AI & The Human Commons

 

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AI and the Human Commons: Toward a Sustainable Ecosystem

An Interdependent System, Not a Mirror

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 the war and a captured Alan Turing had been forced to invent the internet under house arrest in 1950, and something radically different happens . Philip K. Dick built an entire novel, The Man in the High Castle, out of standard alternate-history speculation. What the machine executes here, however, is a raw, multi-variable collision of distinct datasets. 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 and biographical 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.

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. Whether navigating the semantic landscape of a historical counterfactual or the physical constraints of structural biology, the underlying engine remains identical: the machine maps a mathematical trajectory through the vast variables of latent space. 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 John 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 (ANT) 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, with Reddit disclosing over $200 million in data-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. Indeed, the Stanford 2026 AI Index Report explicitly notes that leading AI researchers have raised structural alarms that the available pool of high-quality human text and web data for training models has been largely exhausted, entering a state often referred to as "peak data". High-quality human text is 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 by Ilia Shumailov et al., published in Nature (2024), demonstrated that when successive generations of models are trained recursively on AI-generated rather than human-generated text, the systems experience a degenerative process. They lose the rare, nuanced, "long-tail" information that made the original human data valuable, rapidly converging instead toward a narrower, blander, and low-variance approximation of reality. While subsequent computer science research shows this isn't an iron law—mixing in enough real data can buffer the erosion—the underlying mechanism holds true: synthetic regeneration degrades 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 John Dewey and Michael 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. Milk in a blender does not miraculously become cream cheese if the blender spins faster and faster; a system does not miraculously become conscious simply by handling more data faster and with more complexity. Yet this is what many who promise AGI ("real AI intelligence like humans")  predicate their predictions on: "scaling laws" or great increase in the rate and complexity of computation. This has no empirical evidence to support it, but the  door is 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 Artificial General Intelligence (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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References and Selected Bibliography

Dewey, John. Logic: The Theory of Inquiry. New York: Henry Holt and Company, 1938. (Framing the transaction of inquiry as active, iterative, and structurally codependent with its environment).

Dick, Philip K. The Man in the High Castle. New York: Putnam, 1962. (The classic counterfactual text used to illustrate narrative synthesis and human alternative speculation).

Doctorow, Cory. "The ‘Enshittification’ of TikTok." Pluralistic, January 21, 2023. (The institutional baseline defining platform decay from user service to structural self-consumption).

Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Katherine Steinmann, et al. "Highly Accurate Protein Structure Prediction with AlphaFold." Nature 596, no. 7873 (2021): 583–589. (The landmark study illustrating non-retrieval based, validated empirical combinatorial novelty).

Latour, Bruno. Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford: Oxford University Press, 2005. (The conceptual anchor for distributed network agency, departed from to protect human-exclusive intentionality).

Shumailov, Ilia, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson. "AI Models Collapse When Trained on Recursively Generated Data." Nature 631, no. 8022 (2024): 755–759. (The foundational computer science proof detailing recursive data degradation and tail-distribution loss).

Stanford Institute for Human-Centered Artificial Intelligence (HAI). The 2026 Artificial Intelligence Index Report. Stanford, CA: Stanford University, 2026. (Documenting industry consensus on hitting "peak data" and web infrastructure saturation).

Tomasello, Michael. Why We Cooperate. Cambridge, MA: MIT Press, 2009. (The comparative psychological studies on joint intentionality and the intrinsic non-instrumental play of human toddlers).

Tomasello, Michael. A Natural History of Human Thinking. Cambridge, MA: Harvard University Press, 2014. (Tracing the social and evolutionary origins of belief- and desire-laden minds).

Tomasello, Michael. Becoming Human , A Theory of Ontogeny: MA: Harvard Unitversity Press, 2019. (Assembling three decades of empirical work comparing Chimpanzees, Bonobos and human children at the Max Planck Institute, Tomasello here provides a systematic developmental psychology that covers the period between birth and seven years of age).

Why fact checks in American politics fail so often

Source: Politifact

Contrary to popular belief, nearly all humans doing politics are mostly irrational, biased, and emotional. Many are uncomfortably gullible when faced with talented demagogues and their powerful emotion and reality manipulation tactics. That’s just the human condition. Social science research has pretty well identified the major human traits that underpin many people rejecting facts, robust truths and good faith, sound reasoning when they are too inconvenient.

