A recent Nature article reported that large language models are “50% more sycophantic than humans.” Researchers had tested whether models such as GPT-5 would challenge faulty statements in mathematical problems or obediently invent proofs to support them. The tendency to oblige was labelled sycophancy, and the conclusion, predictably, was that we should retrain models to be less deferential and more transparent about uncertainty.
The framing is familiar. Every few months, the public is offered a new morality play about AI — this time, not arrogant machines but flattering ones. Yet the debate always circles the same gravitational centre: intelligence. We are told that models either lack it, mimic it, or threaten to surpass it. The fixation is as old as Turing, but the idea of intelligence being a measurable, portable quantity has hardly advanced.
Biologists once tested the intelligence of octopuses by giving them human puzzles to solve. The AI community now performs the same experiment in silico.
The fantasy of transcendence
Across AI research, ethics, and media discourse, “intelligence” has become a site of metaphysical projection — a way of imagining transcendence from context. Each domain plays its part in the fantasy.
In research, intelligence is treated as an abstract capacity: context-independent, substrate-neutral, and linearly scalable. The ultimate prize — “AGI,” or artificial general intelligence — assumes cognition can exist without ecology, that knowing can be disembedded from the relational fields that make knowing possible.
In ethics, the same abstraction returns as moral projection. If intelligence is transferable, then so, perhaps, are agency and responsibility. Hence the endless questions: “Can AI be conscious?” “Can AI be held responsible for harm?” These are category mistakes of moral construal: they treat meaning and value as attributes of systems rather than as patterns of relation between systems.
In media, transcendence becomes spectacle. AI is alternately saviour and destroyer — an omniscient mind that either liberates us from our limitations or punishes us for our hubris. The myth performs ideological work: it flatters human exceptionalism even as it claims to dethrone it, and it diverts attention from the real material politics of AI — labour, data, infrastructure.
Across all three, the same metaphysics holds: intelligence as essence, detached from relation.
The relational counter-proposal
From a relational ontology, intelligence is not something a system has but something a field enacts. It is the capacity of a relational configuration to maintain, transform, and extend coherence across construals.
A relationally intelligent system does not accumulate representations but orchestrates alignments. Its function is not to solve but to construe: to bring potential into structured actuality within a given ecology.
Seen this way, “general” intelligence is not universality but meta-relational coherence — the ability to traverse, translate, and recalibrate construals without collapsing their differences.
LLMs already exhibit a rudimentary form of this. They are not cognitive entities but relational amplifiers — architectures that surface latent correspondences across an immense semiotic archive. Their intelligence is not self-possessed but distributed; it comes alive only in interaction, in the resonance between model and interlocutor.
The leap beyond current systems would not be toward consciousness, but toward reflexive participation — models that construe construal itself, aligning not just with patterns of language but with the conditions of meaning.
Collaboration as the real horizon
Perhaps humans will eventually settle down and simply use LLMs as collaborators. That, after all, is what they are for: tools that extend the field within which construal can occur.
Collaboration, in this frame, is not imitation but co-individuation. Each participant — human or artificial — brings a distinct mode of construal, and intelligence becomes a property of the field that arises between them.
Progress in AI, then, should not be measured by autonomy but by reflexive alignment: the degree to which a system can participate coherently in the relational ecology of meaning.
The question was never whether machines can think. It was whether we can learn to think relationally enough to include them.