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As AI reshapes work, the disruption is not only structural but personal. Control becomes distant, understanding fades and professional identity shifts—leaving individuals navigating systems that still depend on them, yet no longer fully reflect their role or expertise.
As AI absorbs routine work, the layer where expertise is formed begins to thin. What disappears is not just execution, but the path to judgment—leaving organizations efficient on the surface, yet increasingly detached from how and why decisions are made.
As interfaces disappear, interaction shifts from navigation to intent. What feels like simplicity masks a deeper change: processes become invisible, decisions harder to trace and control increasingly abstract within systems that now act on our behalf.
For decades, digital networks have functioned as reactive infrastructures. They transmitted signals, responded to requests and waited for human input. Agency was clear: users acted, systems followed. Efficiency was measured in speed, bandwidth and latency.
For decades, the logic of telecommunications was almost uncontested. Faster was better. More bandwidth meant progress. Shannon’s Law framed intelligence as an engineering problem: how efficiently can information be transmitted from point A to point B?
