Jörgen Kihlgren

We are making the AI and entry-level jobs conversation too simple. The easy debate is whether AI will wipe out junior roles. That is where the headlines go because it is dramatic and clean. But work rarely changes in clean lines. The more important issue is what happens to the development path underneath those jobs. Junior people do not become senior because a title changes. They become senior because they spend years building judgment. They sit with messy research. They rewrite drafts. They clean up data. They prepare reports. They fix the deck again. They watch what gets challenged in meetings. They learn which details matter and which ones are noise. A lot of that work gets dismissed as grunt work, but it was doing something important. It was the bottom rung. It gave people repetition, context, pattern recognition, and taste. It taught them what good work actually looks like before they were expected to lead it. Now AI can remove a lot of that. In many cases, that is a good thing. Nobody needs to protect low-value administrative work just because it used to be part of the job. But leaders need to be honest about the tradeoff. When the task disappears, the learning can disappear with it. That is what makes this moment so interesting. The companies using or exploring AI are not necessarily running away from junior talent. Many are moving toward it because AI gives early-career workers more leverage. The grunt work can go to the machine. More of the thinking can go to the person. That sounds like progress. But it also creates a new problem. A junior employee can move faster than ever and still not get better. If AI does the work before they understand the work, speed becomes a trap. The output improves on paper while judgment gets weaker underneath. That is why the Gen Z data matters. Younger workers are already saying they rely on AI too much. Some believe it is weakening their skills. I would not brush that aside as fear or resistance. That sounds like an early warning from the people closest to the change. There is a big difference between using AI well and becoming dependent on it. Using AI well means you can question the output, improve it, explain it, and still own the decision. Dependence means the tool becomes your first move, your shortcut, and eventually your substitute for thinking. Leaders do not need to preserve every old task. They need to preserve the capability those tasks used to build. Remove low-value work, but redesign how people learn. Keep humans close to judgment. Build AI fluency without letting it become dependency. The question is not just whether AI replaces people. The better question is what kind of people your system is building.

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