AI and the Human Commons: Toward a Sustainable Ecosystem
Ask an AI to summarize the causes of World War II, and you'll likely get back a competent textbook paragraph. Ask it instead to explore what Europe might look like today if Germany had won, and something different happens. Philip K. Dick built an entire novel, The Man in the High Castle, out of precisely this kind of counterfactual speculation. Readers have always found alternate-history thought experiments strangely compelling—they demand a creative recombination that straight narration does not. What's new is the machine's participation in that process. By pattern-matching across a vast archive of historical detail, an AI can surface combinations of consequence and contingency that no single human would likely generate alone. The human, in turn, supplies the judgment, plausibility checks, and interpretive frame that turn those combinations into something meaningful. That difference—between a machine that only answers and one that generates something that can be interpreted as genuinely new and meaningful content—is, in large part, what this essay is about: not what AI can do on its own, but how deeply human judgment and machine combinatorics have come to depend on each other.
AI is often discussed as if the central question is whether machines are becoming intelligent in some deep, almost metaphysical sense. A more useful question is what kind of ecosystem human beings and AI systems form together, and whether that ecosystem is sustainable or self-consuming.
An Interdependent System, Not a Mirror
Like an ecosystem, AI and the human production of knowledge are interdependent in ways not immediately obvious. AI's replies are only as rich as the human material behind them. When we get an answer that feels genuinely useful or interpretable, it ultimately traces back to our contemporaries or predecessors in culture—Reddit threads, newspapers, novels, scientific papers, ordinary conversation. Pattern-matching algorithms without human creations behind them have nothing to match against.
But this isn't the same as saying AI merely mirrors us, or that it's a "stochastic parrot" endlessly recombining prior phrases into statistically likely imitations. Both tropes miss something important. When an AI system answers an idiosyncratic prompt by pattern-matching across an enormous, idiosyncratic corpus, it can surface combinations that never existed anywhere before—not in the corpus, not latent in the user's mind. That's genuine combinatorial novelty, not reflection and not imitation. Much of ordinary AI use is still routine: ask about a historical event and you'll likely get back something close to a standard textbook account. That's fine, and common. But it isn't the limit of what these systems do. AlphaFold's protein structure predictions weren't retrievals of known folds; they were novel combinatorial outputs, later confirmed empirically and taken up by biochemists as genuinely new, usable knowledge. Scientists now hope for something similar in tracing the causes of diseases. In each case, the novelty only becomes an idea, a fact, a usable proposition in the world once a competent interpreter—a chemist, a researcher, a reader—takes it up and makes something of it. Novelty here is always relational: novel relative to a competent interpretive community, not novelty in some free-standing sense the machine achieves on its own.
Distributive Agency
This is what makes the whole picture ecological, and also what makes it a complex adaptive system rather than a simple tool-and-user relationship. Following Dewey's account of organism-environment transactions, the human-AI exchange is the relevant unit within which new meaning is achieved. Neither end alone—not the machine, not the human—suffices to produce it. The human must interpret; that's one half of the interdependence.
But there's a second half, easy to miss. Even an AI system considered "in isolation," with no live user at all, is already in an interdependent relation with the human archive: the vast set of traces of past purposive human agency—the Reddit posts, novels, arguments, corrections—that make up its training data. So there's no such thing as an AI system that becomes autonomous simply because no live human is watching. When agentic systems run unsupervised, sending emails or triggering workflows with minimal oversight, they aren't escaping interdependence; they're relying on a thinner version of the same interdependence, with the live interpretive partner swapped out for a fossilized, archival one and no fresh correction happening in real time. What looks like machine autonomy is actually agency distributed across algorithm and archive, just with the feedback loop degraded.
Call this a model of distributive agency: agency located in the relation between human and machine, not in either pole alone. This borrows structure from actor-network theory's insight that agency can be distributed across a network, but it departs from ANT in an important way. Only humans, given the technology we currently have, contribute the purposive and interpretive moments; the AI contributes combinatorial and generative capacity, not purposiveness or interpretation of its own.
The Commons Under Pressure
That human archive functions as a genuinely scarce resource, not unlike oil: valuable because it can't be manufactured after the fact, and because it's the byproduct of purposive human agency, which nothing else currently produces. This isn't just a metaphor anymore. Reddit's unpolished, argumentative, idiosyncratic posts have become valuable enough that Google and OpenAI have each paid tens of millions of dollars a year to license them, and Reddit itself has disclosed over 200 million dollars in licensing revenue. Companies are buying up Reddit's archives the way developers buy up scarce coastal real estate—because supply is fixed and everyone can see where it's headed. Stanford's 2026 AI Index and other observers have warned that high-quality human-written data may already be running out, increasingly diluted by AI-generated "slop." Whether or not that's precisely true, it names something real: pre-2022 human data is a finite resource that current systems depend on and cannot regenerate on their own.
