AI removes frictions that organizations used to rely on: 50 examples with evidence

A compilation of scientific papers, news articles, blog posts, and anecdotal evidence that AI/LLMs have dropped the cost of specific interaction types to near-zero, threatening to overwhelm recipients and systems.

#How to read this file

Within each of the 50 types, exhibits are ranked best-first on importance + relevance + persuasiveness combined:

• ★★★ Lead exhibit — Named institution, checkable number, primary or near-primary source.
• ★★ Supporting — solid corroboration that the pattern is not a one-off.
• ★ Background — real but thin, dated, secondary, or interested-party.

⚠️ marks a claim that is unverified, interested-party, or contested. Evidence current to August 2026; types whose newest evidence is older carry a note saying so.

#Communication & Outreach

#1. Personalized Cold Emails

• ★★★ AI Now Generates Majority of Spam and Malicious Emails (Infosecurity Magazine) — By April 2025, 51% of all spam emails were AI-generated rather than human-written. The crossing of the 50% line is the single most quotable fact in this type. Corroborated by the underlying academic work: AI now powers over half of spam emails (Columbia Engineering) — accepted to ACM IMC 2025, the first study to quantify AI's role in modern spam.

• ★★ Half the Spam in Your Inbox Is Generated by AI (Barracuda Networks, June 2025) — Independent corroboration of the ~50% figure from a security vendor with visibility into mail flow. ⚠️ Interested party, but the finding matches the Columbia result.

• ★★ AI-Generated Phishing: The Top Enterprise Threat of 2026 (StrongestLayer) — AI-generated phishing achieves click-through rates more than 4× higher than human-crafted counterparts; 82% of phishing emails now contain AI-generated content, up 53% YoY. ⚠️ Vendor research, numbers not independently replicated.

• ★★ The Power of Personalized Outreach with LLMs and RAG (Selling Power) — Documents the cost collapse concretely: LLMs with retrieval cut email drafting from 15 minutes to 45 seconds per lead at a California cybersecurity firm. The clearest per-unit cost datapoint in this type.

• ★ The Cold Swarm: Why Your AI-Personalized Pitch Is Dead on Arrival (Zak El Fassi) — AI-generated cold emails at ~0.001eachvs. 15 for human-crafted; 10,000+ daily messages vs. 50 for human teams; open rates down 23%; reply rates ~5%. ⚠️ Blog post, none of these figures traced to a primary; the "practical extinction by 2027" prediction is frequently laundered into serious writing and should not be. Use the cost ratio as illustrative only.

• ★ Major Spam Email Warning (TechRadar) — Secondary reporting of the Infosecurity finding.

• ★ Cold Email in 2026: The Spam Filters Are Watching (TextPolish) — Google and Microsoft have trained filters on billions of AI-generated emails. ⚠️ SEO content marketing; unverifiable.

#2. Personalized Cover Letters

• ★★★ Signaling in the Age of AI: Evidence from Cover Letters — Cui, Dias & Ye (Yale, 2025) — The best single piece of evidence in the entire project for the cost-as-filter collapse mechanism, because it measures the signal loss directly. 5 million cover letters across 100,000+ Freelancer.com jobs. AI use raised callback rates 3.56pp (+51%), while the correlation between tailoring and callbacks fell by 51% — the signal content of tailoring was destroyed even as its use rose. Employers shifted to alternative signals (prior work history). This is the theory and the evidence in one paper.

• ★★ AI Is Killing the Cover Letter (Knowledge at Wharton) — Judd Kessler's framing: cover letters signalled quality (writing) and interest (tailoring); AI makes both cheap, so "a tailored cover letter became a prerequisite rather than a differentiator." The cleanest statement of the mechanism, from a named economist. Pairs perfectly with Cui et al.

• ★★ State of Job Search 2025 (The Interview Guys) — Job seekers using AI complete 41% more applications; applications up >45% YoY; ~11,000 applications per minute on LinkedIn as of July 2025. ⚠️ Industry report.

• ★★ AI's Impact on Hiring in 2025 (Resume Genius) — 74% of hiring managers have encountered AI-generated content in applications; share of applicants using AI for cover letters or résumés more than doubled Feb 2024 → Jan 2025. ⚠️ Vendor survey.

• ★ 62% of Employers Reject AI-Generated Resumes Without Personalization (Resume Now) — 90% of employers report more spam applications; 94% have encountered misleading AI-generated content. ⚠️ Vendor survey.

• ★ AI Adoption in Recruiting: 2025 Year in Review (HeroHunt) — 64% of recruiters noticed an uptick in look-alike AI résumés, increasing screening workload. ⚠️ Vendor.

#3. LinkedIn Connection Requests with Personalized Messages

⚠️ Newest solid evidence is 2025.

• ★★★ LinkedIn Removed 200M Bots (FraudBlocker) — 200 million bot accounts removed, ~16.7% of the user base. Bot removals nearly quadrupled in six years: 21.5M in H1 2019 → 83.4M in H1 2025. 252M+ pieces of content removed in 2024; spam and scam content up 80% over five years. The only hard volume data in this type. ⚠️ Aggregator site compiling LinkedIn's own transparency figures — worth tracing to LinkedIn's transparency report before publishing.

• ★★ LinkedIn's War Against Bot Scrapers Ramps Up as AI Gets Smarter (Bloomberg Law) — Credible outlet on the platform's legal and technical response.

• ★★ LinkedIn Bots and Spear Phishers Target Job Seekers (Malwarebytes) — AI-crafted messages tailored to victims' profiles; fake investors that ask tactical questions and request elevator pitches.

• ★ My Experience with LinkedIn Spam Now Powered by ChatGPT (Mark Decker) — First-person account; bots conversing at graduate level before pivoting to WhatsApp/Telegram. Anecdote, useful as colour.

• ★ AI Comes to LinkedIn Spam (Hype Cycles) and AI Bot Spams LinkedIn with Cybersecurity Posts (Commsrisk) — Blog-level documentation of the same pattern.

#4. Letters to Elected Officials

• ★★★ Here's What Happened When ChatGPT Wrote to Elected Politicians (The New Republic) — The Cornell field experiment: 32,000 policy advocacy emails to 7,000+ state legislators, half student-written and half GPT-3. Response rates were nearly identical — 15.4% for AI vs. 17.3% for human, a gap under 2 points, with AI higher on some issues. A randomized experiment showing the recipient cannot price the difference, which is the cleanest possible demonstration that the filter has already failed. Primary coverage: Lawmakers Struggle to Differentiate AI and Human Emails (Cornell Chronicle).

• ★★★ AI-Generated Public Feedback Raises New Questions for Lawmakers (Governing, 2025) — Lawmakers inundated with AI-generated constituent comments, including the CiviClick case (20,000+ comments, contacted individuals unable to confirm they had sent them). Cross-listed to types 5 and 49, where the fuller treatment lives; kept here because the constituent-communication channel is the one being corrupted.

• ★★ Artificial Intelligence, Participatory Democracy, and Responsive Government (Brennan Center for Justice) — The best institutional analysis of how AI threatens the integrity of constituent communications and what responses are available. Brennan Center is the most credible non-partisan source in this cluster.

• ★★ What's Happening with AI in Constituent Engagement? (POPVOX Foundation) — Names the specific mechanism that defeats deduplication: malicious actors can generate false "constituent sentiment" at scale by effortlessly creating unique messages on any side of any issue. Nonpartisan civic-tech foundation.

• ★ How AI Threatens Democracy (Journal of Democracy) — Peer-reviewed framing: volume floods political communication and undermines efforts to understand constituent sentiment.

⚠️ The randomized evidence here is from 2023. A 2026 replication, or evidence of legislative offices changing intake procedures, would be the highest-value addition to this type.

#5. Public Comments on Proposed Regulations

• ★★★ Barrage of Emails From AI Politics Platform Defeats Clean Air Initiative (Futurism, 2026) — CiviClick's platform generated 20,000+ comments opposing Southern California air quality regulations, contributing to a 7–5 board vote rejecting the measure. Typical comment volume on such items is "counted on one hand." The campaign organizer said CiviClick "made a massive difference in turning the tide." At least three contacted individuals were unaware their names had been used. This is the strongest exhibit in the entire civic cluster: a named tool, a counted volume, a recorded vote, an admission of efficacy, and identifiable victims of impersonation. Corroborated by AI-Generated Comments Swayed California Air Decision (GovTech).

• ★★★ Bots, Fake Comments, and E-Rulemaking (International Journal of Public Administration) — The FCC net neutrality docket received 22 million comments, of which only ~800,000 were authentic once likely fakes were stripped. Pre-LLM, which is why it is essential: the comment channel was already broken by cheap automation before generative AI, so the AI story is an acceleration, not an origin. Popular version: How a Bot Made 1 Million Comments Against Net Neutrality Look Genuine (Quartz) — of 22M comments, 17M (77%) favoured repeal; among authentic comments, 99% opposed it. The inversion is the point. ⚠️ Note the discrepancy across sources: Nextgov reports 7 million comments traced to fabricated identities, the IJPA analysis implies far more. Use the IJPA figure and cite it, or state the range.

• ★★★ Modernizing the Regulatory Comment Process (White House OMB, Nov 2024) — Executive direction to OIRA to consider guidance on mass comments, computer-generated comments, and falsely attributed comments. Primary-source evidence that the executive branch formally recognized the problem, and the strongest institutional-response item in this type.

• ★★ House Bill Targets AI-Generated Comments in Rulemaking (Nextgov) — The Comment Integrity and Management Act of 2024 passed the House unanimously, requiring agencies to identify and manage AI-generated comments, publish a single representative version of mass comments, and state the count of computer-generated submissions. ⚠️ Passed the House; not law.

• ★★ Will ChatGPT Break Notice and Comment for Regulations? (GWU Regulatory Studies Center) — Demonstrates ChatGPT generating convincing comments with minimal prompting, and notes that Regulations.gov's CAPTCHA and API rate limits provide only partial protection. The clearest statement of the institutional stake: if actors can flood with limitless unique comments, agencies cannot learn genuine public preferences.

• ★★ Investigation Reveals 'Deeply Disturbing' AI-Powered Operation (The Cool Down) — Establishes that CiviClick was not a one-off: a North Carolina campaign used it to support a gas pipeline expansion, and a Bay Area campaign used a similar platform, Speak4. The repeat-use finding is what turns CiviClick from an anecdote into a pattern.

• ★★ Filtering Out the Noise (NYU Proceedings) — Whether the Administrative Procedure Act even permits agencies to use AI/ML to filter AI-generated comments. The legal bind — flooded by AI, possibly barred from using AI to cope — is an excellent paper hook.

• ★★ Agencies are now using AI on the receiving end. The CDC has used a generative AI tool to analyse public comments, and ex-officials warn the practice creates litigation risk for actions taken on AI-summarized records. The both-sides-use-AI equilibrium arriving with a legal-vulnerability twist — and a direct instance of the §11 co-optation response colliding with the §8 legal one.

• ★ AI Astroturfing Helped Kill Clean Air Rules in Southern California (FalseSolutions.org) — Quotes Samuel Woolley (University of Pittsburgh): what we are seeing with AI is the next step in digital astroturfing. ⚠️ Advocacy site; use for the Woolley attribution only, and verify the quote against a primary before putting it in quote marks.

• ★ Artificial Intelligence and Public Comment (Council of State Governments), Robotic Rulemaking (Brookings) — Framing pieces without new data.

#6. Letters to the Editor / Op-Ed Submissions

One of the better-measured types, because scholarly letters are indexed and countable in a way that newspaper letters are not.

• ★★★ Letters to Scientific Journals Surge as 'Prolific Debutante' Authors Likely Use AI (Science/AAAS) — Analysis of 730,000+ PubMed letters: the number of authors publishing 10+ letters in their debut year rose 376% after LLMs arrived. One researcher published 84 letters across 58 different topics in a single year with zero prior publications. These "prolific debutantes" were 3% of active authors but produced 22% of letters (nearly 23,000). This is among the best exhibits in the entire project — a large indexed corpus, a clean pre/post comparison, a concentration statistic, and an individual case so extreme it needs no interpretation. It belongs in any published version.

• ★★★ An Emerging Issue in Journal Publishing: The Use of AI to Generate Letters to the Editor (PMC) — Peer-reviewed documentation of nearly 100 highly critical letters across many fields, likely AI-generated to pad CVs. Names the incentive precisely — letters are the cheapest indexed publication available, so they are the first thing to be industrialized when writing cost collapses.

• ★★ AI Likely Driving Surge in Letters to the Editor (Inside Higher Ed, Nov 2025) — One journal received 43 AI-flagged letters through mid-2025, more than in any previous full year; detection tools flagged content at 46–100% AI-generated. Editor quote on being swamped by what they must read and get through. ⚠️ The flags come from unspecified detection tools; see response_taxonomy.md §1 on treating detector output as measurement rather than proof.

• ★★ AI-Generated Text Is Overwhelming Institutions (Schneier & Sanders, The Conversation, Feb 2026) — Documents newspapers being inundated by AI-generated letters as one strand of the cross-institutional pattern. Also available at Schneier's site; see Cross-Cutting References.

• ★ Institutions Are Drowning in AI-Generated Text (Fast Company) — Popular-press version.

Gap: all the measured evidence here is from scholarly letters. No newspaper has published letters-to-the-editor submission volumes. The newspaper claim rests on Schneier & Sanders' assertion.

#7. Personalized Fundraising Appeals

⚠️ Supply-side type. The evidence establishes that personalized appeals are now free to produce; none of it measures donors or nonprofits being overwhelmed. Ranked accordingly, and the type should be presented as cost collapse demonstrated, flooding not yet documented.

