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📰 TL;DR
Box founder Aaron Levie recently called out what he calls "AI psychosis" — the phenomenon where executives who greenlight "AI can replace this job" are often the ones who know the least about what that job actually entails. He's warning that this decision-making blind spot is spreading across tech — decision-makers, overconfident in AI's potential, are rushing to implement mass layoffs without truly understanding workflows, role nuances, or the human judgment required.
In a concrete case, collaboration platform ClickUp just announced cutting 22% of its workforce, explicitly stating it will use AI Agents to take over those functions. This wave of layoffs has pushed 2026's total tech layoffs to nearly match all of 2025 — before even reaching the mid-year point — showing AI-driven workforce reduction is accelerating.
Levie's core argument isn't against AI adoption, but rather a warning: companies rushing to replace human labor with AI lack deep understanding of the work itself. When decision-makers aren't familiar with the actual complexity of the roles being replaced, they tend to overestimate AI's real coverage capability, ultimately hurting organizational efficiency. This "over-AI'd" thinking is becoming a new management risk in Silicon Valley. Full interview available at the source link.
💬 JudyAI Lab Take
The "AI psychosis" Levie pointed out is a red flag worth every AI implementer paying attention to: the execs loudest about "AI can replace this job" are often the ones most unfamiliar with that role — that's the real decision blind spot.
ClickUp's 22% headcount reduction replacing roles with AI Agents has already pushed 2026 tech layoffs near matching all of 2025 — before midyear. There's a wake-up call for the AI builder community here: the环节 where automation design fails most isn't tech selection, but insufficient understanding of the "work being automated." When designing Agent flows or workflows, skipping deep interviews with actual executors easily leads to overestimating AI's coverage — automating visible steps while missing a lot of implicit human judgment and exception handling. Levie's critique is essentially a requirements analysis problem: not understanding the true complexity of a job means more AI investment can lead to bigger organizational efficiency losses.
Before planning any AI replacement plan, talk to the people actually doing that job first. Ask them "what do you know that nobody else knows?" — that's often exactly where AI falls shortest.
References
- What happens when companies become too AI-pilled? | TechCrunch
- What happens when companies become too AI-pilled?
- Is My Company "AI Pilled"?
Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.