The Burnout Before The Layoff
Everyone keeps talking about layoffs.
I think we’re missing an earlier signal.
Burnout.
Not the normal kind.
Not “I need a vacation” burnout.
Not “too many meetings” burnout.
A different version.
The kind that comes from feeling like your profession is moving underneath your feet while you’re still standing on it.
I’ve been noticing something strange.
When people talk about AI displacement, they usually talk about jobs disappearing.
Headcount reductions.
Hiring freezes.
Restructuring.
Those are easy to see.
The harder thing to see is what happens before any of that.
The engineer spending nights learning new tools because they don’t know whether they’ll still be competitive in two years.
The analyst quietly rebuilding workflows every quarter because the expectations keep changing.
The product manager trying to figure out whether they’re becoming more valuable or less valuable every time a new model gets released.
Nobody counts those hours.
Nobody tracks the psychological cost.
Yet.
That cost is real.
And I think it’s becoming one of the biggest labor stories nobody is talking about.
The second job nobody asked for
For most of modern professional life, staying current was relatively straightforward.
You learned your field.
You built experience.
You improved gradually.
The environment changed, but it changed slowly enough that experience remained valuable.
A software engineer who spent ten years becoming excellent at their craft could reasonably expect that expertise to remain useful.
A marketing professional could build deep knowledge of their domain.
An analyst could spend years mastering tools and methods.
The rules changed.
Not overnight.
But fast enough that people noticed.
Now a growing number of knowledge workers have two jobs.
The first job is the one they’re paid for.
The second job is staying employable.
Reading model announcements.
Testing new tools.
Watching what peers are doing.
Trying to determine whether a new capability is hype, meaningful, or existential.
Monitoring the labor market.
Monitoring the technology.
Monitoring themselves.
Most of this work happens after hours.
Most of it is unpaid.
And because everybody assumes everyone else is handling it fine, very few people talk about it openly.
Which creates a strange illusion.
Everyone looks calm.
Many people aren’t.
The adaptation treadmill
One of the most common pieces of advice right now is simple.
Adapt.
Learn the tools.
Stay current.
Keep up.
The advice sounds reasonable.
The problem is that adaptation itself has become labor.
Imagine telling a software engineer in 2015 that they would eventually spend part of every week evaluating new AI capabilities, deciding which mattered, rebuilding workflows around them, and wondering whether those same tools would eventually reduce demand for the work they spent a decade learning.
Most would have laughed.
Now it’s normal.
The expectation isn’t merely that workers perform their jobs.
The expectation is that they continuously reinvent how those jobs are performed.
Some people thrive in that environment.
Others don’t.
Neither response is irrational.
Human beings are not designed to operate indefinitely in a state of professional uncertainty.
At some point uncertainty stops feeling exciting.
It starts feeling expensive.
Something about engineers keeps bothering me
Software engineers are a useful example because they’re often presented as the winners of the AI transition.
They’re technical.
They understand the tools.
Many already use them daily.
From the outside that looks like a strong position.
The closer you get, the more complicated it becomes.
Many engineers are being asked to learn the tools that could eventually reduce demand for certain parts of their work.
They’re expected to do it quickly.
They’re expected to do it mostly on their own time.
They’re expected to become more productive.
Then they watch companies celebrate productivity gains and quietly wonder where those gains eventually go.
Toward higher compensation?
Toward shorter working hours?
Toward larger teams?
Or toward fewer people producing the same amount of output?
That’s not a comfortable question.
A lot of engineers won’t ask it publicly.
Many are asking it privately.
The emotional burden isn’t coming from ignorance.
It’s coming from understanding exactly what is happening.
The people who leave first
Most conversations about displacement assume workers stay in place until they’re replaced.
I don’t think that’s what happens.
I think a growing number leave before displacement arrives.
Not because they lost.
Because they’re tired.
Tired of rebuilding.
Tired of uncertainty.
Tired of constantly recalculating where value lives.
Tired of hearing every productivity breakthrough framed as good news while quietly wondering who benefits from the savings.
Over the next two years I expect this to become more visible.
Not through dramatic announcements.
Through subtle decisions.
People declining promotions.
People switching industries.
People accepting lower-paying roles with more stability.
People walking away from high-status careers because they no longer want to live on the adaptation treadmill.
That won’t show up in AI press releases.
It will show up in career decisions.
And by the time it becomes obvious, it will already be happening at scale.
I’ve spent months writing about who gets displaced next.
Lately I’ve found myself thinking about the people who are still employed.
The people still doing the work.
Still adapting.
Still learning.
Still trying to stay ahead.
A lot of public discussion treats employed workers as evidence everything is fine.
That feels incomplete.
You can be employed and exhausted.
You can be employed and anxious.
You can be employed and quietly wondering how long you can keep running at the current pace.
Employment status doesn’t tell the whole story.
Sometimes it barely tells any of it.
What I’d actually do
This is the part where many newsletters say to learn AI.
You already know that.
The people reading this are not unaware.
Most are already experimenting.
Most are already adapting.
Most are already paying attention.
The bigger question is where you’re positioning yourself.
Run a simple audit.
Forget job titles.
Forget tasks.
Forget the software you use.
Ask one question.
What breaks if you disappear?
Not what stops temporarily.
What genuinely breaks?
What outcome are you responsible for?
What decision relies on your judgment?
What risk sits on your shoulders?
The closer you are to accountability, the stronger your position tends to be.
The closer you are to repeatable process work, the weaker it tends to become.
That doesn’t guarantee safety.
Nothing does.
But it changes the game.
People obsess over protecting tasks.
The more useful move is protecting responsibility.
Tasks move around.
Responsibility tends to stick.
This week, identify one area where you can own an outcome rather than execute a process.
One project.
One customer problem.
One decision.
One result.
Move one step closer to accountability.
Then do it again next month.
The workers who navigate this period successfully won’t necessarily be the smartest people in the room.
They won’t necessarily be the fastest learners.
They won’t necessarily predict every technological shift correctly.
They’ll be the people who understand where human responsibility still matters and position themselves accordingly.
The people still standing in five years won’t be the ones who never felt the pressure.
They’ll be the ones who recognized it early enough to build differently.
The people still standing in five years won’t be the ones who never felt the pressure.
They’ll be the ones who recognized it early enough to build differently.
It is so real, and yet no one talks about how exhausting it is to keep learning and while learning is a good thing, being forced to always learn a new skill and using your personal time for it is seriously exhausting for someone who is at a later stage of their career. Many more people should talk about it. Glad someone bought it up.