Part one of a two-part series on designing a custom dual-Z80 RetroShield PCB. This piece covers the design process: choosing a shared-bus architecture, creating new schematic symbols, modifying the PCB layout programmatically, autorouting with Freerouting, and generating production Gerber files — all done over SSH without touching a GUI.
If AI follows a Jevons trajectory — and five pieces in this series argue that it does — the investment question isn't whether demand will expand, but where the expansion creates bottlenecks. This piece maps the Jevons framework onto concrete investment layers: energy, physical infrastructure, custom silicon, and the application tier. It also addresses the most common objection — that GPU diminishing returns cap the expansion — and explains why multiple overlapping cost curves make the case stronger, not weaker.
I used Claude Code with Opus 4.6 and the Claude Agent SDK to generate three technical handbooks — totaling over 2,400 pages and 232 chapters — from real project source code. The key was a framework that launches 10-12 AI agents in parallel, each reading actual codebases and writing LaTeX chapters simultaneously. This post describes how the system works, what goes right, what goes wrong, and what it means for the future of technical documentation.
Steve Yegge's "The AI Vampire" describes AI-driven burnout as an extraction problem — companies capture the productivity surplus while workers absorb the cognitive toll. But what he's actually describing is Jevons Paradox applied to human attention. AI makes cognitive output dramatically cheaper, demand for it expands exactly as the model predicts, and the expansion concentrates on the one input that can't scale: human judgment.
Matt Shumer's widely-shared essay warns that AI will displace half of entry-level white-collar jobs within five years. He's right about the capability curve. He's wrong about what follows from it — because he commits the same analytical error that has produced incorrect predictions at every prior technological inflection point: modeling displacement without modeling demand expansion.
AI inference costs are declining at a rate that mirrors the early decades of Moore's Law. If the cost per token continues to fall by an order of magnitude every two to three years, the implications extend far beyond making current AI applications cheaper. This piece explores what becomes possible — not what gets displaced — when intelligence-per-dollar follows the same exponential curve that turned computing from a military luxury into a pocket commodity.
