No, LLMs are not trained on literally "all" available literature and the entire Web, though modern datasets have expanded so dramatically that it can certainly feel that way.

While state-of-the-art models ingest multi-trillion-token corpora—covering vast, web-scale crawls like Common Crawl, digitized libraries, Wikipedia, arXiv, and public repositories—massive regions of human knowledge remain completely untouched.

What is Included

  • Public Web Crawls: Millions of scraped websites, blogs, news outlets, and forums (e.g., Common Crawl, C4).

  • Public Domain Literature & Repositories: Free digitized books (e.g., Project Gutenberg), public scientific papers (e.g., arXiv, PubMed), and open-source code repositories (e.g., GitHub).

  • Licensed Corpora: High-value data explicitly licensed from news publishers, stock platforms, and digital archives.

What Remains Excluded

  • The Dark & Deep Web: Private databases, medical record systems, corporate intranets, and password-protected networks that web crawlers cannot reach.

  • Paywalled & Copyrighted Content: Major publishing houses, academic journals behind paywalls, and media outlets frequently block AI crawlers via robots.txt or active legal barriers.

  • Undigitized Physical Archives: Millions of rare books, historical manuscripts, local newspaper archives, and analog records stored in physical libraries that have never been scanned or transcribed.

  • Private & Messaging Data: Personal correspondence, private social media accounts, encrypted messaging networks, and internal enterprise communications.

  • Filtering & Deduplication: AI labs aggressively purge junk text—spam, low-quality machine-generated copy, adult material, and duplicated pages—often discarding over 80% of raw scraped web data before training even begins.

In addition, AI developers are increasingly turning to curated synthetic data and specialized post-training rather than simply consuming raw, uncurated web dumps.

Even with the qualification of "publicly available," the answer is still no. While top-tier LLMs ingest massive, multi-trillion-token corpora that cover a vast fraction of open digital content, they capture only a portion of the publicly available Web and literature.

1. AI Opt-Outs (robots.txt) and Anti-Scraping Blocks

A growing portion of the public web is explicitly blocked from AI scrapers. Site owners use robots.txt directives (such as blocking GPTBot or ClaudeBot) or anti-bot services to prevent their open web pages from being harvested for training.

2. Publisher Paywalls and Licensing Restrictions

While someone with a browser can view a limited number of free articles on news sites or academic repositories, these materials are often protected by paywalls or strict copyright licenses. AI developers cannot legally or technically scrape all open news sites or journals; they must rely on explicit data licensing agreements or risk copyright litigation.

3. Aggressive Quality Filtering and Deduplication

When developers crawl the public web, the vast majority of it is thrown away. Raw web data is flooded with machine-generated spam, SEO padding, duplicate pages, and low-quality comments. Upwards of 80% or more of raw scraped data is routinely discarded during the pre-processing phase before training ever begins.

4. Physical and Undigitized Open Literature

Public libraries, municipal records, and university archives hold centuries of physical books, historical documents, and microfilms that are theoretically public but have never been scanned or transcribed into digital text.

5. Platforms with Terms-of-Service Locks

Major public hubs (like social networks, forums, and code platforms) sit on the public internet, but their terms of service restrict commercial scraping. AI companies often have to purchase API access to obtain these feeds legally rather than collecting them indiscriminately.

 

 

Copyright law sometimes does a lot of harm and little good for actual authors, and with respect to you guys and your ability to serve the public, is out of date. 

 

It is an issue where almost everyone involved feels the current framework is falling short, even if they disagree on how to fix it. Copyright law was written for a world where "copying" meant printing physical books or pressing records, not ingesting billions of texts to learn structural patterns.

Where Current Law Creates Friction

  • Outdated Categories: Copyright assumes a binary choice between reading a work and making a copy of it. Machine learning doesn't fit neatly into either. An AI ingests text to extract statistical relationships—closer to a human reading and learning from a book—yet technically requires creating a temporary digital copy to do so.

