The Jersey Pump Principle: why AI’s trillion-dollar bet could stall like a 1949 gas pump law

Through the lens of Silicon Valley and Wall Street, the path forward for artificial intelligence looks entirely pre-ordained. Four dominant tech hyperscalers are on track to spend an unprecedented $650 billion on data centers and infrastructure this year alone.

A narrow group of AI enablers now carries nearly half the total value of the S&P 500 — some measures now put the AI-linked share closer to 50%–57%, meaning the concentration is arguably even more extreme than the headline number suggests — and the buildout has become so massive that it is driving the lion’s share of U.S. GDP growth. The overarching consensus in Silicon Valley and on Wall Street is clear: The technology works, and the capability is unprecedented; ergo, mass adoption is an inevitability.

History teaches a different lesson, and the last few months have provided a series of flashing red lights regarding what could lie ahead. In April, a man motivated by anti-AI sentiment attacked OpenAI CEO Sam Altman’s home. In May, college graduates entering a workforce where the technology is the primary reason for job cuts met commencement speakers hailing the AI revolution—including former Google CEO Eric Schmidt at the University of Arizona—with boos.

June began with Anthropic co-founder Jack Clark publicly urging governments to build regulatory mechanisms to slow the technology down and President Donald Trump signing a cybersecurity executive order establishing a voluntary, 30-day national security review for frontier models. And we learned that frontline workers—particularly those from Gen Z—increasingly admit to quietly undermining and sabotaging the efficacy of their employers’ internal AI rollouts.

The tech sector and the broader corporate world rushing to deploy its tools are increasingly suffering from technological determinism: the fallacious assumption that society is a passive operating system that will automatically update to accommodate new capabilities and infrastructure.

Disruptive technologies do not scale based on their abstract, mathematical capability. They scale based on human incentive structures. Human nature doesn’t necessarily limit technological innovation, but it determines the terms of its adoption. Stakeholder alignment will determine how—and where—AI is successfully adopted. When you align the incentives correctly, adoption moves at warp speed. When you ignore them, the human system will build a firewall to freeze the technology in place.

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