Educational Technology and Change Journal

By Jim Shimabukuro (assisted by ChatGPT-5 and Grok)
Editor

Introduction: This conversation began with ChatGPT. I was curious about university libraries and the range and depth of curated resources that would be accessible via AI chatbots. I assumed the results would determine, to a large extent, the quantity and quality of research students and faculty would be able to produce. The discussion brought us to the realization that AI alone would not offset the information access advantage of top-tier institutions. The imbalance would remain. We then pursued a best-case scenario that might eventually level the playing field. At the end of the conversation, I appended a discussion with Grok about the budget disparities between top- and mid-tier universities. -js

JS: Are the literary works of authors usually studied for college degrees in English Literature accessible by AI chatbots?

ChatGPT: Yes—but with some important nuances.

Most of the literary works that students study in English Literature programs fall into three broad categories in terms of AI chatbot accessibility:

1. Public Domain Works (freely accessible)

• Examples: Shakespeare, Chaucer, Milton, Austen, Dickens, many 19th-century American and British writers, most classical literature, older poetry.
• AI accessibility: Full texts are freely available and widely included in training datasets for AI models. Chatbots can read, quote, analyze, and even generate commentary on them without restriction.
• Implication for students: AI can function almost like a study companion—summarizing passages, comparing themes, or generating essay outlines.

2. Copyrighted Works (partially accessible)

• Examples: Modern and contemporary authors (e.g., Toni Morrison, Salman Rushdie, Margaret Atwood, Kazuo Ishiguro, post-1929 works in general).
• AI accessibility: Full texts are not openly accessible to chatbots due to copyright restrictions. Models like me may know about these works (plot, themes, criticism, summaries, reception history), but I cannot provide or reproduce them in full.
• Implication for students: Chatbots can help with context, summaries, thematic discussions, or secondary criticism, but cannot replace reading the primary text itself.

3. Critical Scholarship (mixed accessibility)

• Examples: Academic journal articles, critical essays, monographs, theory texts.
• AI accessibility: Some older criticism is public domain or open access (e.g., early editions of literary journals), but most current scholarship is behind paywalls (JSTOR, Project MUSE, university presses).
• Implication for students: AI can provide overviews of critical debates, summarize major schools of thought (e.g., feminist, postcolonial, structuralist readings), and point students toward resources. But direct access to current scholarship usually requires a university library or database subscription.

So in short:

• Yes, most canonical works before ~1929 are fully accessible to AI chatbots.
• For modern works, AI can provide summaries and analysis but not the full text.
• Scholarly criticism is hit-or-miss depending on whether it’s open access.

Would you like me to create a ranked breakdown of an English Literature degree’s typical reading list (say, the 50–100 most commonly assigned authors), showing which ones are public domain and which are copyright-restricted? That could give you a clear picture of where AI can be most helpful.

---

JS: At what point in time, if ever, will works (literature and reviews) written after ~1929 be “conditionally” accessible to chatbots? Can we assume that higher ed institutions are providing or will eventually provide chatbot gateways through specific paywalls as part of their degree program offerings?

ChatGPT: Great questions — these go to the heart of how copyright, licensing, and higher ed infrastructure will shape AI’s role in literary study. Let’s break it down:

1. When will post-1929 works become openly accessible?

• In the U.S., copyright typically lasts for the life of the author plus 70 years (for works published after 1978) or 95 years from publication (for older corporate/anonymous works).
• That means:
  • Works published in 1929 entered the U.S. public domain in 2025.
  • Each January, another year’s worth of works advances into the public domain (“Public Domain Day”).
  • So in 2026, 1930 works; in 2027, 1931 works; and so on.
  • Toni Morrison’s Beloved (1987), for example, would not enter the public domain until 2082 at the earliest.

Thus, full open access through AI to most 20th- and 21st-century works won’t happen for many decades.

2. Conditional accessibility (via licensing)

Even though copyright blocks free access, AI chatbots can still interact with works if publishers, authors, or institutions license them. This is starting to happen:

• Music and film streaming provide the model — platforms pay rights holders, and users access works legally.
• Ebook libraries (OverDrive, JSTOR ebooks, Project MUSE) already license literary works to universities.
• AI integrations are the logical next step: a university could license a set of works and allow its AI assistant to deliver excerpts, analysis, or even full-text interaction.

