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Jingshi Technology is burning through China’s $138 Billion robotics fund by running $250,000 humanoids into the ground Most companies protect their multi-hundred-thousand-dollar prototypes like fragile glass. Jingshi threw their flagship robot onto an Olympic track to sprint at maximum power until its actuators gave out and crashed in front... of thousands. The strategic logic behind this high-speed crash: High-Velocity Stress Testing: Wrecking custom joints on a live track is the fastest way to train neural networks for high-impact physics recovery. Aggressive Capital Burn: Backed by massive state funds, Jingshi willingly sacrifices high-end hardware to master dynamic shock absorption. Real-World Telemetry: Every snapped limb and faceplant generates unscripted impact data, bypassing years of safe, sterile lab simulations.show more

Piggle

1,860 subscribers

125,874 Aufrufe • vor 1 Monat •via X (Twitter)

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19 Kommentare

Profilbild von Sabi

Sabivor 1 Monat

But in 5-10 years this investment will pay off several times over

Profilbild von Piggle

Pigglevor 1 Monat

perhaps even much earlier

Profilbild von Shadow Nick

Shadow Nickvor 1 Monat

and the answer is very simple, because the company is looking at the long term

Profilbild von Piggle

Pigglevor 1 Monat

That's right, bro

Profilbild von 红

红vor 1 Monat

They seem quite expensive, but they might be cheaper in China? In any case, initial investment is unavoidable.

Profilbild von Exlipse

Exlipsevor 1 Monat

It seems like no one was disappointed with this

Profilbild von Piggle

Pigglevor 1 Monat

Why should people be disappointed? This company's employees actively test their ideas and hypotheses

Profilbild von bondy

bondyvor 1 Monat

its crazy broo

Profilbild von Emoon

Emoonvor 1 Monat

不止机器人是新手，赛事组织者也是新手，耐心点，伙计，路是一步步走的，发现问题改就是了。我相信下一届会更精彩，下下一届会更更精彩。

Profilbild von rewind

rewindvor 1 Monat

telemetry worth the crash

Profilbild von Rich Odin

Rich Odinvor 1 Monat

I think I would be faster than this robot

Profilbild von WOWMAX.Exchange

WOWMAX.Exchangevor 1 Monat

I wonder how the investors of this company feel at this moment

Profilbild von Piggle

Pigglevor 1 Monat

I don't think this situation disappointed them at all

Profilbild von ALEXYZ

ALEXYZvor 1 Monat

real world testing beats showroom protection

Profilbild von Piggle

Pigglevor 1 Monat

yep

Profilbild von Caitty

Caittyvor 1 Monat

@grok the game is held in which stadium? Why not use a bigger one?

Profilbild von Laura Emerson

Laura Emersonvor 1 Monat

📰 Useful update. Thanks for posting.

Profilbild von 白夜

白夜vor 1 Monat

智能机器人和大语言模型都是ai革命的产物，全世界只有中国和美国有巨额投入，大哥就别说二哥了，来自华尔街的融资只比机器人基金只多不少，没准还有美国资本下注中国机器人，都是一回事，

Profilbild von Thundaga

Thundagavor 1 Monat

where is this 250k figure coming from?

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281,875 Aufrufe • vor 2 Monaten

Every Wall Street giant that owns an AI data center is suddenly looking for a buyer. And NONE of them want to be the last one holding it. Three of them made their move in the last two weeks: Vantage Data Centers is exploring an exit. Its owners, Silver Lake and DigitalBridge, are weighing a listing at around $100 billion, or a sale, or a stake sale. It would be the largest data center IPO ever done. Three days earlier, CyrusOne started the same process. KKR and Global Infrastructure Partners met Goldman Sachs and Morgan Stanley, and the banks pitched for roles on a listing that could come as early as 2027. Last month, Switch hired Goldman and JPMorgan to take it public at close to $80 billion including debt, possibly by the fourth quarter. Three different companies moved inside the same 14 days, and the same handful of investment banks took every call. And these are the exact same firms that BOUGHT these companies off the public market four years ago. Between June 2021 and early 2022, private equity took the data center industry private. Blackstone bought QTS. KKR and Global Infrastructure Partners took CyrusOne private in a deal worth about $15 billion. DigitalBridge and IFM took Switch private for about $11 billion. Together those deals ran past $35 billion. By 2023 there were only two pure-play data center companies left on the public market. The logic at the time was that data centers burn cash for years before they pay, and public shareholders hate that. But private money was patient, and private money could wait. Four years later, the AI boom arrived and every one of those buildings became a gold mine. So follow this: Switch went private at about $11 billion in 2022. Its owners now want close to $80 billion for it. That is roughly 7x, in four years, on the same buildings. And DigitalBridge sits on both sides of this. It owns a piece of Vantage and it took Switch private. It is now looking for the door on BOTH. The question now is who is supposed to buy. There is no bigger private buyer left to sell to. These are already the largest infrastructure funds on Earth, and the price tags now run to $100 billion. The only pocket deep enough is the public market, which means anyone with a brokerage account or an index fund. The people who bought low from the public are now organizing to sell high back to the public. And they are doing it while telling everyone the buildout is just getting started. KKR raised a record $19.2 billion for its newest infrastructure fund this month, and in June launched a separate company with over $10 billion committed to finance more construction. So one hand raises fresh billions to build more data centers, and the other hand sells the finished ones to whoever will take them. None of this proves anyone thinks the boom is ending. Selling into strength is what these firms are paid to do, and every one of these deals is early stage and might never happen. But the timing tells you something: The most sophisticated infrastructure investors alive spent four years accumulating these assets in private, and all decided in the same two weeks that now is the moment to find someone else to own them. Four years ago these firms decided the public market was too impatient to own data centers. Now they want the public market to own them again, at 7x the price. Quite suspicious.