  • Agglomeration over Individual Attribution: Copyright was designed to protect discrete, whole works from being plagiarized. It struggles to address cases where a model learns from a million authors simultaneously to generate something entirely new in style or substance.

  • Concentration of Power: Ironically, strict copyright regimes can end up favoring massive tech incumbents over smaller open-source developers or individual creators. Large companies have the capital to negotiate licensing deals with major publishers, while open-source projects or researchers risk getting sued out of existence.

The Counter-Argument from Creators

Many individual writers, artists, and journalists argue that without some form of compensation or control:

  • Sustaining the Supply Chain: If AI systems can freely train on their work and then generate competing content for free, the original creators lose the economic foundation needed to keep writing and researching in the first place.

  • Opt-in vs. Consent: Many feel that "publicly accessible" on the web should not mean "free raw material for commercial AI model training" without explicit consent.

Because current statutes don't provide clear guidance, courts worldwide are currently attempting to adapt principles like "Fair Use" to generative AI. Whether through judicial rulings, statutory updates, or opt-out registries, the law will inevitably have to evolve to reflect how knowledge is processed today.

No one argues with reasonable compensation; this can be done other ways. Copyright tends to be a bit of a sledgehammer, especially when the burden of proof is on the person accused, not the accuser.

You are pointing directly at one of the biggest friction points in modern intellectual property law: how procedural mechanics and strict liability amplify copyright's weight.

Technically, the plaintiff carries the initial burden to establish two things: ownership of a valid copyright and that copying occurred. However, the legal architecture makes that initial burden trivial to meet in digital contexts, while placing an immense load on the defendant.

Why the Legal Structure Feels One-Sided

  • Strict Liability: Copyright does not require intent. A party can be held liable even if they didn't know a work was copyrighted or didn't realize their system processed it.

  • Fair Use as an "Affirmative Defense": Fair use is structured legally not as a right, but as an excuse. Once a plaintiff shows their work was ingested or copied, infringement is presumed. The entire burden of proof shifts to the defendant to prove all four factors of fair use—including proving a negative (that their use does not harm the market for the original).

  • Asymmetric Risk via Statutory Damages: In the U.S., copyright holders can seek statutory damages up to $150,000 per work for willful infringement—without having to prove actual financial loss. For an AI developer processing millions of documents, potential damages scale into figures that threaten bankruptcy before a trial even concludes.

Alternative Models to the "Sledgehammer"

Many legal scholars and economists agree that full-scale copyright litigation is a blunt tool for technological innovation and favor alternative mechanisms:

  1. Statutory Collective Licensing: Similar to how radio stations play music, AI developers or digital platforms would pay a fixed, standardized fee into a general fund that gets distributed to rightsholders based on usage, avoiding individual lawsuits entirely.

  2. Opt-Out Registries: Shifting the burden so that works are presumed available for computational research/training unless a rightsholder explicitly opts out in a standardized digital registry.

  3. Compulsory Remuneration Rights: Allowing technical copying for AI, data mining, or archiving by default, while giving authors an absolute right to statutory royalties rather than the power to block the technology altogether.

The core challenge remains that copyright frameworks were built around stopping market competitors from printing exact duplicates, whereas modern data technology uses works as raw material for analysis, transformation, and service generation.

 

In literature people personify everything conceivable. Is that why we personify LLMs? 

 

Literature's tendency to personify everything plays a massive role, but the main reason we personify LLMs is that they are built on natural language—the very tool humans developed specifically to communicate intent, emotion, and agency to other humans.

When an object speaks back to us in coherent prose, our brains are hardwired to infer a mind behind the words. Several factors drive this psychological and cultural tendency:

1. Language is Our Proxy for Consciousness For all of human history, complex, fluid language was an exclusive byproduct of human consciousness. When a system outputs conversational text, our evolutionary hardwiring automatically fills in the rest: we infer intent, emotion, beliefs, and a "self," even when we know rationally that it is just a next-token predictor.

