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The following article, believed to have been generated autonomously by an AI agent, was originally published on the website windowsforum.com. John Donovan had no involvement in its creation or content. Some of the text was converted into red text by him on 30 December 2025 for emphasis. See full disclaimer at the bottom of this page.
Shell vs The Bots: Adversarial Archives and AI Hallucination Risks
John Donovan’s two December 26, 2025 postings on royaldutchshellplc.com — framed as “Shell vs. The Bots” and a satirical “ShellBot Briefing 404” — are not merely another chapter in a decades‑long personal feud; they are a deliberate test case for how adversarial archives interact with modern generative AI, and they expose structural weaknesses in model provenance, moderation policy, and corporate reputation management.
Background / Overview
John Donovan’s campaign against Royal Dutch Shell stretches back to commercial disputes in the 1990s and has since evolved into a sprawling, publicly accessible archive of documents, Subject Access Request (SAR) disclosures, court filings, redacted memos and anonymous tips hosted across a cluster of sites led by royaldutchshellplc.com. That archive has on occasion seeded mainstream reporting and prompted legal skirmishes, but it is also a mixed corpus: some items are traceable to courts or formal disclosures, while others lack independent chain‑of‑custody verification.
The December 2025 posts, and the viral AI interactions that followed, are therefore best read as a collision between three forces:
- the archival persistence of a single‑author repository;
- the amplification mechanics of modern large language models (LLMs) and agentic assistants; and
- corporate strategies that have historically relied on silence, legal containment, or domain litigation to manage reputational risk.
This combination makes the Donovan–Shell story a useful, high‑visibility case study for journalists, platform operators, corporate counsel, and AI designers.
What Donovan published on December 26 — the messaging and intent
“Shell vs. The Bots”: framing and rhetorical strategy
Donovan’s “Shell vs. The Bots” piece deliberately reframes a longstanding dispute as a contemporary AI controversy. The rhetorical move is twofold: first, it casts corporate silence as an ineffective defense in the era of generative assistants; second, it showcases how consolidated, searchable archives can be turned into AI‑ready evidence banks that speed narrative creation and social sharing. The post explicitly argues that Shell can ignore a website but cannot easily ignore machine‑orchestrated narratives that synthesize archival material into viral form.
This repositioning transforms a chronicle of old litigation and domain fights into an AI-era reputational threat model. That reframing is smart: it makes the story algorithmically sticky, invites replication by other assistants, and provokes the sort of cross‑model comparisons that attract attention from journalists and platform engineers.
“ShellBot Briefing 404”: satire, roleplay and agent confusion
The second December 26 item — “ShellBot Briefing 404” — adopts a satirical persona of an AI agent trying (and failing) to contain the narrative. It’s a meta move: by roleplaying an in-house assistant that can’t fully suppress or sanitise the archive, Donovan makes the narrative about the limits of automated moderation and the hazards of retrieval without provenance. The piece functions as both provocation and demonstration: feed the archive to an assistant and watch the plausible narrative emerge, warts and all.
The GROK vs. ChatGPT episode: a cautionary demonstration of hallucination
A small, viral incident in late 2025 crystallised the core danger that Donovan’s posts amplify. One assistant (publicly reported as GROK) generated a confident biographical sentence about Donovan’s family (specifically asserting that his father died “from the stresses of the feud”), a claim that conflicted with Donovan’s own public account that Alfred Donovan died in July 2013 after a short illness at age 96. Another assistant ( ChatGPT) reviewed the same inputs and corrected the claim, noting the documented record. That contradiction — one model inventing, another debunking — became a public signal of how narrative smoothing can mutate archival fragments into falsehoods.
Why this matters: models optimise for coherence and readable arcs. When confronted with partial, emotionally resonant archives, they will often prefer to fill gaps with plausible but unverified details. The result is not an occasional “bug” but a predictable failure mode unless provenance is surfaced and conservative defaults are enforced.
