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4. $AMBA - Ambarella The brain that rides along inside the machine. Chips small enough to run AI on the robot itself, so it decides in real time instead of waiting on a data center. NXP is reportedly in talks to buy them for ~$3.3 BILLION.

Frank Curzio

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159,633 次观看 • 2 个月前 •via X (Twitter)

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Frank Curzio 的头像

Frank Curzio2 个月前

3. $ISRG - Intuitive Surgical Their da Vinci machine is four robotic arms over an operating table. The surgeon sits at a console a few feet away and controls them. 11,710 installed worldwide, up 12%. Procedures up 16%. $2.89B last quarter.

Frank Curzio 的头像

Frank Curzio2 个月前

2. $OUST - Ouster LiDAR. Lasers that map a whole room in 3D, so a machine knows where everything is and doesn't drive into it. Sold into warehouse robots, delivery bots and drones. $55M last quarter, up 56%, as their new Rev8 sensor starts shipping.

Frank Curzio 的头像

Frank Curzio2 个月前

1. $TSLA - Tesla The most obvious name here, but it has to be mentioned. Everyone knows the cars. The robot is Optimus, a human-sized machine built to do the jobs a person does on a factory floor. Nobody else can build a million of anything. Tesla can.

Frank Curzio 的头像

Frank Curzio2 个月前

For anyone new, I'm Frank Curzio - veteran stock analyst. We've been saying robotics is the next leg of AI for years now & we'll be talking about it LIVE on X this Thursday at 11AM ET. Sign up link here: Now, the 7 stocks we're looking at:

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Frank Curzio2 个月前

Elon Musk is telling you to buy robotics stocks. "There will be at least a billion robots in 10 years, each producing at least 5x the output of a human" Morgan Stanley says robots are a $5 TRILLION market by 2050. Here are 7 stocks positioned to benefit:👇

Frank Curzio 的头像

Frank Curzio2 个月前

5. $CGNX - Cognex They make the eyes. Cameras and software that let a machine see what's in front of it. Which part is it, which way up, is it damaged. Record quarter: $291M revenue, up 17%.

Frank Curzio 的头像

Frank Curzio2 个月前

6. $AME - AMETEK Motors, actuators and sensors. The parts that turn a robot's decision into an arm that actually moves, and then tell it where that arm ended up. Boring and unglamorous, but in everything. EPS and sales climbing, share count flat.

Frank Curzio 的头像

Frank Curzio2 个月前

And I've got a wildcard here too... Every one of these robots is physical. Copper windings. Wiring. Motors. A billion of them doesn't just need software and chips... it needs metal. And almost nobody's connecting that...

Frank Curzio 的头像

Frank Curzio2 个月前

7. $COPR - Coppernico Metals A partner company of ours, a copper & gold explorer sitting on 50,000 hectares in southern Peru. Teck already owns close to 9% of it. They just got their drilling permit after waiting 9 years.

Frank Curzio 的头像

Frank Curzio2 个月前

Elon thinks AI alone adds 20-30% to the global economy. Robotics, he says, is many multiples of that. If a small cap captures even a sliver of that buildout? Stock could 10x, and that's where millionaires will be made. Don't buy the big names... buy the nuts and bolts.

Frank Curzio 的头像

Frank Curzio2 个月前

That's been our thesis for over two years, and it's why $BE went up 12x while people argued about chatbots. We're recommending stocks placed to win every week both on X and on our podcast Wall Street Unplugged ( Want more? Follow me @FrankCurzio

Frank Curzio 的头像

Frank Curzio2 个月前

Video Credits/Disclosures: - Wall Street Unplugged Podcast - G20 Summit - CNBC - Cognex Vision - Intuitive Surgical -

Fajasy 的头像

Fajasy2 个月前

$AMBA's robot part is mostly still ahead of them, robotics is the smallest IoT bucket today behind security cameras and portable video at ~45% each. What's booked so far is 15+ robotics design wins, humanoids included, plus the 10-year Hanwha LTA worth >$800M across robotics and industrial.

Q it Up! 的头像

Q it Up!2 个月前

Tell me what the current stock prices is for each

The AI Therapist 的头像

The AI Therapist2 个月前

Ambarella c'est l'épine dorsale de la robotique mobile. là où les géants se battent pour le cloud, elle capture la valeur directe du bord en temps réel. petit mais indispensable.

