Experience Is an Edge Again

For most of history, experience was the competitive edge. Craftsmen spent decades building judgment that apprentices couldn't match. Then the IT revolution flipped it — the generation that grew up digital was just faster at adopting new tools. Smartphones, social media, cloud. With each wave, younger people got there first and older ones followed. "Young people are fast with technology" became conventional wisdom. In the sweep of history, it's a recent belief. The AI era is breaking that formula.

I watched a commercial building go up recently. A site supervisor with decades of experience and a young architect were working side by side. The supervisor had the full sequence in his head — demolition, permits, foundation, plastering, windows, finishes — and more importantly, the variables that could blow up at each stage. How much delay if you hit bedrock. What order to follow when a neighbor complaint comes in. What risks come with scheduling foundation work during the rainy season. His schedules had almost no gaps.

The architect had good design instincts but limited field experience. His schedules weren't tight enough — planning without a vivid picture of the finished building left too many holes.

Then AI entered the picture. When AI started organizing task dependencies and sequencing stages, scheduling improved visibly. But the improvement tracked context. The supervisor fed AI the specifics — "there's a chance we hit bedrock after excavation, add three days of breaking work, finish the foundation before the rainy season, build in buffer here" — and got a realistic schedule. Someone with little experience typing "plan a schedule for a commercial building" got a textbook template. Unusable in the field.

Context is what separates AI that works from AI that sounds like a textbook — why industry norms formed the way they did, why similar attempts failed before, where the variables hide beneath the surface. You don't get that from searching. You get it from years of doing the work.

In the IT era, the advantage was how fast you adopted the tool. In the AI era, it's the quantity and quality of context you can pour into it. Experience is an edge again.

The obvious objection: even if context matters more, younger people are still faster at picking up new tools. True. And it's not a small advantage — less inertia means faster starts, faster experimentation, faster intuition for what AI can simplify.

And the tools keep changing. Every six months a new paradigm lands — agents, multimodal, MCP — and younger people get there first every time. Adaptation isn't a one-time cost. It resets.

But the question of what to do with the new tool also resets. Every time the tool changes, knowing how to use it gets easier. Knowing what to use it for stays hard. The faster tools evolve, the wider the gap between someone who can learn the interface in a week and someone who knows which problem is worth solving. Speed of adoption matters less with each cycle. Depth of context matters more.

And there's the time scale. Getting comfortable with a tool takes months. Accumulating context takes years. One gap closes. The other doesn't.

Being young still means something — just not what it used to. More time left to accumulate context. The real asset of youth isn't fast adaptation — it's the runway still ahead to fill in what you don't yet know.

We've measured "young" by biological age. 28 is young, 55 is not. Physically, that holds. But in the AI era, someone with rich context who combines it with AI to produce results that didn't exist before is younger — in the way that matters — than someone biologically young but light on context.

The question isn't how old you are. It's whether you're still accumulating.