Compound Interest

Overview

Compound interest: a quantity grows at a rate proportional to its current size — growth itself grows — producing exponential accumulation. Formula: A = P × (1 + r)^t. Humans underestimate long-horizon outcomes because cognition extrapolates linearly. Two consequences: Rule of 72 (doubles in ≈ 72/r periods); late-period dominance (most final value comes from the last few periods).

Composes with lindy-effect, hyperbolic-discounting, expected-value-and-kelly, network-effects, deep-work.

When to Use

• Evaluating any long-horizon investment, savings, or wealth decision
• Deciding between starting earlier vs. starting later; intensity vs. duration paths
• Evaluating compound advantages in business (data, brand, switching cost)
• Weighing AI capex, AI adoption timing, or defending against AI-native competition — where data flywheels, ecosystem lock-in, and eval/technical debt compound over years
• Skill-development planning; recognizing compound decay (fees, atrophy, trust erosion)

Not when: horizon is short; rate is so low linear approximation is fine; process is genuinely linear; situation requires immediate one-shot intensity.

Coaching Novices (Adaptive Front Door)

• Engine mode: user has a concrete long-horizon case → run The Process directly.
• Coach mode: user is unfamiliar → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. One-line: duration of compounding dominates rate — starting earlier with small consistency beats starting later with large intensity.
2. Check fit. Short horizon or very low rate? Compound effects are small — save it for genuinely long horizons.
3. Elicit the specific decision, time horizon, and rate.

[WAIT — do not advance until user responds]

4. Walk through Rule of 72, precise compound outcome, late-period dominance, and other life domains one question at a time.

[WAIT — do not advance until user responds]

5. Close: decision informed by compound math + compound dynamics identified + commitment to early consistent action.

[WAIT — do not advance until user responds]

The Process

Step 1 — Specify the situation Starting value / Rate (per period) / Time horizon / Decision / Alternative options

Step 2 — Rule of 72 intuition Doubling time = 72/r | Doublings in horizon | Approximate multiplier = 2^doublings

Step 3 — Precise compound result A = P × (1+r)^t | Linear-extrapolation comparison | Gap between linear and compound

Step 4 — Late-period dominance Value at half-time (much less than half) | Value gained in last 25% (typically 50%+ of total)

Step 5 — Option comparison Option A compound outcome | Option B compound outcome | Where duration dominates | Recommendation

Step 6 — Generalize Other life domains with compound dynamics | Compound decay risks | Commitment to early action

Output Template

Compound Interest Analysis: <decision>
Situation: value / rate / horizon / decision
Rule of 72: doubling time / doublings / multiplier
Compound math: final (compound) vs. final (linear) / gap
Late dominance: value at half-time / last-25%-gains
Options: A vs. B / recommended
Generalization: other dynamics / decay risks / commitments

→ Method in Action: Bernoulli 1683, Graham/Buffett, and the Compound-Advantage Tradition · Franklin's Two-Hundred-Year Trusts → 2026 lens: Compounding in the AI Era — Data Flywheels, Ecosystem Lock-In, and Eval Debt (2023–2026)

Pack: Compound Interest Application Patterns

→ Primary sources: references/sources.md

Common Rationalizations

[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.

Red Flags

• Long-horizon decision made by linear extrapolation, not compound calculation
• Recurring fees or losses dismissed as "small"
• Plan is to "start later when I make more" — intensity substituted for duration
• Compound asset (trust, brand, skill) treated as something other than a compound asset

Verification

• Rule of 72 applied to estimate doubling time
• Precise compound calculation done for the full horizon
• Late-period dominance identified
• Both option compound outcomes computed (if comparing options)
• Compound dynamics identified in non-financial life areas
• Compound decay risks named; early action recommended

Part of deciqAI Knowledge Skills — 253 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/compound-interest · Built by deciqAI · github.com/deciqAI · Contributions welcome.

Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/compound-interest.json