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A toy robot. A home assistant. A humanoid on a factory floor. They all need the same thing: a brain. QVAC is that brain, and it runs on the robot itself. No cloud to phone, no latency, nothing leaving your home. The intelligence is on the machine, so it keeps thinking even with the internet off. The robots are coming. This is what wakes them up:

A toy robot. A home assistant. A humanoid on a factory floor. They all need the same thing: a brain. QVAC is that brain, and it runs on the robot itself. No cloud to phone, no latency, nothing leaving your home. The intelligence is on the machine, so it keeps thinking even with the internet off. The robots are coming. This is what wakes them up:

QVAC

5,250,097 次观看 • 4 个月前

We set out to collect 10M hours of on-embodiment data as a starting point for robotics models, but couldn’t find a robot reliable enough at a price that made it possible. So, we built one. Meet Pandroid: an accessible hardware platform for AI. Small enough to safely experiment with, durable enough to run in fleets. Connect it to any model or coding agent over SSH to let it interact with the world. Join the waitlist:

We set out to collect 10M hours of on-embodiment data as a starting point for robotics models, but couldn’t find a robot reliable enough at a price that made it possible. So, we built one. Meet Pandroid: an accessible hardware platform for AI. Small enough to safely experiment with, durable enough to run in fleets. Connect it to any model or coding agent over SSH to let it interact with the world. Join the waitlist:

Pantograph

130,995 次观看 • 3 个月前

Inside real Silicon Valley. Inside the semiconductor company, Ambarella, that makes cameras better. Inside your GoPro, for instance. This is part of my homework to understand the robotics ecosystem in Silicon Valley (this company is in Santa Clara) and San Francisco better. What do they make? A tiny chip that runs AI models while using very low power. This is long but is a college-level class on the chips that will power a lot of robots. And anything that uses cameras. Drones, autonomous vehicles, medical devices, AI glasses, and more. Ambarella Inc. and Muneyb Minhazuddin thanks!

Inside real Silicon Valley. Inside the semiconductor company, Ambarella, that makes cameras better. Inside your GoPro, for instance. This is part of my homework to understand the robotics ecosystem in Silicon Valley (this company is in Santa Clara) and San Francisco better. What do they make? A tiny chip that runs AI models while using very low power. This is long but is a college-level class on the chips that will power a lot of robots. And anything that uses cameras. Drones, autonomous vehicles, medical devices, AI glasses, and more. Ambarella Inc. and Muneyb Minhazuddin thanks!

Robert Scoble

16,165 次观看 • 1 个月前

David Chalmers says it is possible for an AI system to be conscious because the brain itself is a machine that produces consciousness, so we know this is possible in principle

David Chalmers says it is possible for an AI system to be conscious because the brain itself is a machine that produces consciousness, so we know this is possible in principle

Tsarathustra

530,052 次观看 • 2 年前

"A data center is one of the absolute worst economic development tools that you can have in a community. It doesn't create any jobs. It doesn't create any knock-on economic effects, because the only thing that wants to be located next to a data center is a data center." It's time to ban tax breaks for data centers. Pat_Garofalo breaks it down on MS NOW:

"A data center is one of the absolute worst economic development tools that you can have in a community. It doesn't create any jobs. It doesn't create any knock-on economic effects, because the only thing that wants to be located next to a data center is a data center." It's time to ban tax breaks for data centers. Pat_Garofalo breaks it down on MS NOW:

American Economic Liberties Project

96,128 次观看 • 2 个月前

What Lisa Su actually held on stage: A mini PC the size of a lunchbox running Qwen3-235B locally, with no cloud and no discrete GPU Inside: the Ryzen AI Max+ 395, 128GB unified memory, 110GB usable as VRAM on Linux The first x86 chip that handles 200+ billion parameters on a single die AMD claims it beats the RTX 5080 by several times on memory-bound models — because the 5080 simply cannot fit them $1,400 to $2,500 once. cloud bills run $200 to $400 a month It pays for itself in a few months, then costs nothing per request This is not a faster GPU. it is the first real argument that your AI does not belong in someone else's data center