Every Wall Street giant that owns an AI data center is suddenly looking for a buyer. And NONE of them want to be the last one holding it. Three of them made their move in the last two weeks: Vantage Data Centers is exploring an exit. Its owners, Silver Lake and DigitalBridge, are weighing a listing at around $100 billion, or a sale, or a stake sale. It would be the largest data center IPO ever done. Three days earlier, CyrusOne started the same process. KKR and Global Infrastructure Partners met Goldman Sachs and Morgan Stanley, and the banks pitched for roles on a listing that could come as early as 2027. Last month, Switch hired Goldman and JPMorgan to take it public at close to $80 billion including debt, possibly by the fourth quarter. Three different companies moved inside the same 14 days, and the same handful of investment banks took every call. And these are the exact same firms that BOUGHT these companies off the public market four years ago. Between June 2021 and early 2022, private equity took the data center industry private. Blackstone bought QTS. KKR and Global Infrastructure Partners took CyrusOne private in a deal worth about $15 billion. DigitalBridge and IFM took Switch private for about $11 billion. Together those deals ran past $35 billion. By 2023 there were only two pure-play data center companies left on the public market. The logic at the time was that data centers burn cash for years before they pay, and public shareholders hate that. But private money was patient, and private money could wait. Four years later, the AI boom arrived and every one of those buildings became a gold mine. So follow this: Switch went private at about $11 billion in 2022. Its owners now want close to $80 billion for it. That is roughly 7x, in four years, on the same buildings. And DigitalBridge sits on both sides of this. It owns a piece of Vantage and it took Switch private. It is now looking for the door on BOTH. The question now is who is supposed to buy. There is no bigger private buyer left to sell to. These are already the largest infrastructure funds on Earth, and the price tags now run to $100 billion. The only pocket deep enough is the public market, which means anyone with a brokerage account or an index fund. The people who bought low from the public are now organizing to sell high back to the public. And they are doing it while telling everyone the buildout is just getting started. KKR raised a record $19.2 billion for its newest infrastructure fund this month, and in June launched a separate company with over $10 billion committed to finance more construction. So one hand raises fresh billions to build more data centers, and the other hand sells the finished ones to whoever will take them. None of this proves anyone thinks the boom is ending. Selling into strength is what these firms are paid to do, and every one of these deals is early stage and might never happen. But the timing tells you something: The most sophisticated infrastructure investors alive spent four years accumulating these assets in private, and all decided in the same two weeks that now is the moment to find someone else to own them. Four years ago these firms decided the public market was too impatient to own data centers. Now they want the public market to own them again, at 7x the price. Quite suspicious.

Ricardo

71,544 Aufrufe • vor 1 Monat

I don’t know if we live in a Matrix, but I know for sure that robots will spend most of their lives in simulation. Let machines train machines. I’m excited to introduce DexMimicGen, a massive-scale synthetic data generator that enables a humanoid robot to learn complex skills from only a handful of human demonstrations. Yes, as few as 5! DexMimicGen addresses the biggest pain point in robotics: where do we get data? Unlike with LLMs, where vast amounts of texts are readily available, you cannot simply download motor control signals from the internet. So researchers teleoperate the robots to collect motion data via XR headsets. They have to repeat the same skill over and over and over again, because neural nets are data hungry. This is a very slow and uncomfortable process. At NVIDIA, we believe the majority of high-quality tokens for robot foundation models will come from simulation. What DexMimicGen does is to trade GPU compute time for human time. It takes one motion trajectory from human, and multiplies into 1000s of new trajectories. A robot brain trained on this augmented dataset will generalize far better in the real world. Think of DexMimicGen as a learning signal amplifier. It maps a small dataset to a large (de facto infinite) dataset, using physics simulation in the loop. In this way, we free humans from babysitting the bots all day. The future of robot data is generative. The future of the entire robot learning pipeline will also be generative. 🧵

I don’t know if we live in a Matrix, but I know for sure that robots will spend most of their lives in simulation. Let machines train machines. I’m excited to introduce DexMimicGen, a massive-scale synthetic data generator that enables a humanoid robot to learn complex skills from only a handful of human demonstrations. Yes, as few as 5! DexMimicGen addresses the biggest pain point in robotics: where do we get data? Unlike with LLMs, where vast amounts of texts are readily available, you cannot simply download motor control signals from the internet. So researchers teleoperate the robots to collect motion data via XR headsets. They have to repeat the same skill over and over and over again, because neural nets are data hungry. This is a very slow and uncomfortable process. At NVIDIA, we believe the majority of high-quality tokens for robot foundation models will come from simulation. What DexMimicGen does is to trade GPU compute time for human time. It takes one motion trajectory from human, and multiplies into 1000s of new trajectories. A robot brain trained on this augmented dataset will generalize far better in the real world. Think of DexMimicGen as a learning signal amplifier. It maps a small dataset to a large (de facto infinite) dataset, using physics simulation in the loop. In this way, we free humans from babysitting the bots all day. The future of robot data is generative. The future of the entire robot learning pipeline will also be generative. 🧵

