All Quiet on the Algorithmic Front

Recently, approaching my mid-30s, I have become hyper-aware that my daily runs have been getting shorter, the effort feels heavier, and each mile seems harder than the last. Each breath, each step, is valuable and calculated. So when my running app steered me into a neighborhood I had never seen before.

At first I thought it was being “creative”. Then I remembered, “noooo, it’s just code”. The app noticed a bridge closure on my usual path and shunted me a few blocks east. That’s all an algorithm really is: a set of structured instructions that spits out a solution.

We tend to talk about algorithms like mysterious, all-encompassing cyber deities pulling unseen levers in the background while sitting atop a virtual mountain crafted in 1’s and 0’s. In reality, they’re closer to your grandma’s recipe card for banana cream pie. If you have eggs, do this. If you’re out of eggs, do that. If the oven’s broken, call for takeout. Computers don’t invent, they just follow recipe cards at inhuman speed.

But, what happens when recipes replace chefs? You may still end up full but you’ll never get surprised by a brilliant and unexpected dish.

The Greedy Choice

In All Quiet on the Western Front, Erich Maria Remarque describes the harrowing life of soldiers in the trenches with impossible decisions required to survive. Among the most dangerous roles were the runners: soldiers tasked with carrying mission critical messages through mud, gas, and barbed wire. A runner who thought only of the next step, or the most direct path forward, rarely survived. A straight line often led into a mine field, or machine gun fire.

Today, computer scientists would call this mistake a greedy choice: taking the immediate advantage without considering what lies beyond. That’s the essence of the Greedy Algorithm. This means that at every step of the problem, the algorithm chooses the best immediate option available, without considering the long-term consequences of that choice.

But runners knew better. They thought in detours. They judged when to crawl through a flooded trench, or when to zigzag across shell holes. It was slower in the moment, but it kept them alive and ensured the message reached its destination.

That’s the human difference. An algorithm says: “This move is optimal right now.” A runner says: “This move costs me time now, but it ensures survival, and success, later.”

The Fortune Teller’s Library

Not to age me any further, but I remember as a kid doing a summer reading challenge for the local library to win nosebleed seats for an upcoming Cardinals game. I vividly remember flipping through the bent, yellowed paper library card catalog drawer to find certain books that were on the reading list. These catalog drawers gave me information like title, author, subject, and most importantly the Call number (the code telling you exactly where on the shelves to find it).

The early days of online algorithms were like helpful librarians. In 2010, if you watched a video about the Art Deco era, YouTube recommended Mid-Century Modern art. If it was a newspaper and you read the classified ads section, you were nudged towards Sunday coupons. Predictable, almost quaint.

Today, those same systems behave less like librarians and more like fortune tellers analyzing your scroll speed, the time of day, and your purchase history like a palm reading. Each little interaction becomes an “interest signal,” thousands of them woven together, all in service of one thing: keeping you on the platform.

Algorithms are no longer about finding “what you might like.” It’s about predicting what will glue you to the feed right now, no matter the cost.

The Bytes & the Bees

Algorithms don’t just watch individuals, they watch us as groups. By scanning millions of people at once, they can identify which memes, trends, or influencer personalities will stick hardest. That’s how we’ve birthed oddly uniform, siloed hive minds.

One silo says daily cold-plunges double your productivity by “resetting” the nervous system, starts every argument with “Well, I heard on Joe Rogan’s podcast..”, and experiments with a carnivore diet. Another silo says Disney ruined the entire Star Wars universe, thinks socializing is spamming “KEKW” in the chat of any Twitch stream, and demands the final comment of the SubReddits they follow. These bundles of traits don’t arise naturally; they’re packaged, like fast food combo meals, by algorithms mining for stickiness.

The Dopamine Drive-Thru

The real danger isn’t that algorithms are inherently evil… it’s that they’re lazy. They choose the nearest answer, not the meaningful one. They solve for attention now, not connection later.

Grandma’s from-scratch recipes have become junk food recipes: engineered for taste, not nourishment. It’s like we’re attempting the dopamine version Super Size Me and eating nothing but a #1 Big Mac Meal. We’re always chewing, always full, but never nourished. And after a while, you forget what a real meal even tastes like.

Unlike code, humans can imagine better futures. We have always had the capability to accept short-term boredom, confusion, or challenge in order to cultivate taste, skill, and community. As a professor I didn’t only hand out easy wins, I assigned hard, unpopular studio assignments because they know what growth looks like.

That’s the piece algorithms miss: the ability to hope for something outside the pattern.

The Human Edge

For all their speed and scale, algorithms will can't fully escape the recipes we feed them. They remix our past, they scan our preferences, and they accelerate our choices, but they cannot step beyond the boundaries of the data we have already given them.

Humans, on the other hand, have always lived in the unknown. We improvise when the script fails. We adapt when the rules shift. We think on our feet because survival has always demanded it, from runners zigzagging across no man’s land to someone finding a new path on a run when the bridge is closed.

The truth is that AI only sees our world through the lens of what we choose to record and share. Reality develops outside the dataset, in the surprise, the happy mistake, the hard-earned insight. That is where human imagination thrives.

So while algorithms may guide us efficiently, humans will always hold the edge, not because we move faster, but because we can pause and invent entirely new directions. In the end, it is not the code that defines the story, it is the runner who decides where to run next.