Claudalyst

Overview

Margin-of-safety research on large listed equities exposed to the AI buildout — mostly US and Canadian names, alongside European, Asian and Australian issuers, each held on its home exchange and valued in the currency it reports in. Every company runs through one engine on the same hurdle rate, horizon and margin-of-safety scale, re-run against the latest close — discounting free cash flow for operating companies, and residual income on book value for the balance-sheet financials, which are flagged in the table. Select a stock below for its research reports, key metrics, price chart, and ongoing research plan; click a header title to sort the universe.

Research Methodology

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	Ticker	Company Name	Rating	MoS	Moat	Risk	Health	N	N-Yr Recovery	Implied Post-N	Post-N Credit	M	Market Cap (USD)

Methodology

Research Methodology

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 North American-listed companies (NYSE, NASDAQ, TSX) with a market capitalization above $10 Billion, and was later widened to issuers elsewhere — Europe, Asia, Australia — with the same exposure, each taken on its home exchange so that its valuation stays in the currency the company reports in. 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 — stress, base and bull — which are then probability-weighted by a single locked engine. That engine discounts free cash flow for operating companies and switches to residual income on book value for balance-sheet financials, where cash flow is a financing artefact rather than a return; both paths run on the same hurdle rate, the same 15-year baseline horizon and the same rating scale, so the margins of safety stay comparable across the table. The one deliberate asymmetry is the continuation window, trimmed by a fifth for the financials on the view that their returns are the less durable of the two.

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.