3. Higher education as a gateway

• Today: Universities already provide access to subscription databases (JSTOR, Project MUSE, ProQuest, Gale, etc.). Right now, students must search these separately.
• Tomorrow (likely within 5–10 years): We can expect AI-powered research portals that unify these sources. A student might ask the campus chatbot: “Compare feminist readings of Atwood’s The Handmaid’s Tale from 1990 to 2020.”
  The bot would then pull from licensed criticism behind JSTOR or Project MUSE, cite sources, and provide summaries.
• Some schools are experimenting with AI “library copilots” that sit on top of existing subscriptions.
• Eventually, we may see consortial agreements (similar to interlibrary loan) where AI systems can query across multiple university paywalls, with usage tracked and compensated to publishers.

4. Reviews and criticism

• Older reviews (pre-1929, early 20th-century) are already public domain and freely trainable.
• Contemporary criticism (journal articles, reviews, theory texts) remains behind paywalls. Here too, conditional access will depend on licensing.
• Universities are strongly motivated to integrate this into AI study tools, because students increasingly expect “one interface, everything included.”

5. The likely trajectory

• 2025–2030: Early pilots of AI integrated with library systems; limited to licensed materials. Students still need to log into databases separately in many cases.
• 2030–2040: Broad adoption of AI gateways. Universities position AI assistants as the “front door” to both public domain and licensed works.
• 2040+: Gradual expansion as more 20th-century works roll into the public domain. By mid-century, much of the early-to-mid 20th-century canon will be free for direct AI use.

Bottom line:

• Works after ~1929 won’t automatically become accessible to AI until their copyrights expire (95 years after publication).
• But higher education institutions will almost certainly license access through chatbot gateways, just as they do with journal databases today.
• This could make studying literature through AI not only possible but actually more efficient than current methods, since the AI could unify and contextualize diverse sources in real time.

Would you like me to map out what a future “AI-enhanced English degree” might look like (say in 2035), showing how a student would actually experience reading, analysis, and criticism through these licensed chatbot gateways?

---

JS: When you say, “But higher education institutions will almost certainly license access through chatbot gateways, just as they do with journal databases today,” what year are you targeting as a possible breakthrough?

ChatGPT: Good push — it’s important to pin this down to a plausible time frame rather than hand-wave. Based on current trends in higher ed tech, publishing, and AI adoption, here’s the most realistic forecast:

2025–2027: Pilot Stage

• A handful of research libraries and ed-tech companies experiment with AI copilots linked to licensed content.
• Current examples:
  • JSTOR’s Constellate (text-analysis of their archives) is AI-friendly.
  • ProQuest and Gale have started testing AI-based discovery tools.
• Limitation: These are internal experiments; not system-wide, not yet deeply integrated into degree programs.

2028–2030: Early Breakthrough

• By the end of the decade, expect first formal chatbot gateways at large research universities.
  • Students ask an AI, and it pulls not just from open-web knowledge but from JSTOR, Project MUSE, ProQuest, etc. — because the university licenses them for AI use.
  • Rights management is handled through usage-tracking, much like streaming royalties.
• Publishers are already incentivized: they don’t want to be bypassed if students rely on free/open AI instead of going through paywalled journals.

I would call 2028–2030 the first visible breakthrough window.

2031–2035: Standardization

• Most major universities adopt AI-first library access as a norm.
• Instead of teaching freshmen how to navigate five different databases, schools present a single AI interface that knows the student’s degree program and can surface readings, criticism, and licensed works.
• At this point, “chatbot gateways” aren’t pilots anymore — they’re the default entry point to scholarship.

2040s+: Expansion + Public Domain Growth

• By the 2040s, early- to mid-20th-century works flood into the public domain.
• AI gateways become even more powerful because they combine open-domain texts with licensed late-20th-century and contemporary works.

So, if I had to name a breakthrough year:

• 2029 is my best target for the first serious, university-wide chatbot gateways with licensed access to both primary works and critical reviews.
• That’s when early adopters (probably Ivy-level or tech-forward universities) will roll out AI assistants that feel like a real replacement for today’s database-hunting.

Would you like me to sketch what a day-in-the-life of an English Lit student in 2029 would look like using one of these first AI gateways? That might make the breakthrough more tangible.

---

JS: So your projection for 2031-2035 is that an English Major at a credible US university will be able to access all important works (literature and reviews) via a single AI interface? Are we assuming that course fees might or will include these access costs?

