Great article, I worked on a small team that had explored the use of “service based” robotics in the US for a large hospitality company back in the early 1990’s, the productivity enhancements for human labor tasks looked promising (potential of doubling some unit labor costs productivity). Even brought in Joseph Engleberger and a team from MIT to build out and design several robotic prototypes (vacuum cleaners/floor scrubbers/window washers). Bottom line is that short term economic factors, being only a single company with a limited R&D budget in a highly competitive industry, and western capitalism’s focus on short term quarterly results basically ended this experiment which IMO strengthens the argument that this industry will not get off the ground in the US without being subsidized and nurtured in some fashion by US public/private partnerships involving government funding. I sure hope our future politicians can someday see beyond their current myopic entrapment cycle of fundraising to focus on things we elect them to do, to be visionaries on what the future of manufacturing might become and to promote and write legislation to enable a future that can compete with the manufacturing juggernaut of the world, China.
Excellent reporting. My question is: Who governs the robot?
Not who controls the trade policy. Not who wins the tariff negotiation. Not who builds the supply chain. Who governs the robot at the robot level. When the robot picks up a box and moves it from A to B, what determines whether the move is admissible? The robot's own evaluation? A remote operator? A policy file? The factory floor manager watching the screen?
Every answer to that question is a different governance architecture. Each has different failure modes. The piece discusses how to make robots cheaper, how to make them better, how to build them domestically. The piece does not discuss what happens when a cheaper, better, domestically-built robot performs an action that should not have been performed. A robot that moves a box to the wrong place is an inconvenience. A robot that wires a data-center cabinet incorrectly is a catastrophic failure. A robot that operates in elder care and applies the wrong force is a liability.
The faster the iteration cycle, the more units deployed, the cheaper the hardware, the more urgent the governance question becomes. The piece celebrates iteration speed. Iteration speed without governance is acceleration without brakes. The brakes are not trade policy. The brakes are typed authority separation at the robot level: the robot that proposes an action cannot approve its own action, the sensor that reads the result cannot redefine the acceptance criteria, the system that decides whether the action counts as progress cannot also be the system that performed the action.
The supply-chain race is real. The governance race has not started. The supply-chain race determines who builds the robots. The governance race determines whether the robots are safe to deploy. One of those races is being run. The other is not.
That DJI parallel got me. Everyone wrote off drones as toys right up until one Shenzhen company owned the whole category, and the dancing robot dogs feel like the same setup: the toy phase is what funds the iteration loop.
Financing for robots as we do for cars is right around the corner. Borrow the money. By a robot. Send the robot to work and own the means of production.
We’ll be creating a new slave class but we’ll do it anyway.
Great conversation. The distinction I keep coming back to is that the DJI/BYD playbook explains Unitree’s hardware trajectory, but not yet the job market. A car leaves the factory with transportation already working; a developer robot becomes valuable because the buyer still supplies integration, task design, teleoperation, and failure recovery.
Unitree’s capital plan almost admits this: roughly 48% of proposed IPO proceeds go to intelligent robot model R&D, versus about 15% to the manufacturing base. I wrote a filing-driven version of that argument here: https://tomfuture3labs.substack.com/p/unitree-has-the-byd-playbook-it-still. The deployment evidence I’d most like to see is productive hours per day, interventions per hour, and cost per successful task from a named customer.
One of the best map of the supply side I’ve read. It sharpens my question from the other end.
Your map is drawn around who can build the robots. But there are two sovereignties: production, where China holds the chain today, and deployment, what a society asks its robots to do first.
A country needn’t own the actuator chain to legislate that publicly funded robots go to public work before private convenience, any more than it builds the computers it regulates.
Where you cut into me: whoever controls the magnets controls the tempo and scarcity moves the choice of “first” from parliaments to suppliers.
Though it sharpens my case as much as it complicates it.
If the material is that scarce and that contested, spending it on laundry for people who can afford a robot stops being a question of legitimacy and becomes one of resource security…
I agree with Reyk, having a robot clean the house and pick up children’s and dog’s toys would be great. Even better would be one that safely irons cotton shirts and clothes. I understand how difficult the actuators for arms and hands are to source and build. Might a coordination with the firm making Roombas be an idea. The more intelligent robot could take photos of the floors of the rooms, transmit that data to the Roombalike vacuum and get it started vacuuming. We are in our 80’s and have never programmed or used our Roomba. It is too heavy for me to lift. I understand you are primarily thinking of commercial applications but there must be an enormous market in elder care.
The duty cycle data was the part I found most useful. From the plant floor, the reference point for these machines isn't the worker but the $30k cobot next to him running three shifts on wall power. Seen against that benchmark, going from 5 minutes of work per hour of cooling to 10 per 15 in two years suggests the remaining gap is mostly thermal and battery engineering, which tends to close faster than intelligence problems.