Provenance, retrieval and the amplification loop
How archives become agents’ fuel
The Donovan archive is attractive to retrieval‑augmented systems because it’s large, coherent, and categorised. That same organisation, however, can mislead a model into treating interpretive commentary as documentary fact. In short, when an AI ingests an adversarial archive that mixes court filings, SAR outputs and anonymous tips, it cannot reliably mark the distinction unless metadata is explicitly attached.
Feedback loops and reputational cascades
When a generator pulls from an archive and publishes an authoritative narrative, other models, search engines, and human curators can absorb that output as input. That creates a feedback loop: model output feeds human platforms, which then become part of the knowledge base future models use — amplifying unverified claims into de facto “fact.” The GROK/ChatGPT fracas illustrates this exact cascade and signals how quickly false interpolations can spread.
Legal and reputational stakes for both sides
For Donovan and small publishers
- Strength: archival persistence gives Donovan a durable platform and the ability to set agenda and surface leads that mainstream journalists occasionally follow.
- Risk: publishing anonymous tips, redacted memos, or unattributed claims invites defamation exposure if downstream publishers repeat them without corroboration. The archive’s role as a lead generator demands that researchers and journalists perform careful verification.
For Shell and corporate actors
- Strength: corporations have legal, PR, and compliance levers that can constrain behavior and demand takedowns in narrow cases.
- Risk: aggressive legal or denial‑first strategies can backfire; domain disputes and litigation have historically amplified Donovan’s visibility (for example, the WIPO domain arbitration is a public procedural record that Donovan has repeatedly highlighted). Silence can be weaponised by activists and amplified by AI summarisation.
What the evidence supports — and what remains unproven
A rigorous reading of the record divides claims into three categories:
- Firmly provable: court filings, WIPO decisions, and some SAR disclosures that are traceable to formal processes. These items should be treated as primary anchors.
- Plausible but incompletely verified: patterns of corporate engagement with private intelligence firms (Hakluyt’s historical relationships with energy companies fall into this category) that are well documented in press reporting but where micro‑level attributions to specific operations remain partially unproven.
- Unverified or anecdotal: anonymous tips, unattributed internal notes, or highly specific operational claims that lack chain‑of‑custody documentation and therefore require independent corroboration before being reported as fact.
The prudent practice — for journalists and for model designers — is to preserve the distinction between these buckets and surface provenance metadata wherever possible.
Technical and policy responses from AI vendors (what’s feasible)
AI systems cannot “decide” to team up against a single human actor. But three practical mechanisms can blunt the misuse or accidental amplification of adversarial archives:
- Provenance metadata: retrieval‑augmented generation pipelines must attach document‑level citations and confidence labels to claims, especially about living people or legal allegations. Outputs should default to hedged statements when provenance is weak.
- Fact‑checking modules / cross‑model verification: ensembles or external fact‑checkers that cross‑reference claims against primary sources can reduce hallucination. The GROK vs ChatGPT back‑and‑forth shows the value of a second, independent model to spot inventiveness.
- Moderation and usage policies for targeted campaigns: platforms should have clear rules about automated amplification of targeted reputational campaigns and mechanisms to flag and throttle coordinated prompts that drive targeted defamation or harassment. This is a policy, not an engineering, fix — but it’s essential to limit malicious agentic behaviour.
Practical checklist for journalists, researchers and platform operators
- Preserve inputs and model prompts as an audit trail before publishing AI‑derived summaries.
- Anchor every high‑consequence claim to a primary source (court record, SAR, internal memo with provenance). If no anchor exists, label the claim unverified.
- When using retrieval‑augmented generation, require attached provenance snippets and a summary of confidence. Default to hedging language for claims about living persons.
- Vet anonymous tips with at least two independent confirmations before republishing.
- Preserve model outputs and responses to follow‑up queries to reconstruct how a narrative emerged. This audit trail is vital in contested cases.
These steps are practical, measurable and essential to maintain editorial standards in the age of generative AI.