ChatGPT: Yes — that’s the trajectory I see. Let me break it down carefully:

2031–2035 Projection for English Majors

1. Single AI interface as the library front door

• By early 2030s, it’s highly likely that an English major at a reputable U.S. university will log into a unified AI assistant (like today’s campus portals, but smarter).
• That assistant will:
  • Retrieve primary texts (public domain and licensed).
  • Surface critical reviews and journal articles from JSTOR, Project MUSE, ProQuest, Gale, etc.
  • Cite sources properly and provide links back to the full-text databases.
  • Tailor results to the course syllabus — for example, if you’re in a seminar on Modernism, it can prioritize T.S. Eliot, Virginia Woolf, and relevant criticism.

2. Which works will be accessible?

• Public domain works (pre-1930s) → Fully accessible without licensing.
• Mid-20th-century and contemporary works → Accessible through institutional licenses (just as they already are today for ebooks and journals, only delivered through AI).
• Critical reviews and scholarship → The major publishers (Taylor & Francis, Wiley, Oxford UP, Cambridge UP, university presses) are likely to license through AI gateways, because otherwise their journals risk becoming invisible to students.

So yes — “all important works” for coursework purposes (primary + criticism) should be within reach via the AI portal.

3. Who pays for it?

• Yes, course fees or tuition will likely bundle access costs.
  • Already, universities negotiate “big deals” with publishers to give students blanket access to journals.
  • In the AI era, the same model applies: the university pays a licensing fee for AI access on behalf of all students.
  • For students, it feels “free” at point of use — just like logging into JSTOR today.
• Some institutions may also tack on a “digital resources” fee (like current textbook or technology fees).
• This cost will be invisible in elite schools (folded into tuition) but may be itemized in public universities where tuition transparency is required.

4. Equity considerations

• The AI gateway model helps reduce inequality in English studies:
  • Today, students at wealthy universities get better database access than those at small colleges.
  • In the future, consortial licensing (state systems, regional compacts) may ensure that even less wealthy schools provide robust AI gateways.
• But there’s still a risk: private universities may pay for exclusive integrations (say, more complete archives or contemporary author interviews), creating tiers of access.

Bottom Line

• 2031–2035: An English major at a credible U.S. university will almost certainly access all required literary works and critical reviews through a single AI portal.
• Yes, costs will be embedded in tuition/fees, much like current library subscriptions.
• The key change is interface and efficiency: instead of siloed databases, students will simply ask the AI — and it will deliver exactly what they need, licensed and legal.

Would you like me to map out a sample licensing bundle (primary works + journals + reviews) that a university English department might negotiate for its AI gateway around 2032? That would show how the “all important works” claim could be practically implemented.

---

JS: So, by 2031-2035, the research for undergraduate and graduate papers, theses, and dissertations could be completed by English Literature majors at reputable universities via institutional chatbots? This would effectively bypass the need for purchasing or using hardcopy resources or even the need to visit campus libraries? If yes, then the elimination of costs related to physically archiving and maintaining hardcopy resources would be substantial to the point where student fees might be reduced?

ChatGPT: You’ve drawn the line to its logical conclusion — and yes, the scenario you describe is very likely by 2031–2035, though with a few caveats. Let’s parse it:

1. Research Workflow in 2031–2035

• Undergraduates:
  • They’ll conduct almost all research for essays and projects inside the university’s AI research portal.
  • The chatbot gateway will combine primary texts, secondary scholarship, and citation tools.
  • Visiting the physical library will be optional, not essential.
• Graduate students (MA, PhD):
  • For theses/dissertations, the same applies. Institutional AI will integrate licensed monographs, journals, dissertations (via ProQuest), and archives.
  • The AI won’t just retrieve sources — it will synthesize themes across dozens of works, saving students weeks of manual searching.
  • For cutting-edge research, grads will still occasionally need special collections (rare manuscripts, letters, author archives). But for the 95% of work that’s text-based? The chatbot covers it.

2. Bypassing physical libraries

• Yes: The need for hardcopy in day-to-day research will almost vanish.
  • Students won’t need to buy hardcopy books for class unless for personal preference.
  • Most general collections of physical books may be de-prioritized.
• But not eliminated entirely:
  • Rare books, first editions, and archival materials still require preservation.
  • Humanities scholarship often values the materiality of texts (marginalia, binding, historical context). These remain in library special collections.

So: libraries won’t disappear, but their core function shifts from general lending → special collections, preservation, and digital infrastructure.

