The 100-Year Pattern — Part II

The Gap: Adoption vs. Economic Impact

AI adoption is accelerating. Measurable economic impact lags far behind. This gap is not uncertainty — it is the Solow Paradox in real time. It always closes. But not for everyone the same way.

AI adoption (orgs using AI in ≥1 function)

True integration at scale — actual (BCG)

True integration at scale — projected (slow growth)

Early movers — economic gain (projected)

Late movers — economic gain (projected, compressed)

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2025: 88% of organizations use AI in at least one function (McKinsey). Only 5% have truly integrated it at scale (BCG). Only 6% report meaningful EBIT impact. The gap between the blue and red lines is where most businesses currently live.

Data sources (actual): McKinsey State of AI 2023 (33% regular GenAI use), 2024 (65%), 2025 (88%, 6% EBIT impact). Early adoption 2017–2022: interpolated from NBER/Stanford AI Index and McKinsey surveys. Projections from 2026 onward are illustrative, anchored to historical GPT-wave adoption curves (electricity: Stage 4 productivity explosion ~1920, 38 years after Edison 1882; internet: measurable impact ~2005–2010, 14 years after WWW).