Great article, I worked on a small team that had explored the use of “service based” robotics in the US for a large hospitality company back in the early 1990’s, the productivity enhancements for human labor tasks looked promising (potential of doubling some unit labor costs productivity). Even brought in Joseph Engleberger and a team from MIT to build out and design several robotic prototypes (vacuum cleaners/floor scrubbers/window washers). Bottom line is that short term economic factors, being only a single company with a limited R&D budget in a highly competitive industry, and western capitalism’s focus on short term quarterly results basically ended this experiment which IMO strengthens the argument that this industry will not get off the ground in the US without being subsidized and nurtured in some fashion by US public/private partnerships involving government funding. I sure hope our future politicians can someday see beyond their current myopic entrapment cycle of fundraising to focus on things we elect them to do, to be visionaries on what the future of manufacturing might become and to promote and write legislation to enable a future that can compete with the manufacturing juggernaut of the world, China.
Excellent reporting. My question is: Who governs the robot?
Not who controls the trade policy. Not who wins the tariff negotiation. Not who builds the supply chain. Who governs the robot at the robot level. When the robot picks up a box and moves it from A to B, what determines whether the move is admissible? The robot's own evaluation? A remote operator? A policy file? The factory floor manager watching the screen?
Every answer to that question is a different governance architecture. Each has different failure modes. The piece discusses how to make robots cheaper, how to make them better, how to build them domestically. The piece does not discuss what happens when a cheaper, better, domestically-built robot performs an action that should not have been performed. A robot that moves a box to the wrong place is an inconvenience. A robot that wires a data-center cabinet incorrectly is a catastrophic failure. A robot that operates in elder care and applies the wrong force is a liability.
The faster the iteration cycle, the more units deployed, the cheaper the hardware, the more urgent the governance question becomes. The piece celebrates iteration speed. Iteration speed without governance is acceleration without brakes. The brakes are not trade policy. The brakes are typed authority separation at the robot level: the robot that proposes an action cannot approve its own action, the sensor that reads the result cannot redefine the acceptance criteria, the system that decides whether the action counts as progress cannot also be the system that performed the action.
The supply-chain race is real. The governance race has not started. The supply-chain race determines who builds the robots. The governance race determines whether the robots are safe to deploy. One of those races is being run. The other is not.
That DJI parallel got me. Everyone wrote off drones as toys right up until one Shenzhen company owned the whole category, and the dancing robot dogs feel like the same setup: the toy phase is what funds the iteration loop.
Financing for robots as we do for cars is right around the corner. Borrow the money. By a robot. Send the robot to work and own the means of production.
We’ll be creating a new slave class but we’ll do it anyway.
Cause that’s what we do.
Let’s grow together by supporting one another if you’re also new on Substack, feel free to subscribe and I’ll gladly do the same.
Great conversation. The distinction I keep coming back to is that the DJI/BYD playbook explains Unitree’s hardware trajectory, but not yet the job market. A car leaves the factory with transportation already working; a developer robot becomes valuable because the buyer still supplies integration, task design, teleoperation, and failure recovery.
Unitree’s capital plan almost admits this: roughly 48% of proposed IPO proceeds go to intelligent robot model R&D, versus about 15% to the manufacturing base. I wrote a filing-driven version of that argument here: https://tomfuture3labs.substack.com/p/unitree-has-the-byd-playbook-it-still. The deployment evidence I’d most like to see is productive hours per day, interventions per hour, and cost per successful task from a named customer.
One of the best map of the supply side I’ve read. It sharpens my question from the other end.
Your map is drawn around who can build the robots. But there are two sovereignties: production, where China holds the chain today, and deployment, what a society asks its robots to do first.
A country needn’t own the actuator chain to legislate that publicly funded robots go to public work before private convenience, any more than it builds the computers it regulates.
Where you cut into me: whoever controls the magnets controls the tempo and scarcity moves the choice of “first” from parliaments to suppliers.
Though it sharpens my case as much as it complicates it.
If the material is that scarce and that contested, spending it on laundry for people who can afford a robot stops being a question of legitimacy and becomes one of resource security…
I agree with Reyk, having a robot clean the house and pick up children’s and dog’s toys would be great. Even better would be one that safely irons cotton shirts and clothes. I understand how difficult the actuators for arms and hands are to source and build. Might a coordination with the firm making Roombas be an idea. The more intelligent robot could take photos of the floors of the rooms, transmit that data to the Roombalike vacuum and get it started vacuuming. We are in our 80’s and have never programmed or used our Roomba. It is too heavy for me to lift. I understand you are primarily thinking of commercial applications but there must be an enormous market in elder care.
I appreciated this conversation on emerging robotics.
The duty cycle data was the part I found most useful. From the plant floor, the reference point for these machines isn't the worker but the $30k cobot next to him running three shifts on wall power. Seen against that benchmark, going from 5 minutes of work per hour of cooling to 10 per 15 in two years suggests the remaining gap is mostly thermal and battery engineering, which tends to close faster than intelligence problems.