#27 - Scaling chaos

One summer when I was still in my teens, I was hired for job by a company that tried to automate a tedious reporting process with a script. It felt like a smart move: a few hours of coding to save days of manual work. Making people's lives better, even if only a little, through technology!

The script worked flawlessly. The results... were a disaster. Things would fail silently, produce garbage data, and create more work than it saved. My first instinct was to blame the tool, to debug the code, to find a better library.

But the problem wasn't the script. The problem was that nobody truly understood the process I was automating. It was full of ghosts: a manual workflow full of hidden exceptions, unspoken rules, and subtle human interventions that no one admitted to. The script didn't fail because it was flawed; it failed because it was a perfect, ruthless mirror of a chaotic process. It simply executed a lack of nuanced understanding at speed.

Today, with the rise of AI, this lesson feels more urgent than ever. We are rushing to apply powerful new tools to workflows we barely comprehend, only to uncover the real story behind so many stalled AI efforts: flawless execution of flawed foundations. We see AI as a solution for inefficiency, but we forget that automation amplifies what is already there. If your process, or your understanding of it, is a mess, AI won’t fix it. It will just help you make a mess faster, creating high-speed chaos.

The real work isn't implementing the tool. It's achieving explicit clarity. The real value? Not in the code, but in the choreography of mapping steps, naming exceptions, exposing intent. Before you automate, you must first master the process. Because AI doesn’t solve your confusion. It just executes it at scale.