Jim Fan

165,246 Aufrufe • vor 1 Jahr

The Financial Logic Behind the Parade of the World's Most Powerful Military China Beckons Global Capital - A Strategic Display for the World’s Investors China’s September 3rd grand military parades are far more than spectacles for domestic and world audiences—they are meticulously orchestrated broadcasts to the world’s capital markets. Beneath the orchestrated marches and thunder of steel lies a financial logic, one attuned to the instincts of global capital. The Real Message to Global Capital What do investors see beyond the flags and formations? Not sentiment, not ideology, not the thunder of the crowd. They see security—a clear signal that, in a world roiled by pandemics, war in Ukraine, Middle Eastern conflict, shipping disruptions, and ballooning Western debt, there is a place of stability. Capital cares about only one thing: safety. Money lost can be earned again, but when security vanishes, all assets become bubbles. Militaries worldwide grow their budgets and speak of peace, but markets remain jittery—seeking havens resilient to shock. So when Beijing unveils its missile arrays, naval assets, drone swarms, and integrated systems on live television, Wall Street and City analysts take notice. This is not just a show of strength for the public—it’s a public safety manual for capital: “Place your assets here; they will be protected.” War, if it comes at all, will be short. The balance is no longer in question. Against all of its adversaries combined, China’s advantage is systemic and overwhelming—rooted in industrial scale, electronic dominance, and logistical reach that no coalition can match. Modern war is not won by individual platforms but by networks that see farther, strike faster, and replace losses instantly. In such an environment, the first hours would decide the outcome; within days, the conflict would be over—not through attrition, but through paralysis of the opponent’s entire command, supply, and economic lifelines. Industrial Power as the Ultimate Moat The true foundation of national strength is industrial power. Modern conflict is a contest of supply chains, technology, and resilience. The parade is a window into decades of accumulation: green energy materials, rare earth reserves, advanced chip production, and self-sustaining manufacturing chains. What was once imported or controlled by others—gallium nitride, rare earths, satellite navigation—has become domestically mastered. “Made in China” was expected to falter; instead, the supply chain underwent a decisive reshuffle and upgrade. EVs conquer export markets, shipbuilding leads the world, robotics and high-end machine tools rapidly close the gap. This resilience is the true strategic moat—one that global capital cannot ignore. Weaponry is the manifestation of this industrial might. A deindustrialized nation can neither innovate nor defend. Each missile, tank and drone at the parade signals the health of the wider industrial ecosystem.

The Financial Logic Behind the Parade of the World's Most Powerful Military China Beckons Global Capital - A Strategic Display for the World’s Investors China’s September 3rd grand military parades are far more than spectacles for domestic and world audiences—they are meticulously orchestrated broadcasts to the world’s capital markets. Beneath the orchestrated marches and thunder of steel lies a financial logic, one attuned to the instincts of global capital. The Real Message to Global Capital What do investors see beyond the flags and formations? Not sentiment, not ideology, not the thunder of the crowd. They see security—a clear signal that, in a world roiled by pandemics, war in Ukraine, Middle Eastern conflict, shipping disruptions, and ballooning Western debt, there is a place of stability. Capital cares about only one thing: safety. Money lost can be earned again, but when security vanishes, all assets become bubbles. Militaries worldwide grow their budgets and speak of peace, but markets remain jittery—seeking havens resilient to shock. So when Beijing unveils its missile arrays, naval assets, drone swarms, and integrated systems on live television, Wall Street and City analysts take notice. This is not just a show of strength for the public—it’s a public safety manual for capital: “Place your assets here; they will be protected.” War, if it comes at all, will be short. The balance is no longer in question. Against all of its adversaries combined, China’s advantage is systemic and overwhelming—rooted in industrial scale, electronic dominance, and logistical reach that no coalition can match. Modern war is not won by individual platforms but by networks that see farther, strike faster, and replace losses instantly. In such an environment, the first hours would decide the outcome; within days, the conflict would be over—not through attrition, but through paralysis of the opponent’s entire command, supply, and economic lifelines. Industrial Power as the Ultimate Moat The true foundation of national strength is industrial power. Modern conflict is a contest of supply chains, technology, and resilience. The parade is a window into decades of accumulation: green energy materials, rare earth reserves, advanced chip production, and self-sustaining manufacturing chains. What was once imported or controlled by others—gallium nitride, rare earths, satellite navigation—has become domestically mastered. “Made in China” was expected to falter; instead, the supply chain underwent a decisive reshuffle and upgrade. EVs conquer export markets, shipbuilding leads the world, robotics and high-end machine tools rapidly close the gap. This resilience is the true strategic moat—one that global capital cannot ignore. Weaponry is the manifestation of this industrial might. A deindustrialized nation can neither innovate nor defend. Each missile, tank and drone at the parade signals the health of the wider industrial ecosystem.

