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.
Using Claude to build a complete Z80 emulator under clean room constraints — no existing emulator source code referenced, only the Zilog CPU User Manual. An LLM clean room implementation covering all official instructions, ACIA serial emulation, and CP/M support.
A response to Citrini Research's "The 2028 Global Intelligence Crisis" scenario. Using Jevons Paradox and historical precedent, this piece argues that AI displacement models systematically undercount the demand expansion that follows when a critical input — in this case, cognitive labor — becomes dramatically cheaper. The article examines latent demand, competitive price dynamics, the speed of entrepreneurial response, and why the historical base rate favors expansion over permanent contraction.
