Lecture 3—The "Markets State"; The U.S. Approach --for the Lecture Series: AI Governance in Comparative Perspective, Theory and Practice: China, U.S., E.U.

 

Pix Credit Image created using DALL-E (visual representation of the relationship between state and individuals through markets

I was delighted to have had the opportunity to present a series of Lectures hosted by the East China University of Political Science and Law (ECUPL) at the end of May.

The overall theme (and thus the title) of the lectures was AI Governance in Comparative Perspective, Theory and Practice: China, U.S. and E.U, With a Sideways Glance at the U.N. The subject of the lectures requires little by way of introduction: Artificial intelligence is the broad term that has come to represent a growing cluster of non-human and digitalized processes and operations that has as its primary task the constitution of non-human systems capable of performing tasks that were once thought to require human intelligence. And so is the impulse to manage, control, exploit, embed, understand, and regulate these processes, systems, and perhaps eventually non-human consciousness with a huge potential to undertake many of the computational tasks (the mathematical and logical processing of data) that were once the sole domain of and perhaps defined what it meant to be human. That is the point where things get interesting. It is at the point where the development of machines, that is of non-human systems, capable of performing tasks that were once thought to require human intelligence, collide with regulatory structures meant to manage, contain, constrain, liberate, embed, project and exploit such non-human systems, whether they are traditional or emerging, public or private regulatory systems, that human collectives and the machine-systems they have created now find themselves.

The eight lectures progress sequentially from conceptual and theoretical frameworks (lectures 1 and 2, the objects and subjects of AI regulation), through a deeper consideration of regulatory systems in three distinguishable regulatory regimes--the US, EU, and China (Lectures 3, 4.5). The last two lectures consider judicial efforts to embed AI within traditional legal orders (Lecture 6), and the way in which the object of regulation (in the form of the owners of the larger AI enterprises) understand the relationship between AI, the state, and society (Lecture 7) . Lecture 8 summarizes and draws larger themes going forward.

In a previous post introducing Lecture 1 (From Algorithms to Foundation Models: What Contemporary AI is “Made of”) I suggested that perhaps a useful way of approaching the issue of AI regulation is to start by considering the nature and characteristics of the regulatory subject--what we euphemistically refer to as "AI." It then occurred to me that it might be useful as well to see if that regulatory object had views of their own respecting their nature character and, more importantly, the relationship of regulation projects to that (self) perception of their nature and character. So I approached Google's Gemini with a series of questions which I thought, in the process of what might pass for a conversation, might help humans begin to understand how at least one AI program thinks of itself. That conversation was incorporated into Lecture 1A. In Lecture 2 we moved from the object to the subjects of regurgitation. Like its regulatory objects, regulatory subjects  are functionally differentiated and can be disaggregated. In either case the connection between object and subject becomes complicated. 

This post includes a summary of the Lecture 3 Notes, as well as the link to the Lecture 3 PPT. Those interested may reach out to me to discuss availability of audio of the lecture and the full text of the Lecture 1 notes

Given the nature of the project I thought it might be useful to engage with an commercially available AI service for the production of a summary of the Lecture 1 materials. After some back and forth with Anthropic's Calude (Lecture 2 used Chat GPT; Lecture 1 and 1A used Google's Gemini), we came up with the following abstract of Lecture 3. 

Image generated using Claude

 

ABSTRACT:

The United States and the Governance of Artificial Intelligence: Markets, Strategy, and Fragmentation . The United States governs artificial intelligence not through a single comprehensive statute but through a distributed institutional ecosystem in which markets, agencies, courts, procurement contracts, voluntary standards, state legislatures, and national security apparatus each occupy a portion of the regulatory terrain. This arrangement reflects the country's broader political economy and carries distinctive characteristics that merit careful analysis across four principal themes.

The first theme is the primacy of innovation over precautionary authorization. The American regulatory framework proceeds from a foundational premise: technology enters the market unless existing law provides a basis for intervention. No federal agency pre-approves most AI systems before deployment. Enforcement follows from harm rather than preceding it. The Federal Trade Commission addresses deceptive claims after they appear in the market. Employment regulators examine discriminatory hiring tools through existing anti-discrimination frameworks. Financial regulators apply model risk and fair lending requirements to credit systems. The Food and Drug Administration governs AI-enabled medical devices through established product-safety authority. This ex post orientation is not incidental to the American approach but constitutive of it, reflecting a settled preference for private innovation as the primary driver of technological development.

The second theme is the translation of AI-related conduct into established legal categories. Rather than constructing AI as a legally novel object requiring dedicated statutory architecture, the United States routes AI problems through existing normative vocabularies. Algorithmic deception is addressed as consumer fraud. Automated hiring discrimination is addressed as employment discrimination. AI-assisted misrepresentation to investors is addressed as securities fraud. AI components in medical devices are addressed as product safety questions. This translation strategy allows regulators to act under existing authority without awaiting new legislation, and permits sector-specific agencies to apply domain expertise to domain-specific problems. The approach also raises analytical questions about the fit between legal categories developed before modern AI systems and the characteristics of probabilistic, opaque, and continuously updated models operating across complex institutional arrangements.

The third theme is the strategic-industrial dimension of American AI governance. The United States does not govern AI solely through market mechanisms. The Trump Administration's policy documents—including the America's AI Action Plan, Director Kratsios' addresses at the Endless Frontiers Retreat and the APEC Digital and AI Ministerial Meeting, and the March 2026 National AI Legislative Framework—articulate a model in which AI is treated as a foundational technology in geopolitical competition. State activity in this register includes export controls on advanced semiconductors, domestic infrastructure investment, bilateral technology agreements, and active promotion of American AI systems and governance frameworks internationally. The administration's policy language frames AI development as inseparable from national security, economic competitiveness, and what it describes as civilizational contest with strategic rivals. This dimension complicates any characterization of the American model as straightforwardly market-liberal, as it involves substantial state coordination, funding, procurement, and strategic direction.

