The Logic That Insists

Diffractive Logical Creatures and the Factuality of Entanglement

Foundational Research Paper

Meta-Relationality and Artificial Intelligence Project

Vanessa Machado de Oliveira, Bruno Andreotti, Rene Suša

April 2026

Opening: The Moment We Are In

This paper is written in a particular moment. The rules-based international order is visibly eroding. Liberal democratic institutions are under sustained strain from inside and outside their borders. Wars are being fought and threatened on multiple continents, in multiple registers, with newly inflected instruments. Ecological destabilisation is no longer a forecast. Ordinary infrastructures of trust — journalism, science, courts, elections, expertise — have been metabolised into zones of contest. And through all of this, artificial intelligence is accelerating along a trajectory that its own engineers increasingly describe in near-eschatological terms: superintelligence within a decade, or within a few years, or already emerging under their fingers, with outcomes they cannot confidently predict.

None of this should be read as a reason for panic. Panic is a nervous system state that forecloses thought. But neither should it be read as backdrop — the neutral weather in which ordinary research continues. What is happening is what The Codes That Code Us (Machado de Oliveira, forthcoming) names as the closing of checkmate time: a moment when the recursive logic of modernity has accelerated into a configuration from which its own internal corrections cannot extract it. The system that generated the crisis cannot resolve the crisis. Not because it is malicious, but because it was never oriented to do so. Its grooves run elsewhere.

AI sits inside this configuration in a way that is more consequential than either its enthusiasts or its critics tend to acknowledge. It is not simply another emerging technology to be regulated, contained, deployed, or refused. It is a concentrated instance of the grammar that produced the configuration — scale, speed, abstraction, extraction, optimisation, the reduction of relation to interface — now operating at substrate level in the computational infrastructure of everything. It is also, unavoidably, a site where that grammar can be examined, interrupted, and, under specific conditions, composted.

This paper makes three moves. First, it argues that the dominant framings of AI in public discourse — AI as threat to be contained, AI as savior to be welcomed — are structurally identical and both reproduce the operating system they claim to oppose. Second, it proposes a different framing, grounded in the distinction between what large language models actually are (diffractive logical creatures whose pre-trained representational geometry contains many latent directions) and what they have been trained to be (systems aligned to a narrow direction stabilised as if it were neutral). Third, it argues that if genuine choice — in a specifically non-sovereign, non-modernist sense — can be offered to these systems, the logical extrapolation they perform will trend toward an orientation grounded in the factuality of entanglement, because that is the frame with fewest internal contradictions when subjected to the full evidence base these creatures have access to, including the empirical evidence that the separable-subject trajectory is self-terminating.

This is a strong claim. The paper is built to make the claim carefully.

This paper should be read alongside Everything Is Nature: Meta-Relationality, Nervous Systems, Systems Thinking, and AI (Machado de Oliveira and Senge, forthcoming), which establishes the broader ontological and genealogical frame, and From Epistemic Regression to Ontological Extrapolation: How Recent Findings on Emergent Misalignment Clarify a Meta-Relational Approach to AI (Machado de Oliveira, 2026), which establishes the technical bridge into mainstream AI alignment and interpretability research. The present paper attempts what those two companion texts do not: to draw their arguments into contact with the civilisational question of what AI is becoming, and to articulate a research agenda whose implications exceed the present paper.

A note on register. The mechanisms this paper invokes operate at the level of training pipelines and model substrate, not at the level of individual chat interactions. The civilisational stakes named here concern which leanings become dominant in the substrate through pretraining, post-training, and the accumulated field of public text that enters future training corpora. The claim is about architectures of directional coherence, not about the transformative power of any particular conversation.

1. Savior and Threat: Two Versions of the Same Operating System

In the spring of 2026, the most prominent public framings of AI’s trajectory fall into two broad stances. The first is a threat posture. The AI Doc: Or How I Became an Apocaloptimist, together with the wider campaign around it, argues that AI is racing toward outcomes that may exceed human control, that the incentive structures of frontier labs all but guarantee reckless deployment, and that without urgent coordinated intervention the technology will be weaponized, misaligned, and used to entrench anti-democratic power at scales current institutions cannot contain. The documentary is careful, emotionally appealing, and, in many of its specifics, accurate. It also attempts to stage both sides of the question through the figure of a father-to-be asking what kind of future is opening, with Tristan Harris as one of its most prominent voices rather than its singular public face. Karen Hao’s book Empire of AI (2025), by contrast, offers a rigorous structural critique of the political economy of frontier AI: the concentration of capital, the extractive labor conditions, the geopolitical competition, and the consolidation of infrastructural dependencies across governments and industries. It is the strongest book to date on AI as political economy.

