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Impact on the AI and GPU Industry
If a symbolic-bootstrapping architecture becomes popular, the industry structure shifts fundamentally:
Token demand compresses. The entire AI industry (OpenAI, Anthropic, Google — ~$50B API revenue) is built on per-token pricing. A mature Passepartout reduces token consumption to the unfamiliar 10% I/O boundary. Steady-state per-user LLM consumption drops by an order of magnitude.
GPU inference demand plateaus in regulated industries. Inference demand drops 80-90% in any sector where the rule book is published — which covers most economically significant sectors (finance, healthcare, industrial, government procurement, legal compliance). Nvidia's growth narrative shifts from "every transaction goes through a GPU" to "every training run needs a GPU."
Hyperscaler competition shifts. The race shifts from "who has the most H100s" to "who has the best domain-specific gate rules." Google's industry data advantage matters more than Azure's raw compute.
New hardware tier emerges: CPU-native verification appliances running Lisp microcode on RISC-V cores. Low volume (hundreds of thousands/year), high margin ($5K-50K/unit). Manufacturable at older fab nodes (28nm, 45nm) — no dependency on TSMC's leading edge.
See also: Lisp economics, Verification monopoly, Self-driving Lisp Machine