USSC attacks congressional power and the public interest

Most headlines about Landor v. Louisiana Department of Corrections and Public Safety say the Supreme Court denied a Rastafarian prisoner damages after guards forcibly shaved his dreadlocks in violation of his faith. That’s the facts, but it’s only the surface story that most of the MSM is reporting. Most of the MSM is ignoring the far more important flow of power context. This June 23, 2026 decision is far more important than those facts alone. This decision in Landor is really about (1) gutting Congress’s ability to enforce federal conditions on spending federal money, and (2) gutting civil rights for people affected by federal spending conditions that require respect for people's civil liberties. US Supreme Court won't let Rastafarian man shaved bald in prison sue guards; The Supreme Court Deals Major Blow to Congressional Authority -- In one devastating ruling, the Republican justices disempower ordinary citizens and their elected representatives. ..... with time, the ruling may prove far more disastrous and far-reaching, unleashing illegal behavior that both Congress and the victims are powerless to prevent; Landor v. Louisiana Department of Corrections and Public Safety (2026) -- Although the federal government had the right to attach strings to aid that it provided, any conditions had to be clearly established and agreed to as they would be in most contracts. Although the prisons that had accepted aid had agreed to the conditions Congress had imposed under RLUIPA, their employees had not and were hence not liable. 

Under the Religious Land Use and Institutionalized Persons Act (RLUIPA), Congress promised “appropriate relief” when prisons violate religious rights. For decades, courts treated that kind of statutory language as enforceable creating rights, including enforcement of spending‑clause programs like Medicaid, nursing‑home protections and prisons. In Landor, the 6–3 MAGA majority written by the authoritarian kleptocrat Gorsuch changed that. Now, federal spending‑clause statutes are just contracts with states, not “laws” that individuals can enforce against state officials. This lands a massive blow to Congressional power because it severs civil rights from remedies to enforce those rights. The MAGA judges have been planning this kind of authoritarian attack for some time now. LANDOR v. LOUISIANA DEPARTMENT OF

CORRECTIONS AND PUBLIC SAFETY ET AL. -- Held: Individuals may not be held liable in their personal capacities under a Spending Clause statute unless those individuals have voluntarily and knowingly consented to answer lawsuits under the statute; because the individual defendants in this case did not voluntarily and knowingly consent to face RLUIPA liability in an agreement with the federal government, Mr. Landor’s case cannot proceed against them; Spending Programs and the New Roberts Court -- The Supreme Court is poised to place new limits on Congress’s spending power at the urging of states vying for regulatory dominance, particularly in health care and public health. .....  Congress may not choose to work more with states if the spending power becomes harder to exercise; and, states depend on federal funding for the safety net’s fiscal stability. Also, federal spending has often protected civil rights, so limits on spending programs may contribute to retrenchment.; New Rule: Spending Clause Statutes Cannot Impose Personal-Capacity Damages Liability on Nonconsenting State Employees

Mainstream coverage mostly misses this. Most MSM reporting frame Landor as a narrow question about whether a mistreated prisoner can sue his guards for money, emphasizing the egregious facts and the culture‑war angle around religious liberty. Some highlights the contradiction with the Court’s broader campaign to expand religious‑liberty claims, but still treats the case largely as a doctrinal oddity in that field. The result is fact‑accurate but context‑empty reporting that leaves readers thinking Landor is a one‑off injustice instead of a major shift in federalism and civil‑rights enforcement. The Supreme Court’s campaign to expand religious liberty now has a glaring exception; Supreme Court Bars Lawsuit After Prison Guards Shaved Inmate’s Dreadlocks -- Damon Landor, a Rastafarian, tried to sue Louisiana prison officials for violating his religious rights

