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Comes from the trace model and describes the node's dependency importance.
Comes from the canonical Vein Graph: AI assertions, user corrections, and external sources. Provisional — needs corroboration or an external source before strong wording.
A data-coverage label based on available summary, evidence, sources, tickers, and chokepoint rationale.
Dynamic claims are capped below 100% and AI-only edges remain labeled as inferred.
Model: trace-score-v1
A transformer-based language model trained on web-scale text and served via cloud inference — dependent on specialized silicon, memory bandwidth, and energy-intensive data centers.
Training and inference depend on advanced GPUs and custom ASICs with high transistor density.
Stacked DRAM dies bonded to the GPU package — bandwidth bottleneck for large models.
Chip-on-wafer-on-substrate integration for GPU + HBM modules.
Web crawl, books, code repos, and licensed corpora — quality and rights vary by source.
Hyperscale data centers with liquid cooling and redundant power for 24/7 model serving.
AI clusters draw sustained multi-megawatt loads; regional grid access shapes build locations.
RLHF, red-teaming, and policy filters layered on base models before product release.
Generated analysis — not investment advice.