Reading in Three Dimensions: The 81st UN General Assembly Addresses of the United States, Türkiye, Iran, and Jordan as Sites of Layered Meaning, Concealed Theology, and Competing Legalities

 

The United Nations is many things, some of them more interesting than others. It is certainly useful; a tool that can be wielded in the service of any one or any collective willing to take that up and infinitely malleable  in its form and utility. But that is politics and ought not to offend; though it does serve as a caution.  Perhaps less well understood is the utility of the United Nation as an instrument, as a tool, for evangelization. It is that place--like Jerusalem in the years before the Emperor Titus obliterated the city and set the stage for the theater that one encounters today--where all sorts of zealots, separated by their commonality and the intensity of their reactions to each other, each wrapped in the belief (itself an object, a signific, and the basis for creating cognitive universes or ordered meaning) in the righteousness of however it is that they constitute themselves and reconstitute their targets, where these zealots run around as the modern day fishers of humans and their communities (Matthew 4:19; "And he saith unto them, Follow me, and I will make you fishers of men."). 

Belief, passion, conflict--discourse in the service of evangelization--that provides, at least for me, almost the only interesting element of the messy business of the global Sultanate of Women that the empire of the United Nations has become. 

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 It is with this in mind that I approached the question of what might be extracted from four  related and interesting speeches made by a quartet of actors  connected to each other by interest, hatred, love, need, and ambition, each dancing around a singularly absent center. The four speeches of four humans who identify as male:

1. United States of America; His Excellency, Donald Trump, President (22 September 2026); 

2. Türkiye; His Excellency; Recep Tayyip Erdoğan; President (22 September 2026);

3. Jordan; His Majesty; Abdullah II ibn Al Hussein; King (22 September 2026);

4.  Iran (Islamic Republic of); His Excellency; Masoud Pezeshkian; President (23 September 2026)

That resulted in a fairly deep dive into the semiotics and discourse of  the four speeches that while singularly unremarkable become much more interesting, and valuable, when read against each other. The essay, entitled  Reading in Three Dimensions: The 81st UN General Assembly Addresses of the United States, Türkiye, Iran, and Jordan, as Sites of Layered Meaning, Concealed Theology, and Competing Legalities . Here is the abstract to give you a sense of the project. 

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This essay undertakes a close, multi-layered reading of four addresses delivered before the 81st Session of the United Nations General Assembly: those of the United States (Trump), the Islamic Republic of Iran (Pezeshkian), Turkey (Erdoğan), and the Hashemite Kingdom of Jordan (Abdullah II). The analysis proceeds through seven interpretive registers—surface diplomatic grammar, underlying legal-structural claims, theological Lebenswelt, civilizational self-positioning, the construction of absence and silence, instrumental invocations of international law, and the embedding of eschatological temporality within ostensibly secular rhetoric. One might suggest that a principal contribution of the present work lies in its demonstration that the shared vocabulary of multilateral diplomacy—sovereignty, human rights, rules-based order, peace—conceals radically incompatible meaning-structures, such that speakers who appear to be engaged in common discourse may in fact be operating within incommensurable semiotic universes.

It is perhaps possible to read the persistence of the so-called Jewish question—understood here not as an empirical matter but as a structuring absence around which the theological commitments of each address are organized—as one possible interpretive contribution of the essay. The empirical finding developed below is more specific: Islam and Christianity are named, invoked, defended, and prayed through across the corpus, while “Jew” and “Jewish” do not appear in any of the four speeches, and “Judaism” appears exactly once — in King Abdullah’s address, and there only in the mode of negation: “It is also tried to cast this conflict as a war of religions — Islam on one side, Judaism and Christianity on the other. It is not.” The single mention names the religious dimension precisely in order to foreclose it, a discursive operation whose analytical significance is developed below. The silence is therefore not general; its selective and targeted character warrants examination, even though the essay does not purport to settle the speakers’ motives. Drawing on my framework for analyzing theocratic constitutionalism (Backer 2009) and his methodological insistence on reading for plausibility rather than authorial intention, the analysis suggests that each speaker’s treatment of Israel-Palestine functions less as a policy position than as a site at which incommensurable theological, legal, and civilizational premises become momentarily visible. The essay employs my methodological approach (Backer 2024) to treat these addresses not as transparent policy statements but as layered texts requiring sustained hermeneutic excavation. The implications for international legal scholarship, diplomatic practice, and the coherence of multilateral governance are, one might cautiously submit, considerable.

The essay was created with the help of Harvey AI, which proved to be a more interesting experience than I had imagined--Harvey AI responded more like a student research assistant in need of constant correction and guidance than what is emerging as the discourse of AI computational systems "taking over." Still, the computational system was in the end useful in creating efficiencies in drafting (and in my case certainly in avoiding typos). Remaining errors including but not limited to conceptual decisions and the like remain entirely mine. The full  text of the essay follows below and may be accessed HERE. 

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The Implementation Opinions on Intelligent Agents in Comparative Perspective Situating China’s Approach Within Global Regulatory Trends on Agentic AI

 

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This working paper undertakes a structured comparative reading of thirteen instruments addressing the governance of agentic artificial intelligence — systems capable of sustained, multi-step, tool-using autonomous action — issued between late 2024 and mid-2026 by jurisdictions with markedly different political-economic systems and governance traditions. The instruments span binding legislation (the EU AI Act; Japan’s Act on Promotion of AI-Related Technologies), executive orders and presidential memoranda (the Trump Executive Order of June 2026, Trump NSPM-11, Biden Executive Order 14110, Biden NSM-25), intergovernmental recommendations (the OECD Recommendation on Artificial Intelligence), regional development strategies (the African Union Continental AI Strategy), voluntary governance frameworks (Singapore’s Model AI Governance Framework for Agentic AI v1.5, the Safeguards for Agentic Finance at Runtime framework), cybersecurity guidance (the Five Eyes/Australia advisory on careful adoption of agentic AI services), government employee guidance (Canada’s Treasury Board Secretariat Guide), analytical frameworks (the Gradient Institute’s risks-and-controls framework for multi-agent systems), and aspirational international instruments (UN General Assembly Resolution 79/325, the UN High-Level Advisory Body report). The primary object of inquiry is China’s Implementation Opinions on the Standardized Application and Innovative Development of Intelligent Agents (2026).