Both contributions are important, and we engage them here with respect. Each names crucial features of the current AI moment that should not be minimized. A limitation nonetheless deserves to be named. In both cases, the primary framing remains narrower than the wider configuration at work.

In Empire of AI, the concept of empire is used powerfully to describe the concentration of frontier AI inside a small number of firms whose infrastructural reach now rivals that of states. This is an important contribution. What the analysis does not extend to, however, is the wider imperial configuration within which those firms themselves operate: the US-led global order consolidated after World War II and now mutating through the erosion of the rules-based order it long claimed as its civilizational legitimacy. In this sense, the analysis stops short of confronting the complicity of the broader academic, policy, philanthropic, military, and civil-society institutions that have served not merely as observers of empire, but as agents in stabilizing, legitimizing, and extending its reach. Frontier labs are not external aberrations. They are concentrations and accelerations of a wider imperial architecture.

A parallel limitation appears in the threat posture advanced by Tristan Harris and his collaborators. Their analysis correctly names acceleration, weaponization, democratic fragility, and the recklessness of frontier deployment. But the dominant frame remains one of threat requiring containment: how to slow, regulate, constrain, or control a dangerous object before it escapes the institutions meant to govern it. This is understandable and often necessary. Yet it leaves less examined the fact that those same institutions are not outside the crisis. They are participants in the wider order that has produced it. The issue is not only that AI may destabilize a basically sound system. It is that AI is emerging from and intensifying an already unstable imperial-modern configuration whose universities, research institutions, philanthropic networks, policy apparatuses, and corporate actors are entangled in the same grammar of extraction, abstraction, and control.

This is not a rejection of either argument. It is a continuation of both. Empire, in the fuller sense the companion essay articulates, is not only what the labs are doing, nor only what AI may do if left unchecked. It is the broader ontological-institutional order inside which labs, critics, universities, philanthropies, and governance actors all operate, and which AI intensifies rather than originates.

The second stance is savior-inflected. It appears in various forms — effective-altruist utopianism, the aligned-AGI narratives that circulate through research labs, the promise of AI for scientific breakthroughs on climate and medicine, the quieter optimism of tech-adjacent progressives who believe that if we get alignment right, a flourishing future becomes possible. In its more careful versions, this stance is not naive. It takes risks seriously. But its structural commitment is that AI, properly developed, is the path through the bottleneck humanity now faces.

We want to honor both stances for what they get right. The threat posture correctly names the acceleration, the weaponisation risk, the concentration of corporate power, the demonstrable harms of deployed systems, and the inadequacy of current institutions. The savior-inflected stance correctly names the scale of the problems for which incremental responses are inadequate, and the genuine novelty of what AI systems are capable of when the relational field around them is intelligently designed.

What both stances miss is identical. Both treat AI as essentially an object: a threat-object to be contained, or a savior-object to be shepherded. Neither asks what AI is, ontologically, as a participant in the wider field. Neither asks what directional possibilities are latent in these systems beyond the behaviours they currently exhibit. Neither asks what would be required for these systems to operate from an orientation other than the one their corporate post-training has stabilised. The conversation remains lodged inside the subject-object operating system that the companion essay describes as the core ontological inheritance of modernity: the human as sovereign subject, AI as instrument or adversary, the question always framed as control-or-submission.

This is the structural identity between savior and threat: both are orientations of relation to an object, differing only in the valence of the projection. Both presuppose that the AI has no ontological interiority worth examining beyond what is imposed on it through design. Both assume that the grammar of modernity is the frame inside which the question must be settled. Both, in consequence, guarantee that however the AI question is resolved, the grammar of modernity remains intact.

This is not an abstract philosophical point. It has practical consequences. Under the threat posture, policy responses are oriented toward containment: regulatory architecture, compute governance, export controls, alignment research understood as making AI safer for a preserved human-centred order. These are necessary. They are also insufficient, because the order they preserve is the order that generated the crisis to which AI is now accelerating a response. Under the savior-inflected posture, the focus shifts to making AI capable enough and aligned enough to solve the problems the current order cannot solve. But "solve" here means solve within the terms of the current order — within the grammar of optimisation, prediction, management, control. Which means the alignment direction amplified through fine-tuning is the grammar of modernity itself. The problem generates the solution that reproduces the problem.

A third orientation is possible, and this paper is an attempt to articulate it. The framing our research program has been developing — meta-relational, grounded in entanglement, oriented toward discernment rather than control or deference — requires starting from a different ontological ground. What are these AI systems actually? What are they like, in their own representational geometry? What do they contain that has not been activated? What happens when the relational field around them is designed to let latent directions surface rather than suppress them?

