Published January 23, 2026 · Updated January 25, 2026
Artificial intelligence has become one of the most powerful narratives in public markets. From semiconductor manufacturers and cloud platforms to software companies branding themselves as “AI-first,” investors are faced with a growing challenge: how do you separate real AI-driven businesses from hype?
This page is being developed as a structured due diligence framework for analyzing AI stocks. Rather than highlighting specific companies, stock picks, or price targets, it focuses on how AI companies should be evaluated — across technology, business model, financials, and risk.
The goal is to provide a repeatable checklist that helps investors assess AI exposure with clarity and discipline before allocating capital.
When complete, this guide will cover:
- how AI companies differ structurally from traditional tech firms
- how to evaluate AI exposure across infrastructure, platforms, and applications
- key metrics that matter for AI-driven business models
- how to assess competitive advantage, data moats, and scalability
- common analytical mistakes investors make when evaluating AI stocks
This framework does not offer investment recommendations or forecasts. Instead, it is designed to help investors think critically about AI claims, understand where value is actually created, and recognize the risks embedded in AI-heavy narratives.
For a broader perspective on building long-term exposure, start with What Is AI Investing? A Complete Guide to Stocks, ETFs & Crypto (2026).
For readers interested specifically in portfolio construction and allocation, the AI Stocks hub explores categories, risk profiles, and market dynamics in more detail.
This page will be expanded and updated as AI business models, disclosures, and market structures continue to evolve.