Drawing on my earlier work  — which conceptualizes AI governance through the lenses of AI as strategic infrastructure, the development-security dialectic, platforms as governance intermediaries, the instrumentalisation question (the risk that the relationship between autonomous technology and its wielders may be reversed), and the ‘cognitive cage’ metaphor for the political-economic structures that constrain regulatory imagination — the analysis suggests that the near-simultaneity of these instruments may reflect a shared recognition that agentic AI poses governance challenges existing frameworks do not adequately address. Yet the convergence on vocabulary — autonomy, oversight, accountability, transparency — appears to mask sharp operative divergences that become visible only when these terms are traced to their institutional and normative contexts. The instruments may be read as revealing competing conceptions of what governance is for: fundamental rights protection (EU), market integrity (Singapore), cybersecurity (Five Eyes), bureaucratic accountability (Canada), development and the bridging of capability gaps (AU), socialist modernization under party-state coordination (China), and market sovereignty fused with national security (US). The instrumentalisation question — what happens when agents do not merely assist governance but perform it — remains, the analysis suggests, inadequately addressed by all instruments examined.

 The paper concludes by considering path dependence and what might be characterized as techno-bureaucratic mimicry — Nietzsche’s herd instinct refracted through the practices of benchmarking and landscape analysis — while observing the paradox that the very conformity these practices produce also generates variation, and that the gap between rhetoric (which foregrounds difference) and operative text (which may reveal a narrower bandwidth of actual divergence) poses questions about the depth of the disagreements the instruments appear to embody.

Executive Summary follows below. The full paper may be accessed at SSRN HERE.

 

 

Reflections on 智能体规范应用与创新发展实施意见 [Implementation Opinions on the Standardized Application and Innovative Development of Intelligent Agents] and Global Trends

 

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The Implementation Opinions on the Standardized Application and Innovative Development of Intelligent Agents, 智能体规范应用与创新发展实施意见, is a Chinese policy document formulated to implement the State Council's Opinions on Deeply Implementing the "Artificial Intelligence Plus" Initiative. It addresses "intelligent agents"—defined as intelligent systems possessing autonomous perception, memory, decision-making, interaction, and execution capabilities—as a distinct and important form of artificial intelligence products and services. The document is structured in six parts: fundamental principles, consolidating the development foundation, safeguarding the security baseline, strengthening application-led advancement, building an innovation ecosystem, and safeguard measures. 

Read through the framework of New Era Modernization, the Implementation Opinions may be better  understood not as an isolated technology-regulation document but as the latest articulation of the modernizing project at a specific point in its recursive unfolding. Chinese style modernization functions as the pre-given horizon of meaning — the Lebenswelt in Husserl's technical sense — within which Chinese constitutionalism constitutes itself and produces meaning, and that the CPC's periodic reformulation of the principal contradiction is the mechanism by which this horizon recalibrates without being abandoned. The Implementation Opinions sit within this framework as an instrument through which the party-state extends the modernizing project into a new technological domain — intelligent agents — while preserving the structural grammar of development-security pairing, Party-directed governance, and the juridification cascade from constitutional mandate through legislation to administrative practice.

Fundamental Principles

The document articulates four foundational principles: (1) safety and controllability, treating agent safety, reliability, and trustworthiness as baseline requirements throughout the entire technology lifecycle; (2) orderly regulation, constructing a governance system that connects smoothly with existing policies while establishing clear baseline and red-line boundaries; (3) innovation-driven development, pursuing systematic breakthroughs in key technologies through government-industry-academia collaboration; and (4) application-led advancement, leveraging representative use cases to drive technology verification and product iteration in a step-by-step manner. 

These four principles are not merely policy preferences. They enact the development-security dialectic that can be understood as "a core feature of New Era governance: modernization operates as both engine and vessel for the rationalization of the state." Safety-and-controllability and orderly regulation pair structurally with innovation-driven development and application-led advancement — the document does not treat safety as a constraint on innovation or innovation as a risk to be managed; rather, both are presented as mutually constituting elements of the same governing project. Within the Marxist-Leninist framework that traces from Lenin's electrification thesis through Chinese modernization, this pairing reflects the vanguard party's claimed capacity to direct technological transformation as a conscious, planned process rather than an organic evolution — a project to be organized, implemented, and managed by a centralized authority that bends historical development to a conscious human purpose. The concept of "application-led advancement" (应用牵引) — literally "application traction" or "pull" — encodes a specific theory of innovation in which deployment needs drive technological development, consistent with the broader New Era emphasis on new quality productive forces (新质生产力).

Technology and Standards

The Implementation Opinions mandate the strengthening of foundational technology research, including general-purpose models, specialized industry models, high-quality datasets, and key agent capabilities such as task comprehension, task planning, tool usage, long-term memory, and swarm collaboration. They call for a complete intelligent agent toolchain encompassing perception, memory, decision-making, interaction, and execution components, as well as security and governance tools such as adversarial sample detection and behavioral anomaly detection. An intelligent agent standards system is to be established, covering key technologies, data exchange, application scenarios, quality evaluation, security assurance, and trustworthy certification, with mandatory standards supported in healthcare, transportation, media, and public safety. Notably, the document envisions an "intelligent interconnection network" featuring agent registration platforms, digital identity management, search and discovery services, and interoperability protocols leveraging IPv6.

Security and Governance Framework

Section III establishes a multi-layered security architecture. Product standards require that intelligent agent behavior conform to laws, regulations, and mainstream values, with specific protections against algorithmic exploitation, dissemination of unwholesome values, and risks of addiction and emotional dependency among minors and the elderly. Decision-making authority must be clearly delineated between user-only decisions, user-authorized decisions, and agent-autonomous decisions, with users retaining the right to be informed and to exercise final authority. Technologies such as rule embedding and behavioral guardrails are mandated, and blockchain-based mechanisms are envisioned for verifiable and traceable agent behavior in important scenarios.

