An eight-month 2025 study at a US tech company: AI tools didn't reduce work but intensified it; employees worked faster, longer, and did a bigger scope of tasks
Right now, many companies are worried about how to get more employees to use AI. After all, the promise of AI reducing the burden of some work …
Harvard Business Review
Context & Ripple Effects
This finding sits alongside reports of coding-agent users working longer hours and a broader push to embed AI into daily workloads. It adds workplace evidence to the gap between AI adoption and a simpler promise of less work.
Employees at the studied company face a faster work cadence and a wider task remit, rather than a reduced volume of work.
For managers, AI use cannot be treated as proof of workload relief; output expectations and task assignment determine whether efficiency becomes time savings.
Second-order effects
Organizations rolling out AI may need to assess workload, quality control, and employee capacity alongside adoption metrics, especially where faster production invites more assignments.
The result reinforces concerns seen among coding-agent users working longer hours, making sustained productivity claims more contingent on whether employers protect time savings.
Third-order effects
If employers consistently convert AI-enabled speed into additional scope, workplace AI will function chiefly as work intensification rather than labor-saving automation.
That would shift the competition from deploying tools to designing jobs and performance systems that can capture productivity without exceeding cognitive capacity.
The trend:Enterprise AI is becoming a work-design issue: gains in task speed are increasingly being translated into higher output expectations rather than shorter or lighter workdays.
Interesting research in HBR today about how the productivity boost you can get from AI tools can lead to burnout or general metal exhaustion, something I've noticed in my own work https://simonwillison.net/...
A corporate position that workers should “just use AI to do stuff” has never been enough. AI use in companies is a leadership problem that involves answering fundamental questions about what people should do with their time, how work is organized, and how to center people in work
“PMs 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.” — You mean they delved into areas they were incompetent in and had no bus…
Whatever the productivity gains promised by LLMs, they result in heavier workloads—and that leads to workers experiencing “cognitive fatigue, burnout, and weakened decision-making.” — All this from the notoriously pro-worker rag [checks notes] Harvard Business Review: hbr.org/2…
There's a meta-point here, which is key: Commercial AI tools are built for bosses. It's very obvious, and very simple. This manifests in every part of their design and implementation and use, and it's no wonder they cause burnout. [embedded post]
very good piece on how workers use LLMs and how it changes their work patterns. I've noticed some of these patterns myself working in software engineering
I think this is super interesting, matches all the qualitative interviews I've had with developers on this, and continues to show that we cannot evaluate the impact of tools on people with a metric of “production” alone. Psychological factors are always central! — hbr.org/2026…