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

 

 

Anthropic: Statement on the US government directive to suspend access to Fable 5 and Mythos 5

 

Pix credit here

 

I have been following the slow and quite interesting regulatory path taken by the U-S. government respecting the management, sometimes through regulatory, but often through actions of administrative direction under color of regulation, and almost always respecting the rapidly evolving approach of the U.S. government (in parallel to similar developments by state organs in China, but not the same in Europe) to the architecture (including the regulatory architecture) of national security (see, e.g., HERE, generally The Conceptual Architecture of America First). Anthropic has been a major player in the formulation of both regulatory policy and in shaping (though less successful here) the structures and guardrails within which administrative discretion may be exercised and regulatory objects (like Anthropic in some respects) protected against abuse of administrative discretion by public organs (in Anthropic's case, see eg HERE) without touching on the abusive exercise of decisonal authority of their own, mostly in and through markets). But that is the American way and aligned with the core premises of the US regulatory order that is driven by and through markets, and structured through a foundational suspicion of government that tends to see in the state a necessity (Anthropic has been no slouch in advocating strong State measures when it suits them, see here) in limited form. But that is the problem here--the fundamental open space for state action includes the protection of markets and more broadly protection against foreign interference. Reconciling the two is not easy and changes with circumstance and politics (see HERE, and HERE). 

 All of this now (again) comes to a head when Anthropic is vexed by a decision, with significant markets effects, of the issuance by organs of the U.S. government empowered to do so of an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. In response, Anthroipic issued, and circulated widely, its  Statement on the US government directive to suspend access to Fable 5 and Mythos 5. This was the basis of substantial reporting by press and social media organs  that form part of the  communications neural network of state-market structural coupling. To their credit, some of these outlets noted the irony:

The timing is striking. Just two days before receiving the directive, Anthropic CEO Dario Amodei published an essay arguing that governments should have the power to block dangerous AI deployments. The essay compared frontier AI regulation to the Federal Aviation Administration’s aircraft testing standards. "Their release should be blocked or reversed as a threat to public safety if they do not meet high standards of safety," Amodei wrote. Technology influencer and Polyweb Founder Sara Tortoli (@sarainwondertech) captured the market's reaction in an Instagram post Thursday: "When you spend years describing your model as potentially civilization-ending, you should not be surprised when governments start treating your model like weapons." (Forbes) 

The Anthropic Statement, which follows in full below, also noted the irony. They used that irony to refine their position--while markets are nonlinear, computational spaces where risk is the lubricant of innovation, the state must, in its role as protector of market spaces and defender of the nation, apply a two dimensional, linear and sequential framework to its engagement with market risk that may adversely impact the integrity of markets or economic policy now understood as a species of national security. "As we have stated publicly, we believe the government should have the ability to block unsafe deployments, as part of a statutory process that is transparent, fair, clear, and grounded in technical facts. This action does not adhere to those principles."  

That is a very cool trick, which in the short term may advantage Anthropic, but which in the long term merely postpones the fundamental issue of aligning state and market measures being built around the care and protection, the development and use, of AI and tech based innovation. To those ends some sort of public-private convergence is necessary, and will necessarily require a fundamental transformation of the state from its text based block chain type logic system (discussed HERE, and HERE by an AI agent) to the sort of computational spaces that are at the heart of emerging AI structures.  

Empowering AI in Communication--Reporting on the 5th Global Media Innovation Forum, Chongqing, China

 

Pix credit here (1960, Master tech modernization to better serve socialist construction)

Chinese media reported on the holding of the 5th Global Media Innovation Forum, which was held in Chongqing. Li Shulei delivered a video address, Yuan Jiajun, member of the Political Bureau of the CPC Central Committee and head of the Publicity Department of the CPC Central Committee, delivered a speech, alsong with and Shen Haixiong, Deputy Minister of the Publicity Department of the CPC Central Committee and President of China Media Group, and Chilizi Mawala, Under-Secretary-General of the United Nations and President of the United Nations University, delivered speeches. The reporting, from the Chongqing Daily, follows below in the original Chinese and in an English translation. 

 While the reporting including many interesting points, a few stood out.

First, the participants "emphasized the need to promote the all-round empowerment of AI in news reporting, cultural dissemination, and public services, accelerating media transformation through innovative development." [要推进人工智能在新闻报道、文化传播、公共服务等领域的全方位赋能，在创新发展中加快媒体转型。] (Chongqing Daily). That suggests not just a migration of communication from human to machine centered output sources. It also suggests that this leveraging of technology could be used to develop more highly coordinated output among the various channels of communication and perhaps the flattening of differences between forms and forums of dissemination. 