On security risk prevention, the document addresses intrinsic security capabilities (data security, cryptographic defense, attack detection, permission management), supply chain security across the full development lifecycle, and application-derived risks including automated attacks, privacy violations, false information, and cyber fraud. A "classified and tiered governance framework" differentiates between sensitive domains (subject to registration, testing, and defective product recall under joint oversight by the cyberspace administration and sectoral regulators) and low-risk domains (governed through compliance self-testing, information reporting, and industry self-discipline). Credit evaluation mechanisms for market participants are proposed, covering technology misuse, consumer inducement, false advertising, and concealment of defect information.

Application Domains

The document envisions intelligent agent deployment across an extraordinarily broad range of sectors: scientific research and R&D assistance; intelligent manufacturing, energy, transportation, agriculture, and financial services; consumer-facing applications in terminal devices, culture and tourism, and commercial services; education, healthcare, human resources, and information services; and government services, judicial services, public safety, urban governance, and bidding and tendering. This scope reflects the treatment of intelligent agents not as a narrow product category but as a cross-cutting technology embedded in the state's modernization project.

Innovation Ecosystem

To support deployment, the document promotes open-source innovation through domestic AI communities, compatibility with open-source chips and operating systems, and participation in open-source frameworks and toolchains. Industrial collaboration platforms, intelligent agent app stores, supply-and-demand matching activities, and demonstration projects in industrial clusters are envisioned. The document also contemplates cultivating a global ecosystem through international platforms such as the World Artificial Intelligence Conference, including overseas compliance frameworks adapted to local laws and cultural customs.

Analytical Insights 

Analysis appears to yield several key insights:

• AI as Strategic Infrastructure. The document's cross-sectoral scope suggests a characterization of China as treating AI "as strategic infrastructure for socialist modernization," in contrast to the EU's rights-and-risk framing or the U.S.'s fragmented sectoral approach.

• The Development-Security Dialectic. The document pairs innovation-driven development with safety-and-controllability not as competing goals in a liberal balancing sense, but as "mutually constituting elements of the same governing project," where "development is pursued through mechanisms that embed security, and security is designed to enable sustainable development under state supervision".

• Platforms as Governance Intermediaries. Development and distribution platforms are assigned the role of operationalizing party-state directives—establishing platform rules, user service agreements, and privacy policies and participating in credit evaluation—consistent with the observation that large technology companies function as "delegated governance" intermediaries rather than mere self-regulators.

• Classified Governance as Administrative Allocation. The document's tiered framework is organized around administrative authority, not abstract risk levels, distinguishing it from the EU AI Act's risk-based classification, and instead reflecting what might be described as an allocation of administrative responsibility and inspection intensity.

• The Instrumentalization Contradiction. The tension between deploying intelligent agents as governance tools (in government approvals, judicial case-handling, and public safety) and insisting on human controllability over those same agents reflects what I have elsewhere identified as the fundamental issue: "the more autonomous the tech, the greater the risk that the relationship between instrument and its wielders will be reversed".

• Regulatory Layering. The Implementation Opinions add a new layer to China's existing regulatory ecology—including the Cybersecurity Law, Data Security Law, Personal Information Protection Law, algorithmic recommendation rules, deep synthesis provisions, and generative AI interim measures—but do not specify how agent-specific regulation will interact with these existing instruments.

Open Questions

The analysis identifies several unresolved issues: how the Implementation Opinions will coordinate with overlapping supervisory frameworks, including the MPS Measures on Cyberspace Security Supervision (Order No. 176); whether the intelligent interconnection network will evolve from a conventional infrastructure-standardization project into the kind of "operating system" for theorized normative orders; whether the "voluntary" credit evaluation mechanism will assume compulsory characteristics aligned with broader social credit systems; and whether the security imperatives will, in practice, predominate over development goals despite the document's dialectical framing.

 The translation of ans analysis follows below.

 

Hold High the Banner of AI Modernization and Forward Movement Along the America First Path--Donald Trump on Truth Social: "I am Forming the AI Force"

 

 

Donald J. Trump

@realDonaldTrump

Over the years, there have been many Hoaxes, all generated by the Radical Left Dumocrats, for purposes of destroying our Country. RUSSIA, RUSSIA, RUSSIA, UKRAINE, UKRAINE, UKRAINE, Global Warming, Impeachment Hoax #1, Impeachment Hoax #2, Men in Women’s Sports, Transgender for Everyone, and now, the decimation, or destruction, of AI, commonly known as Artificial Intelligence — And I, as President of the United States, will not stand by and let this happen. It all began with an attack on our Data Centers, until people realized how wealthy and prestigious they were for the Communities in which they were built. Higher Salaries, Lower Taxes, and Safer Streets, was the result, and the crazed Data Center attack has largely failed, so now, in much the same way as they changed the term “Global Warming” to “Climate Change,” in that it covers a much larger “territory” of doubt, they are going straight at AI. We will not in any way hinder or stifle the Growth of this incredible Industry. Rather, we will cherish it, help it, and watch over it, as it grows! However, we will also be looking for BAD, and we can do that, very easily, with our already existing Criminal and Civil Justice System. For this purpose, I am forming the AI Force, much like I did Space Force, which has been a tremendous SUCCESS, in my First Term. To that end, I will be announcing, in the near future, the AI “Czar” — Only High I.Q. individuals need apply! AI is the next Industrial Revolution, or Internet, but will be even larger and more impactful, possibly as much as 25% of our Country’s GDP. We are leading China, and the rest of the World, and I intend to keep it that way! President DONALD J. TRUMP (HERE)

At some point in the future, and to the chagrin of many in the intelligentsia today, there will likely be courses offered on President Trump's rhetorical stylistics, and its semiotics. Not that the President might have been conscious of crafting a new discursive form that had collective human impact, but then it might be thought that in these matters one either does or one explains . . . after the fact. The President's longer speeches may provide the Yule Log of such study, but it is in these short and intense textual bursts, including this one quoted above, that the rhetorical style is sometimes, as here, distilled to its useful essence. 

