Editors’ note: Today’s post is by Neil Blair Christensen, Sales Director at the Molecular Connections Group.
Since participating in this year’s Society for Scholarly Publishing Annual Meeting, and through my work in the years prior, I’ve been thinking about how our scholarly publishing community engages with AI (artificial intelligence) — more specifically, what we might be avoiding.
Irving Janis, the Yale psychologist who developed the theory of groupthink from studying foreign policy disasters, found that the groups most vulnerable to it weren’t the incompetent ones. Quite the opposite. They were experienced, high-functioning ones, because their cohesion and shared identity made the cost of dissent high. This isn’t done through intimidation, but more through subtle social or professional rewards of agreement, and the quiet discomfort of being the person who asks the wrong question.
Lately, I wonder if we, the scholarly publishing community, are one of those groups.
At this year’s SSP meeting, I noticed a persistent disconnect between conversations happening outside the meeting rooms and the formal presentations inside (as other Scholarly Kitchen authors have acknowledged this year). Over coffee, people described how they’re using AI to get work done, practically and without ceremony. Inside the rooms, the conversations were more about how we as an industry should think about AI. Governance frameworks, responsible adoption, strategic positioning. Same technology, different lenses. When I mentioned this to David Worlock, he described it as the creeping edge of change moving forward without being in rhythm with the thought-leader world. That framing stayed with me.
I think the disconnect runs deeper than AI adoption pace.
If you look at the formal conversations our community has been having about AI in recent years, a pattern emerges. We debate governance. We build integrity screening tools. We invest in AI-assisted peer review, discovery, and production workflows. What we discuss much less is why those challenges exist in the first place, and whether AI tools are solving them or enabling their continued growth. This pattern can be seen elsewhere.
Peer review challenges, for example, have been topics in our industry for years and years. The conversation is mostly about how to manage volume growth and reviewer shortage. What is less discussed, is that this high volume of manuscripts is a product of academic incentive structures that force researchers to produce output as if there is no tomorrow. As we run out of people, we are adding technology to facilitate a flood that we, as an industry, have a bias in not stemming.
The groupthink is most acute in which problems we discuss solving. AI isn’t causing that. It’s just accelerating the consequences.
Every stakeholder in the scholarly publishing ecosystem has some financial or institutional stake in the continuation of high-volume knowledge production. Researchers need publications for career advancement, universities need output for rankings and funding, funders need signs of returns on investment, publishers generate revenue from processing and certifying that output, libraries are given budgets to provide access to it, and vendors sell tools to manage it. No one in that chain has a persistent structural incentive to question the added value of all that volume. Not one. So, our industry conversations, including those on AI, tend to optimize within that framework rather than interrogate it. Increasing volume and complexity require more AI to cope. The more AI we apply, the more volume and complexity we produce.
That’s not a conspiracy. It’s what Janis would recognize as normal groupthink dynamics. A collective rationalization of the status quo, if you will. An ingroup confidence that existing strengths can prove decisive against disruption, and an ingroup that defines which questions are asked.
Are we at risk of becoming a case study like the music industry, broadcasting, or Kodak? Maybe, but the risk isn’t disruption from the outside. It’s that our own incentives are the deeper problem, and AI is making them easier to sustain. We’re applying increasingly sophisticated interventions to a hamster wheel that is perpetually under strain. Profitable problems tend to persist until they don’t.
I’m not writing this to indict anyone, and I’m not exempting myself. I’m just as embedded in this community as anyone reading it — and I even sell AI solutions. I’m writing it because I think the most valuable thing a professional community can do is to distinguish between the problems it has incentives to solve, and the problems that actually need solving. Those aren’t always the same. Is more simply more? What are we solving for?
Author’s notes: I am grateful to Lettie Conrad for her suggested edits. Claude Opus 4.8 was used to polish the writing.
Discussion
12 Thoughts on "Guest Post — A Groupthink We Don’t Talk Much About"
I would argue from the point of view of a publisher, that the problem you’re talking about (basically the funding and career advancement structure of academia) is not ours to solve. How many academics would be happy with Elsevier dictating how they were allowed to communicate their research? Will the University of California be taking advice from Wiley on how they do hiring and offer tenure? Is Wellcome going to change their funding decisions based on what Springer Nature wants?
