By Michael Privat

I have been in the same company for over two decades, long enough to watch hiring trends come and go, long enough to see entire belief systems rise, peak, and collapse under their own weight. One of those beliefs has been sitting in plain sight for years, and most leaders still treat it like a law of physics. More experience equals more capability. More years equals better judgment. More tenure equals safer bets. It sounds reasonable. It used to work. It is now breaking in slow motion.

Inside large engineering organizations, including the one I lead today with hundreds of engineers across continents, something subtle has shifted. The output gap between a junior engineer and a senior engineer on many tasks has narrowed in a way that would have been unthinkable even five years ago. It does not mean expertise is gone. It means the way expertise shows up has changed. AI has quietly removed friction from execution, and when execution becomes easier, the value of simply having “done it before” starts to erode.

Yet hiring systems have not caught up. Job descriptions still read like time capsules… Ten years required. Fifteen preferred. “Senior” is used as shorthand for safety. It feels comforting until you look at what teams actually need today. They do not need comfort; they need people who can think when the ground is moving.

When Experience Becomes A Lagging Signal

There is a pattern I keep seeing. A hiring manager opens a senior role. The expectation is clear. Someone who has seen everything, handled scale, navigated complexity, and can operate without supervision. The assumption is that years of experience will deliver all of that in a neat package. Then reality steps in.

The candidate arrives with the résumé. Impressive. Recognizable companies. Long tenure. The conversations go well. Structured answers. Clean narratives. Everything looks right. And then the work starts. This is where things begin to wobble.

The environment they are stepping into is not the one in which they built their experience. The tools have changed. The pace has changed. The expectations have changed. AI is now sitting in the workflow, reshaping how code is written, how problems are explored, and how solutions are validated. What used to take hours now takes minutes. What used to require deep familiarity with systems can now be scaffolded quickly with the right prompts and oversight.

Experience starts to behave like cached knowledge in a system that has already moved on. Meanwhile, a less experienced engineer, someone who has not yet accumulated a decade of habits, approaches the same problem differently. They explore faster. They question assumptions earlier. They are less attached to “how it used to be done,” which turns out to be an advantage when the rules are shifting under your feet. This is not a theory. It is visible in day-to-day output.

The broader data is pointing in the same direction. Nearly 39% of core job skills are expected to change by 2030, which means the half-life of what someone learned over a long career is shrinking, whether we like it or not (Source: World Economic Forum, 2025). In the Asia-Pacific region, two-thirds of organizations expect a steep decline in entry-level hiring due to AI, and 91% say roles are already changing or disappearing as a result (Source: HRM Asia, 2025).

If the ground is shifting this fast, then experience becomes a lagging signal. It tells you where someone has been. It tells you very little about how they will behave when the map no longer matches the territory. And yet, companies keep hiring as if nothing changed.

The Quiet Rise Of Judgment, Curiosity, And Learning Speed

I have seen teams make a decision that would have sounded reckless a few years ago. They open a senior role. They review the requirements. Then they pause and rethink the entire premise. Do we actually need ten years of experience for this?

Sometimes the answer is no. Not because the work is simple, but because the leverage has changed. AI has lowered the cost of execution, which means the bottleneck is no longer writing code. The bottleneck is deciding what should be built, what should not, and how to navigate trade-offs that do not come with clean answers. That is judgment. Not tenure.

So they adjust the role. They hire someone earlier in their career. Someone sharp, curious, slightly restless. Someone who learns quickly and is comfortable being wrong in public. Someone who does not freeze when the playbook runs out. The result is not chaos. It is often acceleration.

This is where most hiring systems break. They are optimized to detect past experience because it is easy to measure. Years, titles, companies. Clean signals. Comfortable signals. Judgment is harder to quantify. Curiosity does not fit neatly into a résumé. Learning speed rarely shows up as a bullet point. So we default to what is easy to filter. And we miss the people who will actually move the system forward.

Inside high-performing engineering teams, the difference becomes obvious quickly. One engineer asks for more requirements. Another starts exploring the problem space. One waits for clarity. Another creates it. One relies on patterns they already know. Another tests new ones in real time, using AI as a multiplier rather than a crutch.

The second profile is not defined by years. It is defined by behavior. This is where hiring needs to go. Not in theory, but in practice.

Stop asking how long someone has been doing the job. Start asking how they approach problems when the job itself is changing. Watch how they think. Watch how they adapt. Watch how quickly they learn when they do not have the answer. Those signals are messy, harder to standardize, and sometimes uncomfortable to evaluate. They are also far more predictive.

What Companies Get Wrong When They Cling To Tenure

There is a hidden cost to overvaluing experience. It does not show up immediately. It accumulates quietly. Teams become slower to adapt because they are staffed with people optimized for stability. Innovation becomes incremental because the system rewards what has already worked. Hiring pipelines become narrower because they filter out unconventional candidates who do not match predefined criteria.

And then something else happens. The people inside the system start to feel it. High-potential engineers, especially earlier in their careers, notice when advancement is tied more to time served than to actual contribution. They notice when curiosity is tolerated but not rewarded. They notice when decisions are driven by hierarchy rather than insight.

That is when engagement drops. Not dramatically, not overnight, but steadily. Organizations then try to fix the symptoms. More processes. More frameworks. More alignment meetings. The underlying issue remains untouched. The system is still anchored to an outdated proxy for capability.

Meanwhile, the external environment keeps moving. AI is not slowing down. It is embedding itself deeper into workflows, making execution faster, making iteration cheaper, and making the gap between idea and implementation smaller. The companies that benefit are not the ones with the longest résumés. They are the ones who can translate this new leverage into real outcomes. That requires a different kind of talent filter.

Hire For How People Think, Not How Long They Have Been Thinking

There is a moment every organization reaches, usually later than it should, where the old assumptions stop working but have not yet been replaced. Hiring is one of those areas right now.

Experience still matters. It always will. The mistake is treating it as the only signal in a world where the nature of work is changing faster than experience can keep up.

The future belongs to teams that hire for judgment, curiosity, and learning speed. Teams that are comfortable betting on potential when the signals are strong. Teams that understand that adaptability is not a soft skill. It is a survival trait.

This requires a shift in how roles are defined, how interviews are structured, and how decisions are made. It requires leaders to be willing to question their own instincts, especially those that feel most comfortable.

Rethink your hiring filters before they quietly filter out the people you actually need.

Michael Privat is a Chief Data and Engineering Officer leading a global team of 500+ engineers. With 25 years in tech, he helps organizations build speed, clarity, and accountability. He works with engineering teams to strengthen performance through ownership, discipline, and modern AI-driven workflows, drawing on his “accountable autonomy” approach. Follow him on LinkedIn or check out his Substack for more.