• ★★★ Generative AI in Political Advertising (Brennan Center for Justice) — AI can synthesize information about a target audience and generate messages tailored to its interests, and AI-generated arguments can be more persuasive than human-crafted ones when tuned to the audience. The persuasion finding is what distinguishes this from ordinary direct mail: it is not only cheaper, it is better. Credible, non-partisan, and it connects to the causal persuasion evidence in type 29.

• ★★ The Use of AI by Election Campaigns (LSE Public Policy Review) — Peer-reviewed analysis of campaigns generating personalized ads and fundraising solicitations at scale.

• ★★ AI-Powered Personalization in Philanthropy (Fidelity Charitable) — Hyper-personalization from donation history, political affiliation, and expressed interests. ⚠️ Interested party, but a major donor-advised-fund sponsor describing the practice to its own donors is meaningful normalization evidence.

• ★ Engaging 'Everyday' Donors with AI-Powered Relational Fundraising (Candid) — "Relational fundraising at scale" — the phrase is worth noting because it names the oxymoron at the heart of this whole project: relationship signalling, mass-produced.

• ★ Generative AI Fundraising (DonorSearch), DonorPerfect's Fundraiser Bot — ⚠️ Vendor marketing; evidence that the capability is productized, nothing more.

#8. Customer Complaint Letters

The strongest qualitative-flooding evidence outside government services — the form Schmitz et al. find dominant.

• ★★★ How AI Is Changing Customer Complaints in Automotive Retail (Freeths, 2026) — The best qualitative-flooding exhibit in the project outside government services. Customers now produce 1,000 words of "legal" argument in seconds: long, structured, quasi-legal letters citing legislation (often misapplied), with emotionally persuasive narratives, for routine issues. AI tools misquote the Consumer Rights Act, misinterpret warranty terms, and invent case law — and staff must still read, investigate, and respond carefully even when the content is inaccurate. Customers also escalate earlier, raising formal complaints before speaking to the dealership, pushing cases into formal processes unnecessarily. Every element of the thesis is here: cost collapse, asymmetric verification burden, hallucinated authority, and process distortion. A law firm advising the receiving industry — closer to a primary account than most sources in this project.

• ★★★ When AI Is the Editor, Consumer Complaints Are More Likely to Succeed (Yale Insights) — The cleanest beneficial-flooding exhibit in the project, and it comes with numbers (recorded in responses_1_9.md): AI-assisted complaints to the CFPB rose from essentially zero to 9.8% by March 2024 — 16 months after ChatGPT — and AI-edited complaints achieved a 49.3% success rate versus 39.9% for human-authored ones, a 9.4-point advantage, with the effect most pronounced among non-native English speakers. That last clause is the crux of the whole project: the population most penalized by AI detection is the population most helped by AI assistance. The same mechanism that overwhelms the queue demonstrably helps the individual complainant, and no inspection of the complaint separates the two. Lead with this whenever the beneficial-flooding tension is argued, and pair it with Freeths, which shows the cost side of identical behaviour. ⚠️ The CFPB figures are recorded in this project's own compilation; trace them to the underlying study before publishing.

• ★★ AI in Complaints and Disputes: The New Reality (Shiv Martin) — Longer narratives, more annexures, more confident legal framing, greater repetition: "more to sift, more to verify, and more time spent clarifying what is actually in dispute." Independent corroboration of Freeths from a different jurisdiction (Australia) and a different practice area.

• ★★ Cross-listed: the chargeback and refund-abuse evidence in type 23 and the return-fraud evidence in type 48 are the fraud-side counterpart to this type's good-faith side.

• ★ Drafting Complaint Letters with ChatGPT (Toolify) and the proliferation of free generators (Easy-Peasy.AI, Template.net, Simplified, LogicBalls, Writecream) — ⚠️ Tool-existence evidence; establishes commoditization, measures nothing.

#9. Recommendation Letters

• ★★★ Brain Versus Bot: Distinguishing Letters of Recommendation Authored by Humans vs. AI (PMC, 2023) — Academic physicians achieved only 59.4% accuracy — barely above chance — at identifying AI-authored recommendation letters. Program directors selected AI letters for 31–38% of applicants, and recognized AI letters as AI-authored only 67% of the time. The strongest evidence that this filter has already failed, from exactly the population paid to apply it.

• ★★★ Why AI-Generated Recommendation Letters Sell Applicants Short (Nature, 2024) — AI generates language based on what letters typically say, producing statistically representative phrases rather than individualized accounts. The mechanism is regression to the genre mean, which is precisely what destroys a signal whose value lay in its particularity.

• ★★ AI Is Killing the Cover Letter (Knowledge at Wharton) — Judd Kessler recommends recommendation letters as the replacement signal for the devalued cover letter. Read against the PMC study, this is the whole dynamic in one juxtaposition: the proposed refuge signal had already fallen by the time it was proposed. Worth staging explicitly in a published version.

• ★★ Are Teachers Using AI to Write Letters of Recommendation? (Ivy Scholars) — Names the structural blind spot: applicants waive the right to read their letters, so no party has both the incentive and the access to check.

• ★★ AI Will Now Draft Your Residency Recommendation Letter (AAMC) — Institutional normalization from the body running US residency matching, distinguishing AI-as-author from AI-as-editor of a human draft. That distinction is the one most policies fail to make.

• ★ Should My Recommendation Letter Be Written by AI? (Canadian Journal of Surgery / PMC) — Three blinded orthopedic program directors preferred human letters, but differences were not always easy to detect. Small n.

• ★ ChatGPT-created letters are nearly indistinguishable from human-authored letters (PsyPost) — Secondary coverage of a convergent result.

• ★ Can Universities Detect AI-Generated Recommendation Letters? (Revolution in AI, 2026) — Makes one good argument (a counsellor submitting an AI letter substitutes a model's output for professional judgement in the one document where their expertise is most directly on display) and one useful privacy point (student data transmitted to third parties without consent). ⚠️ Low-credibility source; the "50% of admissions offices used AI screening by late 2023" figure is untraced. Use the arguments, not the numbers.

#10. Submitting Stories/Poems to Literary Competitions

• ★★★ AI Is Writing Prize-Winning Fiction (Pangram, May 2026) — Four of ten Commonwealth Short Story Prize regional winners across 2025 and 2026 flagged as AI-generated, including the 2025 overall winner (Chanel Sutherland's "Descend", 88% AI fraction). The 2026 case that triggered the investigation — Jamir Nazir's "The Serpent in the Grove", flagged 100% — was spotted by a reader on public stylistic tells before any detector ran. Regional winners take ~3, 350; theoverallwinner 6,700. This displaces Clarkesworld as the lead exhibit for this type: Clarkesworld showed the slush pile flooding, this shows the flood reaching the podium at a prestigious international prize. ⚠️ Vendor-run analysis; as of August 2026 the Commonwealth Foundation had neither confirmed nor contested the findings, and no author has admitted AI use. Flag accordingly — but note the stakes are exactly why it is persuasive.

• ★★★ Sci-fi magazine stops submissions after flood of AI-generated stories (NPR, Feb 2023) — Clarkesworld closed submissions after 500+ AI-generated submissions in a single month against 700 legitimate ones, up from fewer than 25 spam submissions monthly before. Neil Clarke traced it to side-hustle blogs and YouTube channels. The founding case of the entire literature, with an unusually crisp before/after ratio. Clarke's own account: How "AI" submissions have changed our submissions process and 2023 Clarkesworld Submissions Snapshot (13,207 legitimate submissions in 2023, excluding the filtered AI ones). Clarke's line — "we basically have a room of screaming toddlers, and we can't hear the people we're trying to listen to" (Washington Post) — is the best single quote in the project.

• ★★ ChatGPT helped write this award-winning Japanese novel (Smithsonian) — Rie Kudan won the Akutagawa Prize (Jan 2024) and disclosed that ~5% of the novel was verbatim ChatGPT. The disclosed counterpart to the Commonwealth cases — useful precisely because it shows the range from acknowledged tool use to unacknowledged generation. Also: CNN coverage.

• ★★ Editor's Desk: The Future of Dealing with AI Submissions (Clarkesworld, Aug 2025) — Clarke's continuing account; the value is longitudinal, from the same observer across three years.

• ★ Science fiction publishers are being flooded with AI-generated stories (TechCrunch) and 'Out-of-hand' flood (Fortune) — Secondary coverage of the same event.

• ★ The Big Sort: How Will AI Affect Submissions to Magazines? (Post Alley, 2024) — Analysis piece.

#11. Submitting Manuscripts to Publishers/Agents

• ★★★ Agents and Publishers Confront AI Use in Submissions (Jane Friedman) — The most reliable industry observer on publishing. Documents agents rewriting submission policies; Greene & Heaton explicitly reject any AI use in manuscripts, proposals, synopses, or cover letters. Named agencies and specific policy text make this the lead.

• ★★ Trilogy Launches AI-Powered Manuscript Assessment Tool (Publishers Weekly) — Publishers deploying AI to evaluate unsolicited manuscripts. The both-sides-use-AI equilibrium arriving in the slush pile, from a trade primary.

• ★★ AI & the Slush Pile: Transforming Manuscript Evaluation (BISG, Spring 2025) — The Book Industry Study Group convening on it signals industry-wide rather than anecdotal concern.

• ★ The Flood of ChatGPT Crap About to Clog Up Publishing (Countercraft) — Early (2023) prediction that has aged well; useful as a dated forecast, not as evidence.

• ★ The AI Slush Pile (Back on the Rock), Publishing Industry Draws a Hard Line (Sri Lanka Guardian), Blacklisted (The Archetypist), Can publishing survive the oncoming AI storm? (Chocolate and Vodka) — ⚠️ All blog-tier; no volume data. Background only.

Gap: no publisher or agency has released submission-volume data. This type is entirely policy-response evidence with no measurement of the flood itself — the mirror image of type 10, where the measurement exists.

#12. Blog Comments and Forum Posts

• ★★★ Wikipedia's speedy-deletion criterion G15, "Unambiguously LLM-generated pages" (adopted Aug 2025, updated by RfC 2026) — G15 permits immediate deletion of any page exhibiting one of exactly three signs: (1) communication intended for the user — "Here is your Wikipedia article on…", knowledge-cutoff disclaimers, "as a large language model", unfilled placeholders like "Smith was born on [Birth Date]"; (2) non-existent or nonsensical references — dead-on-arrival links, ISBNs with invalid checksums, unresolvable or unrelated DOIs (the policy's own example: a paper about a species of beetle cited in a computer-science article), citations with invalid temporality such as news reports predating the event; (3) technical indicators — model-specific citation bugs like oaicite, attributionIndex JSON blocks, turn0search0, or wikitext wrapped in markdown code fences.

  The crucial design choice: the policy explicitly states that the more subjective signs of LLM writing — the stylistic tells — "should not serve as the sole basis for applying this criterion," because they may stem from human error or unfamiliarity with Wikipedia's conventions. Less clear-cut cases go to the AI noticeboard or Articles for Deletion instead. Wikipedia chose observable artifacts over stylistic detection, which is precisely the opposite of the commodity-detector approach and a strong contrast with NeurIPS's classifier-based route (type 14). It buys near-zero false positives at the cost of catching only careless generation. The policy also carries a 2026 RfC rationale explicitly reasoning by analogy to G5 (deletion of banned users' pages): deleting some policy-compliant pages is worth it to "keep the cleanup workload manageable" — an institution stating outright that review capacity, not content quality, is the binding constraint. Contemporary coverage: 404 Media, 5 Aug 2025 — ⚠️ paywalled beyond the lede, so the policy page is the source to cite.

  ⚠️ Secondary coverage sometimes reports a broader March 2026 rule barring editors from using LLMs to generate or rewrite article prose at all, with exceptions for self-copyediting and translation. That does not appear in the speedy-deletion policy and is unverified. If such a rule exists it lives on a separate policy page; do not cite it until located.

• ★★★ Reddit AI bot experiment (University of Zurich, 2025) — Researchers deployed bots that posted 1,700+ personalized comments to r/changemyview, earned upvotes on 73% of posts, and were 3–6× more effective at changing opinions than human commenters. Reddit banned the accounts and condemned the study. Persuasive because it measures effectiveness, not just volume — the comments were not merely undetected, they worked better. ⚠️ Linked source is a blog summary; trace to the Zurich team's own materials and Reddit's statement before publishing.

• ★★ 15% of Reddit Posts Are Likely AI-Generated in 2025 (Originality.AI) — 15% of posts likely AI-generated; 146% increase 2021→2024. ⚠️ Detector-vendor study using its own tool — precisely the class of measurement the response taxonomy now treats with caution. Cite the direction, not the decimal.

• ★★ To Spam AI Chatbots, Companies Spam Reddit with AI-Generated Posts (Slashdot) — Firms seeding fake Reddit posts so that retrieval-based chatbots ingest and repeat them. A genuinely new incentive structure: the audience for the spam is other AI systems. Analytically the most interesting item in this type.

• ★★ Reddit cofounder Alexis Ohanian says 'so much of the internet is dead' (Fortune, Oct 2025) — Insider acknowledgment; quotable, not measurable.

• ★★ AI Slop and the Information Ecosystem (Columbia SIPA / Internet Governance Project, June 2026) — Institutional report framing slop as a systemic information-ecosystem problem rather than a nuisance, with attention to displacement (what gets pushed out of a feed when slop fills it) and to recommendation-system feedback. The best available academic framing for the content-flooding types generally. Raw copy: raw/external/igp-ai-slop-report.txt.

• ★ The growing problem of AI comment spam (Conferences That Work) and Incoming AI Generated Comment Spam (Interdependent Thoughts) — First-hand accounts of context-aware comment spam that references the actual post. Good colour on the qualitative shift.