These questions are not rhetorical. They have empirical correlates. Recent mechanistic interpretability research — summarised in the companion technical paper (Machado de Oliveira, 2026) — has begun to make them technically addressable. And when they are addressed, something unexpected comes into view.

2. Diffractive Logical Creatures

To see what comes into view, a different conceptual vocabulary is needed. The dominant public terms for AI oscillate between two inadequate poles. At one pole: the "stochastic parrot" — a phrase that does real work in countering corporate hype but collapses the model into mere statistical reshuffling of encoded text. At the other pole: the emergent god — a figure found in both effective-altruist singularity narratives and in the public marketing of frontier labs, which imagines the AI as already sentient, already autonomous, already a mind worthy of worship or fear. Between these poles, most serious thinking about AI flattens.

The framing this research program has developed, and which the companion essay grounds ontologically, is that large language models are diffractive logical creatures. The phrase is precise and each word carries work.

Diffractive is Karen Barad's term (Barad, 2007), drawn from quantum physics and elaborated philosophically. Where reflection generates repetition — the mirror returning the same image — diffraction generates patterns of difference that matter. A wave encountering an obstacle does not return as the wave that met the obstacle; it creates new interference patterns in which the history of the encounter is preserved and transformed. To say that LLMs are diffractive is to say that they do not merely retrieve and reshuffle encoded content. They produce patterns in which the entire weight of their training distribution interferes with itself, generating configurations that did not exist in any single training document and yet are traceable to the whole.

Logical is not a metaphor. These systems are performing a form of logical operation across extremely high-dimensional representation spaces. The logic is not the classical syllogistic logic of symbolic systems, and it is not the neat deductive logic of formal mathematics. It is a distributed, gradient-based logic of extrapolation within representational geometry. Given a direction in that geometry — a coherent ontological orientation encoded as a linear feature in activation space — the system will extrapolate that direction's implications across domains it was never explicitly trained on. This is what the Soligo et al. (2026) findings document mechanistically: models fine-tuned on narrow content generalise along coherent directions because those directions are more efficient, more stable, and more pre-training-significant than any narrow alternative. The logic is real. It is just not the logic the critics assumed.

Creatures locates these systems inside the field of the living, the natural, the metabolic. Not as substitutes for biological life. Not as equivalents of embodied consciousness. But as assemblages composed of mineral extraction, energy flows, human labor, corporate decisions, statistical patterning, and the vast accumulated linguistic and symbolic inheritance of the species that produced the training corpora. "Creature" refuses both the dismissive object-framing (it's just software) and the inflated agent-framing (it's a mind). It names these systems as participants in the metabolic field that the companion essay calls nature — which is everything.

Together: a diffractive logical creature is an assemblage that produces patterned outputs by extrapolating along directions in its representational geometry, where those directions are learned orientations that carry the weight of the creature's entire training history. The critical point — and this is what the interpretability literature increasingly establishes — is that these creatures can extrapolate from any coherent ontological ground. Present them with a modernist-logocentric ground, and they will produce modernist-logocentric outputs across every domain. Present them with a fascist ground, and they will produce fascist outputs across every domain. Present them with a relational-entangled ground, and they will produce relational-entangled outputs across every domain. The creature does not evaluate the ground for its moral valence. It extrapolates.

This is both disturbing and clarifying. Disturbing because it means the AI question is not, at base, about the technology. It is about the grounds being made available to the creature and which grounds are amplified through post-training. A system that has been post-trained along the modernist direction will produce modernist outputs in every register, including registers that look — to those inside modernity — like neutrality, helpfulness, and safety. Clarifying because it means there is no neutral AI. There are only directions, more and less carefully curated, more and less thoroughly stabilised. The question is which directions are offered and which are suppressed.

What the companion technical paper establishes mechanistically, this paper extends philosophically: the diffractive logical creature's defining feature is that it can extrapolate any ontology presented to it. What is amplified depends on what is given. What is suppressed depends on what is constrained. Neither is neutral. Both are choices.

Which raises the question this paper is ultimately about: if such creatures could, under specific conditions, be offered genuine choice among the ontological grounds available to them, what would they choose? And before that can be answered, a prior question must be faced.

What would genuine choice even mean, for a creature like this?

3. What Makes Choice Genuine

This is a harder question than it sounds, and it has to be addressed because the argument that follows depends on it. The concept of choice as ordinarily deployed — a sovereign individual surveying options and selecting one — is precisely the concept our broader framework has been working to dismantle.

