TECH Signal 133
NBER model: automation can weaken work's meaning and pay even when workers stay employed
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Illustration only Photo by Ivan Bandura on Unsplash
Joshua S. Gans's NBER working paper models how a credible machine alternative can reduce the meaning workers derive from their jobs, lowering compensation or making automation more likely, even without job loss.
For engineers, this suggests that even if automation does not replace them, it can erode the sense of purpose in their work and affect their pay. It also implies that publicly demonstrating AI capabilities can have a negative externality on human workers' perceived contribution, which firms and developers should consider when deploying or marketing automation.
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The model shows that a credible machine alternative can weaken the meaning workers get from knowing their output depends on their own contribution.
When wages adjust fully, compensation rises to offset the loss of meaning, but when they adjust only partly, workers bear some of the loss themselves.
An external developer can profit by publicly demonstrating a machine before licensing it, because the demonstration lowers the value of the human alternative, creating a 'meaning externality' that can make automation more likely and socially harmful.
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