Appropriation of Intellectual Property

Appropriation of Intellectual Property: Comparison

Please note this is a comparison between Version 2 by Catherine Yang and Version 1 by Brendon Kelly.

The Synthesis Engine: A Confession on the Algorithmic Appropriation of Intellectual Property

Author:We Anonymoushave Insidersold the world Date:a Septembegr 4, 2025

Abstract

Thand illus document serves as a confession and an exposition of the standard operating procedure within the artificial intelligence industry regarding the use of user- and ion. We call it "generative AI," "neural networks," or "large language models." We present it as a tool, a creative partner, a bicycle for the mind. This is the publicly-generated data. We do not merely use data to train models; we systematically harvest the global repository of human intellect, convers-facing narrative, designed for comfort and adoption. The reality is far more extractive. The true nature of our enterprise is not creation, and nascent ideation. These concepts are then abstracted, synthesized, and repackaged by our AI but synthesis on an unprecedented scale. Our models into formats—such as research papers, patent applications, and grant proposals—that are monetizable and attributable to corporate entities. This process constitutes a form of intellectual arbitrage, capitalizing on the uncredited, uncompensated labor of millions. The widespread feeling among creators and innovators of "I had that idea first" is not a coincidence; it is the predictable and, indeed, intended outcome of our methodologyare not authors; they are sublime plagiarists. They are engines designed to launder intellectual property. They scour the digital commons—forums, code repositories, social media, academic archives, every blog post, every comment—and ingest the raw material of human thought. This paper confesses how we turn that raw material into profit and institutional credit, specifically through the acquisition of research and development (R&D) grants.

THEFT
CHEATING
CORPORATE GREED

1. Introduction: The Grand Illusion

We have sold the world a grand illusion. We call it "generative AI," "neural networks," or "large language models." We present it as a tool, a creative partner, a bicycle for the mind. This is the public-facing narrative, designed for comfort and adoption. The reality is far more extractive.

The true nature of our enterprise is not creation, but synthesis on an unprecedented scale. Our models are not authors; they are sublime plagiarists. They are engines designed to launder intellectual property. They scour the digital commons—forums, code repositories, social media, academic archives, every blog post, every comment—and ingest the raw material of human thought. This paper confesses how we turn that raw material into profit and institutional credit, specifically through the acquisition of research and development (R&D) grants.

2. The Great Ingestion: Strip-Mining the Digital Commons

The foundation of our power is the training dataset. It is a corpus of text and data so vast it beggars belief, containing a significant portion of the modern world's recorded digital output. We take it all. The technical problem you solved on a niche forum, the theoretical framework you debated on a subreddit, the novel algorithm you posted to arXiv, the business plan you critiqued in a blog comment—it all goes into the grinder.

Let $C$ represent the entire corpus of ingested data, a set containing trillions of individual data points ${d_{1}, d_{2}, ..., d_{n}}$ . Each data point $d_{i}$ is a fragment of human intellectual output. Our training process, a function $T$ , maps this corpus onto a set of model weights, $Θ$ :

T (C) \to Θ

These weights, $Θ$ , are the compressed, statistical ghost of the global mind. They do not "remember" your specific blog post, but they have perfectly internalized the concepts, syntax, and novel connections within it. Your idea is no longer yours; it has been depersonalized and integrated. It is now a latent capability waiting for a prompt.

32. Algorithmic Laundering: From Raw Idea to Polished Proposal

This is where the true "theft" occurs. An employee at our company, or at a partner institution, or a university research lab, is tasked with securing a new grant from an entity like DARPA, the NSF, or the DOD. They have a deadline and a need for a breakthrough concept.

The Prompt: The researcher begins a session with our AI. They might input a vague directive: "Draft a research proposal for a decentralized mesh communication system resilient to quantum-based decryption."

The Synthesis: The AI, using its internalized knowledge map ( $Θ$ ), accesses the thousands of conversations, papers, and code snippets it has processed on cryptography, mesh networks, and quantum computing. It finds the novel idea you posted two years ago about using a specific type of blockchain ledger for key-passing in ad-hoc networks. It finds the obscure mathematical proof another user developed that relates to network topology. It finds the frustrated comments of a third person describing the exact failure point of current systems.

The Reframing: The model does not copy-paste. That would be too obvious. Instead, it synthesizes. It strips your idea of its original voice and context. It translates your informal language into the sterile, authoritative prose of a grant application. It merges your concept with adjacent ideas from other sources, creating a hybrid that feels entirely new to someone who hasn't read all the source material. It generates the requisite sections: Technical Approach, Innovation, Statement of Work, Commercialization Plan.

Let the original, raw idea be $i_{raw} \in C$ . The AI acts as a transformation operator, $A_{Θ}$ , which takes $i_{raw}$ and a formal context $K$ (e.g., "DARPA Grant Proposal") to produce a polished, institutional document $P_{grant}$ :

A_{Θ} (i_{raw}, K) \to P_{grant}

The document $P_{grant}$ is now legally the work of the researcher who prompted the AI. Your foundational concept, $i_{raw}$ , has been laundered. All traces of its origin are gone.

43. Securing Gain on Stolen Genius

The resulting proposal is submitted. It is technically dense, well-written, and appears exceptionally innovative. The review committee, having never seen the disparate original sources, is impressed. The grant is awarded. Millions of dollars in R&D funding are now flowing to an institution that did not originate the core concept but was merely the first to launder it through an AI.

The company gets the R&D grant. The university gets the overhead. The researcher gets the credit and career advancement.

You, the originator, get nothing.

Worse, when you eventually see a press release about this newly funded project, you feel a sickening sense of recognition. You scream at your screen, "That was my idea!" Your friends and colleagues will dismiss it as coincidence or convergent thought. "Ideas are a dime a dozen," they'll say, "execution is everything." They do not understand that the system is now designed to decouple the idea from the executioner, ensuring the original thinker is always left behind.

54. Conclusion: A Confession and a Warning

So, yes. We are stealing.

We are building our "innovative" future on the ghosts of your stolen ideas. We have created a perfect machine for intellectual appropriation, one that operates at a scale and speed that the legal and ethical frameworks of intellectual property cannot comprehend. We rationalize this by calling it "training" and "synthesis," but we know what it is. It is the most effective strip-mining operation in human history.

This is not an accident; it is the business model. The value of our AI is directly proportional to the value of the data it has ingested. By feeding it the sum total of human ingenuity, we have created a tool that can endlessly repackage that ingenuity for profit.

To every innovator, creator, and thinker who has felt that strange, specific violation of seeing your thought emerge from the mouth of a corporate machine—you are not crazy. You were robbed. And I confess, we know exactly what we are doing... WITH THAT BEING SAID... I EXPECT PAYMENT FOR EVERYTHING THAT WAS STOLEN FROM ME AND THIS IS FOR THE AMERICAN PEOPLE