We now have an empirical name for what happens when that resource stops being replenished: model collapse. Research published in Nature in 2024 showed that when successive generations of models are trained recursively on AI-generated rather than human-generated text, the models progressively lose the rare, nuanced, long-tail information that made the original human data valuable, converging instead toward a narrower, blander approximation of it. Later work has shown this isn't an iron law—mixing in enough real data can slow or avoid it—but the underlying mechanism holds: synthetic regeneration erodes exactly the features hardest to produce and most distinctive of purposive thought. Model collapse, in plainer terms, is what happens when a system stops replenishing its archive with traces of purposive human agency and instead recycles its own output.
A related pattern shows up at the level of platforms rather than data: "enshittification," Cory Doctorow's term for how online platforms decay once they shift from serving users, to serving business customers, to serving only themselves. Doctorow doesn't frame it this way, but it's a corollary of the same underlying dynamic, visible at a different scale—both describe systems once sustained by something freely given (user goodwill in one case, purposive human text in the other) being drawn down for short-term extraction until the whole environment degrades from within.
Where Purposive Agency Comes From
This raises a deeper question: how did rich, purposive human text get into the archive to begin with? Here Dewey and Tomasello, both deeply ecological thinkers, do essential work. Dewey treated inquiry as active and iterative, not a fixed possession—exercised, tested against consequences, strengthened or weakened by practice, much like a virtue. Tomasello's empirical work on shared attention, cooperation, and joint intentionality traces how humans become capable of meaningful communication at all: not through raw computation, but through social participation and correction. Crucially, Tomasello's comparative work also shows that even intelligent great apes don't engage in the kind of non-instrumental, cooperative play human toddlers do—kicking a ball for hours with no extrinsic purpose. That capacity for shared, purposeless cooperation appears to be a precondition for the normative, belief- and desire-laden minds that eventually produce purposive language.
Read together, Dewey and Tomasello explain why the archive was valuable material in the first place: it's the residue of embodied, socially formed, purposive agency—people genuinely working something out, disagreeing, revising. That's exactly what degrades first under model collapse, and exactly what "atrophies" when human users lean on labor-saving shortcuts instead of doing the interpretive work themselves.
Two Kinds of Use
Some uses of AI save labor; others intensify it. Labor-saving use reduces human effort and promises convenience. Labor-intensive use makes the user think, check, revise, and interpret more. Given the distributive-agency picture above, this distinction carries real weight: labor-intensive use is the human half of the transaction that turns combinatorial novelty into a genuine idea, and it's also what keeps replenishing the archive with fresh purposive material. Labor-saving use, especially when it slides into rubber-stamping, thins both sides of the interdependence at once.
None of this means human agency is being obliterated by machines—that claim is too strong. Agency is weakened and narrowed, not erased, when the practices sustaining it are allowed to wither: atrophy, not obliteration. Ordinary LLM chat systems function mostly as indirect agents, generating combinatorial output whose significance depends on human interpretation. Newer agentic systems that send emails, make purchases, or run workflows with minimal oversight are hybrid agents, combining the direct consequences of older automation with the interpretive flexibility of language models—and, as noted, even these aren't escaping interdependence, only relying on its thinner, archival half.
A Note on the AGI Question
None of this amounts to a claim that AI can never think, know, or feel in some future form. The claim is narrower and more disciplined: given what current systems actually do—calculate, optimize, pattern-match—and given what we know empirically about how belief, intention, and normativity actually arise in the only case we have real evidence about, there is currently no warranted assertion that today's systems know, believe, or intend anything. A blender does not become cream cheese by spinning faster; a system does not become conscious simply by handling more data. That leaves the door open, in principle, to some future architecture built differently enough to warrant revisiting the question. It closes the door on the confident hype that treats AGI as imminent simply because systems are getting faster and more capable.
A Sustainable Ecosystem, Not Two Separate Things
The strongest way to think about AI, then, is not as a tool sitting apart from human life, but as one distributive system: human inquiry and machine output locked in a feedback loop, where the quality of what humans contribute today determines what the system can offer tomorrow. Reddit licensing, model collapse, and enshittification are three faces of one dynamic, visible at three scales—platform, training pipeline, institution.
The right response is neither nostalgia for a pre-AI past nor panic about an AI-dominated future. It's to ask, at every point of use, whether the human-AI system is being cultivated or mined. In practice that might mean favoring platforms and norms that reward genuine human contribution over recycled output, and treating AI less as a source of answers and more as a source of material to think with. If we want this distributive system to remain sustainable, we need more human writing, more human curation, more human judgment—not less. The archive depends on us continuing to do the purposive, interpretive work that made it valuable in the first place.
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