The article explores a common experience many professionals have had since introducing artificial intelligence tools into their workflows: rather than feeling lighter and faster, work often feels heavier, slower and more fragmented. The author explains that this isn’t because AI inherently makes tasks harder, but because it reveals existing fragilities and inefficiencies in how work actually gets done. Before AI, hidden manual coordination and unspoken fixes by humans masked underlying process weaknesses. When AI accelerates execution, these weaknesses become more visible and burdensome.
AI speeds up parts of work that are already structured, but it cannot fix poorly defined systems on its own. When workflows rely on undocumented decisions, individual judgment calls, or patchwork fixes by teammates, acceleration doesn’t help — it amplifies confusion and exposes bottlenecks. That’s why many people feel busier: the fast pace highlights unresolved dependencies instead of smoothing them out.
The article makes a deeper distinction between doing work and designing work. Previously, being quick to respond or knowing answers made someone valuable. With AI, those contributions matter less than designing reliable processes that work even when individuals aren’t present. Success with AI comes not from execution alone, but from structuring workflows so that AI can operate effectively without constant human patching.
Ultimately, the author argues that the feeling of “heavier” work is feedback, not failure. AI shows where friction and waiting still exist, prompting people to rethink their roles. The key question becomes not whether AI will replace jobs, but what types of waiting and inefficiencies a person eliminates. Those who focus on designing smooth flows and reducing dependency will benefit most from AI, while those who rely on outdated friction will feel more pressure as systems move faster.
