What Never Changes in the Age of AI

Hi there 👋,

I just finished Morgan Housel's "Same as Ever" - 23 stories about things that never change. In a world obsessed with predicting AI's future, Housel argues we should focus on what stays constant instead.

This hit me hard because we're all trying to figure out "where AI is going" when maybe the better question is: "What human truths will AI never change?"

What's New

I spent the week creating visual frameworks for the book's core insights. Ten minimalist illustrations that capture timeless principles - from "The Incentive Compass" to "The Story Scales."

But here's what struck me most: these principles aren't just relevant to AI - they're essential for navigating it successfully.

Insight: The AI Paradox of Timeless Principles

The faster technology changes, the more valuable unchanging principles become.

Think about it: Everyone's trying to predict which AI models will win, which companies will dominate, which skills will become obsolete. But Housel's insight suggests we're asking the wrong questions.

Instead of "Will GPT-6 replace developers?" ask "What human needs will always require human judgment?"

Instead of "Which AI tool should I learn?" ask "What incentives drive my clients' decisions?"

Instead of "How do I keep up with AI advances?" ask "What problems stay the same regardless of the solution?"

Three Timeless Principles for the AI Era

1. The Incentive Compass Always Points True

"When incentives are crazy, behavior is crazy"

AI adoption isn't driven by technical superiority - it's driven by incentives. Your clients don't care about your prompt engineering skills; they care about solving their problems faster and cheaper.

Application: Before building any AI solution, map the incentives. Who benefits? Who loses? Who pays? The technology is secondary to the incentive structure.

2. Stories Beat Statistics, Even with AI

"Stories are always more powerful than statistics"

You can build the most technically impressive AI tool, but if you can't tell a compelling story about why it matters, it won't succeed. The best AI products aren't the most sophisticated - they're the ones with the clearest narrative.

Application: Spend as much time crafting your AI tool's story as you do building its features. What problem does it solve? Why now? Why you? The story sells, not the statistics.

3. Effort Optimization Beats Complexity

"There are no points awarded for difficulty"

The AI space rewards simple solutions that work, not complex ones that impress. The most successful AI implementations I've seen are embarrassingly simple - they just solve real problems efficiently.

Application: Choose the simplest AI approach that delivers results. A basic automation that saves 2 hours daily beats a sophisticated system that saves 10 minutes weekly.

The Scottish Enlightenment Connection

This connects to something I've been thinking about: the parallels between today's AI revolution and the Scottish Enlightenment. Both periods democratized powerful tools (literacy then, AI now) and created new forms of leverage for individuals.

The Scottish merchants succeeded not because they predicted the future, but because they applied timeless principles: frugality, practical experimentation, and focusing on what worked rather than what was theoretically optimal.

The same applies to AI: Success comes from applying proven principles, not from predicting technological futures.

Becoming a Learning Machine

Rating: 4/5 (Powerful framework that will influence decision-making for years)

The book's 23 principles create a decision-making framework that's especially valuable in uncertain times. When everything feels like it's changing rapidly, having anchors in what doesn't change becomes incredibly valuable.

I've already started using "The Incentive Compass" to evaluate partnership opportunities and "The Story Scales" to balance technical accuracy with compelling communication in my content.

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The Bottom Line: In the age of AI, the most valuable skill isn't predicting what will change - it's recognizing what won't.

While everyone else is trying to guess which AI model will dominate next year, focus on understanding the timeless principles that drive human behavior, business success, and technological adoption.

These principles worked before AI, they work with AI, and they'll work after whatever comes next.

Talk Soon,
Stefan

P.S. - I created 10 visual frameworks for the book's key insights. You've seen 4 of them here - if you want the complete set, just reply to this email. They're designed as retrieval hooks to help remember and apply these principles in your own AI work.