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EXCLUSIVE INTERVIEW WITH CHRIS CAMILLO AND AMIT KUKREJA We sat down with Chris Camillo and amit for a full length conversation on the state of humanoid robotics, the timeline, the supply chain, and where the investable opportunities actually are. Here is the full video with timestamps: 0:47 The current...

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WOLF

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26,207 次观看 • 12 天前 •via X (Twitter)

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RoboStrategy CEO Andrew Kang sat down with EO to discuss: • The inflection point of robotics • How we could get to $2/hour labor • Manufacturing and supply chain opportunities • The US vs China race in robotics Timestamps: 0:00 Intro to RoboStrategy 1:13 The Figure investment 2:55 Manufacturing opportunities 6:00 Sizing the humanoid market 6:58 The 4th Industrial Revolution 9:57 The real bottleneck of manufacturing 11:45 NVIDIA's open-source play 13:20 US vs China 15:35 Where to build now 19:29 Building a robotics company is hard
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RoboStrategy CEO Andrew Kang sat down with EO to discuss: • The inflection point of robotics • How we could get to $2/hour labor • Manufacturing and supply chain opportunities • The US vs China race in robotics Timestamps: 0:00 Intro to RoboStrategy 1:13 The Figure investment 2:55 Manufacturing opportunities 6:00 Sizing the humanoid market 6:58 The 4th Industrial Revolution 9:57 The real bottleneck of manufacturing 11:45 NVIDIA's open-source play 13:20 US vs China 15:35 Where to build now 19:29 Building a robotics company is hard

RoboStrategy

14,514 次观看 • 13 天前

Not enough people are talking about physical AI and robotics. I sat down with Andrew Kang, one of the best investors of the last decade, to discuss his massive bet on humanoids and robotics. He breaks down the industry, the addressable market, multiple leading companies, and why he launched a publicly-traded fund ($BOT) focused on investing in the top private robotics companies. YouTube: Apple: Spotify: TIMESTAMPS: 0:00 - Intro 1:28 - Why Andrew shifted from crypto to humanoid robots 3:58 - How big is the total addressable market? 8:08 - Building conviction — the $19M bet on Figure AI 16:06 - US vs. China — who wins the robot race? 28:08 - General purpose vs. specialized robots 31:05 - Where does training data come from? 40:24 - Humanoid robots in your everyday life 43:27 - Can Tesla & Elon win the humanoid race? 46:19 - Job displacement & UBI 51:15 - RoboStrategy — the publicly traded venture fund 1:11:17 - What is exciting about Apptronik? 1:13:24 - Addressing the critics
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Not enough people are talking about physical AI and robotics. I sat down with Andrew Kang, one of the best investors of the last decade, to discuss his massive bet on humanoids and robotics. He breaks down the industry, the addressable market, multiple leading companies, and why he launched a publicly-traded fund ($BOT) focused on investing in the top private robotics companies. YouTube: Apple: Spotify: TIMESTAMPS: 0:00 - Intro 1:28 - Why Andrew shifted from crypto to humanoid robots 3:58 - How big is the total addressable market? 8:08 - Building conviction — the $19M bet on Figure AI 16:06 - US vs. China — who wins the robot race? 28:08 - General purpose vs. specialized robots 31:05 - Where does training data come from? 40:24 - Humanoid robots in your everyday life 43:27 - Can Tesla & Elon win the humanoid race? 46:19 - Job displacement & UBI 51:15 - RoboStrategy — the publicly traded venture fund 1:11:17 - What is exciting about Apptronik? 1:13:24 - Addressing the critics

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676,914 次观看 • 2 个月前

This week's episode is mostly about robots. Kyle Vogt is one of very few entrepreneurs to found 3 business valued at over a billion dollars - Twitch in 2006, Cruise in 2013, and The Bot Company in 2024. If you've caught any other Uncapped episodes you've probably heard some investors saying that home robots are close and they'll be a giant new market. Kyle Vogt is building them, and talking to him made me as bullish as ever. Timestamps: (0:00) Introduction (0:34) Why robotics is suddenly booming (3:31) Special-purpose vs generalized (5:32) Designing robots people actually use (9:00) Building for scale and affordability (12:17) The myth of humanoids (15:04) Trust, safety, and privacy in your home (17:51) Robotics intelligence (21:01) Why Kyle keeps starting hard companies (22:32) The 100-person rule and elite teams (26:10) How to actually ship (27:28) What home robotics will do first (35:05) Home security and other applications (38:41) Tesla vs Waymo (41:08) When to sell a company (42:41) Marathons
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Jack Altman

