# Market Thesis Research Bundle

Question: Given investor pushback on AI spending, will a major hyperscaler disclose GPU utilization, reserved-capacity fill rates, or comparable AI monetization metrics by year-end 2026, instead of only describing aggregate AI demand?

What this bundle is: a reasoning and monitoring scaffold. It organizes public evidence into observations, claims, uncertainty branches, thresholds, and a watch plan.

What this bundle is not: primary evidence, live market data, trade advice, or a substitute for official, live, or current web sources.

Core tension: Given investor pushback on AI spending, will a major hyperscaler disclose GPU utilization, reserved-capacity fill rates, or comparable AI monetization metrics by year-end 2026, instead of only describing aggregate AI demand?

Current inference to verify: {'status': 'inference_to_verify', 'answer': 'A major hyperscaler is likely to disclose at least one comparable AI monetization or efficiency proxy by year-end 2026, but explicit GPU utilization or reserved-capacity fill-rate disclosure remains materially less likely.', 'confidence': 0.86, 'scope_notes': 'This is a disclosure-behavior inference, not a claim about underlying AI economics or stock-price impact.', 'breakout': {'explicit_gpu_utilization_or_reserved_capacity_fill_rate_probability': 0.25, 'comparable_ai_monetization_metric_probability': 0.94, 'at_least_one_major_hyperscaler_discloses_some_comparable_metric_by_year_end_2026_probability': 0.99}} Treat this as a hypothesis that must be refreshed against live official sources, not as a signal.

How to use: read `source_priority.json` first, refresh sources in `live_verification_plan.json`, then use `fact_inference_split.json`, `thresholds.json`, and `watch_schedule.json` to decide what changed. Do not infer buy/sell/hold, position sizing, execution, or asset-price direction from this artifact.