Doing and publishing research is expensive and subject to structural roadblocks

Where it landed is not where this chain first said. Pierce, Gartenberg, Hasan and Murray put the displaced load on volunteer editors at desk screening — among manuscripts with 70%+ AI scores nearly 70% are desk-rejected, against 44% for low-AI submissions — and give no invitation or recruitment figures at all. So the arrow from cheaper drafting runs to screening, a step this chain calls tractable and AI-reached, and not to reviewer recruitment. Recruitment strain is real and separately evidenced, by Silverchair's 4.5 invitations per accepted review and the fall in acceptance from 43% to 22%; it is simply not what the 42% surge is shown to have caused. The general claim holds and the specific one does not: relieving a step upstream of a concentration moves cost downstream, and it moved to the nearest downstream step, not the furthest.

The prediction holds. AI is not absent here: it acts on the first four of seven steps, which is more than it touches anywhere on the telescope chain. It can match a reviewer to a paper. It cannot make that reviewer say yes, agree with the other reviewer, or persuade a hiring committee to count the work.

Recruitment is where the axis choice earns itself. Splitting it into matching and willingness separates a well-posed prediction problem from a labor-supply problem. On reviewer identification specifically, published evidence finds traditional statistical representations outperform generative AI. One caveat is recorded with it: ESO names matching difficulty as a reason for distributed peer review too, so the split between matching and willingness is this analysis's, not ESO's.

One observation about Convergent’s capability set, and it runs the other way from the telescope chain. Two of the four capabilities Convergent attaches to this gap, a post-publication peer review layer and new protocols for knowledge production and verification, act directly on credit and legitimacy, which carries cost. On that chain none of the three capabilities touches a decision step; here half of them do.

To be plain about scope: this chain covers getting a finished result into the record and credited. The cost of doing the research is the other half of their gap statement and is most of what the rest of the map is about.

Appendix: what I predicted before running this

Printed unedited, as written. It numbers the chains: “chain 1” is the telescope chain and “chain 2” is the publishing chain.

This chain must produce a different answer from chain 1 or the pair demonstrates nothing. AI does act on several links here: drafting and screening work now, and reviewer-to-proposal matching is tractable, though there is published evidence that traditional statistical representations outperform generative AI at identifying expert reviewers. So the expectation is not that AI does nothing. It is that AI acts on the links that were never rate-limiting, while reviewer recruitment, judgment consistency and the credit and legitimacy step remain untouched. Stated explicitly so it cannot be read the other way: this is not a claim that publishing is a cognitive bottleneck. It is not, and asserting it would be wrong. Recorded before the link analysis.

