── ── Startups
The Second Curve
Every business follows an S-curve: slow start, steep growth, peak, then decline. Companies that endure start a second S-curve before the first peaks. Named by Charles Handy in The Empty Raincoat (1994): the optimal start is during late-growth or early-maturity — when the first curve still funds investment but the team can still see the need. The canonical case is…
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How it works
Step 1 — Diagnose first-curve position. Classify as: early growth / late growth / early maturity / late maturity / decline. Use revenue growth trend (3 years), gross margin trend, market share trend, TAM penetration. Late-growth and early-maturity are highest-leverage moments for second-curve investment.
Step 2 — Identify candidate second curves. For each candidate: business description, distance from core (1=same customers/product new feature; 3=new customers adjacent product; 5=new customers new product new capabilities), sized opportunity, time to meaningful revenue, investment required. Most second curves should be distance 2-3.
Step 3 — Time the start. Late growth: start now. Early maturity: start now, urgently. Late maturity: start now, constrained funding. Decline: too late internally — consider M&A, exit, or restructure.
When to use it
- user says 'our growth is slowing and we need to figure out what's next', 'we keep doubling down on the core but I'm worried about disruption', 'when is the right time to start something new alongside the main business', 'should we diversify now or wait', 'competitors are moving into adjacent spaces'
When not to use it
the company has not yet reached product-market fit; resources are so constrained that any investment outside the core would kill the business.
Worked example
The Incumbent's AI Second Curve (2024–2026)
The 2024–2026 generative-AI wave is a live, textbook second-curve moment for software incumbents. The pattern: a company whose first curve is mature, high-margin legacy software (licenses or seat-based SaaS) must reinvest that cash flow into an AI-native offering — new pricing, new architecture, new customer expectations — before the first curve peaks, while the AI product still cannibalizes the very seats that fund it. Microsoft's Copilot build-out is the most-documented instance and is used here as the worked case; the same audit applies to Adobe…
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