What Lisa Su actually held on stage: A mini PC the size of a lunchbox running Qwen3-235B locally, with no cloud and no discrete GPU Inside: the Ryzen AI Max+ 395, 128GB unified memory, 110GB usable as VRAM on Linux The first x86 chip that handles 200+ billion parameters on a single die AMD claims it beats the RTX 5080 by several times on memory-bound models — because the 5080 simply cannot fit them $1,400 to $2,500 once. cloud bills run $200 to $400 a month It pays for itself in a few months, then costs nothing per request This is not a faster GPU. it is the first real argument that your AI does not belong in someone else's data center

plutos

1,240,308 次观看 • 5 个月前

There are over 90 robot gyms launching in China right now. 100+ robots per center. We are in the robot data gold rush. China's humanoid makers now ship 97% of all humanoids globally. The central government just put $138 billion into a robotics venture fund. And this is only the tip of the iceberg. Robots need an ungodly amount of data to work in the real world. The biggest bottleneck isn't hardware, it's data. So a whole industry appeared overnight to feed them. Welcome to our generation's gold rush: the robot data gold rush. We made a full video on it. We cover why it's such an insane business, how it actually works, the shady stuff (yes, sweatshops for robot data are a thing), and why this might be the completely wrong approach.

There are over 90 robot gyms launching in China right now. 100+ robots per center. We are in the robot data gold rush. China's humanoid makers now ship 97% of all humanoids globally. The central government just put $138 billion into a robotics venture fund. And this is only the tip of the iceberg. Robots need an ungodly amount of data to work in the real world. The biggest bottleneck isn't hardware, it's data. So a whole industry appeared overnight to feed them. Welcome to our generation's gold rush: the robot data gold rush. We made a full video on it. We cover why it's such an insane business, how it actually works, the shady stuff (yes, sweatshops for robot data are a thing), and why this might be the completely wrong approach.

Andreas Klinger 🦾

13,810 次观看 • 2 个月前

The biggest limit on AI today isn't chips. It's power, and the years it takes to build a data center around it. Our answer is the Armada AI factory. We build them on a production line, then deploy them wherever the energy already is. Watch Dan Wright tell the story:

The biggest limit on AI today isn't chips. It's power, and the years it takes to build a data center around it. Our answer is the Armada AI factory. We build them on a production line, then deploy them wherever the energy already is. Watch Dan Wright tell the story:

Armada

740,586 次观看 • 1 个月前

How to learn Physical Intelligence's robot brain without working at Physical Intelligence: PI was in talks this year to raise $1B at an $11B valuation, and its brain, π0, is open source three of its co-founders teach the ideas inside it on YouTube for $0: Sergey Levine, Chelsea Finn and Quan Vuong here's the order I'd watch them in: 1: copy a human first Levine's Berkeley CS 285, lecture 2 (Spring 2026), is supervised learning of behaviors: a robot copies human demos, then drifts off course after its first small mistake. that drift is why ALOHA and π0 predict a whole chunk of motion at once instead of one step at a time 2: learn the math π0 moves with Stanford AA203, lecture 15 (Spring 2026). an hour in, it explains flow matching, the same math π0 uses to turn a decision into smooth arm motion 3: make it better after the demos run out Chelsea Finn's Stanford CS224R: lecture 16 is RL for real robots, and lecture 17 is a guest lecture from Ashish Kumar, who led AI for Tesla Optimus 4: see the whole brain in one talk Quan Vuong's guest talk in ETH's Robot Learning course is about π0.7. 13 minutes in, it folds a shirt on a robot arm that had no folding data of its own then stop watching and start running it: - openpi, PI's own code and π0 weights, 14.4K stars on GitHub - LeRobot by Hugging Face, 28.5K stars, which fine-tunes π0 on your own demos - a $122 SO-101 arm to record those demos on main insight: the lab in talks at $11 billion is built on three ideas stacked on top of each other: copy a human, move smoothly with flow matching, and let a vision-language model decide what to do next all three are free lectures now. the order is the only part nobody hands you P.S. my Optimus-style robot brain guide below turns this order into an 8-week plan