America-China Watcher

55,940 Aufrufe • vor 1 Jahr

Are you watching the Chinese New Year Gala? The Robot Kungfu show is mind blowing!!! They just executed a coordinated martial arts routine with spatial precision, rhythm control, and dynamic balance adjustments in real time. Kung fu, one of China’s most iconic traditional art forms , performed by machines built with cutting-edge AI control systems, advanced actuators, and high-speed feedback loops. Ancient discipline meets algorithmic precision. Last year, humanoid robots stepped onto the Spring Festival Gala stage for the first time. This year, they held synchronized kung fu stances with balance that would humble half of us after leg day. And they did it live!!! On the most-watched television event on the planet. The progress in just one year is magical. That’s what we call China speed. What makes it even sweeter is where this happened. I love how the progress is integrated in culture. In celebration. In a Lunar New Year gala watched by hundreds of millions. It’s music to my ears. The robots didn’t look like they were “trying” anymore. They looked like they belonged. Their joint articulation was smoother. Their formation timing tighter. Their balance recovery almost elegant. Their choreography is expressive. That’s what happens when AI models improve, control systems get smarter, hardware stabilizes, and iteration cycles compress. One year in robotics today is not the same as one year ten years ago. It’s compounding. If this is what 12 months looks like, imagine 36. The Chinese New Year Robot Kungfu Gala is just futuristic. It was quite the statement! The future is getting better very, very fast. It was so beautiful to watch. What do you think?

Are you watching the Chinese New Year Gala? The Robot Kungfu show is mind blowing!!! They just executed a coordinated martial arts routine with spatial precision, rhythm control, and dynamic balance adjustments in real time. Kung fu, one of China’s most iconic traditional art forms , performed by machines built with cutting-edge AI control systems, advanced actuators, and high-speed feedback loops. Ancient discipline meets algorithmic precision. Last year, humanoid robots stepped onto the Spring Festival Gala stage for the first time. This year, they held synchronized kung fu stances with balance that would humble half of us after leg day. And they did it live!!! On the most-watched television event on the planet. The progress in just one year is magical. That’s what we call China speed. What makes it even sweeter is where this happened. I love how the progress is integrated in culture. In celebration. In a Lunar New Year gala watched by hundreds of millions. It’s music to my ears. The robots didn’t look like they were “trying” anymore. They looked like they belonged. Their joint articulation was smoother. Their formation timing tighter. Their balance recovery almost elegant. Their choreography is expressive. That’s what happens when AI models improve, control systems get smarter, hardware stabilizes, and iteration cycles compress. One year in robotics today is not the same as one year ten years ago. It’s compounding. If this is what 12 months looks like, imagine 36. The Chinese New Year Robot Kungfu Gala is just futuristic. It was quite the statement! The future is getting better very, very fast. It was so beautiful to watch. What do you think?

Evrim Kanbur

1,558,509 Aufrufe • vor 7 Monaten

In 2026, Venture Capital will eat Private Equity It used to be that venture capital and private equity lived on two separate planets: VC = San Francisco PE = New York They targeted completely different universes of companies: --> PE - people heavy biz services, stable/low growth, predictable cashflows --> VC - tech-forward, high growth, high risk, massive TAM What was the playbook for B2B VC backed startups? --> Grow to unicorn scale by selling to other early adopter tech companies, then Fortune 500s XX> SMB and mid-market services - think field services, IT staffing, accounting, construction, recruiting - were always tough to sell into for startups Why? -->Thin margins, high labor costs, and small IT budgets >> But as AI eats labor, these businesses are in play << There are 3 ways where VC and PE are colliding: 1/ Private Equity funds will become channel partners for startups. PE funds are focused on financial engineering and cost optimization. Startups building AI products and services can sell across their portfolio to automate the backoffice and uplevel sales and marketing. PE funds have made AI their #1 strategic priority and have hired central leaders to oversee their portfolio adoption efforts 2/ PE portfolio pages are a startup idea menu Private equity will often buyout vertical software companies whose TAM didn’t allow venture scaled returns. As software evolves from data storage and collaboration to agents taking action and completing work, AI should massively expand the TAM for these categories. Founders will set their sights on unseating these legacy incumbents backed by private equity. All they have to do is look at their portfolio pages for category ideas 3/ AI Rollups This is one of the most direct ways that VC is eating PE VC backed AI platform businesses are not just selling software but acquiring legacy business services companies to own the value chain end to end. As an example, our speedrun company AgentAstra is acquiring freight forwarding services businesses with mostly debt and integrating AI deeply into their operations These companies aim to increase margins by at least 2x and make them “AI native” tl;dr - While the west coast, Patagonia-wearing VCs and the east coast, PE suits used to live in different universes, in 2026 with AI, I believe, those worlds converge

In 2026, Venture Capital will eat Private Equity It used to be that venture capital and private equity lived on two separate planets: VC = San Francisco PE = New York They targeted completely different universes of companies: --> PE - people heavy biz services, stable/low growth, predictable cashflows --> VC - tech-forward, high growth, high risk, massive TAM What was the playbook for B2B VC backed startups? --> Grow to unicorn scale by selling to other early adopter tech companies, then Fortune 500s XX> SMB and mid-market services - think field services, IT staffing, accounting, construction, recruiting - were always tough to sell into for startups Why? -->Thin margins, high labor costs, and small IT budgets >> But as AI eats labor, these businesses are in play << There are 3 ways where VC and PE are colliding: 1/ Private Equity funds will become channel partners for startups. PE funds are focused on financial engineering and cost optimization. Startups building AI products and services can sell across their portfolio to automate the backoffice and uplevel sales and marketing. PE funds have made AI their #1 strategic priority and have hired central leaders to oversee their portfolio adoption efforts 2/ PE portfolio pages are a startup idea menu Private equity will often buyout vertical software companies whose TAM didn’t allow venture scaled returns. As software evolves from data storage and collaboration to agents taking action and completing work, AI should massively expand the TAM for these categories. Founders will set their sights on unseating these legacy incumbents backed by private equity. All they have to do is look at their portfolio pages for category ideas 3/ AI Rollups This is one of the most direct ways that VC is eating PE VC backed AI platform businesses are not just selling software but acquiring legacy business services companies to own the value chain end to end. As an example, our speedrun company AgentAstra is acquiring freight forwarding services businesses with mostly debt and integrating AI deeply into their operations These companies aim to increase margins by at least 2x and make them “AI native” tl;dr - While the west coast, Patagonia-wearing VCs and the east coast, PE suits used to live in different universes, in 2026 with AI, I believe, those worlds converge