289,176 次观看 • 9 个月前

My conversation with Sergey Levine (Sergey Levine). Sergey is the co-founder of Physical Intelligence -- a company building foundation models that can control any robot to do any task in any environment. The company's thesis is that generality is more scalable than specialization, meaning that a model trained across many different robots and tasks will ultimately outperform any system built to do one thing well (eg, just wash dishes). Sergey is a researcher by background, but I think you will appreciate how practical and commercially grounded this conversation is. We discuss: - Why changing a diaper will be the last task a robot masters - The simulation v. real-world data debate - How multimodal LLMs give robots common sense - Moravec's Paradox + Robot Olympics - Why robots can do long-horizon tasks now - A realistic timeline for robots in our homes I should note that I am an investor in Physical Intelligence -- I made the investment because I believe it is one of the most important companies tackling the problem of robotics. Enjoy! Timestamps: 0:00 Intro 2:39 Defining Physical Intelligence 5:19 The Challenge of Building General Models 6:34 The Stakes and Future of General Purpose Robotics 8:15 Pros and Cons of Humanoid Robots 10:12 Historical Milestones in Robotics Research 15:31 Combining Generative AI and Deep RL 21:24 Moravec's Paradox 25:33 Kitchen Robots 29:30 Simulation vs. Real-World Data 30:48 The Robot Olympics 36:31 The Physiological Reality of Embodiment 38:56 Controversies in the Robotics Community 44:18 What Makes a Great Researcher 48:27 How Businesses Should Prepare for Robotics 54:09 Tracking Progress Through Research Papers 57:02 The Next Step: Mid-Level Reasoning 1:02:00 The Kindest Thing
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My conversation with Sergey Levine (Sergey Levine). Sergey is the co-founder of Physical Intelligence -- a company building foundation models that can control any robot to do any task in any environment. The company's thesis is that generality is more scalable than specialization, meaning that a model trained across many different robots and tasks will ultimately outperform any system built to do one thing well (eg, just wash dishes). Sergey is a researcher by background, but I think you will appreciate how practical and commercially grounded this conversation is. We discuss: - Why changing a diaper will be the last task a robot masters - The simulation v. real-world data debate - How multimodal LLMs give robots common sense - Moravec's Paradox + Robot Olympics - Why robots can do long-horizon tasks now - A realistic timeline for robots in our homes I should note that I am an investor in Physical Intelligence -- I made the investment because I believe it is one of the most important companies tackling the problem of robotics. Enjoy! Timestamps: 0:00 Intro 2:39 Defining Physical Intelligence 5:19 The Challenge of Building General Models 6:34 The Stakes and Future of General Purpose Robotics 8:15 Pros and Cons of Humanoid Robots 10:12 Historical Milestones in Robotics Research 15:31 Combining Generative AI and Deep RL 21:24 Moravec's Paradox 25:33 Kitchen Robots 29:30 Simulation vs. Real-World Data 30:48 The Robot Olympics 36:31 The Physiological Reality of Embodiment 38:56 Controversies in the Robotics Community 44:18 What Makes a Great Researcher 48:27 How Businesses Should Prepare for Robotics 54:09 Tracking Progress Through Research Papers 57:02 The Next Step: Mid-Level Reasoning 1:02:00 The Kindest Thing