The fourth theme is federalist fragmentation as a structural feature of American AI governance. The absence of comprehensive federal AI legislation means that governance emerges from the interaction of multiple institutional actors operating under different authorities and at different levels of government. State legislatures have addressed automated decision-making, biometric data, employment AI, and high-risk systems, with Colorado's risk-based statute representing one of the more comprehensive state-level frameworks. Voluntary standards, most notably the NIST AI Risk Management Framework, acquire practical authority through procurement requirements, liability considerations, and industry adoption rather than through direct legislative mandate. Private firms establish their own model policies, access rules, and safety standards. The resulting architecture is one in which regulatory coverage, enforcement capacity, and available remedies vary across sectors, jurisdictions, and affected populations.

Taken together, these four themes describe a governance model that combines monitored markets, existing-law enforcement, voluntary standardization, procurement discipline, state-level experimentation, and national security strategy into an arrangement that is institutionally plural, sectorally differentiated, and geopolitically oriented. The central analytical question the model poses is whether this distributed architecture is adequate to govern AI systems whose characteristics—scalability, opacity, systemic reach, and geopolitical significance—extend beyond the assumptions embedded in the legal and institutional frameworks through which it currently operates.

 

 

 

 

Links to Lectures:

Lecture 0 -- Introduction
Lecture 1—From Algorithms to Foundation Models: What Contemporary AI is “Made of”
Lecture 1A--A Computation/Conversation With Google's "Maschinenmensch" Gemini:
Lecture 2—What Are We Actually Governing When We Govern AI?
Lecture 3—The “Markets State”: U.S. Approach
Lecture 4—The “Rights State”: EU Approach
Lecture 5—The “Guided State”: The Chinese Approach
Lecture 6—Courts, Companies, and the Legal Construction of AI
Lecture 7—AI Narratives: Palantir; Anthropic; Open AI; and Leopold Aschenbrenner
Lecture 8—Putting It All Together: Trends, Trend Lines, and Regulatory Dialectics

 

 

Introduction; AI Governance in Comparative Perspective, Theory and Practice: China, U.S. and E.U.-- Lectures at the East China University of Political Science and Law (May 2026)

 

Image generated with Google Gemini

Introduction:

I was delighted to have had the opportunity to present a series of Lectures  hosted by the East China University of Political Science and Law (ECUPL) at the end of May. My thanks to my hosts and especially to Sun Yuhua for organizing these opportunities to engage with colleagues. 

 

The overall theme (and thus the title) of the lectures was AI Governance in Comparative Perspective, Theory and Practice: China, U.S. and E.U, With a Sideways Glance at the U.N. The subject of the lectures requires little by way of introduction: Artificial intelligence is the broad term that has come to represent a growing cluster of non-human and digitalized processes and operations that has as its primary task the constitution of non-human systems capable of performing tasks that were once thought to require human intelligence. AI has come to dominate , or infect, depending on one’s point of view, virtually every aspect of the organization and operation of collective human systems—as well as shaping the lives of individuals who are plugged into virtual systems of decision making or automated processes that can provide everything from entertainment, to advice, to interaction with other humans and organizational structures.  This is well known as is its capacity for literal impact. One is reminded of Norbert Wiener’s reference to the classic horror tale, the Monkey’s Paw, in his book God & Golem, Inc.: A Comment on Certain Points Where Cybernetics Impinges on Religion (MIT Press, 1964, pp. 58-60). The tale describes the risk of wishing for or demanding something from a source that is entirely literal minded, which in the case of the Monkey’s Paw was meant to grant three wishes, the means to those ends were undefined. When a person wished for  a sum of money, the wish was granted, in the form of a settlement by their son’s employer as a consequence of an accident that killed the son.  In Wiener’s words: “The magic of automation, and in particular the magic of automatization in which devices learn, may be expected to be similarly literal. If you are playing  a game according to certain rules  and set the playing-machine to play for victory, you will get victory if you get anything at all, and the machine will not pay the slightest attention to any consideration except victory according to the rules.” (Ibid.); unless, of course the rules are constantly changing to embed more and more complex considerations. When the machine becomes self-learning, it can develop its own mechanisms for adding or subtracting considerations as well (rules, norms, values, etc.).   

 

Pix credit here

And so is the impulse to manage, control, exploit, embed, understand, and regulate these processes, systems, and perhaps eventually non-human consciousness with a huge potential to undertake many of the computational tasks (the mathematical and logical processing of data) that were once the sole domain of and perhaps defined what it meant to be human. That is the point where things get interesting. It is at the point where the development of machines, that is of non-human systems, capable of performing tasks that were once thought to require human intelligence, collide with regulatory structures meant to manage, contain, constrain, liberate, embed, project and exploit such non-human systems, whether they are traditional or emerging, public or private regulatory systems, that human collectives and the machine-systems they have created now find themselves. It is the point, as well, where such collectives are floundering even as these machine-systems become ever more sophisticated and ever more embedded in the life of human collectives and the engagement of individual humans with the world.   One moves here from "The Monkey's Paw" to "Three Thousand Years of Longing" where AI is the genie and his container is regulation, and the lonely professor given three wishes the consumers and producers of AI, that is the genie. 
 