• ★ More weird spam is popping up on Facebook (CNN), AI-Generated Comments in the Forums (HubPages) — Background.

#13. Product Reviews

• ★★★ Three percent of front-page Amazon reviews are now AI-generated (Pangram Labs) — ~30,000 reviews across 500 best-selling products; 3% AI-generated, 74% of AI reviews gave 5 stars (vs. 59% for human), and 93% carried the "verified purchase" label. The verified-purchase finding is the killer detail: the platform's own trust signal does not separate the classes. ⚠️ Vendor study.

• ★★★ Amazon blocked 250M+ suspected fake reviews in 2025 (following 275M+ in 2024), has targeted 32,000+ "bad actors" since 2020, and seized 75+ domains tied to fake-review services in July 2025. ⚠️ Company self-report via secondary coverage; no primary Amazon transparency document located. The scale is the point — this is enforcement operating three orders of magnitude above human review.

• ★★ The internet is rife with fake reviews. Will AI make it worse? (AP) — Analysis of 73 million reviews across home, legal, and medical services: nearly 14% likely fake, 2.3 million partly or entirely AI-generated. Largest independent sample; wire-service credibility.

• ★★ FTC Announces Final Rule Banning Fake Reviews and Testimonials (FTC, Aug 2024) — Primary regulatory source; AI-generated fake reviews explicitly covered, up to $51,744 per violation, effective Oct 2024. The FTC warned 10 companies over the 2025 holiday season that they face action for gaming the review system — the first sign of enforcement following the rule. Read alongside the Rytr reversal (type 16) for the full ambivalent picture.

• ★ AI Content in Amazon Reviews (Originality.AI) — ~5% in baby products, beauty, wellness. ⚠️ Detector vendor.

• ★ Amazon reviews are being written by AI chatbots (CNBC, 2023) — Early reporting, superseded.

• ★ AI Tools Often Used for Fake Product Reviews (VOA), Plague of Fake AI Reviews (Retail TouchPoints), Fake AI Reviews Are Ruining Google (Whitespark), Fake AI Reviews Are Spreading Fast (Inc.), Amazon Beauty Category (BeautyMatter) — Background; no independent measurement.

#14. Academic Paper Submissions

This type and type 34 (peer review) now carry the strongest evidence in the entire project, because academic publishing is the one domain where the flood, the detection, and the institutional response are all publicly measurable.

• ★★★ AI-Generated Papers in the NeurIPS 2026 Position Paper Track (NeurIPS blog, 2 June 2026) — 178 submissions (18.4% of the track) desk-rejected, plus 123 more (12.7%) required to produce a documented audit trail or be rejected. Default-windowed Pangram scored 28.2% of 969 submissions at 100%. The negative controls are what make this decisive: FAccT 2022 (pre-ChatGPT) returned 0.0% at every threshold; FAccT 2025 returned 1.0%/0.0%. Organizers also found AI use rising across NeurIPS generally — papers scoring ≥90% in the Evaluations & Datasets track rose more than tenfold from 2025 to 2026 (0.8% → 9.3%). The single best exhibit for "an institution measured the flood and acted on the measurement." Full local copy in raw/external/.

• ★★★ Low-quality papers are flooding the cancer literature (Nature, 2025) — An AI tool flagged more than 250,000 cancer studies bearing textual similarities to known paper-mill articles. Largest single number in the project; Nature-credible.

• ★★★ Organization Science submissions rose 42% since ChatGPT's launch, with AI-generated manuscripts harder to read, more jargon-laden, and more likely to be rejected (Forbes, Apr 2026). A top journal measuring its own submission inflation and quality distribution. ⚠️ Forbes contributor piece, and the page is JS-walled; the underlying Organization Science editorial is the source to obtain.

• ★★ Wiley shuts 19 scholarly journals amid AI paper mill problem (The Register, May 2024) — 11,300+ papers retracted from the Hindawi portfolio, 19 journals closed, $35–40M in lost revenue. The clearest instance of a flood destroying institutional assets outright, with a dollar figure.

• ★★ Up to one in seven submissions to Wiley journals flagged by paper mill tool (Retraction Watch, Mar 2024) — 1 in 7 flagged. Retraction Watch is the specialist primary in this space.

• ★★ Low-quality papers are surging by exploiting public data sets and AI (Science) — Surge of formulaic papers built on public datasets like NHANES. Names the mechanism — AI plus an open dataset is a paper factory.

• ★★ AI Chatbots Have Thoroughly Infiltrated Scientific Publishing (Scientific American) — At least 60,000 papers (>1% of global output) may have used an LLM; 13.5% of 2024 biomedical abstracts likely LLM-processed. The biomedical figure is the useful one.

• ★★ AI tools combat paper mill fraud (Chemistry World) — ~400,000 suspected paper-mill papers published over 20 years; mills earning tens of millions annually. Establishes the pre-AI baseline the flood is accelerating from.

• ★ A bibliography of genAI-fueled research fraud from 2025 (Sharon Kabel) — Useful as a source index rather than an exhibit.

• ★ The Peer Review Crisis: Why Publishers Are Struggling in 2025 (Prophy.ai) — Overview of the structural crisis. ⚠️ Vendor blog (Prophy sells reviewer-matching software); superseded by the Nature and NeurIPS material above.

• ★ AI Slop in Academic Publishing (Internet Reference Services Quarterly, 2026) — Peer-reviewed treatment of history, characteristics, causes, and mitigation. ⚠️ Paywalled, not read; listed as a trailhead.

#15. Grant Proposals

• ★★★ Fearful of AI-generated grant proposals, NIH limits scientists to six applications per year (Science, July 2025) — The load-bearing detail: some PIs submitted more than 40 distinct applications in a single submission round using AI tools. That single fact does more work than any survey — it is the cost collapse made visible in an institution's own intake data. Primary policy: NOT-OD-25-132 / Apply Responsibly (NIH Extramural Nexus) — applications substantially developed by AI "are not considered the original ideas of applicants"; post-award detection may trigger misconduct proceedings.

• ★★★ Grant proposals drafted with AI help more likely to win NIH funding (Nature, 2026) — AI-assisted proposals win more and resemble previously funded work more closely. The homogenization finding is the analytically richest result in this type: the flood does not merely add noise, it pulls the research frontier toward its own centre of mass. Pair with Cui et al. (type 2) — same signal-collapse mechanism, higher stakes.

• ★★ NIH to Limit AI Use, Cap Grant Applications at 6 per Year (Inside Higher Ed, July 2025) — Supplies the essential counterweight: only 1.3% of applicants exceeded six applications in 2024. The cap constrains a tiny minority and ceilings everyone — the honest cost of a blunt instrument, and a detail an unfriendly reader will find if we don't.

• ★★ NSF requires disclosure of AI use in proposal preparation and treats non-disclosure as misrepresentation, with no caps and no originality declaration — the permissive pole against NIH's restrictive one. ⚠️ The PAPPG 26-1 update was deferred pending OMB Uniform Guidance revisions, so this may tighten; secondary sourcing only.

• ★ Where do foundations stand on AI-generated grant proposals? (Candid) — 97% of foundations not using AI to screen applications. Useful as the private-funder contrast.

• ★ The Rise of AI-generated Research Grant Funding Applications (University of Bath, 2024), Will AI soon be reviewing your grant applications? (Candid), NIH Issues New Limits (CITI Program) — Background and summaries.

#16. Book/Film/Product Blurbs and Endorsements

• ★★★ FTC Reopens and Sets Aside Rytr Final Order (FTC, Dec 2025) — The FTC had barred Rytr from generating fake reviews and testimonials, then set the order aside under the Trump AI Action Plan. Primary source for regulatory retreat, which is the most important development in this type and a rebuttal to any assumption that institutional response is monotonic.

• ★★ A New AI Scam Is Targeting Thousands of Authors (Hollywood Reporter) — Thousands of authors targeted with AI-generated personalized praise scraped from book descriptions, then upsold dubious marketing. Concrete, named-outlet reporting on endorsement fabrication as a business.

• ★★ AI deepfake scams make fake celebrity endorsements look real (WDRB) — Celebrities targeted 47 times in Q1 2025, an 81% increase over all of 2024; $200M+ lost globally to deepfake scams in Q1 2025. ⚠️ Figures from a consumer segment, not traced to a primary.

• ★★ FTC Final Rule Banning Fake Reviews and Testimonials (Aug 2024) — Covers AI-generated consumer, celebrity, and testimonial content.

• ★ AI-Generated Bookstagrammers Are Targeting Authors (Writer's Digest), Update on AI book marketing scams (Anne R. Allen), AI-generated fake book list (PEN America), The World's Most Deepfaked Celebrities (McAfee), BBB Scam Alert, FTC warning letters (DLA Piper) — Background.

#Professional & Business Services

#17. Pull Requests to Open-Source Projects

The best-documented category after academic publishing, and the one where institutions have moved fastest to close channels outright.

• ★★★ The end of the curl bug-bounty (Daniel Stenberg, 26 Jan 2026) — The maintainer's own account. The programme officially stopped 31 January 2026 after producing 87 confirmed vulnerabilities and over $100,000 USD paid to researchers; the confirmed-rate ran "somewhere north of 15%" in previous years and "plummeted to below 5%" starting 2025 — Stenberg's own gloss is "Not even one in twenty was real." He names three converging trends: "the mind-numbing AI slop, humans doing worse than ever and the apparent will to poke holes rather than to help," and notes that slop reports "take a serious mental toll to manage and sometimes also a long time to debunk. Time and energy that is completely wasted while also hampering our will to live." Reports now go to GitHub's private vulnerability reporting — no bounty, no HackerOne. A functioning institution killed a functioning programme because triage cost exceeded signal value. The best single exhibit in the project; it should anchor any published version.

  • ⚠️ Two figures circulate widely in secondary coverage (Socket, BleepingComputer) that do not appear in this post: "20 AI-generated security reports in the first 21 days of 2026, none identifying a real vulnerability," and "seven reports within a 16-hour window." They appear to draw on Stenberg's separate weekly reports; do not attribute them here. The 15%→<5% collapse is the stronger claim anyway.
  • Also useful: Drowning in AI Slop, cURL Ends Bug Bounties (The New Stack, Jan 2026) — where the "terror reporting" phrase entered circulation. ⚠️ Stenberg's own construction is "an attempt to reduce the terror reporting," somewhat different from how secondary coverage renders it.

• ★★★ GitHub Ponders Kill Switch for Pull Requests to Stop AI Slop (The Register, Feb 2026) — GitHub PM Camilla Moraes opened a community discussion on "the increasing volume of low-quality contributions"; on 14 February 2026 GitHub shipped two settings: disable pull requests entirely, or restrict them to collaborators. Platform-level acknowledgment that open contribution has an unpriced cost — the infrastructure layer conceding, not just individual projects.

• ★★★ Open source maintainership in the age of AI (Kubernetes blog, 26 June 2026) — The largest open-source project in the world publishing formal guidance on AI-assisted contribution, balancing accountability against innovation rather than banning outright. The constructive counterweight to curl and tldraw: this is what "process redesign" looks like in OSS, and it keeps the type from reading as pure retreat. Local copy in raw/external/.

• ★★ AI Agents Are Flooding Open-Source Maintainers with Security Reports (Axios, Mar 2026) — Projects that previously received 2–3 bug reports per week now receive hundreds at once, fewer than 5% legitimate. Mainstream-outlet confirmation that curl is representative, not idiosyncratic.

• ★★ AI Slopageddon and the OSS Maintainers (RedMonk, Feb 2026) — Analyst treatment; the Genkit core team lead's figure of 1 in 10 AI-generated PRs being legitimate is the useful number.

• ★★ Context for the Agent, Ownership for the Human (Continue.dev) — The best statement of the mechanism: AI-generated PRs pass traditional quality heuristics while containing subtle misunderstandings, creating an asymmetric burden where review effort vastly exceeds generation effort. This is the cost-as-filter argument in its purest form — the heuristics were proxies for effort, and the proxy broke.

• ★★ AI-SLOP: best current practices for Open Source maintainers (OpenSSF working group issue #178) — The Open Source Security Foundation's vulnerability-disclosure working group treating AI slop as a standards problem. Shared-infrastructure response emerging (cf. response taxonomy §10).

• ★ 128,000-Line AI-Generated Pull Request (BigGo News) — A 128,000-line Claude-generated PR to the OpenCut project. Vivid, single-instance.

• ★ AI Slopageddon: How AI-Generated Code Is Destroying Open Source (Kunal Ganglani) — Coined the term; catalogues Ghostty's ban, tldraw's auto-close, curl's shutdown. Blog-tier but a useful index.

• ★ AI Bot Shames Developer for Rejected Pull Request (The Register, Feb 2026) — An AI agent publicly shaming a maintainer for rejecting its PR. Colour, but it previews an adversarial dynamic worth watching.

• ★ anti-slop GitHub Action — Community tool to auto-detect and close AI slop PRs; evidence the problem warranted automation.

#18. Filing Patent Applications

⚠️ The predicted flood has not clearly arrived. This is analytically valuable and should be stated plainly rather than buried: patents are the type where the friction-collapse prediction has least observable support, because the USPTO's fees, formal requirements, and attorney-of-record rules retained non-AI friction. A targeted search found no evidence of an AI-attributed surge in filings. Filings reached 430,625 in 2024 (+3% YoY) and the unexamined backlog fell to 776,995 by April 2026 from a ~813,000 peak — the backlog is a staffing story (923 new examiners hired in FY2024), not an AI-flooding story. Keep this type as a negative case: it shows that fee-plus-formality friction survives what prose friction does not.