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133,833 次观看 • 4 个月前

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I spoke with Andrew Kang, Co-Founder and CEO of RoboStrategy. RoboStrategy already holds concentrated positions in industry-defining companies including Figure, Apptronik, Standard Bots, and Dyna Robotics. It is the first publicly listed vehicle purpose-built to give investors liquid exposure to private robotics and physical AI giants, applying the MicroStrategy capital-markets playbook to automation. The first episode of the third season! Full interview now live: Ep 101 | Investing $19M in Humanoids with Zero Robotics Experience 📽️ YouTube: 🎧 Spotify: And thanks for making this happen, intern & Humanoid Scott, Jack 🤖! Timestamps 0:00 Why we built a public fund for private robotics 0:35 Building the MicroStrategy for robotics 1:22 Escaping the tiger parent trap 2:11 Video games are elite training for game theory and critical thinking 6:30 Learning arbitrage in RuneScape and Neopets 7:59 Don't believe experts just because of their titles 10:24 Why investing is just a video game with real money 12:40 Harvard nutrition professors don't know what they're talking about 18:48 Recreating global finance from first principles 19:57 Why robotics is the early days of crypto all over again 21:50 Realizing humanoids are no longer sci-fi 22:58 Why I wrote a $19M check when every VC told me not to 27:24 How SPVs are democratizing venture investing 30:55 Bet on the founder and their execution speed, not the pitch deck 36:39 The math behind a $50 trillion physical labor market 41:44 Applying the MicroStrategy playbook to private markets 45:48 Humanoids are the GPUs of physical labor 47:48 Building a new SoftBank for the robotics era 50:51 Why robotics startups need engineers for attention 54:35 Don't be satisfied with surface level answers

Ilir Aliu

16,683 次观看 • 1 个月前

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New Episode. Nils Pihl (Nils), CEO of Auki Labs, discuss the "Real World Web" and making physical spaces accessible to robots. We dive into decentralized mapping and Auki’s latest retail deployments. Nils also shares his take on the US-China robotics race, drawing on 15 years of building ventures in the heart of China’s tech ecosystem. 2:05 Nils' journey into the technology 8:36 Six layers of Robotics value chain 11:00 Use case for AUKI's perception stack 17:04 Compute challenge of distributed mapping 20:16 Decentralized solution to privacy 22:36 Programmable robots vs closed approach 25:43 Most valuable layers or robotics ecosystem 28:53 Behind China's rapid humanoid boom 36:06 140 humanoid OEMs, who's gonna win 41:32 US-China geopolitics 44:52 What can the west do to compete with China 46:55 Technology adoption in Asian countries 54:01 Memes

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39,794 次观看 • 6 个月前

Every conversation I have with Dylan Patel, I'm really just trying to understand the supply and demand of tokens. This is a unique episode in that it's entirely dedicated to talking about both sides of that equation. We discuss: - The infinite demand for the newest models - SemiAnalysis going from $10K on AI spend to $7M - Mythos and Anthropic's compute problem - Why TSMC spending $100B on CapEx could cause a shortage - Robotics as next demand wave - Why memory prices will double again This is my second conversation with Dylan and find myself needing to speak with him more and more often to make sense of it all. Enjoy! Timestamps: 0:00 Intro 1:00 Surging AI Spend 10:27 Token Demand 16:21 When Ideas Are Cheap and Execution is Easy 20:46 Model Hoarding 22:34 Robotics 27:03 The Compute Bottleneck 30:26 The AI Permanent Underclass 31:39 Supply Chain Reality 37:47 CPUs 42:54 Predictions: Public Backlash
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Every conversation I have with Dylan Patel, I'm really just trying to understand the supply and demand of tokens. This is a unique episode in that it's entirely dedicated to talking about both sides of that equation. We discuss: - The infinite demand for the newest models - SemiAnalysis going from $10K on AI spend to $7M - Mythos and Anthropic's compute problem - Why TSMC spending $100B on CapEx could cause a shortage - Robotics as next demand wave - Why memory prices will double again This is my second conversation with Dylan and find myself needing to speak with him more and more often to make sense of it all. Enjoy! Timestamps: 0:00 Intro 1:00 Surging AI Spend 10:27 Token Demand 16:21 When Ideas Are Cheap and Execution is Easy 20:46 Model Hoarding 22:34 Robotics 27:03 The Compute Bottleneck 30:26 The AI Permanent Underclass 31:39 Supply Chain Reality 37:47 CPUs 42:54 Predictions: Public Backlash

