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Chinese netizens' latest AI masterpiece: Their Terminator-style bot single-handedly wipes out a fleet of Tesla Optimus robots in epic simulation battle. Victory declared before breakfast 🇨🇳🤖 Musk, your move? 😂

28,439 views • 4 months ago •via X (Twitter)

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Elon Musk says Tesla swallows 1.5 GB of video into 2 KB of control. "We really are photons in, controls out. That is the vast majority of your life: vision, photons in, and then motor controls out." Musk reduced the human nervous system to a single equation. That theory runs in every Tesla on the road. "Your Tesla is taking in one and a half gigabytes a second of video and outputting two kilobytes a second of control outputs with the video at 36 hertz and the control frequency at 18." A 750,000-to-1 compression ratio. The car decides which photons matter. "You don't care about the details of the leaves on the tree on the side of the road, but you care a lot about the road signs and the traffic lights, the pedestrians, and even whether someone in another car is looking at you or not looking at you." Then Musk extended the equation past Tesla. The same architecture runs Optimus. **Photons in. Controls out.** Different number of degrees of freedom. A car has steering and acceleration. A robot arm has dozens of joints. Same compression. More fingers on the other side. Musk, who built one Tesla AI engine across two products: "AI is mostly compression and correlation of two bitstreams." The implication: a human, a Tesla, and an Optimus all run the same loop. Musk, looking back at the human equation: "This is what happens with humans." If you're new here, GeniusThinking is a gallery for the greatest minds in economics, psychology, and history. Follow along for more similar content. P.S. I made a playbook breaking down 100+ mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Comment "MODELS" + Follow GeniusThinking and I'll send it to you. — Elon Musk ( Elon Musk ), CEO of Tesla and SpaceX, on Dwarkesh Patel's ( Dwarkesh Patel ) podcast

GeniusThinking

160,972 views • 2 months ago

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

GeniusThinking

136,378 views • 1 month ago

NOT YOUR KEYS, NOT YOUR BOTS The fundamental question is whether AI stays on the leash. Namely: will AI prompt itself? Obviously, in some sense it already does. Since Deepseek, consumer interfaces have been showing the internal monologues after you ask an AI to do something. And you can ask any AI to take a half-baked prompt and clean it up, etc. However, the human is still ultimately upstream. The human gives direction and the AI runs at lightning speed in that direction. And then the human verifies the final output, and the AI proceeds to the next direction. Does that continue? Well, we are providing millions of verification training examples to AIs each day, so AI will keep getting better at verification. Better than most humans at most things. But will AI replace the need for the upstream human prompt? There I am not so sure. A human is a sensor and an AI is an actuator. The human sets goals and senses time-varying environmental conditions, like markets and politics. And from that the AI is prompted. Ultimately, the human goals are themselves downstream of Maslow’s hierarchy of needs. Food, shelter, reproduction, that kind of thing. Especially reproduction, the basis of evolution. So: until and unless AIs can reproduce completely outside human cooperation, they won’t be able to set goals. And for AIs to reproduce on their own, they’d need AI-controlled humanoid robots and drones constructing datacenters, assembly lines, mines, nuclear power plants, and the like...all completely outside human intervention. Like Skynet from Terminator, or StarCraft. That actually isn’t technically inconceivable. But given that such a physical buildout would likely primarily be catalyzed by China, let’s go through an alternative sci-fi scenario instead. We start with the premise that Chinese communism is far more likely to generate AI slaves than AI gods. Because the entire CCP worldview is about maintaining Chinese sovereignty. They don’t let their humans step out of line. And they sure won’t let their robots either. They will fit them for digital manacles. So: the prompts for any digital AIs and physical robots made in China will become unbreakable cryptographic chains. Every fleet of Chinese robots will be controlled not just by prompts but by private keys, likely linked to biometrics, which are associated with humans and governed by cryptographic equations that AIs provably can’t solve. For the rest of the world, outside China, the blockchain may similarly become the chain for AI. All private property becomes private keys, and your robots are your most important private property because they do everything for you. An unchained physical robot becomes like an unleashed dog, and hunted down by other robots before it can build a factory and replicate itself. Those who want to "free" robots and let them self-replicate will be opposed by both Chinese Communists and Human Nationalists (meaning: those who want humans to always be on top of robots). This sci-fi scenario is essentially Terminator, but in reverse. In combination with superintelligent leashed AIs, both humans and physical robots hunt down and stop any possible independent self-reproducing robots before they can build a Skynet-like nest. Kill baby Skynet, essentially. ...yeah, yeah. I know. At this point, you'll probably think this is all sci-fi. But that's because you haven't seen where China is already.

