# Market Thesis Research Bundle

Question: Given the AI productivity debate, will more large-cap companies begin quantifying measurable revenue lift or cost savings from AI on earnings calls rather than only describing pilots and anecdotes by the next two earnings seasons?

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 the AI productivity debate, will more large-cap companies begin quantifying measurable revenue lift or cost savings from AI on earnings calls rather than only describing pilots and anecdotes by the next two earnings seasons?

Current inference to verify: {'status': 'current_inference_to_verify', 'summary': 'Yes, but unevenly. The current evidence supports a shift toward quantified AI monetization and productivity reporting on earnings materials, especially among enterprise software and platform companies, but most disclosures are still proxies such as ARR, ACV, paid deal counts, book-of-business, hours saved, ticket deflection, or internal cost savings rather than clean, audited AI-attributed revenue.', 'verification_horizon': 'next two earnings seasons', 'not_a_conclusion': 'This is a live inference to verify, not a prediction signal or a claim that companies will converge on uniform AI revenue attribution.'} 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.