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893,210 次观看 • 3 个月前

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Open source AI just became the biggest policy fight in tech. Our full conversation with Ben Horowitz, where he tells Theo and Sofia why banning open source is nearly impossible, why it's historically the safer path, the risk of one company controlling AI and why distillation isn't the threat people claim. 01:21 The Nvidia-backed open letter on open-weight AI 01:49 The OpenAI–Hugging Face hacking incident 02:27 Why you can't actually ban open source 03:05 Historically, open source has been safer (Linux vs. Windows) 05:17 Jensen's Twitter account and the letter's significance 07:25 China, "sleeper agent" models, and national security risk 09:23 Anthropic defying the US government 15:18 The danger of building your business on Anthropic 18:51 BYD, EVs, and rebuilding US manufacturing 21:30 Why would a company ever release an open model? 23:58 The DeepSeek moment: why nothing actually changed 28:25 Paying 100x more for an AI researcher vs. a janitor 29:06 AI art, the "slop" problem, and a coming renaissance benahorowitz.eth Theo Jaffee sof 𓋹

MTS

11,225 次观看 • 24 天前

Elon Musk revealed the 3 hardest challenges for humanoid robots and how Tesla is solving them There are really only three hard things for humanoid robots: - Real-world intelligence - ⁠The hand and - ⁠Scale manufacturing "I haven’t seen any demo robots that have a great hand with all the degrees of freedom of a human hand. But Optimus does have that" To reach human-level dexterity, Tesla had to design custom actuators, motors, gears, power electronics, controls, and sensors, everything from physics first principles There is no supply chain for this "From an electromechanical standpoint, the hand is more difficult than everything else combined. The human hand turns out to be quite something" This is why Optimus is on a completely different level. Tesla is solving the hardest problems from the ground up
0:55

Elon Musk revealed the 3 hardest challenges for humanoid robots and how Tesla is solving them There are really only three hard things for humanoid robots: - Real-world intelligence - ⁠The hand and - ⁠Scale manufacturing "I haven’t seen any demo robots that have a great hand with all the degrees of freedom of a human hand. But Optimus does have that" To reach human-level dexterity, Tesla had to design custom actuators, motors, gears, power electronics, controls, and sensors, everything from physics first principles There is no supply chain for this "From an electromechanical standpoint, the hand is more difficult than everything else combined. The human hand turns out to be quite something" This is why Optimus is on a completely different level. Tesla is solving the hardest problems from the ground up

X Freeze

20,796 次观看 • 5 个月前

🇺🇸 TRUMP & LUTNICK EYE HUMANOID ROBOTICS REVOLUTION IN 2026: “AI THAT WALKS AND WORKS” Trump and Secretary of Commerce Howard Lutnick are now turning their attention to the next seismic shift in tech: humanoid robotics, with a targeted push beginning in 2026. The move is part of a broader strategy to supercharge American AI leadership, but not just in software. The focus is shifting to physical, labor-replacing machines: humanoid robots that can think, move, and work in real-world environments. Think less ChatGPT, more “steel spine and servers flipping burgers, or building cars.” Why does it matter though? The U.S. is now staring down a future where whoever builds the best humanoid labor force wins the next economy, and we know China’s not waiting. Trump and Lutnick’s entrance into this space signals something serious: this isn’t just a Silicon Valley playground anymore. This is becoming a national priority. For a country dealing with a shrinking workforce, soaring labor costs, and foreign competitors pumping billions into robotics. It’s time to start taking humanoid robots more seriously and build them already. 2026 could be the year the Made in America label gets printed by robots. Source: Disclose.tv / Politico / AOL News
0:45