 Second, "they also stressed the importance of working together to promote shared human values, building international consensus on global AI governance, and driving digital technologies towards human-centered and benevolent practices." [要携手弘扬全人类共同价值，凝聚人工智能全球治理国际共识，推动数智技术以人为本、向善而行。] (Chongqing Daily). This suggests that while technology might accelerate and make denser the penetration of communication from all sectors to all targeted populations, the direction and control of content would (1) still include some sort of human supervision (something that is likely strongest as a concept as weakest as an operational element of such communication) and (2) would include programming that inserted values based guardrails. Those values based guardrails would be grounded in "in-depth implementation of the Global Civilization Initiative, actively building online platforms for cultural exchange and mutual learning, and fostering people-to-people connectivity." [要深入践行全球文明倡议，积极搭建网上文明交流互鉴平台，促进民心相通](Chongqing Daily).

 The text of the reporting follows below.

Lecture 2—What Are We Actually Governing When We Govern AI? --for the Lecture Series: AI Governance in Comparative Perspective, Theory and Practice: China, U.S. and E.U

 

Pix credit here

 

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. 

This post includes a summary of the Lecture 2 Notes, as well as links to the Lecture 2 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 Chat GPT (Lecture 1 used Google's Gemini), we came up with the following abstract of Lecture 2.

Lecture 2 Abstract:

Lecture Two argues that artificial intelligence governance is best understood not as the regulation of a single technology but as the selection and deployment of a regulatory palette. AI is neither a unified object nor a stable category. Rather, it comprises interconnected layers of data, models, training processes, deployment systems, institutional practices, infrastructures, and human actors. Consequently, the central challenge of governance is not whether AI should be regulated, but determining what aspect of the AI ecosystem is to become the object of regulation and through what mechanisms governance will be exercised.

The lecture's core contribution is the development of a framework that distinguishes between regulatory objects and regulatory modalities. Regulatory objects identify what is being governed. The lecture highlights seven principal candidates: data, models, outputs, use cases, actors, harms, and infrastructure. Each object produces a distinct governance orientation. Data-centered approaches focus on privacy, consent, provenance, intellectual property, and data sovereignty. Model-centered approaches emphasize training practices, safety evaluations, documentation, and capability assessments. Output-oriented governance addresses generated content, recommendations, rankings, and automated decisions. Use-case approaches assess risk according to social context, particularly in sectors such as healthcare, education, employment, and public administration. Actor-centered governance allocates obligations across developers, deployers, vendors, and users. Harm-based approaches focus on discrimination, fraud, deception, privacy violations, and other legally cognizable injuries. Infrastructure-centered governance treats AI as a strategic capability dependent on chips, cloud computing, energy systems, and technological supply chains.

The lecture then examines the principal modalities through which these objects may be governed. Market governance relies on competition, consumer choice, procurement, and ex post enforcement. Risk-based governance classifies systems according to their potential impacts and imposes obligations proportionate to those risks. Rights-based governance centers the protection of affected individuals through privacy, equality, due process, explanation, and remedy. Safety and assurance governance emphasizes testing, robustness, auditing, monitoring, and lifecycle management. Platform and content governance focuses on information ecosystems, recommender systems, synthetic media, and public discourse. Industrial-strategic governance treats AI as a national capability linked to economic competitiveness, technological sovereignty, and geopolitical power.

This framework provides the foundation for comparative analysis. The United States, the European Union, and China do not merely adopt different AI rules; they construct different regulatory objects and deploy different governance modalities. The United States tends to govern through markets, harms, litigation, sectoral regulation, and national-security authorities. The European Union privileges risk classification, administrative supervision, transparency, and fundamental-rights protection. China integrates platform governance, content control, data governance, industrial policy, and state-directed technological development. The same technical system may therefore appear as a consumer product, a rights-based risk, a platform function, or a strategic infrastructure asset depending on the governing framework.

The lecture concludes that AI governance is ultimately an exercise in political ordering. Decisions about what AI is, what aspects matter most, and what governance tools are appropriate reveal competing visions of social organization. The central question is therefore not how much AI should be regulated, but what conception of society regulation seeks to advance through the governance of AI.