The main, under-reported impact is on power. Congress uses conditional spending to enforce civil‑rights and social‑welfare protections in the public interest. That includes Medicaid patient rights, nursing‑home standards, nondiscrimination rules in education and health care, and protections baked into environmental cooperative‑federalism regimes like the Clean Air Act. When private enforcement is taken away from injured people, their “remedy” becomes federal agencies cutting off or threatening funds. That is no remedy at all. The HHS itself has said cutting funding is politically implausible, usually harmful to the people Congress meant to protect. Landor shifts serious power from Congress and individual beneficiaries to state governments and the organized interests that dominate state policy, e.g., hospital chains, insurers, fossil‑fuel businesses, charter‑school advocates, and utility giants. The Spending Power and Environmental Law after Sebelius -- The review sheds light not only on environmental law after Sebelius, but also the many other realms of American governance that engage spending power bargaining, such as public education, civil rights law, social service programs, and civic infrastructure. The Article concludes that the impacts of the doctrine will be most palpable not in litigation but in the dynamics of intergovernmental bargaining. States will have more leverage when negotiating design and enforcement terms within spending-power partnerships; Federalism by Waiver after the Health Care Case -- But the Court’s Spending Clause ruling will have potentially an even more far-reaching effect on the constitutionality of other federal statutes enacted pursuant to Congress’s spending power, as states will be prompted to challenge other conditional-spending laws in the education, social welfare, environmental, and civil rights areas as unconstitutionally coercive 

In a normal constitutional order, that would be bad enough. In a political environment where the MAGA movement and its allies are openly hostile to voting rights, equal protection, and independent law enforcement, disabling Congress’s main enforcement tool for civil rights, health care, and environmental protections takes on a different character. It gives state‑level actors aligned with authoritarianism and special interest demands much more room to violate federal spending conditions with impunity. Violators of the law can be confident that neither Washington nor individual plaintiffs can predictably hold them to account. 

Fundamentally, Landor is authoritarian and kleptocratic even though it’s dressed up in contract‑law jargon. Authoritarian, because it concentrates power in less accountable state institutions and a Supreme Court majority that is systematically obliterating space for democratic lawmaking and rights enforcement. Kleptocratic, because the beneficiaries of weakened enforcement are not “the people” but entrenched economic interests that profit when civil‑rights, health‑care, and environmental conditions are treated as optional rather than obligatory. The MSM sells the Landor decision to the public as a tragic but technical dispute about a prisoner’s dreadlocks. In reality, the decision is a major step in dismantling Congress’s capacity to protect the public interest. Special interests[1] win, we lose.

Footnote:

1. Special interests include state governments. By contrast with the federal government, it is usually easier for special interests to capture and corrupt state governments. About 85% of public‑corruption convictions in the US are the result of federal prosecutions, not state or local cases. That suggests states do little to prosecute corruption by their own state and local officials. In effect states acquiesce to federal dominance in corruption enforcement. State governments are, on average, easier targets for corrupt networks than the federal government because federal prosecutors and national media often uncover and punish state‑level corruption. Now the government is neutered by MAGA anti-government and anti-rule of law politics and policy. 

Which States Have the Highest Public Corruption Convictions?

Corruption in America

Public Corruption and Federalism

Trump's lies about election interference by China and the deep state

page 26 of 37

"IC Mainline Judgments: Beijing has not deployed influence efforts 

intended to change the outcome of the US presidential election" 

Influence ≠ interference

On July 16, Trump have a highly publicized prime time speech to reveal huge evidence of massive election vote and voter fraud. As expected, his speech consisted of a pack of unsubstantiated lies, falsehoods and bizarre crackpottery. Trump prominently blamed China and the evil Deep State for shocking vulnerabilities in American voting systems and vote counts. But neutral assessments of the documents he released (available at this link) show that his allegations were false, unsubstantiated and/or idiotic. The documents again show that Russia tried real hard to get Trump elected, but there's nothing of substance about China or any alleged American Deep State fraud. Not much new information was released.

Some analysist assert that Trump's obvious goal is to set the stage for him to directly intervene in and subvert the 2026 mid-term elections.   

Q: Is Trump's allegations credible in view of the contrary evidence in the documents he says proves his story, or is he as usual just a morally rotten dictator lying to the American people?

Info sources: 

Trump Just Did More Damage to American Elections Than China -- ..... what these documents discuss are influence operations—propaganda, proxies who speak for foreign interests, fake stories, and so on—rather than interference, which would involve actual manipulation of data or sabotaging of electoral infrastructure. These classified revelations, despite Trump’s assertions, show that the intelligence community didn’t even agree that China was fully engaged even in these more limited influence operations.

