Biological intelligence to bootstrap robotic intelligence? If robotics is about to enter its golden age, @MeckaAI is one of the companies feeding it, by sensorizing humans to train robots. The biggest challenge robotics faces is the data flywheel cold start problem. LLMs got a free cold start from the internet: trillions of tokens, already written. Robotics gets nothing. Someone has to go create the training data to jumpstart the system. @joshavata is the co-founder and CEO of Mecka, the data and deployment company for physical AI. Josh read every robotics paper he could find in late 2024, visited the labs, and made a bet: humans and humanoids look the same, so humans could act as the training fleet. Egocentric data, first-person video of people doing real work, could become vital fuel. Two years ago, the biggest egocentric dataset was Ego4D at 4,000 hours. The field is now in the millions of hours, thanks in part to Mecka. We’re probably still orders of magnitude of training data away from robotics nirvana. But every rung on the physical intelligence ladder unlocks real commercial tasks. And eventually, Josh imagines a world with an order of magnitude more robots than people, of a plurality of shapes and sizes and use cases, as the Hitchhiker’s Guide to the Galaxy foretold :) A few things we cover in this conversation. - Why robotics has no cold start: pixels, actions and logic, and why pre-training data is a real market in robotics in a way it never was for LLMs - What “teaching robots about carbon life” means: in sci-fi the final frontier is organic. The human brain runs on 20 watts and self-heals - The egocentric bet: teleop couldn’t scale, humans and humanoids share a form, so Waymo-fy the humans. The first pilot was aunties and uncles recording themselves cooking on a mobile app - Force data: video can’t tell a robot how hard to squeeze an egg. Wrist cameras are a hack. Mecka built the gloves instead - Inside the data factory: a consumer app that streams your tasks, a B2B engine recruiting across “the taxonomy of all physical human work,” 100-gigabit upload lines, hundreds of petabytes - Millions of hours vs. Tesla’s decade on two degrees of freedom, and why it’s a slow creep up, not a GPT-3 moment - The home has the most data and is the hardest place to deploy. Laundry folding and assembly-line pick-and-place get solved in the next year or two - Useful robots already exist: hotel delivery and cleaning bots in China and Southeast Asia, industrial cleaning in the US. Humanoids are the terminal state, not the first step - Deployment diffusion: a modern systems integrator whose installed robot does something next week it couldn’t do last week. If Mecka ever helps deploy a robot that mines an asteroid, they’ve done their job - The data pot of soup: a million hours of human data, tens of thousands of UMI, a few hours of in-domain teleop. Tactile and simulation haven’t entered the mix yet - Move 37 for robotics: AlphaGo went behavior cloning, then self-play, then magic. The catch is the rules of the game are the entire physical world, and we don’t have the simulator yet - Will the AGI labs steamroll robotics? “It takes a lot of courage to scale up data with imperfect information.” - The Kawasaki Corleo, the robot panther-dog-horse on Mecka’s conference room wall, and why the future of robotics looks like Hitchhiker’s Guide to the Galaxy Timestamps: 0:00 – Introduction 1:46 – What Mecka is: pixels, actions and logic 2:45 – Is robot data economically feasible without the internet? 4:15 – Teaching robots about carbon life 5:27 – Cloud kitchens, stablecoins and the path to robotics 7:03 – Entering robotics with a fresh lens 8:45 – What egocentric data is and why Mecka bet on it 10:09 – The first pilot: aunties and uncles with a mobile app 10:42 – Force data and tactile gloves 11:55 – Inside the operations of a data factory 14:41 – Heat-mapping the taxonomy of human work 15:42 – Quality gates, fraud and the McDonald’s POV videos 17:44 – Earning a margin as a data intermediary 19:33 – Millions of hours vs. Tesla’s decade 20:55 – Why it isn’t binary: unlocking commercial tasks along the way 22:02 – Home data is plentiful; home robots are the hardest 23:24 – Useful robots can be deployed today 24:35 – When robotics gets an FSD-style flywheel 26:42 – Deployment diffusion and the modern systems integrator 28:21 – Favorite vertical robot categories; mining an asteroid 30:08 – The hardware slate: Waymo-fying people 30:55 – EgoVerse 32:04 – The data pot of soup: the converged training mix 33:54 – Move 37 for robotics: AlphaGo and Federer 36:02 – World models and the training mix 36:58 – Will scaling the current mix get us to the promised land? 39:05 – Will the AGI labs steamroll robotics? 40:50 – Sci-fi roots and the Kawasaki Corleo 42:44 – How many robots in a decade?

and recursive self entertainment

We’re bringing the magic of Waymo to Toronto! 🇨🇦 In the coming days, you’ll begin seeing Waymo’s all-electric vehicles driving through Toronto’s streets as we lay the groundwork for our ride-hailing service. Our goal is to begin serving riders in 2028. Sign up for future updates: waymo.com/waymo-in-canada

Frontier models still struggle to accurately understand human video data for robotics The biggest gap is actions: GPT-Astra hits 71.5% accuracy, Meckas custom trained model achieves 94% @tkkong + @philhchen evaluates Meckas video understanding model against the frontier below

Today, we're sharing results from our partnership with @MeckaAI to see if frontier models can label egocentric videos for robot learning. We had five frontier models caption 200 clips with human grading. GPT-6 Astra which performed the best got 43.5% of clips fully correct.

Are robots ready to accelerate science labs? We created the WetLabs Benchmark to test 9 critical tasks, from easy to hard, and let 3 leading models take 20 attempts per task Astra leads but it comes close...

In 12 weeks, we built a research facility that is run entirely by AI. AI designs, executes, and observes experiments end-to-end across biology, chemistry, and materials science. We’re introducing SciUniverse: a benchmark that measures AI’s ability to do real-world scientific research.