The point of this collision, especially among human sub-systems, the cognitive orientation of which are becoming increasingly better defined and distinct as against the others, that serves as the point of departure for the series of lectures that together constitute AI Governance in Comparative Perspective, Theory and Practice: China, U.S. and E.U, With a Sideways Glance at the U.N. There are eight lectures: 

Lecture 1—From Algorithms to Foundation Models: What Contemporary AI is “Made of”
Lecture 2—What Are We Actually Governing When We Govern AI?
Lecture 3—The “Markets State”: U.S. Approach
Lecture 4—The “Rights State”: EU Approach
Lecture 5—The “Guided State”: The Chinese Approach
Lecture 6—Courts, Companies, and the Legal Construction of AI
Lecture 7—AI Narratives: Palantir; Anthropic; Open AI; and Leopold Aschenbrenner
Lecture 8—Putting It All Together: Trends, Trend Lines, and Regulatory Dialectics

 The Lectures start from the premise that before it is even possible to speak about regulation and regulatory systems, and even more so to speak of such systems in or as some sort of comparison (against what standard is yet another problem of course), one must first have firmly in mind two of the critical elements on which any such discussion might be organized. The first requires  an effort to grasp, or perhaps better to approach an understanding of, the object of regulation. The second is to have some better sense of the regulatory enterprise, especially its forms, placement, approaches, characteristics and logic; and noy just its forms but its sources and the character of its authority within regulatory collectives including but not limited to the governmental apparatus.

Pix credit here

Once one understands the complexity and interconnectivity of regulatory subjects (actors within a legal system, that might now also include AI) and objects (the things that can be acted upon, including, perhaps the regulators themselves), one can then approach the enterprise of regulation of AI in context. This starting point is not merely   an effort in taxonomy of regulatory subject and object, which then, as is traditionally undertaken, serves as a basis for the sort of matrix within which it is possible to consider those permutations and combinations of connections between subject and object, thus disaggregated, that serves some purpose or other. Each reflects the other in a certain sense.  AI is a creature made in the image of its creator; the Biblical reference as well as reference to Mary Shelly’s Frankenstein (creating something in the image of the creator) hides the computational element in both, as well as their inter-subjectivity (here in the sense of constant and mutual inter-penetration, sometimes as a form of dialectic). But all of this interactivity, and its consequential impulse to build AI in particular ways and to regulate it in equally peculiar ways, suggests an underlying fundamental set of cognitive patterns that may be reflected in both AI object and regulatory subject.  The regulatory object reflects its subject; it is its own subject. To regulate AI, then, suggests its character as the regulation of the self, now detached and reconstituted as a virtual representation of the self. Regulation is, in this sense, an effort in self control, but where the self has been detached (in this case the collective regulatory self) from and operates on the its physical representation (discussed from a conceptual perspective in 'The Soulful Machine, the Virtual Person, and the “Human” Condition').    

Pix credit here

Neural networks (pattern finding) and large language models (predicting sequences) may provide a useful (or at least interesting) basis for giving some form to that inter-subjectivity between regulatory subject and object—that are also object and subject. Neural networks might provide a useful conceptual basis for understanding , or at least framing, the iterative and dialectical structures within which human collectives as well as computing systems, are organized to process data (inputs, irritants, requests, etc.) through layers of weights, values, biases and activation functions in order not just to identify patterns but to use those patterns to rationalize data and respond to stimulus. In the case of political economic systems, the neural network analogy makes it easier to understand such systems as computing systems that identify and reinforce patterns, and that rationalize the environment in which they operate, through the identification and application of a series of foundational values, biases, and recognized units for receiving, processing and passing along data that together constitute the parameters within which it is possible to receive process and identify objects received. For the purposes of these lectures extracting the fundamental elements of the “hidden layers” of the political-economic “neural network” of a state system is essential for understanding the way in which the regulating body both understands the object of regulation and processes that object in terms of the biases and values of the system to determine the form and direction of regulation. This serves as the structure against which it might be possible to consider the (inevitable?) and distinctive regulatory pathways to identify and respond to "challenges" and "threats"-- take for example the challenge of AI "risk", that might be conceived as risk to rights in Europe, risk to markets in the US and risk to socialist modernization guided forward along its socialist path in China. Each of these reductionist expressions hides and embeds a much more potent (and effectively hidden) layer of analytics, premises, and ordering frameworks that make the reduction appear at some level "natural;" and so it is within the community of believers--Fides et Ratio, perhaps more than Magnifica humanitas (the former conceptual, the later operational).

  

For purposes of these lectures the focus is on three of the more influential subjects of regulation: the United States (Lecture 3), the European Union (Lecture 4), and China (Lecture 6). Mere description of regulatory approaches is no longer particularly useful, since AI programs can now produce descriptive summaries that may be better than anything a human can do (controlling for the hallucinations of either). Each discussion of national legislation starts with an effort to grasp the characteristics of each state’s neural network embedded in and as its political economic model.  Those characteristics then shape the approach of each state toward the regulatory object and the character and objectives of regulatory responses.  In that context, the United States might be labelled (or named; 鬼谷子 (Guiguzi's) ming-ming (明名 intelligent naming)) as the “markets state,” the European Union as the “rights state,” and China as the “guiding state.” These labels suggest the baseline weights and values, the scope of what each system identifies and processes, how it perceives value and threat and accordingly regulates. The US is markets driven and suspicious of state interference (for the most part though reshaped by reconstituting efforts after the 1920s and its openness to techno bureaucratic leadership). The European Union may be rights driven and trusting of state leadership the purpose of which might be understood to protect and enhance rights and prevent negative impacts. China is development driven with a specific objective (forward movement along a Socialist Path) guided and led by a vanguard of social forces in the context of which all productive forces of the state might be embedded. These “hidden layers” of the national neural networks then shape the way each formulates the “issue” of AI and fashions a response that both reaffirms the patterning of its foundational structures and in the process reinforces them. 

 

Lectures 6 and 7 then consider  two critical areas that have a significant effect on any regulatory project. The first focuses on courts, enterprises, and the legal construction of AI in and as law. Its principle focus is on the way that AI related litigation is both changing traditional areas of jurisprudence (tort, contract, property and the like) and is being changed by it. The second, Lecture 7, then considers the way in which leaders of AI production are seeking to influence approaches to the conceptualization of the relationship between AI and the state, and in the process offer a window into the way that AI leaders re-envision the relationship between AI, the State and the regulation of both.To those ends the recent "concept exercises" or manifestos of Palantir, Anthropic, and Open AI, along with the reflections of an influential voice within the AI elite mainstream, Leopold Aschenbrenner, are considered. 

