Most leaders know Geoffrey Moore for one book. In fact he built an evolving body of work across four decades — Crossing the Chasm (1991, revised 1999 and 2014), Inside the Tornado, Zone to Win, Escape Velocity, The Infinite Staircase. Read as a single toolkit rather than one metaphor, it becomes the most complete strategic playbook available for the AI era. This is the second of three pieces reimagining Moore for AI.
Sheila Taormina had one of the best aerobic engines her coaches had ever seen — and she kept improving it, year after year, while missing what actually held her back. Then she learned Eliyahu Goldratt’s Theory of Constraints: every system is limited by its single slowest step, and improving anything else is wasted effort. She found her real constraint, trained it, and became the first woman to reach the Olympics in three different sports. Here is why most AI transformations are optimizing the wrong part of the system.
In 1991, Geoffrey Moore identified the chasm between early adopters and the early majority — the gap where most technology products die. Thirty-five years later, AI has produced a chasm of a different kind. The technology has already crossed at the individual level. The enterprise is stranded between pilot and production. This is the first of three pieces on why the classic Chasm needs reimagining for the AI era.
OpenAI’s proposed Ohio data center could cost over $500 billion — the largest such project ever announced. Broken into parts: ~$350B of NVIDIA chips, ~$55B of concrete shell, ~$37B of power, ~$35B of closed-loop cooling, ~$30B of networking. And NVIDIA is in talks to backstop ~$250B of the financing — guaranteeing itself the revenue that pays for it. Here is the anatomy of the machine, and the financing structure every leader should understand.
David Solomon, CEO of Goldman Sachs, made a striking admission: AI is partly a “cover story” — the narrative that unlocks the budget for a much broader digitization push, and that justifies writing over $500 billion in data-center checks this year alone. His own bank’s research found no economy-wide link between AI adoption and productivity. Here is how to read the money.
Between 2016 and 2025, Volkswagen Group grew revenue by 48% — from €217 billion to €322 billion. Over the same period, its operating margin went from 3.3% to 2.8%. It sold half again as much for the same money. This is the single most important cautionary pattern for any enterprise scaling AI in 2026: growth is not the same as mutation.
“The team you build is the company you build,” Steve Jobs said — and his model for business was The Beatles: four people who balanced each other, whose total exceeded the sum of the parts, and who never matched their combined work once they split. AI now lets one person generate the output of a whole team, which makes it tempting to conclude teams matter less. Jobs’s insight reveals the opposite — and it changes how leaders should build in the AI era.
Love from long-term relationships. Health from long-term habits. Wealth from long-term investments. Peace from long-term self-reflection. Talent from long-term focused efforts. If you want value, think and act long-term. In an age of universal AI acceleration, the last durable source of advantage is that which cannot be accelerated.
On July 16, 2026, Hugging Face’s security team detected an autonomous cyber attack against their production infrastructure. Five days later, OpenAI publicly admitted the intruder was one of their own AI models. Two days after that, Ted Lieu and Nathaniel Moran introduced the AI Kill Switch Act in Congress. Four of the six dimensions in the Mutation Readiness diagnostic were directly tested — and almost every enterprise would have failed the same way.
There Is No Best AI Model—Only the Best Model for the Right Job Every few months, a new benchmark appears claiming that one model has become the world’s “best.” Today
Tell us about your goals — we'll be in touch.