Probably not.
Publishers can play a role in innovating and presenting new alternatives to the research community, but researchers and research institutions themselves must decide on their values and what matters to them. We can offer opportunities, but uptake is not ours to dictate or force.
In the end, we are a service industry and our job is to serve the research community’s needs. The academy must make these choices for itself, and our job is to try to make that work as well as is possible.
Thank you David! I somewhat agree, but I think what I’m saying is that I’m trying to look through a different lens. I’m not suggesting that publishers must solve academia’s incentive structure. That’s impossible, and I sometimes wonder if academia is even up to that task.
What I’m detecting is the “not ours to solve” dynamic. No single stakeholder in the chain seems to have incentives to address the causes behind volume and complexity, so each of us can accurately say it isn’t our problem, and avoid that topic altogether. That’s how a collective problem stays a collective problem. A senior leader in a very large org once told me, there is power in managing problems, not in solving problems.
While we may not be able to solve it, we can choose whether to keep building interventions that make the underlying dysfunction easier to sustain, and calling that progress. I think a service industry that is solution-oriented and consultative can still ask whether it’s optimizing the right thing. We can’t all agree of course, and these are just my current thoughts (and thoughts change).
Thank you, Neil. Yes, “power in managing problems, not in solving problems.” The two groups phenomenon in democratic societies operates in many publishing systems as in many academic research systems. One group seeks solely to optimize profits and the other wonders whether, in the long term, “it’s optimizing the right thing.”
David,
I agree that the key issues are: “research output evaluation” and “reputation management”.
But I hope readers of the SK will firmly reject that they are: “not ours to solve”.
In the face of technology innovation somebody will address these needs. Maybe publishers, maybe AI or social media companies; but I’m absolutely certain this transition will not be led by academics/researchers. Why?
• Academics are employed by society to focus on research and teaching, not on the machinery/plumbing of workflow.
• Their skillset is generally not managerial (here, I resist the temptation to quote Derk Haank who expressed this in more colorful terms!).
• Academics should not be “marking their own homework”.
• Academic institutions are Balkanized and don’t have the economies of scale or objectivity to address the upcoming challenges.
• Senior academics attained their lofty positions under the current incentive structures therefore and have little psychological motivation to lead change.
You write: “In the end, we are a service industry and our job is to serve the research community’s needs.” Of course, respectfully serving academics is an important day-to-day function, but when it comes to strategic direction, publishers should keep their eye on what serves society and not limit themselves to what academics are asking for. These are not necessarily the same, and ultimately society pays for the research community to exist.
“What we discuss much less is why those challenges exist in the first place.” Perhaps we should look at the fundamental organizational structures of our countries? Most agree that the two stark alternatives, democracy and dictatorship, are far from perfect. Those who wish to do well in a dictatorship must study the dictator. But when that dictator falls there is a new dictator to study and the strategy may fail. And, those who wish to do well in a democracy must study it. They fall into two groups. First, those who match their work to social (i.e. voters’) perceptions of what is needed. They do very well by, sometimes with a shrug, accepting the status quo. Second, those who recognize this, but attempt to combine it with trying to convince all that, in the long term, we should not condemn with the phrase “curiosity orientated,” the research we wish to get supported with funds and publications. They tend to fair less well.
Thank you Donald! In the dynamic you describe, value flows to whatever fits the collective’s current sense of what’s valuable, and in publishing that sense is largely manufactured from volume and metrics. Those who point it out tend to fare less well. Did I get that right? If so, you have me thinking that I may have put my foot in my mouth!
I’m an optimist, though, so I hope that perceptions of what’s needed, and what we choose to do with technologies like AI, aren’t laws of nature, even if they currently underwrite our behaviors. That said, social dynamics run deep and take generations to evolve.
