later. We are using TSMC Taiwan, Samsung Korea, TSMC Arizona, and Samsung Texas — and even after securing all of that, we still don’t have enough capacity. When you ask, “What’s the timeframe to reach volume production?” the answer is: You’ve got to build the fab, start production, climb the yield curve, and reach high-yield volume production — and that takes about 5 years.

Elon Musk: Most engineering is done with people who don’t have PhDs. You don’t need a PhD for that — but you do need competent personnel. Right now, Tesla is pedal to the metal, going as fast as possible to get AI5 chip design into production, reach full scale, and secure every chip fab we possibly can. If everything goes at maximum speed, AI5 will reach production around the second quarter of next year, and then AI6 would hopefully follow less than a year later. We are using TSMC Taiwan, Samsung Korea, TSMC Arizona, and Samsung Texas — and even after securing all of that, we still don’t have enough capacity. When you ask, “What’s the timeframe to reach volume production?” the answer is: You’ve got to build the fab, start production, climb the yield curve, and reach high-yield volume production — and that takes about 5 years.

Ian Miles Cheong

92,034 просмотров • 8 месяцев назад

Robot dogs are now welders! 👨🏼‍🏭 Path Robotics launches a mobile welding robot on quadruped platform, called Rove. This quadruped robotic welding system is paired with their Obsidian physical AI model. Obsidian already delivers autonomous, adaptive welding inside fixed cells. Rove brings intelligent welding to the part rather than moving the part to a fixed workstation. Shipbuilding, heavy construction, and large-scale fabrication share a constraint, massive assemblies and workpieces that cannot be moved or fixtured inside welding cells. These conditions have limited welding automation in environments that need it most, just as skilled welder shortages reach critical levels. Legged robots have long been considered too unstable for precision welding. Rove changes that. Obsidian provides the perception and adaptability needed to operate in high-variability environments. Saronic Technologies, a leader in autonomous maritime vessels, is one of the first early adopters, integrating Rove into its physical AI-powered shipbuilding operations in Franklin, Louisiana. 🇺🇸 Welding requires millimeter-level precision on a moving quadruped platform, considered impossible until AI-based perception and control made it feasible. 🔥 Andrew Lonsberry gotta be honest, but a welding robot dog wasn't on bingo card for 2026. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Robot dogs are now welders! 👨🏼‍🏭 Path Robotics launches a mobile welding robot on quadruped platform, called Rove. This quadruped robotic welding system is paired with their Obsidian physical AI model. Obsidian already delivers autonomous, adaptive welding inside fixed cells. Rove brings intelligent welding to the part rather than moving the part to a fixed workstation. Shipbuilding, heavy construction, and large-scale fabrication share a constraint, massive assemblies and workpieces that cannot be moved or fixtured inside welding cells. These conditions have limited welding automation in environments that need it most, just as skilled welder shortages reach critical levels. Legged robots have long been considered too unstable for precision welding. Rove changes that. Obsidian provides the perception and adaptability needed to operate in high-variability environments. Saronic Technologies, a leader in autonomous maritime vessels, is one of the first early adopters, integrating Rove into its physical AI-powered shipbuilding operations in Franklin, Louisiana. 🇺🇸 Welding requires millimeter-level precision on a moving quadruped platform, considered impossible until AI-based perception and control made it feasible. 🔥 Andrew Lonsberry gotta be honest, but a welding robot dog wasn't on bingo card for 2026. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

10,227 просмотров • 5 месяцев назад

$Elon Musk on Terafab: "It's worth noting that there's not a single high volume computer memory fab in America right now, zero. There's one being built by Micron, but that will not reach volume production until I believe 2028 and there's something built in New York, but they are in, I think, 2029 and 2030, and this is a tiny fraction of the memory that's needed, and in fact, even if you take the best case assumptions of the memory makers and the logic makers, it is not enough to meet the demand that is anticipated, which is why you're seeing stocks of like Micron go to, I think, 1.2 trillion, or some quite high number, so there's just clearly a need for AI logic memory and packaging, AI computers, essentially, that is far beyond what even the best case assumptions of the existing fabricators can do, and that's why we need to do the Terafab. It seems essential, otherwise we will not, there will not be enough chips."$

Elon Musk on Terafab: "It's worth noting that there's not a single high volume computer memory fab in America right now, zero. There's one being built by Micron, but that will not reach volume production until I believe 2028 and there's something built in New York, but they are in, I think, 2029 and 2030, and this is a tiny fraction of the memory that's needed, and in fact, even if you take the best case assumptions of the memory makers and the logic makers, it is not enough to meet the demand that is anticipated, which is why you're seeing stocks of like Micron go to, I think, 1.2 trillion, or some quite high number, so there's just clearly a need for AI logic memory and packaging, AI computers, essentially, that is far beyond what even the best case assumptions of the existing fabricators can do, and that's why we need to do the Terafab. It seems essential, otherwise we will not, there will not be enough chips."

