Pulling an all-nighter because you got inspired right before bed. Forgetting to drink or eat. Missing a meeting because you’re too hyperfocused on your task. Being exhausted the rest of the day after being in the zone and super productive for 2-3 hours. If you’ve got ADHD like me, these all sound familiar. But if you’re neurotypical, they’re kinda weird. Well, good news: you’re not alone anymore. Bad news: this might make AI harder to scale in your company.

Berkeley Haas School of Business researchers found something interesting:

We discovered that AI tools didn’t reduce work, they consistently intensified it. In an eight-month study of how generative AI changed work habits at a U.S.-based technology company with about 200 employees, we found that 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. Importantly, the company did not mandate AI use (though it did offer enterprise subscriptions to commercially available AI tools). On their own initiative workers did more because AI made “doing more” feel possible, accessible, and in many cases intrinsically rewarding.

This really strikes a chord. I’m writing this post while also having Composer 1.5 fix the menu of my CMS and Claude Code convert a Python tool into a web app. On paper this all sounds great (look at all that time saved and extra productivity), but what it actually makes me think of is context switching, burnout, and sick leave.

Our research reveals the risks of letting work informally expand and accelerate: What looks like higher productivity in the short run can mask silent workload creep and growing cognitive strain as employees juggle multiple AI-enabled workflows. Because the extra effort is voluntary and often framed as enjoyable experimentation, it is easy for leaders to overlook how much additional load workers are carrying.

So what should you do? Aruna Ranganathan and Xingqi Maggie Ye recommend adopting an “AI practice”:

a set of intentional norms and routines that structure how AI is used, when it is appropriate to stop, and how work should and should not expand in response to newfound capability. Without such practices, the natural tendency of AI-assisted work is not contraction but intensification, with implications for burnout, decision quality, and long-term sustainability.

Source: hbr.org (via simonwillison.net)