The Convergence Method

A trend is not a single event repeated. It is multiple independent signals from different ecosystem segments all pointing in the same direction. The convergence method identifies trends by tracking signals across five categories — technology capability, market behavior, regulatory movement, investment patterns, and talent flows — and flagging when three or more categories independently produce signals aligned with the same directional thesis. One category signaling is a data point. Two categories is interesting. Three or more is a trend worth analyzing.

1. Define Signal Categories Technology capability: model benchmarks, new features, open-source parity events. Market behavior: pricing changes, new entrants, vendor exits. Regulatory movement: draft legislation, enforcement actions, standards proposals. Investment patterns: funding rounds, M&A, R&D allocation signals. Talent flows: hiring surges, talent migration between companies, new role types emerging.
2. Track Category Alignment For each developing theme (e.g., "multimodal AI adoption"), log signals by category. When a new signal arrives, check whether other categories have produced aligned signals in the trailing 90 days. Three-category alignment triggers a trend assessment. The assessment asks: is this convergence real, or are the signals causally connected through a single root event?
3. Validate Independence Convergence only counts when the signals are truly independent. If a model release (technology) drives a funding round (investment) which drives hiring (talent), all three trace to the same root cause. True convergence means independent actors in different categories are making aligned moves for their own separate reasons. That is the signal that a trend is structural, not reactive.