The Simple Guy

The Antidote to Stupidity Isn’t Intelligence, It’s Strength

Shane Parrish’s Clear Thinking isn’t about becoming smarter. It’s about escaping stupidity. Most mistakes don’t come from ignorance, but from reacting on autopilot.

We all live under four defaults: emotion, ego, social pressure, and inertia. These instincts once kept us alive, but in modern life they betray us. A single tweet fired off in anger can ruin more than a tiger ever could.

The antidote is strength. Parrish reminds us that cleverness is fragile, but strength endures. Strength comes from four practices:

• Accountability: Ask “What part of this is my responsibility?” The more you own, the faster you grow.
• Self-knowledge: Track when you make your worst decisions, tired, rushed, showing off and redesign your habits.
• Self-control: Build the pause. A deep breath before you reply. A day before you buy. Slowness is power.
• Self-confidence: True confidence is the ability to say, “I was wrong.” Weak minds cling; strong ones update.

Tools that compound: Clear thinking is a discipline, not a gift. Rules, guardrails, and mental models are like weights in the gym. They train your judgment:

• Write personal rules: “I don’t argue after midnight.”
• Design guardrails: no Twitter on the phone, no sugar in the kitchen. Design beats willpower.
• Run pre-mortems: “If this fails, why?” Fix the risks before they break you.
• Think second-order: “And then what?” The first consequence is obvious, the second and third decide your fate.

Parrish’s warning is clear: the extraordinary is just the ordinary, managed wisely. In a world run by defaults, the question is simple.

Are you living by choice, or by default?

Algorithms as Gods, Influencers as Priests, Likes as Prayers

“Man is a religious animal.” Pascal wrote that centuries ago. The form has changed, but the instinct remains.

Religion once offered meaning, community, and ritual. Social media offers the same, only faster and cheaper. The church has been replaced by the platform. The priest by the influencer. The scripture by the feed.

Algorithms are treated like gods. They are invisible, all-powerful, and unpredictable. They decide what we see, what goes viral, who gets rewarded. Their commandments are engagement and outrage. As Dostoevsky warned, “If God is dead, everything is permitted.” The algorithm has no ethics, only incentives.

Likes are the new prayers. They do not change reality, but they create belonging. Enough likes and you feel chosen. Too few and you feel abandoned.

Five years ago, social media was entertainment. Ten years ago, it was curiosity. Today, it is infrastructure. It shapes elections, markets, even wars. The digital crowd is no longer background noise; it is the battlefield.

The economics of this new religion are brutal. Attention is currency, and the market never sleeps. Unlike faith, where devotion could be private, online devotion must be visible. If you do not post, you do not exist.

The side effects are obvious. Rising anxiety. Shallow debates. Polarized societies. A generation raised on dopamine spikes instead of deep thought. We are witnessing not the wisdom of crowds but the hysteria of mobs.

Yet every religion produces its rebels. The early adopters of silence, digital minimalists and offline thinkers, look like heretics now but will be seen as saints later.

The future will not be about choosing between God and no God. It will be about choosing between algorithmic faith and human freedom.

So here is the question: if social media is the new religion, do you want to live as a believer, a priest, or an apostate?

The Global Circus: Are We Being Governed by Fools?

How can we truly measure whether humanity is progressing in wisdom or regressing? While IQ scores might fluctuate and access to information is at an all-time high, one of the most revealing metrics may be the leaders we choose and the environments they create.

We live in an age where information is infinite, intelligence is automated, and machines can answer questions in seconds. By all logic, we should be smarter than ever. Instead, many have traded deep thinking for endless scrolling, consuming reels, memes, and bite-sized outrage, outsourcing judgment to algorithms and AI. The result: a public less curious, less informed, and more vulnerable to emotional manipulation. That vulnerability shows up at the ballot box.

In the United States, voters elevated Donald Trump - a leader whose term was defined by erratic decisions, bizarre proposals like buying Greenland or merging Canada, and a focus on personal gain over public service. In Russia, Vladimir Putin has ruled for decades, waging war in Ukraine while tightening his grip on power. In China, Xi Jinping dismantled term limits, silenced critics, and expanded mass surveillance, even as the economy falters. Around the world, the pattern repeats. Hungary’s Viktor Orbán erodes democracy; Turkey’s Recep Tayyip Erdoğan blames interest rates for inflation, wrecking the currency. The UK briefly installed a prime minister whose economic plan imploded in 49 days. Italy can’t hold on to leaders long enough to ensure stability. Corruption and dysfunction fuel global unrest from Mongolia’s youth-led protests to Japan’s political scandals, Brazil’s graft cases, and France’s paralyzing strikes. This isn’t just about bad politicians.

It’s about a distracted, dopamine-chasing society that no longer demands competence. When people stop questioning, stop learning, and let algorithms do their thinking, they become easy prey for strongmen, populists, and con artists. We’re not just electing worse leaders, we’re creating the conditions for them to thrive. In the age of AI, the real danger isn’t that machines will outsmart us. It’s that we’ll stop trying to be smart at all.

