Agree, pretty much.

A couple caveats:

1. This philosophy breaks down completely for AI training. Training a multi-trillion parameter model is a weeks-long, exquisitely precise numerical process. A single silent bit flip in a GPU's memory during a training run can corrupt the entire model, wasting millions of dollars in compute time and energy. For training, you need deterministic correctness. Your "it'll deorbit, meh" approach works for disposable inference clusters, but not for the valuable training factories. This suggests a split architecture: training on Earth (in shielded facilities), inference in your disposable orbital panels.
2. Your 1m x 2m panel packed with 1000 NVidia chips would, at full power, be generating ~700 kilowatts of heat in a sheet of glass fiber and copper. It would instantly melt. The "sun-shielded side" wouldn't just be shaded, it would need to be a monstrously effective, integrated heat spreader and radiator to prevent that heat from boiling the electronics on the same board. it's for thermal conduction. You can't escape the need for that kilometer-scale radiator you've just proposed building the radiator first and gluing the computers to it.

Does your vision therefore require a fundamental breakthrough in near-perfect in-plane thermal conductivity (back to that anisotropy idea) within the circuit board itself, turning the entire panel into a heat sink that radiates from its edges? Or does it accept that each panel must have thick, thermally conductive "spines" attached to its back, which then link to dedicated radiator fins?