Article
The Meaning of Life in the Age of Machines, Algorithms, and Artificial Intelligence
In my previous post, I reflected on what we need for a good life in an era increasingly shaped by artificial intelligence and automation. I concluded that it is meaning, today, tomorrow, and ten years from now. We need our lives and the world around us to have some sense of purpose, or at least for us to be on the path to finding it. When this sense of meaning disappears, it leaves behind an emptiness that most people find difficult to bear.
We typically find meaning in work, relationships, tasks, and hobbies. But what happens when most of these opportunities vanish? When machines and algorithms take over the majority of meaningful activities? When they perform most tasks better than we do? To what will we dedicate our time when the things that once gave our lives purpose become unnecessary? This emptiness arises not because we lack material things but because we lose what truly matters to us. It is one reason I value minimalism, keeping what matters and letting the rest go.
This feeling of emptiness is often referred to as anxiety.
The Phenomenon of Anxiety
I first became deeply interested in the concept of anxiety when I read Being and Time by Martin Heidegger during my university studies. It was challenging reading. In seminars, we dissected the text sentence by sentence. I had never encountered anything like it before, but the ideas this book offers are well worth the effort.
Heidegger describes anxiety as fundamental to existence, something that reveals the true nature of our being. Anxiety differs from ordinary fear. Fear always has a specific object. We fear illness, loss, or failure. Anxiety, however, has no specific object. In a state of anxiety, the world as a whole appears meaningless. Activities and relationships that we usually take for granted seem to lose their significance. This is not fear of something in the world; rather, it is a revelation of the fact that our being is our own responsibility, with no predetermined purpose.
Heidegger explains that anxiety confronts us with the state of Geworfenheit (thrownness), the realisation that we have been thrown into the world without our consent, without a clear guide on how to live within it. This recognition forces us to confront our freedom and responsibility, reminding us that no external framework provides the meaning we seek.
A key aspect of Heidegger’s conception of anxiety is that it grants us access to authentic being. When we recognise that our time is finite, we gain the opportunity to live on our own terms, rather than according to societal expectations. Anxiety, therefore, is not merely an uncomfortable state but a crucial moment of clarity in which we can reclaim the direction of our lives.
In the digital age, where algorithms and machines manage all practical matters, a unique challenge emerges. When everyday obligations that once distracted us disappear, we are left with only ourselves and one pressing question: What now?
In my view, we will stand at a crossroads. One path is to seek out new, meaningful activities. The other is to embrace anxiety and the questions and challenges it brings. Perhaps it is this second path that will ultimately lead us to a good and truly authentic life.
Ignoring these paths takes us somewhere too, just not where we would want to go. The question is how many people will follow other directions. I can imagine a future, a world where living a good life will not be that easy.
Continue reading for free
Enter your email to keep reading for free. This also subscribes you to my monthly newsletter. No spam, unsubscribe anytime.
Sources
Summary
Common questions on this article's topic
What is existential anxiety according to Heidegger?
What is the difference between anxiety and fear in philosophy?
What is Geworfenheit (thrownness)?
How could AI and automation cause a crisis of meaning?
Can existential anxiety be a positive experience?
How can we find meaning in the age of artificial intelligence?
If you have any thoughts, questions, or feedback, feel free to drop me a message at mail@richardgolian.com.
LinkedIn
Related articles
A few weeks ago I installed a small local AI model on my laptop that watches a live camera feed. I turned the webcam on in the dark, and in near total darkness it recognised me and the objects in the room. That such things exist, I have known for a long time. What opened my eyes was the accessibility. I installed it in one prompt, free, and it runs entirely on my machine, sending data nowhere.
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.
More articles
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.
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.