The Physical Layer
Fabs, process nodes, packaging, HBM, equipment - why only a handful of companies can make any of it.
Three things I write about
The Bottleneck Watch
Power, cooling, materials, capacity. What is actually constraining AI buildouts, and what breaks next.
Physical AI Reality Check
What a robotics demo does and does not prove, and what would have to become true for it to ship.
All three end in the same place: what it means for margins, capex, and the companies you may own.
Recent writing
All posts →- Bottleneck Watch
The efficiency number improved. The water number in the same report did not.
Berkeley Lab projects average US data center PUE falling to 1.15-1.35 by 2028 while average site WUE rises to 0.45-0.48 L/kWh, and names the shift to hyperscale and colocation as a cause of both. Most of the water is not on the site meter at all.
- Physical AI
Nobody scraped the robot data. Fifty people performed it.
Language models train on text that already existed. The dataset robotics calls large-scale and in-the-wild is 350 hours, produced by 50 people over a year.
- Physical AI
The battery lasts two hours. No one publishes who changes it.
Charging a humanoid takes less time than draining it, so energy is not the ceiling. Three battery swaps a shift are, and neither maker says who does them.
- Physical AI
A robot hand's warranty is written in cycles, not years
Gripper makers rate hands in cycles, not years. So throughput and wear are one variable, and the case for faster robots is a case for faster wear.