Troy Kirwin

187,474 Aufrufe • vor 9 Monaten

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 Aufrufe • vor 5 Monaten

Nvidia is pulling off the most sophisticated financial loop in tech history. They invested $40 BILLION in its own customers in just 5 months. Here's why this could blow up the entire AI economy: Nvidia generated $97 billion in free cash flow last year. Instead of sitting on it, Jensen started writing checks to every company in the AI supply chain. Not small checks. We're talking about billions at a time. And almost every single one of those companies turns around and spends that money on Nvidia chips. Follow the money: $30 billion into OpenAI. OpenAI is one of Nvidia's largest GPU customers and spends billions annually on Nvidia hardware through cloud providers. $2 billion into CoreWeave, a company that exists exclusively to rent out data centers full of Nvidia GPUs. $2 billion into Marvell for silicon photonics that connects Nvidia systems. $2 billion into Lumentum for optical tech that powers Nvidia data centers. $2 billion into Coherent for the same thing. $2 billion into Nebius, an AI cloud company deploying Nvidia infrastructure. $3.2 billion into Corning, the glassmaker building three new US factories specifically to make fiber optic cables for Nvidia's next-gen systems. $2.1 billion into IREN, a data center operator that just agreed to deploy 5 gigawatts of Nvidia-designed infrastructure. And the list goes on. Every single recipient either buys Nvidia chips directly, builds infrastructure that runs on Nvidia chips, or manufactures components that go inside Nvidia systems. Matthew Bryson, an analyst at Wedbush Securities, said in a research note that Nvidia's dealmaking fits "squarely into the circular investment theme." Bloomberg even published an entire interactive feature this week titled "AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other." The piece maps how capital flows between the same handful of companies and gets counted as revenue multiple times along the way. But here's the part that makes this genuinely complicated: Nvidia's $5 billion investment in Intel from September is now worth over $25 billion. That's a 5x return in months. Their private company portfolio went from $3.4 billion to $22.3 billion on the balance sheet in a single year. They booked $8.9 billion in gains from equity investments alone. So when critics say "circular investing," Nvidia can point to Intel and say "we turned $5 billion into $25 billion, this is just smart capital deployment." And they're not wrong. Some of these bets ARE paying off like crazy. The real question is whether Nvidia is a chipmaker that happens to invest, or a venture fund that happens to sell chips. Because right now Jensen is doing both at a scale that has never existed in the semiconductor industry. No chipmaker in history has EVER invested $40 billion in its own ecosystem in five months. Last fiscal year Nvidia invested $17.5 billion in private companies. Their SEC filing literally says those investments include "AI model companies that purchase its products directly or through cloud service providers." They're saying it themselves: We invest in companies that buy our products. On Nvidia's last earnings call, Jensen told investors their investments are focused on "expanding and deepening our ecosystem reach." Translate that from CEO-speak and it means " we're funding the companies that fund us. The bull case says Nvidia is building an unbreakable moat by financing the entire AI supply chain and ensuring it all runs on Nvidia hardware. The bear case says this is the most elaborate circular revenue scheme since the subprime mortgage era and it all breaks apart the moment one domino falls. Both cases use the exact same evidence.

