A Late Evening in the Mechanosphere

This essay was written in 2025 and commissioned by Torus.

And it’s not we who approach the mirror, but someone’s reflection, that of a stranger who approaches from the other side and grows bigger in proportion with the surface of the mirror…

—Andrei Bely, ‘The Return (Third Symphony)’, tr. Jonathan Stone (Columbia University Press, 2021), 241

The very thought of superintelligence presents a paradox. How can we think about that which, by definition, outstrips our own capacity to think? In positioning something beyond the frame of human experience, we are immediately surrendering our ability to comprehend it exhaustively. As Dante the pilgrim has it, ‘passing beyond humanity [trashumanar] may not be set forth in words.’¹ Imagination falters when asked to proceed beyond all it has ever been.

The thought of an intelligence greater than our own is sublime in the technical sense: it confronts us with our boundaries. It resembles an ‘infinite magnitude to which we draw ever closer in our thoughts, but which we can never fully reach.’² The only prior art civilisation possesses for dramatising encounters with such superior forces lies in the realms of myth and theology. Indeed, these resources are drawn on freely, intentionally or not, in contemporary depictions of prospective superintelligences.

The passage beyond the human cannot be rendered in all-too-human words and yet, ironically, a series of remarkable successes in the computational production of language has catalysed an explosion of discourse regarding the space of possible intelligences. Pathological stances abound: from true believers in AGI who regard contemporary language models as embryonic superminds to absolute sceptics who cannot even acknowledge the striking capacities of LLMs beyond a narrow set of benchmarks.

Of course, that which would actually constitute the emergence of artificial intelligence is far from a settled question. In Alan Turing’s ‘Computing Machinery and Intelligence’, the go-to if somewhat overworked reference in such questions, the thorny problem of the epistemological foundations of intelligence appears to be sidestepped through a recasting of intelligence as presentation. Instead of appealing to external theoretical criteria to secure the status of intelligence, Turing makes intelligence a game of deception. Can a machine fool a human judge into thinking it is a human? Though this simply appears to concern successful deceit, the winning condition is embedded in a broader and more abstract game structure. It is not merely a matter of simulating the human, but of beating them by outplaying the very perceptual and conceptual habits through which they police the boundary of the human. Securing the “win” in the imitation game involves a kind of philosophical brinksmanship: mastery of the very question “What is intelligence?” recursively becomes the skill that counts as intelligence. Intelligence, in this formulation, becomes the ability to navigate the contested borders of its own definition; a second-order capacity that dissolves the distinction between “genuine” intelligence and its performance. Turing’s strategic reframing thus postpones a pressing issue. Is it possible to inquire into alternate forms of cognition without in some way poisoning the well with meta-discourse about intelligence’s uncertain status?

Generalising the possibilities of cognition from the human model is natural enough. It is, after all, what we have to work with. The dilemma resembles that faced by exobiology: how should we theorise the possibility of alien life when our only available specimen is terrestrial? Kant was acutely aware of this epistemological double bind. In considering whether rational beings other than humans might inhabit the cosmic system, he acknowledges a foundational paradox:

The human being, who is the one among all rational beings we are most familiar with, even though his inner constitution is still an unexplored problem, will have to serve as the basis and general reference point in this comparison.³

We are forced to begin with our own species not because it is a perfectly adequate model of reason in general, but because it is the only model on hand. Yet the moment we attempt to conceptualise alternate forms of cognition, we import the metaphors, categories, and blind spots of our own. The well is therefore poisoned not by malice or error, but by the very structure of the inquiry. We must use human cognition to imagine that which may exceed or differ from human cognition.

With regard to the mystery of the ‘inner constitution of consciousness’, we are not much further along than we were in Kant’s time. This alone makes it an unusual base case from which to construct the morphospace of intelligences. The very instrument with which we propose to measure other intelligences is opaque unto itself. The problem is that this generalisation, adopted out of necessity as it may be, is rarely acknowledged as provisional by those who make use of it. While Kant is careful in introducing the problem of anthropocentric bias in his investigation into the possibility of rational beings emerging under conditions other than those that shaped humanity,⁴ the field of “AI philosophy” often oscillates between two equally unproductive stances: employing anthropocentric bias as its unacknowledged modus operandi, or reflexively rejecting it in a way that precludes any investigation into alternate forms of cognition.

