How algorithms rewired us, without us even noticing

When was the last time you decided what to watch, without it being “recommended content”?

Everyone has heard of the algorithm. It’s the thing that “recommends content.” It knows.

But what is an algorithm, really?

An algorithm is a sequence of steps that leads to the solution of a problem.
Instructions that tell you what to do, step by step.

Those steps must be:

• clear (so they can’t be interpreted in multiple ways),
• finite (so they don’t go on forever),
• and effective (so they actually solve a specific task).

A real-life example

Imagine you’re in a supermarket and you want to find the cheapest juice.

Your “algorithm for finding the cheapest juice” could look like this:

1. Go to the juice aisle.
2. Look at the price of the first juice and remember it.
3. Look at the next juice.
4. If its price is lower than the one you remembered—remember the new, lower price.
5. Repeat steps 3 and 4 until you’ve checked all the juices.
6. In the end – take the one with the lowest price.

That’s an algorithm!
You’ve probably used it before, you just didn’t call it that.

Computers don’t understand “roughly” or “approximately.”
You have to tell them everything exactly and precisely. That’s why algorithms in programming are written as clearly defined sequences of commands that tell the computer what to do.

For example, if you want a computer to sort a list of numbers from smallest to largest, it will use a sorting algorithm, like bubble sort, quick sort, and others. Each of these algorithms reaches the same goal in a different way.

Algorithms aren’t just for programmers

They’re everywhere around us, in the kitchen, in traffic, in sports, in education.

A good algorithm is one that solves a problem quickly, efficiently, and with as few errors as possible.

Artificial intelligence, search engines, recommendations, all of that runs on smart algorithms.

When we mention algorithms, most people immediately think of social networks like Instagram, TikTok, or YouTube.

They use algorithms that try to understand what interests you, and then show you exactly that kind of content.

These algorithms track how you use the app:

• What you like
• How long you watch a video or look at an image
• What you comment on
• Which profiles you follow
• What you send to others
• What you search for

Based on all of this, they build your digital interest profile.

When an app decides what to show you, it uses a combination of:

• Relevance (is it similar to what you like?)
• Popularity (how many people are watching/liking it)
• Freshness (is the content new?)
• Shared interests (with people you follow)

What’s interesting is that the algorithm constantly changes as you use the app.

For example:

• If today you watched more travel videos, you’ll soon see even more of them.
• If you skip sports clips, the algorithm “understands” that and shows fewer of them.

There are also some hidden factors that affect your feed, such as:

• Device type and phone language (local content is often shown more)
• Time of posting (greater reach when more people are online)
• Interactions with specific people (if you often like one person’s posts, you’ll see them more)

From a technical perspective:

TikTok uses machine learning, algorithms analyze massive amounts of behavioral data and learn to predict what you’ll like next.
Instagram (under Meta) uses a similar system, but combined with multiple content types (photos, Reels, Stories, IGTV, etc.).

All of these algorithms work in real time and rely on a huge number of so-called behavioral signals.

At the beginning, the algorithm observes your behavior:

• what you watch longer,
• what you like,
• what you react to.

But that’s only the first step. Over time, it doesn’t just show you what you like, it starts pushing what it wants you to like.

TikTok, Instagram, YouTube, and others have a simple goal: keep you there as long as possible.

To do that, they use mechanisms such as:

• emotional triggers (anger, shock, laughter),
• controversy (so you’ll comment),
• trends (so you don’t miss “what’s new”),
• personalized content (so you feel “seen”).

How this looks in practice:

• The algorithm shows you content that isn’t necessarily your choice, but is known to provoke reactions (e.g., a provocative video).
• You watch it because it intrigues or shocks you.
• The system concludes: “Aha, they stayed, give them more of this.”
• Next time, that kind of content grabs your attention even faster.
• Over time, your taste changes, you’re not getting what you like, you’re learning to like what you’re constantly given.

This system leads to:

• habituation to certain types of content (e.g., negative news, conspiracy theories, drama),
• being trapped in “bubbles”, you only see content that confirms your worldview,
• gradual redirection of interests, often without you noticing.

Why every second of your attention matters to them:

• more ads,
• more data about you,
• more profit.

You are not the user, you are the product.
Your attention is what’s being sold. That’s the main reason the product is free.

Where’s the line between useful and dangerous?

The line is where the algorithm stops serving you, and you unconsciously start serving it.

In other words, if an algorithm helps you explore what you genuinely like, that’s useful.
If it drags you into content that doesn’t benefit you, steals your time, but brings clicks to the platformm that’s manipulation.

How do you know you’ve crossed that line?

• You didn’t plan to stay online, but hours passed
• You watch things that annoy you, yet you don’t stop
• You feel like the algorithm “reads your mind”
• You feel drained, but keep scrolling

The real danger lies in manipulation

Algorithms choose for us, and when they choose news, products, and even political views, they exert a powerful (and often invisible) influence.

How algorithms choose news

Goal: show you the news you’re most likely to click on

Not necessarily what’s most accurate or important, but content that:

• has a strong headline (emotional, shocking),
• aligns with your past clicks,
• provokes reactions (comments, shares, anger).

Problem: If you once clicked on a conspiracy theory or sensational story, the algorithm may keep feeding you similar content, because it “works.”

How algorithms choose products

Goal: make you buy, more, more often

They track every click, search, and pause on a product.
They match you with people who behave similarly.
They show you products that “people like you” bought.
They push “personalized discounts” and “only a few left in stock” to trigger FOMO (fear of missing out).

Problem: You don’t buy because you need it, but because it’s constantly in front of you and feels “made just for you.”

How algorithms influence political views

Platform goal: keep you engaged, not informed

• Like a post with a certain stance → you’ll see more of that stance
• Over time, you’re exposed to only one side and hear fewer opposing views
• More anger, outrage, and certainty → more engagement → more time on the platform

Problem: Instead of forming opinions based on facts, the algorithm pushes you toward the stance that triggers the strongest emotions, polarizing society.

Conclusion

Algorithms are like a coin with two sides.

On one side, they save time, match interests, and personalize the world.
On the other, they quietly decide for us, shaping what we think and what we want.

The problem is that we often see only the side we like.
And the other one… feels invisible, until it might already be too late.

BONUS RECOMMENDATION →→→ 🎁

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Watch carefully.
Stay curious.