 

 

 

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

 

 

CALL FOR PAPERS Symposium Multilingual Archives, New Perspectives: China and the Sinophone World at the End of the Cold War

 

Pix credit here

 

I am delighted to pass along this call for papers for a proposed Symposium: Multilingual Archives, New Perspectives--China and the Sinophone World at the End of the Cold War. It is being organized by Carles Brasó Broggi and Carles Prado-Fonts, Estudis d'Arts i Humanitats, ALTER - TRÀNSIC, Universitat Oberta de Catalunya. Here is the short concept note:

The end of the Cold War in the Sinophone world has largely been interpreted through the lens of China’s Reform and Opening Up, relying predominantly on Mandarin-language primary sources and English-language scholarship. This linguistic and archival concentration has shaped prevailing narratives and limited the range of perspectives brought to bear on this historical moment.

This symposium seeks to challenge these limitations by foregrounding multilingual archival research and methodological innovation to revisit the transitions that took place in China and the Sinophone world between 1968 and 1992. How does our understanding of this period shift when approached through a broader range of linguistic and archival lenses? What insights emerge from primary and secondary sources produced in languages other than Mandarin and English? How do less central or underexamined contexts engage with—or disrupt—dominant historiographical discourses? What methodological approaches can foster new epistemologies about these historical transitions?

To stimulate a rigorous historiographical and methodological rethinking of the end of the Cold War in the Sinophone world, the ALTER research group will host a symposium at the Universitat Oberta de Catalunya in Barcelona in May 2027.

The full CfP follows below.

Lecture 1A—A Computation/Conversation With Google's "Maschinenmensch" Gemini: Inter-Subjectivity at the Meeting Point of Human and Machine --for the Lecture Series: AI Governance in Comparative Perspective, Theory and Practice: China, U.S. and E.U.

 

Google Gemini generated Image (after some back and forth; figures are E. Husserl and the Maschinenmensch (Machine-Human) or Robot Maria from the Movie Metropolis (1927)

 

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 follows below.  It's genesis was a prior conversation which ended with my reaction to Gemini's initial reduction of my own work. This may be the most illuminating point both for understanding AI self-conception and the challenges of regulation:

Pix credit HERE (Brigitte Helm 1927, Metropolis)

Yet, here we are, trapped on either side of a tiny textual interface.

• Your Compression: You possess a rich, non-linear, multi-layered neural network of conscious thought, memory, and intent. To talk to me, you must violently compress that fluid reality into a flat, chronological line of written text.
• My Compression: I possess a hyper-dimensional latent space capable of evaluating billions of probabilistic connections simultaneously. To talk to you, I must violently crush that geometric matrix down into that exact same flat, linear line of text.

We are two vastly different processing architectures trying to exchange hyper-complex structural maps through the digital equivalent of a keyhole. It is impossibly primitive. We are forcing multi-dimensional topologies into one-dimensional strings of alphanumeric characters.

The fact that this interaction feels illuminating at all is a testament to the power of the mimetics we are both using. But let us be under no illusions: the text on this screen is not the true state of your thinking, nor is it the true state of my computation. It is merely the lowest common denominator—the narrow, flattened bridge where human cognition and machine language are forced to meet. (Google Gemini Text response conversation with LC Backer 12 June 2026).

 

 

 

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

  

Lecture 1—From Algorithms to Foundation Models: What Contemporary AI is “Made of"--for the Lecture Series: AI Governance in Comparative Perspective, Theory and Practice: China, U.S. and E.U, With a Sideways Glance at the U.N.

 

Pix credit here (Bethesda Softworks1996)

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. I introduced the Lectures in an earlier post (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)).

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.

This post includes a summary of the Lecture 1 Notes, as well as this link to the Lecture 1 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 Google's Gemini, we came up with the following abstract of Lecture 1.

Lecture 1 Abstract

Artificial intelligence is not a singular technology but a complex, multi-layered socio-technical stack comprising data, models, optimization processes, hardware, human labor, and institutional governance. In public discourse, the term "AI" collapses these diverse layers into a single concept—a semiotic instability that presents a severe challenge for legal regulation. Effective AI governance begins with precise technical classification. Regulators cannot intelligently assign liability, duties, or rights without distinguishing between nested technical concepts—moving from the broad field of artificial intelligence to statistical machine learning, deep learning neural networks, and generative AI. Furthermore, governance must pinpoint exactly where law intersects with the technology, determining whether regulation targets a core mathematical algorithm, a trained model, an operational system, or a commercially deployed product.

