Experience Isn't Gone, We Just Stopped Looking For It.

It’s strange how 20 years of experience can suddenly feel irrelevant.

You spend decades building data centers, keeping systems alive, troubleshooting at 2am, understanding every piece of what makes an enterprise tick… And yet today, it can feel like none of that matters anymore. Not because the work has lost its value but because the way hiring works now makes it seem that way.

I’ve noticed a pattern over the years. Every time a new tech trend comes along, the job descriptions change overnight. It used to be Big Data. Then it was Containers and Kubernetes. And now, of course, it’s AI.

You start seeing lines like:

“Must have at least 5 years of AI experience.”

And I can’t help but wonder… what does that even mean?

Is it the ability to use AI tools? Or the ability to build the infrastructure that powers them. Because let’s be honest — outside of Silicon Valley, how many of us actually get to build AI systems from the ground up?

Most Asia Pacific teams are focused on sales, delivery, or customer success. We’re great at what we do, but not everyone gets hands-on exposure to core AI R&D. So why do recruiters and hiring managers still write job descriptions that sound almost impossible to meet?

Some say, “We’re open to people without direct experience if they have the right attitude.” Sure, that sounds nice in theory. But if that’s really true, why make the requirement sound like a wall instead of a door? It doesn’t just filter candidates. It discourages good ones from even applying.

To make things worse, most resumes today are screened by — you guessed it — AI.

If your CV doesn’t have the right buzzwords, it might never even be seen by human eyes. So what do people do? They start padding their resumes. Adding “AI” everywhere just to get through the filter. And now we’ve built a system that rewards keyword stuffing instead of honesty.

Recruiting in 2025 needs to come back to the basics.

Look at people as people, not as keyword matches.Because there are folks who have spent decades designing and maintaining the very systems that today’s AI depends on. Just because someone can’t instantly tell you the difference between a large language model and a vector database doesn’t mean they can’t learn it or even master it faster than you expect.

In a world where layoffs have become normal, maybe it’s time for hiring managers and recruiters to pause and reflect. Somewhere out there, there are still diamonds in the rough. They may not shine on paper. But they’ve held entire systems and companies together for years.

Sometimes, all they need is a human eye to see them again.