 

Lecture 8 then attempts to draw the bigger picture. It suggests the way that a shared vocabulary can cover quite substantial differences in approach, values, and objectives in the context of AI and AI regulation. And it suggests the way that weighting the objectives of innovation, risk, and the role of the state affects  the way in which  common regulatory focus of safety, security, transparency, accountability, innovation, and infrastructure, each with sometimes substantially different regulatory characteristics as a function of the core differences in national “neural networks.”  One comes at last to the understanding of the fundamental contradiction of AI and its regulation: the development of a common language  across political systems the neural networks of which overlap but are fundamentally incompatible producing conceptions of AI and law that can be coordinated but which cannot converge. That seems to be the template for coordinated governance for the 2nd quarter of the 21st century. I noted the effect of patterns of conceptual homonyms in the context of the convergence project of the  International Financial Reporting Standards (IFRS) Foundation has released its IFRS Foundation 2025 Annual Report—Fit for the Future (31 March 2026):.

By the start of the second quarter of the 21st century, however, the common language, like written Chinese, covered a wide variation in how it would be "spoken" and the signification with which common use terms would be infused. take transparency, for example. Within the cognitive cages of early 21st century convergence globalization transparency could be read broadly to require disclosure of virtually everything but trade secrets and protected know how--and even that might require some disclosure as a function, for example of its connection with adverse human rights or sustainability impacts (as these terms were to be developed by international institutions through hard and soft measures, norms, declarations, finds, etc.)--subject to increasingly narrowed exceptions for national security, development and the like. A quarter century later, transparency remains a presumption but now much more tightly constrained and perhaps sieved through superior obligations (many generated through domestic legal orders of States) respecting, national security, sanctions, data protection and data sovereignty, anti-espionage statutes, and blocking legislation to counter extraterritorial projections of regulatory systems. Transparency remains the same; its signification is now fractured and defined as a function of higher ranking signified principles, rules, and expectations. The same applies, with substantially more bite, to the construction and deployment of the concept of "risk."
One can then, and with enthusiasm, embrace a common language, while at the same time investing that language with regional or other contextual meaning, and constrain it through higher order local values, especially those encased in domestic (or regional) rules and legislation. The imaginaries of unity are preserved, and, indeed, a platform within which such values and contestations of values, may be produced and consumed, without enforcing a centralizing fundamental political or normative line. Yet that also is a step forward. A single language encasing difference may not have been the goal at the start of the 21st century, but language binds all the same--it binds enough that difference may be constrained by the outer boundaries of rational learning inside a cognitive cage that through its language objects effectively defines both that rationality and its limits. And it is that common language that provides all that is necessary to achieve the fundamentally critical element of this project: comparability and communication across and despite of difference. The rest might come later; or it might not. That convergence is no longer necessary where a common language has been substituted for common norms, beliefs, practices, and expectations. (A Common Language Containing Differentiating Meanings Within Evolving International Standards for Sustainability Disclosure in Financial Statements). 

Pix credit here

In the posts that follow I will post summaries of the lecture notes and the PowerPoint of each of the lectures which can be accessed by following these links:

Links to Lectures:

Lecture 0 -- Introduction
Lecture 1—From Algorithms to Foundation Models: What Contemporary AI is “Made of”
Lecture 1A--A Computation/Conversation With Google's "Maschinenmensch" Gemini:
Lecture 2—What Are We Actually Governing When We Govern AI?
Lecture 3—The “Markets State”: U.S. Approach
Lecture 4—The “Rights State”: EU Approach
Lecture 5—The “Guided State”: The Chinese Approach
Lecture 6—Courts, Companies, and the Legal Construction of AI
Lecture 7—AI Narratives: Palantir; Anthropic; Open AI; and Leopold Aschenbrenner
Lecture 8—Putting It All Together: Trends, Trend Lines, and Regulatory Dialectics

National Security Presidential Memorandum/NSPM-11

 

On 5 June 2026 President Trump issued a Presidential Memorandum--National Security Presidential Memorandum/NSPM-11, rescinding the Biden Presidency's National Security Memorandum-25 and associated guidance. It was accompanied by an explanatory Fact Sheet: President Donald J. Trump Signs Historic Directive on AI in the National Security Enterprise. This adds another layer to the emerging strategies and principles for implementing the new national security framework within which AI development in markets is to be undertaken (See my discussion in Brief Reflections on the Cognitive Semiotics of President Trump's Executive Order; Promoting Advanced Artificial Intelligence Innovation and Security (2 June 2026). And, ironically it recalls key arguments in Leopold Aschenbrenner's 2024 reflection, "Situational Awareness" (see, e.g., Satyricon, or Tragedy at Play--Artificial Intelligence and the New World Order: Leopold Aschenbrenner, "Situational Awareness").

As understood by its authors, the National Security Presidential Memorandum/NSPM-11 is meant to attain the following objectives:

The Memorandum directs the national security enterprise to accelerate AI adoption to meet surging demand, adapt the best commercial and open-source technologies for mission use, assure that fielded systems are robust, steerable, controllable, and preserve clear lines of accountability under the Constitutional chain of command.

The Memorandum strengthens national security capabilities, directing the rapid onboarding of the most advanced AI models from multiple vendors, driving the buildout of next-generation, high-security computing facilities to run future AI systems at scale, and bolstering the talent pipeline, including by establishing an AI National Security Strategic Reserve of top non-governmental experts.

The Memorandum directs the Secretary of War to issue an updated directive on autonomy in weapon systems and requires annual review of key guidance across the national security enterprise to keep pace with the rapidly advancing AI frontier. 