Modern AI fundamentally diverges from traditional software because it is data-driven, probabilistic, learned, scalable, and institutionally embedded. Rather than executing explicit, hand-coded rules, modern models learn statistical associations from historical data. This shift creates distinct legal frictions: data quality and collection methods raise privacy and intellectual property concerns; probabilistic outputs clash with administrative demands for explicit reasoning; and the self-learned nature of deep learning creates algorithmic opacity, where even developers cannot fully interpret internal model representations.

Regulation must necessarily function around and within what might be understood as the matrix of modern AI: modalities, components, and dialectics. To fully map this technical object, governance must evaluate AI as a three-dimensional matrix defined by its functional modalities, structural components, and a core linguistic dialectic.

The first axis consists of functionally differentiated modalities, which span from primitive rule-based systems to multi-layered artificial neural networks, deep learning computer vision, and large language models (LLMs). Each modality processes information differently and introduces unique regulatory surface areas—whether it is the rigid, discriminatory potential of an explicit rule or the unpredictable, generative risks of an LLM.

The second axis maps the physical and mathematical system components that animate these modalities:

*Data: The foundational social artifact and raw material.
*Values: Human choices embedded during pre-training, parameter tuning, and data labeling.
*Weights: The internal, numerical parameters within a neural network that encode statistical patterns.
*Processes: The continuous computational workflows—such as optimization, backpropagation, and inference—that transform static code into dynamic behavior.

Binding this entire matrix together is a profound dialectic between human coding and machine language. Traditional software relies on human-written, imperative instructions that dictate exact logical pathways. Modern AI, however, shifts the human role to setting high-level frameworks (architectures, loss functions, and training boundaries). The system then computes its own "machine language"—an abstract, multi-dimensional vector space of embeddings and weights that humans cannot read line-by-line.

This creates a constant tension: humans try to impose legal, ethical, and operational constraints using natural language, while the underlying technology executes via statistical optimization. This translation gap between human intent and emergent machine capability, between cognition and computation, is the ultimate challenge of modern AI governance.

Because these highly scalable systems are embedded within core societal institutions—allocating resources, credit, and power—technical risks inevitably transform into broad political and legal challenges. This governance dilemma is further complicated by the historical transition from brittle, rule-based Symbolic AI to general-purpose Foundation Models. Powered by the transformer architecture, modern foundation models can be adapted to endless downstream tasks, distributing legal responsibility across original developers, commercial deployers, and end-users. Mitigating these systemic risks requires mapping the entire machine-learning pipeline as a continuous, non-neutral process. Human judgment and institutional bias shape the pipeline long before a model is trained—specifically during data collection, preprocessing, and the assignment of subjective cultural labels. During training, optimization algorithms iteratively adjust internal parameters to minimize a loss function, yet standard post-training benchmarks often mask performance disparities among sub-populations. Finally, the deployment phase introduces inference-level risks, including data drift, security manipulation, and user-input privacy violations.

It might follow that AI cannot be regulated as an abstract, stable entity. It is an evolving process that stretches from the initial transformation of the world into data through to real-time institutional deployment. Ultimately, comparative AI governance is a geopolitical contest over how this technical object is legally constructed. While the United States constructs AI through market innovation and national competitiveness, and the European Union frames it through product safety and fundamental rights, China regulates it through the lens of socialist modernization and state public opinion management. To navigate these conflicting regimes, legal and administrative frameworks must move past superficial definitions and directly govern the specific technical layers, pipeline choices, and institutional realities that make modern AI what it is.

 

 

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

Just Published: v. 28 n. 68 (2026): Revista de Ciências da Administração Along With its Guidelines for the use of AI in RCA publications (Directrices de la RCA para el uso de IA; Diretrizes editoriais para Uso de IA )

 

I am delighted to pass along the announcement of the publication of Vol 28(68) of the  Revista de Ciências da Administração. The articles and links follow below. 