Nvidia is pulling off the most sophisticated financial loop in tech history. They invested $40 BILLION in its own customers in just 5 months. Here's why this could blow up the entire AI economy: Nvidia generated $97 billion in free cash flow last year. Instead of sitting on it, Jensen started writing checks to every company in the AI supply chain. Not small checks. We're talking about billions at a time. And almost every single one of those companies turns around and spends that money on Nvidia chips. Follow the money: $30 billion into OpenAI. OpenAI is one of Nvidia's largest GPU customers and spends billions annually on Nvidia hardware through cloud providers. $2 billion into CoreWeave, a company that exists exclusively to rent out data centers full of Nvidia GPUs. $2 billion into Marvell for silicon photonics that connects Nvidia systems. $2 billion into Lumentum for optical tech that powers Nvidia data centers. $2 billion into Coherent for the same thing. $2 billion into Nebius, an AI cloud company deploying Nvidia infrastructure. $3.2 billion into Corning, the glassmaker building three new US factories specifically to make fiber optic cables for Nvidia's next-gen systems. $2.1 billion into IREN, a data center operator that just agreed to deploy 5 gigawatts of Nvidia-designed infrastructure. And the list goes on. Every single recipient either buys Nvidia chips directly, builds infrastructure that runs on Nvidia chips, or manufactures components that go inside Nvidia systems. Matthew Bryson, an analyst at Wedbush Securities, said in a research note that Nvidia's dealmaking fits "squarely into the circular investment theme." Bloomberg even published an entire interactive feature this week titled "AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other." The piece maps how capital flows between the same handful of companies and gets counted as revenue multiple times along the way. But here's the part that makes this genuinely complicated: Nvidia's $5 billion investment in Intel from September is now worth over $25 billion. That's a 5x return in months. Their private company portfolio went from $3.4 billion to $22.3 billion on the balance sheet in a single year. They booked $8.9 billion in gains from equity investments alone. So when critics say "circular investing," Nvidia can point to Intel and say "we turned $5 billion into $25 billion, this is just smart capital deployment." And they're not wrong. Some of these bets ARE paying off like crazy. The real question is whether Nvidia is a chipmaker that happens to invest, or a venture fund that happens to sell chips. Because right now Jensen is doing both at a scale that has never existed in the semiconductor industry. No chipmaker in history has EVER invested $40 billion in its own ecosystem in five months. Last fiscal year Nvidia invested $17.5 billion in private companies. Their SEC filing literally says those investments include "AI model companies that purchase its products directly or through cloud service providers." They're saying it themselves: We invest in companies that buy our products. On Nvidia's last earnings call, Jensen told investors their investments are focused on "expanding and deepening our ecosystem reach." Translate that from CEO-speak and it means " we're funding the companies that fund us. The bull case says Nvidia is building an unbreakable moat by financing the entire AI supply chain and ensuring it all runs on Nvidia hardware. The bear case says this is the most elaborate circular revenue scheme since the subprime mortgage era and it all breaks apart the moment one domino falls. Both cases use the exact same evidence.

Ricardo

162,581 Aufrufe • vor 4 Monaten

$‼️🇺🇲 Lockheed Martin has officially unveiled a game-changing addition to the global air defense arsenal, introducing the PAC-3 Adapted Capability Effector (ACE)—a highly advanced, ultra-affordable Patriot interceptor specifically designed to shatter the economics of modern aerial warfare. The revolutionary missile is engineered to complement existing Patriot frameworks while slashing production costs to less than half the price of the current $4 million PAC-3 MSE interceptor. Developed through a high-impact collaboration between U.S. and European defense industries, the PAC-3 ACE is a direct response to the grueling tactical lessons of the war in Ukraine, where the massive proliferation of cheap enemy drones and loitering munitions has demanded a far more cost-effective defensive solution. Lockheed Martin confirmed that development is moving at an aggressive pace, with full-scale production projected to hit the assembly lines within the next 36 months. This breakthrough represents a massive strategic pivot that destroys Russia's and Iran's strategy of trying to bankrupt Western air defense systems through mass-produced drone swarms. By delivering a lethal, high-tier interceptor at a fraction of the traditional cost, the alliance ensures that defenders can protect critical infrastructure without depleting multi-million-dollar missile stockpiles on low-cost targets. As production gears up, the PAC-3 ACE will provide Ukraine and its allies with an unsustainable cost curve for the enemy, cementing long-term dominance over contested skies and ensuring the democratic world can out-produce and out-last any adversarial threat. See the latest updates with us: Visioner$

‼️🇺🇲 Lockheed Martin has officially unveiled a game-changing addition to the global air defense arsenal, introducing the PAC-3 Adapted Capability Effector (ACE)—a highly advanced, ultra-affordable Patriot interceptor specifically designed to shatter the economics of modern aerial warfare. The revolutionary missile is engineered to complement existing Patriot frameworks while slashing production costs to less than half the price of the current $4 million PAC-3 MSE interceptor. Developed through a high-impact collaboration between U.S. and European defense industries, the PAC-3 ACE is a direct response to the grueling tactical lessons of the war in Ukraine, where the massive proliferation of cheap enemy drones and loitering munitions has demanded a far more cost-effective defensive solution. Lockheed Martin confirmed that development is moving at an aggressive pace, with full-scale production projected to hit the assembly lines within the next 36 months. This breakthrough represents a massive strategic pivot that destroys Russia's and Iran's strategy of trying to bankrupt Western air defense systems through mass-produced drone swarms. By delivering a lethal, high-tier interceptor at a fraction of the traditional cost, the alliance ensures that defenders can protect critical infrastructure without depleting multi-million-dollar missile stockpiles on low-cost targets. As production gears up, the PAC-3 ACE will provide Ukraine and its allies with an unsustainable cost curve for the enemy, cementing long-term dominance over contested skies and ensuring the democratic world can out-produce and out-last any adversarial threat. See the latest updates with us: Visioner

