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.
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.
In the 1950s, the Liverpool Institute for Boys had both Paul McCartney and George Harrison as students. The music teacher missed both. Elvis Presley’s Tupelo school teachers said the same about him. Sir Ken Robinson used these stories to make a point: human resources, like natural resources, are often buried deep. The failure mode is producing systematic AI-native talent underestimation right now.
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
On October 15, 2025, researchers published LLMs Can Get Brain Rot — the first controlled experimental evidence that a specific category of information input produces measurable, persistent, non-fully-reversible cognitive decline in the systems exposed to it. The systems the paper studied were machines. The implications extend to every knowledge worker in the modern economy.
The phrase talangka mentality — often translated as crab mentality — originated in the Philippines. Ninotchka Rosca coined it in a 1988 essay. The framework describes crabs in a bucket pulling each other down. The framework is wrong at its root: crabs did not evolve in buckets. Corporate structures create the container that produces the behavior. The reframe is the sharpest executive tool for what’s actually happening.
On April 1, 1972, five engineers walked out of IBM’s Mannheim office and founded a company they called Systems, Applications, and Products in Data Processing. Fifty-four years later, SAP handles approximately 87% of global commerce transactions. And the real-time data project they started in 1972 is still unfinished. The unfinished project is the sharpest available executive framework for AI transformation.
In 1965, Nintendo hired a young engineer named Gunpei Yokoi and assigned him to maintain the playing card machines. His philosophy — lateral thinking with withered technology — produced the Game Boy, which outsold its full-color competitors by 10 to 1. The framework is the sharpest counter-intuition available for enterprise AI in 2026.
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