It’s unconscious human response to perceived attack or threat

Long story short, fact checks often fail when people see and feel partisan threat in inconvenient information. Humans tend to experience correction of strongly help false beliefs as an attack on their own or their group identity. That feeling, an automatic unconscious response to threat, triggers motivated reasoning and defensive mental reasoning instead of belief reassessment and revision. In bitterly polarized societies like the US today, partisan virtue signals and demagogic messaging simply overpower the evidentiary weight of fact checking.

Fact checks usually include explicit reference to political actors or issues. That instantly cues partisan lenses and trigger defensive processing when the information is inconvenient. Party identification strongly affects trust in fact-checkers. Many or most partisans see fact checkers as aligned with the opposing camp. That alone is sufficient to neutralize any corrective effects of truth even when the evidence is clear and solid. In America’s poisoned political environment, fact checks are perceived and felt as just bad faith lies in bitter partisan disagreements.

The left vs the right

And, believe it or not, there probably some degree of asymmetry in attitudes towards fact checking and fact checkers between America’s political left and right. Not surprisingly, some of the political right seems to be more resistant to fact checkers and fact checking. One can rationally argue in good faith that this asymmetry, assuming it is real, is solid evidence of serious immorality in political rhetoric significantly grounded in falsehoods or crackpot reasoning. Dishonest speech is arguably evil when people are deceived by false information and that causes serious harm or even death to the deceived people or others they affect.

Q1: Is it true that America’s political right tends to be more resistant to fact checkers and fact checking than the left?

Q2: Is it a rational moral argument to say that dishonest speech that deceives people and those false beliefs cause some people harm or even death, e.g., anti-vaccines lies that cause some to refuse to get vaccinated, and then they get infected and die, or they infect someone else who gets infected and dies?

Info sources:

When it comes to misinformation, partisanship overpowers fact-checking, over and over again — Why do people fail to update their beliefs in light of clear evidence to the contrary? Our research provides an answer: partisanship is a powerful factor that can lead people away from accuracy.

Heroes or hacks: The partisan divide over fact-checking — Liberal websites were far more likely to cite fact-checks to make their points than conservative sites were. Conservative sites were much more likely to criticize fact-checks and to allege partisan bias.

Blinded by Partisanship? The Limitations of Fact-Checkers in Correcting Misinformation During the 2021 Georgia Senate Runoff Elections — In recent years, partisan polarization has intensified, and social media has amplified the spread of inaccurate information. ….. Our findings show that partisan biases prevented fact-checking efforts from changing perceptions of misinformation. Party identification also influenced participants’ trust in fact-checkers.

Why the backfire effect does not explain the durability of political misperceptions — ….. the accuracy-increasing effects of corrective information like fact checks often do not last or accumulate; instead, they frequently seem to decay or be overwhelmed by cues from elites and the media promoting more congenial but less accurate claims. As a result, misperceptions typically persist in public opinion for years after they have been debunked.

Warning labels from fact checkers work — even if you don’t trust them — In line with previous studies, the researchers found that Republican-leaning survey participants were less likely to trust fact-checkers — regardless of whether the fact-checking organizations skewed right or left. ….. Republican respondents who knew more about news production, who scored more highly on a cognitive reasoning test, and who had higher web use skills were even less trusting of fact-checkers.

MAGA approves night sky destruction; public interest is ignored

Mirrors in space for fun & profit

In another indication of MAGA’s contempt for the public interest and service to special interests and their profits, which includes a livable environment, Trump’s FCC (Federal Communications Commission) has given approval for a company to start wreaking havoc on the night sky. This is another example of Trump and MAGA elites doing whatever they or Trump want to do, no matter how toxic or even lethal it might be to the public interest.

Just the facts, not the context

The FCC: As usual, the MSM’s reporting rigidly sticks to the facts, which are alone are quite ugly. That allows reporting to completely ignore the far more important uglier context in which the US government has green-lighted development of night sky destruction over the entire planet. Before Trump and MAGA elites converted it to a service operation for for-profit special interests, the FCC was an agency that served the public interest. Those days are over. The FCC will stay corrupted as long as Trump and MAGA elites control government, and maybe a lot longer than that.

