AI is on track to become a superhuman hardware designer, but design is only the beginning. The interface between bits and atoms is lossy, and turning designs into real hardware gets harder at every level: 1. Sourcing off-the-shelf parts that work together (a 2006 Falcon 1 engine test was delayed by one capacitor, in stock 6,000 miles away) 2. Custom parts, where a master machinist translates CAD into machine instructions 3. Assemblies, where every part can meet its drawing and still not fit 4. Outside vendors (even SpaceX has 591 employees with supply chain titles) 5. Doing all of it at a scale of 1,000,000 "Superhuman AI might be great at pure design, meaning at defining the platonically ideal part for a particular job, but struggle with 'design for manufacturing,' or finding a point design that meets the requirements while remaining easy to build/procure and inexpensive." Full piece from a16z's Christian Keil on what it takes to turn AI-designed hardware into the real thing: a16z.news/p/superhuman-hardw…

many builders in physical AI are focused on deployment. you may have noticed how little robots are actually deployed by leaders of the space. we are so, so early. i've been working on this issue for some time. drop your own experience below, soon to be many in-person group discussions on this topic. here is my current read: -only 30,000 robots are deployed per year in the US across sectors. mostly programmed, not learned models. china does 10x that. its growing fast, but the obstacles are still there -the advantages of learned robotic models currently struggle to outperform programmed robotics in industrial environments. expect hybrid solutions to blend the strengths of the two -contract manufacturing, or high mix would greatly benefit from adaptive robotics, but the contract manufacturing space in the USA is..broken. fixing it to deploy becomes a whole economy solution. -dependability and capex remain the barriers to entry, building a deployment that survives those plus future regulation, liability, and maintenance is how you win -most industrial spaces lack the data readiness or network infra to support learned robotics. this is a huge problem that labs have either chosen to solve themselves in a fragmented way or avoid wholesale by adding local compute. big opportunity. -large labs that have focused on model & hardware development are struggling to divert focus to deployment, for good reasons. if you find a way into market, consider that your moat for now. -as LLMs, world models compete with classical architectures you will see these labs begin to focus on getting deployment partners. picking the right stack is less important than getting the right customers. treat the bitter lesson as your own litmus for vendor choices. all the companies you admire or consider to be saturating the opportunity are not even close to scaled deployment, you are sitting on the ground floor. get building.

HERE IT IS. The Next Generation Roadster developed by SpaceX and Tesla! There is a fan-suction system at the bottom of the car that can pull up to 2 gs allowing it to stick upside down. It just flipped UPSIDE DOWN with cold gas thrusters and landed. HOLY SHIT.