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Everyone’s tracking AI exposure like it predicts layoffs.
It doesn’t.
The real predictor is demand.
More specifically: price elasticity.
When AI makes a task cheaper and faster, two things can happen.
Demand explodes.
Or demand barely moves.
If demand explodes, companies often hire more people.
Because there’s suddenly more work worth doing.
If demand barely moves, automation turns into headcount cuts.
Same AI.
Opposite outcome.
Example.
If AI cuts the cost of writing product descriptions by 70%.
An e-commerce brand might go from 5,000 SKUs to 50,000.
That creates new needs: QA, brand voice, compliance, testing, localization.
Demand surged.
Now flip it.
If AI cuts the cost of basic meeting notes by 70%.
Most teams won’t run 10x more meetings.
Demand stays flat.
So the “savings” show up as fewer roles.
Here’s what smart leaders do ↓
↳ Map your work into elastic vs inelastic buckets.
↳ Ask: if this gets 50% cheaper, do we do 2x more?
↳ Invest where volume can scale, not where it can’t.
Economist Alex Imas argues we need a Manhattan Project for elasticity data.
Not just for groceries.
For every major job task we’re betting careers on.
Because guessing your future is not a strategy.
In your industry, where will demand surge when AI drops costs?
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