Visioner

82,265 Aufrufe • vor 2 Monaten

Nvidia's next generation chips are about to make a handful of companies impossible to ignore and here is how you can benefit from this (Save this). Goldman projects networking content per AI compute system could rise from $315,000 today to $9.4 million in Nvidia's next generation, a 29x increase in what companies spend just wiring their chips together. Morgan Stanley, Citi and Goldman all agree on the same direction, projecting the total addressable market for AI networking to grow from around $11 to $15 billion today to $154 billion by 2028, roughly a 9x jump in just a few years. As AI clusters scale into the hundreds of thousands of GPUs, the chips themselves stop being the bottleneck and the connections between them become the limiting factor instead. Every GPU needs to talk to every other GPU almost instantly to keep a training run synchronized and that requires far more sophisticated switches, optical modules and cabling than today's networks can provide, which is exactly why Nvidia's next generation systems are expected to need dramatically more networking hardware per system just to keep up. Optical modules specifically are becoming the chokepoint. The global optical module total addressable market is projected to grow from roughly $6.7 billion in the first quarter of 2025 to nearly $69 billion by 2028, with 1.6T modules going from essentially zero market share today to capturing the majority of shipments by 2028 as speeds keep climbing. Nvidia sits at the center of this story since it designs the switches and networking silicon that go into every one of these systems, meaning that 29x jump in networking content per system flows straight into Nvidia's own revenue per AI factory, not just its GPU sales. Marvell is one of the biggest direct beneficiaries here, since it makes the custom networking chips and interconnect silicon that hyperscalers rely on to move data between GPUs at these speeds. Credo Technology plays a slightly different role, supplying the high speed connectivity chips that clean up signals moving through copper links, which becomes more valuable as networks push toward higher bandwidth with lower power draw. AAOI, or Applied Optoelectronics, sits closer to the physical optics layer, making the lasers and optical components that go into the transceivers carrying data across these networks, positioning it directly in the path of that jump from $6.7 billion to $69 billion in optical module demand. Coherent and Lumentum round out the optical side, both supplying lasers and photonic components that scale directly with 1.6T module adoption as it goes from near zero to the majority of shipments by 2028. Switch and connector makers benefit almost mechanically from this trend too, since every additional dollar of networking content per system has to physically pass through a switch, a cable, or a connector, meaning revenue scales with network complexity regardless of which chipmaker's GPUs sit inside the rack. Bullish on AI networking, make sure to follow Melvin for more semiconductor insights and check out the link below for more details.

Nvidia's next generation chips are about to make a handful of companies impossible to ignore and here is how you can benefit from this (Save this). Goldman projects networking content per AI compute system could rise from $315,000 today to $9.4 million in Nvidia's next generation, a 29x increase in what companies spend just wiring their chips together. Morgan Stanley, Citi and Goldman all agree on the same direction, projecting the total addressable market for AI networking to grow from around $11 to $15 billion today to $154 billion by 2028, roughly a 9x jump in just a few years. As AI clusters scale into the hundreds of thousands of GPUs, the chips themselves stop being the bottleneck and the connections between them become the limiting factor instead. Every GPU needs to talk to every other GPU almost instantly to keep a training run synchronized and that requires far more sophisticated switches, optical modules and cabling than today's networks can provide, which is exactly why Nvidia's next generation systems are expected to need dramatically more networking hardware per system just to keep up. Optical modules specifically are becoming the chokepoint. The global optical module total addressable market is projected to grow from roughly $6.7 billion in the first quarter of 2025 to nearly $69 billion by 2028, with 1.6T modules going from essentially zero market share today to capturing the majority of shipments by 2028 as speeds keep climbing. Nvidia sits at the center of this story since it designs the switches and networking silicon that go into every one of these systems, meaning that 29x jump in networking content per system flows straight into Nvidia's own revenue per AI factory, not just its GPU sales. Marvell is one of the biggest direct beneficiaries here, since it makes the custom networking chips and interconnect silicon that hyperscalers rely on to move data between GPUs at these speeds. Credo Technology plays a slightly different role, supplying the high speed connectivity chips that clean up signals moving through copper links, which becomes more valuable as networks push toward higher bandwidth with lower power draw. AAOI, or Applied Optoelectronics, sits closer to the physical optics layer, making the lasers and optical components that go into the transceivers carrying data across these networks, positioning it directly in the path of that jump from $6.7 billion to $69 billion in optical module demand. Coherent and Lumentum round out the optical side, both supplying lasers and photonic components that scale directly with 1.6T module adoption as it goes from near zero to the majority of shipments by 2028. Switch and connector makers benefit almost mechanically from this trend too, since every additional dollar of networking content per system has to physically pass through a switch, a cable, or a connector, meaning revenue scales with network complexity regardless of which chipmaker's GPUs sit inside the rack. Bullish on AI networking, make sure to follow Melvin for more semiconductor insights and check out the link below for more details.

