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.
Steve Jobs refused to show the embarrassing version. Eric Ries says you must. They’re both right — about different audiences. Here’s why most enterprise AI programs are getting MVP exactly wrong.
Eric Ries was right. Steve Jobs was right. Elon Musk is doing something different from both of them. Most leaders don’t realize there are six kinds of MVP — each doing a fundamentally different job. Here’s the taxonomy.
“If you’re not prepared to be wrong, you’ll never come up with anything original.” This idea is widely associated with Ken Robinson, who argued that modern systems often educate people
70% of major transformations fail. Cloud, digital, platforms, agile, now AI. The technology changes. The failure rate doesn’t. Here’s what the 2025-2026 McKinsey and BCG data actually says — and what the 5% who succeed are doing differently.
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