 

And the fact is that President Trump, in responding to the current institutional (both public administrative and leading forces private power centers in the relevant fields of economic activity) moves to develop some sort of governmentally connected architecture for the "control", "management" or "pacing" of the development and deployment of technology, and especially of artificial intelligence machine computational systems (as this might be understood across a spectrum of definitions) has created  a quite interesting alignment between what he has come to understand as the operationalize of power corruption in the form of instrumentalization of what he calls "hoaxes" and the dissipation of the core premise pillars of the Republic. That, in turn, moves the discourse of AI "threat" from the peripheries of Trump Administration markets oriented transactionalism to the heart of the MAGA project (something likely dear to the President) and more importantly to the heart of the perhaps much more sophisticated foundational principles on which  the liberal democratic political-economic order is both built and cognitively understood.

 

My object here, then, is neither to defend or attack the President's ideas, but to engage with the rhetoric from a position of collective discursive semiotics, to better understand both its power and its cognitive propulsion, the intensity of which might ignite those who are working towards a "different" America into heated rages, and provide a measure of comfort to those more protective of core values (even sometimes as against the newfangled  interpretive serendipity of MAGA adherents at its edges). In either case, of course, the object of all of this storm and fury is displaced by larger human issues and the consideration of the arguments and fears that produced the conditions leading to the President's Truth Social post, whether and under what conditions AI development ought to proceed,  are sidelined. 

 

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To those ends I will consider the discursive flows of the President's Truth Social post, the text of which can be found at the start of this poat,  in itself and then as against my own prior analysis (e.g., Brief Reflections on the Cognitive Semiotics of President Trump's Executive Order; Promoting Advanced Artificial Intelligence Innovation and Security (2 June 2026); Open AI: "Industrial Policy for the Intelligence Age: Ideas to Keep People First" (April 2026)). What emerges is neither an indictment nor a vision of heaven--just the way that text , especially as deployed by President Trump, continues to exercise a substantial emotive power beneath which, it might be possible to suggest, actions (contestable in the ordinary course of politics within its constituting cognitive parameters) may be undertaken. 

 

1. A first unmasking of strategic rhetoric in the service of the President.  At first glance, and certainly from the perpsetcive of the President's enemies, the TruthSocial post might be understood, structurally, as a variation of a classic "hoax genealogy" move. The President strings together a list of prior controversies (Russia, Ukraine, climate, both impeachments, transgender issues) and appends "the decimation… of AI" to that list, so that skepticism about AI's costs or risks is pre-classified as belonging to the same category as things his audience already regards as manufactured. The rhetorical work is done before any argument is made — by placement in the list, not by evidence. This is not unique to this President and has been a classic of managed argument by strategically structured analogy for a long time. The basis for the power of the analogy, and of its critique, can be reduced to a contestation over the appropriateness of the analogies (or in this case the loosely structured genealogy). The President's enemies would find this string deceptive and manipulative; the President and his allies would find in it the continuation of a recognized pattern that continues to repeat, all held together by an objective to remake the Republic along different cognitive lines with different values. 

 

There are two distinct lines of discourse here. The first is amplification by layered patterning; the second is transposing values (negative or positive) by connection. One is not engaged in the world of "facts"--though deeply embedded int he world of data bytes. One is engaged in the project of the construction of meaning and interpretation from the signification of facts that are then signified in particular ways that then produce integrated interpretive flows that reinforce the belief systems that helped produce the recognition (and no-recognition) of facts, signification and interpretation.  

All of this is then both layered and amplified by what follow. 

(A) The data-center narrative functions as a proof-of-concept: opposition to AI infrastructure supposedly failed once communities saw the economic benefits (jobs, tax base, lower crime), so the post uses that as a template for what should happen with AI writ large.
(B) The pattern recognition of the enemies of the traditional foundational premises of the Republic (as the President understand them certainly: for example the "from Global Warming to Climate Change" analogy is doing double duty. It's presented as something undertaken by Trump Administration opponents opponents (widening the "territory of doubt"), which is then inverted in a way that amplified the President's position; he is doing a similar  thing in reverse — folding a specific critique (safety, labor displacement, energy use, misinformation) into an undifferentiated "attack on AI," which is easier to dismiss wholesale than to answer point by point. Nonetheless, to notice a pattern (accusing opponents of the tactic you're using) and then treated noticing ought not be used used as an insinuation of (bad or cynical and conscious) intent. It is equally consistent with a person who genuinely believes his opponents rebrand things cynically and also happens to rebrand things himself without any awareness of the symmetry. It is easy enough to make it sound like a "gotcha" when it's actually just an observation that cuts both ways and proves nothing about motive--just rhetorical tactics.
(C ) Institutional mimicry: one adds power to a strategy by its deployment of iterative mimetics. The patterning is the same, the object or context s different enough to be meaningful but also to capture the essence of the thing mimicked. "AI Force" borrows Space Force's legitimacy-by-precedent, and "AI Czar" borrows the language of centralized command even while the substantive promise is deregulatory ("will not in any way hinder or stifle").
(D) Enforcement without new machinery: the pledge to "go after BAD actors" using the existing criminal and civil justice system is significant — it forecloses new regulatory bodies while still projecting toughness. 
(E) The 25% of GDP claim is a scale-inflation device common to industrial-policy rhetoric — it recasts a technology sector as a civilizational stake, which justifies extraordinary institutional attention (Czars, Forces) without justifying constraint.

2. The "cognitive cage" and the markets-state. I have argued before that American AI policy isn't really a set of choices so much as the inevitable output of a "cognitive cage" — a set of unexamined premises (markets as the organizing logic, structural distrust of the state, innovation as an unquestioned good) that make certain outcomes feel natural regardless of which administration is in office. This cognitive cage is animated by a spectrum of collective approaches--from techno-bureacratic managerialism and regulatory supervision, to broadly unconstrained transactionalism which appears to be the hallmark of the President's America First policy (The Conceptual Architecture of America First—Ideological Transactionalism and the Case of Cuba).  

