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The 'Waymo effect': how frictionless AI quietly erodes research collaboration

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The Waymo effect: how AI is quietly making research less collaborative

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Riding a driverless Waymo in San Francisco, the author—an executive in the Holtzbrinck group—noticed he was delighted by a technology whose entire appeal is that you never have to talk to anyone. He names this the ‘Waymo effect’: when a tool removes the friction of dealing with another person, we register the removal as pure gain, because the costs of that friction were always obvious while its benefits were diffuse and unadvertised. Drawing on Tim Wu’s ‘tyranny of convenience’ and Albert Borgmann’s ‘device paradigm,’ he notes that the taxi driver was often the last stranger obliged to hand us an unchosen conversation—and we surrender that not by decision but one comfortable ride at a time.

The real subject is research. Large language models, he argues, are the Waymo of intellectual life: available at 2am, carrying no rival agenda, and willing to challenge your work only exactly as hard as you ask. A human collaborator’s inconvenience—the unbidden insistence that you’re solving the wrong problem, that a rival group abandoned this in 2019, that your framing collapses on contact with another field—isn’t a bug in collaboration; it largely is the collaboration. Research is a community of practice held together by argument, apprenticeship, and shared ordeals, and every conversation redirected from a colleague to a chatbot withdraws a thread from that fabric. At scale, he calls the result ‘decollaboration.’

What makes this more than a matter of personal discipline is the incentive structure. Tightening budgets first cut the unmeasurable line items—travel, workshops, sabbaticals—that form the physical infrastructure of serendipity. Evaluation systems still reward output and speed, which LLMs promise above all. And unlike a co-author, a model never demands a share of the credit, making it not just convenient but, in a system where credit is survival currency, a form of arbitrage. The piece is a warning to research leaders and funders that collaboration is being engineered away by default, not by choice.

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