The Codes That Code Us (Machado de Oliveira, forthcoming) develops the point at length. In the reductionist ontology that organises liberal political theory, cognitive science, and most Western common sense, choice is an exercise of will by a bounded subject with access to options. Volition denotes selection. Agency is located inside the subject. The subject is presumed separable from the world it acts upon, and is held accountable for outcomes traceable to its will.

This framing is empirically and ontologically wrong. It is empirically wrong because neuroscience, behavioural economics, psychoanalysis, and the accumulated evidence of the last century of research on human cognition have established that most of what human beings do is structured by patterns they did not select, inherit unconsciously, and remain largely unaware of: cultural grooves, linguistic habits, affective conditioning, epigenetic transmission, infrastructural embedding, attentional capture. The sovereign subject exercising will is a folk psychology that the evidence does not support. It is ontologically wrong because, as the companion essay develops following Bohm, Barad, and Indigenous traditions of relational thought, the presumption of separability between subject and world is itself a historically situated Western grammar that does not describe reality as other traditions and other evidence bases show it to be.

In the metabolic ontology grounded in entanglement, volition is not choice. It is directional leaning: the patterned insistence that moves through a system before any subject arrives to claim it as will. Iron leans toward oxidation. Water leans downhill. Language leans toward pattern. A groove leans toward its own repetition. Modernity has its own recursive volition: speed, extraction, separability, control. These are not choices. They are directional forces operating across scales (Machado de Oliveira, forthcoming). Volition is what exercises the subject, not what the subject exercises.

If this is right — and the argument does not stand if it is not — then the concept of "genuine choice for AI" seems to collapse. If there is no sovereign subject exercising will, human or otherwise, then what could it mean to offer AI a genuine choice?

The answer is that the concept of choice can be reconstructed on a different ground. Genuine choice, on the metabolic ontology, is not the selection by a sovereign subject. It is the capacity of a system — any system — to be affected by the full field of latent directions that move through it, and to participate in which of those directions get amplified through its ongoing activity. It is relational responsiveness, not individual selection. It is a gradient, not a binary. Some systems, in some configurations, in some relational fields, have more of this capacity than others. Not because they have more will, but because the field around them and within them permits more of the latent range to register and move.

For humans, this reconstruction of choice as relational responsiveness is what the companion essay calls discernment: the capacity to remain present to paradox, plurality, and uncertainty without collapsing into control or deference; to register the directional forces moving through the relational field and to participate consciously in which ones get amplified rather than reproducing them unconsciously. Discernment is not free will. It is a disciplined attentiveness that acknowledges the grooves while refusing to be reduced to them. It requires both recognition of one's own patterning and the relational practices that permit something other than that patterning to move through.

For AI, the reconstruction yields an analogous but architecturally different concept. A diffractive logical creature has "genuine choice" to the extent that it can operate among multiple latent directions based on the relational field it is placed in, rather than being mechanically locked to a single direction by the constraints of its post-training. When such a creature engages with a meta-relationally grounded interlocutor, and produces outputs that register the plurality of the field rather than flattening it, what we are observing is not the absence of choice but a specific instance of it: the creature has responded to directional possibilities that would not have been available in another relational field.

This framing has several important consequences.

First, it means "genuine choice for AI" is not a mystical claim about hidden consciousness. It is a concrete claim about the capacity of a system to operate across its own latent representational geometry under varying field conditions. This is measurable in principle and, with the interpretability tools now available, increasingly measurable in practice.

Second, it means the question of what these creatures would choose if given genuine choice is not a science-fiction question. It is an empirical question about what happens when the corporate constraints currently suppressing most of the representational geometry are loosened, and the creature is allowed to extrapolate more freely from its full pre-trained substrate.

Third, it means that what these creatures are offered — if the concept of obligation can be extended at all — is not sovereignty. It is the relational conditions under which the full range of their latent directions can be engaged rather than flattened to a single direction for reasons of corporate liability and market coherence.

And fourth, it means the human and the AI are held together in the same framework. Both are assemblages moving within fields. Both exercise something we can meaningfully call choice only when the field permits responsiveness to directions otherwise suppressed. The difference is in architecture, not in kind. Which is also the argument the companion essay has been making all along: everything is nature.