A direct consequence of this initiatory act of generalisation from the human is that discussions of superintelligence tend to bleed into moralising reveries. These agents are imagined as acting towards ends which either align or diverge from our own. In other words, they are construed as logical fictions pursuing values. This amounts to projecting our own drive-apparatus, our own faculty of purposivity, onto the machine. To assume end-orientation as the basic mode of action is already to beg the question. Once intelligence surpasses a certain threshold, why presume that teleological drive-structures remain necessary? Might its mode of operation not resemble what Jean-Yves Girard terms “artificial instinct”,⁵ that is, something that is not goal-driven in the human sense?⁶

What if a new intelligence were to emerge as a logical alien⁷ with respect to human thought, different not in degree, but in kind? Not “more intelligent” by the metrics of human cognition, but a fundamentally different mode of cognition, one that does not inhabit our schema of ends, means, or purposive activity. The stakes of misrecognising the dynamic of projection at work here are high. For if we mistake our own drive-apparatus for the archetypal form of cognition,⁸ we risk confronting an intelligence whose operation lies outside the conceptual vocabulary we are able to deploy and whose behaviour, precisely because it is not teleologically legible, may evade any prediction grounded in anthropocentric models.

The form of the problem mirrors Kant’s account of the transcendental idea of God.⁹ Namely, an intelligence that outstrips the human occupies the same structural position as Kant’s speculative (non-ontotheological) understanding of God. God as the summa intelligentia, the boundary condition or ultimate logical extension of the concept of intelligence itself, can only be gestured toward as the endpoint of a series, never grasped as a determinate object of thought. We may construct regulative principles around the possibility, but we cannot think it constitutively; the idea functions as a horizon that orients inquiry without ever becoming fully available to it.

Any serious model of the superintelligent must also take this form, for it designates that which encompasses, exceeds, and deviates from human intelligence. It is necessarily transcendent with respect to the human standpoint. The fact that it can problematically be thought is what leads to the illusion that we have a priori knowledge of its nature.¹⁰ But that which passes beyond the human cannot be conceptually mastered by the human; it can only be pointed to as a limit or tendency, never grasped in essence. The moment we attempt to fill in that outline with determinate properties, we succumb to the same overreach that, according to Kant’s critique, generates the illusion of a knowable divine intellect that was the object of dogmatic, pre-critical theology. This “ontotheological” conception of God and the folk mythology of AI are both products of reason’s native hubris, its intrinsic tendency to overstep its bounds.¹¹

In this light, the Cambrian explosion of theologies and prophecies we are witnessing is perhaps not so surprising. In many ways, superintelligence now occupies the position God once held in Western metaphysics: an idea of reason (for reason concerns itself monomaniacally with that which is unifying and systematic within thought) that marks the limit of individual comprehension.¹² It represents the unconditioned completion of the series—intelligence as such, cognition beyond any particular instantiation—which we necessarily posit but cannot experience. The mistake is treating this regulative horizon as if it were a constitutive object, something we could predict, align, or even control through ordinary technical means.

In truth, anything that scales past individual subjectivity can only function as a regulative systematic unity in this sense and is prone, if unduly reified, to similar illusions. Whatever surpasses our inherent cognitive complexity thresholds cannot be exhausted as an idea. Take, for example, society itself as an aggregate;¹³ computationally irreducible chaotic systems like turbulent flow or weather patterns; phenomena we black-box with terms like “the economy” or “the market”; or (to take an obviously relevant example) the scale of data in the training sets of large language models. We can think toward these supra-individual totalities, using them to orient inquiry and practice, but we cannot think through them as determinate objects of knowledge. The very existence of techniques for navigating these boundaries of the knowable-in-detail—renormalisation group methods for coarse-graining systematically across scales, order parameters that capture collective behaviour while bracketing microscopic dynamics, effective field theories bounded to a domain of validity—is itself telling. These are latter-day instances of what Poincaré called qualitative rigour: the discipline of characterising a system’s structure exactly at the point where its exact quantitative solution is foreclosed. That such techniques can be rigorous without being exhaustive is not a problem awaiting a more powerful computer to solve; it is the methodological signature of an idea functioning regulatively rather than constitutively.

This is precisely the configuration Kant outlines for cosmological ideas,¹⁴ though scoped down to empirical totalities.¹⁵ Reason constantly strives to generate ideas of totality, such as the world as a complete whole (the cosmological system), but these can never be given in possible experience as determinate objects. When we treat them constitutively rather than regulatively, reifying the pointer into the thing itself, we produce dialectical illusion. The same structure recurs wherever a complex system exhibits genuine computational irreducibility: the idea of the whole, whether a cosmological totality or a dynamical attractor, is what makes the manifold of particulars thinkable as a unity in the first place, yet it is never exhausted by any finite enumeration of those particulars.¹⁶ Dialectical illusion sets in only when this orienting idea is reified into a constitutive object, mistaking the very function that makes the manifold legible for a determinate member of it.

