This is basically Covey's "sharpen the saw" for knowledge workers. [1]
When you're cutting trees, sharpening the saw looks like you're not working. When you're doing software or organizational work, figuring out what actually matters can also look like you're not working.
This resonates a lot if you’ve ever worked in system integration.
In practice, most of the complexity comes exactly from what’s described here: every system has a rich internal model, but the moment data crosses a boundary, everything degrades into strings, schemas, and implicit contracts.
You end up rebuilding semantics over and over again (validation, mapping, enrichment), and a lot of failures only show up at runtime.
I’m skeptical about “one model to rule them all”, but I strongly agree that losing semantics at system boundaries is the core problem.
I sometimes think the same, but look at it this way: you’re an IT worker—probably a specialist, at least a bit of a nerd in something. The days when we were seen as “wizards” are gone, sure. Right now a lot of people in business, and at the interface between business and IT, are riding a wave of confidence because of AI.
But the difference is: you’re a critical thinker. They’re just using the tool at face value.
Anyone can ask AI: “write me this code.” But you understand how things actually work in production—trade-offs, edge cases, long-term consequences. Thats the real leverage.
So use AI differently. Don’t just generate code, ask better questions:
How does the business actually work? Why is this process done this way? What are we optimizing for? In my perspective it's the normies knowledge that's at risk.
A critical thinker with technical skills will have the edge and should get far more out of AI than someone who just feels empowered by it.
No worries. You are in the right place. This is how others feel as well and how software engineering will feel for new generations so yeah the bicycle comparison fits well.
sounds unrealistic to me. For military and industrial use cases yes but I think we are at least 5 probably 10 and more years away from that moment. I think there is not enough data, and hardware is not software so the scaling will be more difficult and use cases are so wide that I can not imagine a humanoid cooking and mowing the lawn and repairing the roof. Look how long the autonomous driving takes and this is just moving a car.
every day waking up, reading hn and thinking "This new AI xy is insane - it will change everything" then start my daily work in low code integration and thinking "How long will this last?