Academia Is Enshittifying. AI Made It Faster.
A small retraction notice opens onto a large structural shift. AI did not start the rot in academic publishing. AI is the catalyst that is finishing it.
In May 2025 a meta-analysis in Humanities and Social Sciences Communications told higher education exactly what it wanted to hear. ChatGPT, the paper concluded, improves students’ learning performance, learning perception, and higher-order thinking. The paper was widely read and cited. Last month it was retracted. Thirty thousand people have read the retraction. The citations have not.
The retraction is the easy part. The harder part is the system the retraction sits inside, and the role AI now plays in accelerating that system past anything human-paced oversight can hold.
In this post I will:
Use one retracted meta-analysis to look at a much larger structural shift in academic publishing.
Walk through the three stages of enshittification, the way Cory Doctorow named them, and show what each stage looks like inside a journal.
Trace what AI catalyses at every stage (production, peer review, retrieval, metrics, publishing) and where the last human handhold in the chain still sits.
Disclosure: I serve as Chief Executive Editor of Geoscience Communication and as an editor for Humanities and Social Sciences Communications, the journal that published and retracted the paper this post discusses. I had no editorial role in the paper, its peer review, or the retraction decision. Across both roles I have edited several hundred submissions and read several thousand more inside the academic publishing system this post examines. I am writing here as a scholar working inside that system, not as a representative of any journal or publisher.
What the retraction notice does and does not say
The retraction notice runs to forty-five words. It says:
“The Editor has decided to retract this paper owing to concerns regarding discrepancies in the meta-analysis. These issues ultimately undermine the confidence the Editor can place in the validity of the analysis and resulting conclusions. The authors have not responded to correspondence regarding this retraction.”
It does not say what the discrepancies were. It does not say which conclusions are now in doubt and which (if any) still hold. It does not say what to do if you have already cited the paper, or built a teaching plan around it, or pointed to it in a doctoral thesis, or quoted it in a Department for Education briefing.
The notice does what retraction notices are designed to do. It marks a record. It does not chase the record’s downstream effects.
Why the paper was always going to find what it found
I want to resist a particular reflex here. The reflex is to look at a retracted paper and ask whether the authors were dishonest, sloppy, or unlucky. That is a story about people. It is comforting because it has villains.
The slower story is about the system that produces these papers and cannot stop producing them. Publish-or-perish rewards positive findings, fast turnaround, and alignment with whatever the dominant narrative happens to be. Many open-access journals run on article-processing-charge funding, and the pipeline rewards volume. Humanities and Social Sciences Communications sits in that publishing model alongside many of its peers. New research shows that LLMs are changing the language of academic papers. AI-assisted writing has arrived at the throughput layer of academic publishing. The pipeline now runs faster than the oversight it depends on.
The question ‘why was this paper retracted’ has an answer. The question ‘why does the system keep producing papers like this’ does not, because answering it would require redesigning the system. It’s easier to just retract the paper.
Confirmation did the work of evidence
The paper confirmed what readers were already inclined to believe. The author affiliations and the open-access logo did the credibility work. The doubt sat where doubts usually live, in a tone designed not to interrupt the reader’s plan to cite the abstract.
I wrote earlier in the year about the ICLR 2026 peer review collapse, where roughly one in five reviews submitted to the world’s largest AI research conference was written by AI. That story and this one share a layer. The visible event was a security breach at ICLR and a retraction here. The structural event is the same in both: the system that is supposed to validate research can no longer keep up with the volume of research it is asked to validate, and AI is now sitting on both sides of the validation step. The reviews are AI-written. The papers are AI-written. The retraction is a human noticing.
The three stages of enshittification, applied to academia
Cory Doctorow’s word for what happens to platforms when they mature is enshittification. The arc has three stages. First the platform is good to its users, because it needs them. Then the platform extracts from its users to attract business customers, because the business customers pay. Then the platform extracts from its business customers to enrich shareholders, until the platform is bad for everyone except the people taking money out of it. Search, social media, retail, transport, accommodation. Same arc, different decade.
Doctorow’s stages are about users, business customers, and shareholders. In academic publishing the equivalent positions are scholars, publishers, and metric-keepers. The translation is not exact (the capital is differently distributed, the time horizons are slower), but the structure is recognisable. Academic publishing is on the same arc, with longer timelines.
Stage one. Journals served scholars. Editors were scholars. Reviewers were scholars. Readers were scholars. The journal existed because scholars needed to talk to each other across distance, and the production cost of a journal was paid out of learned-society subscriptions and university libraries. This phase ran from the Philosophical Transactions in 1665 until roughly the post-war scaling moment.
Stage two. Journals served publishers. Five companies (Elsevier, Springer, Wiley, Taylor and Francis, Sage) control more than half of all peer-reviewed publications in the natural and medical sciences and the social sciences. The scholars continued to write, review, and edit for free, because the metrics they were judged on were issued by the publishers. Subscription bundles became obscenely expensive. Article processing charges quietly became a parallel business model. The publisher’s loyalty shifted from the scholars who produced the work to the institutional libraries who paid for it. The product was still usable. The friction was real but bearable. This is the phase that ate the second half of the twentieth century.
