Every few months, I hit the same wall. I’ll have spent weeks building a work project, something for my portfolio, or usually both at once. Then a wave of exhaustion hits me that has nothing to do with how much sleep I got the night before.

I finally close the IDE, and instead of feeling proud, I’m oddly hollow. My brain starts asking questions I don’t have good answers for.

Things like: What am I supposed to build next? Is there even a point?

It’s not sadness per se, but closer to that moment when your battery hits 1%, and you don’t even remember the last time it was full 🪫

For a long time, I figured this was what being a developer felt like. Then 2026 happened, and I started noticing this exact exhaustion showing up everywhere outside my own head too.

Developers on forums, in thread after thread, describe something that sounded a lot like what I was experiencing. Except now it had a name and research behind it.

So I did what I always do when something’s bothering me: I went looking for answers.

What I found wasn’t simple, and it definitely wasn’t one-sided. This is what this post tackles.

AI is tangled up in this burnout wave, but blaming everything entirely on it wouldn’t be honest, and neither would pretending it’s not part of this at all.

Why Developer Burnout Got Worse in 2026 Instead of Better

The pitch for AI coding tools promised less grunt work and more time for the parts of the job you actually like. And for many people, it holds because boilerplate and scaffolding got faster.

Half of senior developers said AI improved their work-life balance last year, and burnout scores actually dropped 15% when AI took over tedious, repetitive tasks, simply because the work got more enjoyable.

But a study covered by Harvard Business Review earlier this year found that people who use AI the most are also burning out the most.

Why? Because every minute saved gets immediately converted into a higher expectation for what “enough” looks like.

The average burnout score in a 2026 developer burnout survey was 7.4 out of 10, with most answers landing between 7 and 9. That’s nearly three-quarters of devs saying they’d felt this way for at least six months, and a third said over a year.

Always-on culture topped the list of causes, matching what I expected going in, but “AI pressure to do more” made the top four. It’s a category that didn’t exist in this conversation two years ago, and now it’s sitting right next to the usuals.

AI does help, but it also raised the bar for what “helped enough” even means. Both things are true at once.

The problem is that nobody adjusted the second half to match the first, and that mismatch is the root of this exhaustion. It’s the same tension I keep running into as the tool gets faster, and somehow my sense of being caught up never does 😮‍💨

Related: You Need To Work Smarter, Not Harder, With AI

AI Coding Assistants Didn’t Cut My Workload, They Relocated It

I wasn’t doing less work with AI in the loop. I was doing a different kind of work, and that kind turned out to be more tiring.

Not everyone agrees, but most devs would say that writing code is the enjoyable, almost meditative part of this job.

Reviewing someone else’s output, even when that someone is an LLM, takes more mental effort and gives back a lot less satisfaction.

I called this the “Debugging Tax” back in January. What I didn’t clock at the time is how it compounds when you’re auditing three or four codebases at once (more on that in a minute).

Swap enough writing time for reviewing time, and speed the whole loop so it repeats several times a day. There’s just not much left in the work to enjoy after that.

It’s the difference between writing an essay and editing an essay, except you’ve less to ponder since it’s raw syntax.

I’d finish a session where I technically got a ton done, except most of that time was spent reading through generated code. I was catching what didn’t fit, rewriting the parts that were technically correct but wrong for my actual project, and guiding the AI toward what I actually needed.

On paper, sure, I shipped more.

In my head, I felt like I’d done more too, just not the fun kind of more.

Note 👁️
I’m not arguing against using AI in your workflow. I’ve written plenty about how to approach it in a way that actually works. What I’m saying is that “faster” and “easier” aren’t automatically the same thing, and that gap is most clear when the speed comes from a task you never liked doing in the first place.

What nobody tracks is that prompting, checking, correcting, and guiding AI output drain the same mental battery as writing code does. That labor doesn’t show up as a visible line on any board, so it’s easy to feel behind even when you objectively shipped more than usual.

Explore: Why AI Code Review Is Now Secretly Killing Developers

What “AI Brain Fry” Actually Feels Like When You’re Juggling Repos

There’s a term going around this year that captures this exhaustion pretty well: AI brain fry. Some describe it as what happens when you’re managing several AI agents at once.

The constant context switching wears down your ability to think clearly, so you start making more mistakes and worse decisions the further into the day you go.

I experience this one personally, and not from managing a swarm of agents on one project.

Mine shows up when I’m running an AI workflow through one repo, then immediately switching to run a similar workflow on a completely different one.

Different stack, different packages, and an entirely different purpose. One might be a client integration, the other a personal experiment in a language I barely touch day-to-day.

I can juggle it; that’s not actually the hard part 🙈

The hard part is what’s left afterward.

I end the day having technically “handled” three or four different projects, and somehow I only have a surface-level grasp of what actually happened in each one.

It’s a strange kind of tired, less like I ran out of energy and more like I ran out of depth.

I know the code exists. I couldn’t really walk you through all of it without opening the repo first and asking the AI for a quick summary.

That’s the part that gets me most: I did the work, and I still couldn’t defend it in a code review without a refresher.

Note: The “AI brain fry” effect doesn’t creep up slowly the way regular overwork does. One week you’re managing it fine. Then a few weeks later, you can’t hold any single project in your head anymore, only fragments of all of them at once. But putting those fragments back together takes real effort every time you switch.