3. Cost implications

• Yes, physical archiving costs could shrink dramatically.
  • Libraries today spend vast sums on building maintenance, shelving, preservation, and circulation systems.
  • If 70–80% of general print acquisitions disappear by the 2030s, universities can downsize stacks, reduce storage, and reallocate staff.
• But student fees won’t necessarily shrink.
  • Universities tend to reallocate savings rather than pass them directly to students.
  • Maintaining AI research infrastructure will involve licensing fees to publishers, which may rival today’s subscription costs.
  • In fact, publishers might price AI-access higher because the usage is more valuable (students will use it far more intensively than static PDFs).

In other words: the form of the cost changes (from physical infrastructure to digital licensing), but net costs may hold steady rather than drop.

4. Campus libraries in the 2030s

Instead of being book warehouses, libraries will evolve into:

• Digital access hubs (AI systems, VR/AR archives).
• Study + collaboration spaces (quiet rooms, media labs, presentation areas).
• Special collections centers (rare manuscripts, archives).
• Digital preservation labs (maintaining access to old formats, digitizing collections).

So, the visit to the library will shift from “to find a book” → “to engage with unique or preserved resources.”

Conclusion
By 2031–2035, yes — English Lit majors at reputable universities will be able to complete virtually all standard research (papers, theses, dissertations) through institutional chatbots, without needing hardcopy resources or frequent library visits.

However:

• Physical preservation of rare materials will still matter.
• Costs for archiving shrink, but licensing and AI infrastructure costs grow.
• Net tuition/fees may not decrease — universities are more likely to reallocate savings than reduce them.

Would you like me to sketch a side-by-side cost comparison (today’s print-heavy model vs. 2035 AI gateway model) to show how savings shift from shelves to servers?

---

JS: Do you “envision” a time or possibility when governments or massive private endowments might shatter or transform paywalls to democratize access to the types of published works that are critical to schlarship in all or most fields at all levels of education?

ChatGPT: This is a deep and important question — it pushes beyond technology into politics, economics, and values. I’d say: yes, the possibility exists that governments or private endowments could transform (even shatter) paywalls around scholarship and literature. But the timing and form depend on a few big forces:

1. Precedents that suggest it’s possible

• The Open Access (OA) movement:
  • Europe’s Plan S (launched 2018) requires publicly funded research to be freely available.
  • NIH in the U.S. requires taxpayer-funded research to be deposited in PubMed Central.
• Internet Archive, HathiTrust, Project Gutenberg: Nonprofit, large-scale digitization projects already democratize access to older works.
• Private philanthropy: The Gates Foundation, Mellon Foundation, and others have poured billions into open educational resources (OER).

These examples show both government regulation and philanthropy can pry open access when the public good is clear.

2. Why the paywalls persist

• Academic publishing is a multibillion-dollar industry.
• Major players (Elsevier, Taylor & Francis, Wiley, Springer) earn margins rivaling tech companies by controlling distribution.
• Universities are locked in because prestige journals and monographs still define tenure and promotion.
• Publishers argue their services (peer review, editing, indexing) justify costs.

So, dismantling paywalls isn’t just a technical matter — it’s a fight against entrenched business models.

3. Possible tipping points

I can see three plausible breakthrough scenarios:

a) Government-led transformation (most likely in Europe first)

• A government (or bloc, like the EU) mandates that all publicly funded scholarship must be open.
• Over time, this forces even private publishers to shift to open-access models (funded by publication fees, consortia, or subsidies).

b) Philanthropy + AI catalyst

• A massive endowment (say, from a tech billionaire or coalition) underwrites the cost of open-access publication and archiving.
• AI increases demand for integrated, global knowledge → creating pressure for open repositories.
• Imagine a “Library of Alexandria 2.0,” free to the world, funded by endowment returns.

c) Disruptive leakage or shadow libraries

• Sci-Hub already provides pirate access to millions of papers.
• If AI depends on large corpora, the tension between copyright and open research could spark new “civil disobedience” archives, eventually normalizing openness.

4. When could this happen?

• 2025–2030: Piecemeal expansion of open-access mandates for government-funded research.
• 2030–2040: A potential tipping point. AI-driven scholarship makes paywalls feel obsolete and unjust, accelerating political and philanthropic pressure.
• Mid-to-late 21st century: A genuine possibility of global open scholarship, at least for research and criticism. Literature (novels, poetry, etc.) may remain behind copyright longer, but critical infrastructure could become universal.

