Practicing Wisdom — Issue #17

A distillation of the most interesting things I explored, learned, and thought about.

1. What I Learned This Time

The playbook is not the game.

There’s a strange thing that happens when you stare long enough at a field— any field— and start to notice that the rules everyone follows aren’t really rules. They are habits which were formed in a different era, for a different environment, and got mistaken for wisdom somewhere along the way.

That’s the thread connecting most of what I read this week.

Jerry Neumann’s essay “We Have Learned Nothing” is, on its surface, a takedown of startup punditry. The Lean Startup. Customer Development. Design Thinking. The whole canon of entrepreneurial methods that Silicon Valley has been teaching for two decades. His argument is blunt: startup survival rates haven’t improved. Not a bit. Thirty years of frameworks, and the data is flat. So either the theories are wrong, or they’re so obvious they provide no edge, or— and this is the one that resonates— they’re self-defeating. Once everyone adopts the same playbook, it stops being a playbook and becomes noise. The Red Queen doesn’t sleep. I remember becoming interested in body-building in college and hearing an interview where someone said “most people do not succeed in the space because of what they do, but rather in spite of what they do.”. It’s amazing what grit can overcome, but how much is actually necessary?

What I find more interesting than the critique is what Neumann gestures towards as the alternative: a meta-theory. Not “here’s what works” but “here’s how to keep finding what works before everyone else does.” The insight is almost Darwinian in its logic. In any ecosystem under competition, the only durable advantage is the capacity to generate new advantages. ‘The secret to life is finding balance.’

William Hockey, in his conversation on Invest Like the Best, lives this instinctively. He reads 2,000-page books on 19th-century Chinese banking. He goes to Kinshasa. He spends time in the places nobody from Silicon Valley bothers to go. Not because he’s eccentric, but because consensus is the enemy of edge— and doing what everyone else is doing is the surest way to get what everyone else gets.

Benn Stancil’s piece, “Compacting,” layers in a quieter unease. When Anthropic interviewed 80,000 people in a week using AI— dwarfing what the World Bank accomplished with 400 researchers over two years— the headline felt triumphant. But Stancil slows down, and asks the harder question: what is lost when understanding travels at that speed? The World Bank’s researchers cried at the stories they heard. They felt the poverty. They were changed by it. The AI classified it. These are not the same thing. Knowledge, as Ezra Klein puts it in a quote Stancil surfaces, isn’t downloaded— it’s earned through the act of grappling. The more we compress, the more we risk knowing things we don’t actually understand.

This connects to a market truth buried in the Palm Valley piece, “Would You Rather.” The whole essay is about forced choice in a broken market— every asset class overvalued, every option uncomfortable— and yet most investors keep playing because sitting out feels worse than being wrong. The game isn’t real anymore. It’s a consensus trap. And the most contrarian act available is to simply… not choose. To hold the T-bill. To wait. To resist the social pressure of permanent deployment.

There’s a common throughline here, and it’s more philosophical than strategic: the danger of systematizing things that work precisely because they haven’t been systematized. Whether it’s startup methodology, research at scale, or investment allocation— the moment a working edge becomes a known process, it starts to decay. This isn’t cynicism. It’s the Red Queen. You can’t stop running. But you can at least understand why. As someone who really enjoys the universal truths expressed in taoism, it is easy to get a certain sense of comfort from this reality check. ‘True mastery can be gained by letting things go their own way. It can’t be gained by interfering.’

Hockey’s model for staying ahead is discomfort— read the boring thing, go to the inconvenient place, build the company nobody else wants to build. Neumann’s model is contrarianism— if everyone does X, do something else. Stancil’s is slower: immersion, attention, the willingness to be changed by what you encounter, not just informed by it.

Three different writers. One shared warning: the faster you process experience, the more you risk mistaking information for understanding.

Sources Referenced

William Hockey — Invest Like the Best (link)

Compacting — Benn Stancil (link)

Would You Rather — Palm Valley Capital Management (link)

We Have Learned Nothing — Colossus (link)

2. Key Distillations

• “The best strategies work until they’re the only strategy.”

• “Speed compresses data. Slowness produces understanding.”

• “The playbook is a starting point, not a destination—and only useful until everyone else reads it.”

• “Patience isn’t passivity. Sometimes it’s the highest-conviction position in the room.”

• “If you go where nobody goes, you learn what nobody knows.”

3. One Contrarian Viewpoint

Scale is not understanding. More data processed faster is not the same as knowing something.

The dominant narrative in AI and research right now celebrates compression: more interviews, faster synthesis, broader coverage. Anthropic’s 80,000-person qualitative study in three months is treated as a triumph. And in certain ways it is.

But there’s a category error hiding in that story. The World Bank researchers who cried taking notes in Bosnia weren’t being inefficient— they were doing something the classifier cannot do. They were being changed. And being changed is different from being informed.

The contrarian view: our obsession with scale may be systematically destroying the thing that makes research, knowledge, and leadership actually work— empathy forged through friction. The AI can surface patterns. It cannot feel the weight of the story it’s summarizing.

As we apply this logic to business, investing, and strategy, the implication is uncomfortable: the leaders who will have the deepest instincts in an AI world may be those who deliberately go slower, engage more intimately, and resist the pull toward maximum compression. The edge may lie in what can’t be automated— not despite inefficiency, but because of it.

4. One Investable Idea

The Boredom Moat

William Hockey’s framework for competitive advantage is deceptively simple: find the thing that is genuinely boring, go deep for decades, and build proprietary knowledge nobody else bothered to accumulate.

In a world where AI can surface consensus insights in seconds, the last true moat is the knowledge that nobody thought to build because it wasn’t interesting enough to build. Column isn’t winning because it out-iterated competitors— it’s winning because Hockey spent years studying the history of banking systems that most founders would never read, in markets most founders would never visit.

The investable thesis here is structural. Seek out businesses— or found them— in domains so unglamorous and technically demanding that the talent pool is thin not because the opportunity is small, but because nobody wanted to do the work. Financial infrastructure. Industrial software. Regulatory-moated services in emerging markets. The categories that Silicon Valley consensus has actively avoided.

In a Red Queen environment, the businesses most likely to compound are not those chasing the same frontier as everyone else— they’re those that chose a different frontier before anyone else thought it was worth choosing.

Thesis: The next durable businesses won’t be built on the best ideas—they’ll be built on the most ignored ones, held longest by people willing to find the boring thing genuinely fascinating.

5. From the Archives: A Recall Highlight

“The longer something has survived, the more likely it is to survive.”

From Issue #10 — still one of the most quietly powerful ideas in this newsletter. In a week dominated by systems, frameworks, and the temptation to optimize for novelty, the Lindy effect is the antidote. The things that have endured — institutions, mental models, businesses, principles — have been stress-tested in ways no forecast can replicate. Survival is the original signal. Everything else is noise.