DogeDesigner

148,233 просмотров • 2 месяцев назад

500 humanoid robots replacing humans in high-voltage operations What does that look like? Steel against steel,instead of flesh and blood. This marks a turning point for China’s State Grid, shifting from human-based maintenance to autonomous operations. This year, State Grid announced plans to procure 8,500 embodied AI robots, with a total budget of RMB 6.8 billion (~$1 billion). These robots will be deployed across four major scenarios: power inspection, live-line operations, emergency response, and warehouse logistics,covering more than 600 specific task scenarios. Among them, humanoid robots for live-line operations are the most expensive and strategically critical: 500 units with a budget of RMB 2.5 billion (~$370 million). They will be deployed in distribution network live-line work and ultra-high-voltage (UHV) projects, replacing humans in high-risk tasks. Workers will transition into supervisory roles, ready to take over remotely when needed. As early as last year, State Grid had already validated the feasibility of humanoid robots for substation inspection. Tienkung can autonomously perform inspection tasks at a State Grid substation in Beijing. Of course, suppliers are not limited to X-Humanoid,players like Unitree, AGIBOT, DeepRobotics, UBTECH, and Fourier are all involved. These 500 humanoid robots will also collaborate with 5,000 inspection quadruped robots and 3,000 dual-arm wheeled robots for indoor substation maintenance,together forming an intelligent, automated, and collaborative network for autonomous grid operations. What does this change? According to State Grid, each embodied AI unit can save RMB 500,000 to 800,000 (~$70,000–$110,000) in annual labor costs, with a payback period of around 2–3 years. Inspection efficiency increases by 5x, fault response time is reduced by 60%, and power supply reliability improves by 0.5 percentage points. More importantly, over 90% of human exposure to high-risk operations can be eliminated, reducing safety incidents by 80%. At another level, for humanoid robot companies, the center of R&D and iteration is shifting to the customer site. Real-world physical interaction becomes the fastest feedback loop,accelerating innovation and evolution. And 8,500 units are just the beginning of scaled deployment. Based on current plans, embodied AI robots will cover 30% of key areas in State Grid by 2026, 80% of high-risk operation scenarios by 2027, and enable fully autonomous operations by 2030. The demand roadmap is clear: define use cases ->deploy at scale->improve models and robots->expand further. 8,500… 50,000… 100,000… But remember,power grids are just one part of China’s vast infrastructure system. The experience of autonomous robotic operations here can be replicated across other sectors, such as broader energy systems. That, in itself, is another story. P.S.The video shows Tienkung 1.0 autonomously performing substation inspection tasks (2025).

500 humanoid robots replacing humans in high-voltage operations What does that look like? Steel against steel,instead of flesh and blood. This marks a turning point for China’s State Grid, shifting from human-based maintenance to autonomous operations. This year, State Grid announced plans to procure 8,500 embodied AI robots, with a total budget of RMB 6.8 billion (~$1 billion). These robots will be deployed across four major scenarios: power inspection, live-line operations, emergency response, and warehouse logistics,covering more than 600 specific task scenarios. Among them, humanoid robots for live-line operations are the most expensive and strategically critical: 500 units with a budget of RMB 2.5 billion (~$370 million). They will be deployed in distribution network live-line work and ultra-high-voltage (UHV) projects, replacing humans in high-risk tasks. Workers will transition into supervisory roles, ready to take over remotely when needed. As early as last year, State Grid had already validated the feasibility of humanoid robots for substation inspection. Tienkung can autonomously perform inspection tasks at a State Grid substation in Beijing. Of course, suppliers are not limited to X-Humanoid,players like Unitree, AGIBOT, DeepRobotics, UBTECH, and Fourier are all involved. These 500 humanoid robots will also collaborate with 5,000 inspection quadruped robots and 3,000 dual-arm wheeled robots for indoor substation maintenance,together forming an intelligent, automated, and collaborative network for autonomous grid operations. What does this change? According to State Grid, each embodied AI unit can save RMB 500,000 to 800,000 (~$70,000–$110,000) in annual labor costs, with a payback period of around 2–3 years. Inspection efficiency increases by 5x, fault response time is reduced by 60%, and power supply reliability improves by 0.5 percentage points. More importantly, over 90% of human exposure to high-risk operations can be eliminated, reducing safety incidents by 80%. At another level, for humanoid robot companies, the center of R&D and iteration is shifting to the customer site. Real-world physical interaction becomes the fastest feedback loop,accelerating innovation and evolution. And 8,500 units are just the beginning of scaled deployment. Based on current plans, embodied AI robots will cover 30% of key areas in State Grid by 2026, 80% of high-risk operation scenarios by 2027, and enable fully autonomous operations by 2030. The demand roadmap is clear: define use cases ->deploy at scale->improve models and robots->expand further. 8,500… 50,000… 100,000… But remember,power grids are just one part of China’s vast infrastructure system. The experience of autonomous robotic operations here can be replicated across other sectors, such as broader energy systems. That, in itself, is another story. P.S.The video shows Tienkung 1.0 autonomously performing substation inspection tasks (2025).

