Business, tech, and the weird path between them.

I have always liked the point where imagination meets execution.

When I was a kid, I wanted my LEGO figures to move on their own. They didn't. So I picked up a camera and started making stop-motion films instead. They still didn't move on their own, but on film, they finally did.

It was not the solution I had imagined, but the problem was solved.

That probably says more about how I think than most lines on my CV ever could. I rarely take the most obvious route, and I am not particularly good at pretending otherwise. But I like finding ways that work, especially when the original plan turns out to be less realistic than expected.

I studied business administration, although I was never really drawn to the polished corporate version of business. I like business when it is about building something: turning vague ideas into structure, creating momentum, connecting people, shaping products, and making things less messy than they were before. That is also how I look at AI.

The funny images, impressive demos, and futuristic use cases are entertaining, and sometimes genuinely useful. But what fascinates me most is the economic layer underneath: who captures value, which business models survive, where productivity actually increases, what becomes cheaper, what becomes scarce, and which parts of the value chain quietly become more important.

This Cost of Intelligence blog is partly about that perspective. Not just AI as a technology trend, but AI as a shift in incentives, margins, workflows, companies, and markets. I am interested in what happens after the initial excitement: where the real businesses emerge, where the hype breaks, and where seemingly small technical changes create large economic consequences.

I have been an early adopter for as long as I can remember. I used the OpenAI Playground back when the models were still called Davinci and Curie, long before ChatGPT became part of everyday vocabulary. I have always liked playing with tools before everyone agrees they matter.

In general, I tend to think sideways. If you hand me a problem with the solution already fixed in your mind, I might not be the right person. I will probably get somewhere useful, but not necessarily by the route you expected.

Outside of that, you can pull me into a conversation with almost anything: economics, AI, football, classic cars, especially combustion engines built before everything became a touchscreen, video games, and Silicon Valley. The show, not the place.