What happens to social order when AI removes ritual constraints?

Classical social theory from Goffman, Giddens, and others explains why AI disrupts the conditions for trust and shared meaning.

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V.a Tokenization of Intelligence (Adrian's capstone thesis)

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Does AI actually commodify expertise or tokenize it?

The standard framing treats AI output like mass-produced commodities, but does AI's contextual, mutable nature fit better with token economics than commodity theory?

Where does the value of AI output actually come from?

If AI-generated intelligence has no intrinsic content-value like physical goods do, what determines whether it's valuable to someone? This explores whether value lives in the token or the receiver.

Is the LLM a tool or a new form of intelligence itself?

Does framing AI as merely delivering pre-existing intelligence miss what's actually happening? This explores whether the model itself constitutes a fundamentally new intelligence-medium with distinct cultural effects.

Can exchange value exist entirely without use value?

Does AI-generated knowledge represent a genuinely new category of goods where exchange-value (market price, social credibility) operates independently of use-value (actual accuracy, practical utility)? This matters because it suggests AI disrupts markets in ways Marx's commodity analysis did not predict.

Why does AI output change with every prompt and context?

Explores whether the variability of AI-generated intelligence across contexts and audiences is a fundamental feature or a flaw to be fixed. Examines what this mutability means for how we should evaluate and understand AI systems.

Is AI fundamentally changing how value gets produced?

Rather than automating commodity production, does AI represent a shift from making identical stockpiled objects to generating contextual tokens on demand? And what makes this genuinely new?

Does Marxist alienation theory explain what AI does to cognitive work?

Marxist alienation frames AI as degrading authentic labor. But does that framework actually describe the shift happening with tokenization, or does it misdiagnose the transformation occurring in intelligence itself?

Does AI abundance actually devalue knowledge itself?

If AI generates vastly more claims than humans can evaluate, does the sheer volume undermine the social processes that normally establish what counts as reliable knowledge? And what would that erosion look like?

Can AI generate knowledge faster than humans can evaluate it?

Explores whether AI-driven content production is outpacing human judgment capacity, mirroring monetary hyperinflation dynamics. Why this matters: understanding this gap reveals whether our evaluation infrastructure can sustain epistemic confidence.

Why do search tools fail against AI generated content?

Internet search worked for finding needles in haystacks of fixed documents. But AI generates new content on demand with no underlying corpus to search. Does this require fundamentally different solutions?

Why can't search tools handle AI-generated content?

Search infrastructure was built for stable, pre-existing items. AI generates ephemeral content on-demand. Can the indexing tools that solved information overload work when there's nothing stable to index?

What actually backs the value of AI-generated intelligence?

If AI produces intelligence tokens at near-zero cost, what constrains their value and prevents inflation? Exploring whether training data, expert validation, or statistical probability can serve as a genuine backing mechanism.

When do users stop checking whether AI output is actually backed?

What causes users to accept AI-generated content at face value without verifying its basis? Understanding this receiver-side acceptance reveals how intelligence-token systems maintain value despite lacking real backing.

XI.d Argumentation Theory and the Periodic Table (Wagemans)

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Can three axes organize all possible argument schemes?

Can a small set of orthogonal distinctions—subject vs. predicate, order level, and proposition types—capture the full space of valid argument structures? This matters because it could replace ad-hoc scheme lists with a systematic framework.

Can argument schemes be organized by formal principles instead of lists?

Argumentation theory has accumulated 60+ overlapping scheme lists with no principled boundaries. Can a structured classification based on formal ordering principles replace this ad-hoc approach and provide a coherent target space for analysis?

Do first-order and second-order arguments unify classical and modern divisions?

Does the formal distinction between first-order and second-order arguments map onto both the classical internal-external topoi divide and the modern reasonable-fallacious distinction? If so, it would reveal a single structural axis underlying two separate critical traditions.

XI.e Toward a Science of Deep Learning (Learning Mechanics)

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Can deep learning theory unify around training dynamics?

Is learning mechanics—focused on average-case predictions and training dynamics rather than worst-case bounds—the emerging framework that finally unifies fragmented deep learning theory?

Can humans understand deep learning before AI does?

Explores whether investing in human-parseable deep learning theory remains valuable even if AI systems eventually develop their own self-understanding. Centers on why this matters for safety oversight.

Are text-only language models fundamentally limited by abstraction?

Explores whether text's compression of physics, geometry, and causality into symbols creates an irreducible ceiling for language-only AI, and whether multimodal approaches can overcome this structural constraint.

Why can't cosine space retrievers distinguish word order?

Dense retrievers using unit-sphere cosine spaces struggle to capture non-commutative linguistic structures like negation and role reversal. Understanding this geometric constraint explains why training fixes have limited reach in compositional retrieval.

Why do Shannon and Kolmogorov measures fail to value data?

Shannon information and Kolmogorov complexity assume unlimited computational capacity. But do these classical measures actually capture what bounded learners can extract from real data?

What can a bounded observer actually learn from data?

Classical information measures treat all high-entropy content equally, but computationally bounded learners can only extract certain types of structure. What distinguishes learnable regularity from random noise that bounded agents face?

XI.f Agentic-Age Security and Structural Convergence

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What security threats emerge when machines read the web?

The web's trust infrastructure evolved for human readers—visual cues, domain reputation, rendering semantics. As AI agents become primary readers, what new attack surfaces and manipulation strategies does this architectural mismatch create?

Do efficiency techniques across agent components reveal shared structural constraints?

Despite targeting different parts of agentic systems, efficiency techniques converge on similar principles. This raises a question: are these convergences independent discoveries, or do they reflect deeper architectural constraints that all agent systems face?

Pass 3 Additions (2026-05-03)

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Can language models simulate belief change in people?

Current LLM social simulators treat behavior as input-output mappings without modeling internal belief formation or revision. Can they be redesigned to actually track how people think and change their minds?

What should a world model actually be designed to do?

Current AI research treats world models as either video predictors or RL dynamics learners, but what if their real purpose is simulating actionable possibilities for decision-making rather than predicting next observations?

Does autoregressive generation uniquely enable LLM scaling?

Is the autoregressive factorization truly necessary for LLM scalability, or do other generative principles like diffusion achieve comparable performance? This matters because it shapes which architectural paths deserve investment.