Glossary
Key terms for understanding AI markets and investor holdings — concise, bilingual, and easy to reference.
Investing principles
- Moat
- A durable advantage that fends off competition and sustains high returns — network effects, switching costs, scale, proprietary data, or ecosystem lock-in.
- Circle of competence
- The domains you genuinely understand well enough to judge their business and competition. Investing only within it is a core Buffett discipline.
- Margin of safety
- The cushion between price paid and intrinsic value. The wider it is, the more room for error and bad luck.
- Drawdown
- The peak-to-trough decline. AI is volatile with deep drawdowns; position sizing decides whether you can hold through them.
- Dollar-cost averagingDCA
- Investing a fixed amount on a fixed schedule to smooth cost and reduce timing risk — suited to long-term themes.
- Core-satellite
- A portfolio approach: a stable core in broad index or platform leaders, plus small satellites for high-beta themes.
Markets & data
- 13F filing13F
- A quarterly SEC filing where large U.S. institutions disclose their U.S. equity holdings. Useful for tracking the greats, but lagged (~45 days after quarter-end).
- Capital expenditureCapex
- Spending on long-term assets (data centers, chips). AI’s massive capex is the core driver of compute and infrastructure demand.
- Price-to-earningsP/E
- Share price divided by earnings per share — the price paid per unit of profit. High multiples are very sensitive to any slowdown.
- Free cash flowFCF
- Operating cash flow minus capex — the cash a business can truly deploy, a gauge of earnings quality.
AI & compute
- Picks and shovels
- Selling the tools rather than the end product — in AI, the chips, compute, and infrastructure with the most certain cash flows.
- Custom silicon
- In-house AI accelerators from hyperscalers (Trainium, TPU, Maia) that erode merchant GPU share over time.
- High-bandwidth memoryHBM
- High-speed memory paired with AI accelerators; tight supply lifts volume and price, benefiting names like Micron.
- Training / Inference
- Training teaches a model from data (compute-intensive); inference serves the trained model to users (at scale, with ongoing compute).
- Large language modelLLM
- Large AI models trained on vast text (GPT, Gemini, Ernie) — the foundation of this AI application wave.