In 1993, in The New Economics, Deming wrote a sentence about theory that has been quietly explaining why organizational learning fails for three decades. Experience teaches nothing without theory. Examples without theory teach nothing. The application to enterprise AI in 2026 is direct.
Steve Jobs’s most famous line — “good artists copy, great artists steal” — was stolen from T.S. Eliot, simplified in the stealing, and has been quoted in defaced form ever since. The original 1920 Eliot version contains a clause that the popular version drops, and that clause is the single most useful framework for AI transformation in 2026.
Charlie Munger’s “iron prescription” — “whenever you think that some situation or some person is ruining your life, it’s actually you who are ruining your life” — is the single most useful psychological technology any executive can carry into the age of AI. He forged it while burying his nine-year-old son. That’s the part most retellings strip out — and the part that matters most.
Jim Simons died as the richest mathematician in history. He made his fortune by hiring scientists and refusing to hire anyone from Wall Street. The Medallion Fund delivered ~39% net annual returns for thirty years — beating Buffett, Soros, Lynch, Cohen, and Dalio combined. The six principles that produced those returns are now the foundational operating principles of every AI-native organization. Most enterprises still haven’t adopted them. Here’s what they’re getting wrong.
Jeff Bezos’s two-way doors policy says it’s cheaper to try a reversible idea than to argue about it. It’s the framework behind AWS, behind the two-pizza team, and behind Amazon’s invention rate. In 2026, with AI collapsing the cost of experimentation, it’s also the single most important decision framework for any enterprise serious about AI transformation.
In 1994, anthropologist Eugenie Scott coined the term “Gish gallop” to describe a debate technique where a speaker overwhelms an opponent with so many false claims that no rebuttal is possible in the time available. The technique has now migrated from creationist debates into the default information environment of senior decision-making — and AI has industrialized it. Here’s how to recognize it and how to defeat it.
A waterfall would never sound melodious if there weren’t rocks in its way. The very thing we love about waterfalls is created entirely by the obstacles we’d remove if we could. Most executives spend their careers trying to smooth the path. That’s exactly why their careers are silent — and why AI is the biggest rock the executive class has encountered in a generation.
In 1950, an F1 pit stop took 67 seconds. Today the world record is 1.80. The 96% reduction has almost nothing to do with making mechanics faster — and everything to do with redesigning the work itself. The same lesson saved thousands of children’s lives at Great Ormond Street Hospital. The same lesson explains why most AI transformations are failing right now.
Edward de Bono coined the term “lateral thinking” in 1967 and compressed the entire framework into one sentence: “You cannot dig a hole in a different place by digging the same hole deeper.” Most senior executives in 2026 are digging a deep hole in the wrong location — with all the wrong instincts, all the right credentials, and a culture that rewards intensification over reorientation. This is what to do instead.
A clickbait Reel is going viral claiming Microsoft banned engineers from using AI because AI costs more than humans. Almost every part of that framing is wrong. The actual story — about token-based pricing creating a new enterprise cost class that finance teams aren’t prepared for — is harder, more important, and the most useful lesson available to any leader running an AI program in 2026.
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