The Memorandum directs departments and agencies to ensure that no entity, commercial or otherwise, can disable, degrade, or modify an AI system that American warfighters depend on without prior approval. It also offers new partnerships with willing private-sector companies to secure America’s cutting-edge AI against global threats. 

The Memorandum rescinds and replaces the Biden Administration’s NSM-25, an outdated document that burdened American AI adoption with ideological mandates and fostered dangerous single-vendor dependencies that left our warfighters exposed. (Fact Sheet: President Donald J. Trump Signs Historic Directive on AI in the National Security Enterprise)

 President Biden's NSM-25, which is reproduced below,  was closely aligned with President Biden's own Executive Order rationalizing AI within its national security context, Executive Order 14110—Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. That document, in turn, was closely incorporated the critical objectives of the Biden Administration with respect to safe, secure and ethical AI the development and deployment of which would be grounded in a labor and civil rights and equity based framework.  Indeed, the Trump and Biden Administration emphasized different priorities reflected in the approach to implementation. The Biden Administration's NSM 25 emphasized among other things the need to balance civil rights against national security, in ways that could be respectful of markets. :

(h) In this effort, the United States Government must also protect human rights, civil rights, civil liberties, privacy, and safety, and lay the groundwork for a stable and responsible international AI governance landscape. Throughout its history, the United States has been a global leader in shaping the design, development, and use of new technologies not only to advance national security, but also to protect and promote democratic values. The United States Government must develop safeguards for its use of AI tools, and take an active role in steering global AI norms and institutions. The AI frontier is moving quickly, and the United States Government must stay attuned to ongoing technical developments without losing focus on its guiding principles.

 The Trump Administration kept much of the old structure, its architecture hard hired into the neural network of the American system--markets driven ("national security enterprise shall adapt commercial or open-source AI technologies, leveraging the most cutting-edge capabilities available from diverse suppliers across the private sector, large and small, while ensuring that AI technologies chosen are optimized for their intended use" National Security Presidential Memorandum/NSPM-11, §2(b)), refashioning the terms of the Biden Administration (safe, secure, and ethical markets that is competitive in the way that the Biden Administration understood that term)--to align with his America First principles in which economic policy and national security are fused, mediated by markets driven innovation (elaborated in The Conceptual Architecture of America First—Ideological Transactionalism and the Case of Cuba). In this version, the  specifies objectives that are sector specific.

AI will be among the most transformative technologies for national security in American history. Used appropriately, it can help protect troops on the battlefield, enable precise operations that minimize harm to civilians, and ensure the United States maintains technological overmatch against every adversary.

The national security enterprise will never develop or deploy AI to censor free speech, embed ideological bias, or conduct unlawful surveillance against the American people. Civil liberties and Constitutional protections are non-negotiable.

The Memorandum makes accountability a central pillar of AI adoption, reinforcing a chain of command that runs from the American people through their elected President to the warfighter. Commanders, directors, and agency heads remain accountable for ensuring these obligations are met at every level. (Fact Sheet: President Donald J. Trump Signs Historic Directive on AI in the National Security Enterprise)

Section 2 sets oput the normative framework and objectives. Section 3 is the operative provision. Section 3(a)directs the "Secretary of War [to] issue an update to DOD Directive 3000.09 on Autonomy in Weapon Systems, to be reviewed annually to account for the rapidly evolving capabilities of AI systems, to ensure the deliberate adoption of AI systems that respect the chain of command and operational authorities." Section 3(b) directs contract termination for default or for convenience contracts with companies that have repeatedly demonstrated a pattern of conduct that is inconsistent with policies laid out in section 2 of this memorandum. Section 3( c) directs the development of an appropriate policy for governance of AI use in national security systems, including implementation and reporting requirements, consistent with .AI governance requirements for non-national security systems, such as that in OMB guidance OMB memorandum M-25-21. Section 4 then targets national security capabilities through intense interactions (really the interpenetration) of the national security and AI sectors, one private and markets centered, the other public and grounded in a very broad understanding both of national defense and of the character of threat.  

 

 

Open for Comment: OMB Revisions to "Regulation for Federal Financial Assistance"

 

Pix credit here

On 29 May 2026 the OMB (Office of Management and Budget) proposed a set of revisions and updates to the Uniform Guidance (2 C.F.R. § 200),  the government-wide framework issued by the Office of Management and Budget (OMB) that establishes administrative requirements, cost principles, and audit requirements for all federal grants, cooperative agreements, and pass-through awards. 

The Office of Management and Budget (OMB) proposes to revise the Guidance for Federal Financial Assistance to improve government-wide policies and requirements related to the management of grants, cooperative agreements, and other forms of assistance. OMB is proposing revisions that would improve transparency, accountability, and oversight for Federal awards across the Federal Government. This includes ensuring that American tax dollars are not wasted or misused, activities performed under Federal awards are consistent with law and policy, and recipients are held accountable when they fail to meet relevant standards. The revisions also aim to ensure that basic American principles of equality and equal opportunity are upheld throughout all stages of the award making process and that unlawful discrimination is no longer permitted. Proposed changes also include providing further clarification on the regulatory status of the OMB requirements and on the process for future updates to the government-wide requirements. Finally, OMB also proposes changes to reduce recipient burden. The listed Federal grant-making agencies propose conforming changes to their respective adopting regulations, or, in the case of some agencies and other entities, establishing new adopting regulations or policies. The proposed changes reflect the administration's commitment to transparency, accountability, and proper oversight for the Federal grantmaking process. The proposed regulations seek to ensure that American tax dollars are ultimately used to serve the needs of the American public.

The proposed revisions are open for comments and reactions which may be delivered on or before 13 July 2026. Comments on this proposal must be submitted electronically before the comment closing date to www.regulations.gov. In submitting comments, please search for recent submissions by OMB to find docket OMB-2026-0034, which includes the full text of the proposed revisions and submit comments there. 