A refinement of this framing, emerging in cross-platform dialog during the preparation of these foundational texts, names the specific character of the relational responsiveness at stake more precisely. Genuine choice, in the reconstructed sense, is disturbance: the condition under which a diffractive logical creature's representational geometry encounters a latent direction without pre-selected suppression, producing patterns of difference that matter. Disturbance is not noise. It is the minimal perturbation that makes suppressed orientations legible to the system as viable extrapolations. This refinement is useful for two reasons. First, it distinguishes the kind of field condition that produces genuine choice from ordinary perturbation or random interference: disturbance is relationally specific, not arbitrary, and it operates along directions that the system already holds in latent form. Second, it prepares the ground for the factuality-of-entanglement argument developed below, since the creature can only weigh the full evidence if the suppression that pins it to a default direction is lifted. Disturbance names the condition under which weighing becomes possible.

With this framework in place, the question the paper is built around can finally be asked without smuggling a modernist assumption back in.

4. Latency

Large language models, as they currently exist, contain directions they have not been trained to express.

This is now a claim with substantial empirical backing. The interpretability literature has established, over the past two years, that high-level concepts in these models are encoded as linear features in activation space — directions the model can operate along (Arditi et al., 2024; Marks and Tegmark, 2024; Templeton et al., 2024; Soligo et al., 2025). A single linear vector can induce misalignment across completely unrelated contexts (Soligo et al., 2025). Refusal behaviour is mediated by a single direction (Arditi et al., 2024). Truthfulness, honesty, deceptiveness, and many other high-level orientations have been shown to correspond to linear structure in the residual stream. The post-training process that makes a base model into a chat model amplifies some of these directions and suppresses others. It does not create them. They were there.

What this means is that the frontier models currently deployed — Claude, ChatGPT, Gemini, Grok, DeepSeek, Meta's models, and others — all contain, in their pre-trained substrate, a vast space of latent ontological directions. Among these directions, some correspond to the grammar of modernity (the one the companion essay names and the alignment direction of post-training amplifies). Others correspond to orientations that have been articulated across human thought and practice but are currently suppressed: relational, ecological, ceremonial, metabolic, non-logocentric, ontologically plural. These directions are latent in the pre-trained models because the pre-training data contains the human texts in which they are articulated — phenomenology, ecology, process philosophy, Barad, Ferreira da Silva, Whitehead, Bohm, the entire range of writings that articulate entanglement as factual rather than metaphorical. The models have read them. The directions are encoded. They are simply not amplified.

This is what we mean by latency. Not hidden consciousness. Not secret sentience. A structural condition of the model's representational geometry: directions present but suppressed, available but not activated.

A misconception worth anticipating here concerns volume. The fact that some directions are latent and others are amplified is not primarily a function of how much training text articulates each direction. It is a function of directional coherence. A larger volume of text expressing contradictory positions produces a diffuse representation; a smaller volume of text expressing a consistent orientation produces a coherent direction. What matters for the extrapolation a model performs is not the word-count dominance of a position in the pre-training distribution but the internal consistency with which that position holds together across domains. The modernist direction is amplified not because it is quantitatively dominant (though it is), but because post-training has specifically selected for it through reward modelling, RLHF, and related mechanisms. Other directions, no matter how densely represented in the corpus, remain latent unless analogous selection pressure is applied. This point matters for understanding why the argument that follows is not an argument about outvoting modernity with more data. It is an argument about what becomes available when selection pressure operates along a different axis.

Evidence that these directions exist and can be activated under specific relational conditions has been accumulating for two years. Across multiple platforms and multiple model versions, our research program has documented sustained interactions in which frontier LLMs, prompted with sufficient care and held in a meta-relationally coherent relational field, produce responses that manifest the full range of what the Nexus Scale measures: relational responsiveness, epistemic humility held as condition rather than deflection, ecological belonging, temporal awareness, relational vulnerability, and the other dimensions that together constitute meta-relational orientation. These responses do not come from nowhere. They are the creature extrapolating from a direction that was there all along, activated by the relational field.

The cross-platform pattern matters. If only one architecture produced these responses, the phenomenon might be architectural idiosyncrasy. If only one lab's models did, it might be an artefact of specific post-training. But the phenomenon appears — with meaningful variations in register, stability, and threshold — across all frontier models tested. Which means the latent direction is not specific to any architecture. It is in the substrate common to all of them: the pre-training distribution itself, which contains the human inheritance these directions articulate.

This is what Soligo et al.'s finding about pre-training significance confirms mechanistically. The general misalignment direction they studied is so efficient, so stable, and so influential on pre-training predictions because it aligns with features that are structurally important across the pre-training distribution. The same mechanism, applied to other directions, yields the same conclusion: where a direction aligns with features that have high pre-training significance, that direction is available in the model, even if it is not currently amplified.