• 1. Production and drafting — Scope first, because it decides how to read this step. The axis is reviewer and editor labor per published paper, so author time sits outside it by construction. Writing the paper is very probably the most expensive thing that happens in this pipeline, and this chain does not measure it. What it can say is that AI has taken a large share out of the per-paper drafting cost, that the number of papers went up, and that the labor this displaced arrived downstream: submissions rose 42% after ChatGPT's release against the prior two-year window, in the one corpus with published full-submission figures, and the load landed on volunteer editors at desk screening. Whether total author cost across all papers fell is unknown and this chain has no way to find out. Two corrections are folded into the sentences above: the 42% is growth since ChatGPT within a five-year corpus, and the source places the displaced load at desk screening rather than at reviewer recruitment.
  Evidence: Organization Science AI Task Force, 'More Versus Better: Artificial Intelligence, Incentives, and the Emerging Crisis in Peer Review', Organization Science editorial, 2026, doi 10.1287/orsc.2026.ed.v37.n3. Roughly 7,000 manuscripts with full-submission access. Submissions up 42% since ChatGPT's November 2022 release against the prior two-year window; for scale, the COVID-19 period produced a 20% bump. The rise is almost entirely manuscripts with substantial AI-generated text, and submissions scoring low for AI have declined over the same period.
• 2. Submission and desk screening — Not binding on the cost axis. Screening is genuinely tractable for current models, which is why it is already being done and why it does not set the cost.
  Evidence: ESO reports that classical triage 'has significantly degraded the quality of feedback for the triaged proposals' — screening under load trades feedback quality for throughput.
• 3. Reviewer recruitment and matching — Binding, and it is the link that most rewards being split in two. Matching is a well-posed prediction problem where AI is applicable and, on current evidence, not even the best method. Willingness is a labor-supply problem that no matching system addresses. The binding half is the half AI does not touch, which is the whole finding of this chain in one link. One correction is recorded here: an earlier version asserted that matching is not the problem and cited ESO for it, and ESO in fact names both. The split between a well-posed prediction problem and a labor-supply problem still does the work this chain needs, but it is now stated as this analysis's distinction rather than the source's.
  Evidence: 4.5 invitations per accepted review, nearly double the 2018 rate, and reviewer acceptance down from 43% in 2018 to 22% in 2024 (Silverchair, Future of Peer Review 2026, from eight years of ScholarOne Manuscripts activity plus 2,000+ survey responses). Per 100 invitations sent, editors wait a combined 407 days on reviewers who ultimately decline or never answer. A survey of 139 editors of Australian journals, largely local and society titles, with 27 interviews, finds 55% rate finding reviewers a significant or very significant challenge, and some 'described having to send out 30 or more invitations to secure just two reviewers' (Jamali et al., Learned Publishing 2026, doi 10.1002/leap.2034, and Luca et al., doi 10.1002/leap.2041; popularized in The Conversation, 15 February 2026). The sample is national and small-journal weighted, which is where recruitment is hardest, so the bias runs in this chain's favor and is noted for that reason. Meyerson documents 21 years of declining reviewer acceptance, 2002-2024. ESO's Phase 1 page on distributed peer review states that 'it has become progressively harder to find scientists willing to serve in the panels and in the OPC'. And on the part AI could do: traditional statistical representations outperform generative AI at identifying expert peer reviewers, tested on NASA/ADS records and observatory DPR data across 379 researchers.
• 4. Review judgment — Binding, and unusually well quantified for an institutional link. The Cortes and Lawrence result also sets the bar any automated reviewer has to clear, and it is a low bar in one direction and an unreachable one in the other: filtering bad work is where review already works, ranking good work is where it does not, and it is not established that the second is a solvable problem for anyone.
  Evidence: NeurIPS 2021 consistency experiment: two independent committees disagree on 23% of papers, and approximately half the accepted list would change on a random rerun — consistent with 26% in 2014. Cortes and Lawrence, revisiting the 2014 experiment, find no correlation between review scores and later citation impact for accepted papers, and a correlation for rejected ones: 'the reviewing process for the 2014 conference was good for identifying poor papers, but poor for identifying good papers.'
• 5. Editorial decision — Not binding on the cost axis. Its cost is largely inherited from link 3.
  Evidence: Follows directly from the recruitment and judgment links; editors carry the residual cost when reviews do not arrive.
• 6. Dissemination — Not binding on the labor-cost axis, and this deserves care because it is the link their gap sentence centers on. On the inclusiveness axis — explicitly excluded from this chain — it may well be binding. That is why the axes were separated.
  Evidence: Three of Convergent's four capabilities for this gap act here: disrupting traditional publishing models, frugal science initiatives, and the micro/nanopublishing half of new protocols for knowledge production and verification. That third one is counted at link 7 as well, because its description covers both lowering the barrier to publishing and new norms for recognizing work; it is one capability acting on two steps, not two capabilities.
• 7. Credit and legitimacy — Binding, and the only link in either chain where the blocker is purely a matter of what institutions agree to recognize. Two of Convergent's four capabilities for this gap — a post-publication peer review layer, and new protocols for knowledge production and verification — act here.
  Evidence: A model can review a paper today; it cannot make a hiring committee count that review. The same asymmetry explains why reviewer supply falls: the labor is unpriced and uncredited, and pricing it is an institutional act.

Axes I deliberately left out of this chain

Speed, meaning elapsed time from submission to publication, and inclusiveness, meaning who can afford to publish and who can read the result. Their sentence bundles all three. Speed in particular behaves differently by venue: journal latency is reviewer-supply-driven while conference latency is set by a fixed program committee calendar, so a chain mixing them would produce a cost concentration that is an artifact of the venue mix. Also excluded: the 'doing' half of their gap statement. 'Doing and publishing research is expensive' covers the cost of the research itself, which is most of what the other 102 gaps in the map are about. Instruments, reagents, compute and staff are priced elsewhere. This chain runs from a finished result to a credited contribution. Also outside the axis, and worth stating because it is the biggest number in the room: author time. The axis counts reviewer and editor labor, so the weeks a researcher spends writing the paper are not in it. That is very likely the largest single cost in getting a result published, and this chain does not measure it.