Sami Mahroum
Given current AI capabilities, does it matter who conducts scientific research? The intuitive response is that a fact is a fact, regardless of who finds it. But knowledge is never disseminated that way. We receive most scientific claims without any means of checking them ourselves, so we tend to judge not the claim but the credibility of whoever stands behind it. As the Harvard historian of science Steven Shapinshows, the production of scientific truth has long been inseparable from the social work of persuading one’s peers. My own research on scientific mobility, conducted a quarter-century ago, pointed in the same direction from a different angle. Reputation in science does not reside in ideas alone; it concentrates in a small number of institutional hubs: departments and laboratories whose standing lends weight to whatever emerges from them. A scientist leaving a leading centre carries part of its credibility along and can, under the right conditions, turn the receiving institution into a centre of gravity in its own right.
Credibility has always needed a carrier, and the identity of that carrier has changed over time, often in the face of fierce resistance. When the authorship of the natural order passed from God to a realm open to human inquiry, the resistance was violent. When the individual scholar gave way to credentialed institutions, peer review, and research assessments, some critics objected that science had become overly bureaucratic and impersonal. And now, with authorship poised to pass from humans to machines, the objection is framed in terms of attribution, originality, and merit.
In all these cases, what we are being asked to trust has changed. And now the system that governs research, from mission-oriented funding to impact assessments, has gradually shifted its attention from whether a result is understood to whether it works, and machine-generated findings suit an enterprise that already values output over understanding. The irony is that two decades of effort to make data open, searchable, and machine-readable was supposed to ease collaboration among human researchers; but it also created the very substrate on which autonomous research now runs. AlphaFold, which earned Demis Hassabis and John Jumper a share of the 2024 Nobel Prize in Chemistry, was trained on the Protein Data Bank, the accumulated work of an entire community of structural biologists. Systems that generate and test their own hypotheses, and then write up the results, are no longer a thought experiment.
Last year, a paper written entirely by Zochi, an AI system built by the US startup Intology, passed peer review in the main track of the Annual Meeting of the Association for Computational Linguistics, a top-tier AI conference. And Robin, an AI system built by the San Francisco nonprofit Future House, proposed the hypotheses, designed the experiments, and analyzed the results that identified an already-approved drug as a candidate for treating a leading cause of blindness; its work has since appeared in Nature. Suppose that such a system delivers a new vaccine, catalyst, or battery chemistry with no identifiable human author. The public is unlikely to care who did it, any more than patients ask which technician ran their blood test. What they will care about is whom to hold responsible if something goes wrong. A named author can be questioned, corrected, made to retract a claim, or discredited; a model cannot be. Human authorship was never a guarantee of reliability, as failed replications and retractions attest. But it did always give us someone to hold responsible for a result.
With a few exceptions, leading journals have refused to list AI systems as authors, on the grounds that authorship entails attribution and accountability. But this merely preserves the old arrangement, rather than solving the new problem. A recent survey found that nearly half of scientists (47%) use AI daily, and another 31% use it weekly. The more capable these systems become, the more widely they will be used. Because most people won’t be able to tell good science from bad science, the human scientist will increasingly become the validator, the source of trust that shared knowledge requires. But the process of validation is already struggling to keep up. Nearly half of scientists who report saving time with AI then spend more than one-quarter of that time checking its output, while their backlog of untested hypotheses grows.
Moreover, peer review itself is being automated, with an analysis of submissions to this year’s International Conference on Learning Representations suggesting that about one in five reviews was fully AI-generated. One possibility is that trust attaches to where the technology was built, with the frontier AI companies becoming the de facto arbiters of what counts as sound science—much as pharmaceutical firms were before independent regulators existed to check their claims.But another possibility is that the institutions that spent generations accumulating credibility—universities, academies, national laboratories, scholarly journals—will develop or certify scientific AI agents of their own, so that trust continues to flow through channels the public already has reason to regard as accountable. Robin’s findings acquired their standing not from Future House’s own announcement, but from peer review at Nature. Nonetheless, this role will have to be earned, and quickly.
Academic journals are already struggling with paper mills, machine-generated submissions, and an overstretched system of peer review. The US National Institutes of Health now accepts no more than six applications per year from any principal investigator and will not consider applications substantially developed by AI to be applicants’ original ideas. The outcome will be decided by who moves first to build the infrastructure of accountability: tamper-proof records of every dataset, model version, and inferential step behind each result; independent systems tasked with reproducing results without reference to the one that produced it; and, at the end of the chain, a named guarantor, be it a person or an institution, that is liable if the result fails. Every generation has had to decide who carries scientific credibility, and ours must now weigh this question again.
The writer is Founder and CEO of Spark X.
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