How to learn Physical Intelligence's robot brain without working at Physical Intelligence: PI was in talks this year to raise $1B at an $11B valuation, and its brain, π0, is open source three of its co-founders teach the ideas inside it on YouTube for $0: Sergey Levine, Chelsea Finn and Quan Vuong here's the order I'd watch them in: 1: copy a human first Levine's Berkeley CS 285, lecture 2 (Spring 2026), is supervised learning of behaviors: a robot copies human demos, then drifts off course after its first small mistake. that drift is why ALOHA and π0 predict a whole chunk of motion at once instead of one step at a time 2: learn the math π0 moves with Stanford AA203, lecture 15 (Spring 2026). an hour in, it explains flow matching, the same math π0 uses to turn a decision into smooth arm motion 3: make it better after the demos run out Chelsea Finn's Stanford CS224R: lecture 16 is RL for real robots, and lecture 17 is a guest lecture from Ashish Kumar, who led AI for Tesla Optimus 4: see the whole brain in one talk Quan Vuong's guest talk in ETH's Robot Learning course is about π0.7. 13 minutes in, it folds a shirt on a robot arm that had no folding data of its own then stop watching and start running it: - openpi, PI's own code and π0 weights, 14.4K stars on GitHub - LeRobot by Hugging Face, 28.5K stars, which fine-tunes π0 on your own demos - a $122 SO-101 arm to record those demos on main insight: the lab in talks at $11 billion is built on three ideas stacked on top of each other: copy a human, move smoothly with flow matching, and let a vision-language model decide what to do next all three are free lectures now. the order is the only part nobody hands you P.S. my Optimus-style robot brain guide below turns this order into an 8-week plan

Martin

15,288 次观看 • 18 天前

Karoline Leavitt on AI Datacenter Concerns: - ratepayer protection will be put in place to ensure those involved building them will PAY for it out of their pockets, not the state taxpayers - commitments have been made so far that cover 80% of all power in the U.S… - Americans will never be left footing the bill so private companies can benefit— Trump instead is leverage the data center boom to benefit families and small businesses

Karoline Leavitt on AI Datacenter Concerns: - ratepayer protection will be put in place to ensure those involved building them will PAY for it out of their pockets, not the state taxpayers - commitments have been made so far that cover 80% of all power in the U.S… - Americans will never be left footing the bill so private companies can benefit— Trump instead is leverage the data center boom to benefit families and small businesses

MJTruthUltra

23,305 次观看 • 3 个月前

$Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.$

Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.

Dustin

160,794 次观看 • 7 个月前

you don’t need a robot to see physical ai in action, you just need a browser. this week I finally tried something I've wanted to do for a while: design an SO-101 scene in three.js, connect it to a real leader arm, collect teleoperation data, train an ACT model, then port it to run in the browser. the result is the first ever demo of ACT that runs locally on the web. try it yourself now at

you don’t need a robot to see physical ai in action, you just need a browser. this week I finally tried something I've wanted to do for a while: design an SO-101 scene in three.js, connect it to a real leader arm, collect teleoperation data, train an ACT model, then port it to run in the browser. the result is the first ever demo of ACT that runs locally on the web. try it yourself now at

Binh

122,825 次观看 • 4 个月前

News just broke that Rick Jackson is currently investing in a Texas Data Center and wants to bring them here to Georgia. Same Rick Jackson that accused me of putting a data center on my land! So to put the record straight, here it is! The fake data center they have been lying about is just green beautiful Georgia pasture.

News just broke that Rick Jackson is currently investing in a Texas Data Center and wants to bring them here to Georgia. Same Rick Jackson that accused me of putting a data center on my land! So to put the record straight, here it is! The fake data center they have been lying about is just green beautiful Georgia pasture.