The Documents Trump Declassified To Blame China For The 2020 Election… Actually Show Russia Was The One Meddling For Him -- At a fundamental level, none of it makes sense. He’s focused on the 2020 election, when HE was in charge of the government. Assuming that there really was a huge foreign effort then, wouldn’t it have also worked in 2024 when Trump won, but Biden was president?

Trump Accidentally Proves That Putin Did Help Him Run for President -- Among the newly declassified intelligence documents Trump released Thursday are assessments that the Russian president and senior Russian officials directed proxy efforts to spread allegations about Joe Biden and Burisma, ..... orchestrate a corruption scandal against the Democratic nominee, and “ensure the President’s victory,” a reference to Trump, who was the incumbent at the time.

Trump Election Documents Reveal China, Russia Targeted Joe Biden -- A Newsweek review of the documents posted on the White House’s website found no evidence supporting Trump's claim that foreign interference or fraud had altered the outcome of any election, including the 2020 presidential election.

‘Trump Is Trying to End Our Democracy’: Alarm as President Attacks Elections Ahead of Midterms -- Trump, who has said his administration should “take over” US elections that are currently run by states, asserted in his speech that the American voting system was “left vulnerable to being rigged and stolen” by his political enemies and accused China of “illicit acquisition of 220 million US voter files” in an effort to undermine him.

Trump’s Presidential Address: Accuses China, ‘Deep State’ of Election Interference

Trump's White House speech shows he's the real threat to U.S. elections

Trump setting stage to declare emergency around midterms, former White House attorney says

Declassified documents undercut Trump’s ‘rigged’ election claim

Trump's damage to the US nationally and internationally

International relations

A Foreign Affairs magazine article, The World Is Giving Up on America, summarizes reasons that our allies and some other nations now have negative opinions of the US government, its trustworthiness, and its disrespect for personal freedoms. Foreign nations that Trump has threatened and insulted are undergoing a slow process of de-coupling from American entanglements when possible. A 36 nation survey found that solid majorities (correctly) believe that the US government does not respect the personal freedoms of its people. 

Biden was viewed more positively than Trump, and approval of the United States’ approach to world affairs and its respect for individual liberties at home, returned or nearly returned to pre-Trump levels in many countries during his presidency. That has reversed since Trump took office in Jan. 2025. Foreign publics mostly disapprove of Trump’s approach to immigration, trade, climate policies, international relations and military interventions. Allies no longer trust the US to be a reliable partner for addressing major international problems.

Foreign public opinion is generally not far out of synch with majority US public opinion on the same issues.

Economic impacts

Loss of trust and dislike of Trump and his politics is starting to generate significant economic losses. There are measurable economic costs from reduced tourism and exports, higher risk assessments on US assets, increased macro‑uncertainty, and damage to the “safe haven” status of the US economy. Cost estimates range from tens of billions of dollars annually to significantly more. Damage has negatively affected both growth and borrowing costs. 

Info sources: 

The biggest economic risk from Donald Trump’s presidency is a loss of confidence in US governance -- Trump’s economic policies may prove surprisingly benign in the short term. But steps that undermine domestic US institutions and international alliances would do serious and lasting damage.

The World Is Cutting Ties With America. It’s Already Costing Us. -- As Mr. Trump muses about making Canada a 51st state, it has embarked on a “new strategic partnership” with China, opened its market for the first time to 50,000 Chinese electric vehicles and joined a more than $150 billion European defense fund aimed at breaking the dependency on the American defense industry.

Unusual sell-off in the dollar raises specter of investors losing trust in the U.S. under Trump

Businesses, investors ‘flying blind’ when trust in government statistics declines

Costs from Trump's tariffs paid mainly by US firms and consumers, NY Fed says

THE COST OF TRUMP'S FOREIGN POLICY: DAMAGE AND CONSEQUENCES 

FOR U.S. AND GLOBAL SECURITY

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).