Research Methodology & Orchestration: This page reproduces the two documents that govern the analysis: the research plan that defines the pipeline, and the literal, word-for-word subagent prompt that drives each company's deep-dive. The process began by filtering all North American-listed companies (NYSE, NASDAQ, TSX) with a market capitalization exceeding $10 Billion. We then deployed autonomous screening agents to run qualitative passes on small groups of these companies, eliminating those whose business models were unaffected by our core AI industry thesis. The remaining candidates were sorted into priority tiers based on market cap and index weight. Finally, we launched a dedicated deep-dive subagent for each target company, seeded with historical pricing data and key fundamentals, to model three distinct long-term valuation scenarios through a discounted cash flow (DCF) framework.

Much of this qualitative and quantitative assessment hinges directly on the macrotrends and technical bottlenecks outlined in the central thesis essay: The AI Industry: From Molten Tin to the Trillion-Dollar Buildout. The structural limits of hardware (e.g. memory bandwidth, CoWoS packaging, KV cache scaling) and the economics of agentic deployment heavily influence the stress and bull cases modeled for each company.