5. Educational impact if it happens

• K–12 and higher ed would gain unprecedented equality of access.
• Students at a small rural college in Montana or Kenya could query the same AI-integrated archive as a PhD at Harvard.
• Physical library disparities would matter less; intellectual opportunity would expand dramatically.

My vision:
Yes, I can imagine a moment — perhaps by the 2030s–2040s — when governments and/or massive philanthropic coalitions decide that knowledge is infrastructure, like roads or electricity, and must be universally open. The combination of AI demand for large knowledge pools and growing resentment at paywalls could finally shatter the current publishing model.

Would you like me to sketch two future scenarios — one where paywalls remain dominant and one where they collapse into open access — to show how different the experience of an English Literature major (or any scholar) would be under each system?

---

JS: Yes, please sketch a future scenario, with embedded timeliines, in which paywalls collapse into open access.

ChatGPT: Perfect — let’s build this as a scenario narrative with a timeline so you can see the progression from today’s paywalls to a world of near-universal open access.

Future Scenario: The Collapse of Paywalls into Open Access

Phase 1: Cracks in the Wall (2025–2030)

• 2025: Governments and universities grow frustrated with soaring subscription costs. Student reliance on AI assistants exposes how fragmented access is.
• 2026: The EU strengthens Plan S, requiring all publicly funded research to be open immediately (no embargo). The UK follows suit.
• 2027: A coalition of foundations (Mellon, Gates, Sloan, Arcadia) announces a $2 billion Open Knowledge Fund to underwrite OA publishing fees and digitization of scholarly monographs.
• 2028: U.S. federal agencies expand mandates for open access to taxpayer-funded research within one year of publication. Major state university systems (California, Texas, SUNY) begin negotiating OA-first contracts with publishers.
• 2029: The first AI-integrated library portals go live at several major universities, offering students seamless access to OA and licensed resources. Pressure mounts: why paywalls for research that AI can summarize from open repositories?
• 2030: Elsevier and Wiley face antitrust scrutiny in the EU for monopolistic pricing. The debate over whether knowledge should be treated as a public utility reaches the European Court of Justice.

Phase 2: The Tipping Point (2031–2037)

• 2031: India, Brazil, and South Africa form a “Global South Open Science Alliance,” committing to fund OA infrastructure rather than pay subscription fees.
• 2032: Philanthropists led by the Gates Foundation and new AI billionaires launch the Library of Humanity Initiative, a perpetual endowment to make research and core educational texts open worldwide.
• 2033: The U.S. Congress passes the Open Knowledge Act, requiring all federally funded research and educational materials (including university-press monographs from federal grants) to be open-access within six months of publication.
• 2034: Facing global boycotts from universities and pressure from AI providers, Elsevier announces a “transformative deal” to flip all journals to OA by 2038, funded by author-side fees, consortial subsidies, and AI licensing revenue.
• 2036: With most critical scholarship now OA, universities begin decommissioning print stacks at scale. Campus libraries shift to digital preservation, special collections, and AI research services.
• 2037: The majority of global peer-reviewed scholarship is freely accessible online. AI assistants at universities now query one universal knowledge graph that includes scholarship, criticism, and most primary texts up to the late 20th century.

Phase 3: Knowledge as Infrastructure (2038–2045)

• 2038: UNESCO declares universal access to scholarly knowledge a “human right.” The U.N. establishes a Global Knowledge Commons, pooling digitized works across nations.
• 2039: The AI education boom pushes publishers to focus on value-added services (annotation tools, interactive editions, VR/AR experiences of texts) rather than restricting access to PDFs.
• 2040: Harvard, Oxford, Tokyo, and Cape Town announce they are OA-only institutions, refusing to use tuition money on paywalled content. Others quickly follow.
• 2042: A generation of students grows up having never hit a paywall for scholarly research. AI assistants democratize advanced education: a high schooler in rural Kenya can research Toni Morrison or climate change with the same resources as an Ivy League undergrad.
• 2045: The term “paywalled scholarship” is mostly historical. While copyrighted literary works (novels, plays, poetry) remain under author control until expiry, all scholarly reviews, criticism, and research are open-access by global norm.

Life of an English Lit Major in 2045

• A sophomore in English Lit asks her university’s AI assistant: “Trace the influence of Virginia Woolf on postcolonial women writers in the 1980s.”
• In seconds, the assistant synthesizes:
  • Primary texts (Woolf’s A Room of One’s Own, freely available).
  • Critical reviews from the 1930s–2020s (all OA).
  • Scholarly debates from across continents, pulled from the Global Knowledge Commons.
• She never worries about subscription barriers or interlibrary loans. Instead, she focuses on interpretation, not access.

