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Task expansion. Because AI can fill in gaps in knowledge, workers increasingly stepped into responsibilities that previously belonged to others. Product managers and designers began writing code; researchers took on engineering tasks; and individuals across the organization attempted work they would have outsourced, deferred, or avoided entirely in the past.

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Blurred boundaries between work and non-work. Because AI made beginning a task so easy—it reduced the friction of facing a blank page or unknown starting point—workers slipped small amounts of work into moments that had previously been breaks. Many prompted AI during lunch, in meetings, or while waiting for a file to load. Some described sending a “quick last prompt” right before leaving their desk so that the AI could work while they stepped away.

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More multitasking. AI introduced a new rhythm in which workers managed several active threads at once: manually writing code while AI generated an alternative version, running multiple agents in parallel, or reviving long-deferred tasks because AI could “handle them” in the background. They did this, in part, because they felt they had a “partner” that could help them move through their workload.

While this sense of having a “partner” enabled a feeling of momentum, the reality was a continual switching of attention, frequent checking of AI outputs, and a growing number of open tasks. This created cognitive load and a sense of always juggling, even as the work felt productive.

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