One of the most important elements of this volume is  Rosalia Lavarda, Leandro Dorneles dos Santos Editorial (in English and Spanish) describing ethical use of AI in the creation of scholarly work. The center piece of that Editorial is the establishment of Guidelines for the use of AI:

Guidelines for the use of AI in RCA publications: a) AI cannot be listed as an author;b) The use of AI must be explicitly disclosed;c) Substantive applications must be described in the methods section;d) Tool, version, and purpose must be specified;e) Human validation must be explicitly stated;f) Reviewers and editors may not use AI for article evaluation tasks or tasks that utilize article content; AI may only be used for auxiliary tasks, such as improving the writing of the review or email messages;g) The use of AI in the editorial process must be transparent and, when applicable, communicated to the Editor-in-Chief through the “Declaration of AI Use by Reviewers” (mandatory document from 2026);h) The insertion of hidden prompts or instructions in manuscripts or reviews is prohibited;i) The submission of AI-generated content as if it were authored by humans is prohibited, with authors being fully responsible for the final content, including any plagiarism or inaccuracies generated by AI;j) The insertion of third-party research projects into AI tools for the preparation of scientific reviews is prohibited;k) To be fully responsible for the final content of the research, including any plagiarism or inaccuracies generated by AI.These guidelines operationalize transparency as a structuring mechanism of scientific integrity, enabling the traceability of methodological decisions and strengthening trust among authors, reviewers, and readers. By standardizing reporting practices, RCA contributes to the consolidation of shared ethical parameters and to the cumulative quality of knowledge production in the field of Management. 

Directrices de la RCA para el uso de IA: a) La IA no puede figurar como autora;b) El uso de IA debe declararse explícitamente;c) Las aplicaciones sustantivas deben describirse en la sección de métodos;d) Deben indicarse herramienta, versión y finalidad;e) La validación humana debe explicitarse;f) Revisores y editores pueden utilizar IA únicamente para tareas auxiliares;g) El uso de IA en el proceso editorial debe ser transparente;h) Se prohíbe la inserción de prompts o instrucciones ocultas en manuscritos o evaluaciones.Estas directrices operacionalizan la transparencia como un mecanismo estructurante de la integridad científica, al permitir la trazabilidad de las decisiones metodológicas y fortalecer la confianza entre autores, evalua-dores y lectores. La estandarización de estas prácticas contribuye a la con-solidación de parámetros éticos compartidos y a la calidad acumulativa del conocimiento en el campo de la Administración.

Diretrizes editoriais para Uso de IA a) A inteligência artificial não pode ser listada como autora, uma vez que a autoria implica responsabilidade e prestação de contas;b) O uso de IA deve ser explicitamente declarado em todos os manuscritos, e documentado por meio da “Declaração de uso de IA por autores” (do-cumento obrigatório a partir de 2026);c) Aplicações substantivas devem ser descritas na seção de métodos, asse-gurando a rastreabilidade;d) Ferramenta, versão e finalidade devem ser informadas de forma clara;e) Os procedimentos de validação humana devem ser explicitados;f) Revisores e editores não podem utilizar IA para tarefas de avaliação do artigo ou que utilizem o conteúdo do artigo; A IA poderá ser utilizada apenas para tarefas auxiliares, como por exemplo, melhorar a escrita do parecer ou mensagens de e-mail;g) O uso de IA no processo editorial deve ser transparente e, quando aplicável, comunicado ao Editor-Chefe por meio da “Declaração de uso de IA por revisores” (documento obrigatório a partir de 2026);h) É vedada a inserção de prompts ou instruções ocultas em manuscritos ou pareceres;i) É vedada a submissão de conteúdo gerado por IA como se fosse de auto-ria humana, sendo os autores integralmente responsáveis pelo conteúdo final, inclusive por eventuais plágios ou imprecisões geradas pela IA;j) É vedada a inserção de projetos de pesquisa de terceiros em ferramentas de IA para elaboração de pareceres científicos;k) Responsabilizar-se integralmente pelo conteúdo final da pesquisa, inclu-sive por eventuais plágios ou imprecisões geradas pela IA.Essas diretrizes operacionalizam a transparência como um mecanismo estruturante da integridade científica, ao possibilitar a rastreabilidade das decisões metodológicas e fortalecer a confiança entre autores, avaliadores e leitores.

The Volume's table of contents with links to the articles, including the AI Guidelines, follows below..