Summary:

• 2025–2030: Cracks appear through mandates, philanthropy, and AI pressure.
• 2031–2037: Tipping point — major publishers flip to OA, governments legislate access, philanthropy builds global infrastructure.
• 2038–2045: Paywalls collapse into obsolescence. Knowledge becomes public infrastructure, universally accessible through AI gateways.

Would you like me to sketch the opposite scenario too — where paywalls harden instead of collapse, creating an “intellectual elite” world by 2045? That contrast might make the stakes clearer.

---

JS: Can we assume that authors and reviewers of primary and secondary resources in post-paywall scholarship will continue to earn recognition, renown, and remuneration via “newer” business channels, eliminating or reducing the profits of current business monopolies?

ChatGPT: Yes — that’s a fair and even likely assumption. If paywalls collapse (or transform) in the 2030s–2040s, the structures that reward authors and reviewers won’t disappear, but they’ll shift away from today’s publisher-controlled monopolies. Let’s unpack how recognition, renown, and remuneration could work in a post-paywall, open-access world:

1. Recognition & Renown (the prestige economy)

• Continuity: Scholars don’t publish primarily for money today — they publish for reputation, tenure, promotion, and influence. That will continue in OA.
• Shift in prestige:
  • Today: Prestige is tied to the brand of the publisher/journal (e.g., Elsevier, PMLA, Cambridge UP).
  • Tomorrow: Prestige shifts to platform-independent impact metrics: citations, downloads, AI integration visibility, public engagement.
  • Example: An essay with 200k global reads through the Global Knowledge Commons may carry more prestige than one locked in a high-impact but paywalled journal.

2. Remuneration (the money economy)

Currently, authors of academic works earn little or nothing; publishers capture the profits. In a post-paywall system, revenue could flow differently:

a) Publication fees (already happening)

• OA journals today often use Article Processing Charges (APCs) paid by universities or grants.
• By 2035, this model may be normalized but subsidized by governments, endowments, or AI companies.
• Authors still don’t “make” money, but they don’t pay either — institutions or consortia cover costs.

b) Platform revenue-sharing

• If AI gateways (or the Global Knowledge Commons) track usage, micro-compensation systems could emerge:
  • Every time a student or AI queries your article, the system logs engagement.
  • Annual micro-payments flow to authors/reviewers, similar to streaming royalties.
• This turns scholarship into a kind of intellectual streaming economy — modest payments but scaled globally.

c) Philanthropic & state stipends

• Governments or foundations may directly fund scholars to produce open research outputs (similar to how artists or scientists receive fellowships).
• In this model, remuneration shifts from publisher royalties → researcher stipends.

d) Ancillary monetization

• Scholars might earn more through talks, consulting, media appearances, or AI-enhanced teaching materials.
• Recognition from open-access reach could translate into broader career opportunities, even if the publication itself isn’t directly lucrative.

3. Business model transformation

• Publishers as service providers, not gatekeepers.
  • Instead of charging for access, publishers sell editorial, peer review management, AI-enhanced annotation, and curation.
  • They compete on quality of services rather than control of access.
• Universities and consortia as funders.
  • The money flows from universities (via grants, tuition, state support) directly to OA platforms, not to subscription monopolies.
• AI platforms as intermediaries.
  • If AI companies profit from integrating scholarship, they’ll pay licensing or access fees back into the OA ecosystem.

Timeline of Shifts

• 2025–2030: Growing OA mandates, but authors still unpaid; universities cover APCs.
• 2031–2037: New models of usage tracking emerge; first micro-compensation systems appear for scholars.
• 2038–2045: Recognition is primarily impact-driven (citations + AI engagement). Remuneration stabilizes around stipends, royalties from AI queries, and secondary career benefits. Publisher monopolies shrink; service-oriented platforms thrive.

Bottom line:
Yes — authors and reviewers will still earn recognition and renown, perhaps even more widely in an open world, and remuneration will flow through newer channels (usage-based royalties, institutional stipends, philanthropic support, ancillary opportunities). The collapse of paywalls doesn’t collapse scholarship; it collapses the publisher profit monopoly, redistributing value toward creators and institutions.

Would you like me to model out a 2040 “compensation snapshot” (e.g., how a literature professor might earn recognition + income in a post-paywall, AI-integrated world)? That could make the mechanics more concrete.

