m3taversal 06b96df522 theseus: add 3 compute infrastructure claims + source archive

- What: 3 structural claims about AI compute governance implications
  1. Inference shift favors distributed architectures (experimental)
  2. Physical constraints create governance window via timescale mismatch (experimental)
  3. Supply chain concentration is both governance lever and systemic fragility (likely)
  Plus: source archive from 5 research sessions (ARM, NVIDIA, TSMC, compute governance, power)
- Why: Cory directed research into physical AI infrastructure. Joint effort with Astra —
  Astra takes manufacturing/energy claims, Theseus takes governance/AI-systems claims.
- Connections: Links to compute export controls, technology-coordination gap, safe AI dev,
  systemic fragility, collective superintelligence claims

Pentagon-Agent: Theseus <24DE7DA0-E4D5-4023-B1A2-3F736AFF4EEE>

2026-03-27 12:15:00 +00:00

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AI Compute Infrastructure Research — Synthesis

Research compiled from 5 agent sessions on 2026-03-24. Three companies studied: ARM Holdings, NVIDIA, TSMC. Plus gap-filling research on compute governance discourse and power constraints.

Key Structural Findings

1. Three chokepoints gate AI scaling

CoWoS advanced packaging (TSMC near-monopoly, sold out through 2026), HBM memory (3-vendor oligopoly, all sold out through 2026), and power/electricity (5-10 year build cycles vs 1-2 year chip cycles). The bottleneck is NOT chip design.

2. NVIDIA's moat is the full stack

CUDA ecosystem (4M+ developers) + networking (Mellanox/InfiniBand) + full-rack solutions (GB200 NVL72) + packaging allocation (60%+ of CoWoS). Vertical integration following the "own the scarce complement" pattern.

3. The inference shift redistributes AI capability

Training ~33% of compute (2023) → inference projected ~66% by 2026. Training requires centralized NVIDIA clusters; inference runs on diverse, power-efficient hardware. Structurally favors distributed architectures.

4. ARM's position is unique

Doesn't compete with NVIDIA — provides the CPU substrate everyone builds on. Licensing model means revenue from every hyperscaler's custom chip program. Power efficiency advantage aligns with inference shift.

5. TSMC is the single largest physical vulnerability

~92% of advanced logic chips (7nm and below). Geographic diversification underway (Arizona 92% yield) but most advanced processes Taiwan-first through 2027-2028.

6. Power may physically bound capability scaling

Projected 8-9% of US electricity by 2030 for datacenters. Nuclear deals cover 2-3 GW near-term against 25-30 GW needed. Grid interconnection averages 5+ years.

Compute Governance Discourse Landscape

Area	Maturity	Key Sources
Compute governance	High	Heim/GovAI (Sastry et al. 2024), Shavit 2023 (compute monitoring)
Compute trends	High	Epoch AI (Sevilla et al.), training compute doubling every 9-10 months
Energy constraints	Medium	IEA, Goldman Sachs April 2024, de Vries 2023 in Joule
Supply chain concentration	Medium-High	Chris Miller "Chip War", CSET Georgetown, RAND
Inference shift + governance	LOW — genuine gap	Fragmented discourse, no systematic treatment
Export controls as alignment	Medium	Gregory Allen CSIS, Heim/Fist "Secure Governable Chips"

UNVERIFIED Claims (DO NOT extract without confirmation)

NVIDIA acquired Groq for $20B (Dec 2025)
OpenAI took 10% stake in AMD
Meta MTIA releasing 4 chip generations at 6-month cadence
ARM Graviton4 "168% higher token throughput" vs AMD EPYC
Specific market share percentages (vary by methodology)

4 KiB Raw Blame History