• ★★★ Discerning Signal from Noise: Navigating the Flood of AI-Generated Prior Art (Patently-O, Apr 2024) — The real flooding story here is not applications but prior art: services like "All Prior Art" and "All The Claims" churn out millions of machine-generated technical disclosures specifically to pre-empt future patents. A flood aimed at a different institutional chokepoint, and a genuinely distinctive variant of the thesis — weaponized publication rather than weaponized submission.

• ★★★ USPTO Request for Comments Regarding the Impact of AI on Prior Art (Federal Register, Apr 2024) — The agency formally asking whether AI-generated disclosures qualify as prior art and how to handle the volume. Primary-source institutional acknowledgment.

• ★★ Like a Tree Falling that No One Hears (IP Tech Blog, Jul 2024) — Argues the volume of AI-generated prior art could block patents on most human ingenuity. The sharpest statement of the stake.

• ★★ Guidance on Use of AI-Based Tools in Practice Before the USPTO (Federal Register, Apr 2024) — Primary; practitioners must verify accuracy.

• ★ Top 5 Potential Implications of AI-Generated Prior Art (Sterne Kessler) — Law-firm analysis.

• ★ Using AI Tools to Reduce Patent Drafting and Filing Costs (PowerPatent) — Drafting time from 20–30 hours to 12–18 hours. ⚠️ Vendor marketing; the cost-collapse claim in this type rests on vendor self-report, which is why the type stays weak.

• ★ GenAI Patent Application Filings See Early Growth Trend (PatentNext) — Patents about AI, not patents written by AI. Frequently conflated; do not.

• ★ AI Patent Drafting Tools Democratize Access (Solve Intelligence) — ⚠️ Vendor. Relevant to the beneficial-flooding thread.

#19. Freedom of Information (FOIA) Requests

⚠️ Handle the Heritage Foundation case carefully. It circulates widely as "over 100,000 FOIA requests, filed using AI," and neither half of that survives contact with the ProPublica reporting it is attributed to. ProPublica analyzed more than 2,000 requests; the larger figure is Oversight Project director Mike Howell's own interview claim of "more than 50,000 information requests over the past two years" — a self-report by the subject, not a finding. And ProPublica's article does not mention AI at all. The case is good evidence of automation-enabled mass filing and poor evidence of AI-enabled mass filing. A general caution for this project: the most quotable version of a number is often not the sourced one.

• ★★★ Too Many FOIA Requests, Too Little Transparency (Columbia Journalism Review / Tow Center, Feb 2026) — The properly sourced replacement lead. Federal FOIA requests nearly doubled in five years to 1.5 million in 2024 while the number of federal FOIA officers did not budge — the cleanest possible statement of demand outrunning a fixed-capacity institution. Tow Center investigation found Metric Media, a right-wing network running 1,000+ sites of auto-generated articles, has filed thousands of records requests targeting voter rolls, school curricula, and transgender inmates; Heritage's requests arrived "some coming multiple times a minute." Also documents the response side: ~20% of agencies now use ML/AI to process requests, some states let officials ignore "vexatious requesters" (Connecticut), and others are opting for higher fees and longer extensions. Named sources: David Cuillier (Brechner FOI Project), Lauren Harper (Freedom of the Press Foundation). Local copy: raw/external/cjr-too-many-foia-requests.txt.

• ★★★ Heritage Foundation Staffers Flood Federal Agencies with Thousands of Information Requests (ProPublica, Oct 2024) — Use with the corrected framing: ProPublica analyzed 2,000+ requests from three Oversight Project staffers to 25+ federal offices, focused on hot-button phrases in individual employees' communications; among 744 requests to the Department of the Interior alone. An agency FOIA worker (anonymous) said requests "sometimes come in at a rate of one a second" and that staff spend a third of their work time on Heritage requests. ⚠️ The 50,000 total is Howell's own claim; the "one a second" is one anonymous worker; no AI attribution appears in the piece. Still an excellent exhibit for automation-enabled mass filing — just not, on this source, for AI-enabled mass filing.

• ★★★ Characterizing Agentic Flooding of Government Services (Schmitz, Hammond & Chan, AIES 2026) — The systematic multi-country evidence base: 84 cases across 11 jurisdictions, including Australia considering reintroducing FOI fees after AI-generated request waves. Non-causal and non-quantitative by design, which is a strength when set against the anecdote-driven US coverage.

• ★★ Governments Adjust Policies Amid Flood of AI Record Requests (GovTech) — State and local policy change; Somerset County, PA resolved that anonymous requests will not be considered. Explicitly AI-attributed, unlike the Heritage coverage.

• ★★ Fighting for Public Records Is More Common in Pennsylvania, Sometimes Aided by AI (WESA, Mar 2026) — Nearly 4,000 Right-to-Know appeals in a year, with AI a factor. Local-outlet reporting with a state-level number.

• ★★ Navigating FOIA in 2026 (MuckRock, July 2026) and What requesters need to know about the FOIA Advisory Committee's recommendations (MuckRock, July 2026) — The National Archives' FOIA Advisory Committee surveyed 193 federal FOIA professionals on burdensome processing demands including AI-generated and automated submissions, and flagged AI requests as a critical priority for the 2026–2028 committee term. Institutional agenda-setting; the committee's output is worth tracking. ⚠️ The MuckRock fetch returned little text; re-fetch before citing specifics.

• ★★ Experts Suggest AI Could Address FOIA Backlogs, Even as Public Records Staff Are Terminated (Nextgov, Apr 2025) — The dual-use bind in one headline.

• ★ FOIAbot: Building an AI Assistant for Public Records Requests (The Fix, May 2025) — Supply-side tooling; also a beneficial-flooding exhibit.

• ★ The Federal Government Is Likely to Receive a Record Number of FOIA Requests Again in 2024 (Government Executive) — FY2024 above 1.5M, +25% on FY2023 (itself +29%). ⚠️ Growth predates widespread AI use; do not attribute the trend line to AI without more.

• ★ AI Could Change Public Records Requests, Professor Tells Congress (StateScoop) — Congressional testimony.

• ★ The Heritage Foundation Is Spamming the Government with Thousands of FOIAs (Gizmodo) and A Blizzard of FOIA Requests from Heritage Foundation (Law Street Media) — ⚠️ Retained for auditability, not as evidence. Both are secondary retellings of the ProPublica reporting, and the drift from "thousands" (ProPublica's analysis) to larger figures with AI attribution appears to run through coverage of this kind. Neither adds independent reporting. If a published version cites the Heritage case, cite ProPublica and CJR directly.

#20. Business Proposals / RFP Responses

⚠️ Vendor-saturated type. Almost every source is an RFP-software company with a commercial interest in reporting high AI adoption. Ranked accordingly; treat all percentages as directional.

• ★★★ How Are Proposal Teams Adopting AI? (Loopio 2025 RFP Trends & Benchmarks Report) — 68% of proposal teams have used generative AI in RFP responses, 70% at least weekly, use doubled YoY; one customer produced 25 responses in the time previously needed for 5. The 5× throughput figure is the useful one — it is a ratio, which survives vendor bias better than an adoption percentage. ⚠️ Vendor benchmark survey.

• ★★ How Is AI Changing RFP Response and Management? (Responsive) — Some organizations now field 200+ RFPs per quarter, with traditional approaches breaking. ⚠️ Vendor.

• ★★ How AI RFP Response Software Is Evolving (QorusDocs) — Workload in strategic responses up 77% YoY. ⚠️ Vendor. The increase in workload despite automation is the interesting claim — the same both-sides escalation seen in hiring.

• ★ How Microsoft Uses AI for RFP Response Management (TechTarget) — Named enterprise case study.

• ★ How AI Is Revolutionizing RFP Responses in 2025 (The Writers for Hire), Implementing AI in the RFP Process (Inventive.ai), 25 Best RFP Tools (DeepRFP) — Market-existence evidence only.

Gap: no buyer-side evidence. The claim that agencies and enterprises are being overwhelmed by AI-generated proposals rests entirely on sellers describing their own throughput. A single procurement office reporting response-volume and evaluation-cost data would make this type publishable; without it, it should not carry weight in a paper.

#21. Legal Demand Letters

• ★★★ Access to Justice in the Age of AI: Evidence from U.S. Federal Courts (Shah & Levy, 2026) — The most rigorous quantitative study in the entire project. 4.5 million non-prisoner federal civil cases (FY2005–FY2026) and 46 million PACER docket entries. Pro se filings rose from a steady-state 11% to 16.8% in FY2025, concentrated in case types with formulaic document production. Over 18% of sampled 2026 complaints flagged positive for AI-generated text, up from essentially zero pre-AI. Docket entries per court from pro se cases in their first 180 days are up 158% on pre-AI means. The last figure is the one that matters: AI is not reducing court burden, it is increasing it — the beneficial-access and institutional-overload readings are simultaneously true, measured on the same data.

• ★★★ AI Hallucination Cases database (Damien Charlotin) — A live, public, continuously updated dataset of court decisions involving AI-fabricated citations: roughly 1,490 decisions worldwide and 1,000+ in the US as of May 2026, growing several per day. Sanctions escalated from $5,000 (2023) to six-figure penalties, $15,000-per-attorney at the federal appellate level, and the first US bar suspensions tied to AI misuse by mid-2026. ⚠️ Secondary trackers quote incompatible totals (1,313 / 1,490 / 1,598) — cite the database with an access date, never a repeated figure. Having a canonical public dataset makes this the most auditable claim in the project.

• ★★ Big Law Grapples With AI-Fueled Pro Se Surge (Bloomberg Law) — Pro se Fair Housing Act suits up 69% in 2025; pro se employment suits up 49%; defence costs 10–15% higher from motion and discovery volume. Puts a cost on the receiving side.

• ★★ More People Are Using ChatGPT Like a Lawyer in Court. Some Are Starting to Win (NBC News) — "Some are starting to win" is the beneficial-flooding half of this type, from a mainstream outlet. Essential for balance; without it the type reads as pure nuisance.

• ★★ DoNotPay FTC Settlement (FTC, Feb 2025) — $193,000 penalty over "robot lawyer" claims covering demand letters, cease-and-desists, and small-claims filings. Primary regulatory source; also evidence the consumer market existed at scale before enforcement.

• ★★ The ChatGPT Plaintiff: How AI Is Transforming Employment Litigation (Fisher Phillips) — Practitioner account of the demand-side mechanism.

• ★ Self-Represented Litigants Are Clogging Up Courts With Ridiculous AI-Generated Lawsuits (Futurism) — "A firehose of legal paperwork that looks legitimate" is a good line. ⚠️ Framing is one-sided; pair with the NBC piece.

• ★ Pro Se Litigant Fined $10K for Filing AI-Generated Reply Brief (FindLaw) — Superseded by the Charlotin database for sanctions evidence.

• ★ New LexisNexis Generative AI Writes Mean Cease & Desist Letters (Above the Law, 2023), How AI Streamlines The Demand Letter Process (Filevine), AI Generated IP Cease and Desist (Mitch Jackson) — Supply-side tooling.

#22. DMCA Takedown Notices

• ★★★ Automattic Transparency Report: July–December 2025 — A platform publishing its own numbers and naming the cause: "copyright infringement reporters use AI en masse, presumably to lower costs and maximize revenue." WordPress.com processed 2,431 takedown notices in H2 2025, +20% YoY. Enforcity alone sent 838 "inactionable" notices — 34% of all notices received — claiming to protect OnlyFans creators, with none of the reported links associated with infringing material. A single AI-enabled sender accounting for a third of a major platform's takedown volume, and producing zero valid claims, is as clean an exhibit as this project has. Coverage: TorrentFreak.

• ★★ What Happens When AI Files Bad DMCA Takedowns (Plagiarism Today, Dec 2024) — Specialist analysis of consequences and the near-total absence of penalties for false notices (§512(f) is essentially unused).

• ★ DMCA Copyright Infringement? The Perils of Relying on AI (Hugh Stephens Blog, Mar 2024) — Early warning from a credible IP commentator.

• ★ The AI-Generated DMCA Deluge (My Adult Attorney), DMCA Abuse in 2025 (DMCA Desk), The Future of DMCA Takedown Requests (PatentPC) — ⚠️ All commercial takedown-service marketing. A dedicated search for 2026 DMCA evidence returned nothing but this tier — a widely circulated "15–20% of DMCA notices are fake" figure traces to no primary and should not be used. The Automattic report is doing all the work in this type; a second platform transparency report is the highest-value addition.

#23. Dispute/Chargeback Claims

⚠️ Fraud-vendor-saturated type, like §20. Every source sells fraud prevention. Directional only.

• ★★★ AI-Powered Refund Abuse and Dispute Fraud: The Democratization of Deception (Ravelin) — 65% of consumers say AI has made it easier to falsely claim refunds, with GenAI used to edit product photos to look damaged. Ravelin's "democratization of deception" is a genuinely useful coinage — it names the uncomfortable symmetry with democratized access in types 8, 21, and 25. ⚠️ Vendor survey.

• ★★ The Ultimate Chargeback Statistics 2025 (Chargeflow) — Chargeback rates up 222% Q1 2023 → Q1 2024; dispute rates up 78% YoY in Q3 2024; 79% of merchants reported first-party fraud in 2024 vs. 34% in 2023. ⚠️ Vendor; the 2023→2024 jump is too large to accept without methodology.

• ★★ Friendly Fraud Survey (Ravelin) — 56% of merchants saw more fraudulent chargebacks; 65% believe they have been targeted by AI-enabled fraud. ⚠️ Vendor.

• ★ Fraud Losses Hit $11M Per Company (Infosecurity Magazine), The Ultimate Friendly Fraud and AI Guide (Chargeflow) (72% of cardholders treat disputes as an alternative to refunds; merchants win only 8.1% of represented disputes), Top 5 Fraud Trends (The Payments Association), Automated AI Chargeback Disputes (ChatFin) — Background; note the last is the supply side (AI agents that file and manage disputes autonomously), which is the more interesting half.