The Developer Burnout That Has Nothing To Do With AI: My Own Side-Project Habit

Long before agentic tools existed (i.e., AI that can carry out multi-step tasks on its own, not just respond to a single prompt), I already had a pattern. I’d finish a stretch of demanding work, then immediately fill any open time with a personal project.

Not because anyone asked me to.

Sitting still and doing nothing has never come naturally to me. Building has always felt like the productive version of rest.

That pattern predates every AI tool I use now. AI just made it faster to act on.

I could fit more building into the same amount of free time, which I did, without ever pausing to ask if I should 😬

Developers are talking about this exact loop right now too. They’re using AI-accelerated side projects to escape day-job burnout, without noticing they’ve quietly built themselves a second job that runs on weekends and evenings.

It’s a completely understandable trade to make in the moment. It’s also the trade that keeps the exhaustion going long after the day job clocks out.

I don’t think the fix is to stop building things you love. That would be a strange thing for someone who writes a web dev blog to recommend.

But, if I’m honest, my drive to keep building, even during rest periods, was never really about curiosity alone.

Some of it was proving to myself that I was still worth something as a developer. That’s a much heavier thing to carry into a Saturday afternoon than “I felt like coding.”

It took me longer than I’d like to admit to tell the two apart.

Tip: Curiosity feels light. Proving something feels like a job you never applied for, and the moment you notice which one you’re running on, it changes everything.

How I Decide What AI Coding Tools Get to Touch (And What I Still Write Myself)

I’ve gotten pickier over the past few months about what actually goes to AI. Mostly by paying attention to which sessions left me tired and which ones didn’t.

Repetitive, low-stakes work goes to AI without much hesitation. Boilerplate, config files, routine setup–none of that costs me anything to hand off.

Core logic is different. That’s the stuff where a subtle mistake costs me an hour of debugging later.

I either:

• write it myself
• pre-plan and outline it before using AI to carry it out
• review it so closely that letting AI touch it first barely saves any time anyway

The last one is where a lot of the invisible review labor lives. If checking or planning the output takes as long as writing it would have, I’ve saved nothing.

I’d rather feel a little slower and actually understand what I built. It sounds like a small distinction, but it’s the one that’s actually changed how drained I feel by the end of a work session.

Tip: Correcting AI output should not take you longer than writing that section yourself. If that’s the case, then that’s also your signal to switch back. It’s not a sign you’re doing it wrong.

Now, being selective about what AI touches won’t necessarily eliminate the exhaustion. But it does shrink the gap between how productive I looked from the outside and how drained I actually felt on the inside.

That gap really matters, mostly because closing it even a little made the next work session easier to walk into.

I’m still tired at the end of most days. I’m just tired in a way that matches what I actually did, instead of a way that feels disconnected from it.

The Weekly Digital Detox That Actually Helps My Developer Burnout

If I don’t have something specific planned, my default move, even on a weekend, is to open my laptop and learn something new.

That almost always turns into building something new.

So I stopped leaving that default unguarded. Roughly every week, or every other week depending on how things are going, I block out a day or two for something offline.

Sometimes that’s a day or two where I don’t have laptop access at all. Sometimes my phone stays mostly untouched instead.

Or, sometimes that’s a small trip somewhere. Maybe a staycation at home where I don’t bring my devices into the plan.

Other times I keep it simpler. I build a to-do list of offline tasks, errands, chores, whatever, that keeps me busy most of the day without a screen in the loop.

The point isn’t the specific activity. This isn’t a no-AI day where I still code by hand to keep my skills sharp, but a no-laptop day.

The goal is to occupy my hands and head with something that isn’t a keyboard at all

It’s not a strict rule I never break, nor is it some universal fix, though it works with how my brain actually behaves.

That’s more than I can say for the vague “just take a break” advice I ignored for years.

Knowing my own default (build, always build) meant I had to design around it instead of relying on willpower to override it in the moment.

Willpower alone had never once worked for me. I’m not sure why I kept expecting it to start now 🤦‍♀️

It’s a Wrap

Some of my exhaustion this year was never AI’s to take credit for. AI fatigue and developer burnout aren’t really the same kind of tired.

AI just made an existing exhaustion pattern in me and the industry worse, rather than invent a completely new one out of nothing.

The part that’s actually new—review labor, context switching across unrelated repos, brain fry—is also baked into how these tools work. No amount of willpower was going to fix that on its own.

But the part where I fill every open hour with building because stopping feels like falling behind? That one’s older than any of this (my fellow hustlers get it).

AI just handed that habit a faster engine that does more damage in the same amount of time.

I was mainly stuck because I treated both AI-caused tiredness and self-caused tiredness the same. I’d take a weekend off, feel the battery fill a little and then wonder why it was back down at 1% by the following Tuesday.

The AI stuff needed different boundaries: what I hand off, how much I juggle at once. While the older stuff needed me to admit that building on a Saturday isn’t the same thing as resting, no matter how good it feels in the moment.

One of them is a workflow problem; the other one is a me problem.

Knowing which kind of tired I’m dealing with, on any given week, has made it a lot easier to actually do something about it.

What’s the part of your own burnout you’ve been blaming on the wrong thing?

I’ll see ya next time.

Ta-ta for now 👋