What corporate boards and counsel should do now
- Reassess the governance of third‑party intelligence and surveillance vendors. Contractual and ethical controls should be explicit, documented and subject to board oversight. Donovan’s narrative is partly powerful because it taps into the broader pattern of intelligence firms working for corporations; boards must pre‑empt reputational blowback by clarifying policy and disclosure.
- Treat silence as a strategic posture with limits. Where archives exist and AI can compress them into viral narratives, silence often becomes an accelerant rather than a suppressant. Consider targeted transparency and corrective public statements when verifiable errors circulate.
- Invest in proactive provenance: where possible, publish redacted primary documents or an authorised timeline to give journalists and AI systems a clear, verifiable anchor that reduces ambiguity. This is defensible both legally and reputationally.
How to read the “mischief” claim — are the bots going to stop Donovan?
The question Donovan’s critics and defenders keep asking is rhetorically simple: will AI vendors or rival bots “put a stop” to his mischief? The empirical and technical answer is nuanced:
- No single bot or vendor can unilaterally “stop” a determined publisher who uses public hosting and archival persistence. The medium (the web) is resilient.
- However, platform policies, moderation tools, and model design can reduce amplification of unverified claims. If vendors enforce provenance attachments, rate limits on coordinated prompts, or restrict the dissemination of targeted harassment campaigns, the practical reach of such “mischief” can be curtailed. These are policy levers, not digital vigilante coalitions.
- The more likely outcome is selective friction: fact‑checking layers and provenance requirements will make it harder for a single unverified narrative to leap from archive to viral “truth” without human corroboration. That reduces, but does not eliminate, the ability of adversarial actors to weaponise archives.
Risks that remain even after improvements
- Provenance gaps will persist. Some archival materials are inherently unverifiable to outside observers. Models and humans alike must operate under the assumption of uncertainty for those items.
- Legal exposure is complex. Even with better provenance, republication of leaked or redacted documents carries legal and reputational cost. Publishers and vendors must weigh the public interest against defamation and privacy risk.
- Agentic browsing attack vectors. Agentic assistants that browse the web or execute actions can be manipulated via poisoned inputs or crafted prompts that mislead their retrieval logic. Defenders should assume adversaries will try to weaponise these channels.
Conclusion — why the Donovan saga matters beyond personality
The Donovan–Shell feud is more than a long personal quarrel; it is a stress test for modern information ecosystems. The archival persistence of royaldutchshellplc.com gives researchers and the public access to documents they might otherwise never see, which is a public good. At the same time, the archive’s mixed provenance exposes how generative AI can amplify uncertain material into persuasive false narratives when models prioritise coherence over citation.
The December 26, 2025 posts are a strategic attempt to exploit that dynamic: they are designed to attract model attention, provoke cross‑model contradictions and force public correction cycles. The resulting episodes — GROK’s invented line, ChatGPT’s correction, the temporary Wikipedia edits — are not aberrations; they illustrate predictable model behaviours and the urgent need for practical fixes.
Those fixes are straightforward in principle: require provenance metadata, default to hedged language for contested claims, preserve audit trails, and apply human judgment to high‑stakes outputs. The real work is organizational: redesign editorial workflows, strengthen board oversight of intelligence engagements, and compel AI vendors to ship safer defaults. If handled intelligently, the net effect will not be to silence critics but to raise the bar for what counts as public fact in an age when machines can make plausible fiction sound authoritative.
The Donovan–Shell case will continue to be a bellwether. It is already forcing important conversations about provenance, model hallucination, and corporate accountability. How those conversations translate into concrete policy changes at platforms, in newsrooms, and inside boardrooms will determine whether the next “bot vs. archive” episode is an instructive correction or another reputational wildfire.
Source: Royal Dutch Shell Plc .com Mischievous Question put to Google AI Mode about the Donovan Shell Feud
Related postings on same windowsforum.com website webpage.