CyberRobo

46,782 просмотров • 5 месяцев назад

NEWS: Figure has unveiling its 3rd generation humanoid robot. • Features a completely redesigned sensory suite and hand system. Wireless charging in feet. • Next-generation vision system engineered for high-frequency visuomotor control. Its new camera architecture delivers twice the frame rate, one-quarter the latency, and a 60% wider field of view per camera within a more compact form factor • Soft goods, wireless charging, improved audio system for voice reasoning, and battery safety advancements • "Engineered from the ground-up for high-volume manufacturing. In order to scale, we established a new supply chain and entirely new process for manufacturing humanoid robots at BotQ." • Lower manufacturing cost • Softer, more adaptive fingertips increase surface contact area, enabling more stable grasps across objects of varied shapes and sizes. Each fingertip sensor can detect forces as small as three grams of pressure - sensitive enough to register the weight of a paperclip resting on your finger. • Multi-density foam to protect against pinch points, and is covered in soft textiles rather than hard machined parts. 9% less mass and less volume than Figure 02 • 10 Gbps mmWave data offload capability, allowing the entire fleet to upload terabytes of data • Upgraded audio hardware system for better real time speech-to-speech. Its speaker is twice the size and nearly four times more powerful, while the microphone has been repositioned for improved performance and clarity. • Charging coils in the robot’s feet allow it to simply step onto a wireless stand and charge at 2 kW Figure: "BotQ is Figure’s dedicated manufacturing facility designed to scale robot production. BotQ’s first-generation manufacturing line will initially be capable of producing up to 12,000 humanoid robots per year, with the goal of producing a total of 100,000 robots over the next four years. Instead of relying on contract manufacturers, Figure brought production of its most critical systems in-house to maintain tight control over quality, iteration, and speed."

NEWS: Figure has unveiling its 3rd generation humanoid robot. • Features a completely redesigned sensory suite and hand system. Wireless charging in feet. • Next-generation vision system engineered for high-frequency visuomotor control. Its new camera architecture delivers twice the frame rate, one-quarter the latency, and a 60% wider field of view per camera within a more compact form factor • Soft goods, wireless charging, improved audio system for voice reasoning, and battery safety advancements • "Engineered from the ground-up for high-volume manufacturing. In order to scale, we established a new supply chain and entirely new process for manufacturing humanoid robots at BotQ." • Lower manufacturing cost • Softer, more adaptive fingertips increase surface contact area, enabling more stable grasps across objects of varied shapes and sizes. Each fingertip sensor can detect forces as small as three grams of pressure - sensitive enough to register the weight of a paperclip resting on your finger. • Multi-density foam to protect against pinch points, and is covered in soft textiles rather than hard machined parts. 9% less mass and less volume than Figure 02 • 10 Gbps mmWave data offload capability, allowing the entire fleet to upload terabytes of data • Upgraded audio hardware system for better real time speech-to-speech. Its speaker is twice the size and nearly four times more powerful, while the microphone has been repositioned for improved performance and clarity. • Charging coils in the robot’s feet allow it to simply step onto a wireless stand and charge at 2 kW Figure: "BotQ is Figure’s dedicated manufacturing facility designed to scale robot production. BotQ’s first-generation manufacturing line will initially be capable of producing up to 12,000 humanoid robots per year, with the goal of producing a total of 100,000 robots over the next four years. Instead of relying on contract manufacturers, Figure brought production of its most critical systems in-house to maintain tight control over quality, iteration, and speed."

Sawyer Merritt

140,241 просмотров • 1 год назад

Elon just confirmed how long every working human has left before the robots take over. "AI will probably increase the global economy by 20 to 30%. That's my rough estimate. Meaning, on the order of 20 to 30 trillion per year." So that's a second United States economy appearing out of thin air, EVERY single year. And Elon's whole point is that the machines produce it, not the people. He even gave us the exact date for the digital half: "AI will be able to do anything digital, anything that does not require the shaping of atoms by hand, by the end of next year." By the end of next year... And on software specifically: "It's going to be impossible for a human to compete in writing software with AI." So every coder, every analyst, every job that lives on a screen is on a 12 to 18 month timer, straight from the richest man alive. But that's only the part that runs on electricity. And this is where it gets crazy... "There will be at least a billion robots in 10 years, and each will produce at least five times the output of a human." "The billion humanoid robots will be more productive than all humans combined." A billion machines, out-working ALL 8 billion of us. And he put a deadline on it: 10 years. And he even called that estimate small. So the version where robots out-produce the entire human race is the number he treats as safe. He said he'd bet serious money on it. But how does a billion of anything get built that fast? Robots building robots building robots. It starts slow and then it goes vertical. That’s how a few thousand becomes a billion. Elon also explained his own formula for how useful a robot actually is. He said it's the quality of the AI software, times the quality of the AI chip, times the dexterity of the hands. Software and chips are the exact things Elon and Nvidia have spent a decade making exponential. The third one is the hand. And the hand is the thing robotics has been stuck on for 40 years. A machine can crush the best human alive at chess and still can't pick a strange object off a messy table the way a toddler can. Grip, pressure, touch, knowing how hard to squeeze. That problem has barely moved while everything digital went vertical. The thing he named as the hard part is the exact thing his whole 10 year number depends on. So it all comes down to one variable, and it's the slowest-moving one in the entire machine: The robot hand. The brain is basically solved. The fingers have to catch up to it this decade, at planetary scale. Now to be fair, this is also where the most money on Earth is pointed right now. Tesla and a dozen others are throwing everything at exactly this problem. Elon's software calls tend to land but his atoms calls tend to fail. Because anything physical always takes longer than anything digital. He's also made this exact robot call before with wildly different numbers, 10 billion of them by 2040 in one speech, five per human in another. So here's the real question: Do you believe robot hands get solved inside 10 years? Because that one bet is the core of Elon‘s prediction. Everything else already came true.

