Why I Think Long-Term About Work, Money and Life

The development of artificial intelligence changes how we look at which paths in life make sense. Which studies and which profession to choose.

It was Thursday, 31 March 2022, when I handed over the last of my responsibilities to my colleagues, closed my laptop and went to the library. I left my job for several months and immersed myself in philosophy. And I am immensely grateful for it. Today I am, like most people, on a merry-go-round, and it is hard for me to stop and think about the fundamental questions of my own life.

It was September 2023. My life, my work, everything was completely different from now. It was simpler. I owned almost nothing. I needed to film an advert, I went up to a floor inside the company where the product I needed for that advert was, and there I noticed her for the first time. She laughs at me when I describe it like this, but at that moment the universe told me, that is her.

How and why I am gradually leaving social media and the big platforms. When I read that Meta is testing a new way to charge for organic posts that link outside its platform, it confirms how right I was to become steadily less dependent on the big platforms and on social media. It does not mean I have left them completely. It means that whether or not I stay in touch with my readers depends on them less and less, and today almost not at all.

One of my family members described a firm that has its data in several different tools, and its employees still join it by hand, in spreadsheets. That is the ordinary state of firms. When somebody tells such people not to do it by hand and to use AI, they will not understand. Most firms do not even have the basic connectors between the tools they use. These firms need help with AI transformation.

The same task, two models. Fable 5 against Opus 4.8. On paper Fable is the better model, with a larger context and stronger specs. And still it lost. Opus handled the task with a single round of checking for 721,000 tokens, while Fable needed nine rounds and burnt through 2.78 million tokens. The difference was not in the model, but in how I set the task. And I know it, because I measured it.

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 once wrote about building my own privacy-friendly analytics tool. It had bot detection from the first version, yet it was not enough. Direct visits took a strangely high share of my traffic. When someone claims that 20% of their visits are bots and 80% are humans, I used to think the same. Today I would say the opposite ratio is closer to the truth. This is how I got there.

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.

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.