Melvin

42,220 Aufrufe • vor 2 Monaten

Jensen Huang is investing in every photonics company he can find and the reason why tells you everything about where AI is headed (Save this). Lip-Bu Tan, the CEO of Intel says, when he looks for investment opportunities, he looks for the bottleneck and right now, the bottleneck is the interconnect, the pipes that move data between chips inside an AI data center. That is why he backed Credo Semiconductor, Astera Labs and Celestial AI on the optical side. Here is the simple version of what the interconnect bottleneck actually means. Think of an AI data center like a city, the GPUs are the buildings where all the work happens but for those buildings to function, you need roads connecting them, fast roads that can carry enormous traffic without congestion. And those roads are now the single biggest constraint on AI performance. As clusters scale to hundreds of thousands of GPUs, traditional copper wiring is hitting its physical limits and that is where this entire sector comes in. Credo Semiconductor (CRDO) is the most direct pure play on this theme, Credo makes high speed cables and optical chips that connect GPUs inside data center racks. Their revenue tripled in fiscal 2026 to $1.3 billion, growing 272% year over year at its peak and four of the world's largest hyperscalers each individually account for more than 10% of Credo's revenue. Astera Labs (ALAB) solves the connection problem between different chip types. Astera makes the PCIe and connectivity chips that manage data flow between GPUs, CPUs, and memory without errors or slowdowns. Their revenue grew 93% year over year to $308 million in Q1 2026 alone. The optical companies are where the longer-term and potentially larger opportunity lives. Copper has physical limits, you can only push electrical signals so far before the signal degrades, the heat spikes and power consumption explodes. The solution is light, fiber optic connections that move data using photons instead of electrons which is faster, cooler and far more energy efficient. Jensen Huang made this clear at Computex 2026 because copper works as long as physically possible but at greater distances and larger scale, optics takes over. Coherent (COHR) is the most established optical company in this space. Coherent makes the lasers, transceivers, and optical components at the foundation of all fiber optic communications. Nvidia signed a multibillion-dollar purchase commitment and invested $2 billion directly into the company and their customer order books are already extending out to 2028. Marvell (MRVL) is the most comprehensive bet across the entire connectivity stack. Marvell makes chips for optical networking, PCIe switching and custom AI silicon. Jensen Huang called Marvell the next trillion dollar company at Computex 2026 and backed it with a $2 billion Nvidia investment. Marvell also acquired Celestial AI, the exact company Lip-Bu Tan backed for $3.25 billion, gaining photonic fabric technology delivering 16 terabits per second of bandwidth. Lumentum (LITE), Corning (GLW), and Ciena (CIEN) round out the major public names. Lumentum received a $2 billion Nvidia investment for laser and photonics components. Corning known mostly for phone glass received $500 million from Nvidia for optical connectivity work and is up over 100% year to date. Ciena runs the optical networking systems between data centers and is seeing analyst price targets raised on the back of the AI optics boom. Every time a hyperscaler spends a billion dollars on Nvidia GPUs, the surrounding infrastructure, cables, switches, transceivers, optical components has to be upgraded to match. The smarter the GPU gets, the more the interconnect matters. Nvidia has committed at least $6.5 billion to photonics companies in the past 4 months alone and the companies building the roads between the GPUs may end up being just as valuable as the companies building the GPUs themselves. Follow me Melvin for more AI, semis and the next big market themes.

Jensen Huang is investing in every photonics company he can find and the reason why tells you everything about where AI is headed (Save this). Lip-Bu Tan, the CEO of Intel says, when he looks for investment opportunities, he looks for the bottleneck and right now, the bottleneck is the interconnect, the pipes that move data between chips inside an AI data center. That is why he backed Credo Semiconductor, Astera Labs and Celestial AI on the optical side. Here is the simple version of what the interconnect bottleneck actually means. Think of an AI data center like a city, the GPUs are the buildings where all the work happens but for those buildings to function, you need roads connecting them, fast roads that can carry enormous traffic without congestion. And those roads are now the single biggest constraint on AI performance. As clusters scale to hundreds of thousands of GPUs, traditional copper wiring is hitting its physical limits and that is where this entire sector comes in. Credo Semiconductor (CRDO) is the most direct pure play on this theme, Credo makes high speed cables and optical chips that connect GPUs inside data center racks. Their revenue tripled in fiscal 2026 to $1.3 billion, growing 272% year over year at its peak and four of the world's largest hyperscalers each individually account for more than 10% of Credo's revenue. Astera Labs (ALAB) solves the connection problem between different chip types. Astera makes the PCIe and connectivity chips that manage data flow between GPUs, CPUs, and memory without errors or slowdowns. Their revenue grew 93% year over year to $308 million in Q1 2026 alone. The optical companies are where the longer-term and potentially larger opportunity lives. Copper has physical limits, you can only push electrical signals so far before the signal degrades, the heat spikes and power consumption explodes. The solution is light, fiber optic connections that move data using photons instead of electrons which is faster, cooler and far more energy efficient. Jensen Huang made this clear at Computex 2026 because copper works as long as physically possible but at greater distances and larger scale, optics takes over. Coherent (COHR) is the most established optical company in this space. Coherent makes the lasers, transceivers, and optical components at the foundation of all fiber optic communications. Nvidia signed a multibillion-dollar purchase commitment and invested $2 billion directly into the company and their customer order books are already extending out to 2028. Marvell (MRVL) is the most comprehensive bet across the entire connectivity stack. Marvell makes chips for optical networking, PCIe switching and custom AI silicon. Jensen Huang called Marvell the next trillion dollar company at Computex 2026 and backed it with a $2 billion Nvidia investment. Marvell also acquired Celestial AI, the exact company Lip-Bu Tan backed for $3.25 billion, gaining photonic fabric technology delivering 16 terabits per second of bandwidth. Lumentum (LITE), Corning (GLW), and Ciena (CIEN) round out the major public names. Lumentum received a $2 billion Nvidia investment for laser and photonics components. Corning known mostly for phone glass received $500 million from Nvidia for optical connectivity work and is up over 100% year to date. Ciena runs the optical networking systems between data centers and is seeing analyst price targets raised on the back of the AI optics boom. Every time a hyperscaler spends a billion dollars on Nvidia GPUs, the surrounding infrastructure, cables, switches, transceivers, optical components has to be upgraded to match. The smarter the GPU gets, the more the interconnect matters. Nvidia has committed at least $6.5 billion to photonics companies in the past 4 months alone and the companies building the roads between the GPUs may end up being just as valuable as the companies building the GPUs themselves. Follow me Melvin for more AI, semis and the next big market themes.

Melvin

153,158 Aufrufe • vor 3 Monaten