Elon just confirmed how long every working human has left before the robots take over. "AI will probably increase the global economy by 20 to 30%. That's my rough estimate. Meaning, on the order of 20 to 30 trillion per year." So that's a second United States economy appearing out of thin air, EVERY single year. And Elon's whole point is that the machines produce it, not the people. He even gave us the exact date for the digital half: "AI will be able to do anything digital, anything that does not require the shaping of atoms by hand, by the end of next year." By the end of next year... And on software specifically: "It's going to be impossible for a human to compete in writing software with AI." So every coder, every analyst, every job that lives on a screen is on a 12 to 18 month timer, straight from the richest man alive. But that's only the part that runs on electricity. And this is where it gets crazy... "There will be at least a billion robots in 10 years, and each will produce at least five times the output of a human." "The billion humanoid robots will be more productive than all humans combined." A billion machines, out-working ALL 8 billion of us. And he put a deadline on it: 10 years. And he even called that estimate small. So the version where robots out-produce the entire human race is the number he treats as safe. He said he'd bet serious money on it. But how does a billion of anything get built that fast? Robots building robots building robots. It starts slow and then it goes vertical. That’s how a few thousand becomes a billion. Elon also explained his own formula for how useful a robot actually is. He said it's the quality of the AI software, times the quality of the AI chip, times the dexterity of the hands. Software and chips are the exact things Elon and Nvidia have spent a decade making exponential. The third one is the hand. And the hand is the thing robotics has been stuck on for 40 years. A machine can crush the best human alive at chess and still can't pick a strange object off a messy table the way a toddler can. Grip, pressure, touch, knowing how hard to squeeze. That problem has barely moved while everything digital went vertical. The thing he named as the hard part is the exact thing his whole 10 year number depends on. So it all comes down to one variable, and it's the slowest-moving one in the entire machine: The robot hand. The brain is basically solved. The fingers have to catch up to it this decade, at planetary scale. Now to be fair, this is also where the most money on Earth is pointed right now. Tesla and a dozen others are throwing everything at exactly this problem. Elon's software calls tend to land but his atoms calls tend to fail. Because anything physical always takes longer than anything digital. He's also made this exact robot call before with wildly different numbers, 10 billion of them by 2040 in one speech, five per human in another. So here's the real question: Do you believe robot hands get solved inside 10 years? Because that one bet is the core of Elon‘s prediction. Everything else already came true.

Ricardo

450,993 просмотров • 1 месяц назад

Most students chasing quant jobs are optimizing for the wrong thing: Agustin Lebron (Agustin Lebron), ex-Jane Street trader and author of The Laws of Trading, explains: "The number of seats at good shops doing interesting work is actually relatively small. Probably only a couple thousand new hires per year worldwide." "If you ask students why they want this, they'll say the math and stats and ML are interesting. But there are plenty of places to apply those skillsets." "If you dig deep, the real answer is it's a high status thing to do. At MIT or Harvard, the high status internship is Jane Street or HRT." "A lot of people say 'I've wanted to be a trader my whole life.' The true answer is probably 'I wanted to be rich my whole life, and this is my best plan to get there.'" "The vision from the outside of what the work is — is very, very different from what the actual work is." "Some people find it super engaging. For others it's like, this really isn't my life's work. But I'm kind of stuck here." "Quant trading is just a tiny sliver of what's interesting."