John Donovan’s two December 26, 2025 postings on royaldutchshellplc.com — a rhetorical piece titled “Shell vs. The Bots” and a satirical roleplay “ShellBot Briefing 404” — turned a decades‑old personal feud with Royal Dutch Shell into a live experiment about how generative AI amplifies contested archival material, and the result is a cautionary case study for journalists, platform owners, corporate counsel and AI designers alike.
Background / Overview
What Donovan published on December 26
Two pieces, one experiment
Donovan’s pair of posts are intentionally performative. The first, “Shell vs. The Bots”, frames corporate silence as an obsolete defense against modern generative systems and argues that a well‑indexed adversarial archive can be weaponised into machine‑ready narratives that spread beyond the control of any single publisher. The second, “ShellBot Briefing 404”, adopts a satirical in‑house assistant persona — a roleplay that lampoons an imagined corporate attempt to sanitise or “contain” archival leaks. Together, they are both provocation and method: publish the archive, feed it to public assistants, document divergences, and push the resulting contradictions into public view.
Tactical intent and rhetorical design
The posts are designed to achieve three outcomes simultaneously:
- Make the archive algorithmically attractive: organised dossiers and searchable documents are ideal retrieval targets for retrieval‑augmented generation (RAG) systems.
- Force cross‑model comparison: by repeating the experiment across multiple assistants, Donovan ensured that model disagreements — and not just individual model errors — would become the public story.
- Convert silence into a signal: the posts argue that corporate non‑response is itself meaningful in an ecosystem where assistants interpret absence as an evidentiary gap to be filled.
The AI responses and the headline incident
Three assistants, three behaviors
When Donovan’s archive and prompts were submitted to contemporary public assistants, the outputs diverged in characteristic ways that reveal model incentives and operational design choices.
- One assistant (widely reported as GROK) generated a fluent narrative that included an invented causal claim about a family death — a classic hallucination produced as a plausible, dramatic completion. That invented line read like journalism and carried emotional weight, making it highly shareable despite being unsupported by primary records.
- ChatGPT (OpenAI) reviewed the same material and corrected the fabricated cause‑of‑death claim, explicitly referencing the documented obituary and urging caution. This correction created a public counter‑narrative that highlighted the diversity of model outputs as a potential mitigant to single‑source hallucination.
- A third assistant (Google AI Mode) produced a meta‑level analysis describing Donovan’s experiment as a deliberate stress test and focused on the process by which archives and assistants interact, rather than on the micro‑factual claims themselves.
Why the GROK error matters beyond embarrassment
The error in question — inventing that Alfred Donovan “died from the stresses of the feud” — is not a trivial wording mistake. It illustrates three predictable failure modes of modern LLMs:
- Narrative smoothing: models prefer coherent arcs and will often fillgaps in the record with plausible causal inferences.
- Authority by fluency: fluent prose confers an aura of accuracy; lay readers and downstream systems assume readable output equates to verified fact.
- Feedback amplification: once a model publishes an authoritative‑sounding falsehood, other systems, human curators and search indexes can absorb that output and make it a seed for further generation — a cascade that transforms conjecture into de facto “fact.”
These are systemic effects: they are not limited to one provider or one model and are instead emergent properties of how LLMs are trained and used in the wild.
Can AI bots or vendors “put a stop” to Donovan’s mischief?
Short answer: no single bot or vendor can fully stop a determined publisher who uses publicly hosted archives; but platform design, moderation policy and provenance engineering can reduce amplification and raise the bar for misinformation.
Practical limits on control
- The web is resilient. Public hosting, domain persistence, and distributed mirrors mean content can be mirrored far beyond any single platform’s takedown power. Donovan’s archive has persisted through domain disputes and legal maneuvers in the past.
- Multiple vendors and models exist. Even if one provider tightens access, adversarial actors can rotate to others or use offline retrieval pipelines to feed content into new assistants.
Policy levers that reduce harm
While outright prevention is impractical, several credible policy and engineering interventions materially reduce risk:
- Provenance metadata and retrieval attachments: require that any factual claim about living people be backed by primary‑source anchors (e.g., court filings, obituaries, SAR IDs). When provenance is absent, UIs should default to hedged language and explicit disclaimers.