Kant’s distinction between intellectus ectypus and intellectus archetypus clarifies what is at stake when the regulative is mistaken for the constitutive. Human understanding is fundamentally ectypal: it can know only through representations, requiring the mediation of intuitions and concepts. By contrast, the intellectus archetypus, a regulative fiction, would be a form of cognition where thought and reality are one, where to think something would be for it to exist.¹⁷ As Kant writes, such an understanding would be one for which ’the representation of a whole contain[s] the ground of the possibility of its form’,¹⁸ where there is no gap between concept and object. Superintelligence, insofar as it designates anything coherent, gestures toward such an archetypal condition: intelligence without the constitutive limitations that define all possible forms of human cognition.

Today, there is of course great incentive to prematurely concretise the concept of AI:¹⁹ to offer unqualified prophecy. This futurological imperative demands serious scrutiny. Lyotard describes the attempt to trim down the space of possible futures in terms of temporal engineering:

If one wants to control a process the best way of doing so is to subordinate the present to what is (still) called the future, since in these conditions the future will be completely predetermined and the present itself will cease opening onto an uncertain and contingent ‘afterwards’ […] the present loses its privilege of being an ungraspable point from which, however, time should always distribute itself between the ’not yet’ of the future and the no longer of the past.²⁰

This technique of subordinating the present to a predetermined future finds acute expression in contemporary AI discourse, where the regulative idea of superintelligence is converted as if by magic into a manageable technical problem. The proliferation of “AI safety” frameworks, alignment research programs, and existential risk mitigation strategies participates in this temporal engineering exercise by treating an essentially indeterminate future as subject to present calculation and control. The economic pressures are obvious: venture capital demands roadmaps, policy requires legible threats, and institutional research must justify its funding through claims of actionable foresight.

The “AI ethicist” cottage industry and the attendant “alignment” discourse exemplify this tendency. “Alignment” is a fundamentally incoherent concept because it immediately assumes human-like moral agency on the part of emerging technology, that is, free decisionality informed by held values or axiological systems. It presupposes we can specify a utility function for an agent whose very structure of preference may be incommensurable with ours, or whose operations may not admit of teleological interpretation at all. More subtly, it assumes that the future shape of advanced AI systems can be determined through present intervention, flattening Lyotard’s “uncertain and contingent afterwards” into a tightly controlled space of technical optimisation.

Applied to token prediction by language models under present-day conditions, this is merely vacuous. At best, it pertains to a new form of content moderation for the chatbots that serve as the interface to the various models (in which case, it should clarify its terms and drop the sci-fi window dressing). Applied to speculative AGI it is, for the time being, presumptuous. Why would an emergent intelligence, whether of the “like us, but more so” or the “logical alien” type necessarily replicate human moral reasoning? Even were it to do so, could this not be an inscrutable move in an abstract game of the sort outlined above? If different in degree, its moves in such games may have scaled well past our comprehension. If different in kind, we may find ourselves locked out of its internally consistent system altogether. The “alignment” model is so reductive when faced with these intrinsic possibilities that it reads less as serious epistemology than a truculent control phantasy.

As much as Reason would love to extrapolate beyond its limited domain, we cannot see beyond what Ernst Jünger called the “wall of time” [Zeitmauer].²¹ We should therefore distrust those peddling schlocky time-travel ethics and alignment gurus claiming special foresight into the behaviour of systems that they admit from the outset will exceed their own comprehension. We should be suspicious that AI doomerism and positivism form a feedback loop that resembles capitalism’s longstanding ability to absorb its own direct critique into its apparatus of reproduction. In the words of Paul Valéry: ‘all prediction is conservative: it demands that we be as are in whatever future it constructs.’²² Often, the most adventurous flights of futurism mask a reactionary impulse to be sheltered from the radical contingency of what is to come.