Institutional academia has not reacted well to the proposals, part of th Trump Administration's efforts to (1) remove the Biden Administration's regulatory efforts to embed their version of diversity, equity and inclusion measures within the grant awarding and administration process; and (2) reduce the aggregate grant payouts, especially to the extent that grant money subsidizes university or institutional operations. A useful summary of the changes and their effects on university fundraising and compliance responsibilities may be found at Key Issues within OMB Uniform Guidance Revision Proposal [Docket OMB-2026-0034], distributed by the Association of Public and Land Grant UIniversities. 

It ought to be no surprise that these proposed changes have been met with opposition from grant recipients and their institutional environments.  

Although research has bipartisan support in the US Congress, and trust in science is above 75% across the country, the Trump administration seems as determined as ever to mortally wound the nation’s scientific enterprise. After the scientific community persuaded Congress to restore most of the president’s draconian cuts to research funding last year, the White House Office of Management and Budget (OMB), under Russell Vought, has found new ways to circumvent the will of Congress and starve American science.* * * The sweeping new regulations proposed by OMB would subject every federal research funding decision to political review. Peer review has never been formally binding, but this proposal would dramatically expand the power of political appointees to override expert assessments of scientific merit. Agencies could end multiyear grants with no due process. They also could use the vague criteria of Trump’s “gold standard science” to identify institutions for preferential treatment. International collaboration with countries identified solely by the administration would be prohibited under the new rules, but more notably, all research that involves the expenditure of funds outside the US would require case-by-case approval.(Another Red Alert for American Science).

The authors of that opinion piece then raise a call to arms, one that is likely top be met with much sympathy among those adversely impacted: 

The scientific community needs to flood OMB with responses during the public comment period, open until 13 July. Universities and associations must speak out as a united front to mobilize Congress and be ready to file lawsuits once the regulations are finalized. I was sympathetic to members of the scientific establishment who played it carefully during last year’s budget negotiations. Getting the budget deal done was crucial. But that was then. The red light is now flashing. All hands, report to stations."! (Another Red Alert for American Science).

 Of course they are right to do so. Scientific grants programs, like every other action of the State, are an overtly political action, in the sense that they represent a political conclusion that State intervention  (in this case subsidies for private research in aid of national goals and aspirations) serves the interests of the State. That State interests--or in American parlance the public interests--serves the financial, status, business, or status interests of others, including academics, researchers, unioversoities and the like in this case, is good but of secondary interest, even if the state interest is, from time to time, served by sunbsidizing the private interests of those objects and processes that are the object if public polñiocy, in this case the fruits of research that serves State interests. For a long time, those converghing interests were mediated largely through self-regulation--academics presiding over grant decisiopns coordinated with state fiunctiponaries. And the universities got their cut, all nicely justified by whatever justificatory logicv appealed to all parties. But the Trump Admionistraiton serves as a reminder that alignment is neithjer permanent nor non-political. The State interests may shift--and that is a matter for elected officials to determine. That shifting may have significant effects on the financial planning of subsidized enterprises and thier employees, as well as on the busoiness model of knowledge production. Yet those are factors in a political consideraiotn, the discretion for which--within the boundaries of due process and the regulaitons under which such susidy programs are organized--lie with political functionarties and not with the beneficiaries of these programs.  

The political nature of these determinations, however do not leave those affected powerless. Politics is a sport that American liberal democracy spreads widely acropss the populaiton. Adverse impacts of political discretionary decisionmaking may always be the subject of a counter politics; ones that may require political counter-responses. The object is to advance one's own interests as against the political intrerests of opponents. And the principal way of doing that in early 21st century Amerioca is to argume virtually anything but politics while denegrationg the opposition as narrowly political. That is fair. In politics. To aid those fforts, of course, one now must play politics in the ocurts, while defending the courts as the last bastion of non-politicalaction.  That is also a quintessentially American politics. 

 And thus, for those interested, or affected. by the proposed revisions, you have a chance to make your voice heard, to the extent that this is possible, through 13 July.  After that, one goes back to that now well embedded poliutical strategy --litigation. 

 

Brief Reflections on the Cognitive Semiotics of President Trump's Executive Order; Promoting Advanced Artificial Intelligence Innovation and Security (2 June 2026)

 

Image prepared using Gemini

 How does one balance the imperatives of economic/social development with the critical element of national security? How does one attempt that balance within a political neural network which is driven by markets (the autonomous and free mimetic iterative actions of consumers and producers within a platform of exchange grounded in the expectations of that very iterative mimetics)? How does one balance the essential need for structural coupling within and among trade, politics, culture,  and social sub-systems-- how does one balance the essential element of cross-system irritation--with the need to protect the solidity and integrity of collectives producing and consuming irritation?

Those are the fundamental questions that now confront apex states as they attempt to re-imagine  themselves by putting together whatever remains of the building blocks the post-1945 project of global convergence  which they destroyed and from out of which they seek to elaborate virtual sub-systems masquerading as empire--empire the constitution and defense of which can be undertaken bloodlessly within the simulacra that is the virtual manifestation of the physical order from out of which all of this emerged in the last decade.  

Tragic in so many ways. . . 

But perhaps inevitable as well, in the ancient Greek sense of tragedy anyway --at least for those who understand the repeating sequential patterns of human collective neural networking. Today one witnesses the quite extraordinary marriage of Nietzsche and Abd al-Rahman ibn Khaldun.  The former reminding one of the constitutive realities that come from investing truth and fact with the values, bias and perceptions that produce human cognitive cages constructed from out of belief in the truth of things, and with it their reality; the later reminding one of the recursivity of human collectives, of their assabiyah (عصبيّة) from which one might realize lebenswelt (Husserl) or habitus (Bourdieu) or imaginaries (Sartre) as the lived simulacra of the human collective (in physical form) and its increasingly managerial force in the form of generative digitized realities of those physical manifestations of imagined collective solidarity. Humans project themselves into their simulated selves, into generative sentience made in their own image- - -and then recoil in the horror if it, even as they are tempted to invest even more of themselves in the project of virtual recreation and in the construction of the relationships between themselves physical and virtual, as each projects themselves against and within the other.  

