7 Kinds of Knowledge Worth Remembering
What Memory Is For (Now)
Most facts are only a few clicks away. What matters is what sticks when you’re offline, under pressure, or making sense of something new.
In a high-access world, memory is no longer a warehouse. It is a workshop.
You don’t need to store everything. But what you do keep should serve a purpose. It should shape how you think, what you notice, how you decide, and who you become.
What follows is a practical breakdown: seven kinds of knowledge worth remembering. Each one offers a different kind of leverage. Each one changes how the rest of the world makes sense.
1. Mental Models and Heuristics
Why: These help you think faster and more clearly.
Examples: First principles thinking1, opportunity cost2, expected value3, Bayesian reasoning4, marginal gains5, Hanlon’s Razor.6
Benefit: You’re not just recalling facts—you’re improving how you process any new situation.
2. Core Conceptual Frameworks
Note: Some core frameworks are also mental models, and many heuristics are built from conceptual insights. But the distinction matters. Mental models are tools for thinking—flexible and tactical. Conceptual frameworks are scaffolds for understanding—structural and explanatory. One helps you decide. The other helps you see.
Why: Understanding doesn’t happen through Google—it happens in your head.
Examples: Supply and demand7, entropy8, the Big Five personality traits9, cognitive biases10, reinforcement learning.11
Benefit: With these in memory, you can integrate new facts more efficiently and generate original insights.
3. Identity-Defining Knowledge
Why: Memory shapes your sense of self and guides action under uncertainty.
Examples: Core values, life goals, personal philosophies, ethical boundaries, critical experiences.
Benefit: You don’t Google who you are. This knowledge informs your choices when context is messy or ambiguous.
4. Creative Inputs and Cultural Fluency
Why: You can’t remix what you never absorbed.
Examples: Key literary works, foundational movies, musical styles, historical events, influential thinkers.
Benefit: Rich memory here enables cultural literacy, creative output, and social connection.
5. Procedural and Embodied Knowledge
Why: These are hard to outsource and essential for mastery.
Examples: Playing an instrument, navigating interpersonal conflict, giving a speech, serving an ace in tennis.
Benefit: These skills require memory built through repetition—no amount of Googling can replace practice.
6. Frequently-Used Knowledge
Why: Lookup costs aren’t just time—they’re cognitive load12 and lost momentum.
Examples: Keyboard shortcuts, basic arithmetic, colleague names, common workflows.
Benefit: Keeping these handy preserves mental bandwidth.
7. Foundational Knowledge in Your Domain
Why: Experts aren’t just faster—they see differently.
Examples: Vocabulary, case patterns, and conceptual schemas in your field.
Benefit: Stored knowledge changes your perception of problems and your ability to solve them.
Which kind of knowledge has served you best—and why?
Leave a comment and let’s map the memory tools that matter.
So, What Should You Forget?
Arbitrary facts with no emotional, practical, or conceptual resonance.
Information you can retrieve instantly and don’t need to use flexibly or frequently.
Memory, like education, is not passive recall but active construction. We carry what helps us think, choose, and change. As Dewey wrote,13
“The present, in short, generates the problems which lead us to search the past for suggestion, and which supplies meaning to what we find when we search.”
These seven kinds of knowledge aren’t just facts to remember. They are tools for becoming. In a world where almost everything can be looked up, what you keep in mind is what keeps you moving.
FOOTNOTES:
First principles thinking is a method of reasoning that breaks problems down to their most basic truths and builds up from there. Rather than relying on analogy or convention, it asks: What do I know for sure? What can be stripped away? What remains when all assumptions are challenged? It’s often credited to Aristotle, revived by physicists, and championed in tech circles for its ability to cut through complexity with clarity.
Opportunity cost is what you give up when you choose one option over another. It’s not just the price of what you do—it’s the value of what you didn’t do. Every yes is a no to something else. Economists frame it in dollars. But it applies just as much to time, attention, and identity. The best choices often depend not on what something costs, but on what it costs you not to choose differently.
Expected value is a way of weighing outcomes by both their likelihood and their influence. It’s not just what could happen—but how often, and how much it matters. You multiply the value of each possible result by its probability, then sum the total. High expected value doesn’t mean guaranteed success—it means a smart long-term bet. Think of it as decision-making with both eyes open: one on risk, one on reward.
Bayesian reasoning is the practice of updating your beliefs based on new evidence. You don’t start from scratch each time—you adjust your prior assumptions in proportion to how surprising or confirming the new data is. It’s probability as learning. Think of it as mental calibration: not “Is this true?” but “How much more (or less) should I believe this now?” Over time, it helps you steer closer to truth without swinging wildly with each new headline.
Marginal gains is the idea that small, consistent improvements—often just one percent better—can compound into significant transformation. Popularized in cycling and systems design, it’s a strategy of refinement over revolution. Rather than chasing breakthroughs, you focus on tiny edges: sleep, setup, sequence, mindset. Over time, those edges add up. Big change, not from leaps, but from layers.
Hanlon’s Razor is a mental shortcut that advises: “Never attribute to malice what can be explained by stupidity”—or, more charitably, by oversight, constraint, or miscommunication. It’s a filter against paranoia, reminding us that most harm is accidental, not intentional. Use it to defuse conflict, lower reactivity, and make space for more generous interpretations—without surrendering discernment.
Supply and demand describes how prices and availability shift based on how much people want something and how much of it exists. When demand rises or supply drops, prices tend to go up. When supply floods the market or interest fades, prices fall. It’s not just about markets—it’s a lens for understanding scarcity, value, and trade-offs in everything from attention to time.
Entropy is a measure of disorder or randomness in a system. In physics, it reflects how energy spreads out and becomes less useful. In everyday life, it shows up as drift, decay, and the natural slide toward chaos. Left unchecked, things fall apart—not out of malice, but because order takes work. Entropy isn’t the enemy, but it is the cost of structure. To build anything lasting, you have to fight it—on purpose.
The Big Five personality traits—openness, conscientiousness, extraversion, agreeableness, and neuroticism—form a widely accepted model for describing human personality. Each trait exists on a spectrum and shapes how we think, feel, and act. Unlike typologies, this framework allows for nuance. It doesn’t box you in—it maps your tendencies. And those tendencies, once understood, can be nudged.
(Curious how you score? You can take a free version of the Big Five at big-5-personality-test.com. It’s based on the IPIP model—no signup, no gimmicks, just a snapshot of how your traits shape the way you move through the world.)
Cognitive biases are systematic thinking shortcuts that help us make quick decisions—but often lead us astray. They’re not flaws so much as adaptations: rules of thumb that worked well in the past but can misfire in complex modern contexts. From confirmation bias to availability bias, they shape how we perceive, remember, and judge—usually without our awareness. Understanding them isn’t about becoming perfectly rational. It’s about catching yourself just early enough to ask better questions.
Reinforcement learning is a way of learning through trial and error, guided by rewards and penalties. In both brains and machines, it’s how systems figure out what works: try something, get feedback, adjust. It underlies how habits form, how AI teaches itself to play games, and how animals—from mice to humans—learn from experience. Success isn’t pre-programmed; it’s discovered, one iteration at a time.
Cognitive load refers to the total mental effort being used in your working memory. When too much information competes for attention, thinking slows and decisions suffer. It’s why multitasking backfires and why simplicity—when done right—is not just elegant but essential. Managing cognitive load isn’t about doing less. It’s about designing thought space that lets insight emerge.
— John Dewey, Democracy and Education (1916)