The most expensive AI mistake may not be the model.
It may be assuming people are the flexible cost.
As AI adoption scales, many organizations are treating token spend as unavoidable while headcount becomes the easiest lever to pull. But that logic is starting to show cracks.
Cutting teams can create short-term budget room. It does not automatically create better products, stronger customer experiences, or higher ROI.
The smarter question is not “How many people can AI replace?”
It is “How efficiently are we using AI in the first place?”
There are practical ways to reduce AI costs before touching talent:
Better prompt design
Caching repeated context
Routing simple tasks to smaller models
Using retrieval instead of dumping full documents
Setting clear usage guardrails
The real opportunity is not just cost reduction. It is reinvestment.
The companies that win with AI will likely be the ones that optimize token budgets and use the savings to upskill, retain, and amplify their people.
Are we building AI strategies that reduce waste, or just reducing teams?
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