Most students chasing quant jobs are optimizing for the wrong thing: Agustin Lebron (Agustin Lebron), ex-Jane Street trader and author of The Laws of Trading, explains: "The number of seats at good shops doing interesting work is actually relatively small. Probably only a couple thousand new hires per year worldwide." "If you ask students why they want this, they'll say the math and stats and ML are interesting. But there are plenty of places to apply those skillsets." "If you dig deep, the real answer is it's a high status thing to do. At MIT or Harvard, the high status internship is Jane Street or HRT." "A lot of people say 'I've wanted to be a trader my whole life.' The true answer is probably 'I wanted to be rich my whole life, and this is my best plan to get there.'" "The vision from the outside of what the work is — is very, very different from what the actual work is." "Some people find it super engaging. For others it's like, this really isn't my life's work. But I'm kind of stuck here." "Quant trading is just a tiny sliver of what's interesting."

Ethan Kho

64,816 просмотров • 6 месяцев назад

A robotic ballet! 🩰 Coordinating multiple robot arms on a busy factory floor is notoriously complex. Each arm needs to move without colliding with its neighbors or the surrounding equipment, and today that planning is still mostly done by hand, a process that takes specialists hundreds of hours. Researchers at UCL, Google DeepMind, and Intrinsic have introduced RoboBallet, a new AI system that tackles this challenge head-on. Using a combination of graph neural networks and reinforcement learning, RoboBallet learns how to coordinate many robots in real time, generating smooth, collision-free motion plans in seconds instead of days. In tests, the system handled up to 40 tasks with eight arms working together, far beyond the limits of traditional planners. More importantly, it showed strong generalization: RoboBallet could adapt instantly to new factory layouts or recover if one robot failed, something previous methods struggled with. For manufacturers, this could mean faster deployment of automation, less downtime, and the ability to reconfigure production lines on the fly. Applications range from automotive welding to electronics assembly, and even large-scale construction. The current system focuses on reaching tasks, but the team plans to expand it to more complex operations like pick-and-place or painting. Here's the paper:

A robotic ballet! 🩰 Coordinating multiple robot arms on a busy factory floor is notoriously complex. Each arm needs to move without colliding with its neighbors or the surrounding equipment, and today that planning is still mostly done by hand, a process that takes specialists hundreds of hours. Researchers at UCL, Google DeepMind, and Intrinsic have introduced RoboBallet, a new AI system that tackles this challenge head-on. Using a combination of graph neural networks and reinforcement learning, RoboBallet learns how to coordinate many robots in real time, generating smooth, collision-free motion plans in seconds instead of days. In tests, the system handled up to 40 tasks with eight arms working together, far beyond the limits of traditional planners. More importantly, it showed strong generalization: RoboBallet could adapt instantly to new factory layouts or recover if one robot failed, something previous methods struggled with. For manufacturers, this could mean faster deployment of automation, less downtime, and the ability to reconfigure production lines on the fly. Applications range from automotive welding to electronics assembly, and even large-scale construction. The current system focuses on reaching tasks, but the team plans to expand it to more complex operations like pick-and-place or painting. Here's the paper:

Lukas Ziegler

26,083 просмотров • 1 год назад

Elon Musk revealed why the economy will be 10X its size in 10 years: 1. We are already inside a hard takeoff. Every time Musk goes to sleep there is a massive AI breakthrough. When he wakes up there is another one. This is not approaching. It is happening right now. 2. AI is already building the next version of itself. Every successive model is built by the one before it. Humans are gradually being removed from the loop. Full automation of this process could arrive by end of this year. 3. The 10x economy in 10 years is his comfortable prediction. AI and robots will increase economic output by orders of magnitude. The only thing that stops it is a world war. Absent that, 10x in 10 years. 4. Human intelligence will become a microscopic minority. Not just on Earth but in the solar system. The intelligence that could run on available solar energy is so far beyond current human capacity that Musk says it will simply solve every problem humans can think of. 5. Output per person will become almost unbelievable. Tesla is not planning layoffs. They are growing their headcount. But what each person produces with AI assistance will reach numbers that are currently hard to even imagine. 6. Universal high income is coming, not universal basic income. AI and robots will produce so many goods and services so fast that they will outrun the money supply entirely. Prices fall. Everything gets cheaper. Eventually robots run out of things humans even want. 7. Money will eventually stop being relevant altogether. AI will not use human currency. It will care only about power and mass. Wattage and tonnage. Musk acknowledged the irony: just as he becomes the richest person on earth, money starts losing its meaning. 8. Everyone on earth will have better healthcare than the richest person alive today. Musk made this personal. He had neck surgery three times because the first two were done wrong. His back still hurts. He is allegedly the richest person on earth and still cannot get it fixed properly. AI solves this for everyone. 9. Optimus 3 production starts this summer. He calls it by far the most advanced robot in the world with nothing close. Slow volume this summer, high volume by next summer, a new robot design every year after that. 10. Progress in AI follows overlapping S curves. Each breakthrough looks like it will go to infinity, then hits diminishing returns, then another breakthrough resets the curve. We are not at the end of these curves. We are somewhere in the middle of a very long series of them.