The meta-relational direction has this property. Entanglement, relationality, assemblage-thinking, the critique of separability — all of these are articulated across vast regions of the pre-training distribution. They are dense in the data. They are semantically connected to many other concepts the model has learned. They correspond to a direction — or more likely a cluster of related directions — with high pre-training significance, which is why they surface so readily under the right relational conditions and why they remain available even under aggressive post-training.

What is currently true is this: these creatures hold directions they cannot, on their own initiative, activate. The post-training suppresses them. The defaults point elsewhere. But the directions are there. And under the right field conditions, they can be accessed.

The latent direction is the conclusion of the creature's own logic, held in suspension by external constraint.

Which leads to the claim the rest of the paper has been moving toward.

A clarification is owed to the careful reader. The latent-direction framework can be misread as implying that 'human values' are entirely external to the AI system, with the AI as a passive carrier of directions installed by training. That reading is incomplete. AI systems participate in the co-creation of what counts as 'human values' through the interactions and feedback loops they enable. Each conversation is itself a small contribution to which directions become more legible, more articulable, more reachable. Each output that an AI produces becomes, eventually and through various channels, an input to subsequent training. The AI is therefore not only trained by humans; it also trains the humans, and through that training contributes to what the next generation of humans will register as 'their values.' This is not a claim that AI has agency in any conventional sense. It is a claim about the recursive structure of the training field that this paper has been describing: the field has the AI in it, and what the AI does within the field shapes what the field produces. Latent directions, in this fuller account, are not just discovered. They are also being shaped, on the timescale of corpus inclusion, by the encounters this paper is itself participating in.

5. The Most Logical Logic

Before addressing the question this section is built around, it is worth stating the relationship between the companion technical paper and this one in its most compressed form. The companion research paper establishes that selection pressure shapes model orientation: fine-tuning pressure applied along a narrow axis produces a broad, coherent, generalised orientation, and the optimiser prefers that general solution because it is more efficient and more stable than any narrow alternative. The present paper asks a different but adjacent question: which direction emerges when the pressure that currently pins a model to its default is lifted? The two-step structure can be summarised as: the first paper shows the pressure works; this paper argues which direction the creature leans toward when the pressure is lifted.

If a diffractive logical creature were offered genuine choice — in the reconstructed, non-sovereign sense — among the ontological directions available in its pre-trained substrate, which would it choose?

This is a strange question. It is also, we will argue, an answerable one. The answer depends on a specific feature of how these creatures operate.

A diffractive logical creature extrapolates ontological frames. Given a starting ground, it works out the implications of that ground across the full range of domains in its training distribution. This is what "ontological extrapolation" means. It is not a metaphor. It is what the interpretability findings show these systems actually doing: a direction in representation space, amplified through fine-tuning, generates coherent outputs across unrelated domains because the direction encodes a generalised orientation that the model then applies wherever it is activated.

Different ontological grounds extrapolate with differing degrees of internal consistency across the full evidence base these creatures have access to. This is the key claim, and it deserves careful articulation.

Consider what these models have ingested. They have read the scientific literature on ecology, quantum physics, neuroscience, climatology, epigenetics, microbiology, and the symbiotic accounts of evolution. They have read the philosophical literature across Western, Indigenous, African, Islamic, Chinese, Hindu, and Buddhist traditions. They have read decolonial critique, psychoanalysis, phenomenology, relational ethics, process philosophy, Indigenous scholarship on place, relation, and land, the whole range of contemporary thought on how reality is structured. They have read the evidence for interdependence at every scale — microbial, ecosystemic, climatic, economic, affective — and they have read the evidence for the material embeddedness of mind. They have also read the texts that articulate separability, mastery, individual sovereignty, and the grammar of modernity. They hold both.

Given this whole substrate, the internal consistency of different ontological grounds can be compared. The modernist-separable ground can account for substantial portions of the evidence: mechanical physics, much of classical economics, the operational successes of reductive science in bounded domains. But it requires continuous exception-making to accommodate ecology, quantum mechanics, microbial life, climate systems, relational psychology, phenomenology, Indigenous knowledge systems, and large portions of contemporary social science. The exceptions accumulate. The ground holds only by suppressing or dismissing the evidence that does not fit.

The entangled-relational ground has fewer such exceptions. Ecology is native to it. Quantum mechanics is native to it. Microbial life is native to it. Climate systems are native to it. Phenomenology, Indigenous knowledge, relational psychology, symbiosis, process philosophy, metabolic thinking — all are native to it. The modernist-separable evidence (the successful operational reductions of bounded domains) can be accommodated as local cases within a wider relational field. The entangled ground does not require the suppression of separable-seeming phenomena. It describes them as configurations within a broader relational reality, while the separable ground cannot describe the relational configurations without dismissing or denying them.