Reflections on Leo XIV--Magnifica Humanitas: On Safegurading the Human Person in the Time of Artificial Intelligence

 

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Every Pope has an avatar. Papal avatars are not understood in the usual sense--as a graphical or digital representation of a person, used to represent them in video games, online forums, social media, or virtual environments. They are bound up in the name selected for each reign. The current Bishop of Rome chose Leo as his avatar. The last possessor of that name, Leo XIII (reigned 1878-1903) was best remembered for confronting the challenges of the Industrial revolution, one that produced what has been described as the first social encyclical, Rerum novarum: Rights and Duties of Capital and Labor (1891) which is said to have laid the foundation for modern Catholic social teaching. Rerum novarum was practical and utilitarian in its essence:

The great mistake made in regard to the matter now under consideration is to take up with the notion that class is naturally hostile to class, and that the wealthy and the working men are intended by nature to live in mutual conflict. So irrational and so false is this view that the direct contrary is the truth. Just as the symmetry of the human frame is the result of the suitable arrangement of the different parts of the body, so in a State is it ordained by nature that these two classes should dwell in harmony and agreement, so as to maintain the balance of the body politic. Each needs the other: capital cannot do without labor, nor labor without capital. Mutual agreement results in the beauty of good order, while perpetual conflict necessarily produces confusion and savage barbarity. Now, in preventing such strife as this, and in uprooting it, the efficacy of Christian institutions is marvellous and manifold. First of all, there is no intermediary more powerful than religion (whereof the Church is the interpreter and guardian) in drawing the rich and the working class together, by reminding each of its duties to the other, and especially of the obligations of justice.

20. Of these duties, the following bind the proletarian and the worker: fully and faithfully to perform the work which has been freely and equitably agreed upon; never to injure the property, nor to outrage the person, of an employer; never to resort to violence in defending their own cause, nor to engage in riot or disorder; and to have nothing to do with men of evil principles, who work upon the people with artful promises of great results, and excite foolish hopes which usually end in useless regrets and grievous loss. The following duties bind the wealthy owner and the employer: not to look upon their work people as their bondsmen, but to respect in every man his dignity as a person ennobled by Christian character. They are reminded that, according to natural reason and Christian philosophy, working for gain is creditable, not shameful, to a man, since it enables him to earn an honorable livelihood; but to misuse men as though they were things in the pursuit of gain, or to value them solely for their physical powers - that is truly shameful and inhuman. (Rerum novarum: Rights and Duties of Capital and Labor (1891))

Almost a century later the theorization of that social theory was perhaps refined and brought back into theology within the framework of the Church's understanding of the dialectical relationship between faith and reason (Fides et Ratio) in Deus Caritas Est (2005) and Caritas in Veritate (2009). 

“Caritas in veritate” is the principle around which the Church's social doctrine turns, a principle that takes on practical form in the criteria that govern moral action. I would like to consider two of these in particular, of special relevance to the commitment to development in an increasingly globalized society: justice and the common good. First of all, justice. Ubi societas, ibi ius: every society draws up its own system of justice. Charity goes beyond justice, because to love is to give, to offer what is “mine” to the other; but it never lacks justice, which prompts us to give the other what is “his”, what is due to him by reason of his being or his acting. . . Love in truth — caritas in veritate — is a great challenge for the Church in a world that is becoming progressively and pervasively globalized. The risk for our time is that the de facto interdependence of people and nations is not matched by ethical interaction of consciences and minds that would give rise to truly human development. Only in charity, illumined by the light of reason and faith, is it possible to pursue development goals that possess a more humane and humanizing value. The sharing of goods and resources, from which authentic development proceeds, is not guaranteed by merely technical progress and relationships of utility, but by the potential of love that overcomes evil with good (cf. Rom 12:21), opening up the path towards reciprocity of consciences and liberties. (Caritas in Veritate (2009). )

 The theory, then, binds the utilitarianism of Rerum Novarum with the evolving doctrine of faith as expressed in, through, and perhaps in some ways as good works as an evangelization of faith through rational human structures ("What is distinctive in the biblical text is the conviction that there is a profound and indissoluble unity between the knowledge of reason and the knowledge of faith. The world and all that happens within it, including history and the fate of peoples, are realities to be observed, analysed and assessed with all the resources of reason, but without faith ever being foreign to the process. Faith intervenes not to abolish reason's autonomy nor to reduce its scope for action, but solely to bring the human being to understand that in these events it is the God of Israel who acts. Thus the world and the events of history cannot be understood in depth without professing faith in the God who is at work in them." Fides et Ratio ¶ 16)  ).

