In the last issue, we looked at tech’s big promises and asked whether it delivered. This week, we’re diving into one specific promise and something Kelly Vaughn talks about in her newsletter After Burnout. AI promised efficiency. Instead, it’s making us work harder.

The promise? A productivity revolution. AI would take care of the boring stuff, freeing us to be more creative

But instead, many of us are finding more work to fill the time AI “saves” us. And it’s not necessarily the kind of work we wanted more of.

So where did it go wrong?

The illusion of efficiency

There’s an illusion of efficiency. Developers often think AI is making them 20% faster, when in reality: studies show they take 19% longer to complete tasks using AI than without it.

On top of that, individuals are reporting a decrease in the amount of time they spend on valuable work as AI adoption increases.

It’s similar to when a company hires a new employee. The expectation is an immediate boost in productivity and speed, but that’s rarely what happens. Most teams see a decrease in productivity at first, to teach, mentor, and onboard. This is referred to as the “J-curve trajectory” of productivity; that small dip before any real gains appear.

The mental load shift

Even when AI does help, it’s often by shifting where we spend our energy.

AI tools are moving us from implementation, to constant quality control, and quality control is mentally draining. I witnessed it firsthand.

There was a period when I was the only senior engineer on my team. I spent far more time reviewing PRs than writing them, and let me tell you, it was not fun. I started questioning whether I wanted to keep being an engineer at all. The joy of the job is thinking creatively and solving problems, not constantly reviewing someone else’s work. Hats off to QA engineers whose entire role requires this level of vigilance.

AI has created a similar shift: from writing code to “critical specification, orchestration, and post-hoc validation.” While it is valuable, it’s not always fulfilling.

On Burnout

Chasing perfect optimization often comes at the expense of our mental health and actual effectiveness.

Kelly’s advice:

• Reframe productivity. Instead of asking, how much time can I save? ask, can I absorb, process, and act effectively on what’s being produced?

• Give yourself permission to be selective. Even experienced professionals misjudge which AI tools are truly helpful. Trust your own experience over the hype. If a tool makes you feel scattered, anxious, or exhausted, stop using it.

• Plan for the adjustment period. Just like onboarding a new teammate, it takes time before AI pays off.
  

Personally, I’ve found AI most useful as a writing partner. I’ll draft about 80% of something, then let AI help me polish it. If I try to outsource the whole thing, I end up spending more time tweaking it to sound like me than if I had just written it myself.

Don’t get me wrong, I still spend a little too much time getting things just right, but at least now it has more of a chance of seeing the light of day.

Social media has a tendency to warp our perspective. Every day there’s a new AI tool, a new workflow, a new productivity hack. It seem like everyone else is miles ahead, and if you’re not keeping up, you’re already falling behind. At least that’s how it feels to me — and I’m willing to bet I’m not the only one.

And at the end of the day, as Kelly puts it:

“The real productivity hack is learning to recognize when tools serve us versus when we’re serving the tools.”

📖 Read the full article: AI promised efficiency. Instead, it’s making us work harder.

And if you haven’t already, make sure you subscribe to Kelly’s newsletter After Burnout.

If you have suggestions for interesting videos or articles I should cover, send them my way.

If you enjoyed this newsletter, consider sharing it on social media and tagging me! Or share it with someone who might appreciate the perspective.