🇺🇸 TRUMP & LUTNICK EYE HUMANOID ROBOTICS REVOLUTION IN 2026: “AI THAT WALKS AND WORKS” Trump and Secretary of Commerce Howard Lutnick are now turning their attention to the next seismic shift in tech: humanoid robotics, with a targeted push beginning in 2026. The move is part of a broader strategy to supercharge American AI leadership, but not just in software. The focus is shifting to physical, labor-replacing machines: humanoid robots that can think, move, and work in real-world environments. Think less ChatGPT, more “steel spine and servers flipping burgers, or building cars.” Why does it matter though? The U.S. is now staring down a future where whoever builds the best humanoid labor force wins the next economy, and we know China’s not waiting. Trump and Lutnick’s entrance into this space signals something serious: this isn’t just a Silicon Valley playground anymore. This is becoming a national priority. For a country dealing with a shrinking workforce, soaring labor costs, and foreign competitors pumping billions into robotics. It’s time to start taking humanoid robots more seriously and build them already. 2026 could be the year the Made in America label gets printed by robots. Source: Disclose.tv / Politico / AOL News

Mario Nawfal

64,898 次观看 • 8 个月前

🦔Workers in India are wearing head-mounted cameras for 12 cents an hour to collect training data for humanoid robots. The footage of them doing everyday tasks like cooking, cleaning, sorting, and walking through public spaces gets sold to robotics companies building the models meant to replace those same kinds of jobs in higher-wage countries. The arrangement has been running for roughly two years. Workers do not own the data, do not get residuals, and in many cases are not told what their footage is being used to train. My Take The workers wearing the cameras live in a country where robotics automation will hit decades later, so they are training their own future replacements at a delay that hides the consequence from them personally. The companies buying the data are mostly US and Chinese, building humanoid robots aimed at warehouses, retail, and service jobs in countries paying $15 to $25 an hour rather than 12 cents. Robotics companies need motion data that mimics how humans actually move through real environments, and synthetic data has not been good enough yet. Paying 12 cents an hour in Bengaluru is cheaper than running motion capture studios in Boston, and it works at scale because the worker absorbs the cost of the camera, the discomfort of wearing it, and the long-term loss of any rights to their own movement data. The robotics labor market that eventually emerges from this footage will displace far more wages than the data collection cost to gather. That is the trade investors funding humanoid robotics startups are betting will pay off, and the workers in the videos are the ones paying the tab up front. Hedgie🤗
0:43

🦔Workers in India are wearing head-mounted cameras for 12 cents an hour to collect training data for humanoid robots. The footage of them doing everyday tasks like cooking, cleaning, sorting, and walking through public spaces gets sold to robotics companies building the models meant to replace those same kinds of jobs in higher-wage countries. The arrangement has been running for roughly two years. Workers do not own the data, do not get residuals, and in many cases are not told what their footage is being used to train. My Take The workers wearing the cameras live in a country where robotics automation will hit decades later, so they are training their own future replacements at a delay that hides the consequence from them personally. The companies buying the data are mostly US and Chinese, building humanoid robots aimed at warehouses, retail, and service jobs in countries paying $15 to $25 an hour rather than 12 cents. Robotics companies need motion data that mimics how humans actually move through real environments, and synthetic data has not been good enough yet. Paying 12 cents an hour in Bengaluru is cheaper than running motion capture studios in Boston, and it works at scale because the worker absorbs the cost of the camera, the discomfort of wearing it, and the long-term loss of any rights to their own movement data. The robotics labor market that eventually emerges from this footage will displace far more wages than the data collection cost to gather. That is the trade investors funding humanoid robotics startups are betting will pay off, and the workers in the videos are the ones paying the tab up front. Hedgie🤗

