We are seeing an increased public narrative about the potential for job losses from AI deployment. These claims receive a great deal of media attention and are rewarded in the social media landscape for being as pessimistic as possible. Mass job loss remains highly speculative and many claims skew to the highly implausible. But this is causing mass harm.

The increasingly popular narrative of inevitable, catastrophic, long-term job loss due to artificial intelligence is not grounded in robust empirical evidence. It is overwhelmingly speculative, framed in worst-case abstractions, and presented to the public with a level of certainty that far exceeds what the data justifies. That alone would be intellectually questionable. But the deeper issue is ethical: the psychological and social harm caused by repeatedly presenting extreme scenarios as near-certainties.

There is a very real human cost to this discourse. People are not reading these forecasts as academic hypotheticals. They are internalizing them as personal futures. Students reconsider career paths. Mid-career professionals experience anxiety and loss of motivation. Workers in already uncertain labor markets feel prematurely obsolete. This is not a trivial side effect. It is a measurable psychological burden placed on millions of people based on projections that remain deeply uncertain and, in many cases, methodologically weak.

Serious economic forecasting requires discipline, historical grounding, and humility about technological diffusion. What we are instead seeing in many public conversations is a pattern of extrapolation from capability demos directly to labor market collapse, skipping entirely over the realities of workflow integration, governance constraints, liability frameworks, organizational inertia, and economic adaptation. That is not analysis. That is narrative acceleration.