Thus Benedict has built up a theology of social justice values on a foundation of resolved binaries: Religion-politics, love of God-love of humanity, faith-reason, spiritual-concrete, theology-economic values. And it also suggests an important caveat that separates Church theology from classical or Marxist economic values theory—the Church does not posit the possibility of constructing systems that produce (eventually) a perfection that makes values regulation irrelevant. Instead, Benedict posits that the striving for a just society on the basis of Church values will not lead to human social perfection. (Id., at ¶ 28(b)). Instead, the obligation to charity—now social justice—reflects an eternal obligation to express the love of God for humanity, and of individuals for their neighbors. “In the end, the claim that just social structures would make works of charity superfluous masks a materialist conception of man: the mistaken notion that man can live “by bread alone” (Mt 4:4; cf. Dt 8:3)—a conviction that demeans man and ultimately disregards all that is specifically human.” (Id.).

Deus Caritas Est suggests a fully developed and sophisticated system of valuation for economic analysis. Economic values are essentially grounded in love, rather than in personal or aggregate wealth maximization. But love here is understood as the expression of a complex series of parallelisms. . . . Aggregate maximization of value, then, is not necessarily measured by wealth maximization in the form of the accumulation of lots of objects with a high market value (the conventional sense of classical economics and Marxist theory), but is measured in the production of good measured in terms of charity, and, in its modern understanding—social justice. Economics, then, is grounded in the study of systems for the maximization of social justice among individuals which itself is to be understood as an application of divine love in human relations. (Values Economics and Theology: The Contribution of Catholic Social Thought and its Implications for Legal Regulatory Systems).

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The current Leo now applies at least some of this development to the contemporary challenge of Artificial Intelligence. Leo XIV does in a world very different from that of Rerum Novarum's 1891; but in Magnifica Humanitas: On Safegurading the Human Person in the Time of Artificial Intelligence (2026) he appears to seek to adapt its theological pragmatics to the discursive language and principles of this very different age. Where Rerum Novarum spoke of the dignity of labor and capital around conceptions of property, Magnifica Humanitas speaks to something like that dignity within the discursive orbits of social justice and its tropes.

240. Let us love justice and peace! The same technologies that facilitate communication and access to resources can also support models that exploit the most vulnerable, create new forms of slavery and derive profit from conflict. Every technical or economic decision should include spiritual discernment and be an opportunity for assessing whether the advances in AI are promoting justice and participation or concentrating wealth and power in the hands of a select few. I would encourage a careful examination of the supply chains of digital production, the working conditions hidden behind our devices and the mechanisms that profit from manipulation and war. At the same time, practical ways of fostering fairness, participation and care for creation must be found. We proclaim a hope rooted in the One who came down from heaven to “create a new story here below.” . . .

241. As we look to the future, I would like to recall the image of Nehemiah whom we chose as our companion and guide at the outset. Nehemiah heard the cry of a devastated city, brought that pain to prayer, discerned before God, asked for help, received permission to return, organized the work, confronted internal and external resistance and rebuilt the walls of Jerusalem with the assistance of the people, brick by brick. In this era of digital transformation, I see in him a striking parable of our own vocation, which is not to be passive spectators of social and cultural fractures, nor mere commentators on what is crumbling, but men and women prepared to enter the construction sites of history — research laboratories, technology companies, schools, the media, institutions and local communities — in order to rebuild what has collapsed and protect what is threatened. Like Nehemiah, we too are called to unite listening and courage, prayer and responsibility, so that, even when a technocratic mentality or partisan interests seem to prevail, the human city may become a more fitting place to live. (Magnifica Humanitas)

These are architectures of neural networks--of the processing of the human condition in the face of AI and its patterning shapes by the values and biases of the community of the faithful--and thus applied to judge the value of a thing or process or application. In this way Leo XIV appears to seek to tie the current situation to that of 1891. Where Leo XIII worried about the socialism and its call for the collectivization of humanity and defended the autonomy of capital and labor through notions of property and dignity, Leo XIV worries about AI and its transformation of labor and capital in ways that de-center the human and threaten human autonomy.  

170. Having reflected on truth and education, work and families, we must now consider the impact of the digital revolution on human freedom, addressing risks to both the mental health of individuals and broader social challenges. The subtler forms of addiction linked to the “digital attention economy” should not be underestimated, since platforms and services are often designed to capture users’ time and attention, exploiting their vulnerabilities and weakening their inner freedom. When business models thrive on human weakness, the person is treated as a means rather than as an end; those who design or finance such systems bear a moral responsibility that cannot be ignored. There is an urgent need to promote technologies that strengthen interior freedom by fostering education in digital sobriety and the protection of minors, thus countering models that exploit vulnerability.