Hedgie

162,025 次观看 • 2 个月前

Elon just dropped a MAJOR nugget on how Tesla is going to be training Optimus to do real world tasks. They are building an Optimus Academy, which is a large scale, dedicated real-world training facility to accelerate the development of Optimus. The Academy will deploy thousands of Optimus units, potentially 10,000 to 30,000 robots, in a controlled realistic environment where they perform self-play, experiment with tasks, iterate on behaviors, and continuously generate training data through trial and error. The Tesla bots will also run millions of simulations in Tesla’s high-fidelity physics-accurate engine, allowing Optimus to close the “sim-to-real gap” by using these real-world observations to refine and validate the simulations! “You’re actually highlighting an important limitation and difference from cars. We’ll soon have 10 million cars on the road. It’s hard to duplicate that massive training flywheel. For the robot, what we’re going to need to do is build a lot of robots and put them in kind of an Optimus Academy so they can do self-play in reality. We’re actually building that out. We can have at least 10,000 Optimus robots, maybe 20-30,000, that are doing self-play and testing different tasks. Tesla has quite a good reality generator, a physics-accurate reality generator, that we made for the cars. We’ll do the same thing for the robots. We actually have done that for the robots. So you have a few tens of thousands of humanoid robots doing different tasks. You can do millions of simulated robots in the simulated world. You use the tens of thousands of robots in the real world to close the simulation to reality gap. Close the sim-to-real gap.”
1:20

Elon just dropped a MAJOR nugget on how Tesla is going to be training Optimus to do real world tasks. They are building an Optimus Academy, which is a large scale, dedicated real-world training facility to accelerate the development of Optimus. The Academy will deploy thousands of Optimus units, potentially 10,000 to 30,000 robots, in a controlled realistic environment where they perform self-play, experiment with tasks, iterate on behaviors, and continuously generate training data through trial and error. The Tesla bots will also run millions of simulations in Tesla’s high-fidelity physics-accurate engine, allowing Optimus to close the “sim-to-real gap” by using these real-world observations to refine and validate the simulations! “You’re actually highlighting an important limitation and difference from cars. We’ll soon have 10 million cars on the road. It’s hard to duplicate that massive training flywheel. For the robot, what we’re going to need to do is build a lot of robots and put them in kind of an Optimus Academy so they can do self-play in reality. We’re actually building that out. We can have at least 10,000 Optimus robots, maybe 20-30,000, that are doing self-play and testing different tasks. Tesla has quite a good reality generator, a physics-accurate reality generator, that we made for the cars. We’ll do the same thing for the robots. We actually have done that for the robots. So you have a few tens of thousands of humanoid robots doing different tasks. You can do millions of simulated robots in the simulated world. You use the tens of thousands of robots in the real world to close the simulation to reality gap. Close the sim-to-real gap.”

Teslaconomics

42,563 次观看 • 6 个月前

🚨 The Robot Episode @jason sits down with FOUR leaders in robotics to discuss the future of the industry, the $1/hour robo-worker, China's threat, robo-dog vs humanoid, and why this time is different: - Dr. Péter Fankhauser of ANYbotics - Bernt Børnich of 1X - Amanda McMaster of Boston Dynamics - Jonathan Hurst of Agility (0:00) Intro: Humanoids, Robots, & AI+ (0:57) ANYbotics' Dr. Péter Fankhauser: Why ANYbotics Bet the Company on Four-Legged Robot Dogs, Not Humanoids (13:18) Dr. Péter Fankhauser: China's Armed Robot Dogs Are "Stupid and Risky": Inside the Terminator Debate (15:01) NEO’s Bernt Børnich: Neo Ships in 2026 and Becomes an Open Robot Platform (22:18) Bernt Børnich: Robots Building Robots in Just 3 Years: “Hard Takeoff” (33:45) Boston Dynamics' Amanda McMaster: Spot Is Now the Most Deployed Robot on Earth, Atlas is the Most Capable (42:36) Amanda McMaster: Chinese Robotics Security Risks (47:14) Agility Robotics' Jonathan Hurst: Why "This Time Is Different" for Humanoids After 100 Years of False Starts (58:45) Jonathan Hurst: When Do Robots Outnumber Humans on the Factory Floor? --------------------------------------- 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 20% off! Which is also available on Amazon: (Code: ALLIN20X)
1:08:34

