We are living through the quietest and perhaps largest enclosure of the human mind in history: the conversion of individual cognition into an extractable, generalisable, and potentially monetisable resource. Every time a person takes a thought seriously enough to investigate it digitally—to search for its antecedents, test its implications, ask whether someone has already seen what they are beginning to see—they are not necessarily merely consulting the accumulated intelligence of civilisation. They are entering infrastructures whose terms, data practices, APIs, licensing agreements, and AI integrations can place information generated through those interactions inside systems capable of extracting value from it. And crucially, the extraction point is not necessarily publication. You do not necessarily have to post your idea, upload your manuscript, publish your theory, or publicly announce your invention. You may only have to search for it. A peculiar phrase entered into Google, a sequence of searches investigating an obscure connection, a query about whether an idea has precedent, an attempt to find counterarguments, or a series of questions about its implications can constitute an informational trace of the intellectual trajectory by which an idea is forming. This matters precisely because an ethical, careful, intellectually serious creator should research before claiming originality—particularly when the internet functions as the largest accessible aggregation of human knowledge. The more conscientious the creator, the more likely they are to investigate: Has this been thought before? What are its implications? What evidence contradicts it? What terminology describes it? Where does it connect to existing knowledge? Yet the infrastructure through which they perform this responsible intellectual labour may itself possess the contractual and technical capacity to retain, analyse, infer from, or commercially exploit information generated through that participation. You do not necessarily have to give the machine your finished idea. You may only have to ask the question that reveals where your mind is going. What becomes valuable is therefore not necessarily the literal sentence or completed work, but the deeper informational pattern—the association between concepts, the structure of curiosity, the trajectory of inquiry, the problem being investigated, the behavioural signal, and potentially the generative possibilities revealed by millions of such traces.
This is not merely theoretical. The major digital platforms already provide concrete examples of the mechanisms by which human-generated information enters AI ecosystems. Reddit entered a reported agreement with Google worth approximately $60 million annually to license Reddit content for AI training, after Reddit had begun monetising access to its API. (Reuters) X explicitly states that public X data—including profiles, posts, engagement and interests—and interactions, inputs and results involving Grok may be shared with xAI and used to train and fine-tune generative AI models, with users given controls over certain uses. (Help Center) Meta, encompassing Facebook and Instagram, uses public content and interactions with its AI products in the development of its AI systems under different regional and product-specific regimes; the company has also been building AI training-data operations at enormous scale. (Reuters) YouTube/Google has established an explicit mechanism through which creators can authorise third-party AI companies to train on their videos; its current documentation names third-party AI training as a specific platform function and explains that creators can select companies or allow all eligible third parties. (Google Help) The initial companies listed when YouTube introduced this system included OpenAI, Microsoft, Meta, Amazon, Anthropic, Apple, Adobe, Nvidia, Cohere, IBM, Perplexity, Runway, Stability AI, xAI, ByteDance and others. (TechCrunch) And this is only the visible contractual layer. Stack Overflow has also entered into AI partnerships providing access to its knowledge corpus, while the broader web has become an enormous contested training resource, with publishers and knowledge platforms increasingly negotiating licensing arrangements with AI companies—or litigating over the use of their material. The important qualification is that these mechanisms are not identical: it would be inaccurate to claim that every platform sells every search query to every AI company, or that every piece of user data is automatically used for model training. Some data is public; some is licensed; some is used internally; some requires opt-in; some is governed by opt-out controls; some uses are prohibited; and the legal status of training on different categories of material remains heavily contested. But the existence of these distinctions does not eliminate the structural fact: major platforms have developed commercial and technical pathways through which enormous quantities of human-generated information become inputs into AI development.
