AI Is Making Us Busier — Not Better
There is a pattern that repeats every time a new productivity technology arrives at the office. A tool promises to eliminate the dull, slow parts of our jobs. Enthusiasm builds around all the deeper, higher-value work we will finally have time for. And then, almost without fail, we end up more overwhelmed than we were before — not less.
Cal Newport, author of Deep Work, has been tracking this dynamic for years, and he recently raised a pointed concern: we may be walking into the same trap with AI.
The cycle Newport describes tends to unfold in three familiar steps:
A new technology promises to speed up annoying, time-consuming tasks.
Workers and organisations get excited about reclaiming time for high-value output.
Everyone ends up busier than before — without a corresponding increase in meaningful work.
It happened with the rise of email. What started as a faster alternative to fax machines and voicemail quickly transformed the workday into a constant stream of back-and-forth messaging. Each individual email was faster to send, but the volume exploded — and the quality of deep, focused thinking quietly declined.
What the data actually shows
ActivTrak, a workplace analytics company, tracked the digital behaviour of over 160,000 workers across more than 1,000 employers. Crucially, they followed individual AI users for six months both before and after they adopted these tools — giving the study an unusual level of before-and-after clarity.
The findings are uncomfortable. After adopting AI, workers roughly doubled the time they spent on email, messaging, and chat applications. Their use of business-management software jumped by nearly 94%. AI didn't slow activity down — it sped everything up.
More activity is not the same thing as better output. We have confused the two before, and it cost us.
The one category that did not grow was focused, uninterrupted work — the kind of sustained concentration required for solving complex problems, writing clearly, and thinking strategically. Time spent on deep work actually fell by 9% among AI users, compared to virtually no change among non-users.
In other words: AI is accelerating the shallow end of our jobs while quietly eroding the deep end.
Why this keeps happening
Berkeley professor Aruna Ranganathan, quoted in the Wall Street Journal piece, offers an illuminating explanation: AI makes additional tasks feel easy and accessible, which creates a powerful psychological sense of momentum. When tasks feel effortless, we take on more of them — without stepping back to ask whether they're worth doing at all.
This is almost identical to what happened with email. The problem was never that email was inefficient. It was that its low friction made it irresistible to overuse. Now AI may be doing the same thing, but for a wider range of tasks: drafting memos, bouncing ideas off a chatbot, iteratively refining slide decks, spinning up agents to run tasks in parallel. All of it feels productive. Much of it may not be.
Newport puts the essential question bluntly: are we sure we are accelerating the right parts of our jobs?
What this means in practice
The risk isn't that AI is bad at its job. The risk is that it's very good at a particular kind of job — fast, self-contained, low-stakes tasks — and that success at those tasks creates a seductive feeling of forward motion that substitutes for deeper work rather than enabling it.
For individuals, this is worth taking seriously as a personal workflow question. Is AI helping you protect and expand your time for focused thinking? Or is it filling your calendar with faster versions of the same shallow tasks?
For organisations, it is a strategic question disguised as a productivity one. If AI adoption is being measured primarily by activity levels — emails processed, documents generated, tickets closed — the metric itself may be obscuring a slow erosion of the output that actually moves the needle.
History gives us reason for humility here. Every wave of office technology has promised us more time for meaningful work. We are still waiting. Before we assume AI is different, it's worth looking honestly at what the early evidence suggests — and course-correcting while we still can.
SOURCE
The pattern and data in this post draw on Newport's newsletter analysis and a Wall Street Journal article ("AI Isn't Lightening Workloads. It's Making Them More Intense"), which cites original research by workforce analytics firm ActivTrak.
Sources: Cal Newport's newsletter; Wall Street Journal; ActivTrak research (164,000+ workers, 2024–2025)