Elon Musk revealed why the economy will be 10X its size in 10 years: 1. We are already inside a hard takeoff. Every time Musk goes to sleep there is a massive AI breakthrough. When he wakes up there is another one. This is not approaching. It is happening right now. 2. AI is already building the next version of itself. Every successive model is built by the one before it. Humans are gradually being removed from the loop. Full automation of this process could arrive by end of this year. 3. The 10x economy in 10 years is his comfortable prediction. AI and robots will increase economic output by orders of magnitude. The only thing that stops it is a world war. Absent that, 10x in 10 years. 4. Human intelligence will become a microscopic minority. Not just on Earth but in the solar system. The intelligence that could run on available solar energy is so far beyond current human capacity that Musk says it will simply solve every problem humans can think of. 5. Output per person will become almost unbelievable. Tesla is not planning layoffs. They are growing their headcount. But what each person produces with AI assistance will reach numbers that are currently hard to even imagine. 6. Universal high income is coming, not universal basic income. AI and robots will produce so many goods and services so fast that they will outrun the money supply entirely. Prices fall. Everything gets cheaper. Eventually robots run out of things humans even want. 7. Money will eventually stop being relevant altogether. AI will not use human currency. It will care only about power and mass. Wattage and tonnage. Musk acknowledged the irony: just as he becomes the richest person on earth, money starts losing its meaning. 8. Everyone on earth will have better healthcare than the richest person alive today. Musk made this personal. He had neck surgery three times because the first two were done wrong. His back still hurts. He is allegedly the richest person on earth and still cannot get it fixed properly. AI solves this for everyone. 9. Optimus 3 production starts this summer. He calls it by far the most advanced robot in the world with nothing close. Slow volume this summer, high volume by next summer, a new robot design every year after that. 10. Progress in AI follows overlapping S curves. Each breakthrough looks like it will go to infinity, then hits diminishing returns, then another breakthrough resets the curve. We are not at the end of these curves. We are somewhere in the middle of a very long series of them.

Yasmine Khosrowshahi

44,696 просмотров • 4 месяцев назад

People don't realize that factories in Asia and factories in the USA are totally different. I think AI is going to help the Asian factories much more than the American ones. Factories in Asia are high mix. Factories in the USA are low mix. This means that factories in the USA make significantly fewer products, whereas factories in Asia are way more flexible, mainly because of the huge differential in labor costs. The gigafactory in Austin, one of the biggest factories in the world, makes 2 products: Tesla Model Y The Cybertruck That's it. The factory in the video makes 1000s of different products every year. When you're making the same thing over and over again, maybe in a few different colors, you're much better off using normal automation which is lower error than AI and human design. But when you're making 1000s of different products, using AI to spin up product 1001 starts to kind of make sense. There is not time or logic to using traditional automation; you're better off using AI to make a fast quality control mechanism and then repair errors using your inexpensive human labor. People are viewing AI in factories like there is one type of factory, but the way factories run in high cost countries vs. low cost countries is wildly different. We're just at the beginning of what will be an epic change for not only manufacturing, but the cost and quality of the goods we consume. The real internet of things has arrived.

People don't realize that factories in Asia and factories in the USA are totally different. I think AI is going to help the Asian factories much more than the American ones. Factories in Asia are high mix. Factories in the USA are low mix. This means that factories in the USA make significantly fewer products, whereas factories in Asia are way more flexible, mainly because of the huge differential in labor costs. The gigafactory in Austin, one of the biggest factories in the world, makes 2 products: Tesla Model Y The Cybertruck That's it. The factory in the video makes 1000s of different products every year. When you're making the same thing over and over again, maybe in a few different colors, you're much better off using normal automation which is lower error than AI and human design. But when you're making 1000s of different products, using AI to spin up product 1001 starts to kind of make sense. There is not time or logic to using traditional automation; you're better off using AI to make a fast quality control mechanism and then repair errors using your inexpensive human labor. People are viewing AI in factories like there is one type of factory, but the way factories run in high cost countries vs. low cost countries is wildly different. We're just at the beginning of what will be an epic change for not only manufacturing, but the cost and quality of the goods we consume. The real internet of things has arrived.