Charlie Munger said we need a latticework of mental models. The beauty of science is that it gives us patterns that don’t just stay in textbooks. They spill over into daily life, career, and relationships. Here are a few science-based models you can lean on in real decision making.

1. Entropy

Left on its own, everything drifts toward disorder. Your room, your body, your projects—they don’t maintain themselves. Entropy is why maintenance is not optional. If you don’t actively put in energy, decay takes over. This explains why gym habits slip, codebases rot, and relationships fade. The lesson is simple: if something matters, schedule the upkeep. Don’t wait for collapse to remind you that order isn’t free.

2. Critical Mass

Big shifts often feel sudden, but they’re the result of small pushes stacking up. A nuclear reaction doesn’t happen until enough fuel is gathered, and then it looks like magic. Building an audience, forming a habit, or launching a product works the same way. At first you see nothing, then suddenly you see everything. Most people quit before hitting that invisible threshold. Keep going, your critical mass might be closer than you think.

3. Feedback Loops

Biology runs on feedback. Some loops stabilize you, like how your body regulates temperature, while others amplify until they spiral out of control. Life is no different. Social media outrage is a runaway loop. A nightly journaling routine can be a stabilizing one. Recognizing which loops you’re in is half the game. If a loop keeps dragging you in the wrong direction, cut it early. If it’s healthy, reinforce it until it becomes automatic.

4. Limiting Factor

In chemistry, reactions stop not because of abundance but because one ingredient runs out. That’s the limiting reagent. In real life, growth is also constrained by the rarest input. A business might have funding but no skilled engineers. A student might have motivation but no clear study plan. Instead of trying to optimize everything, find your bottleneck. Fix that first, because until you do, nothing else moves forward.

5. Inertia

Physics teaches that objects at rest stay at rest, and objects in motion keep going. Humans aren’t much different. Starting a project is often harder than doing the project. But once you’re in motion, momentum carries you. That’s why the hardest part of going to the gym is putting on your shoes. Lower the barrier to starting and you’ll beat inertia. The trick isn’t willpower, it’s designing tiny starts that make momentum inevitable.

6. Natural Selection

Biology reminds us that only what works survives. Nature discards the inefficient, and so does the world. Careers, businesses, and even personal habits face the same filter. The ideas that adapt stick around, the rest fade. Instead of resisting change, evolve with it. Test small variations, keep what works, and let the rest die quietly. Survival, whether in ecosystems or economies, belongs to those who can learn and adjust fastest.

If you’ve ever heard Charlie Munger talk about decision-making, you’ll know he insists on building a “latticework of mental models.” One of the richest sources of these models is math—not the abstract equations you struggled with in school, but the patterns of growth, risk, and reality checks that quietly shape everything in daily life. In this post, we’ll look at a handful of math models you can actually use—and how they show up in real-world choices.

1. Compounding — The Quiet Superpower

Compounding is exponential growth in action. Money, habits, and even knowledge all compound. If you invest ₹10,000 at 12%, it doubles in about six years (rule of 72). If you learn one new coding skill every week, in a year you’re not just 52 steps ahead—you’re connecting skills, multiplying your options.

Real-life use: Start early, stay consistent, and avoid interruptions. A gym habit, a savings account, or a daily journal all get dramatically easier to maintain if you give them time to compound.

2. Power Laws — Focus on the Vital Few

The 80/20 rule is a power law in disguise: 20% of inputs often drive 80% of results. In business, a few customers often bring in most of the revenue. In studying, a few chapters carry most of the test weight.

Real-life use: Ask, What are the few things that really matter? Cut the trivial many. Spend 80% of your effort on the 20% of tasks that actually move the needle.

3. Regression to the Mean — Don’t Get Fooled by Extremes

When something swings to an extreme - an unusually good exam score, a lucky stock pick—it usually drifts back toward average next time. People often mistake extremes for new norms.

Real-life use: Don’t over-celebrate one win or despair over one failure. Look for trends, not outliers. That coding bug that mysteriously fixed itself? Don’t assume you’ve leveled up permanently—verify it.

4. Base Rates — Start With What Usually Happens

Before making a prediction, ask: what happens on average in similar situations? If most startups fail within 5 years, that’s your base rate. If 70% of job applicants don’t hear back, that’s the reality you start with.

Real-life use: Before betting on exceptions (“This stock will triple!”), ground yourself in the norm. If the base rate is against you, you need extraordinary evidence or a smarter plan.

5. Expected Value — Thinking in Probabilities

Not all wins and losses are equal. Expected Value (EV) = probability × payoff. A 10% chance of winning ₹10,000 is worth ₹1,000 in expectation. A guaranteed ₹900 may be better, depending on your risk appetite.

Real-life use: Apply EV thinking to career moves, not just gambling. A risky project with 30% chance of big rewards might still beat a “safe” option, if failure won’t ruin you.

6. Sensitivity Analysis — What Really Moves the Needle?

Change one variable at a time and see how the outcome shifts. In financial models, it’s interest rates or customer growth. In coding, it’s usually one performance bottleneck.

Real-life use: Don’t tweak everything at once. When debugging, change one input, test, then note the effect. Same with habits—if you want to sleep better, change caffeine first before overhauling your whole routine.