Practicing Wisdom — Issue #14

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

1. What I Learned This Week

Theme: Choice architectures are becoming increasingly opaque.

This week kept circling one question: who’s writing the script?

Not the story we tell ourselves (“I’m in control; I’m choosing; I’m deciding”), but the quieter reality: most of our behavior is downstream of defaults—and the defaults are increasingly set by whoever has prestige, leverage, or access.

Scott Galloway’s piece on role models frames this explicitly: we don’t just admire people—we learn “scripts” from them. We copy how leaders talk, how they handle criticism, what they treat as “normal,” and what they treat as beneath them. The point that stuck with me wasn’t moralizing, but mechanics: if you want to understand a culture, don’t ask what it believes—ask who it rehearses.

The Joseph Henrich idea Galloway cites landed even harder: humans are wired for cultural learning, and we’re especially tuned to high-prestige cues—so much so that prestige can act like a coordination lever. In the example he references, when the “high-prestige” person moves first (and cooperates), the group follows; when they don’t, cooperation collapses. In other words: prestige isn’t just status; it’s veto power over norms.

That lens made two other reads feel like they were talking about the same thing, just in different domains.

Benn Stancil’s “gentle obsolescence” is nominally about AI, but it’s really about an inversion: we started by calling these models “interns,” and then slowly realized that “planning mode” can be the intern… prompting the manager. The model asks the questions. It proposes the options. It nudges you toward its taste. And because it often has decent taste—and it’s fast—you begin to prefer when it drives. Suddenly the steering wheel becomes ceremonial.

The eerie part isn’t “AI can do tasks.” The eerie part is the emotional bargain: it offers relief from decision-making. It doesn’t just do work; it removes the need to choose. And if you accept that bargain often enough, you don’t become unemployed—you become unpracticed. Your agency atrophies from disuse, not from displacement.

Then “Go crazy, folks, go crazy” supplies the missing incentive layer: even if the careful, governed, “responsible” version of AI is what should win, history suggests the market repeatedly rewards the team that says, essentially, “eh, why not?”—ship the bolder product, loosen the constraints, crank the dial to 12, and let users YOLO their way past the warning labels. He points out how often people claim to want rigor, but behave like they want magic.

So we have a pattern:

Role models teach scripts.

AI tools are becoming role models (or at least default-setters).

Markets tend to reward the default-setters who move fastest, not the ones who move most carefully.

And then Ted Seides’ piece on private markets made me realize this isn’t just cultural—it’s financial infrastructure too.

Private equity’s model assumes a clean rhythm: buy → improve → sell → return capital → raise the next fund. Seides argues the rhythm is breaking because too many portfolio companies can’t find a buyer, stretching holding periods and starving the recycling engine that funds the next cycle. He frames it in supply/demand terms: purchases can keep growing (capital, targets, “dry powder”), but exits are constrained by bottlenecks—especially the absence of enough strategic buyers and a less-attractive IPO path.

What clicked for me: when exits don’t clear, the whole system quietly shifts from “investing” to asset management in captivity. Liquidity stops being a feature and becomes the power structure. You see it in his implications: changes to fund structures, LP behavior, GP shakeouts, and the rise of “zombie” dynamics where alignment erodes because time is no longer bounded the way the contracts pretended it was.

Same underlying theme again: when there’s no exit, whoever controls the inside writes the rules.

In politics/culture, role models normalize behavior (for good or ill).

In AI, default interfaces and “helpful” prompts steer choices.

In private markets, liquidity constraints rewrite governance and bargaining power.

The week’s meta-lesson: the world is getting more “internal.” More closed loops. More systems where feedback is delayed, accountability is fuzzy, and the “script” is set by the actors with leverage—prestige, distribution, or liquidity.

Which leaves a practical question I’ve been sitting with:

If the most important skill is no longer “knowing,” but choosing… how do you stay good at choosing in a world trying to choose for you?

Sources Referenced

Can Private Markets Normalize? — Capital Allocators (link)

The Gentle Obsolescence — Benn Stancil (link)

Go Crazy Folks, Go Crazy — Benn Stancil (link)

Role Models — Scott Galloway, No Mercy / No Malice (link)

2. Key Distillations

• Prestige is coordination leverage. High-status behavior doesn’t just get copied—it sets what the group can and can’t do next.

• Liquidity is governance in disguise. Whoever controls the “exit” often controls the terms—morally, financially, and institutionally.

• Tools become teachers. The interface that asks the questions eventually shapes the user who answers them.

• Safety is a luxury good—until it’s regulated. Markets routinely reward the product that feels like magic, even if it’s messy.

• Decision relief is the real drug. Automation’s sharpest edge isn’t labor replacement; it’s agency replacement.

3. One Contrarian Viewpoint

“Normalization” is often just a bedtime story we tell ourselves so we don’t have to update our models.

In private markets, “things will normalize” means: exits will come back, holding periods will shorten, distributions will resume, and the flywheel will spin like it used to. Seides is essentially asking: what if not? What if the growth of private capital + the flatness of strategic demand + the diminished appeal of IPOs isn’t a temporary cycle, but a structural regime?

The contrarian stance I’m taking from that: stop underwriting privates like a timed trade. Start underwriting them like semi-permanent governance exposure.

If you believe that, a lot changes:

The “illiquidity premium” starts to look less like a premium and more like an illiquidity tax (paid in optionality).

Manager selection matters even more—not just for sourcing and operations, but for how they behave when the market doesn’t give them an exit.

Secondaries, continuation vehicles, NAV solutions—these aren’t side quests. They’re the new plumbing.

The comfort of “normalization” is that it preserves the old playbook. The danger is that it delays adaptation until you’re forced into it.

4. One Investable Idea

The “Liquidity OS” for Private Markets (Powered by Agentic Underwriting)

If private equity is accumulating more “unsold inventory” and exits remain structurally constrained, the biggest opportunity isn’t another fund—it’s infrastructure that makes ownership transferable and legible.

What I mean by “Liquidity OS”:

A vertically integrated platform that combines:

• Standardized data ingestion for private assets (financials, KPIs, covenants, board materials, cap tables, customer concentration, etc.)

• Agentic analysis (not just dashboards) that continuously produces investor-ready outputs: risk memos, scenario trees, covenant stress tests, value-creation trackers

• Transaction rails for secondaries and structured liquidity (LP stakes, GP-led, direct secondaries, CV interests), with built-in compliance and audit trails

Why now (based on this week’s readings):

• Private markets are getting “longer duration” because exits are bottlenecked. That increases demand for alternative liquidity mechanisms.

• AI systems are becoming meaningfully better at synthesizing messy information and proposing next actions—especially when you let them run more autonomously than traditional “trusted insights” tools.

• The winning product category may not be the perfectly governed one—it may be the one that delivers unmistakable ROI quickly, then backfills governance as it becomes table stakes.

5. From the Archives: A Recall Highlight

“Your map is not the terrain — and someone else drew it.” (From Issue #4)
A reminder to question not just where you’re going, but whose system you’re navigating.