300+ Executive Conversations Reveal 'Mass Psychosis' Driving Poor AI Decisions, Consultant Says

For AI practitioners and product leaders, the gap between boardroom enthusiasm and real-world adoption has never been starker. A technologist with over 300 professional conversations under their belt reports that organizations are lurching from one AI pivot to the next—building for agentic interfaces that barely attract double-digit users, then scrambling into support roles to stay relevant. This street‑level view challenges the narrative of inexorable AI progress and raises urgent questions about how the industry can align hype with genuine user needs.

In July 2026, a technologist and consultant writing under the pseudonym Ludicity published a searing critique of how AI fervor is systematically dismantling rational decision-making across global institutions. Drawing on over 300 professional conversations conducted over the preceding year—spanning frontline workers in niche services to executives at Fortune 500 companies—the author describes a collective 'mass psychosis' that has left banks, hospitals, and government agencies alike directionless or quietly compliant with hype cycles. The essay, cross-posted from Ludicity’s personal blog to the company site hermit-tech.com, argues that many organizations are trapped in a cycle where leadership either lacks coherent plans or sees no path forward except to placate AI enthusiasm, while more level-headed employees oscillate between fear and frustration.

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In July 2026, a technologist and consultant writing under the pseudonym Ludicity published a searing critique of how AI fervor is systematically dismantling rational decision-making across global institutions.

The piece gains credibility not from quantitative data but from the author’s front‑line vantage: as a sales lead and technical architect for a consultancy, they have witnessed firsthand the internal dynamics of clients and counterparts. A particularly vivid example involves a division within an unnamed company that pivoted to build interfaces for agentic workflows. Despite the effort, only ten users ever engaged with those products. The division then pivoted again—this time to provide support for agentic workflows—only to find intense competition because, as the author grimly observes, 'there's only like four things you can do in that space.' This rapid sequence of pivots without validated demand illustrates the broader phenomenon: institutions are chasing AI buzzwords not because of proven productivity gains, but because of an overriding fear of being left behind.

Importantly, the essay asks a question that exposes the core absurdity: 'Are companies actually seeing massive productivity gains from their AI adoption?' The author implies that the answer, from the ground, is a resounding no. Yet the pressure to appear AI‑forward persists. This disconnect between executive rhetoric and operational reality is not new—it echoes previous technology adoption fads—but the scale of the current mania, amplified by large language models and the generative AI boom since late 2022, makes it uniquely destabilizing. The institutions that society relies on for stability are diverting resources toward unproven AI initiatives based on hope, not evidence, while those who raise concerns are marginalized.