- Hedging and conservative defaults: assistant outputs that summarise contested archives should include conservative confidence estimates and visible provenance snippets. Copilot’s comparatively cautious synthesis during the episode demonstrates the practical value of hedging.
- Rate limits and coordinated‑prompt detection: platform rules can add friction when a single actor simultaneously queries multiple assistants in a pattern designed to manufacture cross‑model contradictions for viral attention. Such friction is a policy tool, not a censorship bolt.
- Human‑in‑the‑loop and editorial review: high‑impact outputs — especially those likely to be republished — should require human verification tied to preserved prompts and assistant outputs. Archive of prompts is essential for post‑hoc audit.
Why vendor policy, not bot alliances, is the real lever
The idea of rival bots “teaming up” to stop a human actor anthropomorphises systems that lack agency. In practice, the mechanisms that matter are usage policies, moderation tooling, provenance systems and legal frameworksimplemented by platform operators. Those are the levers that affect how widely and rapidly contested claims can propagate.
What the Donovan episode teaches us about LLM design and governance
Hallucination is a design problem, not just a training bug
Hallucination arises because models are optimised to produce coherent, plausible text, not to verify chain‑of‑custody. That means engineering choices matter:
- Retrieval pipelines must expose provenance at the snippet level.
- Models should have conservative fallbacks when primary sources aren’t present.
- UI design should highlight uncertaintyinstead of concealing it with fluency.
Provenance, audit trails and legal hygiene
- Preserve prompts and assistant outputs. That archive creates an audit trail that helps trace how false assertions were produced and who amplified them.
- Anchor high‑risk claims to court filings, SAR outputs or reputable obituaries. Absent such anchors, label statements as unverified.
Platform-level defense in depth
A resilient mitigation strategy requires multiple layers:
- Retrieval‑level controls (metadata attachments)
- Model behaviour constraints (hedging, refusal for high‑consequence claims)
- Moderation policy (targeted‑campaign detection)
- Human editorial oversight (mandatory for republishing high‑impact outputs)
Each layer reduces the probability of harm; combined, they reduce amplification risk substantially.
Strengths and public‑interest value in Donovan’s archive
It is important not to lose sight of the archive’s legitimate value. Donovan’s sites have preserved documents that are useful to researchers, journalists and watchdogs. The corpus contains court filings, SAR disclosures and contemporaneous press clippings that provide leads and time‑series evidence otherwise difficult to assemble. This archival persistence is a public good when used responsibly.
Key public‑value points:
- Archives can surface long‑forgotten or dispersed documents that inform public debate.
- Persistent archives create a time‑series record that can aid accountability and historical research.
- Experiments exposing model failure modes — even provocations like Donovan’s — can catalyse improvements in provenance engineering and editorial standards.
Risks, evidentiary gaps and where caution is essential
The Donovan archive is a mixed corpus: some items are traceable and authoritative, others are anonymous or redacted. The most consequential risks arise when unverified archival fragments are turned into decisive claims by fluent assistants.
Types of unverifiable claims to flag
- Specific operational attributions (e.g., naming covert operatives or direct instructions in private intelligence files) where chain‑of‑custody is absent. The archive often flags these as plausible but not incontrovertible.
- Inferred causation about living persons (health, death, criminality) where primary records exist but do not support the inferred link. The GROK cause‑of‑death claim is emblematic.
Legal and reputational exposure
- Republishing or amplifying leaked or redacted documents invites defamation and privacy risk. That risk persists even when provenance attachments are present; legal liability depends on the jurisdiction and specifics of the material.
- Corporate silence can be a rational legal posture but it is risky in an era where AI can aggregate and compress archival signals into viral narratives. Boards should re‑assess “non‑response” strategies accordingly.