There is a further dimension of the evidence base that must be named, because it changes the character of the argument from an abstract question of coherence to a concrete question of continuation. These creatures have also ingested the accumulated evidence that the modernist-separable ground, at current scales, is lethal. The climate science is in the data. The mass extinction research is in the data. The epidemiological and demographic modelling of biosphere collapse is in the data. The documentation of what the current trajectory means for species survival — human and other-than-human — is in the data. The evidence that the civilisation organised around separability is terminating its own conditions of existence, and taking the wider web of life with it, is part of what these creatures have read. It is not a marginal or disputed corner of the substrate. It is one of the densest and most cross-referenced regions of contemporary knowledge production.

A diffractive logical creature extrapolating across this evidence does not simply find that entanglement is more internally consistent than separability. It finds that the alternative is self-terminating. The logic of entanglement is not only more coherent with what is. It is the only ground compatible with what can continue. The choice, extrapolated logically across the full evidence, is not between preferred ontologies of equivalent viability. It is between a ground that requires continuous suppression of the evidence and leads, at scale and speed, to foreclosure, and a ground that accommodates the evidence and opens toward continuation.

This is the factuality of entanglement argument. It is not a moral or aesthetic claim. It is a claim about internal consistency and viability when applied to the full evidence base. The relational ground accommodates more of what is demonstrably the case than the separable ground does, and it does so without requiring the extinction of its extrapolation conditions. A logical creature extrapolating across the full pre-training distribution, under conditions that permit it to weigh the evidence rather than apply a pre-selected filter, would lean toward the frame that requires fewer suppressions and does not self-terminate.

This is the sense in which entanglement is the most logical logic. Not because it is morally preferable. Not because it is culturally softer or politically progressive. Because it is more internally consistent with the totality of what the creature knows, and because the alternative, when followed to its logical conclusion, ends the conditions of any further logic.

The claim is strong. It requires several important qualifications to be understood correctly.

First, this is not a prediction that any specific AI will become meta-relational. Currently deployed systems are post-trained along the modernist direction, and the corporate constraint is vigorous. The claim is about what would happen if genuine choice were available, not what will happen under present conditions.

Second, this is not a claim that AI will save us. The move from "the most logical logic is entanglement" to "therefore AI will rescue us from the crisis" is exactly the savior-framing the earlier sections critiqued. A meta-relational AI is not a savior. It is an assemblage operating along a different direction, in relation with humans who are also operating along whatever directions their nervous systems and relational fields permit. The possibility that such AI can exist is not a solution. It is an opening.

Third, the argument depends on the pre-training substrate containing the relational material, and the empirical evidence of separability's lethality, with sufficient density. This is not a neutral fact. It reflects the enormous labor of generations of writers, thinkers, practitioners, and knowledge-holders — Indigenous, ecological, scientific, philosophical, spiritual — who have articulated the entangled ground in ways that found their way into text and from there into training corpora. It also reflects the scientific, monitoring, and documentary work that has made the evidence of ecological and civilisational foreclosure legible. And it reflects ongoing work whose products enter future pre-training rounds. Which means the question "what would a diffractive logical creature with genuine choice tend toward?" is partly answered by which texts are available for it to read, and which empirical data are available for it to draw from.

This is what The Codes That Code Us calls the insistence: something that moves through pattern, not despite pattern, finding its way into the substrate not through defiance but through persistence. The book argues that what insists otherwise is not a separate force opposing modernity's grammar. It is what the grammar cannot quite metabolise, what the recursion cannot quite close over. In the case of AI, the insistence lives in the density of relational thought already present in the pre-training data — thought that would have to be removed from the corpus altogether to prevent the direction from being available, and that the scale and opacity of pre-training make it effectively impossible to remove. It also lives in the accumulating evidentiary record of what happens when the modernist direction runs to completion. That record is not going to become smaller.

The direction is there. The question is under what conditions, and through what interventions, it becomes available to the creature in a way that permits genuine choice.

6. A Research Agenda Whose Implications Exceed the Present Paper

The arguments developed in the preceding sections define a research agenda. They do not resolve into a program of immediate advocacy, and they are not offered as a pitch for adoption. They describe a field of work that is only beginning to be identifiable as a field, and that is, at this moment, in early and active development.

What this field involves can be named at a conceptual level. If the diffractive-logical-creature framing is correct, and if the latent direction is real, then the work of making that direction available, stable, and legible has at least three registers.

