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18 lines
1.6 KiB
Org Mode
18 lines
1.6 KiB
Org Mode
:PROPERTIES:
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:ID: 5f55bbe6-d243-5766-8ccf-5c5cc88a6542
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:END:
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#+title: Impact on the AI and GPU Industry
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#+filetags: :passepartout:economics:industry:ai:gpu:nvidia:
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If a symbolic-bootstrapping architecture becomes popular, the industry structure shifts fundamentally:
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**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.
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**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."
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**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.
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**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.
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See also: [[file:lisp-economics.org][Lisp economics]], [[file:verification-monopoly.org][Verification monopoly]], [[file:self-driving-lisp-machine.org][Self-driving Lisp Machine]]
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