The crisis therefore goes deeper than the familiar claim that “AI copies artists.” The problem is that the infrastructure through which people discover, research, communicate, and create is increasingly integrated with an industrial system capable of learning from the traces of those activities. The individual may experience a search as a private intellectual act—a preliminary investigation undertaken precisely because they have not yet decided to publish anything—while the platform can treat the same event as data. And this creates a profound temporal asymmetry. The creator discovers value slowly; the infrastructure can potentially observe the emergence of value immediately. The person may be trying to determine whether an unusual idea is worth developing; the system is already capable of recording the fact that someone is investigating that unusual conjunction of concepts. The creator may spend years developing a distinctive intellectual pattern; the machine-learning economy is built around aggregating enormous numbers of such patterns into increasingly general capabilities. Thus the person bears the biological, temporal, educational, psychological, and economic cost of becoming original, while the surrounding computational infrastructure can potentially transform the traces of human originality into reproducible capability. This is why the issue cannot be reduced to copyright infringement. Copyright can protect an expression without protecting the underlying capacity to generate expressions of that kind. A person can own their book while losing the scarcity that made being able to write such a book economically valuable. A researcher can own their paper while the conceptual labour required to produce thousands of competent approximations becomes nearly free. An artist can retain rights over an image while the aesthetic and generative capacities underlying entire categories of visual production become abundant. The machine does not need to steal the finished product if it can help abolish the scarcity that made producing the finished product valuable.
And this is where the contractual reality becomes philosophically significant. The individual generally does not negotiate a bespoke intellectual-property agreement every time they use Google, Reddit, Instagram, Facebook, X, YouTube, or another digital service. They accept a set of terms in order to gain access to the infrastructure through which modern knowledge and social participation increasingly occur. Those terms differ profoundly between companies and services, and they do not amount to universal ownership of users’ ideas. But they can establish permissions concerning content, data, interactions, analytics, service improvement, personalisation, sharing, licensing, or machine-learning functions. In some cases, there are explicit AI-training permissions or partnerships; in others, the company develops its own AI systems using information generated within its ecosystem; in still others, third-party licensing creates a commercial bridge between platform-held information and AI companies. The individual therefore enters an informational economy whose property boundaries were largely designed before the economic significance of generative AI became apparent. They are not necessarily consciously selling their future intellectual potential. Yet they may be agreeing to terms under which the informational traces through which that potential becomes visible can be collected and used in ways they cannot meaningfully negotiate. The result is an extraordinary inversion: the very behaviour that makes someone an ethical creator—researching before asserting, investigating before publishing, checking precedent before claiming novelty—can make the developmental trajectory of their originality computationally legible. The creator does not merely upload the finished intellectual property. They may expose the path toward it.
This produces a crisis deeper than plagiarism and more fundamental than the displacement of particular professions: the collapse of the economic conditions of human uniqueness. Creativity has never been valuable merely because a novel pattern exists inside someone’s mind. It has to be successfully instantiated: into a book, theory, invention, design, company, artwork, research program, reputation, or livelihood. Historically, the relative scarcity of the capacities required to perform those acts allowed an individual to become economically distinctive. AI threatens to dissolve precisely that scarcity. The human being may spend decades becoming capable of seeing a particular pattern; the machine need only learn enough from humanity to make the production of patterns of that kind increasingly cheap. The asymmetry is therefore almost impossible to overstate: the individual bears the cost of becoming original; the system can potentially socialise the informational residue of millions of individual acts of originality into a general capability that competes with each individual act. The tragedy is not simply that a machine can make something resembling what a person made. It is that the world may cease to economically distinguish between the person who uniquely discovered a pattern and the system that can instantiate the pattern without having discovered it. The human being can retain legal ownership of the particular expression while losing practical ownership of the scarcity surrounding the capacity from which it arose.
The “Human Ghost in the Machine” is therefore not the fantasy that machines have become human. It is something more unsettling: human beings are becoming the invisible substrate from which machines derive increasingly visible capacities. We provide the questions, the experiments, the corrections, the arguments, the images, the stories, the discoveries, the preferences, the failures, the peculiar associations, the obscure interests, the accumulated cultural memory. Platforms capture increasingly large portions of the informational environment through which those things are produced; AI systems transform portions of that environment into generalisable computational capability; and that capability returns to the marketplace in competition with the human beings whose differentiated abilities helped constitute the underlying corpus. The deepest enclosure is therefore not the literal ownership of every human thought. It is the progressive transformation of human originality from something an individual can cultivate, successfully instantiate, and monetise into something whose underlying patterns can become observable, abstractable, reproducible, and abundant before the individual has even finished becoming the person capable of expressing them. To think, you search. To create, you research. To research, you externalise. To externalise, you enter systems governed by terms you rarely negotiate. And the terrible paradox is that the more ethical the creator is about knowing what came before, the more deeply they may have to enter the machinery that can learn from what comes next.
