Robot Wars World

I am leaving the Foundational Research team at DeepMind! I just wanted to take the time to reflect on this truly amazing journey. It was such an intense and fulling ride that I will always cherish. Two efforts shaped me in particular: the reasoning work and building the continual learning infrastructure for robotics. They taught me what it takes to turn ambitious ideas into real systems. Here are some of my biggest takeways: 1. Iteration speed, iteration speed, iteration speed: the teams that win arent neccessarily the smartest but the ones able to execute on a thousand ideas in the time their competitors excute on five. This became way more obvious when we were working on reasoning for humanoids where the iteration contains hardware in the loop. You have to really deeply think about what it takes to test your hypothesis and how to greatly simplify the iteration loop to move faster. 2. Building scalable infrastructure from day 1: Researchers sometimes think that moving fast means building unscalable infrastructure. My time at DeepMind taught me that there is always one more experiment that requires refactoring the entire repo, as those come up, we should figure out how to better build the stack from the ground up to support more and more wacky experiments. 3. Having fun is probably the most important thing at work: When you truly enjoy your colleagues’ company and you are motivated by the success of the larger team, the late nights become memorable, not exhausting. I never truly understood this until the 1am nights at work all huddled near one of our humanoids trying to figure out why its behaving this way. I’m especially gratefJ to my mentors @sippeyxp , Jie Tan, @Kanishka_Rao, and @carolina_parada for constantly finding harder challenges for me and pushing me to grow. Peter Pastor, @keerthanpg, and Stefani Karp thank you for the late-night hacking sessions and the PEAK dinners. Those are some of my most treasured memories! @claudiofantacci, Alex Lee, @Sumeet_Robotics, and Ken Caluwaerts thank you for teaching me how to build scalable infrastructure, from building the new inference stack to scaling experiments. @Stacormed, @xiao_ted, @ColinearDevin, and Giulia Vezzani I learned so much from you. Thank you for entertaining all my hypotheses (especially the weird ones) and helping me learn through them. I can go on and on.. I just can’t thank each one of you enough. Truly thankful for the time we spent together! Will share more soon 👀

@adcock_brett @Figure_robot Curious how much you’ve liquidated yourself @adcock_brett ? Corporate buyback or secondary?

🥊🤖🇺🇸 USA vs CHINA 🇨🇳 ? Official invitations have been sent. Tesla Optimus 🇺🇸 vs Unitree 🇨🇳 03.10.2026 Poland The world's first Humanoid Robot Boxing Show. We provide the arena. You bring the future. @elonmusk @Tesla @UnitreeRobotics #RobotWars #USAvsChina #Tesla #Unitree #AI #Robot @RobotWarsWorld

🥊🤖🇺🇸 USA vs CHINA 🇨🇳 ? Official invitations have been sent. Tesla Optimus 🇺🇸 vs Unitree 🇨🇳 03.10.2026 Poland The world's first Humanoid Robot Boxing Show. We provide the arena. You bring the future. @elonmusk @Tesla @UnitreeRobotics #RobotWars #USAvsChina #Tesla #Unitree #AI #Robot @RobotWarsWorld

I am leaving the Foundational Research team at DeepMind! I just wanted to take the time to reflect on this truly amazing journey. It was such an intense and fulling ride that I will always cherish. Two efforts shaped me in particular: the reasoning work and building the continual learning infrastructure for robotics. They taught me what it takes to turn ambitious ideas into real systems. Here are some of my biggest takeways: 1. Iteration speed, iteration speed, iteration speed: the teams that win arent neccessarily the smartest but the ones able to execute on a thousand ideas in the time their competitors excute on five. This became way more obvious when we were working on reasoning for humanoids where the iteration contains hardware in the loop. You have to really deeply think about what it takes to test your hypothesis and how to greatly simplify the iteration loop to move faster. 2. Building scalable infrastructure from day 1: Researchers sometimes think that moving fast means building unscalable infrastructure. My time at DeepMind taught me that there is always one more experiment that requires refactoring the entire repo, as those come up, we should figure out how to better build the stack from the ground up to support more and more wacky experiments. 3. Having fun is probably the most important thing at work: When you truly enjoy your colleagues’ company and you are motivated by the success of the larger team, the late nights become memorable, not exhausting. I never truly understood this until the 1am nights at work all huddled near one of our humanoids trying to figure out why its behaving this way. I’m especially gratefJ to my mentors @sippeyxp , Jie Tan, @Kanishka_Rao, and @carolina_parada for constantly finding harder challenges for me and pushing me to grow. Peter Pastor, @keerthanpg, and Stefani Karp thank you for the late-night hacking sessions and the PEAK dinners. Those are some of my most treasured memories! @claudiofantacci, Alex Lee, @Sumeet_Robotics, and Ken Caluwaerts thank you for teaching me how to build scalable infrastructure, from building the new inference stack to scaling experiments. @Stacormed, @xiao_ted, @ColinearDevin, and Giulia Vezzani I learned so much from you. Thank you for entertaining all my hypotheses (especially the weird ones) and helping me learn through them. I can go on and on.. I just can’t thank each one of you enough. Truly thankful for the time we spent together! Will share more soon 👀