Stage three. Journals serve their own metrics. Impact factor, h-index, citation count. The metrics started life as proxies for quality and quietly became the thing itself. Junior scholars optimise for the proxy. Senior scholars judge by the proxy. Publishers raise prices on the proxy. Editors are increasingly chosen and judged by the proxy. The quality the proxies were supposed to track is, at this stage, almost incidental. This is the phase we are in.
What does the user experience in each stage? In stage one the user is the producer; they get what they wanted. In stage two the user pays in money and labour, and gets something usable with delays. In stage three the user runs a literature search and receives thousands of plausible-looking results, most of which exist to feed the metric, many of which are noise, a small subset of which contain the signal they were looking for. The user does the filtering work the system was supposed to do.
The Wang and Fan retraction is a stage-three artefact. The journal caught a flawed paper, eventually, and that catch is what the editorial layer is supposed to do. It is also the part of the apparatus the rest of the system has stopped supporting. The notice is shorter than the paper. The retraction does not propagate to the metric. The paper continues to accrue citations from systems that did not get the memo. The catch is real. The catch is the bare minimum, performed by the one layer the metrics have not yet succeeded in cannibalising.
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What AI catalyses, at every stage
The catalyst frame matters because AI did not start this. The enshittification arc was already in motion. AI catalyses every stage at once.
At the production stage, AI writes the paper. Abstracts, literature reviews, methods sections, discussions. Meta-analyses are particularly vulnerable because they look like aggregation work and AI aggregates fast. The marginal cost of producing a publishable-looking paper has collapsed.
At the review stage, AI writes the review. Roughly one in five reviews at ICLR 2026, as above. The labour to slow down and check is precisely what the system stopped rewarding once stage three began. I see this from inside the editorial workflow: reviewers under volume pressure increasingly return reports that read like model output, and the tooling to tell the difference is not keeping up with the rate at which the practice is spreading.
At the retrieval stage, AI summarises the literature for the next researcher. The summary may include the retracted paper without flagging it. The summary may include papers that do not exist. The summary may include citations that were invented for plausibility.
At the metric stage, AI suggests citations, generates them, and helps researchers game the h-index. Citation-recommendation services are now AI-driven; some are honest, some are not. A small industry exists to inflate scholar metrics directly.
At the publishing stage, AI is now used in some journal workflows to draft editorial decisions, retraction notices, marketing copy, and institutional responses to controversy. From my own editorial work I see this entering the layer; from other studies it is clear that the rollout is wider than the individual editor sees. The forty-five words by the editor of Humanities and Social Sciences Communications are one of the last places in this whole apparatus where a human still has to write something specific about a specific paper.
The retraction is the last human handhold. Everything around it, before and after, is increasingly produced by a layer in which AI does the work and humans assist the AI, rather than the other way round.
Citations outlive their evidence
Retraction Watch tracks retractions. Google Scholar tags retracted papers. Most other systems do not. Citation managers, institutional repositories, doctoral theses, government strategy papers, university policy documents, consultancy decks, slide packs for vice-chancellors, briefings for ministers, marketing copy for EdTech vendors: none of these propagate retractions backward.
Imagine the finding cited in a ministerial briefing, or in the procurement decision behind a chatbot rollout across tens of thousands of undergraduate accounts. The retraction never reaches the briefing. The chatbot is still rolled out. The citing paper is still cited by the next paper.
The Wang and Fan paper has been cited in subsequent academic work since May 2025. Some of those citations will be revised. Most will not. The downstream evidence base for “ChatGPT helps students learn” is now standing on one less leg than it was last month, and the people who built on top of it have not been told.
The system, not the technology
The publishing pressure that produced this paper would have produced a flawed paper without AI. AI accelerated the throughput. The rot was already there. I have written before that AI is just a catalyst, a way of speeding up whatever the surrounding system already rewards. This case is a textbook version of that argument. The system was rewarding speed and positive findings before ChatGPT existed. It will reward them after.
The slippage I want to flag is this: the question ‘is AI making research worse’ and the question ‘is research itself getting worse’ are different questions. The first one has a satisfying answer (we can ban AI, mandate disclosure, run detection software). The second one does not. The first one occupies most of the discourse. The second one is the one that matters.
The publish-or-perish machine
The career structure of academia rewards the production of artefacts. It does not reward the validity of those artefacts. The exact rate varies by field and country, but the structural pressure does not: an early-career researcher with a three-year postdoc, in most disciplines, needs to leave it with somewhere between five and a dozen publications to be competitive on the next contract. That works out to one paper every two to four months, including weekends, including teaching, including the bench work or fieldwork or coding that the paper is supposed to describe. The arithmetic was already tight before AI arrived to assist with the writing. AI did not create the timetable. It just made the timetable survivable.