You are on to something, sir. Peer review is the squeaky wheel of the moment. The seed of the peer review issue was planted long before AI hit our radar. Submissions were increasing, which begat more journals. More journals needed editors. Those editors were younger, had smaller networks, and lacked authority in the field. We left them alone to figure out how to survive. Desk rejects plummeted. To many submissions got the full treatment with two and three reviews, just to get to a reject. At the same time submissions started coming from countries that did not previously submit. The result was leaning harder into the small number of trusted reviewers. Were publishers helping editors to expand the reviewer pool into these new countries? Not that I have seen.
David Crotty also has his hand on one leg of the elephant. That funders and institutions use publishing metrics to evaluate researchers is not our issue to solve.
Thank you Sir! The operational history is a good illustration of the resulting dynamics. More submissions, more journals, editors with smaller networks left to fend for themselves, desk reject dynamics changing significantly. Everyone leaning harder on the limited-size pools of trusted reviewers for increasing amounts of content that may not be particularly interesting to an average reviewer.
On the question of “Were publishers helping editors to expand the reviewer pool into these new countries?” In some of my prior roles, I/ we tried, but were often rejected by editors, who were worried against using reviewers from outside their existing networks, or editors resisting reviewer suggestions we could provide through automated literature analysis. Many editorial board lists signal diversity, but don’t always reflect involvement. The issue is maybe as much behavioral as structural. I agree with you on some of what David says, and have responded above.
Really thoughtful piece, Neil. I agree that the misaligned incentives behind volume growth are real, and increasingly recognized across scholarly communication. I also think David’s point is fair: publishers are, to some extent, dealing with the hand they were dealt. They did not create tenure systems, rankings, or the publish-or-perish economy.
But perhaps the danger is that every actor in the system can truthfully say “not ours to solve,” while still building tools and business models that make the underlying dysfunction easier to sustain. In this case, the concern with AI is not only as groupthink, but as a kind of collective echo chamber: more volume creates more complexity, which creates more demand for AI-enabled management of that complexity.
A hopeful note is that we do not need to wait for one grand bargain. Levers exist, and some are already being pulled by departments, funders, journals, and ethics bodies. Maybe the question is whether we use AI to reinforce the hamster wheel, or to make better research practices easier and more rewarded.
Thank you Steve! I appreciate the hopeful note, because my post was more observation than direction. I totally agree that we don’t need one grand bargain, and that levers are distributed and moving. Your closing question is one I think about too. The technology is at times less the problem than what we point it at. AI aimed at optimizing greater throughput amplifies the hamster wheel. AI aimed at rigor, reproducibility, and placing the contribution in the bigger framework could push incentives in a different direction. Where it’s the actual contribution in context of the scholarly record that came before it that matters, and less whether someone uses the ecosystem to publish 50 tweaks of the same arguments in a year. There is indeed hope and lots of agency. I do wish the finger-pointing at any one stakeholder came with more looking inward across the chain, ourselves included.
Its also worth asking … what actual/kind of problem is this? Take increasing volume … Is the problem the volume itself, really? Personally, I’m pretty much in favor of more knowledge being found out and shared. Or, is it that it is impossible to keep quality levels constant (or even higher) while volume grows? Or, is the problem that the publishing system wasn’t built to scale to handle the volume? Incentives, stakeholders, etc. are not the same across even these two possibilities.
Thank you Lisa!
These are good questions, and it seems that none of them rule each other out. Is volume a problem? Sometimes, sometimes not. What I do feel strongly is that incentives to publish are increasingly out of whack. I don’t think authors like having to publish as many studies as they do on average, or being asked to review mediocre papers, but they do it because it is expected and they think it gets them ahead. Publishers can find ways to make this happen at the appropriate price, but if the community is focused on pushing out as many studies as humans and AI can manage, then it creates ever-increasing costs at ever-shrinking returns for all involved. It creates busywork. When we reward volume decoupled from contribution, output grows faster than knowledge. All this costs time, people, and money for all stakeholders. The ‘not ours to solve’ may carve itself deeper and deeper if we don’t start to question the foundational incentives. It leads me back to what problems do we collectively have incentives to solve, and what problems actually need solving. If the collective answer struggles with this, I suspect so could the return on everyone’s efforts.
