📈 The warning has been clear for years now — workers are experiencing isolation, anxiety, and eroded meaning as AI transforms their professional lives. Mental health experts document identity erosion and dissociation. Entry-level talent pipelines are drying up. Yet organizations continue racing toward AI adoption at breakneck speed, often intensifying their investments even as evidence of psychological harm mounts. The question isn’t whether we know about these costs — we do. The question is: why are we so willing to pay them?

The USD tn imperative: The answer begins with numbers that make human concerns feel almost quaint by comparison. Morgan Stanley research estimates that widespread AI implementation throughout major corporations could yield close to USD 1 tn in yearly gains and potentially expand equity values by USD tns — representing a quarter to a third increase in total worth. McKinsey calculates the extended horizon value proposition at several USD tns in enhanced economic output.

These aren’t abstract projections. They represent concrete competitive advantages that will separate market leaders from the obsolete. When the vast majority of enterprises anticipate expanding their AI spending through the coming years, the calculation becomes existential: invest now and risk employee wellbeing, or hesitate and risk corporate survival. McKinsey’s analysis warns that business leaders face greater danger from thinking too conservatively rather than too ambitiously about AI’s scope. This framing captures the central tension — in an environment where AI adoption is positioned as a zero-sum game, the human cost becomes a strategic externality rather than a dealbreaker.

The FOMO factory: Perhaps no force drives AI adoption more powerfully than fear itself. Recent research reveals that more than one third of companies experience anxiety about falling behind in the AI race, with nearly half holding the conviction that firms avoiding this technology have already lost ground against rivals and face declining prospects. This isn’t paranoia — US investment in AI dwarfed spending elsewhere by a factor of 10 during the previous year. The message from competitors, investors, and market analysts is unambiguous: adopt or die. The pressure extends beyond corporate leadership: surveys indicate that roughly one in six employees falsely claim to utilize AI tools simply to appear competent despite having no genuine need for the technology.

The seduction of AI lies in its measurability. While psychological harm manifests slowly and ambiguously, productivity gains appear immediate and quantifiable. Employees describe reclaiming several hours each week by delegating tasks to automated systems. Teams handling customer inquiries report double-digit percentage improvements in output — all numbers that populate executive dashboards and shareholder presentations.

But the human costs are difficult to quantify. How do you measure the erosion of meaning? How do you put a price tag on professional identity fragmentation? As workplace mental health expert Chistina Muller notes, genuine satisfaction derives not from speed or volume but from believing your contributions matter and create impact. From a corporate finance perspective, a dissociated worker who increases productivity looks like a success story, not a cautionary tale. And when the fulfillment doesn’t appear on quarterly reports and efficiency does, the choice becomes obvious — even if tragically misguided.

The myth of managed transition: Organizations justify accepting human costs with promises of thoughtful implementation and substantial operational transformation, believing that technological advancement could enhance rather than diminish human value — even in roles with high automation potential. But these reassurances ring hollow against the reality of implementation. McKinsey’s executive surveys reveal that fewer than one in five leaders report meaningful revenue growth from AI investments, and under a quarter observe beneficial cost impacts.

The gap between promise and performance suggests that organizations are accepting human costs because they lack the power to resist larger market forces. Organizations are accepting the human cost of AI because the alternative — in the current market structure — appears to be corporate suicide. The result is a race to a bottom that everyone recognizes but no one can stop, a feature of a system designed to prioritize efficiency over wellbeing, quarterly returns over long-term sustainability, and competitive advantage over human growth.