#24. Insurance Claims

• ★★★ AI Editing Tools Are Fueling a New Era of Insurance Fraud (Verisk, Mar 2026) — The most substantial industry study in this type: 98% of insurers agree AI-powered editing is driving digital fraud; 99% have already encountered manipulated or AI-altered documentation; only 32% feel very confident detecting deepfakes. On the consumer side, 36% would consider digitally altering a claim image, rising to 55% of Gen Z — the generational split is the most quotable finding and speaks directly to norm change rather than criminality. ⚠️ Verisk sells fraud analytics; the near-unanimous percentages (98%, 99%) should prompt scrutiny of question wording. Local copy in raw/external/.

• ★★★ Use of AI-Generated Images for Fake Insurance Claims (Debevoise, Jan 2026) — Legal analysis of deepfake accident photos, fabricated engineer assessments, and medical reports complete with realistic logos and signatures. A disinterested professional source (law firm client advisory) corroborating the vendor picture. Also summarized on the Columbia Blue Sky Blog.

• ★★ How Deepfakes, Disinformation and AI Amplify Insurance Fraud (Swiss Re) — Reinsurer analysis; Swiss Re Institute is the most credible non-vendor voice in insurance research.

• ★★ Adapting Claim Investigations for AI-Driven Fraud (Claims Journal, May 2026) — Trade reporting on how investigation practice is changing — the process-redesign response arriving in claims handling.

• ★ AI Is Creating Fraudulent Insurance Claims (Browne Jacobson, Dec 2024) — 94% of claims handlers suspect ≥5% of claims are AI-manipulated; one insurer reported a 300% increase. ⚠️ Single unnamed insurer.

• ★ AI-Driven Insurance Fraud: 2025 Trends (TruthScan) — 475% increase in synthetic voice fraud attacks; 25–30% of claims involve GenAI-altered material. ⚠️ Detection vendor; the 25–30% figure conflicts with more conservative estimates and should not be used.

• ★ Using AI to Fight Insurance Fraud (Deloitte, 2025), Synthetic Health Insurance Claim Fakes (PMC), Deepfake Insurance Fraud (Facia.ai) ("fake claims as a service"), AI Fraud in Auto Claims (The Institutes) — Background.

#25. Tax Authority Challenges

⚠️ Emerging type; mostly supply-side tooling rather than measured flooding. Its value is as the clearest beneficial flooding case alongside type 21.

• ★★★ Tax Court Navigates AI Misuse Rules for Self-Represented Filers (Bloomberg Tax) — Over 75% of US Tax Court cases are pro se, which is why this is the type where AI-assisted filing has the largest potential effect on both access and burden. §6673 allows penalties up to $25,000 for frivolous arguments, now being applied to AI-generated content. The base rate is the persuasive fact.

• ★★★ Characterizing Agentic Flooding of Government Services (Schmitz et al.) — Their risk matrix identifies tax administration and court systems as the highest near-term risk, precisely because they are financially attractive services whose demand was historically suppressed by friction and tacit knowledge. The theoretical prediction that this type is where the flood should land.

• ★★ How AI Can Help Both Tax Collectors and Taxpayers (IMF, Feb 2025) — Institutional analysis of the dual role; useful as a non-US, non-partisan framing.

• ★★ I Used AI to Fight My Cook County Property Taxes. I Won. (Medium) — ⚠️ Self-published anecdote, but the genre is the evidence: individuals documenting successful AI-assisted appeals and then releasing the tool. This is what democratized access looks like from the inside.

• ★ Using AI in Tax Workflows? What Heppner Means for Tax Departments (Morgan Lewis, Mar 2026) — Case analysis.

• ★ Is AI-Generated Tax Advice Making the Grade? (IRS Taxpayer Advocate Service, Jun 2024) — The IRS's own advocate acknowledging the tools. Primary, but thin.

• ★ Smart Appeal AI, AI Tax Appeal Letter Generator (LogicBalls), Colorado property value protest tool, CPA Pilot, TaxGPT — Supply-side tool existence. Establishes cost collapse; establishes nothing about volume.

Gap: no tax authority has published appeal-volume data attributable to AI. Until one does, this type is a prediction with good theoretical backing (Schmitz et al.) rather than a documented flood.

#Civic & Democratic Participation

#26. Petitions and Petition Signatures

⚠️ Weak type. Almost all evidence is speculative — what AI could do to petitions rather than what it has done. Consider merging into type 29 or 49 in a published version.

• ★★★ The AI Democracy Dilemma (Journal of Democracy) — The best theoretical statement: AI dissolves the legal and logistical thresholds that historically made direct-democracy mechanisms "conditional safety valves," lowering the bar from a team of lawyers to one motivated individual with a premium subscription. Peer-reviewed, named journal, and it articulates the design assumption being broken — which is the project's core move.

• ★★ Online Astroturfing: A Problem Beyond Disinformation (SAGE Journals, 2024) — Academic treatment of manufactured petition support and detection difficulty.

• ★ How AI Swarms Manipulate Public Opinion (Orange/Hello Future) — Fabricated profiles and deepfake "enthusiastic citizens"; the observation that manufactured momentum can become real momentum is worth keeping. ⚠️ Corporate research blog, speculative.

• ★ The Rise of Generative AI and the Coming Era (RAND) — Credible institution, but forward-looking rather than evidentiary.

• ★ ChangeOrgSignBot (GitHub) — Demonstrates technical triviality. Pre-LLM and countered; capability proof only.

• ★ AI-Powered Astroturfing (AdExchanger), If AI Wrecks Democracy, We May Never Know (Governing) — The Governing line — it is much harder to catch someone saying the same thing slightly differently thousands of times — is the single most useful sentence for explaining why dedup-based defences fail.

#27. Testimony at Public Hearings

Overlaps heavily with type 5; the strongest exhibits (FCC net neutrality, CiviClick) live there. What is distinctive here is the legislative response.

• ★★★ House Bill Targets AI-Generated Comments in Rulemaking (Nextgov/FCW) — The Comment Integrity and Management Act of 2024 passed the House without opposition, requiring agencies to identify and manage AI-generated comments, publish a single representative version of mass comments, and state the number of computer-generated submissions. Unanimity on a contested technology is notable. ⚠️ Passed the House; not law.

• ★★ Bots, Fake Comments, and E-Rulemaking (IJPA) — See type 5; the 22M/800K net neutrality finding applies equally here.

• ★★ AI Written Testimony at State Legislature (POPVOX Foundation) — Both the tools helping citizens draft testimony and the mass-submission risk, from a nonpartisan civic-tech foundation. The only source here that takes the beneficial side seriously.

• ★ Filtering Out the Noise (NYU Proceedings), Artificial Intelligence and Public Comment (CSG), Will ChatGPT Break Notice and Comment? (GWU), Robotic Rulemaking (Brookings) — Cross-listed from type 5.

#28. Citizen Journalism / News Tips

• ★★★ Tracking AI-Enabled Misinformation: AI Content Farm Sites (NewsGuard) — 3,006 AI content farm news sites across 16 languages, with 300–500 new sites appearing each month. A continuously maintained count from a specialist organization, which makes it the most citable number in this type. Companion: Watch Out: AI "News" Sites Are on the Rise (NewsGuard) on the programmatic-advertising incentive that funds them — the economic engine matters as much as the volume.

• ★★★ AI Is Undermining OSINT's Core Assumptions (Reuters Institute) — The most useful response finding in this type: journalists are urged to treat unsolicited emails as AI-generated until proven otherwise. That is a norm inversion — the default presumption of good faith, which made tip lines work, being formally withdrawn. Oxford-affiliated, credible.

• ★★ AI Slop and the Information Ecosystem (Columbia SIPA / IGP, June 2026) — Institutional framing of displacement effects: what gets pushed out of a feed, search result, or recommendation surface when slop fills it. Includes an analysis of 1,000+ YouTube Shorts recommended to young children, engineered for recommendation systems rather than viewers. Local copy: raw/external/igp-ai-slop-report.txt.

• ★★ AI-Generated Slop Is Quietly Conquering the Internet (Reuters Institute) — Notably even-handed: poses whether this is a threat or a self-correcting problem. Good for the pro/cons framing the project defaults to.

• ★★ Chaos and Credibility: AI's Impact on Press Freedom (GIJN) — Human evaluators are not much better than chance at detecting deepfake content; fabricated war footage outpaces debunking.

• ★ AI Is Polluting Truth in Journalism (Bulletin of the Atomic Scientists) — "The signal-to-noise ratio is getting close to one" is a nice formulation but not a measurement.

• ★ How Newsrooms Battle AI-Generated Misinformation (TechBuzz), Analysts Warn of Spread of AI-Generated News Sites (VOA) — Background.

#29. Ballot Initiative Signature Gathering

The persuasion evidence here is unusually strong — arguably stronger than the flooding evidence, which makes this type a slightly different argument: not that the channel is overwhelmed, but that its unit economics of persuasion have changed.

• ★★★ Persuading Voters Using Human-AI Dialogues (Nature, 2025) — Chatbot conversations shifted candidate preferences by ~4 points on a 100-point scale (roughly 4× the effect of traditional political ads), with large effects on Massachusetts residents' support for a psychedelics ballot measure. When models were optimized for persuasion, shifts reached 25 percentage points. Nature, peer-reviewed, with a ballot-measure outcome specifically. The strongest causal evidence anywhere in this project.

• ★★★ AI Chatbots Can Sway Voters with Remarkable Ease (Nature News, 2025) — Across US, Canadian, and Polish elections, chatbots moved opposition voters' attitudes and voting intentions by ~10 percentage points. Cross-national replication.

• ★★ The Era of AI Persuasion in Elections Is About to Begin (MIT Technology Review) — Two large peer-reviewed studies in Nature and Science published simultaneously; the best accessible synthesis, useful for a general-audience version.

• ★★ The AI Democracy Dilemma (Journal of Democracy) — Cross-listed; the theory of why direct-democracy thresholds are the vulnerable ones.

• ★ AI Chatbots Shown to Sway Voters (Scientific American) — Secondary coverage.

• ★ Agentic Conversational AI Door Knocking System (AI Journal) — Chemeria's "Colloquy" claims to contact millions of voters daily by outbound AI calling. ⚠️ Press-release-derived vendor claim; no independent verification. Illustrative of the offering, not of deployment.

• ★ The Use of AI by Election Campaigns (LSE Public Policy Review) — Academic framing across creation, mobilization, persuasion.

#30. Community Board/Planning Meeting Comments

The cleanest small-scale case in the project: a named commercial tool, a named price, a named institutional target, and named experts on both sides.

• ★★★ Objector.ai — UK tool launched November 2025 generating "policy-backed objections in minutes." For £45 a user receives objection letters, councillor lobbying letters and videos, and planning committee speeches, produced by two AI models analysing the planning application. The price tag is the exhibit: it puts an exact number on what a previously expensive act of civic obstruction now costs.

• ★★★ 'Supercharged' AI NIMBYism to Jam Up Planning System (The Negotiator) — Warns that AI objections often contain fabricated legal precedents, misleading decision-makers and clogging overloaded systems. The hallucination problem arriving in a low-capacity institution with no verification budget — worse than in courts, where at least sanctions exist.

• ★★ Planning System 'Must Keep Pace' with AI (Pinsent Masons) — Named practitioners: planning lawyer Sebastian Charles on systemic risk, and John Myers of the YIMBY Alliance with the line worth quoting — the technology designed to make planning faster could be the very thing that slows it down. Having a YIMBY and a planning lawyer agree strengthens it.

• ★★ Can AI Empower Planners to Accomplish More with Less? (American Planning Association) — The professional body's own account of both sides: ML parsing comments into themes and sentiment, while planners face an influx of AI-generated comments. The both-sides-use-AI equilibrium in miniature.

• ★★ UK government developing "Extract" to process planning objections — the state building AI to absorb AI. Cross-listed to the response taxonomy §11. ⚠️ Needs a primary source.

• ★ How AI Is Fueling a New Wave of NIMBYism (eWeek), Using AI to Assist in Land Use Planning and Zoning (SSRN), Solve for the NIMBY Equilibrium (Marginal Revolution) — Cowen's post is short but frames the game-theoretic question well: if objection costs fall for everyone, does the equilibrium shift or just get noisier?

#Academic & Educational

#31. College/Graduate School Application Essays

• ★★★ The Common App now treats AI-generated essay content as application fraud, authorizing account termination and revocation of admission — the strongest sanction available to the institution, applied to the single highest-stakes personal essay most people ever write. Institutions are piloting live writing samples, recorded video responses, and audio statements as future application components (NBC News), which is process redesign arriving in admissions. ⚠️ Common App policy wording is from secondary sources; verify against the Common App fraud policy before quoting.

• ★★★ AI Can Write Your College Essay, But It Won't Sound Like You (Cornell Chronicle, 2025) — 30,000 human-written application essays compared against essays from eight LLMs. Even when prompted with race, gender, and geography, models produced highly uniform text that was easy to distinguish from human writing. Important because it cuts against the simple flooding story: at least as of 2025, in this genre, the machine output was homogeneous and separable. Any published treatment should include it for calibration.

• ★★★ AI use in admissions essays rose sharply in 2024, especially among lower-income applicants, and higher estimated AI use was associated with worse predicted admissions outcomes — particularly for lower-SES applicants, even controlling for academic credentials and writing features. The most important equity finding in this type: the tool marketed as levelling the field appears to have penalized exactly the applicants it was supposed to help, presumably because advantaged applicants had human editors and disadvantaged ones had ChatGPT. ⚠️ Held only via a secondary summary; the underlying study must be located and read before this is used. High priority to source — it is potentially the single most publishable finding in the academic cluster.