🚨 The Robot Episode @jason sits down with FOUR leaders in robotics to discuss the future of the industry, the $1/hour robo-worker, China's threat, robo-dog vs humanoid, and why this time is different: - Dr. Péter Fankhauser of ANYbotics - Bernt Børnich of 1X - Amanda McMaster of Boston Dynamics - Jonathan Hurst of Agility (0:00) Intro: Humanoids, Robots, & AI+ (0:57) ANYbotics' Dr. Péter Fankhauser: Why ANYbotics Bet the Company on Four-Legged Robot Dogs, Not Humanoids (13:18) Dr. Péter Fankhauser: China's Armed Robot Dogs Are "Stupid and Risky": Inside the Terminator Debate (15:01) NEO’s Bernt Børnich: Neo Ships in 2026 and Becomes an Open Robot Platform (22:18) Bernt Børnich: Robots Building Robots in Just 3 Years: “Hard Takeoff” (33:45) Boston Dynamics' Amanda McMaster: Spot Is Now the Most Deployed Robot on Earth, Atlas is the Most Capable (42:36) Amanda McMaster: Chinese Robotics Security Risks (47:14) Agility Robotics' Jonathan Hurst: Why "This Time Is Different" for Humanoids After 100 Years of False Starts (58:45) Jonathan Hurst: When Do Robots Outnumber Humans on the Factory Floor? --------------------------------------- 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 20% off! Which is also available on Amazon: (Code: ALLIN20X)

The All-In Podcast

341,020 次观看 • 22 天前

We recently sat down with Brett Adcock at Figure to discuss Helix 02, the future of Humanoids, and Brett's vision for Figure. It's a full blown Humanoid takeover. (w/ @DavidBlundin ) (0:00:00) - Neural Nets, Data Moats & The Age of Abundance (0:04:06) - Going All‑In on Neural Nets (0:12:22) - LLMs vs Physics: Why Language Models Can’t Run Robots (0:16:31) - Global Competition & Industry Consolidation (0:24:27) - Helix in Production: Logistics & Long‑Horizon Autonomy (0:30:00) - Scaling Figure 3: Robots Building Robots (0:52:44) - Jarvis‑Style Digital AI (1:02:25) - Healthcare, Surgery & Robots in the Home (1:15:20) - Safety, Privacy & Trusting Robots with Your Family (1:29:37) - Billions of Humanoids & The Global Robot Economy
1:43:47

We recently sat down with Brett Adcock at Figure to discuss Helix 02, the future of Humanoids, and Brett's vision for Figure. It's a full blown Humanoid takeover. (w/ @DavidBlundin ) (0:00:00) - Neural Nets, Data Moats & The Age of Abundance (0:04:06) - Going All‑In on Neural Nets (0:12:22) - LLMs vs Physics: Why Language Models Can’t Run Robots (0:16:31) - Global Competition & Industry Consolidation (0:24:27) - Helix in Production: Logistics & Long‑Horizon Autonomy (0:30:00) - Scaling Figure 3: Robots Building Robots (0:52:44) - Jarvis‑Style Digital AI (1:02:25) - Healthcare, Surgery & Robots in the Home (1:15:20) - Safety, Privacy & Trusting Robots with Your Family (1:29:37) - Billions of Humanoids & The Global Robot Economy

Peter H. Diamandis, MD

174,220 次观看 • 6 个月前

Here's my conversation with Marc Raibert, founder and long-time CEO of Boston Dynamics, and head of their new AI Institute, all about the history and future of robotics. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 1:43 - Early robots 6:47 - Legged robots 25:27 - Boston Dynamics 28:45 - BigDog 36:52 - Hydraulic actuation 38:44 - Natural movement 44:31 - Leg Lab 51:23 - AI Institute 54:41 - Athletic intelligence 1:02:35 - Building a team 1:05:37 - Videos 1:13:25 - Engineering 1:16:53 - Dancing robots 1:21:40 - Hiring 1:25:32 - Optimus robot 1:34:02 - Future of robotics 1:38:56 - Advice for young people
1:43:38

Here's my conversation with Marc Raibert, founder and long-time CEO of Boston Dynamics, and head of their new AI Institute, all about the history and future of robotics. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 1:43 - Early robots 6:47 - Legged robots 25:27 - Boston Dynamics 28:45 - BigDog 36:52 - Hydraulic actuation 38:44 - Natural movement 44:31 - Leg Lab 51:23 - AI Institute 54:41 - Athletic intelligence 1:02:35 - Building a team 1:05:37 - Videos 1:13:25 - Engineering 1:16:53 - Dancing robots 1:21:40 - Hiring 1:25:32 - Optimus robot 1:34:02 - Future of robotics 1:38:56 - Advice for young people