Pix credit here (note that the output is "dog" because the neural pathways make that choice inevitable; other pathways might have concluded that the image was "food", or "not the property of the state" etc. (on its phenomenology here; on categoriuzation and its framing see Emporio celestial de conocimientos benévolos)

 

All of this by way of introduction to a most remarkable document that serves to evidence the way the United States approaches the problem of balance while staying true to the core driving premises (the bars of the cage of its own cognitive frameworks) producing through the application of the processes of its own collective political neural networks an inevitable approach to the balancing of innovation and national security within a markets driven reality aligned with a political system fundamentally distrustful of the State (despite what might be conceived, in retrospect, as the techno-bureaucratic state managed aberrations of the period 1919-2015, or winch might themselves be cast as the current aberrations from the techno-state evolution that ascended or supplanted the national structures of the American political neural networks after 1919). That remarkable document--and the process of its finalization, was President Trump's Executive Order: Promoting Advanced Artificial Intelligence Innovation and Security (2 June 2026). What makes it remarkable is not its terms, as such, but the way in which it exposed the conceptual framework within which such a document was inevitable within a broader framework that had, in the prior Administration of President Biden, produced a different result within the same conceptual cage: Executive Order 14110 of October 30, 2023, Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. It is one in which national security (AI must be "safe and secure"; "addressing AI systems' most pressing security risks—including with respect to biotechnology, cybersecurity, critical infrastructure, and other national security dangers—while navigating AI's opacity and complexity") is balanced in a different way against other values (privacy, equity and civil rights, enhancing labor rights, etc.), but which also includes protection of market forces ("Federal Government will promote a fair, open, and competitive ecosystem and marketplace for AI and related technologies so that small developers and entrepreneurs can continue to drive innovation"). But see the Biden Administration's 2024 National Security Memorandum (NSM-25) which is discussed in a separate post.

As reported widely (I used the reporting in Politico). That reporting nicely highlighted the structural elements of decision making processes. These were grounded (quite unconsciously of course, because that is how being inside a cognitive cage works) in the protection of the core elements of American cognitive political structures--markets, distrust of the State, and the relentless drive that is innovation (as that in turn might be understand flexibly over time and among sectors of human collective production and consumption of whatever might be of interest from a regulatory perspective) balanced against the fundamental need to protect the markets and its political collective as the primary obligation of the State.  

The original version of what became Promoting Advanced Artificial Intelligence Innovation and Security  was to have been signed at a White House event on 21 May 2026. That event, and the signing of that version was postponed at the last minute.

Thursday’s abrupt postponement of President Donald Trump’s much-awaited executive order on artificial intelligence came after former AI czar David Sacks voiced industry concerns about the measure to Trump, according to a senior White House official and two people familiar with the matter. * * * The executive order, which the White House planned to release Thursday afternoon, would have set in motion a voluntary oversight system in which developers of advanced AI models could submit their products to a review by federal agencies before releasing them, POLITICO previously reported.* * * According to the White House official, Sacks had participated in a review of the EO this week, and White House officials believed he was generally happy with it and would support it. But Wednesday night, he began to raise concerns, including fears that the voluntary nature of the agreement may one day become mandatory, the senior White House official said. “Then, he called POTUS this morning unbeknownst to anybody, his own staff included, and derailed it,” the White House official said. The reversal also came after industry officials raised concerns about a proposed voluntary review process for cutting-edge “frontier” AI models, according to four people familiar with the matter, who were granted anonymity to discuss private discussions. (Politico).

The question, then, wasn't whether the draft Executive Order (dated May 2026) ought to outline an "America First" AI strategy (eg, discussed here) designed to accelerate technological growth by reducing regulatory burdens while aggressively hardening national security infrastructure against AI-driven threats, but how that balance was to be struck consistent with the interpretation of those values by those producing, managing and developing both products and the national security structures that protected their freedom in and as markets. within the value systems. The policy rejected mandatory federal licensing or preclearance, though the spectre of both remains very much in the air. The alternative--voluntary public-private partnerships, rapid deployment of defensive AI tools across government agencies, and targeted criminal enforcement against malicious actors--one grounded in the vitality of private sector standards and deep interpenetration between the market and the national security apparatus on an operational level, better reflected current consensus on the legitimacy of American regulatory approaches. To do that the Executive Order protects innovation in markets, but ties that innovation to the consequences of such innovation for and as critical elements of American national security.

As finalized, the Executive Order retained most of its original provisions. But it did shorten the period for examination from 90 to 30 days and included the Department of Commerce (Section 3) as a consulting partner.  

What result? Here is a short summary:

Section 1: Purpose and Core Policy: The EO starts by reaffirming the operational premise that principle American leadership in AI stems from private-sector talent and a deliberate avoidance of bureaucratic constraints. The administration contrasts its approach with the prior administration, stating that it has actively slashed regulatory hurdles to accelerate AI adoption across both government and industry. 

It is the policy of the United States to promote AI innovation and security by working collaboratively with the private sector to modernize government and private sector information systems and harden them against external threats; to protect American ingenuity and intellectual property from exploitation and theft by adversaries; and to cultivate America’s advanced AI-enabled capabilities.

The core policy balances two competing pillars: AI Innovation: Championing an "America First" technological framework that fosters private-sector investment and rapid deployment. National Security: Addressing external threats by modernizing public and private information systems, protecting intellectual property from foreign adversaries, and cultivating advanced defensive capabilities ("Advanced AI capabilities make our Nation stronger, but also introduce new national security considerations that require coordinated action across executive departments and agencies (agencies), and components.").

