:PROPERTIES:
:CREATED:  [2026-05-24 Sun]
:ID:       5f55bbe6-d243-5766-8ccf-5c5cc88a6542
:END:
#+title: AI Industry Impact
#+filetags: :passepartout:economics:industry:ai:gpu:nvidia:

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 [[id:28c46769-c14b-42aa-ac7a-69d310157f8f][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 [[id:13e6ae54-2d24-5aa0-b1cd-a7e8e749aa70][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. This hardware embodies [[id:9af13fff-9725-542b-93b1-a555bc74ad72][Lisp economics]] — the cost of verification approaches zero once the symbolic engine is running on dedicated silicon. The outcome is a [[id:827bc546-e887-5b7c-9b65-6392beaf0920][verification monopoly]] for agent safety — the same certification dynamic UL provides for electrical safety.