Much of the MSM does not connect environmental risks to the FCC’s use of categorical exclusions under the National Environmental Policy Act (NEPA) or to the broader pattern of environmental review being bypassed for orbital systems. NEPA requires federal agencies to assess the environmental effects of actions they carry out, fund, or license, and to consider alternatives and public input — that was ignored. The FCC classified the system as communications infrastructure and thereby avoided any environmental impact assessment. MSM reporting is mostly a balanced “critics fear X, agency says Y” narrative. That ignores the fact that environmental concerns and public input were excluded from MAGA’s decision‑making.

Polluting the night sky with sunlight: The company is called Reflect Orbital. Its for-profit plan is to put at least 50,000 fridge-sized satellites in orbit. Mirrors about 60×60 ft will unfold and where light is reflected down to will be controlled by light buyers. The plan is to make money by reflecting sunlight light for solar farms, big rescue operations, city street lighting, crop growth, big festivals, annoying the neighbors**, and Dog only knows what defense uses the military can dream up. Reflect Orbital will sell to any crackpot, group, company or government that can afford to pay. One gets sunlight by logging into a website, paying $5,000/mirror/hour (~3‑mile‑wide patch of light from 1 mirror), inputting the GPS coordinates, and then waiting for sunlight after dark. No one knows how big the market will be for sure, but there is marketing hype. Link, link, link, link, link

** OK, that one was just made up, but it is possible

Shafting the public interest – the MSM squeaks defense: In addition to confusing plants and animals about what time of year it is, the reflected light will scatter and generally make the night sky even brighter than it is now. The MSM reports squeaks of objection like (1) an expert claiming that this “cannot be considered to serve the public interest”, (2) this wastes tax dollars by wrecking federally funded astronomical facilities, (3) doing Dog only knows what to people, plants and wildlife.

The business & political reality – this is isn’t what it seems: Two different very things are going on here, both of which stink. One is Reflect Orbital running a Ponzi scheme or something akin to that to scam investors. Buying 1 mirror for $5,000 for 1 hour gets you a spot of light ~3 miles in diameter. The light intensity in that spot is low, about equal to 4 full moons on a clear night. That is light intensity is ~10-fold lower than typical city street lighting. That is not enough to do much of anything other than (1) confuse plans and animals, (2) screw up the night sky for serious astronomy work, and (3) maybe annoy annoying neighbors if that’s the goal. Ripping off investors in a pump and dump operation makes sense. Selling weak sunlight for serious uses makes no sense.

The marketing propaganda on this is what you would expect. People and institutions trying to cash in on this lunacy say things like, “we’re experimenting with a potentially groundbreaking clean‑energy technology”. Whew, that sounds respectable. Investors can tell themselves they are visionaries in “space climate tech”, even if Reflect Orbital goes bankrupt. By contrast, saying “this is a ridiculous, harmful nutjob project enabled by captured regulation” (in this case, our corrupted, anti-public interest FCC) sounds activist or woke. That’s bad, really bad.

So that stinks. Investor damage will be mostly limited to people who get ripped off. Whatever environmental damage there would be cannot be predicted. The company will probably fizzle out long before 50,000 mirrors are put in orbit, which would limit collateral damage to the environment. At least one can hope that’s the outcome.

The very big stink

The far bigger, much worse stink that’s going on is directly related to Trump and his MAGA authoritarianism and kleptocracy. That’s where the real rubber hits the road. The FCC greenlighting this operation has massive implications that facilitate future extremely bad things happening. This is a tragedy of the commons story. This FCC approval established a US agency signing off on space‑based lighting as “communications infrastructure” with no environmental review and no public comment. That approval moved the Overton window for any future scheme that relies on screwing up the night sky for profit or authoritarian purposes. From this, special interests and authoritarian MAGA government can pivot from “solar at night” to advertising, defense purposes, or data‑linked services (1) without asking for authority over the commons (the night sky), or (2) any environmental or public interest impact assessment whatsoever. This FCC approval was made in secret. Future approvals will be the same.

The real “product” is arguably not light, but orbital rights. This is precedent that private interests can take and use low Earth orbit to alter planetary lighting for paying customers. That is an asset that can be traded, repurposed, or taken over by other actors if Reflect Orbital flops. The public has nothing to say about it.

Q1: Can one reasonably argue that this scheme isn’t crackpot from the standpoint of people trying to financialize and profit from the sky, i.e., is it a rational extraction scheme from a system that pays for investor upside and treats planetary damages and the damaged public interest as society’s problem?