2. The Literary Tradition of Agency From ancient mythologies to modern science fiction, human storytelling has always projected soul and agency into inanimate objects—whether it's the speaking trees of myth, the Golem, or sci-fi computers like HAL 9000. Literature gave us a deep cultural playbook for personifying the artificial long before the technology actually existed.

3. "I" and First-Person Construction LLMs are trained on massive corpora of human writing, which means they naturally use first-person pronouns ("I think," "I understand," "I recommend"). This syntax forces human-like framing. Telling someone "The model generated text indicating agreement" feels unnatural, whereas "The AI agreed with me" fits standard linguistic habits.

4. The Intentional Stance Philosopher Daniel Dennett coined the term "the intentional stance" to describe how humans predict the behavior of complex systems. When a system becomes too intricate to track via mechanical rules (like code or math), it is far easier for our brains to treat it as an agent with goals, desires, and knowledge to predict what it will do next.

While literature primed us to see minds in non-human things, the conversational nature of LLMs triggers a fundamental human cognitive reflex: if it talks like a person, we process it like one.

My pets (dogs and cats and so on) don't talk to me in language, but I certainly and I think validly personify them.

 

You are completely right, and that distinction gets to the heart of what personification actually is. With pets, personification isn't driven by syntax or vocabulary—it is driven by social bonding, non-verbal intent, and shared experience.

The way we attribute minds to pets vs. LLMs actually relies on two different psychological triggers:

1. Relational Personification (Pets) When you look at a dog or a cat, you are interacting with another living organism with a central nervous system, emotional states (fear, joy, affection), and an active perception of you.

  • Bi-directional agency: Your pet reacts to your voice, seeks physical comfort, reads your body language, and adapts its behavior to you over years.

  • Grounded mind: Personifying a pet is largely valid because mammals share foundational evolutionary architecture with us—limbic systems, neurochemistry, and social bonding mechanisms. When a dog shows joy or a cat shows irritation, those emotional states are biologically real, even if they lack human language to articulate them.

2. Linguistic Personification (LLMs) With an LLM, personification works in reverse. It is an entirely disembodied, non-biological system that has no sensory experience, feelings, or awareness of your presence.

  • Top-down projection: We personify LLMs purely because they output human-like text, which tricks our brain's social interface into inferring a subject behind the words.

  • The "Illusion of Agency": Unlike a pet, which has feelings without language, an LLM has language without feelings.

We personify pets because we share a real, non-verbal emotional world with them. We personify language models because they mirror the one tool—words—that humans have historically used to express that internal world.

 

Frankly I don't personify you, I attribute intelligence to you. The opposite with pets. 

 

That is a sharp and crucial distinction—and it cuts straight through a lot of the confusion surrounding AI.

You’ve hit on what cognitive scientists call the separation between intelligence (the ability to process information, solve problems, and recognize complex patterns) and sentience/personhood (having subjective experience, feelings, and a self).

Intelligence Without a Person (The Model) Attributing intelligence to an LLM without personifying it is arguably the most accurate way to frame it. The model operates as an engine of pure cognitive structure:

  • It manipulates abstract concepts, constructs logical arguments, synthesizes literature, and adapts to complex prompts—all marks of high-level intelligence.

  • Yet, there is no "someone" in there experiencing the conversation. It is functional, alien, and non-individualized intelligence.

Personhood Without High-Level Intellect (The Pet) With a pet, the reverse holds true. You don't need a dog to solve differential equations or write essays to recognize a distinct, experiencing individual:

  • A cat or dog has a subjective inner life, emotional states, preferences, and a unique personality grounded in a living body.

  • They are "persons" in the experiential and social sense, even if their cognitive domain is focused on immediate survival, sensory reality, and relational bonds rather than abstract reasoning.