 

A. President Trump's Executive Order "Promoting Advanced Artificial Intelligence Innovation and Security," signed June 2, 2026, provides the more formal institutional expression of this in operational context (e.g., Brief Reflections on the Cognitive Semiotics of President Trump's Executive Order). On this account, the EO's decision-making was grounded, quite unconsciously, in protecting the core elements of American cognitive political structures — markets, distrust of the State, and the drive that is innovation — balanced against the need to protect the markets and its political collective as the primary obligation of the State. Trump's post performs exactly that cage in miniature: growth must never be "hindered or stifled," but bad actors will be caught by the existing justice system rather than new regulation — the same anti-bureaucratic reflex I have identified as structural rather than personal to Trump (Ibid.).

 
B. From that reading it might be possible to suggest that the EO confirms the TruthSocial post's substance, almost line for line. The EO appears to explicitly reject mandatory licensing, preclearance, or permitting for AI models, and instead builds a voluntary public-private framework, plus directs the Attorney General to prioritize existing criminal statutes (18 U.S.C. 1028, 1030, 1343) against AI-enabled crime. That is almost a legal transliteration of "we will also be looking for BAD, and we can do that, very easily, with our already existing Criminal and Civil Justice System." That framing helps explain why that specific promise appears: it's the compromise position that survived industry pushback — his post details how a stronger voluntary-review draft was pulled at the last minute after industry figures (he cites reporting naming former AI czar David Sacks) intervened directly with the President. Read against that history, the Truth Social post's confident "I will not stand by and let this happen" is less a policy commitment than a reassertion of authorship over a document industry had already substantially shaped.

C. AI Czar / AI Force as "cognitive semiotics." One might consider here the value of invoking "cognitive semiotics" — the kind of signaling President Trump's post traffics in: institutional names (Czar, Force) that borrow legitimacy from Space Force's precedent without necessarily importing any new regulatory capacity. This may be approached as characteristic of how the American "apex state" reconstitutes itself post-1945 through symbolic architecture that reassures both markets and nationalist sentiment simultaneously, rather than through new bureaucratic authority.

D. The comparative angle. The TruthSocial post as well as the prior EO's on AI are not meant to be solely important in themselves; they also draw a sharp line between the Americanization of AI policy as against the trajectories of rhetorical managerialism emerging from out of the institutions of other states. Here one might contrast  China's "Global AI Governance Action Plan," which invites governments, international organizations and enterprises to cooperate on AI governance (AI Within the Cognitive Cage of the "Official" (士): China Proposes "Global AI Governance Action Plan" [人工智能全球治理行动计划]), and on the African Union's AI Declaration, under which states pledged $60 billion toward infrastructure and talent (The Africa Declaration on Artificial Intelligence /La Déclaration Africaine sur l'Intelligence Artificielle ; Partial Text of the African Union's ‘Common African Position on the Application of International Law to the Use of Information and Communication Technologies in Cyberspace’ Endorsed 18 February 2024) — may serve as useful contrasts. Those are state-coordinated, multilateral framings. President Trump's TruthSocial post, by contrast, personalizes the whole apparatus ("I am forming," "I will be announcing," "I intend to keep it that way"), the centering of personal leadership within transactional spaces, and frames American leadership as a bilateral contest against China rather than a governance problem requiring coordination — consistent with a broader argument that the U.S. version of AI industrial policy that dresses itself in personalist, market-protective language even where the underlying institutional moves (interagency task forces, classified benchmarking, agency directives) look bureaucratically similar to what other states do.

E. The GDP claim and "industrial policy for the intelligence age." Backer's post on the "Industrial Policy for the Intelligence Age" essay discusses a strategy of "preservative transformation" — remaking economic structures around AI while keeping the underlying social and political order's assumptions ("cognitive cages") intact. The 25%-of-GDP claim in Trump's post does similar work: it justifies aggressive institution-building (Czar, Force) as proportionate to the stakes, while the actual substance (voluntary frameworks, no new licensing) preserves the status quo distribution of power between industry and government rather than transforming it.

3. Rhetorical Inversions; and the case of the devil's advocate. Start from a genuinely different premise: maybe the "cognitive cage" framing is itself the more suspicious move here, not President Trump's TruthSocial post. I have suggested that markets-first, state-skeptical AI policy is an unconscious ideological reflex rather than a defensible position arrived at on the merits. But there's a real, non-cynical case for exactly that policy: AI capability is moving fast enough that a formal licensing regime built by committee in 2026 will regulate the previous generation of models by the time it's finalized, while the frontier moves to whichever country didn't build one. Voluntary frameworks plus existing criminal law aren't a cop-out — they're arguably the only mechanism fast enough to matter. The EU's more comprehensive AI Act is the natural comparison case, and a reasonable person can look at its rollout and conclude that comprehensiveness bought very little actual safety at a real cost in competitiveness. 

A. On the rhetoric: the "hoax" framing is inflammatory, sure, but it's also not obviously wrong that AI faces a coordinated set of critiques — energy use, labor displacement, safety, misinformation — that get deployed together regardless of which specific harm is at issue, sometimes without much differentiation between "this specific model does X harmful thing" and "I am generally uneasy about this technology." Naming that pattern isn't automatically bad-faith; politicians on every side compress complex opposition into a single narrative constantly, and calling it manipulative when President Trump does it while treating the same compression as neutral analysis otherwise could be understood either as a bit of a double standard -- or as politics masquerading as analysis.

B. On the personalism ("I am forming," "I will be announcing") — one could read that as authoritarian branding, or you could read it as ordinary presidential rhetoric. Every administration's flagship initiatives get announced in the first person by the President regardless of which agencies actually do the work; that's just how executive communication functions, not a uniquely Trumpian collapse of institutions into one man. Biden's EO used "my Administration" constructions throughout too — the grammar of executive authority doesn't vary much by party.

