Between 1830 and 1886, American railways faced a coordination crisis: 23 independent gauge decisions created a fragmented network where the Southern Railway & Steamship Association's coordinated conversion of approximately 11,500 miles in May-June 1886 solved the integration problem through institutional coordination.
In leading edge research from the University of Washington, frontier LLMs now exhibit 71-82% output homogeneity, potentially linked to RLHF (Reinforcement Learning from Human Feedback) alignment, suggesting that enterprises relying on multi-model decision-making inherit a coordination solution that has accidentally reversed itself - diversity in form, convergence in substance.
Both episodes reveal how systems driven by local optimisation and local switching costs create paradoxical fragility: railroads needed standardisation to escape fragmentation, but AI needs the reverse - escape from the standardisation that alignment inadvertently engineered, risking epistemic monoculture in open-ended problem-solving contexts where diverse perspectives strengthen solutions.
If railroads required crisis-driven coordination to reverse fragmentation, what institutional innovation can reverse AI's unintended convergence?
Profiled research:
The Artificial Hivemind - https://arxiv.org/pdf/2510.22954.
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