One of the promises of AI is that it can reduce workloads so employees can focus more on higher-value and more engaging tasks. But according to new research, AI tools don’t reduce work, they consistently intensify it: In the study, employees worked at a faster pace, took on a broader scope of tasks, and extended work into more hours of the day, often without being asked to do so. That may sound like a win, but it’s not quite so simple. These changes can be unsustainable, leading to workload creep, cognitive fatigue, burnout, and weakened decision-making. The productivity surge enjoyed at the beginning can give way to lower quality work, turnover, and other problems. To correct for this, companies need to adopt an “AI practice,” or a set of norms and standards around AI use that can include intentional pauses, sequencing work, and adding more human grounding.
AI Doesn’t Reduce Work—It Intensifies It
Aruna Ranganathan and Xingqi Maggie Ye from Berkeley Haas School of Business report initial findings in the HBR from their April to December 2025 study of 200 employees at a …
What happens when AI replaces the parts of work people love?
We’re using AI to move faster. I’m not yet convinced we’re using it to work better.
Doing nothing at work
Many engineers should be doing less work. I don’t necessarily mean producing less code or fewer changes, but literally working fewer hours in the day. When they do work, they should be working at a slower pace. I like to aim to be running at 80% utilization by default: unless I have a high-pressure project going on, I spend 20% of my workday away from the computer.
How does AI impact skill formation?
Two days ago, the Anthropic Fellows program released a paper called How AI Impacts Skill Formation. Like other papers on AI before it, this one is being treated as proof that AI makes you slower and dumber. Does it prove that?
Labor market impacts of AI: A new measure and early evidence
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
Why AI hasn’t replaced software engineers, and won’t
Arvind Narayanan and Sayash Kappor take on the question of AI job losses through the lens of a profession that is uniquely suited to AI disruption - software engineering. In …
AI fatigue is real and nobody talks about it | Siddhant Khare
You're using AI to be more productive. So why are you more exhausted than ever? The paradox every engineer needs to confront.
I don't know if I like working at higher levels of abstraction
AI tools push us to higher abstraction. I'm not sure I like what that costs us.
What AI coding costs you | Tom Wojcik
What's the effect of the prolonged AI usage among coders and is it tracked correctly, if it all?
I work, I think? - Annotated
How AI may quietly dismantle the feedback loop that turns inexperienced people into competent ones, and why my work matters to me.
AI and the Fall? of the Creative Class
America is Losing Hundreds of Thousands of Jobs in Media, Film & the Arts. How Much is AI to Blame?
Should You Use AI for a Task? Here’s a Simple Way to Decide - Schneier on Security
This essay originally appeared in The Guardian. I teach public policy at the Harvard Kennedy School and the Munk School at the University of Toronto. And it will come as no surprise to you that my students regularly use AI to complete their writing assignments. Doing so is a waste of their tuition money. But if their entire career is going to include AI writing assistants, why shouldn’t they embrace their future? The best way I’ve found to explain the dilemma comes from the AI researcher Daniel Meissler: it’s the difference between work and the gym...
Is AI causing a repeat of Frontend’s Lost Decade? | Mastro Blog
AI is doing to programming what framework-brain did to the frontend before. Deskilling, or just working at a higher level of abstraction?
Women Who Use AI Seen As Incompetent; Men Who Use AI Seen As Pragmatic
A 2026 study found that when women and men use AI tools to create identical resumes, evaluators view the women as less competent, while crediting the men with initiative.
Andrew Ng: The Biggest Opportunities in AI Aren't Where You Think
Harness Engineering for Self-Improvement
The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965), where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. Yudkowsky (2008) used the phrase “recursive self-improvement” for a specific feedback loop: an AI uses its current intelligence to improve the cognitive machinery that produces its intelligence. This feedback loop in modern AI may indicate the model rewriting its own weights directly, or more broadly the model improves the training pipeline and the deployment system, which in turn enables a better successor model with improved performance across economically valuable tasks. The speed of research development in AI has been shown to drastically accelerated in frontier labs (Anthropic; OpenAI).