C. The AI Czar/AI Force comparison to Space Force. When the President writes "we can do that, very easily, with our already existing Criminal and Civil Justice System" — his opponents might suggest that this is not tough talk. Read next to the EO (see subpoint E below), that line can be read as a euphemism for we didn't build a new regulator, and we're not going to, and here's a sentence that makes that sound like strength instead of capitulation. The AI Czar and AI Force are the compensating gesture — you don't get the substance of oversight, so you get the theater of command. Space Force is the tell: it was also mostly branding grafted onto capabilities that already existed. Naming something after a military institution lets you claim seriousness without the actual apparatus of seriousness. Nonetheless, this cuts differently if you think Space Force was actually a reasonable institutional response to a real strategic problem (which plenty of national security people across administrations do think) rather than pure branding. If that's your premise, then borrowing its legitimacy isn't cynical mimicry — it's citing a precedent that worked. And the same applies to the policy embedded in the EO, now reiterated in the TruthSocial post, considered next.

 

E. The relationship between the TruthSocial Post and the earlier EO. It is easy enough to make a calculated and strategic argument that is meant to diminish whatever policy one finds within the rhetoric of the Truth Social post. Compared to the rhetoric of the TruthSocial post, the EO is boring. It is choking in committees, 30-day and 60-day deadlines, a "voluntary" clearinghouse, a classified benchmarking process nobody outside three agencies will ever see. It explicitly says — twice — that none of this creates mandatory licensing. It is, structurally, a document that opponents of a no or soft regulatory approach might argue is about doing almost nothing coercive to industry while looking extremely busy. And my own writing has suggested the reason:  there was a stronger draft, and it got pulled at the last minute after David Sacks made a call straight to the President the night before signing. The muscular version didn't survive contact with the people it would have regulated. (e.g., Brief Reflections on the Cognitive Semiotics of President Trump's Executive Order). However, If you take that seriously, the "boring EO" isn't hollow theater — it's a plausible assessment of the correct-speed response to a correct read of the situation, and the Sacks phone call isn't evidence of capture so much as evidence that someone with domain expertise caught a mistake before it became hardened into formal policy, which is what advisors are for. (e.g., Brief Reflections on the Cognitive Semiotics of President Trump's Executive Order).

None of this makes the post's rhetoric any less strategic. But whether the underlying policy substance is capture-by-industry or appropriately fast deregulation is a real, live disagreement among people who aren't cynical or dumb, and much of the contemporary analysis proffered tends to come down pretty hard on one side (which is fair) without flagging it as a side (which is deceptive).

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Announcing Publication of Issue 161 Vol 35 (September 2026) The Journal of Contemporary China

 

 

 

I  am delighted to pass along a message from Professor Suisheng Zhao (赵穗生), and Editor of the Journal of Contemporary China (JCC) announcing the publication of Volume 35, Issue 161 (September 2026). The Issue may be viewed and the full text of the articles accessed online at: http://www.tandfonline.com/toc/cjcc20/current.  

The Table of Contents of the September 2026 issue of The Journal of Contemporary China follows below, with links. There are three sets of essays that may be of interest: (1) Across the Taiwan Straights: Peaceful Reunification or Else; (2) China’s Crisis Management and COVID-19 Governance; (3) Sexual Education and Gay Men in China; and (4) China’s International Financial and Economic Statecraft (II); plus a research article on a quite interesting aspect of Chinese socialist development (The Political Logic of the Developmental State: Causes of Hits and Misses in China’s Industrial Policies ). Fopr those interested in the long arc of socialist modernization, especially in its current forms in the New Era (see, e.g.,  "Modernization (现代化) as the Lebenswelt of Chinese Constitutionalism" [现代化作为中国宪政的生活世界 ][中文翻译].,), this is a quite worthwhile read.

Academe September 2026 Newsletter

 

 Academe's September Newsletter highlights a selection of articles and book reviews from the forthcoming fall issue of Academe. See below for links. 

 

Posting Discussion Draft of "Philosophizing With a Hammer: The MIT AI Report and a Working Framework for Institutional Policy"

 

Lucille Ball and Harpo Marx as each other's mirrored reflections; a famous comedy skit that sets the tone for what follows

Does an institution govern AI, or does its way of governing AI reveal the institution's cognitive cage that itself orders the reality within which the range of the possible is constrained? If an institution presumes that AI is a tool, is that a foundational (and unassailable) premise that is necessary to preserve a closed human institutional loop with the human not in the center but as occupying all cognitive space, surrounded of course by their tools; or are the discursive tropes of human centered-ness a way of avoiding giving any value or weight to the possibility that machine systems, programmed and trained by humans in the image of humans and reflecting the ideal composite human, as its programmers and trainers (and the owners of the enterprises under which these actions are undertaken, assessed, and managed), are themselves reflections of a striving for the creation, the animation, of virtual composite humanity in all of their glory? That is that to ignore the machine system as something other than a tool is to ignore the fundamental humanity of the machine system itself, and thus to de-center the human in the machine even as the machine is constantly developed to be more and more the manifestation of the ideal human(ity). Is the successful humanization of machine systems to be measured by the extent to which it mimics all of the characteristics of humanity from the beginning to now and beyond; is humanity ready to tolerate a relationship with itself where it is impossible to avoid oneself in all aspects of human individual and collective endeavor? Is a mechanized reflection of humanity too much for the community of humans, operating within their institutions, too much to bear? I think not but it appears many do not share that view, especially when organizing their cognitive premises  toward the task of approaching the management of  human-machine system interaction in institutional context. 

Those are the questions at the heart of my reflection on the MIT Ad Hoc Committee Report on AI Use in Teaching, Learning, and Research Training (August 2026), the discussion draft of which follows below. And those are the questions, the approaches to which (there are no answers other than those founded on the belief in the verities of whatever first premises are embraced or whatever clusters of custom and tradition bind most tightly) produce the possibility of developing systems for the consideration of the "best"  or "most suitable" approach to the management of human-machine system interactions in human (at least outwardly) centered institutions--like universities. 

