The Setup: AI Was Supposed to Save Us Time
Imagine this: You’re finishing up a long week, dreaming of dinner with family, but your to-do list just keeps growing. We were told AI was going to fix that. All around us, headlines promised freedom, no more slogging through boilerplate or wrangling dependency updates. The dream was simple: let the bots handle the grunt work so we could focus on the interesting stuff and actually log off at a reasonable hour. On paper, it sounded perfect. Who would say no to breathing room and fewer routine tickets?
What Really Happened: More Agents, More Chaos
Here’s what those blue-sky predictions missed: once AI tools landed, work changed, but not always for the better. Yes, they took care of some of the repetitive pieces. But now, instead of grinding on a couple of major features, you end up babysitting a whole crew of agents, each one demanding your attention, one after another, never on your schedule. It’s like trying to play conductor with an orchestra that keeps adding new instruments mid-song. The result? A workday splintered into endless context switches, leaving your brain feeling like mashed potatoes by evening.
The Numbers Don’t Lie
You can see this shift in the stats. Take this engineer’s GitHub contributions: in 2020, they logged 2,711 commits. That dropped off for a few years. Then, after AI joined the workflow, the numbers bounced back, by October 27, 2026, there were 2,102 contributions and still climbing. At first glance, it looks like a productivity jackpot. But zoom in and it’s clear: the work isn’t just more, it’s different. Instead of diving deep on a handful of game-changing features, the days fill up with bite-sized tasks, issues, enhancements, reviews. AI made it easier to say yes to extra work, so everyone just... did. Before you know it, "taking on more" becomes business as usual.
Bursts, Burnout, and the Never-Ending Sprint
It doesn’t help that productivity now comes in wild bursts. January and February 2026, for example, saw a surge of contributions as shiny new AI models rolled out. That excitement didn’t last. After riding the wave, the engineer pulled back as fatigue set in, with output winding down from March to June. It’s a familiar story: the tools promise to make us faster and, for a while, they do. But by expanding the pool of what’s possible, they quietly raise the bar for what counts as a “good” day. The cycle repeats, and the burnout comes faster than before.
When “Quiet Periods” Aren’t So Quiet Anymore
Here’s the kicker: even on weeks that feel slow, the actual volume of completed tasks is higher than the old pre-AI days. What once would’ve looked like a packed week now feels like catching your breath. The baseline has shifted. Expectations creep upwards, and suddenly everyone’s normal is a lot busier than it used to be. It’s not just new tools, it’s a new definition of what a full plate looks like.
Where Do We Draw the Line?
AI hasn’t exactly delivered on its original promise of less work or more downtime; instead, it’s reset the standards for what teams can (and should) handle. Sure, we’re getting more done, but at what cost? Maybe the real paradox is that by making things easier, AI has tempted us to fill every free moment with even more work, leaving less time, not more, for what matters outside the terminal. Makes you wonder: if the tools keep getting better, will we ever really get our time back?
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