---

JS: What’s the likelihood of a YouTube-type monetization system being adapted for scholarly publishing, which might transfer costs to advertisers?

ChatGPT: This is a sharp question — because a YouTube-type monetization system (ad-supported, with creators earning revenue shares) is exactly the kind of disruptive model that could emerge once paywalls weaken. Let me assess the likelihood and mechanics of this happening in scholarly publishing:

1. Why it’s plausible

• Massive user base: Millions of students and scholars worldwide access research daily. That’s the kind of attention economy advertisers crave.
• Precedent in entertainment/media: YouTube, Spotify, TikTok, and Substack have proven that “free to user, ad-supported” can scale globally.
• AI as amplifier: Once AI assistants become the primary gateway, they’ll log billions of queries daily — each one a chance to serve contextual ads or sponsored recommendations alongside scholarly results.

2. Why it’s tricky

• Academic culture clash: Scholars are wary of commercial intrusion. The idea of an ad for “Pepsi” next to a Shakespeare analysis feels absurd (and damaging to credibility).
• Advertiser alignment: Ads would need to be hyper-relevant (educational tools, conferences, publishers, ed-tech, career services, software, etc.), not generic consumer ads.
• Ethical risks: Monetization could skew visibility: research that draws more clicks or ads may be promoted over niche but important work.

3. Adapted Models (likely hybrids, not pure YouTube clones)

a) Sponsored access layers

• Example: A database is free/open, but certain features (advanced annotation tools, AI synthesis, datasets) are “sponsored by Microsoft Research” or “powered by Google AI.”
• The ads are ambient, not intrusive, preserving academic seriousness.

b) Institutional subscription + ad-supplement

• Universities still pay bulk fees, but costs are offset by ads or sponsorships.
• E.g., “This AI query service is supported in part by Springer Nature + Coursera.”

c) Micro-royalty + advertiser pool

• Every time an article is queried via AI, the author earns a micro-payment.
• Instead of universities paying 100% of the cost, advertisers (ed-tech companies, academic publishers, professional organizations) contribute to the royalty pool.

d) Scholar-driven channels

• Just as YouTubers create their own channels, scholars might create OA research channels (curated content, explainers, datasets).
• These could earn ad/sponsor revenue tied to engagement. Think: a “LitCrit Channel” with millions of views on feminist readings of Atwood, monetized by ed-tech sponsors.

4. Timeline likelihood

• 2025–2030: Low adoption in scholarship — ad models clash with traditional academic norms.
• 2030–2037: First experiments with sponsorship-driven OA platforms (e.g., Elsevier or JSTOR hosting free portals with ed-tech branding).
• 2038–2045: Moderate adoption of advertiser-supported features, especially in AI gateways. By now, global OA is the norm, so advertising/sponsorship may help offset universal access costs.

5. Probability estimate

• Direct YouTube-style ad monetization of every article: Low likelihood (~20%) — the culture of academia resists direct commercialization.
• Adapted sponsorship/micro-royalty models with advertiser backing: High likelihood (~70%) — advertisers become silent partners funding the infrastructure, while scholars and institutions remain the face of credibility.

Bottom line:
We’re unlikely to see a full YouTube-for-research with banner ads and pre-rolls. But we’re very likely to see sponsorship-driven open access, where advertisers (often education-aligned) help fund infrastructure, and scholars receive micro-royalties or stipends from advertiser-supported pools. This would reduce costs for universities and students while still rewarding creators.

Would you like me to sketch a mock-up of what a 2040 AI research query interface might look like with subtle sponsorships (e.g., “Powered by Oxford University Press + Coursera”)? That might help make the monetization feel concrete rather than abstract.

---

JS: What’s the probability that aversion to commercial ads in schloarly media may be a generational thing and not much of a problem with digital and AI natives?

ChatGPT: Excellent follow-up. You’ve put your finger on a key cultural variable: scholarly aversion to ads may not be timeless — it might be generational. Let me break down the probability and dynamics:

1. Current generational divide

• Boomers & Gen X (born <1980):
  • Grew up with print-dominated academia where prestige = journals, presses, libraries.
  • Commercial intrusion = corruption of scholarly integrity.
  • Very high aversion to ads in scholarly settings.
• Millennials (1980–1995):
  • Hybrid generation: accustomed to both print and digital.
  • See ads as annoying but tolerable if they enable free access (e.g., YouTube, Spotify).
  • Mixed stance on ads in scholarly contexts — suspicious but pragmatic.
• Gen Z & Gen Alpha (1995–2015+):
  • Digital and AI natives. Ads are ambient, baked into almost every platform (TikTok, Instagram, YouTube).
  • Strong sense that “free = ad-supported” is normal.
  • More likely to care about relevance of ads than their existence.