Lex Fridman

484,938 次观看 • 2 年前

.Sergey Levine is one of the world's leading robotics researchers (and co-founder of Physical Intelligence). He thinks fully autonomous robots are much closer than people realize - when I pushed him on a prediction, he said 5 years to robots that can autonomously run your household). The big game changer, in his view, is that we finally have LLMs with common sense and prior knowledge that we can scaffold the robot models around. We get into all the reasons this might not happen, the data flywheel from deployment, architecture, simulation, manufacturing, and much more. Enjoy! 0:00:00 – Timeline to widely deployed autonomous robots 0:22:12 – Why robotics will scale faster than self-driving cars 0:32:15 – How vision-language-action models work 0:50:26 – Improvements needed for brainlike efficiency 1:02:48 – Learning from simulation 1:14:08 – How much will robots speed up AI buildouts? 1:22:54 – If hardware’s the bottleneck, does China win by default?
1:33:14

.Sergey Levine is one of the world's leading robotics researchers (and co-founder of Physical Intelligence). He thinks fully autonomous robots are much closer than people realize - when I pushed him on a prediction, he said 5 years to robots that can autonomously run your household). The big game changer, in his view, is that we finally have LLMs with common sense and prior knowledge that we can scaffold the robot models around. We get into all the reasons this might not happen, the data flywheel from deployment, architecture, simulation, manufacturing, and much more. Enjoy! 0:00:00 – Timeline to widely deployed autonomous robots 0:22:12 – Why robotics will scale faster than self-driving cars 0:32:15 – How vision-language-action models work 0:50:26 – Improvements needed for brainlike efficiency 1:02:48 – Learning from simulation 1:14:08 – How much will robots speed up AI buildouts? 1:22:54 – If hardware’s the bottleneck, does China win by default?

Dwarkesh Patel

211,158 次观看 • 11 个月前

Why Galaxy Might Be the Most Undervalued Crypto x AI Stock w/ Duncan What do Meta, Amazon, Google, and Microsoft …have in common? They’re spending $330B on AI infra this year. And one crypto company is building the AI infrastructure to power them. Tune in to know more ⏱ TIME POINTS ⏱ 00:00 – Intro 00:58 – Where Are We in the Bull Market? 03:11 – Is the 4-Year Cycle Dead? 06:25 – How Public Markets Are Changing Crypto 12:13 – Figure Markets 12:48 – KGeN 13:18 – Why Duncan Loves $GLXY 16:10 – The AI Data Center Opportunity 27:23 – Risks of Building at AI Scale 33:33 – What’s Galaxy’s Endgame? 36:05 – AI & Robots: What’s Coming and When 43:46 – How to Invest in Humanoid Robots 47:50 – What Could Slow Down Robot Adoption? 50:18 – Where Duncan Finds Alpha 53:29 – Final Thoughts
55:26

Why Galaxy Might Be the Most Undervalued Crypto x AI Stock w/ Duncan What do Meta, Amazon, Google, and Microsoft …have in common? They’re spending $330B on AI infra this year. And one crypto company is building the AI infrastructure to power them. Tune in to know more ⏱ TIME POINTS ⏱ 00:00 – Intro 00:58 – Where Are We in the Bull Market? 03:11 – Is the 4-Year Cycle Dead? 06:25 – How Public Markets Are Changing Crypto 12:13 – Figure Markets 12:48 – KGeN 13:18 – Why Duncan Loves $GLXY 16:10 – The AI Data Center Opportunity 27:23 – Risks of Building at AI Scale 33:33 – What’s Galaxy’s Endgame? 36:05 – AI & Robots: What’s Coming and When 43:46 – How to Invest in Humanoid Robots 47:50 – What Could Slow Down Robot Adoption? 50:18 – Where Duncan Finds Alpha 53:29 – Final Thoughts

Milk Road

59,357 次观看 • 11 个月前