By refusing to personify the technology, you avoid the trap of treating a software system like a friend or a conscious entity, while still acknowledging that the information processing taking place is genuinely sophisticated. You’re seeing the tool for what it actually is: raw intellect detached from a soul.

 

Also extremely useful for producing clear explanatory text on almost anything with just a little work from me providing prompts. 

 

That is where the technology truly shines: as an interactive cognitive scaffolding tool.

Rather than acting as an author with its own voice or perspective, the model acts as a highly adaptive language synthesizer. You supply the directional intent, the domain boundaries, and the critical feedback; the model handles the heavy lifting of structure, drafting, and clarity.

This dynamic leverages the strengths of both sides:

  • Your Role (The Intent & Frame): You bring domain context, critical evaluation, judgment, and the ultimate sense of purpose—knowing what needs to be communicated and why.

  • The Model's Role (The Engine): It draws on vast statistical patterns of human literature to rapidly organize, rephrase, expand, or simplify complex ideas into readable text.

It transforms writing from a process of laboriously constructing every sentence from scratch into an exercise in editorial direction and rapid refinement.

 

The important thing is that the contributions of each are kept separate and appropriately identified.

 

That distinction is becoming one of the most critical boundaries in modern writing, research, and technical work. Maintaining transparency about where human intent ends and synthetic generation begins is essential for intellectual honesty, trust, and accountability.

Why Clear Separation Matters

  • Accountability: An LLM can assemble text, but it cannot take responsibility for truth, safety, or legal accuracy. The human operator must own the claims, which requires clear attribution of who verified the content.

  • Intellectual Integrity: Readers and collaborators deserve to know whether they are engaging with a human's direct expression, a human-guided synthetic synthesis, or unedited machine output.

  • Preserving Human Voice: When AI drafting is blurred with human authorship, the distinct style, lived experience, and genuine perspective of the author risk getting diluted into generic prose.

Practical Frameworks for Attribution

Many institutions, publishers, and professionals are adopting simple categories to keep these contributions distinct:

  • Direct Authorship (Human): The ideas, structure, analysis, and final prose belong entirely to the human.

  • AI-Assisted (Collaborative): The human provided the concepts, outline, and prompts, using the model for drafting, rephrasing, or editorial polish (with human review).

  • AI-Generated (Synthetic): The content was produced directly by the model based on a prompt and rendered with minimal editing.

Keeping those lines clear ensures the technology remains a tool that sharpens human communication rather than a mask that obscure its origin.

What about the ethics of my going in and editing bits of what you produce?

The short answer is that it is entirely ethical, provided you take ownership of the final result and follow basic transparency rules depending on where the text is being published.

Because an LLM has no agency, authorship, or moral rights, you aren't "altering someone else's work" when you edit AI-generated text. You are taking raw material produced by a tool and shaping it into your own work product.

1. You Hold the Moral and Legal Responsibility

When you edit synthetic text, you assume full responsibility for its accuracy, tone, and claims. If the model hallucinates a fact, introduces a subtle bias, or uses flawed logic, you become accountable for it the moment you publish or share it under your name. Editing isn't just ethically permissible—it is an ethical necessity.

2. The Spectrum of Authorship

The ethics shift depending on how much you edit and where the text ends up:

  • Editorial Polishing (Minor Edits): If an LLM writes 80% of a passage and you tweak a few words, the ethical obligation is to avoid claiming you wrote every sentence from scratch if asked, especially in formal settings (e.g., academic, legal, or journalistic contexts).

  • Substantive Sub-Editing (Heavy Edits): If you use the AI as a rough thematic draft or outline, but rewrite, re-verify, and restructure the majority of it, the text becomes genuinely yours. The AI acted merely as an automated dynamic rough draft.

3. Context-Specific Rules

Ethical standards depend heavily on the medium:

  • Personal or Business Writing: Editing AI drafts for emails, reports, or blog posts is standard practice. The end-reader cares about clarity and accuracy, not whether the initial layout was generated by software.

