will ai level the playing field...

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There's a comfortable assumption baked into how most organisations manage their workforce: that productivity in knowledge-based roles peaks young, plateaus in middle age, and slowly declines from there. It's part of the reason that over 50's have often struggled to find work after redundancy. Cost versus output. The maths, apparently, doesn't favour them.

That assumption was always more convenient than accurate. But now, in the age of AI, it may just be wrong.

What the old productivity curve actually measured

The traditional model had some truth to it. Younger workers — roughly 25 to 35 — bring genuine advantages in raw cognitive speed: faster pattern recognition, quicker uptake of new information, higher capacity for volume work. In knowledge industries, that translated to output.

But the research on judgment, decision quality, and domain expertise has always told a different story. These compound with age. A seasoned professional knows what to ignore, where the real risk sits, who to trust and who to question. That kind of filtered thinking is enormously valuable — it just doesn't show up neatly in productivity metrics designed for a pre-AI world.

What AI changes

AI commoditises cognitive speed almost entirely. The tasks where younger workers historically had an edge — research, synthesis, drafting, processing volume — are now largely augmented or automated. The gap closes fast.

What AI cannot replicate is the quality of the questions you ask it, the frameworks you apply, and your ability to critically evaluate what it gives back. That is an experience game. Knowing when to trust the output, when to push back, and what the right answer should smell like — these are skills that accumulate over decades, not months.

The result is that AI amplifies experience in a way it simply cannot amplify raw potential. The more you know, the better your outputs become. The curve that once bent downward after 50 may now bend the other way.

The adoption caveat

Younger workers still have a real edge in one area: adoption speed. They integrate new tools faster, with less friction, and without the psychological weight of having done things a different way for 20 years. That advantage is genuine.

But it's temporary. As AI becomes ambient — embedded in every workflow rather than a novel tool requiring deliberate adoption — that edge narrows to nothing. What remains is judgment. And judgment compounds.

The talent strategy mistake hiding in plain sight

Organisations that have spent the last decade quietly "restructuring out" their experienced 50-plus cohort to manage wage costs may have made a calculation that looks increasingly poor in retrospect.

They shed the layer that AI would have made most productive. They retained the layer whose primary advantages AI is now replacing. And the domain knowledge that walked out the door — particularly in specialised fields like industrial automation, engineering, and complex B2B sales — won't be rebuilt quickly or cheaply.

In sectors where expertise is deep and scarce, that's not a cost saving. It's a structural liability.

The question worth asking isn't whether your experienced people are worth what you're paying them. It's whether you've correctly valued what they'd produce if you gave them the right tools — and whether you'll have time to find out before someone else does.

The productivity curve is being redrawn. The organisations that see it first will have a significant advantage in the talent market over the next decade.