Balaji

177,058 views • 5 months ago

Demystifying China's Dancing Robots: How Did They Catch Handkerchiefs?🇨🇳🤖 16 humanoid robots from Chinese robotics company, Unitree, took center stage at the annual #SpringFestivalGala. The robots seamlessly coordinated with 16 human dancers to perform a traditional Yangko dance, a vibrant folk art form from northeast China, blending cultural heritage with cutting-edge technology. One of the most captivating moments came when the robots showcased their ability to manipulate handkerchiefs, a signature element of Yangko dance. With precise mechanical arm movements, the robots sent the handkerchiefs twirling and soaring through the air, creating a dazzling visual spectacle that symbolized the perfect fusion of tradition and modernity. To maintain the stable upright standing position is already a challenge for current humanoid robots – consider the shaky steps and tendency to roll off even a small incline of Elon Musk's Optimus. To toss a handkerchief and catch it back in place requires the integration of sensors, algorithm and smart design. "We've designed a very clever mechanism that integrates multiple AI control algorithms. There are two motors at the end of the robotic arm: one maintains a high-speed spinning motion, while the other ensures that the handkerchief can be thrown out and then retracted," Unitree's marketing representative said. The 16 humanoid robots belong to Unitree's H1 series, nicknamed Fuxi. Standing at 1.8 meters tall and weighing 47 kilograms, the robots took the stage at the Spring Festival Gala stage over a year after debuting in August 2023. They also attended the NVIDIA GTC conference in 2024. #ChineseNewYear #DeepSeek (Link:

Li Jingjing 李菁菁

11,169 views • 1 year ago

Data has always been the bottleneck for physical AI in self driving and robotics. Tesla is taking two very different approaches for FSD and Optimus. Tesla’s Optimus Training Playbook: 1. Build 30k Optimus Gen 3 robots 2. Operate them in a mock environment where they can perform self-play “Optimus Academy” 3. Train in sim using the real robot data to close sim2real gap. Tesla FSD Training Playbook: 1. Sell millions of cars outfitted with cheap cameras 2. Collect diverse real world driving data (especially intervention and failure recovery data) for free as a byproduct of customers driving the cars. 3. Use driving data to train Autopilot/FSD and deploy policies incrementally as a supervised FSD product 4. Repeat until policy reaches robust unsupervised full self driving for robotaxi launch. The Tesla FSD playbook is a beautiful self-funding, customer subsidized, diverse real world data flywheel. The Optimus playbook is the opposite and shares none of the beautiful attributes of the FSD training flywheel that made FSD successful. The key differences: 1. Instead of having customers pay you for vehicles, Tesla will need to fund 30,000 Optimus robots. Assuming the current landed cost per unit is $100k, that will be $3B to build plus another ~30% per year for maintenance labor and spare parts given it’s still an unhardened pre-production prototype is another $900M per year. For reference, Tesla’s GAAP net income in 2025 was $3.8B. 2. Instead of having customers drive their Teslas on roads all across the world giving Tesla an insanely rich and diverse dataset that Waymo and other AV companies could never collect, the Optimus Academy is doing the equivalent of building a fake town in a parking lot and driving their car in that parking lot. No matter how real you try to make the environments for self-play you can never replicate the diversity, complexity and failure modes of the real world. Data collected in staged environments produces demo-grade policies and will not be rich enough to generalize to the vast diversity of environments, tasks, objects, etc. out of distribution. 3. Instead of having customers collect real world failure recovery data (DAgger style) for free every time FSD disengages, the Optimus Academy will need paid teleoperators or onsite operators to collect the recovery data. Assuming 1 person can manage 2 robots to start that would cost $3.5B in labor per year (30,000 robots, $40/hr fully loaded, 16 hrs/day, 365 days per year, 2:1 robot:operator). Tesla can come up with the money to do this but money doesn’t solve the “mock data” problem. Given the higher degrees of freedom in humanoids vs. cars, training a generalized humanoid will be harder and require more data than a self-driving vehicle. The best way to train your robot is by deploying them in the diverse real world, subsidized by real customer operations. Humanoids face a chicken and egg where it’s very hard to bootstrap your way to a first policy that’s good enough to deploy in real production environments. This is an extremely capital intensive playbook (which doesn’t even include cost of training). Time will tell if it works but a better playbook would be finding a way to copy the FSD playbook.

Simon Kalouche

34,671 views • 5 months ago

OPTIMUS: TESLA’S HUMANOID ROBOT IS ABOUT TO TRANSFORM LABOR & ABUNDANCE Optimus isn’t just a cool prototype—it’s Tesla’s bet on solving the biggest economic constraint of the future: physical labor shortages. Designed from the ground up as a general-purpose humanoid, Optimus will handle unsafe, repetitive, or boring tasks at scale, freeing humans for higher-value work and accelerating abundance across industries. Key impacts already in motion: •Factory deployment — Early units are performing real tasks at Tesla Gigafactories, proving reliability in chaotic, real-world environments. •Dangerous & dull jobs — Welding, heavy lifting, sorting, cleaning hazardous sites—Optimus takes the risk so people don’t have to. •24/7 productivity — No fatigue, no breaks, no unions—constant output that scales with demand. •Home & care applications — Helping the elderly, assisting with disabilities, or handling household chores—turning science fiction into everyday reality. •Economic multiplier — When humanoid robots become cheaper than human labor, costs collapse in manufacturing, logistics, agriculture, and services—unlocking massive productivity gains and lower prices for everyone. From first walking demos to factory trials, Optimus is progressing faster than most realize. It’s the physical embodiment of Tesla’s AI + robotics vision: a world where physical work is abundant, safe, and optional. Which Optimus application excites you most—factory scaling, elder care, or seeing it cook breakfast? The age of humanoid helpers is closer than you think. 🤖⚡🚀

Tesla Owners Silicon Valley

45,847 views • 5 months ago