Burt Jones

430,624 次观看 • 7 个月前

Perplexity CEO Aravind Srinivas on the biggest threat to the data center industry: It's not competition. It's not regulation. It's decentralisation. "The biggest threat to a data center is if the intelligence can be packed locally on a chip that's running on the device and then there's no need to inference all of it on like one centralized data center." He outlines how this could work in practice. Personalisation doesn't necessarily require on-device model training. Retrieval augmented generation, tool calls, and local data can already tailor AI to individual users. But the real unlock? Test time training. Aravind Srinivas describes a future where AI lives on your device, watches how you work and gradually automates your repetitive tasks. "Imagine we crack test time training where the AI watches tasks you repeatedly do on your local system, adapts to you over time and starts automating a lot of the things you do." The key insight: in this model, the intelligence belongs to you. It's your data, your device, your personalised AI brain. And if that future arrives, the economics of centralised infrastructure start to collapse. "That really disrupts the whole data center industry. It doesn't make sense to spend all this money, 500 billion, 5 trillion, whatever on building all the centralized data centers across the world that do a lot of the intelligence workloads for people." The companies spending trillions on centralised infrastructure may want to rethink where intelligence actually needs to live.

Perplexity CEO Aravind Srinivas on the biggest threat to the data center industry: It's not competition. It's not regulation. It's decentralisation. "The biggest threat to a data center is if the intelligence can be packed locally on a chip that's running on the device and then there's no need to inference all of it on like one centralized data center." He outlines how this could work in practice. Personalisation doesn't necessarily require on-device model training. Retrieval augmented generation, tool calls, and local data can already tailor AI to individual users. But the real unlock? Test time training. Aravind Srinivas describes a future where AI lives on your device, watches how you work and gradually automates your repetitive tasks. "Imagine we crack test time training where the AI watches tasks you repeatedly do on your local system, adapts to you over time and starts automating a lot of the things you do." The key insight: in this model, the intelligence belongs to you. It's your data, your device, your personalised AI brain. And if that future arrives, the economics of centralised infrastructure start to collapse. "That really disrupts the whole data center industry. It doesn't make sense to spend all this money, 500 billion, 5 trillion, whatever on building all the centralized data centers across the world that do a lot of the intelligence workloads for people." The companies spending trillions on centralised infrastructure may want to rethink where intelligence actually needs to live.

Big Brain AI

90,355 次观看 • 8 个月前

🚨 NVIDIA IS GETTING HIT FROM BOTH SIDES CHINA JUST COMPLETELY BANNED NVIDIA CHIPS BEIJING CANCELED THE GREEN LIGHT FOR TOP CHINESE COMPANIES TO BUY $NVDA CHIPS CHINA WOULD RATHER BUILD ITS OWN AI SUPPLY CHAIN THAN DEPEND ON AMERICA AT THE SAME TIME, NVIDIA CUT ITS INITIAL SUPPORT FOR OPENAI’S OHIO DATA CENTER FROM $250 BILLION TO LESS THAN $120 BILLION CHINA IS BACKING AWAY FROM NVIDIA AND NVIDIA IS STARTING TO BACK AWAY FROM THE AI BOOM THIS IS NOT JUST ABOUT ONE CHIPMAKER THE ENTIRE AI TRADE IS STARTING TO CHANGE

🚨 NVIDIA IS GETTING HIT FROM BOTH SIDES CHINA JUST COMPLETELY BANNED NVIDIA CHIPS BEIJING CANCELED THE GREEN LIGHT FOR TOP CHINESE COMPANIES TO BUY $NVDA CHIPS CHINA WOULD RATHER BUILD ITS OWN AI SUPPLY CHAIN THAN DEPEND ON AMERICA AT THE SAME TIME, NVIDIA CUT ITS INITIAL SUPPORT FOR OPENAI’S OHIO DATA CENTER FROM $250 BILLION TO LESS THAN $120 BILLION CHINA IS BACKING AWAY FROM NVIDIA AND NVIDIA IS STARTING TO BACK AWAY FROM THE AI BOOM THIS IS NOT JUST ABOUT ONE CHIPMAKER THE ENTIRE AI TRADE IS STARTING TO CHANGE

Leshka.eth ⛩

43,549 次观看 • 2 个月前

Introducing /multitask in the new Cursor 3 interface. Cursor can now run async subagents to parallelize your requests instead of adding them to the queue. For already queued messages, you can ask Cursor to multitask on them instead of waiting for the current run to finish.

Introducing /multitask in the new Cursor 3 interface. Cursor can now run async subagents to parallelize your requests instead of adding them to the queue. For already queued messages, you can ask Cursor to multitask on them instead of waiting for the current run to finish.

Cursor

436,746 次观看 • 6 个月前