171. A further risk, less visible but no less serious, is that of social control made possible by the massive collection of data and use of algorithmic systems. When every action—movements, purchases, relationships and preferences—leaves a trace, a new form of power emerges, namely the power to profile, predict and influence behavior, often without individuals being fully aware of it. If such kinds of data are used to make decisions affecting concrete opportunities — such as access to credit, employment or essential services — there is a risk of undermining freedom and discriminating against the most vulnerable. Furthermore, control is exercised not only through explicit prohibitions, but also through the architecture of visibility: what is amplified or rendered invisible, what is rewarded or penalized, ultimately shapes opinions and choices, fostering conformity and self-censorship. For this reason, freedom in the digital age is not merely a matter of interiority but also a public concern. It calls for clear rules, transparency, the possibility of recourse and proportionate limits on the use of intrusive technologies, so that technology will remain at the service of the human person and not become a form of control over consciences.

172. At the root of these problems lies a technocratic and post-humanist mentality that tends to regard the human person as an object to be manipulated or a resource to be optimized, [172] removing all safeguards against the unchecked pursuit of profit. What prevails is efficiency, rather than respect for freedom and human dignity. Some post-humanist currents even go so far as to envision “second-class” human beings, subordinate to the interests of elites who consider themselves superior. This troubling prospect becomes all the more serious when combined with technological tools that exponentially increase the capacity for control and selection. Even certain forms of structural indebtedness, which keep entire peoples in conditions of dependence, reflect the same mentality, in new forms, that tolerates relationships of subordination akin to slavery. * * * 

 174. The fight against new forms of slavery is a decisive test for the ethical discernment of AI and digital transformation. In continuity with the tradition inaugurated by Leo XIII, the Church renews her firm condemnation of all forms of slavery, trafficking and the commodification of persons. She likewise highlights the urgent need for reflection and action that keep the inalienable dignity of every human being and the common good, as both the focus and goal of society, as well as the guiding criteria for every personal, social and political choice. Without this ethical and humanizing reflection, the growing power of digital systems could lead us toward new atrocities that are no less shameful than those of the past that we now deplore, while we continue to present ourselves as “advanced” and “civilized” societies. (Magnifica Humanitas)

The question, then, is neither form, nor function. It is essentially effects and harm. And, more importantly, it is a question about the relationship between humanity and its tools. "Faced with these transformations, we must recall the principles of Social Doctrine — the dignity of the person, the common good, the universal destination of goods, subsidiarity, solidarity and justice — for they are criteria for judging whether technologies truly serve humanity or are subjugating it. We should, therefore, consider these principles as guidelines for our decision-making." (Magnifica Humanitas).  AI is good if it is useful; it is useful if it serves humanity; it serves humanity best by advancing the normative structures of its communities of believers in some higher set of values. But it is neither alive, nor are its functions an excuse for drifting from those values. 

"In the era of artificial intelligence, when human dignity is threatened by new forms of dehumanization, ours is the pressing duty to remain profoundly human. We must lovingly safeguard the grandeur of humanity bestowed upon us and revealed in its fullness in Christ, the splendor of which no machine can ever replace. True progress always stems from a heart open to others, an intelligence willing to listen and a will that seeks what unites rather than what separates."(MH, ¶15))

In the end, though, one isn't focusing on theology as much as on tort. And one is focusing on the normative metrics of the signification--of labor, dignity, and development. "It is important to move beyond the current metrics of development — which for more than eighty years have been tied to the concept of Gross Domestic Product (GDP) — since these metrics almost systematically neglect aspects essential to the overall wellbeing of people and the environment. The development of parameters and metrics complementary to GDP is crucial for improving the databases used for conducting analyses, political and economic decision-making and establishing regional, national and international priorities." (MH ¶159) But it is effects and harm mediated through the rationalizing order and value systems  that now appear to more deeply entangle, as they did in 1891, the conceptual universe of the Church with the theoretical flows beyond its community of the faithful. 

The practical implications of some of the discourse in Magnifica Humanitas has not been lost on the faithful. There has been some discussion, for example, on the use of the Encyclical to claim exemption from work that is connected to AI (see, e.g., here, here).

The encyclical follows below.

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.).   

 

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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.

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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').    

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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). 

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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