Might DePIN be the bottleneck breaker for one of this century’s biggest economic shifts? Over the past two decades, we’ve witnessed three tech shockwaves that tore up the rulebook: 2007 – iPhone: Mobile became the remote control of life, birthing an app-driven economy. 2009 – Bitcoin which led to Web3: Redefined money, ownership & coordination. 2022 – ChatGPT: Turned AI from sci-fi into a daily tool, collapsing the idea to execution loop. While Web3 and AI are still playing out, the next revolution is already brewing: Humanoid Robotics. You can feel the shift. Capital, talent, and ambition have flooded in at a blistering pace: Tesla is all-in with Optimus. Figure, 1X, Apptronik, and Agility have raised monster rounds. Foxconn and Nvidia are mapping humanoids into global supply chains. Momentum is real, yet something’s missing. To get from demo videos to real-world ubiquity, two ingredients matter: Hardware progress and software progress. And one of them is lagging behind. Hardware is no longer the bottleneck. Torque-dense actuators rival human muscle. Lightweight composites + next-gen batteries enable all-day operation. Edge compute shrinks datacenter power into a backpack. We’ve solved the body. What’s left is the brain. The race will be won by embodied AI - software that learns by doing. Software that interacts with the messy, unpredictable physical world. The biggest bottleneck for that: data. Not just visual data, but real-world experience - across space, time, friction, feedback, failure. And right now, our current solutions for collecting it are broken: - Teleoperation → expensive, low-throughput - Simulation → always diverges from reality - AR → low headset usage - Video learning → just in early research phases Trying to train physical AI this way is like teaching a child to walk using only YouTube clips - no scraped knees, no balance checks, no feedback loop. This is where DePIN & DePAI as the data flywheel come into play. I can't forget what @hosseeb once said at a panel I listened to: "If crypto has mastered one thing, it’s one thing: Give people tokens, and they’ll do things." We’ve already seen it with early real-world networks: @NATIXNetwork crowdsourcing urban camera data, incredibly valuable for autonomous driving @silencioNetwork mapping global soundscapes, potentially becoming the ear of robots @OVRtheReality building an AR twin of Earth with smartphone video data Now, humanoid-native DePINs like @reborn_agi and @PrismaXai are popping up and tackling this same challenge for embodied AI. Projects like @peaq and @AukiNetwork are going a layer deeper, positioning themselves as the coordination backbone for physical AI on a global scale. Here’s the unlock: We don’t need a few labs simulating the world, but a permissionless, real-world data layer fueled by incentives. Imagine millions of edge agents - robots, wearables, users - interacting with the physical world, feeding learnings back into a shared intelligence layer. Train once → Deploy everywhere → Learn continuously. That’s how we leap from prototypes to practical utility. That’s how we scale humanoids without relying on centralized R&D bottlenecks. Obviously, this is a thesis, but if you believe in it, it might be one of the most asymmetric opportunities of this decade: Own the data layer for physical intelligence Because that’s what humanoid robots will eventually run on. We're entering a phase where: – Anyone can contribute physical data – Anyone can own part of the learning stack – Anyone can build on top of it Most are still focused on the robots themselves. But the real unlock (and probably the only accessible exposure anyways) is underneath: Networks. Protocols. Flywheels.