molson 🧠⚙️

66,129 просмотров • 10 месяцев назад

Robotics Expert on How Robots Learn: At First Much Slower Than Humans, Then Infinitely Scalable Agility Jonathan Hurst: “I don't believe that there's a singularity. I do believe that things are going to get better and better. Think of it more like a snowball picking up steam going down a hill. But the reason that it's snowballing like this is because people are putting money, and resources, and engineering time, and engineering effort in as they explore everything and start to figure all of this stuff out. Humans, for example, we've evolved to learn. We are very good at learning, and it takes very little data to show us how to do something, and then we practice and practice, iterate. Robots are not very good at learning yet. Robots take so much more data and so many more examples than a person. We're still figuring out how to teach robots how to learn. But one of the benefits that robots have in the long run is they have Wi-Fi. When you learn how to play the violin, you can't just load that to somebody else and then they know how to play the violin based on your learnings. Robots will be able to do that. @jason: “One robot learns to play violin, all robots know how to play a violin.” Jonathan Hurst: “Or all robots of that type know how to play the violin, right? And then minor variations for the next type and the next piece of hardware.” ------------------------------ Thanks to our partners for making this possible! Most advertisers have never heard of the platform with an $11B annual run rate in ad spend. AppLovin Ads. 1B+ daily active users, full-screen video ads watched for a median of 35 seconds, and businesses are profitably spending hundreds of thousands of dollars a day on it. Now open to everyone. Sign up at If your work depends on conversations — meetings, deal flow, interviews, customer calls — Plaud helps you capture and organize everything with highly accurate AI-generated notes that are not just simple summaries, but also highlight pain points, key decisions, next steps, and customizable summary templates. Check out Plaud at and use code ALLIN for up to 10% off! Which is also available on Amazon: (Code: ALLIN10X)

Robotics Expert on How Robots Learn: At First Much Slower Than Humans, Then Infinitely Scalable Agility Jonathan Hurst: “I don't believe that there's a singularity. I do believe that things are going to get better and better. Think of it more like a snowball picking up steam going down a hill. But the reason that it's snowballing like this is because people are putting money, and resources, and engineering time, and engineering effort in as they explore everything and start to figure all of this stuff out. Humans, for example, we've evolved to learn. We are very good at learning, and it takes very little data to show us how to do something, and then we practice and practice, iterate. Robots are not very good at learning yet. Robots take so much more data and so many more examples than a person. We're still figuring out how to teach robots how to learn. But one of the benefits that robots have in the long run is they have Wi-Fi. When you learn how to play the violin, you can't just load that to somebody else and then they know how to play the violin based on your learnings. Robots will be able to do that. @jason: “One robot learns to play violin, all robots know how to play a violin.” Jonathan Hurst: “Or all robots of that type know how to play the violin, right? And then minor variations for the next type and the next piece of hardware.” ------------------------------ Thanks to our partners for making this possible! Most advertisers have never heard of the platform with an $11B annual run rate in ad spend. AppLovin Ads. 1B+ daily active users, full-screen video ads watched for a median of 35 seconds, and businesses are profitably spending hundreds of thousands of dollars a day on it. Now open to everyone. Sign up at If your work depends on conversations — meetings, deal flow, interviews, customer calls — Plaud helps you capture and organize everything with highly accurate AI-generated notes that are not just simple summaries, but also highlight pain points, key decisions, next steps, and customizable summary templates. Check out Plaud at and use code ALLIN for up to 10% off! Which is also available on Amazon: (Code: ALLIN10X)

The All-In Podcast

70,595 просмотров • 2 месяцев назад

What happens when mobile manipulators go on a grid❓ This week Brightpick announced Gridpicker, its new robotic system for high-throughput applications. Combining AI-powered mobile manipulation with a high-density grid design, Gridpicker achieves up to 2x the throughput of shuttle systems at 40% lower cost and using 5x less labor. Most fascinating: The cost of Gridpicker DECREASES as automation increases 🤯 Traditional systems separate storage and picking. If you want more robotic picking, you add expensive robotic cells, more infrastructure, and more complexity. Gridpicker was designed differently: Each robot both retrieves items and picks them. The greater the share of robotically pickable items: The more productive each robot becomes The fewer robots are needed The lower the cost per order That means progress in AI and VLAs directly translate into better system economics over time. Hats off to Jan Zizka that! 👏 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

What happens when mobile manipulators go on a grid❓ This week Brightpick announced Gridpicker, its new robotic system for high-throughput applications. Combining AI-powered mobile manipulation with a high-density grid design, Gridpicker achieves up to 2x the throughput of shuttle systems at 40% lower cost and using 5x less labor. Most fascinating: The cost of Gridpicker DECREASES as automation increases 🤯 Traditional systems separate storage and picking. If you want more robotic picking, you add expensive robotic cells, more infrastructure, and more complexity. Gridpicker was designed differently: Each robot both retrieves items and picks them. The greater the share of robotically pickable items: The more productive each robot becomes The fewer robots are needed The lower the cost per order That means progress in AI and VLAs directly translate into better system economics over time. Hats off to Jan Zizka that! 👏 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