The metric is the h-index, not the insight. The job application is a citation count, not a body of thought. In the biomedical and physical sciences, senior scholars run labs of fifteen or twenty postdocs and put their name on everything those postdocs produce; author lists grow accordingly. In the humanities and social sciences the lab-as-factory model does not hold, but the citation and productivity pressures translate into other shapes: sole-authored monographs replaced by edited collections, single-author journal pieces replaced by co-authored ones, the same volume pressure transmitted through different infrastructure. The PI’s name (or the senior author’s) accumulates citations on work they did not always write. Whether the work is good is a separate, slower question that the metrics do not ask.
Then the citations beget citations. A finding that confirms what the field expects gets cited more than one that complicates it. A finding that aligns with the funding climate gets cited more than one that questions it. The ‘ChatGPT improves student learning’ result was always going to be highly cited because there was already a demand for the citation. Universities, ministries, EdTech companies, consultants and managers all needed a reference to point at. The paper supplied a reference. Whether the analysis behind it held up was, in the literal sense, the editor’s problem to discover later. I write that with the perspective of someone who handles a steady volume of editorial decisions across multiple journals: the structural pressure on editors to clear submissions in weeks rather than months is real, and it is not balanced by any structural reward for taking the extra month.
A retraction does not undo a citation. A retracted paper continues to appear on the CV of the people who wrote it, where some assessors will count it and some will not. The citation count of a retracted paper continues to rise, slowly, because new papers cite it from old bibliographies. None of the structural rewards for producing the paper are recovered when the paper is withdrawn. The system makes it cheap to publish and expensive to retract, and the asymmetry sits on the side of whichever party benefits from the original publication.
What academic publishing looks like now
Take a step back and ask what the publishing layer actually looks like, in 2026, when AI is at every step.
AI drafts abstracts. AI generates the literature review (the most automatable part of any paper). AI writes the methods section, especially the boilerplate. AI produces plausible-looking meta-analyses by aggregating effect sizes that a careful reader would interrogate and a hurried reader will accept. AI drafts the peer review reports that go back to the authors. At the ICLR 2026 collapse roughly one in five reviews submitted to the world’s largest AI research conference was written by AI. AI suggests citations, including, sometimes, citations to papers that do not exist. AI writes the editorial decisions in some workflows. AI writes the institutional response when a journal has to explain a retraction.
The system response to this is to publish more. Major publishers now produce hundreds of thousands of articles a year each across their open-access stables. The retraction rate is rising fast; Retraction Watch tracked record annual numbers across 2023, 2024 and 2025. The validation infrastructure was built when knowledge production was slow and validation was tractable. Both assumptions are now dead. The infrastructure has not been redesigned; it has been overloaded, and overload looks like a healthy publishing rate if you measure it from the wrong angle.
What does this look like to a reader? Every literature search returns thousands of plausible-looking results. Every citation in any bibliography may itself rest on a citation in another paper that may be retracted, or that may rely on a methods section the authors did not entirely write, or that may have been peer-reviewed by an AI that did not really read it. The ground beneath your reading shifts. Some of it is solid. Some of it is hollow. You cannot tell from the surface.
The retraction notice is one of the few places where a human still has to write something specific about a specific paper. Forty-five words. They are the last load-bearing human handhold in the chain. Everything around the notice, before and after it, is increasingly the product of a system in which AI has moved from feature to substrate. We are no longer publishing ‘with AI’. We are publishing inside it.
This is the part the discourse keeps stepping around. Calling it ‘AI in academic publishing’ makes it sound like a tool we are using. The honest description is closer to a layer we are now operating within. The tool framing implies an off switch. The layer framing implies a building we did not design and cannot exit, only modify from inside while it continues to function around us.
Questions I do not have answers to
I am putting these down here anyway.
Who is responsible for the citations of a retracted paper? The authors of the retracted paper? The authors of the citing papers? The editors? The institutions that funded the citing work?
If a piece of evidence that supported a policy is retracted, does the policy need to be re-justified, or is the original justification preserved because it was made in good faith at the time?
If retraction is bookkeeping after the fact, what would prevention look like? Caps on publication rates? Smaller journals? Mandatory replication before citation count accrues?
When a finding everybody wanted to be true turns out not to be, who is left holding the consequences? The students who were taught with the policy built on it? The doctoral candidates who built their thesis on its scaffolding?
I want to resist the impulse to round these off into a neat answer. The instinct of a newsletter is to land. The instinct of the publishing system that produced the Wang and Fan paper is also to land. Maybe the more honest move, when the system is rewarding speed over judgement, is to refuse to land.
Go slow.
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The IT industry is similar in that it rewards deception and superficial quick wins, while punishing effort and care. The same, it would seem applies through much of life in all in all its forms. The silver lining of AI, at least in how I see things, is that it accelerates this to the point where the strategy no longer works.
I am so glad that I dropped out of the publish or perish race because it's pointless in a way. Good ideas need time to grow.
Enshittification that's what it is. 😂
I think authors need new models of publishing — Substack is one way of doing this.