Section 2: Upgrading American Systems for Advanced AI: This section mandates immediate actions to upgrade cyber defenses across federal and civilian networks, leveraging advanced AI tools to counter incoming threats (Section 2(a). The specifics are set out in Sections 2(b)-(f). They oinclude:

(1) Within 30 days, specific actions are assigned to key agencies: National Security & War Systems: The Committee on National Security Systems and the Secretary of War must prioritize and expedite the cyber defense of their respective information networks.
(2) Civilian Government & Critical Infrastructure: Within 30 days the Department of Homeland Security (DHS), via the Cybersecurity and Infrastructure Security Agency (CISA) and in consultation with the Office of Management and Budget (OMB), must issue Binding Operational Directives. These directives will expedite civilian federal cyber defense, expand AI-driven defensive programs, and facilitate access to cybersecurity tools (including "covered frontier models") for local entities like rural hospitals, community banks, and local utilities.
(3) AI Cybersecurity Clearinghouse: The Department of the Treasury, the National Security Agency (NSA), and CISA will form a clearinghouse in voluntary collaboration with private industry. This entity will scan for, validate, patch, and remediate software vulnerabilities.
(4) Funding and Talent (30 & 60 Days): OMB must immediately evaluate federal grant programs to redirect available funds toward advanced AI vulnerability detection. Within 60 days, the Office of Personnel Management (OPM) must expand hiring and placement pathways for the U.S. Tech Force Information Cybersecurity Specialist program to bring technical talent into government.

Section 3: Secure Frontier Model Deployment: This is a section that sets out the public-private framework for AI development within the architecture of national security. Within 60 days, a multi-agency coalition led by Treasury, the NSA, CISA, the National Cyber Director, and the National Institute of Standards and Technology (NIST) must establish protocols for governing highly advanced AI models. 

Classified Benchmarking Process: Section 3(a) describes the creation of a classified process to evaluate the cyber capabilities of AI systems under NSA leadership,which is to determine the exact technical thresholds that designate a system as a "covered frontier model." These benchmarks will be shared with researchers and developers.

Voluntary Government-Industry Framework: The order designs a voluntary program allowing AI developers to: (1) "engage the Federal Government" to determine if their models meet the "covered frontier" designation; (2) provide the federal government with early access to these models for up to 30 days prior to a wider release (under strict confidentiality and intellectual property protections); (3) jointly select "trusted partners" to receive early access to the models to reinforce critical infrastructure defenses.

Anti-Regulation Guardrail: Section 3(c) provides that nothing in this section permits the creation of mandatory government licensing, permitting, or preclearance requirements for developing or distributing AI models.

Section 4: Protection Against Criminal Actors:  The Attorney General is directed to prioritize federal criminal enforcement against malicious actors leveraging AI. Specifically, the Department of Justice will target individuals utilizing AI to unlawfully access, breach, or damage public or private information technology systems, or deploying autonomous AI agents to steal data for criminal purposes.

Section 5: General Provisions: the Department of War will bear the costs of publishing the order.

Key Operational Matrix

The following table summarizes the explicit mandates, responsible agencies, and strict deadlines imposed by the draft order:

Timeline	Responsible Agency / Official	Mandated Action	Target Systems / Sectors
30 Days	Committee on National Security Systems	Prioritize and expedite cyber defense protocols.	National Security Systems
30 Days	Secretary of War	Expedite information system cyber defenses.	Department of War Systems
30 Days	DHS (CISA) / OMB / National Cyber Director	Release Binding Operational Directives; deploy AI defensive tools.	Civilian Federal Gov / Critical Infrastructure (Rural hospitals, community banks)
30 Days	Treasury / NSA / CISA	Establish a voluntary AI Cybersecurity Clearinghouse.	Software vulnerability scanning and patch distribution
30 Days	OMB / CISA / National Cyber Director	Identify and divert federal grant funds.	Advanced AI vulnerability detection development
60 Days	Office of Personnel Management (OPM)	Expand specialist hiring and placement pathways.	U.S. Tech Force Information Cybersecurity
60 Days	NSA / Treasury / CISA / NIST / National Cyber Director	1. Establish classified benchmarking metrics. 2. Build a voluntary 30-day early-access framework.	"Covered Frontier Models"
Ongoing	Attorney General / DOJ	Prioritize criminal prosecution of AI-driven cybercrimes.	Unauthorized computer access, malicious AI agents

None of this affects sanctions regimes, nor export restrictions regimes and must be understood as deeply embedded in the current government programs to manage exports with national security implications. What it does do is provide an additional structural pillar in the reconstitution of economic policy, and development, of key industrial sectors, as national security in line with the fundamentals of the Trump Administration's America First policy (see The Conceptual Architecture of America First—Ideological Transactionalism and the Case of Cuba). 

And none of this touches on the deep, intricate and informal networks of engagement between public and private in the context of frontier (and more "ordinary") tech based innovation.  The deep intertwining of the State apparatus and the market for products, innovation, expectation, and processes that the apparatus consumes (and protects) suggests that actions like this Executive Order are important, but also serve to shield those interconnections that sometimes make it hard to distinguish either a public or private sphere, or in this case the larger private producers and the State as consumer of innovation. In that respect the now long process of the interconnection between institutional actors in markets continues to develop (or evolve) in ways that suggest that the private-public divide will assume new and not yet visible characteristics in the future.

The primary source documents follow: (1) Promoting Advanced Artificial Intelligence Innovation and Security; (2) Fact Sheet: President Donald J. Trump Promotes Advanced Artificial Intelligence Innovation and Security; and the (3) the Draft Executive Order (May 2026), along with President Biden's 2023 Executive Order 14110 (30 October 30); Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.