The Algorithmic Hive: Why You’re Not As In Control As You Think

In 2020, a teenager posted a TikTok dance that turned an obscure pop song into a global hit. Within weeks, millions had copied the moves, the song shot up the charts, and the artist’s streaming numbers went through the roof.

This wasn’t magic. It wasn’t even marketing. It was an algorithm.

A few lines of code decided what got seen. And the rest of us—users, creators, critics—fell into line. Welcome to the hive.

Algorithms: From Tools to Organisers

We like to think we run the show and that we use technology, but more often than not, it’s actually using us.

Algorithms—those rule-based instructions driving your news feed, Netflix queue, or trading app—aren’t just suggestions. They’re directing attention, influencing behaviour, shaping culture. They are, quite literally, reorganising humanity.

Not through some grand conspiracy, but via feedback loops. They observe what you do, then give you more of it. You click, they learn. You linger, they optimise. And like an ant colony responding to pheromones, individual actions build up to emergent patterns—virality, panic, financial chaos, political bubbles.

It’s biology meets big data, minus the immune system.

We’ve Seen This Before—In Nature

Biologists refer to it as emergence: complex outcomes emerging from simple rules. Birds flock. Neurons fire. Ants build superstructures without plans. No one’s in charge, yet everything functions (until it doesn’t).

Algorithms operate in eerily similar ways. They adapt, self-correct, and scale. However, unlike biological systems, they lack evolutionary safeguards—no negative feedback loops, no death-by-mistake. They simply respond faster and with greater intensity, sometimes useful, often not.

In 2010, the stock market plummeted 1,000 points in just 10 minutes, a sudden event now called the Flash Crash. It wasn’t caused by war, politics, or human error—rather, it was high-frequency trading algorithms bouncing off each other in a feedback frenzy. No one could halt it because there was no one truly in control.

This is what occurs when the system is too quick to control and too complicated to foresee.

Algorithms Don’t Just Predict Us—They Change Us

Let’s talk TikTok again. The algorithm doesn’t just guess what you like—it teaches you what to like. It warps taste, rewires attention, and shapes cultural norms. That’s why teens across the world can develop identical behaviours—speech patterns, tics, even mental health symptoms—without ever meeting.

It’s called algorithmic social contagion. We used to adopt behaviours from our social circles. Now we catch them from For You pages.

Algorithms determine what’s normal, trending, and true. If that doesn’t seem like the role of culture, religion, or government—congratulations, you’re beginning to understand it.

The Illusion of Agency

You might think: “But I choose what I click!” Sure. But your choices are curated. Your ‘free will’ operates within a system designed to shape it. You’re still in the driver’s seat, but the road was built by someone else, and the signs are algorithmically updated in real time to steer you toward where you're most likely to spend, scroll, or stay.

The result? A society subtly steered not by ideology or intention—but by engagement metrics.

And no, this isn’t a tinfoil hat scenario. It’s just capitalism with better math.

So What Now? Biology Might Offer Clues

If we’ve built a system that mimics biology, maybe biology can help us regulate it.

Biological systems depend on negative feedback loops to keep stable. You sweat when you get too hot. You blink when your eyes feel dry. If not controlled, biological processes can spiral into problems—think cancer or cytokine storms.

The same applies to algorithms. Without checks, they tend to spiral: outrage fuels more outrage, echo chambers turn into echo prisons, and a single viral post can spark mass hysteria—or a run on the banks.

What we need are digital immune systems. Checks and balances. Meta-algorithms that slow things down, introduce friction, reward diversity, and punish virality for virality’s sake.

Some platforms are trying. Most aren’t.

The Ethics of Outsourcing Agency

As these systems develop, we’re faced with some tricky philosophical questions. Who is responsible for what the algorithm decides? Is it the programmer, the platform, or the user? And what occurs when the system learns things no human can fully explain?

Do we still have accountability? Or just plausible deniability in code?

There’s something unsettling about systems that understand us better than we understand ourselves—and then profit from that imbalance.

The Hive Is Already Here

Algorithms aren’t coming for us. They’re already here. Quiet. Ubiquitous. Normalised.

They organise your inbox, playlists, and job applications. They influence elections, markets, and moods. They shape how teenagers think about gender, how investors respond to news, and how pandemics are understood (or misunderstood).

They are the new infrastructure of human life—except they weren’t designed with humanity in mind.

If we want a future where humans still matter, we’ll need to design systems that remember what it means to be human. That prioritise understanding over engagement. Nuance over noise.

We built the hive. But we don’t have to be drones.