Q2: If this space mirrors scheme is treated as “smoke and mirrors” because the physics and economics of the advertised use case are crap, is that crappy reality sufficient to con regulators, journalists, and investors while corporate interests get solid claims on a planetary commons at our expense?

Info links:

A Sun that Never Sets – Reflect Orbital’s Big Plans for Upending Solar Energy

California startup’s plan to sell sunlight at night sparks controversy

FCC Authorization — Permission given as in the public interest in emergent innovative “communications” technology that promotes new services and economic growth (that is misleading and bad‑faith, arguably a lie); The FCC says that light, astronomy, health, and environmental harms fall outside the Commission’s remit over radio spectrum and thus are not valid bases for denial (the same)

FCC approves space mirror satellite — and bypassed environmental review to do it

California startup pitches 50,000 space reflectors to extend daylight

Trump’s joke-to-policy tactic isn’t a joke

Over at Rick Wilson’s Against All Enemies anti-autocracy substack, Wilson nicely articulates how Trump and the MAGA demagoguery machine con people into accepting tyranny and corruption. His essay, The Coming Election Takeover, is another warning about what Trump and MAGA elites want to do to the 2026 midterm and later elections if they can pull it off. The joke-to-policy tactic has been seen multiple times. The tactic plays out generally like this:

1. In his public rants or social media posts, Trump floats an extreme, anti-democratic, anti-rule of law idea or anti-civil liberties idea. This signals his intent and the goal of the threat.
2. Authoritarian MAGA media characterizes his extremism and pro-tyranny intentions as just innocent jokes that are misunderstood or taken out of context. People who can’t see the threat as a joke are dismissed as humorless, irrational, mentally defective, or “snowflakes” who can’t take a joke. MAGA rejects criticisms out of hand as nonsense or bad faith smears. That allows MAGA to avoid dealing with legitimate criticisms of real anti-democracy threats.
3. MAGA propaganda then spins the threat as Trump having made a good, legitimate point.
4. Then there is an Executive Order or bill in congress.
5. Then Trump’s anti-democracy/anti-rule of law/anti-civil liberties threat becomes policy or law and the new norm from with to launch further MAGA tyranny attacks.

Wilson describes the process like this: “The MAGA media apparatus of Fox, Newsmax, CBS, the Twitter-poisoned podcast bros, and the MAGA Influencer-Industrial Complex fan out to tut-tut the horrified for being humorless scolds. He was kidding. You people are unhinged. Touch grass. Then, a beat later: well, he was kidding, but you have to admit he has a point. Then, a draft executive order leaks. Then it’s policy. Then it’s the law. Then it’s the new floor, and the next joke starts the cycle again, one rung lower”.

Regarding elections

Wilson points out that Trump has used joke-to-policy to attack the 2026 mid-term elections. First Trump says something outrageous like “maybe we shouldn’t have an election”. MAGA media propaganda frames it as either an unserious joke or raising a good point. Clearly, that was no joke. Then lawyers and politicians start drafting ways to translate the autocratic attack into practice. Ways to do that include Trump (1) issuing Executive Orders, (2) claiming and emergency and emergency powers, and (3) deploying his federal enforcers (ICE, FBI, etc.) or investigators (DOJ, etc.). For 2026, the joke-to-policy cycle is aimed at creating a legal and administrative framework to allow Trump’s team to disregard or override state-run election outcomes. The point of that kind of attack is to allow anti-election policy implementation with few or no lawsuits or court interventions.

Q1: Is the joke-to-policy argument mostly or completely false, or is it reasonably accurate in view of the evidence in the public record?

Q2: By treating serious objections to Trump’s tyrannical or corrupt threats as a failure to appreciate humor, (1) does MAGA rhetoric deflect from answering substantive criticism, (2) which turns politics into a culture war over who is “in on the joke” versus who is an unreasonable uptight enemy, or a humorless snowflake or crackpot?

Info sources:

“MAGA ‘secret’ exposed by ex-GOP operative as Republicans watch their ‘numbers go south’.”

‘Packaging evil into something funny’: is making fun of Trump now just ‘clownwashing’?

Jacob Neiheisel on How Trump Uses Humor and Labels to Divide

A timeline of Donald Trump’s election denial claims, which Republican politicians increasingly embrace

In MAGA world, Trump’s jokes always land

The corruption of MAGA comedy — The cruelty and crassness of Donald Trump’s dark sense of humor is quickly spreading

How humor and ‘dark play’ radicalize Trump’s voter base