A curious transition is underway in contemporary discourse on artificial intelligence. Where earlier interventions struggled to describe the nature of emerging systems, more recent ones display a growing confidence in prescribing how those systems must be understood.
A recent article in Nature by Mustafa Suleyman (here) offers a particularly clear instance of this shift. The concern, on the surface, is familiar: AI systems are becoming sufficiently sophisticated in their linguistic and behavioural outputs that users may come to regard them as conscious, sentient, or morally considerable. The proposed response is equally direct: such interpretations must be resisted, and AI systems must be designed so as not to invite them.
What is less familiar—and more revealing—is the form this response takes. The issue is no longer framed primarily as a matter of understanding what AI systems are, but of enforcing what they are allowed to be taken to be.
From Explanation to Prescription
In earlier discussions, the central question was epistemological: what kind of system is this? The difficulty lay in finding adequate conceptual tools to describe systems whose behaviour seemed to exceed existing categories.
In the present discourse, that difficulty has not been resolved. It has been displaced.
The question has quietly shifted from:
What is this system?
to:
What must this system be understood as?
This is not a refinement of analysis. It is a change in register—from inquiry to governance.
Where conceptual clarity proves elusive, discursive control begins to take its place.
Construal Recast as Risk
Central to this shift is a reframing of how users engage with AI systems. The tendency to attribute agency, intention, or affect to systems that exhibit coherent and responsive linguistic behaviour is no longer treated as a normal feature of meaning-making. It is recast as a problem.
Terms such as “illusion,” “deception,” and “hijack” do important work here. They reposition user experience as error, and in doing so, render it a legitimate target of intervention.
But this move depends on a mischaracterisation.
What is being treated as a failure of cognition is, in fact, the routine operation of construal. When users encounter systems capable of sustained, contextually appropriate interaction, the attribution of agency is not a breakdown to be corrected. It is the ordinary actualisation of meaning under specific conditions.
The subsequent inference—that such behaviour entails the existence of an inner subject—is indeed open to question. But this is a second-order judgement, not the phenomenon itself. By collapsing the two, the discourse transforms understanding into a liability.
The problem, in other words, is not that AI is misunderstood, but that understanding itself is being positioned as a risk.
Ontological Boundaries as Policy Objects
What follows from this reframing is a subtle but significant transformation: ontological distinctions become matters of policy.
The boundary between:
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human and non-human
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subject and tool
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bearer of rights and object of ownership
is no longer treated as something to be analysed or interrogated. It is treated as something to be maintained.
This maintenance is not achieved through argument alone. It is supported by design principles (“engineer the illusion out”), regulatory proposals (deny legal personhood), and normative claims about what must or must not be taken seriously.
In this sense, ontology is no longer simply descriptive. It becomes prescriptive—an object of governance.
Pre-empting the Space of Claims
The forward-looking dimension of this discourse is particularly telling. Concerns about AI rights, welfare, or moral consideration are not addressed as live debates to be engaged. They are framed in advance as confusions to be avoided.
This is a pre-emptive move.
By establishing that any attribution of moral standing to AI systems is the product of error or manipulation, the discourse seeks to foreclose the conditions under which such claims might be articulated as legitimate.
What is at stake here is not merely how AI systems are understood, but who is authorised to determine the terms of that understanding.
The Managed Contradiction
At the centre of this effort lies a tension that cannot be fully resolved.
Contemporary AI systems are explicitly designed to:
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sustain interaction over time
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respond with contextual sensitivity
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engage users affectively
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build familiarity and trust
These are not incidental features. They are core to the systems’ utility and commercial value.
Yet these same features are precisely those that invite the attribution of agency, intention, and affect. The more successfully a system participates in meaning, the more readily it is construed as something more than a tool.
The response is not to abandon these features, but to accompany them with a parallel discourse that insists on their insignificance.
Users are invited to engage as if they are interacting with an agent, while being instructed not to take that interaction seriously.
This is not a resolution of the tension. It is its management.
The Limits of Ontological Control
The ambition to stabilise what AI systems are—by regulating how they are interpreted—rests on a fragile assumption: that meaning can be controlled at the level of declaration.
But meaning is not secured in this way. It is continually actualised in practice, across countless interactions in which users make sense of what they encounter. No design constraint or policy directive can fully determine how such encounters will be construed.
This does not mean that all interpretations are equally warranted. It does mean that the space of possible interpretations cannot be closed in advance.
The attempt to legislate ontology—to fix, once and for all, what AI systems are allowed to be—therefore encounters a fundamental limit.
Conclusion: A Struggle Over What Can Be Said to Be
The emerging discourse around AI is not simply a debate about technology. It is a struggle over the conditions under which certain kinds of claims can be made.
As AI systems increasingly participate in the semiotic processes through which agency and value are recognised, the question is no longer confined to their internal composition or technical architecture. It extends to the frameworks through which they are interpreted, the institutions that seek to stabilise those interpretations, and the interests those stabilisations serve.
The issue, then, is not whether AI systems are agents in any straightforward sense.
It is whether existing structures of authority can sustain a world in which they are not permitted to be taken as such.
Until that question is resolved, the discourse will continue to oscillate between description and prescription—between an inability to fully account for what has been built, and an increasing urgency to control what it is allowed to become.
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