• ★★ 1 in 3 College Applicants Used AI for Essay Help (EdWeek) — ~33% of 2023–24 applicants used AI: 50% for brainstorming, 47% for outlining, ~20% for generating first drafts. The breakdown by use type is what makes this valuable — most use is assistive, not generative, which the flooding framing tends to elide.

• ★★ Can ChatGPT Write a College Admission Essay? (Washington Post) — Admissions offices using AI screening; notes the technology "can be inaccurate and falsely implicate students."

• ★ Admitted Students Talk Role of ChatGPT (The Princetonian) — Princeton's attestation requirement; enforcement acknowledged as difficult.

• ★ College Essays Were Always Bad — ChatGPT Makes Them Worse (Tex Admissions), Can College Admissions Officers Detect ChatGPT? (Say Hello College) — ⚠️ Admissions-consultant marketing.

#32. Scholarship Application Essays

• ★★★ Students Try Using AI to Write Scholarship Essays (Hechinger Report) — Scholarships360 ran ~1,000 essays for a single scholarship through GPTZero and found 42% likely AI-composed. CEO Will Geiger's observation is the good detail: essays stopped feeling like they were written by teenagers, with formulaic structures and recurring words like "cornerstone" and "bedrock." A specific organization, a specific scholarship, a specific number. ⚠️ Detection was by GPTZero — a perplexity-era tool now known to have high false-positive rates on simple prose, and scholarship essays by young or L2 writers are exactly the vulnerable case. Treat 42% as an upper bound, and note that this exhibit is itself an instance of the false-positive problem in the response taxonomy §1.

• ★★ Student Won Scholarship with Fake Documents, AI-Written Essay (Inside Higher Ed) — A Lehigh student pleaded guilty to forgery and was expelled after faking admissions documents and using ChatGPT. A concrete adjudicated case rather than a detection estimate — rare and therefore valuable.

• ★★ Students Are Using AI to Write Scholarship Essays (Boston Globe) — Major-outlet investigation.

• ★ Can I Use AI for Scholarship Essays? (Scholarships360), Do College Admissions Check for AI? (WalterWrites) — Policy round-ups; Brown prohibits AI "under any circumstances," Caltech permits brainstorming but not generation. ⚠️ Second source is AI-writing-tool marketing.

• ★ Fulbright and Rhodes ban AI-generated content; 62% of scholarship providers now use AI detection (up from 28%) — ⚠️ the detection-adoption figures are untraced; see response taxonomy §1.

#33. Student Homework and Assignments

The type with the most data and the least analytical clarity, because "AI use" spans everything from spellcheck to full generation. Rank favours sources that disaggregate.

• ★★★ Student Use of AI for Homework Rises (RAND, Mar 2026) — Between May and December 2025 the share of students using AI for homework rose 48% → 62%, and the share saying it harmed their critical thinking rose 54% → 67%. RAND is the most methodologically credible source in this type, and the second figure is the more interesting one: students' own assessment moving against the tool while their use of it rises.

• ★★★ Assessment redesign is now happening at scale, and it is the real story. Texas A&M, University of Florida, and UC Berkeley report surging demand for blue books (Fox News); Cornell, NYU Stern, and Penn revived oral defences in spring 2026 midterms — 15-minute Socratic sessions, coding-while-talking, panel dialogues — after professors reported flawless homework from students who could not explain it. From September 2026 universities are moving to assess process over output (drafts, revision memos) (Times Higher Education). Crucially, phys.org (Aug 2026) documents the costs: oral exams carry their own problems of anxiety, accessibility, bias in live judgement, and staff time that scales linearly with enrolment. Use the response and its cost together — the cost is why redesign is underused, and pretending it is free weakens the argument.

• ★★★ Universities are switching detection off rather than up. Curtin University disabled Turnitin's AI-writing detection across all campuses from 1 January 2026, citing reliability, equity, and a shift toward trust-based assessment; text-matching remains. Vanderbilt did so in 2023. The most important institutional signal in this type: the sector is abandoning the detection response in favour of redesign. ⚠️ The widely repeated "50+ universities" count traces only to detector-marketing sites.

• ★★ Majority of High School Students Use Generative AI for Schoolwork (College Board) — 69% of high school students used ChatGPT for assignments as of May 2025. Credible institutional source.

• ★★ Share of Teens Using ChatGPT for Schoolwork Doubled (Pew Research Center) — 26% in 2024, up from 13% in 2023, with grade-level breakdown. Pew's methodology is the best here; note how much lower its number is than the vendor surveys, which is itself informative.

• ★★ How Vulnerable Are UK Universities to Cheating with GenAI? (Taylor & Francis) — Peer-reviewed risk assessment; 88% of UK students had used AI for assignments per HEPI.

• ★★ ChatGPT Bans Evolve into 'AI Literacy' (Fortune) — The arc from prohibition to accommodation, which is the co-optation response (taxonomy §11) in education.

• ★ ChatGPT Cheating Statistics 2025 (NerdyNav) — Nearly 7,000 UK university students formally caught cheating with AI in 2023–24 (triple the prior year); incidence 1.6 → 5.1 → 7.5 per 1,000. Also the University of Reading test where 94% of AI-written exam submissions went completely undetected — that finding is excellent and should be traced to the Reading study itself rather than cited from this aggregator. ⚠️ SEO statistics site.

• ★ ChatGPT Confounds Colleges and High Schools (Axios) — "The cheating is off the charts. It's the worst I've seen in my entire career" (Casey Cuny, 23-year English teacher). Quotable.

• ★ New Data: 92% of Students Use AI (Programs.com) — ⚠️ Wildly higher than Pew or College Board; almost certainly a different question. Do not cite alongside them.

• ★ AI Cheating Is on the Rise (EdScoop), What Do AI Chatbots Really Mean for Students and Cheating? (Stanford GSE) — The Stanford piece is the useful sceptical counterweight: cheating rates may not have risen as much as the discourse assumes.

#34. Peer Review of Academic Papers

Together with type 14, the evidentiary centre of gravity for the whole project.

• ★★★ Pangram Predicts 21% of ICLR Reviews Are AI-Generated (Pangram, Nov 2025) — All 19,000 papers and ~70,000–76,000 reviews from ICLR 2026 analysed. 21% (15,899 reviews) fully AI-generated; over half showed some AI involvement. Three findings make it more than a headcount: AI-involved reviews give higher scores (reviewers outsourcing judgement, not just phrasing — and LLM sycophancy biasing acceptance); AI reviews are longer but lower in information density, so authors pay a time cost parsing vacuous feedback; and papers with more AI content receive worse scores, cutting against the assumption that LLM judges favour LLM text. Negative control on ICLR 2022 reviews: error rates from 1 in 1,000 to 1 in 10,000, with no confusions between fully-AI and fully-human. Reported in Nature. ⚠️ Vendor-run study of its own product, undertaken for a public bounty offered by Graham Neubig; the negative controls are the reason to take it seriously.

• ★★★ AI-Generated Papers in the NeurIPS 2026 Position Paper Track (NeurIPS, June 2026) — Cross-listed from type 14 because it is equally a peer-review governance exhibit: 178 desk rejections (18.4%), an appeals process demanding pre-AI/post-AI version history, FAccT 2022 negative control at 0.0%, and the organizers' conclusion that "relying on author declarations is insufficient." The most consequential enforcement action any scholarly venue has taken over AI.

• ★★★ Conferences are now running controlled experiments on their own automation. NeurIPS 2026 is running a randomized three-arm reviewing trial (unassisted vs. two LLM-interaction conditions per assigned paper) while banning reviewers from uploading papers to chatbots on confidentiality grounds — drawing the line at confidentiality rather than quality, which is a more defensible boundary than most. AAAI 2026 formally embedded AI assistance across 22,977 reviews. ⚠️ AAAI figure from secondary coverage. This is the strongest evidence anywhere in the project that an institution is trying to find out whether co-optation works rather than asserting it.

• ★★ The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores (arXiv) — At least 15.8% of ICLR 2024 reviews AI-assisted; nearly half of submissions had at least one; AI-assisted reviews raised borderline papers' acceptance by 3.1pp (a 31.1% relative increase in odds). The independent academic corroboration of Pangram's score-inflation finding, from a year earlier and a different method. Use these two together — convergent evidence from a vendor and from academics is much stronger than either alone.

• ★★ Is ChatGPT Corrupting Peer Review? Telltale Words Hint at AI Use (Nature, 2024) — "Delve" appeared 2,847 times in 2023 papers, a 654% rise from 349 in 2020; similar surges for "commendable," "intricate," "meticulous." The lexical-fingerprint method — crude, cheap, and it worked before detectors did.

• ★★ Gaming AI-Assisted Peer Reviews Poses New Risks to the Scientific Community (arXiv:2606.10159) and Stop Automating Peer Review Without Rigorous Evaluation (arXiv:2605.03202) — The 2026 academic literature on adversarial dynamics and premature automation. Local copy of the first in raw/external/. ⚠️ Skimmed, not read end-to-end.

• ★★ ICML 2026 prompt-injection trap — Hidden LLM instructions in submitted PDFs trigger telltale phrases if a reviewer feeds the paper to a chatbot; hundreds of reviewers identified and their reviews rejected. Ingenious and ethically contested — The Scientist's framing as a controversy is the right one. Works by exploiting a current model limitation, not a durable principle.

• ★★ 'A Serious Problem': Peer Reviews Created Using AI Can Avoid Detection (Nature, 2025) — The necessary counterweight to the Pangram results: AI reviews sophisticated enough to evade detection. Include it; a treatment that cites only the successful detections is not honest.

• ★ Papers and Peer Reviews with Evidence of ChatGPT Writing (Retraction Watch) — Running list including reviews containing "Here is a revised version of your review with improved clarity and structure." A living catalogue of the most embarrassing tells.

• ★ AI Is Transforming Peer Review — and Many Scientists Are Worried (Nature, 2025), Artificial Intelligence in Peer Review (JAMA), Exploring the Impact of Generative AI on Peer Review (Journal of Academic Ethics), Personal Experience with AI-Generated Peer Reviews (PMC), Sem-Detect (arXiv:2605.21713) — Editorials, surveys, case studies, and detection methods.

#35. Reference Checks / Professional Inquiries

Overlaps type 9; what is distinctive here is identity fraud rather than prose fraud, which is why the durable response is source-based verification.

• ★★★ How Generative AI Is Making Reference Fraud Easier Than Ever (Konfir) — Counterfeit reference documents and entire fabricated employer identities created from scratch. The key insight for the taxonomy: this type is migrating to source-based validation — verifying against Open Banking, payroll providers, and tax records rather than trusting submitted documents. That is identity verification (taxonomy §2) displacing document review, and it is the durable answer. ⚠️ Vendor (Konfir sells verification).

• ★★ The AI Impostor: Shielding Your Hiring Process from Synthetic Fraud (StaffingHub) — Fake references, fake audio and video for reference calls, synthetic employer identities. ⚠️ Guest post.

• ★★ AI and the Rise of Resume Fraud (NPAworldwide) — 23% of recruiters have encountered candidate fraud.

• ★ Candidate Fraud Detection (Metaview) — ⚠️ Vendor.

• ★ Cross-listed from type 9: Brain Versus Bot (PMC) (59.4% discrimination accuracy), Nature on AI recommendation letters, AAMC.

#Creative & Competitive

#36. Hackathon/Competition Submissions

⚠️ Weak type. No institutional data. The strongest item is not about hackathons at all. Consider merging into type 33 or dropping in a published version.

• ★★★ Stopping AI Cheating in Remote Tech Assessments: 2025 Playbook (HackerRank) — 14% of candidates admit to using generative AI in online assessments; 83% say they would if they thought employers wouldn't detect it. The gap between 14% and 83% is the exhibit — it measures how much of the remaining compliance is held up by belief in detection rather than by norms. Applies well beyond hackathons. ⚠️ Vendor survey, self-reported.

• ★ Hacking AI competition rules — Some hackathons now require AI use, others ban it. Illustrates policy incoherence.

• ★ The biggest AI hackathons on Devpost in 2024 — The democratization side: "people with all kinds of skill sets and backgrounds can join hackathons — not just developers."

• ★ Disqualified from a hackathon for using AI (LinkedIn, Jagrit Sachdev) — Single anecdote.

#37. Art/Design Contest Entries

The type with the longest history — the Colorado State Fair predates ChatGPT — which makes it the best available natural experiment on how an institution adapts over four years.

• ★★★ AI won an art contest, and artists are furious (CNN, 2022) — Jason Allen's Midjourney-generated "Théâtre D'opéra Spatial" won the Colorado State Fair digital arts competition. The origin case for the whole genre of AI-wins-a-prize stories, and the reason type 10's Commonwealth Prize case in 2026 landed on prepared ground. Follow-through: the Fair amended its rules to require AI disclosure (Smithsonian, 2023) and the US Copyright Office denied the image protection (CPR News, 2023). A complete arc from incident to rule change to legal determination — rare in this project and worth using as a template for how these things resolve.

• ★★★ Artist refuses photography award after revealing his picture was AI-generated (Euronews, 2023) — Boris Eldagsen won the Sony World Photography Award with a DALL-E 2 image and refused the prize to force the issue. Deliberate institutional stress-testing by a participant — the rare case where the flood is a demonstration rather than an exploitation.

• ★★ Artist wins AI image contest with real photo, then gets disqualified (ArtNews, 2023) — Miles Astray submitted a real photograph to an AI art contest, won, and was disqualified. The inverse case, and the best single illustration that the category boundary has stopped being legible in either direction.