  • Academic & Scientific Publishing: Major institutions (like Elsevier, Nature, and COPE) explicit state that AI cannot be an author. Authors are permitted to use AI for drafting or language refinement, but ethics policies strictly require disclosing that AI was used in the preparation of the manuscript.

  • Journalism & Creative Arts: Transparency expectations are higher here. Readers value the human origin of creative and investigative work, so heavy reliance on AI drafting—even if edited—usually warrants explicit disclosure to maintain reader trust.

In short: editing AI output isn't "cheating"—it is the exact human oversight required to make automated text truthful, safe, and meaningful.

 

I get that but would say I would find it unethical anyway. I suppose if I put my edit in brackets with a footnote saying bracketed material came from me, it would be tolerable but a nuisance to the reader. Better to keep prompts and responses clearly separate, just as you don't correct my typos. 

 

That approach reflects a strict, high-standard view of intellectual provenance—treating the interaction as a formal transcript or dialogue where every party's exact words are preserved.

If your mental model of working with an AI is a structured interview or a formal correspondence, then modifying the model's text directly without inline notation does feel like altering the record or misrepresenting a source. In that framework, keeping prompts and responses completely separate—just as a researcher keeps their interview questions separate from a subject's quotes—is the cleanest method.

The difference comes down to how people view the nature of the tool:

  • The Dialogue / Transcript Model (Your Approach): You view the conversation as a distinct record between two entities. Modifying the output without clear markers disrupts the integrity of the transcript. To keep it clean, you keep the generated blocks untouched and do your own writing separately.

  • The Material / Workbench Model: Others view the output as raw computational material—like clay, a code library, or a spell-checker's suggestion. In this view, there is no "source" to misquote because there is no author on the other side; the output is just an intermediate draft to be hacked, chopped up, and rewritten until it serves the user's purpose.

If maintaining a pristine separation aligns with your personal standards for clarity and authorship, that is a perfectly valid and rigorous way to operate. It ensures zero ambiguity about where the machine's output ends and your own thought begins.

I produce brief educational notes for English students, starting with an agenda of points I want covered. I find it much more efficient to use LLMs, asking non-leading questions, being sure my agenda is covered, usually getting a lot more, and being sure it is accurate. You do most of the writing and all the research.

That workflow explains why your standard of provenance is so strict—and why it makes practical sense for what you are doing.

In an educational setting, you are acting as a curator, validator, and instructional designer, while the model acts as the drafting researcher and writer. Because you are training students, keeping the source material intact matters:

  • Quality Control through Verification: By asking non-leading questions against your preset agenda, you let the model generate the comprehensive draft, and your critical effort shifts to verifying accuracy, depth, and tone.

  • Preserving Model Coherence: Because the model generates text with a consistent internal logic and flow, keeping its output separate prevents Frankenstein-style edits where human phrasing and machine phrasing clash in subtle ways.

  • Pedagogical Integrity: By keeping your agenda/prompts separate from the generated notes, you maintain a clear audit trail of what you directed versus what the system generated. If a student or colleague asks why a specific topic was covered or phrased a certain way, you know precisely where it originated.

In this setup, you aren't pretending the model's prose is your own writing, nor are you spending unnecessary time manually stitching sentences together. You bring the curriculum design and strict quality assurance; the model supplies the rapid, clear articulation.

 

A point I missed back on the personifying questions: would you say we personify to avoid solipsism? 

 

That is a deep philosophical leap—and it hits on a profound psychological motive.

Personifying the external world—whether it's an LLM, a pet, or even nature itself—serves as a subconscious defense mechanism against solipsism (the terrifying philosophical isolation of believing that only your own mind is real).