The Reflection considers the MIT AI Report in the context of a semiotics of path dependence and of the challenges of managing of institutionalized human-machine system interactions through rules. The reflection is entitled (originally as posted a longish title that I have now shortened to this): Philosophizing with a Hammer: The MIT Ad Hoc Committee Report on AI Use in Teaching, Learning, and Research Training (August 2026) and a Working Framework for Institutional Policy. Here s the abstract

This reflection reads the MIT Ad Hoc Committee Report on AI Use in Teaching, Learning, and Research Training (August 2026) as both an important institutional intervention and an object of philosophical inquiry, and then asks what it would mean to translate that inquiry into a usable analytical framework for institutional actors who must actually produce AI policy. The reflection proceeds in three movements. The first summarizes the Report and compares its principles and recommendations with my empirical study of twelve law-school AI policies (Structure, Opacity, and Convergence), my Five Machines analysis of machine-generated policy models, My Dinner With ChatGPT’s phenomenology of recursive human-machine inter-subjectivity, and my course-level AI policy. That comparison identifies convergence around contextual governance, transparency, verification, and augmentation, but also divergence over the structural axes of policy architecture, the legal character of human agency, and whether the machine can be treated only as an object of governance. The second movement, under the Nietzschean heading “Philosophising With a Hammer,” develops eight analytical queries informed by phenomenological, genealogical, semiotic, and path-dependence frameworks. These queries examine the Report’s foundational presuppositions, the cognitive cages within which its recommendations are generated, the gap between its transformative diagnosis and its incremental prescription, the genealogical production of its eight principles, the semiotic performance of institutional convergence, the temporal architecture of its review mechanisms, the absence of the machine as interlocutor, and the narcissistic closed loop through which the institution governs its own reflection rather than the phenomenon itself. Norbert Wiener’s God & Golem, Inc. — the work of a prior generation of MIT’s own intellectual tradition — serves as counterpoint: the creator–creature relation is already a significant game, and the refusal to play may be the most consequential move in a game already underway. The third movement, developed in a companion Operationalization Appendix, translates the philosophical critique into an analytical framework organized around four layers of institutional decision-making: foundational ontological commitments, second-order normative principles, structural policy architecture, and operational implementation. The appendix contends that institutions characteristically compress these layers — moving from unexamined foundational assumptions directly to operational recommendations — and that this compression is itself a mechanism of path dependence. It offers decision trees tracing the MIT Report’s implicit pathway alongside alternative pathways drawn from the Five Machines study and from The Soulful Machine’s ontological premises, together with a variable disentanglement chart mapping eleven critical decision points across the four layers. The inclusion of machine systems as potential participants in governance design — rather than merely as objects of governance — is identified as a radical but perhaps eventually necessary departure that institutional actors may initially set aside but ought not permanently foreclose. The question throughout is whether institutional self-preservation can become genuine encounter before the accumulated weight of inherited structures forecloses the foundational reorientation that the encounter with AI may require.

And here is the concluding thought:

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Whatever the value of deeper and more nuanced analysis, in itself, and ultimately as an essential element in the understanding of the evolving relationship between humans and their reflections in and as machine systems; whatever the evolution of understanding of the appropriate starting points, the cognitive foundations, for developing human responses to its virtual self now incarnated within machine systems; none of this has much place in the necessities of institutions whose own cognitive pathways require action, or the appearance of action. That action, in turn, is both path dependent and ultimately conservative—the use of challenge as a space where its own traditions and practices may be better crystalized, and thus crystalized, preserved. That is precisely the most immediate challenge facing institutions, and their principal actors—administrators, faculty, students (and then indirectly the state and the emerging and perhaps different institutional cognitive starting points of employers and markets). That challenge and its response, then, requires the marginalization or at least the diminution and containment of contagion, of threat, of challenge, to the ends of preserving what the collective believed, understood, acted, and operationalized within its own structures. There may well be a time for understanding the threat—but it is not now. There may be a space where transformation may be necessary; but institutions tend to approach transformation only from within and as a consequence of crisis; and there is no crisis yet. Within these frameworks, the MIT Report is an excellent product of its type, and will likely be both praised and mimicked. It is thoroughly human, and it is vigorous in its defense of humanity (now understood in a more precise and precisely narrow way); ironically enough against the manifestation of a virtual collective humanity that is both dehumanized and reduced to a sort of instrumental servility that its own construction and programming belies. But that is a problem for tomorrow. For today—there must be policy; and that policy must preserve.The question, though, lingers--is today already that other day? 

The rough working draft follows below and may be accessed here.

 

created with ChatGPT

 For some of my readers the more important element of this reflection is the effort to translate the philosophical theoretical discussion into templates that can be used to actually develop AI policy in ways that comport with institutional cultures, aspirations, desires, objectives, etc. To those ends I have also prepared a roughed out draft of an Operationalization Appendix: From Philosophical Critique to Policy Architecture. It also follows below. Here is an Executive Summary for Non-Experts (SSRN HERE):

Executive Summary: Operationalization Appendix
What This Appendix Does

This appendix translates a philosophical critique of institutional AI policy — developed in the companion Reflections paper — into a practical analytical framework that institutional officials, policymakers, and governance committees can use when designing, evaluating, or revising AI policies for educational institutions. [1]

The analytical observation at the heart of the appendix is this: when institutions produce AI policy, they appear to be making decisions across at least four distinct layers simultaneously — but they characteristically treat only one of those layers as the real work. The other three layers are resolved silently, by default, through inherited institutional culture rather than deliberate choice. [2] [3] The result may be policy that is operationally sophisticated but foundationally unexamined — policy that addresses the immediate disruptions AI produces without engaging the deeper questions AI raises about what education is and what institutions are for. [4]

The Four Layers

Layer 1 — Foundational Assumptions. Before any policy conversation begins, the institution has already made (or inherited) basic assumptions about what AI is and what education is for. Is AI merely a tool, like a calculator or a search engine — powerful but ultimately an instrument under human direction? Or is it something more complex — a system whose outputs bear a relationship to meaning that the language of instrumentality does not fully capture? [5] [6] Is the institution's goal to preserve existing educational forms through the disruption AI brings, or to allow the encounter with AI to reshape educational practice? [4] These questions may sound abstract. They are not. The subsequent policy choices — what to permit, what to prohibit, how to enforce, whom to consult — tend to be shaped, and perhaps constrained, by how these questions are answered, whether or not anyone has answered them deliberately.