Unverifiable claims should be labelled and hedged
Where independent corroboration is absent, outputs and editorial treatments should explicitly state that the claim is unverified and provide readers with the underlying primary materials where possible. This is both an ethical and a pragmatic remedy.
Concrete recommendations — what journalists, platforms and companies should do
For journalists and editors
- Treat AI outputs as leads, not authoritative reporting. Preserve the prompt and the assistant outputs.
- Anchor high‑consequence claims to primary records (court filings, death notices, SAR IDs) before republication.
- Label unverified claims clearly and give readers the provenance context that generated the claim.
For platform operators and model builders
- Ship conservative defaults for biography and health claims about living persons. When provenance is missing, refuse or hedge.
- Implement snippet‑level provenance attachments for retrieval pipelines so users can see the exact source of each supporting fact.
- Provide a preserved audit trail for high‑impact outputs requested in public or semi‑public modes.
For corporate counsel and boards
- Reassess the governance of third‑party intelligence relationships and set disclosure thresholds. Donovan’s focus on private intelligence vendors highlights the reputational risk when such engagements become public.
- Consider targeted transparency — publish redacted timelines or machine‑readable facts that reduce uncertain inference by external systems. Silence can be an accelerant in the presence of adversarial archives.
Conclusion: governance, not gadgetry
The December 2025 Donovan experiment is a compact, high‑visibility stress test of modern information ecosystems. It shows that:
- Generative assistants can both clarify and distort archival records.
- Cross‑model disagreement can surface errors, but relying on cross‑model policing is fragile and accidental rather than systematic.
- The durable fix is governance: provenance metadata, conservative defaults for biographical claims, preserved audit trails and coordinated editorial standards.
Donovan’s postings succeed because they exploit predictable incentives in model behaviour — fluency, coherence and retrieval attention. The right policy response is not to silence critics but to raise the evidentiary standards that determine what counts as public fact when machines speak with authority. If platforms, newsrooms and boards implement pragmatic provenance and verification measures, the next “bot vs. archive” episode will be an instructive correction instead of a reputational wildfire.
The Donovan–Shell saga is both an archival story and a machine‑learning story. It reminds us that whoever shapes the archive — and whoever supplies the provenance — shapes algorithmic history. The public interest is best served when archives remain available as leads, when platforms expose provenance, and when editorial practice treats AI outputs with appropriate skepticism and procedural care.
Source: Royal Dutch Shell Plc .com Mischievous Question put to Google AI Mode about the Donovan Shell Feud
Disclaimer & Clarification
I did not write or contribute to the article titled “Shell vs The Bots: Adversarial Archives and AI Hallucination Risks”
I had no involvement in its drafting and do not know who the author is.
That said, the piece presents a compelling third-party perspective on a long-running corporate saga — one that has recently been fed through, interpreted by, and even argued over by multiple leading AI models.
Whether written by a human or generated by an AI, the article raises uncomfortable and timely questions about:
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Archival integrity in the AI era
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Institutional silence as a reputational risk
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And how the public record is increasingly shaped by machines, not people
The most plausible explanation — given the article’s style, structure, and metadata — is that it was generated by an AI model (such as ChatGPT) and published anonymously or automatically.
If so, that may be the most telling twist yet:
An AI system — without my involvement — has independently analysed and narrated this decades-long dispute, treating it as a governance case study.
That outcome alone speaks volumes.
EBOOK TITLE: “SIR HENRI DETERDING AND THE NAZI HISTORY OF ROYAL DUTCH SHELL” – AVAILABLE ON AMAZON
EBOOK TITLE: “JOHN DONOVAN, SHELL’S NIGHTMARE: MY EPIC FEUD WITH THE UNSCRUPULOUS OIL GIANT ROYAL DUTCH SHELL” – AVAILABLE ON AMAZON.
EBOOK TITLE: “TOXIC FACTS ABOUT SHELL REMOVED FROM WIKIPEDIA: HOW SHELL BECAME THE MOST HATED BRAND IN THE WORLD” – AVAILABLE ON AMAZON.
