Critical Thinking and Confirmation Bias: The Habit of Just Consuming

I have conducted roughly one hundred and fifty practical interviews over the past four years. Fifty for data specialist roles. A hundred for advertising and performance marketing specialists. Almost every one of them involved sitting down with a candidate over a practical task, something close to a real problem we actually need to solve at the company. Not theory. Not trivia. Applied problem-solving. Over time, I started noticing a pattern.

Before you can teach AI to understand anything, you need to see what it is hiding from you.

What is artificial intelligence? In plain terms, it is applied mathematics that predicts the most probable next word in a sequence. It does not think, and it does not understand. When we hear the phrase, many people imagine something mysterious, something that thinks, something that understands. It does not. I work with AI every day, and the more I use it, the clearer the truth becomes: it predicts what the next word should be. That is the entire mechanism.

I have Heidegger and my notebook beside me. I am asking where all of this is heading, where artificial intelligence is taking us.

Seventy per cent. That is where the first AI output begins, even when you give it the full company context and the best examples from the past. We are talking about the kind of output that cannot be defined programmatically. It is more complex. Often it is creative work. On one repeated type of output I reached eighty per cent within a week. Every further percentage point is harder than the one before.

For a long time we treated the internet as the main road. The place where work and relationships happen. Yet most of what we see on it today is, or soon will be, AI-generated: text, images, profiles and comments. The internet is turning into an online game full of bots, where you cannot be sure that a human is on the other side of anything. So I ask: was the online world the main road, or only a temporary detour that part of us will return from, back offline?

A few days ago I interviewed a senior marketer. An experienced man, years of practice. I asked him about AI. He said he barely uses it. He had one bad experience with the output and decided he was too senior for it to add value when it is not perfect. I know the other side too: professionals who automate everything that can be automated.

Europe does not have the capacity to face a full-scale, mass drone war of the kind we see in Ukraine. Three dependencies weaken it: China supplies the physical material for defence systems, the United States supplies capabilities Europe does not have, and twenty-seven states cannot agree how fast, or who pays. Rearmament plans exist, but they are being carried out slowly.

AI produces the graphic, the newsletter and the product page faster than a person. What is left for the one who used to do it is the judgement, knowing whether the output is good. But most people have worse judgement than AI. And whoever cannot judge quality cannot delegate either. How do you tell whether yours is the judgement a company relies on, or the kind it can replace?

In April, in the first part of this series, I wrote about an AI prediction system I had started building on my own machine. At the time the software was a few hours old and the prediction record was empty. The record since then has shown one thing: the system does not yet understand the market it is being asked to forecast. It can pull macro context, book value, earnings. But it cannot put those together into something that helps it understand the price.

Prague, 13 May 2026. On my way to work I started thinking about something that stayed with me for days. If most routine work on a computer disappears in the next ten years, and a large share of repetitive manual work disappears with it, what happens to the flow of money? Who pays whom for what? Which economic layers will exist, how large will they be, and what relationships will run between them? This is the six-layer map I sketched as an answer.

I am building an AI system to predict the S&P 500. It runs on my own machine, uses free public data (yfinance, FRED, the Shiller dataset), and grades every forecast against reality. This series documents the build itself: the decisions, the methodology, the mistakes. What I will eventually share from the running system is a separate question, and an honest one.

Yesterday I could not tear myself away from the computer. When I lifted my head, it was half past eight in the evening. I had been sitting alone upstairs for about three hours.

Four days in Catalonia. No computer, no AI, almost no social media. I bought this notebook so that I could write down what I would think about, and what I would come across and learn on the trip.