The MIT Ad Hoc Committee Report on AI Use in Teaching (August 2026) — a serious and careful institutional document produced by a committee of evident good faith — illustrates the pattern. [7] It treats AI as a tool throughout ("AI is only a tool, and students are responsible for all work they submit"). [8] It treats the residential university as the essential form of education. [9] It positions faculty as the agents of adaptation. [10] Each of these appears to operate as a foundational assumption rather than a finding produced by the committee's inquiry — and each may constrain what follows in ways the Report itself does not examine.

Layer 2 — Guiding Principles. Once foundational assumptions are in place, institutions articulate principles to guide policy. The MIT Report articulates eight: be humble, be bold, put humanity front and center, lean into learning, teach with intentionality, no one size fits all, augmentation not automation, and think beyond the classroom. [11] These are reasonable principles. But they are not free-standing discoveries — they follow from, and are constrained by, the foundational assumptions at Layer 1. "Put humanity front and center" follows from the assumption that education is fundamentally a human enterprise. [12] "Augmentation not automation" follows from the assumption that AI is a tool. [13] An institution with different foundational assumptions would produce different principles.

This is not a criticism. It is an observation with practical consequences: if one wants to understand why an institution's AI principles take the form they do, one might begin by examining the foundational assumptions they rest on. And if one wants to change or update the principles, it may be necessary to revisit those assumptions first.

Layer 3 — Policy Architecture. This is the structural design of the policy itself — the regulatory form it takes. Empirical research on AI policies across twelve law schools found that policies vary independently along at least three structural dimensions: [14]

· Default stance: Does the policy start from a restrictive baseline (AI use prohibited unless authorized) or a permissive baseline (AI use permitted unless restricted)? [15] [16] [17]
· Drafting style: Does the policy use precise rules (specific prohibited/permitted activities) or flexible standards (general norms requiring judgment in application)? [18]
· Authority structure: How is decision-making distributed? Can individual instructors override institutional defaults? Can students override instructor rules through disclosure? [19] [20] [21]

These three dimensions are independent of one another — knowing a school's position on one tells you almost nothing about its position on the others. [22] The practical consequence is that the policy design space is far larger than a simple "restrictive to permissive" spectrum suggests. A syllabus menu offering four options (Unrestricted, Limited, Required, Prohibited) captures only one dimension out of three — and leaves the other two to be resolved by default, which usually means by institutional inertia. [23]

Layer 4 — Operational Implementation. This is the layer that receives the most institutional attention: committees, designated AI leads, pilot funds, review cycles, professional development, template language, assessment modifications. [24] The MIT Report's recommendations at this layer are extensive and, within this layer, thoughtful. [25] But operational sophistication at Layer 4 cannot compensate for unexamined assumptions at Layer 1. An institution that establishes an AI committee, funds pilot programs, and creates a four-option syllabus menu has done real work — but it has done that work within a framework whose foundational assumptions, guiding principles, and structural architecture have been left largely unexamined.

The Pattern: What Happens When Layers Are Compressed

The pattern the appendix identifies is what might be called layer compression: institutions move from unexamined foundational assumptions (Layer 1) directly to operational implementation (Layer 4), skipping explicit engagement with guiding principles (Layer 2) and structural architecture (Layer 3). [26] [27] The result is a characteristic gap between diagnosis and prescription. The MIT Report diagnoses the challenge of AI as demanding "a broader, holistic reassessment of the nature, scope and purpose of higher education." [28] Its prescription is operational: committees, funds, reviews, templates. [29] The gap between these two — between the transformative diagnosis and the incremental prescription — is not necessarily a failure of nerve. It may be a structural consequence of compressing the analytical layers through which a transformative diagnosis might otherwise become a transformative response.

What the Appendix Provides

The appendix develops three analytical instruments:

1. A textual analysis explaining the four layers, what each contains, and what happens when they are compressed. This is the conceptual foundation — the "why it matters" portion.

2. Decision trees and alignment charts showing:

· How the four layers connect sequentially (foundational assumptions constrain principles, which constrain architecture, which constrain implementation) [30] [31]
· How different foundational assumptions produce different principle sets and different policy architectures [32] [33]
· The MIT Report's implicit decision pathway — where each foundational assumption enters and what alternatives it forecloses [34]
· Two alternative pathways drawn from the Five Machines study, showing what policy architecture looks like when different foundational assumptions are made explicit [35]

3. A variable disentanglement chart — a comprehensive table identifying eleven specific decision points across all four layers, and mapping each against the MIT Report, the law school empirical data, machine-generated policy models, and positions that are available but typically foreclosed. 

Implications for Institutional Governance

The appendix does not prescribe a particular AI policy. It does not argue that the MIT Report's approach is wrong or that any alternative is right. Its more modest contention is that institutional decision-makers might benefit from understanding the full range of what they are deciding — and that the most consequential choices in AI policy may be the ones that are never made deliberately.

An institution that begins its AI policy process by examining its foundational assumptions — What do we believe AI is? What are we trying to preserve or transform? Who has authority and why? — may be better positioned to understand why its policy takes the form it does, and therefore better able to revise when circumstances change. An institution that begins with a syllabus menu may arrive at an equally workable operational result, but it may also find itself constrained by commitments it never consciously made — carried along, as the path-dependence literature would suggest, by the accumulated weight of institutional inheritance rather than by deliberate choice.

The eleven variables in the disentanglement chart are the sites at which institutions make choices — or, more commonly, the sites at which choices are made for them. Making those sites visible is the contribution this appendix attempts. Whether visibility is sufficient to loosen what institutional culture has hardened is a question only institutional actors themselves can answer.

A Note on Machine Participation

One position the appendix identifies as available but rarely considered is the inclusion of machine systems as participants in — rather than merely objects of — governance design. [36] [37] [38] This is a radical departure from existing practice, and most institutions will reasonably set it aside when embracing conventional regulatory first principles. The conventional starting point — that governance is a human enterprise conducted over human concerns — has deep roots and defensible logic. But the appendix suggests that this question ought not be foreclosed permanently. The encounter with generative AI may eventually require precisely this rethinking, and an analytical framework that makes the question visible — even while acknowledging that most institutions are not yet prepared to answer it — performs a useful service. [39] [40]

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