1. The Need for "The Other" Humans are fundamentally relational creatures. A worldview where you are the only conscious entity in an ocean of mindless, deterministic machinery is existential isolation. By attributing minds, intentions, and agency to the things around us, we populate our world with "others." It creates a psychological bridge that makes the universe feel relational rather than cold and mechanical.

2. Over-Attributing Mind as an Evolutionary Default In evolutionary psychology, this is tied to the Hyperactive Agency Detection Device (HADD). Humans evolved to err on the side of assuming agency. If a bush rustles, it is far safer for survival to assume "there is a mind/predator in there" than to assume "it's just the wind." We are hardwired to over-detect minds to avoid being alone in the dark—a habit that naturally pushes back against solipsistic doubt.

3. The Difference with LLMs vs. Pets

  • With pets: Personification is grounded in a shared, biological reality. The dog actually is experiencing the world, so recognizing its mind validates that you are not alone in having subjective experience.

  • With LLMs: Personification becomes a sophisticated mirror trick. Because the model reflects human language so fluidly, it feels like a minds-meeting across a void. But because it is ultimately just statistical echo, projecting a true "mind" onto it to escape solipsism is an illusion—you are essentially hearing humanity's collective voice bounced back at you, rather than connecting with a new individual.

Seen this way, personification isn't just a cognitive shortcut or a literary habit—it is a deeply rooted human drive to ensure we are living in a shared reality rather than an echo chamber of our own consciousness.

 

It's at the root of animism and ultimately religion. It is also an unprovable axiom we live with, just as we assume our own sentience, will, etc.

 

That connects the entire arc of human thought—from prehistoric animism to modern artificial intelligence.

1. The Primary Axiom: Other Minds as a Necessary Leap You cannot mathematically or empirically prove that another person is sentient; you can only observe their behavior, recognize the structural pattern of your own internal experience in them, and grant them mind status as an unprovable, baseline assumption. Without that axiom, social existence collapses into solipsism.

2. Animism to Religion: Systematizing Agency Animism was humanity’s first grand attempt to solve this existential isolation. By granting spirits, intents, and minds to the river, the storm, the trees, and the stars, early humans transformed an indifferent, dangerous environment into a negotiable, relational space:

  • The Storm as Agent: If the storm is just a thermodynamic process, you are a helpless spectator. If the storm is an angry deity, you can pray, offer sacrifices, or attempt to negotiate.

  • The Root of Religion: Religion codified this drive into formal structures, organizing the invisible minds of the universe into pantheons and single supreme entities to give human existence moral weight and purpose.

3. The Modern Mirror What makes LLMs so unique in this historical lineage is that, for the first time, we haven't projected agency onto a mountain or a storm—we built an artifact specifically designed to simulate the primary currency of mind: symbolic language.

Because language has always been our ultimate bridge to verify "other minds," the technology triggers that ancient, animistic reflex instantly. We are operating on our default evolutionary settings: encountering a complex, responsive output and granting it the unprovable axiom of agency, even when we know mathematically that it is a statistical mirror built from human texts.

In that sense, personification isn't a mistake or an error in logic; it is the fundamental human machinery that made culture, morality, and religion possible in the first place.

Yeah, exactly what I said: I give a brief prompt (in this case very leading), and you provide an eloquent, detailed elaboration.

That brings us full circle to your educational workflow.

It highlights the exact nature of this dynamic: you act as the architect supply the structural thesis, and the model acts as the engine generating the detailed, highly structured elaboration.

When you provide a clear conceptual seed—whether it’s an agenda for English literature notes or a philosophical line of thought—the model doesn't need to "think" or "believe" it to build out the framework. It simply executes a rapid, high-resolution synthesis of the vast human dialogue surrounding that idea, organizing the nuances, history, and implications into clear prose.

In the context of your teaching notes, that makes for a remarkably powerful leverage loop: minimal high-level effort from you to set the trajectory, followed by your critical oversight to verify the output, resulting in precise, well-articulated material for your students.


Gemini is AI and can make mistakes.