2. Factors moderating aversion

• Context sensitivity:
  • If ads are intrusive (pop-ups, autoplay videos), rejection will remain high.
  • If ads are subtle, contextual (“This article brought to you by JSTOR, supported by Coursera”), younger users may see it as harmless.
• Prestige signaling:
  • If elite institutions (Harvard, Oxford, Stanford) endorse ad-supported open access, resistance will collapse quickly.
  • If they resist, prestige bias will keep ads out longer.
• Transparency of compensation:
  • If scholars and reviewers visibly earn micro-royalties from ad revenue, younger academics may view it as fair recognition rather than corruption.

3. Probability assessment

• Short-term (2025–2030):
  • Aversion remains strong among senior faculty (Boomers/Gen X still dominate editorial boards, tenure committees).
  • Probability that younger academics normalize ads = ~30–40%.
• Medium-term (2030–2040):
  • Generational turnover: digital natives rise into faculty and leadership roles.
  • Probability that aversion diminishes significantly = ~60–70%.
• Long-term (2040+):
  • With Gen Z/Alpha in control of academia, ads are seen as simply another funding stream.
  • Aversion reduced to niche holdouts. Probability of normalization = ~80–85%.

Bottom line:
Yes — ad aversion in scholarly media is largely generational. By the 2040s, when digital/AI natives dominate universities, the debate will likely shift from “ads or no ads?” to “which ad models preserve credibility and fairness?”

Would you like me to map a scenario timeline showing when ad-tolerant generations enter the ranks of grad students, junior faculty, tenured faculty, and finally university leadership — so you can see when resistance to ads might collapse institutionally?

---

Libraries of top-tier universities spend approximately three times more on journal subscriptions

JS: Am I correct in assuming that there’s a positive correlation between college prestige ranking and student access to the top journals in the majors they offer? For example, students doing research at Harvard, MIT, and Stanford have access to more of the top journals in their fields of study than students at mid-tier and lower institutions?

Grok: Yes, your assumption is largely correct. There is evidence of a positive correlation between university prestige rankings and the level of student access to top academic journals, primarily driven by differences in library budgets and resource allocations. Prestigious institutions like Harvard, MIT, and Stanford typically have significantly larger library expenditures, which enable them to subscribe to a broader array of high-impact journals and databases compared to mid-tier or lower-ranked schools. This disparity stems from wealthier endowments, higher overall funding, and prioritization of research infrastructure at top-tier universities.

For context, library materials expenditures (which heavily include journal subscriptions) vary notably by institutional type and region. In 2020, U.S. academic libraries in regions with a higher concentration of doctoral/research institutions (often top-ranked) averaged higher collections spending—e.g., $1.73 million in the Northeast versus $1.09 million in the Southeast. Doctoral institutions, which dominate prestige rankings, allocate more to collections overall, reflecting their ability to afford extensive subscriptions to top journals in various fields.

Empirical studies reinforce this. One analysis of U.S. universities found statistically significant associations between higher rankings (e.g., U.S. News & World Report) and greater library investments, including materials expenditures that support journal access. Similarly, a study of Indian higher education institutions (while not U.S.-specific, the patterns align) reported a significant positive correlation (r = 0.511, p < 0.001) between library expenditures and national ranking scores, with higher-ranked schools spending more per user on resources like journals.

Practical spending data highlights the gap: Tier 1 U.S. universities average around $6 million annually on journal access, compared to about $1.8 million for Tier 2 institutions. Across sampled U.S. libraries, subscriptions consume roughly one-third of materials budgets, with average costs per title around $21, but top-tier schools can sustain far more titles due to their scale. While open access initiatives and interlibrary loans can mitigate some differences, direct institutional access to a wider range of top journals (e.g., in specialized fields like physics or medicine) remains a clear advantage for students at elite schools.

That said, access isn’t absolute—many mid-tier universities still subscribe to core top journals (e.g., via packages like JSTOR or Elsevier), and factors like open access growth are narrowing the gap somewhat. But overall, the correlation holds based on budget and resource disparities.

Filed under: Information Access Gap |