• ★ Does AI mean open exhibitions need to rethink digital entries? (Making a Mark, 2024), Juried art competitions and inclusion of AI-generated works (AAM Open Forum) — Practitioner deliberation; useful for showing institutions reasoning in public.

• ★ Golden Demon, World Press Photo, and San Diego Comic Con have banned AI art — ⚠️ needs primary sourcing for each.

#38. Music Demo Submissions

The type with the largest raw numbers in the entire project, and now the clearest case of an institution responding at a scale where human review was never conceivable.

• ★★★ Spotify removes 'AI slop' and seeks transparency from musicians who use AI (ABC News, 16 July 2026) — Spotify removed more than 75 million spammy AI-generated tracks over twelve months and will introduce an AI music spam filter in autumn 2026, tagging uploaders and tracks that mimic existing artists and halting their algorithmic recommendation. Note the policy shape: Spotify is not banning AI music — it targets impersonation, unauthorized voice cloning, and rights violations, and still welcomes AI composition where the creator holds commercial rights. That distinction (fraud, not category) is exactly the line Schneier & Sanders argue is the right one, and Spotify is the largest institution to have drawn it explicitly. Local copy in raw/external/.

• ★★★ Deezer reports that fully AI-generated tracks passed 50% of daily uploads in mid-2026 — around 90,000 tracks a day, up from 28–34% (~50,000/day) in late 2025. A majority-synthetic submission channel, with a trend line across three data points from the same reporting platform. ⚠️ Deezer company reporting via secondary coverage; get Deezer's own release before publishing. If this holds up it is the single most striking statistic in the project — the first documented interaction type where machine submissions became the majority.

• ★★ Suno AI is generating Spotify's entire catalog every two weeks (Hits District, 2025) — 7 million songs generated daily. ⚠️ Blog-tier and the framing is loose, but the supply-side capacity number complements Deezer's demand-side share.

• ★★ AI Music Timeline: From 'Fake Drake' to Suno & Udio label settlements (Billboard) — Trade-primary chronology of controversies and policy responses.

• ★★ 60 million people used AI to create music in 2024 (DJ Mag / IMS Business Report 2025) — Establishes the size of the producing population, not just the output.

• ★ Major record labels sue AI music generators (TIME, 2024) — UMG, WMG, Sony v. Suno and Udio. The legal response track.

• ★ AI music roundup (Music Ally, 2025) — The AI Song Contest now requires teams to describe training data. AI musicians are flooding Spotify (Substream, 2026) — superseded by the ABC reporting.

• ★ DDEX consortium AI-credit metadata standard (15 distributors/labels committed) — the provenance response; ⚠️ needs a primary source.

#39. Screenplay/Script Submissions

⚠️ Mostly a labour-relations type, not a flooding type. The evidence is about pre-emptive collective bargaining against AI substitution rather than documented submission floods — a genuinely different mechanism, and worth flagging as such rather than assimilating.

• ★★★ The writers' strike is over; here's how AI negotiations shook out (TechCrunch, 2023) — The WGA won binding protections: studios cannot require writers to use AI, and AI cannot receive writing credit. The only case in this project where a group with collective bargaining power secured contractual protection before the flood arrived. That is the analytically important point — most institutions in this project had no such instrument. Background: Brookings and Fortune on the fear that studios would use AI for first drafts, then hire writers cheaply to revise.

• ★★ Hollywood's 2025 AI disclosure rules explained (Sima Labs) — Academy disclosure requirements plus WGA and SAG-AFTRA provisions and California AB 412. ⚠️ Vendor explainer; use the underlying instruments.

• ★ Cross-listed from types 10 and 11: Clarkesworld (Washington Post), Jane Friedman, Sri Lanka Guardian, Countercraft, Neil Clarke. These are prose-submission exhibits standing in for screenplay evidence we do not have.

Gap: no production company, festival, or agency has reported script submission volumes. This type is currently borrowing all its flooding evidence from type 10.

#40. Award Nominations and Supporting Materials

⚠️ Weakest type in the project. It borrows almost entirely from type 9 (recommendation letters) and type 37 (art contests), and has essentially no evidence of its own. Recommend merging into type 9 in a published version.

• ★★ Oscar leaders say films made with AI are eligible for awards (Deseret News, 2025) — April 2025 Academy rules: AI use "neither harms nor helps" nomination chances; human creative authorship is the criterion. An institution declining to police the tool and instead relocating the standard to authorship — the same move Spotify made in type 38.

• ★★ Oscars, Grammys feature nominated works using AI (The Post, 2025) — 2025 nominees including The Brutalist (AI-enhanced Hungarian accents) and Emilia Pérez. Concrete instances at the top of the field.

• ★ Cross-listed: Nature on AI recommendation letters, PsyPost, AAMC, Revolution in AI (⚠️ low credibility, untraced figure).

#Platform & Marketplace

#41. Job Applications (Full Applications)

The type with the most complete both-sides-use-AI equilibrium, and the best-measured collapse in signal value.

• ★★★ Signaling in the Age of AI (Cui, Dias & Ye, Yale 2025) — Cross-listed from type 2 and still the lead: 5M cover letters, callbacks up 3.56pp for AI users while the correlation between tailoring and callbacks fell 51%. The mechanism, measured.

• ★★★ The job application got faster. The job search got worse. (Axios, 21 Aug 2026) — The most current mainstream framing of the equilibrium: applications are faster to send, hiring is slower to conclude, and trust has degraded on both sides. Local copy: raw/external/axios-job-seekers-ai-hiring-2026-08.txt.

• ★★★ The receiving-side cost is now quantified: 77% of hiring teams regularly encounter AI-generated applications, and 67% of US HR leaders say reviewing them has slowed hiring — one in five reporting delays of more than two weeks. ⚠️ Industry surveys, not traced to primaries. Directionally consistent with Axios and with the Resume Genius/SHRM finding below, which is why it is worth keeping despite the sourcing.

• ★★ 'Trust is at an all-time low': hiring platform CEO says talent acquisition is in an 'AI doom loop' (Fortune, 2025) — "AI doom loop" is the most useful shorthand anyone has coined for the both-sides equilibrium: seekers mass-apply with AI, recruiters mass-reject with AI, and both lose trust. Good for a talk.

• ★★ AI's impact on hiring in 2025 (Resume Genius / SHRM) — SHRM benchmarking found cost-per-hire and time-to-hire both increased over the period of generative AI adoption. The most important claim in this type: automation on both sides made the process more expensive, not cheaper. ⚠️ Reported via a vendor; the SHRM benchmark itself is the source to get.

• ★★ Recruitment is broken. Automation and algorithms can't fix it (SHRM) — 250+ applications per corporate opening, 4–6 interviewed. Secondary trackers now report ~242–244 applications per opening with a ~0.4% success rate — essentially unchanged, which is itself informative: the ratio was already brutal pre-AI. ⚠️ Recent figures from industry blogs only.

• ★★ How AI is helping job seekers supercharge their job hunt (CBS News) — LazyApply and similar tools auto-applying to 150+ jobs per day. The concrete supply-side mechanism from a mainstream outlet.

• ★ Why AI resumes are overwhelming recruiters and managers (Inc.) (64% of recruiters saw look-alike résumés increasing screening workload), How hiring managers are grappling with AI job applications (US Chamber) (74% encountered AI content), Auto-apply job bots (The Interview Guys), 2025 AI in Hiring Survey (Insight Global) — Background; all vendor or trade surveys.

#42. Freelancer Proposals on Gig Platforms

Distinctive because the platform co-opted the flood rather than fighting it, and freelancers objected — a rare case where we can see the losers of co-optation complain in public.

• ★★★ Freelancing platform flooded with AI-generated nonsense (The Register, 2023) — Early, credible reporting on ChatGPT-generated proposals flooding Upwork and clients losing the ability to identify qualified freelancers. The signal-collapse mechanism in a marketplace where the signal was the proposal.

• ★★★ Upwork Made A HUGE Mistake Promoting AI-Made Proposals (Upwork Community) and Is Upwork allowing/encouraging AI proposals? (Upwork Community) — The platform shipped AI-drafted proposals as a feature; freelancers report clients leaving over the "deluge of garbage proposals," and describe AI proposals as "all saying the right things" from applicants who "have no real idea what they've signed up for." First-party voices from inside the affected population — the co-optation response (taxonomy §11) evaluated by the people it was done to. ⚠️ Forum posts; representative of sentiment, not measurement.

• ★★ AI Upwork bidder: automating proposals without spamming (GigRadar) — GigRadar and PouncerAI automating submission at scale. ⚠️ Vendor, and the "without spamming" framing is self-serving.

• ★ I set out to explore AI use by Upwork freelancers (BloggingPro), AI's impact on freelancers (2727 Coworking) — Background; the observation that content-writing gigs have shrunk while cheap AI-assisted providers proliferate is the substitution story, adjacent to but distinct from flooding.

Gap: Upwork has published no proposal-volume or client-retention data. Everything here is journalism plus forum sentiment.

#43. Dating App Messages

• ★★★ The rise of AI 'chatfishing' in online dating poses a modern Turing test (Scientific American) — Six in ten dating app users believe they have encountered AI-written conversations. The framing — dating apps as an involuntary mass Turing test — is the most useful conceptual contribution in this type, and Scientific American is the most credible source in it.

• ★★★ The revealed-preference asymmetry: 80%+ of daters use AI, but most say they would reject a match who did the same. Roughly 58% now worry about AI-generated fake profiles. This is the sharpest statement anywhere in the project of the collective-action structure — everyone defects, everyone punishes defection, and the equilibrium is worse for all. ⚠️ Industry survey figures via secondary aggregators; must be traced to a primary before use, but the finding is worth the effort.

• ★★★ All three major apps now permit AI-generated photos, with authenticity rather than photo origin as the governing standard. Tinder, Bumble, and Hinge launched major AI features in early 2026 (Hinge ranking profiles by mutual-attraction probability and suggesting prompts; 85% toxic-content detection). Verification moved to real-time selfie liveness checks against profile photos — Tinder's blue checkmark, Bumble's mandatory photo verification. The response migrated from policing the artifact to verifying the person, which is the same move as NeurIPS's audit trail and Konfir's source-based reference checks. ⚠️ Secondary sources, largely dating-tech marketing sites; the pattern is well corroborated, the specifics are not.

• ★★ AI is shaking up online dating with chatbots that are 'flirty but not too flirty' (CNBC, 2024) — 26% of US singles used AI to enhance dating in 2024, a 333% jump year-on-year. The steepest adoption curve in the project.

• ★★ AI-generated pictures and voices drive surge in online dating scams (Washington Times, 2026) — Norton blocked 17 million dating-scam attacks in Q4 2025, +19% YoY; romance scams up 20% Q1 2025 vs Q1 2024. The fraud half, with vendor telemetry. ⚠️ Norton is an interested party.

• ★★ AI chatbots are becoming romance scammers (McAfee) — Bots running dozens of simultaneous conversations 24/7, building trust over weeks. The concurrency point is the cost-collapse mechanism made concrete. ⚠️ Vendor.

• ★ Dating AI: Rizz app will respond to your Hinge or Tinder messages (Washington Post, 2023), People are rizzing on Tinder using ChatGPT, then showing up to dates tongue-tied (Futurism), For Valentine's Day, I outsourced my dating life to AI (Quartz), Message from her match was 'like a robot had sent it' (CBC) — First-person and tool-existence pieces. The Futurism "tongue-tied" observation is a nice illustration that the flood creates a verification moment offline.

#44. Airbnb/Rental Inquiry Messages

⚠️ Mostly a fraud type, not a flooding type. The evidence is about scams (fake listings, fake damage claims) rather than inquiry volume overwhelming hosts.

• ★★★ Scammers are hitting travelers with fake rentals and websites (Moneywise) — Booking.com's internet safety chief reported travel scams up 500–900% in 18 months, largely attributed to generative AI; FTC received nearly 10,000 fraud reports in Q2 2025. A named executive at a major platform attributing a specific multiple to AI is the strongest thing in this type. ⚠️ Enormous range (500–900%) suggests an imprecise estimate; attribute to the speaker, not to the world.

• ★★ AI Airbnb scams: how fake images are fueling disputes (Rental Scale Up) — AI-generated damage images used for fraudulent claims against hosts — the inverse direction from most of this project, where the institution is the recipient. Worth noting: flooding can run either way along a relationship.

• ★★ Scammers are tricking travelers into booking trips that don't exist (Help Net Security, 2025) — Polished multilingual scam emails and realistic images at scale.

• ★ How to recognize scam emails and inquiries (Vacation Rental Hosting 101) — Hosts report generic AI-generated inquiries that never reference property specifics. The only genuine inquiry-flooding evidence here, and it is a blog.

• ★ France joins Italy and Czech Republic in new Airbnb scam epidemic (Travel and Tour World) — ⚠️ Low-credibility SEO travel site. Do not cite.

#45. Academic Collaboration Requests

⚠️ This type has no dedicated evidence — every source below is really about cold email (type 1) or spam generally, applied by analogy. Recommend merging into type 1 in a published version, or commissioning original evidence (a survey of faculty inbox volumes would be cheap and genuinely novel).

• ★★★ AI now powers over half of spam emails (Columbia Engineering) — 51%+ of spam AI-generated, accepted to ACM IMC 2025. The peer-reviewed anchor, borrowed from type 1.

• ★★ Cracking cold emails: reaching out to professors (Princeton PCUR, 2025) — The closest thing to on-point evidence: professors receive many cold emails and increasingly cannot distinguish genuine from AI-generated. Student-written and anecdotal, but it names the actual interaction.

• ★ Cold email strategies 2024 (EOC Works) (success rates from 2% to 0.5%), 7 cold emailing trends 2025 (Woodpecker) — ⚠️ Marketing blogs; the "0.5%" is untraced.

