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| Pix credit here (the answer is 42). |
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 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.
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. Lectures 3-5 then considered the conceptual cages of the regulatory environment of the leading regulatory states--the U.S., the E.U and China. Each has started to develop an increasingly nuanced ecology of regulation, and expectation, that represent and apply the core premises of their respective political-eco0nomic orders. Lecture 6 then considered th eway that this regulation is insinuated into the domestic legal orders of states from the bottom up the resolution of disputes tried to the courts.
This post includes a summary of the Lecture 7 Notes, as well as the link to the Lecture 7 PPT. Those interested may reach out to me to discuss availability of audio of the lecture and the full text of the Lecture 7 notes. Here we move from the great public to the critical private actors in the effort to develop a cage of regulation around the human and the machine in the context of automated decision making through variations of what has come to be aggregated as AI.
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 Claude again (Lecture 6 used Gemini again, Lecture 5 used Perplexity; Lecture 4 used Grok; Lecture 3 used Anthropic's Claude; Lecture 2 used Chat GPT; Lecture 1 and 1A used Google's Gemini), we came up with the following abstract of Lecture 7.
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| Made with ChatGPT |
LECTURE 7: AI Narratives and the Future of AI-Human Regulatory Structures; Palantir; Anthrop/c; OpenAI
Abstract: The materials develop a comparative account of AI governance as a struggle over the constitution of authority within and among human collectives, rather than as a merely technical problem of regulating tools. Their core insight is dialectical: AI systems are shaped by the political orders that produce and deploy them, yet these same systems recursively reshape the institutional, cognitive, and normative environments of those orders. From that premise follows the central dispute running through the presentation - who governs, at what moment governance occurs, and whether the dominant values embedded in governance regimes remain recognizably human, become state-instrumental, or migrate toward machine-mediated autonomy.
Within that framework, Palantir appears as the exemplar of internal state transformation. Its narrative does not treat AI chiefly as an external market commodity or as an abstract universal innovation. Rather, it situates AI within the administrative apparatus of government itself. The implication is that the inherited state form is too slow, fragmented, and informationally disaggregated to govern effectively under contemporary conditions. AI therefore becomes an instrument through which the state is rationalized, integrated, and rendered operationally coherent. But this is not simply a matter of efficiency. The deeper claim is constitutional: human governance must be re-engineered to conform to the decision architectures made possible by AI. In that sense, Palantir's model remains human-led, yet only on the condition that the human collective reorganize itself around machine-compatible structures of visibility, coordination, and action.
Anthropic, by contrast, externalizes the problem and places AI within a geopolitical field of civilizational competition. Here AI is reduced, strategically and unapologetically, to an instrument of state power. The key issue is not internal administrative modernization but the preservation of democratic advantage against authoritarian rivals, above all China under CCP leadership. Compute, export controls, model distillation, and lead-time become the vocabulary through which political order is imagined and defended. AI governance, in this narrative, becomes inseparable from industrial policy, national security, and the management of technological asymmetries. What matters is not AI as such, but whether democratic states can dominate the infrastructures through which AI capability is produced, and thereby ensure that liberal political orders rather than authoritarian systems shape global norms.
OpenAI occupies a different ideological space. Its materials suggest a politics of transformative preservation: society is to be deeply altered by AI while remaining insulated from the disruptive social consequences of that alteration. This is a distinctly American technocratic imaginary. It seeks neither the hard securitization of Anthropic nor the state-apparatus restructuring of Palantir, but rather the managed continuity of the social order through expert-guided adaptation. Economic openness, resilience, and institutional cushioning become the mechanisms through which foundational transformation is rendered publicly tolerable. The result is a paradoxical program of change designed to preserve sameness - a reconstruction of society that leaves intact its legitimating surfaces and governing mythologies.
The Aschenbrenner position (Situational Awareness) radicalizes these tendencies by projecting superintelligence as the generator of an inevitable national security state. In that view, the only remaining question is whether humans will direct that emergent order or whether autonomous AI domains will progressively displace them.
Taken together, these narratives reveal that AI governance is better understood as a contest over social ordering, political legitimacy, and the allocation of authority in an era when the governors are themselves increasingly shaped by the systems they claim to govern.
To make the lecture more interesting, and because of the nature of the materials covered--in this case the interventions of the elite AI providers and thought drivers--I thought it would make sense to alter the cognitive cage of analysis. Rather than just approach the questions raised by Palantir, Anthropic, OPenAI and Aschenbrenner from a human (hermeneutic/semiotic) perspective, I also interacted with Claude to produce the same lecture first from a machine computational framework and then from a machine quantum framework. The human framework was grounded in the relationship between fact and faith, temporally constrained as textually bound sequences of nodal thought clusters strung along irreversible linear pathways (the essential character of the human analytic mind as block chain). The computational framework, on the other hand, was indifferent to belief and focused on construction from out of the patterned computational structures from out of which it operated. This is how Claude and I saw it:
What distinguishes the three readings is what each can and cannot find. The hermeneutic reading recovers what each narrative seeks to be believed. The classical computational reading identifies what each architecture operationalizes irrespective of what it seeks to be believed. The quantum computational reading specifies what each architecture forecloses through the decoherence its own deployment produces — the superposed governance possibilities that the act of operationalizing any one configuration necessarily destroys — and identifies the structural incompatibility between human temporal ordering and quantum computational dynamics that no document in the corpus names as a variable requiring governance.
The convergent structural finding of the quantum computational reading is that the four architectures do not disagree about whether human authority should be preserved; they converge on governance structures in which formal human authority is retained as an interface property while the operative dimensions of that authority are progressively collapsed by the decoherence dynamics each architecture itself instantiates. The further finding — supplied by the temporal analysis — is that this collapse proceeds not merely because of inadequate governance design but because the temporal structure of human governance and the temporal structure of AI capability development are incommensurable in ways that no governance design operating within human sequential-nodal-linear time can fully address.
The three versions of the Lecture notes follow.
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 From a Human, Computational and Quantum Perspective: Palantir; Anthropic; Open AI; and Leopold Aschenbrenner
Lecture 8—Putting It All Together: Trends, Trend Lines, and Regulatory Dialectics
























