Name It, Frame It, Leverage It
The Real Value of Mental Models in the Age of LLMs
Everyone’s talking about what AI can do. Almost no one is talking about what you still have to do.
As LLMs become more powerful, there’s a growing assumption that human cognition can take a back seat. That if the machine knows everything, maybe we don’t need to. That prompt engineering is just typing smarter. That judgment is optional.
It’s not.
Here’s the paradox: AI doesn’t eliminate the need for structured thinking — it multiplies the cost of not having it.
If your inputs are vague, shallow, or structurally confused, the model will mirror them. You'll get plausible nonsense, ungrounded synthesis, and seductive answers that look smart but aren't. And you won’t even know it.
AI is a force multiplier. But only for those who already know how to think.
Mental Models: Still the Core OS of Strategic Thinking
Mental models are cognitive power tools. They compress complexity, reveal leverage, and help you see what's really happening beneath the surface of decisions, markets, or behaviors.
Charlie Munger famously said:
“80 or 90 important models will carry about 90% of the freight in making you a worldly-wise person.”
Mental models are not academic curiosities. They are named patterns that unlock recognition and action.
First-principles thinking lets you deconstruct problems instead of playing by inherited rules.
Second-order thinking shows you the consequences others miss.
Inversion flips the problem to expose blind spots.
Opportunity cost keeps you from mistaking activity for strategy.
These aren’t concepts. They’re weapons.
“Name It to Tame It” – Why Language is Leverage
There’s a deep principle in cognitive science, popularized by Dr. Daniel Siegel:
“Name it to tame it.”
When you label something — an emotion, a thought pattern, a strategic tradeoff — it becomes external. Visible. Workable. You go from swimming in it to working on it.
This principle holds at every level of thinking. If you can name the pattern, you can recognize the moment, choose the model, and execute the move.
That’s why the act of naming is so powerful: it makes thought modular, transferable, and sharable — whether with a team or with an LLM.
Design Patterns, Mental Models, and Structured Brains
In programming, we have design patterns — reusable solutions to common architectural problems. They come from architecture (Christopher Alexander), not software, and they let engineers build fast without reinventing the wheel.
Mental models serve the same role in strategic thinking. They let you:
Compress hard-won knowledge into a usable pattern.
Move fast through complexity with structural clarity.
Solve better by seeing the type of problem you’re in.
Both design patterns and mental models are how high-leverage people name reality, not react to it.
Why LLMs Still Need Your Brain
Here’s the strategic blind spot in AI hype: LLMs don’t "think." They autocomplete. And their output depends entirely on the structure of your input.
A great prompt is not just clearer — it’s smarter, because it reflects:
Your recognition of what kind of problem you’re facing.
Your framing of what actually matters.
Your intentionality about what you want out of the system.
LLMs respond brilliantly to mental models embedded in your language. Prompt with “use second-order thinking to evaluate this acquisition target,” and the model performs. Prompt vaguely, and you get garbage at scale.
Tree vs. Graph: How Real Learning Works
Traditional knowledge is often structured as a tree:
Roots = foundational truths (math, logic)
Branches = disciplines (econ, psychology)
Leaves = facts, theories, frameworks
But that’s not how knowledge works in reality — or in LLMs.
In reality, knowledge is a semantic graph: messy, interconnected, cross-disciplinary. “Opportunity cost” lives in economics, decision theory, startup strategy, and personal productivity. “Entropy” shows up in thermodynamics, information theory, and even organizational decay.
And if your mind still runs on a tree-based model, you're bottlenecked. AI operates on graphs. To use it well, you need to think like a graph — and navigate it like a pro.
Sequential Learning in a Nonlinear World
There’s a twist: humans still learn linearly. We read page by page. We listen in sequence. But the structure of knowledge is nonlinear.
Mental models act like nodes on your personal graph of understanding. The key is not to memorize them randomly, but to build them in the right order, for your goals, from where you are.
That’s where AI becomes a tutor — if you bring structure to the table. If not, you’re just bouncing between YouTube videos and chat prompts with no spine of understanding.
From Books to Live Thinking Systems
Reading used to be the best way to learn. It still matters. But it’s static.
With LLMs, you don’t just read a book — you interrogate it, challenge it, remix it, apply it.
But this only works if you know what to ask, what to connect, and what to discard. The quality of your questions becomes your learning curve. Mental models sharpen those questions. AI scales them.
From Insight to Execution
Here’s the real game: not learning, but acting.
With the right model + the right prompt + the right framing, LLMs become strategic engines:
Model a market entry
Simulate second-order effects
Compare mental models side-by-side
Translate abstract ideas into business decisions
But without that framing, all you get is noise — fast.
The Risks of Model-Free Thinking
Without models, the danger is clear:
You mistake confident-sounding output for actual insight.
You become a passive consumer of plausible language.
You offload thinking and lose the ability to audit your own mind.
The result? False certainty, slow decisions, and dependency on black-box logic.
Mental models are your guardrails, your debugging tools, and your thinking scaffolds.
The Strategic Edge: Clarity × Leverage
In the coming years, access to AI will be commoditized.
The differentiator will not be what tools you use, but how structured your thinking is when you use them.
That means:
Clarity > Content
Framing > Facts
Structure > Speed
The thinkers who name faster, frame sharper, and navigate complexity with confidence will dominate.
Final Word: Don’t Outsource Thinking. Augment It.
Mental models aren’t going away. They’re becoming more essential.
Because they’re what let you:
Name the problem
Frame the context
Leverage the machine
If you don’t know what to name, you won’t know what to ask.
If you don’t know what to frame, you won’t know what to do.
And if you can’t leverage it, you’re not thinking — you’re just typing.
Name it. Frame it. Leverage it.
That's how you stay human in an AI world — and stay ahead of everyone who isn’t.