Predictable Society: Living in The Age of Algorithms

I was no longer surprised when Netflix suggested a few good movies for my weekend. After years of watching the same kinds of genre, I think it became easier for the platform to decide what suited me best. For a moment, it even felt personal, like the algorithm somehow understood me.

Personalized service is nothing new today. But two decades ago, this level of prediction was something most people never experienced.

At the beginning of the new millennium, around the year 2000, only about 6% of the world’s population used the internet. By 2025, that number had reached roughly 74%. Yet before all of this, people were still perfectly capable of making choices about music, movies, books, even identity without digital recommendations constantly guiding them.

I remember the 1990s vividly. MTV, VH1, radio stations, magazines, our friends, and sometimes even the person we had a crush on shaped our taste in music. I once bought a cassette compilation of The Simpsons songs simply because I heard “Do the Bartman” on television. In the early 2000s, I could spend almost an hour browsing movies inside Blockbuster store, walking shelf by shelf with no guarantee I would find something good. There was uncertainty in choosing, and somehow that uncertainty was normal.

A 2007 study by Pew Research Center showed that more people were beginning to use the internet for product research including music, mobile phones, and real estate. It marked a transition, choices that were once shaped mostly by social circles, institutions, and mass culture slowly became influenced by personalized digital systems.

In the past, shopkeepers helped us decide what to buy. Librarians recommended books. People trusted critics, newspapers, or communities. Information moved slowly. Choices were limited. Uncertainty was expected.

Today, prediction comes from data extraction.

Every click. Every location shared. Every movie watched. Every website visited.

And the logic behind this system strangely resembles something economists call Bayesian thinking.

Now, “Bayesian” sounds intimidating, like another theory full of impossible graphs and equations. But honestly, humans use Bayesian thinking all the time without realizing it.

Imagine this simple case.

“A” has a friend who is always late for weekend lunch gatherings. Before the meeting even starts, “A” already assumes that friend will probably arrive late. Then the friend calls and says, “Sorry, traffic is terrible, I’ll be late.”

That new information strengthens the original assumption. Unfortunately, in this story, the late friend is me.

Dating works the same way. Someone does not text after a date, and suddenly we start updating our conclusions:

“He probably isn’t into me” . Remember that movie with the same title, “He’s Just Not That Into You” sounds like a Bayesian updated conclusion.

At its core, Bayesian thinking is actually simple:

• What did I believe before?
• What new evidence do I have now?
• How much should I change my mind?

In economics, this approach matters because economists understand there is no such thing as perfect certainty. Inflation changes. Markets panic. Consumers behave irrationally. Predictions are constantly updated because new information constantly appears.

But now this logic no longer belongs only to economists. It powers algorithms.

Platforms learn from us continuously. The more data we generate, the more predictable we become. Algorithms shape what we watch, buy, believe, fear, and even desire. Our attention is no longer simply observed, it is engineered.

And this raises an uncomfortable question:

What happens when uncertainty is no longer accepted and continued monetized?

Modern society often tells us we are being celebrated as unique individuals. Everything feels personalized: playlists, advertisements, shopping suggestions, political content, dating apps. But are we truly being understood as individuals?

Or are we simply being grouped into predictable behavioral patterns?

The more predictable we are, the easier we are to influence.

In many ways, today’s digital economy does not profit from who we truly are, but from how accurately we can be anticipated. Human behavior has become a marketable asset. Attention is extracted, analyzed, and sold.

We are not only users anymore. We are data.

We once lived in a society that accepted uncertainty. Now we live in one that profits from prediction.

And somewhere between convenience and control, we became the product.