What does “senior” even mean in the AI era?

Not long ago, being senior meant years.
More years, more tickets, more systems survived.

That definition is dying.

AI didn’t just speed things up. It compressed experience. Tasks that used to take years to master can now be done in weeks with the right tools. Writing tests, scaffolding code, generating docs, even debugging. AI leveled the execution field.

So if everyone can execute faster, what separates a senior from everyone else?

It’s not speed.
It’s not output.
And it’s definitely not job title.

A senior today is someone who understands impact.

They know what not to build.
They know what not to automate.
They know when a flaky test is a signal and when it’s noise.
They can tell the difference between an app bug, infra instability, and test debt without blaming the wrong team.

In the AI era, juniors can ship. Seniors decide.

Seniors ask better questions.
“What problem are we actually solving?”
“What happens if this breaks at scale?”
“Is this risk acceptable, or just ignored?”

They design systems that survive change, not just pass today’s pipeline. They think in tradeoffs, not absolutes. They communicate clearly when things go wrong, without drama and without hiding reality.

AI makes everyone faster.
It does not make everyone wiser.

Being senior now means:

  • owning outcomes, not tasks
  • giving clarity when things are messy
  • protecting quality without blocking delivery
  • knowing when to push back and when to let go

The uncomfortable truth is this:
some people with 10 years of experience are no longer senior.
And some with 3 years already are.

Seniority is no longer about how long you’ve been around.
It’s about how well you see.

And in an era full of automation, vision is the rarest skill left.