29,457 просмотров • 6 месяцев назад

What's the number one mistake that people make about satiety? Also, how can chicken breast and broccoli be high satiety per calorie (SPC), if people do not really feel full eating them? This may be the biggest misunderstanding that stops people from using satiety to lose weight and improve their metabolic health. As Ted ⚡️ Naiman shares in this clip, the fundamental misunderstanding is mixing up satiety with satiety per calorie. The best way to get a lot of satiety? Go to a buffet and eat a lot of everything you see. You'll get a lot of satiety, but at a cost of thousands and thousands of calories. Now, if you instead eat extremely high satiety per calorie foods, lets say egg whites and broccoli, you will likely not eat a lot of food. You'll probably end up eating quite few calories. So few, in fact, that you won't feel all that much satiety. In the latter case you ate super-high satiety per calorie foods, but very few calories. Therefore, the satiety you feel still isn't that high. Think of it like this. The higher the satiety per calorie number of the foods you eat, the fewer calories you're likely to end up eating. Period. However, the goal should not be to eat the very lowest number of calories you can. You can eat too little, and that won't make you feel your best. For example, eat only skinless chicken breast (76) and broccoli (89), and your average satiety per calorie may be 80. You'll eat very few calories and sure, you'll lose weight. But it will probably be fairly miserable. It's just too high, too effective. The goal should be to find a good balance for you. If you're looking for weight loss, that might be in the 50-70 range of average satiety per calorie. That's it for today. Don't mistake satiety and satiety per calorie. They are different ways of looking at things. Find the right balance for you. Not too high. Not too low. Just right. Or like we say in Sweden: Lagom. What's your favorite SPC range? More Try our Hava satiety app for free: Check the satiety score of any food: Visual guides:

What's the number one mistake that people make about satiety? Also, how can chicken breast and broccoli be high satiety per calorie (SPC), if people do not really feel full eating them? This may be the biggest misunderstanding that stops people from using satiety to lose weight and improve their metabolic health. As Ted ⚡️ Naiman shares in this clip, the fundamental misunderstanding is mixing up satiety with satiety per calorie. The best way to get a lot of satiety? Go to a buffet and eat a lot of everything you see. You'll get a lot of satiety, but at a cost of thousands and thousands of calories. Now, if you instead eat extremely high satiety per calorie foods, lets say egg whites and broccoli, you will likely not eat a lot of food. You'll probably end up eating quite few calories. So few, in fact, that you won't feel all that much satiety. In the latter case you ate super-high satiety per calorie foods, but very few calories. Therefore, the satiety you feel still isn't that high. Think of it like this. The higher the satiety per calorie number of the foods you eat, the fewer calories you're likely to end up eating. Period. However, the goal should not be to eat the very lowest number of calories you can. You can eat too little, and that won't make you feel your best. For example, eat only skinless chicken breast (76) and broccoli (89), and your average satiety per calorie may be 80. You'll eat very few calories and sure, you'll lose weight. But it will probably be fairly miserable. It's just too high, too effective. The goal should be to find a good balance for you. If you're looking for weight loss, that might be in the 50-70 range of average satiety per calorie. That's it for today. Don't mistake satiety and satiety per calorie. They are different ways of looking at things. Find the right balance for you. Not too high. Not too low. Just right. Or like we say in Sweden: Lagom. What's your favorite SPC range? More Try our Hava satiety app for free: Check the satiety score of any food: Visual guides:

Dr. E

24,577 просмотров • 2 лет назад

Elon Musk reveals why he’s building rockets, AI, and humanoid robots at the same time "The overall goal of my companies is to maximize the probability that civilization has a great future, and to expand consciousness beyond earth" "We need to assume that life and consciousness is extremely rare and it might only be us" "The image in my mind is of a tiny candle in a vast darkness, a tiny candle of consciousness that could easily go out" "That's why it's important to make life multiplanetary, such that if there's a natural or man-made disaster on earth, that consciousness continues" "That's the purpose of SpaceX" "With robotics and AI, this is really the path to abundance for all. The only way to solve global poverty and give everyone a very high standard of living is AI and robotics" "My prediction is there'll be more robots than people. Everyone on earth is going to have one and going to want one. We will make so many robots and AI that they will saturate all human needs. You won't be able to even think of something to ask the robot for" "Who wouldn't want a robot to watch over your kids, take care of your pet, or protect an elderly parent? There just aren't enough young people to take care of the old people" "Overall I'm very optimistic. We're headed for a future of amazing abundance. We are in the most interesting time in history"

Elon Musk reveals why he’s building rockets, AI, and humanoid robots at the same time "The overall goal of my companies is to maximize the probability that civilization has a great future, and to expand consciousness beyond earth" "We need to assume that life and consciousness is extremely rare and it might only be us" "The image in my mind is of a tiny candle in a vast darkness, a tiny candle of consciousness that could easily go out" "That's why it's important to make life multiplanetary, such that if there's a natural or man-made disaster on earth, that consciousness continues" "That's the purpose of SpaceX" "With robotics and AI, this is really the path to abundance for all. The only way to solve global poverty and give everyone a very high standard of living is AI and robotics" "My prediction is there'll be more robots than people. Everyone on earth is going to have one and going to want one. We will make so many robots and AI that they will saturate all human needs. You won't be able to even think of something to ask the robot for" "Who wouldn't want a robot to watch over your kids, take care of your pet, or protect an elderly parent? There just aren't enough young people to take care of the old people" "Overall I'm very optimistic. We're headed for a future of amazing abundance. We are in the most interesting time in history"

Jaynit

618,499 просмотров • 4 месяцев назад