One of my teams once brought me an open senior engineering position. Real responsibility, important projects, and the general assumption that we needed someone seasoned to step in and immediately carry the load. The job description reflected this. If you had seen it, you probably would not have blinked. I approved an offer for one of our interns instead, and I have not questioned that hiring call once.
What that decision did, though, was push me to examine something most organizations treat as settled: why we trust years of experience as much as we do, and whether that trust still earns its place in how we hire.
Why Years of Experience Became the Default Hiring Metric
For most of my career, I believed that experience was a reliable signal. Someone with ten years behind them had usually seen more, failed more, and built better instincts than someone two years in. Not always, but often enough that the logic held. The system was imperfect in ways everyone quietly acknowledged, but it worked reasonably well because the environment it was designed for rewarded accumulated exposure. Knowledge moved slowly. Access to expertise was genuinely limited. Time in a role translated to something real.
This environment is no longer what we are hiring into, and many of our filters have not kept up.
The Hiring Shortcut Nobody Questioned
Experience became a proxy for competence because hiring is fundamentally a prediction problem. You are trying to estimate how someone will perform before they have actually performed, which means you need signals. Years of experience became one of those signals because, in theory, more time meant more situations, more failures, and more hard-earned lessons. It was a shortcut, but it was a defensible one.
When I started out, learning genuinely depended on proximity to people who already knew things. You figured out a hard technical problem by sitting next to someone who had already solved it, or by digging through whatever documentation existed, or by slowly accumulating context through repetition. Information was not unavailable, but access was slower, more uneven, and heavily tied to where you happened to be and who happened to be around you. Under these conditions, someone with ten years of exposure had a real, structural advantage over someone with two.
What has shifted is that the gatekeeping of that knowledge is largely gone now. Engineers today can get technical documentation, architecture guidance, debugging support, and implementation context almost on demand. Artificial intelligence (AI) tools have compressed learning curves that used to take years into much shorter timelines, and that compression is accelerating. The growing role of artificial intelligence (AI) in daily professional work is evident across industries as organizations continue to weave these tools into how work actually gets done (Source: Stanford AI Index, 2026). Most hiring systems, though, are still behaving as though the gate is there.
To be clear, this is not an argument that experience stops mattering. Someone who has spent years navigating real production failures, managing difficult tradeoffs, and building judgment through actual consequences brings something that is not easily replicated. This is still true. What is worth questioning is whether years of experience should remain the dominant filter at the top of the funnel, before any of those deeper qualities even get a chance to surface.
Why Adaptability Matters More Than a Long Résumé
The more interesting shift I keep observing is not about knowledge at all. It is about what happens after someone accesses knowledge, and whether they can actually do something useful with it in a situation they have never encountered before.
Nearly 40% of workers’ current skill sets are expected to change as technology continues reshaping jobs and industries (Source: World Economic Forum, 2025), and adaptability has become one of the most emphasized capabilities as organizations try to keep pace with constant change (Source: LinkedIn Workplace Learning Report, 2025).
These numbers reflect something I regularly see playing out within engineering teams. The people generating the most value are not reliably the ones with the longest track records. They tend to be the ones who stay curious, adapt without drama when circumstances shift, and trust their own ability to learn through unfamiliar problems rather than waiting for a situation that matches past experience exactly.
The Hiring Decision That Changed My Perspective
The intern who got that senior role stood out for exactly that reason. He loves the work, and you can actually feel it in a conversation with him. He thinks carefully, asks questions that go a level deeper than most, and approaches problems like someone who genuinely wants to understand what is happening rather than someone who wants to close the ticket and move on. His experience level was not what impressed me. He had none, and I knew it. It was the quality of his thinking, which does not automatically appear after a certain number of years in a role and does not disappear just because someone is early in their career.
This distinction matters more now than before, because AI handles an increasing share of the knowledge-retrieval work in engineering. It can surface an answer. It cannot supply the drive to keep questioning after the answer shows up, and it does not generate the sense of ownership that makes someone stay invested in a problem past the point where it stops being assigned to them.
What Hiring Managers Should Measure Instead of Tenure
None of this is a case against experienced engineers. Judgment comes from real decisions with real consequences, and that cannot be shortcut. The perspective someone builds by having actually led through a hard project, absorbed a real failure, or navigated competing priorities under pressure is genuinely valuable and does not transfer through a prompt.
The question worth sitting with is a narrower one: should years of experience continue to serve as the primary gatekeeper, determining who gets evaluated and who gets screened before the evaluation even starts?
Most hiring processes are still structured that way. Tenure-heavy requirements filter candidates out before anyone has had a chance to understand how they think, how they learn, or what they actually do when they hit a problem without a playbook. Years of experience can add context, but they do not reveal much about how someone will handle a situation they have genuinely never seen before, which is increasingly the category of problem that matters most.
What I find more telling is how someone processes information when the path forward is unclear. Whether they move toward a problem or wait for more structure. Whether they take ownership of an outcome or treat it as someone else’s responsibility once their piece is done. Whether their curiosity is real or performed. These qualities show up in how someone talks about their work, how they respond when pushed on a decision, and how they describe something that did not go the way they expected. They do not reliably appear as a number next to a job title on a résumé.
The organizations that keep running tenure as their primary screening mechanism will keep losing candidates they would have wanted, to companies willing to look a little earlier and trust what they are seeing. Those who learn to evaluate thinking, adaptability, and ownership directly will be better positioned than those still counting years and hoping the count means something.
The Best Hires Are Still Out There. You Might Just Be Filtering Them Out.
That intern decision came down to one honest question: if what we actually want is someone who can create value, learn fast, and grow with the organization, are we measuring that, or are we just measuring time? Most job descriptions, if you look at them squarely, are measuring time.
Experience matters and will keep mattering. But in a world where knowledge is no longer the scarce resource that gave years-of-experience its original meaning, the organizations that learn to recognize potential before the résumé catches up to it will have a real advantage over the ones still counting.
If you want to follow more of these ideas, you can find me on Substack or connect on LinkedIn.
Hiring FAQs
The intern stood out for the quality of his thinking, not his experience. He asked deeper questions, wanted to understand problems rather than close tickets, and showed the kind of curiosity and ownership that Privat now sees as stronger predictors of performance than tenure.
Hiring is a prediction problem, and experience served as a proxy for competence because knowledge moved slowly and access to expertise was limited. More time in a role meant more exposure to hard problems, so it was a defensible shortcut.
Yes. Michael Privat argues that AI has compressed learning curves that used to take years, so the structural advantage someone with ten years of exposure once had over someone with two has largely eroded.
No. Michael Privat is clear that judgment built through real decisions and real consequences isn’t easily replicated. His argument is narrower: tenure shouldn’t remain the primary filter that decides who gets evaluated in the first place.
How someone processes information when the path forward is unclear, whether they move toward a problem or wait for structure, whether they take ownership of outcomes, and whether their curiosity is genuine or performed.
Michael Privat argues that organizations relying on tenure as their primary filter will keep losing candidates they’d want to companies willing to evaluate thinking and adaptability directly.
Further Reading
Michael Privat
Michael Privat is a Chief Data and Engineering Officer leading a global team of 500+ engineers. With 25 years in tech, he helps organizations unlock speed, clarity, and accountability. He turns stalled engineering teams into high-performing systems built on ownership, discipline, and modern AI-driven workflows through his “accountable autonomy” model. Follow him onLinkedIn or check out hisSubstackfor more.
