An eight-month study at a US tech company finds AI tools didn't reduce work but intensified it, as employees worked faster and took on a broader range 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 company-level evidence sits alongside reporting that coding-agent users may work longer even when they offload tasks, contributing to productivity pressure among executives and engineers. It challenges the simple equation of AI adoption with fewer hours of human work.
The broader coverage also finds a mixed employee outcome: AI can ease burnout for some workers while creating mental fatigue from intensive AI use. That makes workload design—not just tool uptake—central to the value proposition.
First-order effects
Employees at the studied company face a denser job: faster task completion is paired with a broader task range rather than a reduced workload.
Managers evaluating AI deployment cannot treat adoption or output speed alone as evidence that work has been eliminated; capacity is being reallocated into additional work.
Second-order effects
Employers may raise delivery expectations as AI accelerates individual tasks, putting pressure on teams to absorb more scope unless staffing and workload rules change.
AI vendors and internal platform teams will face greater demand to demonstrate useful-task outcomes and sustainable workflows, not merely usage or time saved.
Third-order effects
If this pattern persists, workplace AI will function less as a labor-saving tool than as a mechanism for work intensification, shifting the productivity debate toward job quality, fatigue, and who captures the extra capacity.
Organizations may need to distinguish automation that removes work from augmentation that expands it; otherwise apparent efficiency gains can conceal rising workforce strain.
The trend:Enterprise AI is moving from isolated task assistance toward a redefinition of performance expectations, with faster work often converted into more work rather than less.
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
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]
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…
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
“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…