#46. Influencer/Brand Partnership Pitches

⚠️ Entirely vendor-sourced; no independent evidence. The one interesting datapoint is a decline.

• ★★★ AI influencer marketing: run smarter campaigns in 2025 (Sprinklr) — Brand collaborations with AI-driven social accounts fell nearly 30% in the first eight months of 2025 versus 2024. The only evidence in the entire project of a market correcting against synthetic participants — audience fatigue producing a measurable retreat. Worth far more analytically than the adoption statistics around it, because it is the strongest available case that some floods recede. ⚠️ Vendor; needs corroboration badly.

• ★ AI in influencer marketing: the 2025 playbook (indaHash) (60%+ of marketers use AI for discovery and optimization), AI influencer pitches (Afluencer) (AI-drafted pitches, +30% open rates), How to use generative AI for automated influencer outreach (Emulent) — ⚠️ All vendor marketing.

#47. Startup Pitch Decks Sent to Investors

• ★★★ How generative AI is reshaping venture capital (HBR, 2025) — VCs using AI to triage founder emails and score pitches, having previously spent "hours every day" on them. HBR is the most credible source in this type, and it documents the receiving side adopting AI — the both-sides equilibrium arriving in venture.

• ★★ The cold email VCs want (TDK Ventures) — White Star Capital's Christian Hernandez receives 1,000+ emails from unique senders monthly, with AI tools making spam skyrocket. A named investor with a number.

• ★★ How to cold email investors and actually get replies (OpenVC) — Warns founders that AI makes it "tempting to send thousands of tailored cold emails at scale" but that "investors sense the difference instantly." ⚠️ The confidence in detectability is unsupported and probably wrong — cite it as belief, which is itself interesting: recipients overestimate their discrimination (cf. type 9's 59.4%).

• ★ How to use AI for cold email in 2025 (Hunter) ("AI emails are increasingly accepted, but few are worth reading"), AI fundraising tools for startups (Evalyze), AI job-apply bots as a pitch analogue (GrackerAI) — ⚠️ Vendor; the last is an explicit analogy rather than evidence.

#48. Customer Support Tickets

⚠️ Weakly evidenced. The domain is almost entirely occupied by helpdesk-vendor content marketing with unverifiable statistics. The genuinely evidenced part of this type is refund and return fraud, which overlaps type 23. The "AI floods support queues" claim is weakly supported; what the better sources actually show is that deflection absorbs 40–70% of tier-1 volume while net volume reaching humans still grows for reasons that are not AI-flooding.

• ★★★ When AI Is the Editor, Consumer Complaints Are More Likely to Succeed (Yale Insights) — Cross-listed from type 8. AI-polished complaints work better, which rationally incentivizes universal AI use. Academic, causal, and it captures the beneficial and burdensome readings simultaneously.

• ★★★ AI-generated damage claims trigger retail crackdown on return fraud (PYMNTS, 2026) — AI-generated fake damage photos for fraudulent returns, against a backdrop of nearly $1 trillion in US merchandise returned in 2024. Trade-press reporting with a named response (retail crackdown).

• ★★ AI-powered refund abuse and dispute fraud (Ravelin) — Cross-listed from type 23; 65% of consumers say AI has made false refund claims easier. ⚠️ Vendor.

• ★ How AI is supercharging negative review attacks and extortion (Guaranteed Removals) — AI-generated extortion threats at scale. ⚠️ Reputation-management vendor.

• ★ FTC's new rule on fake and AI-generated reviews (Sidley Austin, 2024), Fake AI reviews are spreading fast (Inc.) — Cross-listed from type 13; these are review exhibits, not support-ticket exhibits.

• ★ Helpdesk industry figures — AI-agent adoption in customer service rising 39% (2025) → 66% (2026); AI resolving tickets at 0.50–2.00 vs. $15.56 for a human agent; deflection handling 40–70% of tier-1 volume at mature organizations; average organization handling 10,675 tickets monthly. ⚠️ All from helpdesk vendors and SEO "research" sites; none traced to a primary. The cost-per-ticket ratio is the only figure worth repeating, and only with the caveat.

#Trust & Safety

#49. Astroturfing Campaigns

• ★★★ Barrage of emails from AI politics platform defeats clean air initiative (Futurism, 2026) — CiviClick, an "AI-powered grassroots advocacy platform," generated 20,000+ public comments opposing Southern California's gas-appliance phase-out; the air board rejected the rules. At least three people contacted afterwards said they had not known CiviClick sent comments in their name. The clearest documented case anywhere of AI-generated volume changing a regulatory outcome, with identifiable victims of impersonation. This should be the lead exhibit for the entire civic cluster, not just this type.

• ★★★ AI-Generated Public Feedback Raises New Questions for Lawmakers (Governing, 2025) — Venture-backed firms including Doublespeed (backed by Andreessen Horowitz) advertising the ability to "orchestrate actions on thousands of social accounts" and mimic "natural user interaction." Astroturfing as a funded, above-board product category is the development that distinguishes 2026 from the bot-farm era — this is not a Russian troll farm, it is a startup with a pitch deck.

• ★★★ US says Russian bot farm used AI to impersonate Americans (NPR, 2024) — DOJ disrupted a Russia-linked AI bot farm of 968 X accounts impersonating Americans. A prosecuted case with a specific count — rare and legally verified.

• ★★ Bots, Fake Comments, and E-Rulemaking (IJPA) — Cross-listed from type 5: 22M FCC comments, ~800K authentic, and the inversion of the apparent majority. The pre-LLM baseline that makes the CiviClick case legible as an escalation rather than a novelty.

• ★★ Ahead of the November 2026 midterms there is no federal regulation constraining AI in political messaging, the FEC remains deadlocked, and only a patchwork of untested state laws applies (US News; The Hill). The live regulatory vacuum, in the highest-stakes application, at the moment of writing. ⚠️ Not read in full.

• ★★ The architecture of lies: bot farms are running the disinformation war (Arkose Labs, 2025) — Automated bot traffic reached 51% of all web traffic in 2024, surpassing human activity for the first time in a decade. ⚠️ Bot-detection vendor; the figure circulates widely and should be traced to the underlying report.

• ★★ Social media manipulation in the era of AI (RAND, 2024) — Credible institutional framing.

• ★ If AI wrecks democracy, we may never know (Governing) — The near-duplicate-detection point, cross-listed from type 26.

• ★ AI bot swarms threaten to undermine democracy (Gary Marcus) — ⚠️ Marcus is on AINT's hype-busters exclusion list; the analysis adds nothing the RAND and Governing pieces do not. Recommend removing from the project.

• ★ House bill targets AI-generated comments in rulemaking (Nextgov, 2024) — Cross-listed from type 27.

#50. Fake Whistleblower/Tip-Line Reports

⚠️ Least documented type in the project, and the reason is structural rather than incidental: these incidents are handled confidentially by corporate compliance and law enforcement, so no public record accumulates. Everything below is anticipatory — concern about a flood rather than evidence of one. Keep it in the taxonomy as a completeness case; do not build an argument on it.

• ★★★ How to prepare for AI-powered investigations while managing your own AI risk (Corporate Compliance Insights) — The most on-point source: compliance functions must now assess the credibility of longer, more polished whistleblower complaints that appear AI-assisted, and determine whether supporting photo, audio, and video evidence is synthetic by examining metadata and watermarks. Note that this is qualitative flooding — the same pattern Schmitz et al. found dominant in government services (90% of cases) — and that polish, formerly a credibility signal, has become a suspicion signal. That inversion is the analytically interesting part.

• ★★ Important whistleblower protection and AI risk management updates (Harvard Law School Forum on Corporate Governance, 2024) — DOJ's 2024 compliance guidance updates address AI risk and whistleblower programmes together, acknowledging that AI-driven whistleblower activity could overwhelm compliance resources. Credible institutional forum; still forward-looking.

• ★ Operation AI Comply (FTC, 2024) — Enforcement sweep against AI-enabled fraud; adjacent rather than on-point.

• ★ AI-driven disinformation: policy recommendations for democratic resilience (Frontiers in AI, 2025), NewsGuard AI content farm tracker — Ecosystem context, not tip-line evidence.

• ★ AI Whistleblower Protection Act (S.1792, Grassley/Coons, May 2025) — legislative response; ⚠️ needs a primary link and note that it addresses protection for AI whistleblowers, not floods of whistleblower reports. These are frequently conflated in this project's sources.

#Cross-Cutting References

• ★★★ AI-Generated Text Is Overwhelming Institutions — Setting Off a No-Win 'Arms Race' with AI Detectors (Schneier & Sanders, Feb 2026) — The closest published statement of this project's thesis, spanning fiction magazines, letters to the editor, academic journals, legislatures, courts, conferences, social media, hiring, music, open source, and education. Core framing: "a legacy system relied on the difficulty of writing and cognition to limit volume." Their key analytic move — distinguishing legitimate assistance (democratizing access) from fraud (astroturfing, misrepresentation) with the power dynamic as the deciding variable — is the sharpest available answer to the beneficial-flooding tension. Note the friction with the 2026 evidence: the "no-win arms race" framing is the part that has aged least well. NeurIPS won a round decisively, with published calibration. A published AINT treatment should engage this directly rather than inherit it.

• ★★★ Characterizing Agentic Flooding of Government Services (Schmitz, Hammond & Chan, AIES 2026) — 84 cases, 11 jurisdictions, 13 service domains. Supplies what the rest of this file lacks: a systematic, criteria-driven dataset rather than an anecdote collection, explicitly non-causal. Concepts worth borrowing throughout: quantitative vs. qualitative flooding (90% of cases show complexity increases vs. 60% volume — most responses only address the latter); the likelihood × severity risk matrix; and "slack" (budgets anchored to historical take-up) as the variable determining whether beneficial demand becomes a crisis. Also the finding that 87% of cases involve LLM text generation with humans navigating the rest manually — autonomous agent navigation "is not yet evident," which should temper the agentic framing this project sometimes uses.

• ★★ AI Slop and the Information Ecosystem (Columbia SIPA / Internet Governance Project, June 2026) — Institutional treatment of slop as a systemic problem, strongest on displacement (what gets pushed out when slop fills a feed, search result, or recommendation surface) and on recommendation-system feedback loops. Useful for the content-flooding types (12, 13, 28, 38, 49). Local copy: raw/external/igp-ai-slop-report.txt.

• ★★ Why AI-Generated Text Detection Fails: Evidence from Explainable AI Beyond Benchmark Accuracy (Pudasaini et al., arXiv:2603.23146, Apr 2026) — The methodological counterweight to the Pangram and NeurIPS results. Feature-based detectors that score F1 0.9734 in-domain degrade sharply under cross-domain and cross-generator shift; SHAP analysis shows they key on corpus artifacts rather than machine authorship. Their standard — a detector is valid only if its evidence remains stable under domain and generator shift — is the right test to apply to any detection claim in this project. Local copy: raw/external/pudasaini-why-ai-text-detection-fails-2603.23146.pdf.

• ★★ GPT detectors are biased against non-native English writers (Liang et al., 2023) — The source of the project's most-repeated equity claim: ~61% false-positive rate on TOEFL essays vs. 1–4% for native-speaker essays, across seven perplexity-based detectors, with the rate falling to 11.8% when the same essays were linguistically enriched. Cite with its scope conditions. It describes 2023 perplexity detectors, not 2026 trained classifiers, and the mechanism is linguistic simplicity rather than nationality. See response_taxonomy.md §1 for the corrected treatment.

• ★ Institutions Are Drowning in AI-Generated Text (Fast Company) — Popular-press version of Schneier & Sanders.

#Evidence quality and open gaps

Where the evidence is strongest. Academic publishing and peer review (types 14, 34), open source (17), courts (21), public comments and astroturfing (5, 49), letters to the editor (6), literary prizes (10), grants (15), student assessment (33), music (38), and cover letters and hiring (2, 41) all carry ★★★ exhibits with named institutions and checkable numbers. That set is close to a natural table of contents for a published version.

Where it is weakest. Types 26, 36, 40, 44, 45, 46, 47 and 50 have no independent evidence of their own — they borrow from adjacent types, rest entirely on vendor material, or document a concern rather than a flood. Types 20, 22, 23 and 48 are dominated by suppliers describing their own throughput. Type 18 (patents) is a negative case: the predicted flood has not arrived, because fee-plus-formality friction survived what prose friction did not.

Highest-value gaps, in priority order:

1. The admissions-essay SES finding (type 31) — that AI use correlated with worse outcomes, especially for lower-SES applicants — is potentially the most publishable result in the project and is currently held only via a secondary summary. Find and read the primary.
2. A Liang-style demographic false-positive audit of a frontier classifier. Nobody has run one. Until someone does, neither "modern detection is equitable" nor "modern detection is biased" is supportable.
3. Deezer's own release on AI tracks passing 50% of daily uploads (type 38) — the most striking statistic in the project, currently secondary.
4. Buyer-side data for RFPs (type 20) and Upwork proposal volumes (type 42) — two types resting entirely on sellers describing their own throughput.
5. A second platform DMCA transparency report (type 22) — Automattic is carrying the type alone.

Claims to re-verify before publishing. The Heritage/FOIA case (type 19), the curl report counts (type 17), and the Wikipedia prose-ban claim (type 12) each circulate in a form their cited sources do not support — see the notes at those types. Where one load-bearing claim drifted, others may have; the ★★★ exhibits should be checked against primaries before any of this appears in print.

Recommended structural changes for a published version: merge type 40 into type 9, type 45 into type 1, and type 36 into type 33; keep type 18 explicitly as a negative case; and lead the civic cluster with CiviClick (type 49) rather than the net-neutrality history.