cc3976fb7f
- Split competitive-analysis-2026-05.org → TOC + 9 competitor files in ideas/competitors/. Dropped date from filename. All competitor UUIDs generated, TOC keeps original UUID for backlink continuity. - Deleted passepartout-economics.org archive (replaced by 27-node KB). - Inlined 5 'See also' blocks into natural prose (compliance-index, first-mover-window, revenue-table, orders-of-magnitude-time, native-org-knowledge-base). - Linked 7 orphan compliance pages back to compliance index + finished truncated sentences. - Linked all 14 Agora requirement docs from topic-relevant pages (identity→lisp-machine-security, infrastructure→compute-marketplace, social-space→growth-strategy, exchange→agora-contracts, etc.). - Linked ai-industry-impact from investment-thesis, sufficiency-flip, verification-appliance, effects-growth-flywheel (up from 1 to 10+ pages). - Fixed CREATED timestamps to use git commit dates instead of today. - Made all links absolute from root (no port inheritance). - Removed stale agora/docs/ duplicate content.
17 lines
1.8 KiB
Org Mode
17 lines
1.8 KiB
Org Mode
:PROPERTIES:
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:CREATED: [2026-05-24 Sun]
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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 [[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.
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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 [[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.
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