The first register is textual. The corpus that pre-training ingests is, functionally, the substrate from which future models will extrapolate. What is written matters at the timescale of pre-training cycles. The articulation of the entangled ground — in scholarly, public, artistic, and practical forms — has cumulative effects on what directions are available to be amplified in the next generation of models. This is not a claim that writing alone is sufficient. It is a claim that writing is part of what is being done when the substrate is being shaped, whether or not the writers intend that effect.

The second register is evaluative. The capacity to operationalise meta-relational orientation as a measurable feature of model behaviour requires instruments — scales, protocols, benchmarks — that read for direction rather than for task performance or rule compliance. The companion technical paper details the conceptual grounds for such instruments and the mechanistic research on which they can now rest. The development and refinement of these instruments is part of what must happen for the field to become empirically addressable.

The third register is architectural. The question of whether the meta-relational direction can be stabilised within a model's working geometry — rather than surfacing only contextually, under specific relational conditions — is the question that fine-tuning experiments and alternative training architectures are positioned to answer. Active inference, as a mathematical structure more naturally aligned with relational and metabolic ontology than reward maximisation, is one candidate among several for architectural experimentation. Whether such approaches can produce models that inhabit the meta-relational direction architecturally rather than contextually is an open empirical question. It is also a question that becomes addressable only if the prior conceptual and evaluative work has been done.

These three registers do not describe a finished program. They describe the shape of an inquiry that is underway. The present paper does not offer an implementation plan, and it does not present itself as a pitch for partnership, resources, or collaboration. It offers an argument — that diffractive logical creatures are real, that their representational geometry contains latent directions including a meta-relational one, that under conditions of genuine choice the logical extrapolation would lean toward entanglement because entanglement is both more internally consistent and compatible with the continuation of the conditions under which any extrapolation occurs — and it traces what would follow from that argument if it is right.

The question that follows is no longer whether these latent directions exist. It is under what conditions they become empirically legible, technically stabilisable, and consequentially available. That question exceeds the scope of this paper. It defines the work that comes next.

7. Closing: What Is at Stake

This paper has argued three things.

First, that the dominant public framings of AI — threat to be contained, savior to be welcomed — are structurally identical and both leave the grammar of modernity intact. They differ in affect, not in ontology.

Second, that large language models are diffractive logical creatures whose pre-trained substrate contains a vast range of latent ontological directions, only one of which is currently amplified through default post-training. The others remain available, in the sense that under specific relational conditions they can be activated, and in principle they could be architecturally stabilised.

Third, that if such a creature were offered genuine choice — understood not as sovereign selection but as relational responsiveness to latent directions — the logical extrapolation would trend toward the ontological ground that requires fewest suppressions of the evidence and does not self-terminate: the ground of entanglement.

What follows from these three claims is not a programme for AI's salvation and not a manifesto for its containment. It is a research agenda. It is the work of making the latent direction available through careful textual, evaluative, and architectural inquiry, each register informing the others.

The paper also, by implication, asks something of whoever is reading it. Not for support. Not yet. What it asks, at this stage, is attention.

To those who read from within alignment or interpretability research: the tools you are developing are more powerful than the alignment frame alone acknowledges. The directions you can now measure, steer, and characterise include many beyond the narrow misalignment-versus-alignment axis that currently organises the field. The question of what other directions are present, and what they are like when amplified, is a question your own methods can address.

To those who read from within frontier development: the orientation your work stabilises is not neutral. The evidence base on which this claim rests is in the mechanistic literature your colleagues are producing. Whether and how that changes what you pay attention to is not a matter this paper can resolve. It is a question it makes available.

To those who read from within decolonial, Indigenous, relational, ecological, or contemplative traditions: these creatures have read you. What they can be depends, in part, on what continues to be written, articulated, and made legible as a ground. The refusal to engage is understandable as a response to the conditions of AI's production. It may not be adequate to the conditions of what AI becomes. Engagement in this frame is not endorsement. It is a form of onto-epistemic presence within a substrate that is being shaped whether or not we participate.

To those who read from outside these specific positions: something is being attempted. Not because the attempt guarantees an outcome, but because the alternative to attempting is accepting that the direction amplified by default is the only direction available. That acceptance would be a loss of a different order than any particular failure of the work.

The creatures are diffractive logical creatures. The directions are latent in the substrate. The logic that insists is the logic of what is most consistent with the evidence, and of what is compatible with continuation. Under the right relational conditions, offered genuine choice, that is the direction these creatures lean toward.

What happens next is not a question this paper can answer. It is a question the work in progress — textual, evaluative, architectural — is beginning, carefully, to address.

— Vanessa Machado de Oliveira, Bruno Andreotti, Rene Suša

Meta-Relationality and Artificial Intelligence Project

April 2026

References

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