Connect AI adoption, agents, pricing, revenue quality, margins, capital expenditure and return on invested capital across company disclosures.
22 source-backed guides. Start with the broader frameworks, then follow the related research paths.
An AI agent can chain research steps—retrieving filings, checking figures and drafting a view. This guide shows where that saves time and where human verification remains essential.
A practical workflow for AI financial analyst, connecting dated sources and unit economics to valuation, portfolio and risk decisions.
A source-checked framework for AI capex, with the evidence, calculations and failure modes an investor should examine.
Evaluate hyperscaler capex through primary evidence, a worked calculation and the assumptions that can break the thesis.
An investor’s research guide to AI revenue vs spending: what to measure, how to verify it and where confident conclusions can fail.
Evaluate AI agent trading through primary evidence, a worked calculation and the assumptions that can break the thesis.
Evaluate AI inference vs training through primary evidence, a worked calculation and the assumptions that can break the thesis.
An investor’s research guide to AI token economics: what to measure, how to verify it and where confident conclusions can fail.
A source-checked framework for AI cloud economics, with the evidence, calculations and failure modes an investor should examine.
Evaluate AI infrastructure backlog through primary evidence, a worked calculation and the assumptions that can break the thesis.
A source-checked framework for open source AI economics, with the evidence, calculations and failure modes an investor should examine.
Evaluate AI model commoditization through primary evidence, a worked calculation and the assumptions that can break the thesis.
An investor’s research guide to AI software economics: what to measure, how to verify it and where confident conclusions can fail.
A practical workflow for AI agents saas, connecting dated sources and unit economics to valuation, portfolio and risk decisions.
A source-checked framework for AI monetization, with the evidence, calculations and failure modes an investor should examine.
Evaluate AI productivity margins through primary evidence, a worked calculation and the assumptions that can break the thesis.
An investor’s research guide to AI adoption metrics: what to measure, how to verify it and where confident conclusions can fail.
A practical workflow for AI revenue quality, connecting dated sources and unit economics to valuation, portfolio and risk decisions.
A source-checked framework for AI partnerships, with the evidence, calculations and failure modes an investor should examine.
An investor’s research guide to AI backlog rpo: what to measure, how to verify it and where confident conclusions can fail.
A practical workflow for AI gross margin, connecting dated sources and unit economics to valuation, portfolio and risk decisions.
An investor’s research guide to AI mentions vs revenue: what to measure, how to verify it and where confident conclusions can fail.