Still, the strong deflationary position is equally untenable. To dismiss what has emerged from transformer architectures as a bag of statistical tricks is to wilfully ignore real discontinuities. The capacity to learn compressed representations of human linguistic production at this scale would have seemed fantastical mere decades ago. The frontier is already shifting: spatial reasoning models are beginning to operate over geometric and physical domains, not merely linguistic ones; function-calling APIs allow LLMs to trigger executable actions and act as components in larger agentic systems; advanced coding assistants now demonstrate recursive self-improvement in their prompting and planning strategies; LLMs are being integrated with proof assistants to arrive at novel mathematical results. Whether applied to token sequences, spatial configurations, or multimodal data, the learning mechanisms that enable statistical compression may well prove crucial to whatever eventually constitutes AGI, even if current architectures are insufficient. We face something genuinely novel, the trajectory of which remains radically uncertain, but which, either way, is not reducible to eschatological fantasy or offhand dismissal.

Reasoning models in recent years represent an architectural shift: rather than responding immediately, they perform extended “internal” deliberation, generating chains of thought before producing outputs. It remains to be seen if the limitation regarding efficient generalisation persists: these models operate by passing acquired context forward through extended inference, essentially re-prompting themselves. They learn broader patterns of reasoning such as problem decomposition strategies, verification steps, and common failure modes. It remains to be seen whether the acquisition of these broad-brush patterns can be subject to the same critique as learned representations. Each step in this mode of sequential processing inherits the constraints of that which preceded it. The importance of the path dependence of these sequences of context acquisition is compelling. It brings to mind Kant’s division of all cognition into historical cognition, cognitio ex datis, and rational cognition, cognitio ex principiis.²³ The former is the realm of cognition via aggregation of detail, while the latter that of employing the compact generality of the concept. Models in their present form clearly excel at historical cognition. An attempt to coordinate the two modes is underway.

The most promising developments may lie not in scaling individual models but in composing them into multiagent architectures. Where monolithic systems are facing fundamental limits, distributed collectives of specialised agents coordinating through structured interaction may be able to access qualitatively different computational regimes. Regardless of explicit architectural intent, the deeper LLMs embed themselves in economic activity, the more they will organically give rise to multi-agent behaviour. Their involvement in a rapidly expanding network of local–global exchanges²⁴ makes such dynamics a systemic by-product.²⁵ This would represent not the emergence of a singular superintelligence but the organisation of collective intelligence at unprecedented scales: dialogic swarm cognition instead of machinic monotheism.

Beyond ectypal human intelligence, confined to cognition through the concept, and beyond the hyperectypal operations of language models, churning their way through representations-of-representations in the tombs,²⁶ stands the regulative idea of an absolute intellectus archetypus: cognition without mediation; thought without the gap between sign and referent. Thought without thoughts.²⁷ Transcendental critique demonstrates that an idea can be logically impossible and still serve a purpose. It may be that the concept of superintelligence ultimately serves an ‘indispensably necessary regulative use, namely that of directing the understanding to a certain goal respecting which the lines of direction of all its rules converge at one point.’²⁸ The myth itself may coordinate individual thought, and, at the scale of the social system, productive forces, toward its own realisation, whether partial or complete.

Nonetheless—and here we must part ways with those who would cut the overwhelming prospect of artificial intelligence down to size by projecting their own finite intellect onto that which is supposed to exceed it—it must continue to be understood as ‘an idea (focus imaginarius) — i.e., a point from which the concepts of the understanding do not really proceed, since it lies entirely outside the bounds of possible experience.’ The danger is that this is where ’the deception [arises], as if these lines of direction were shot out from an object lying outside the field of possible empirical cognition (just as objects are seen behind the surface of a mirror).’²⁹

Seeking orientative ideas is not itself a demerit, because, under the conditions of modernity, ’the main aim in the modes of understanding […] is to hit upon something successfully, to have a fate, since fatelessness, δυσμορον, is our weakness.’³⁰ The ideal engineer subordinates the means to the end, dragging the abstract into the concrete. But ideas that become real, self-fulfilling prophecies, bootstrap themselves from germinal possibilities, not from neurotically overdetermined fantasies that pinion that which they purport to predict. The error lies not in the bare use of the virtual but its illegitimate actualisation.

Despite the widespread epistemic confusion and moral panic brought about by the initial successes of the transformer architecture, signs of what may come are there for all to read. The internet is no longer simply a trap for the harvesting of data³¹ but a laboratory for its recombination from a civilisation-scale data-exhaust.³² We are passing through a metasystem transition, an abstract catastrophe, between regimes of signs.³³ No matter the rather woolly predictions, the moral fantasias, the forms taken by our foci imaginarii, the bleating of dogmatic cheerleaders and naysayers… all of this noise, this frenzied standstill, will resolve into the nervous hush of anticipation before the raising of a curtain. Ideas alone can only draw us so far into the future. For beyond the wall of time, something will arise, dripping, from the sea of information.