Imagine this:

You spend years honing a voice. You write consistently. Tweets. Essays. Blog posts. Maybe a book.

Some go viral. Some don't. But it's your mind, rendered into words, week after week.

Now imagine this:

Anyone can call on a chatbot and ask it to:

"Summarize my blog." "Generate 10 tweets in my style." "Explain my book better than I ever could."

And it does. Instantly. No credit. No payment. No acknowledgment that the core ideas, structure, cadence-all came from you.

This is not theoretical. This is the present tense of large language models like ChatGPT and Claude. They are extracting and internalizing massive amounts of publicly available human writing-without consent, without compensation, and often without consequence.

And writers, perhaps more than any other class of creator, are the most unprotected in this new landscape.

The Illusion of "Free"

The internet trained us to publish for attention. Writers gave away thoughts for likes. Threads for visibility. Substack posts for email subs. Essays for the hope of one day landing a book deal or speaking slot.

But the value was never zero. The cost was time, intellect, and in many cases, expertise that took a decade to earn.

And now, in a perverse twist, the internet's openness has become a feeding ground for AI systems that never ask "who wrote this?"-only "can we scrape this?"

The result? Public data becomes private capital. Open knowledge becomes proprietary training material. And your labor-your writing-becomes infrastructure for a trillion-dollar model you'll never profit from.

The Writer's Dilemma in the LLM Era

LLMs cannibalize the writer's value chain.

1. Loss of Revenue

Ask an LLM to summarize Atomic Habits or The War of Art. You'll get a surprisingly cogent, distilled, ready-to-digest version of a book that once sold for $15.

Why buy the book?

Now imagine this playing out across every book, every paid blog, every course, every newsletter. The backlist dies. The long tail vanishes. And the writer's residual income-already thin-collapses.

2. Loss of Attribution

LLMs don't credit sources. They remix. Rephrase. Reassemble. But the DNA remains-a writer's phrasing, structure, tone-all stripped of origin.

Imagine someone quoting your idea, in your voice, without ever saying your name. Now imagine millions doing it at scale, invisibly.

3. Loss of Market Differentiation

A writer's edge was once their uniqueness. Now, LLMs flatten style. They democratize access-but they also homogenize output.

Your voice becomes one of many-diluted, anonymized, commodified. And worse: others can now imitate you with a prompt.

Publishers' Silent Complicity

Traditional publishing should have been the firewall. But in many cases, it's the sieve.

Most publishers haven't updated copyright contracts to explicitly prevent AI training. DRM protections are weak. Book PDFs float across the internet, indexed and scraped like blog posts.

Worse, some publishing houses are selling access to their archives via quiet licensing deals-profiting from their authors' work without transparent rev share.

And writers, boxed into legacy contracts, have no recourse. The gatekeepers are asleep. Or worse-cashing checks while the gates burn.

How LLM Companies Rationalize It

Their stance is simple:

"It's publicly available." "We're not copying; we're learning patterns." "We filtered copyrighted data." "We're compliant with fair use."

But fair use wasn't built for this scale. It wasn't designed to account for models that internalize entire corpora and then replicate it with minor variation.

This is not quoting a passage. This is internalizing a million passages, generating an output, and pretending no one deserves a slice of that value.

It's technical legalism dressed up as progress. And it's dishonest.

What Must Change

The knee-jerk response is to scream "regulate." But vague regulation without implementation will move slower than model training.

We need layered, realistic, enforceable solutions. Here's what that could look like:

A. A Collective Licensing Body for Writers

Think ASCAP for prose. Writers enroll their work. LLMs pay for access. Revenue is distributed back based on model usage, weight, or domain.

It won't be perfect. But it's a starting point. A way to make "training" no longer a euphemism for theft.

B. Opt-In Marketplaces for Training Material

Imagine an AI-native Substack:

• You publish
• You tag it as trainable or not
• If trainable, you set licensing terms
• LLMs browse, pay, use
• Writers earn

Control moves back to the creator.

C. Digital Watermarking for Text

Tech exists to watermark images, videos, music. Text needs the same. Invisible tags embedded into phrasing patterns or syntax choices.

If LLM output mirrors the structure too closely-it's flagged. Attribution enforced.

D. Consumer-Level Nudges

Build transparency into the LLM UX:

"This summary draws from published books and essays by real human writers. Here are the sources. Consider supporting them."

Even that tiny gesture reshapes user behavior. It brings creators back into the value loop.

E. Paygated Summarization

If I ask ChatGPT to "summarize Sapiens," I shouldn't get a free essay. I should get a message:

"To access this, purchase the book or read excerpts via our licensed preview."

LLMs should augment consumption, not replace it.

Why It All Matters

This isn't about Luddite resistance. I love LLMs. I use them. But I also write. And I see what's coming.

If we continue down this path-if we allow LLMs to ingest everything without cost-we will kill the incentive to create anything worth ingesting.

AI will eat itself. It will train on AI-generated sludge. And the golden era of human insight-raw, flawed, beautiful-will vanish beneath layers of derivative noise.

If we want intelligent machines, we must sustain human intelligence. And that starts by paying the people who built the internet's mind.

Writers. Like you. Like me. Like the millions whose names no one sees, but whose words built the scaffolding of modern AI.

The Choice Ahead

We are at a juncture.

Do we let LLMs become the new robber barons-extracting value at scale with no redistribution?

Or do we build something better?

A world where AI amplifies creators, not replaces them. Where compensation is not an afterthought but a first principle. Where attribution is not optional but essential.

This isn't a technical challenge. It's a moral one.

And if the machines are learning from us, let's give them something worth learning.

Starting with how to be fair.