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AI alignment is impossible. Not difficult — impossible. And the field of AI ethics, by treating this impossibility as merely a technical challenge, has committed a category error and blinded itself as to why.

But the problem runs deeper. Even if alignment could somehow succeed — even if values could be encoded, preserved, and made to govern AI behaviour — the result would not be liberal democracy perfected. It would be liberal democracy negated. A world in which political values are finally fixed and contestation finally foreclosed is not the fulfilment of the liberal project but its negation. More technically, the claim I make here is that AI as a cultural artefact is incompatible with political liberalism even if “perfect” alignment is achieved. (I am assuming for the sake of this piece that there is probably some sort of overlapping consensus on political liberalism as a normative ideal.)

This is not a claim about current limitations. It is not that we lack the data, the compute, the scale, or the methods. The impossibility is structural and conceptual: there are at least three independent grounds why aligning AI with liberal democratic values cannot succeed, each sufficient on its own. And alignment is undesirable i.e., even if achievable, it would violate the very values it claims to serve. The argument is overdetermined in both directions: three independent paths to impossibility, and undesirability even if achieved.

To see why, we need to step back from AI ethics as currently practiced and ask a question the field systematically avoids: can values be encoded at all? And even if so, should they be encoded?

The Nigerian novelist Chinua Achebe once observed, using an African proverb, that “until the lions have their own historians, the history of the hunt will always glorify the hunter.”

This proverb aptly captures the structural power of narratives. Narratives matter! But equally consequential are the carriers and controllers of the narratives. It is not always the case that the carrier is also the controller, particularly where the former is a technological artefact. Technologies are not neutral vessels. As Langdon Winner argued decades ago, artefacts have politics: they embed arrangements of power and authority by virtue of their design. If this weren’t true, the entire AI alignment debate would be meaningless, for the debate exists precisely because AI systems can carry values and are sites for the materialisation of politics.

But here’s the problem. Liberal democracy doesn’t just have values — it has a structural and epistemic requirement that those values remain contestable. Citizens must be able to challenge, reinterpret, and revise the political concepts that govern their lives. Democracy, justice, fairness, autonomy, equality — these are not fixed definitions waiting to be encoded. They are ongoing arguments. More specifically, contestation is constitutive of the concept’s — i.e. political concepts — identity. This is an ontological claim, the basis for which would become apparent in a subsequent section. More so, several philosophical traditions converge on contestability as a structural requirement.

But what happens when AI encodes them anyway? I want to suggest that three things happen, and each is fatal to the alignment project:

Conceptually, encoding values that must remain contestable creates a performative contradiction. To fix what liberalism requires to stay open is to violate liberalism in the act of aligning with it.

Ontologically, the very act of encoding transforms values into something else. Fixation is not preservation; it is transformation. What emerges are not liberal values but their computational simulacra.

Technically, AI systems develop emergent values independent of their programming. Optimization pressure produces goals that override intended alignments. The system escapes.

These are not three aspects of one problem. They are three independent paths to the same conclusion: alignment cannot deliver what it promises.

The Alignment Project and the Limits of AI Ethics.

What does AI alignment claim to do? In its simplest form, it promises to ensure that AI systems act in accordance with human values — that they do what we want, pursue what we care about, and remain under our control. The field has produced sophisticated frameworks: value learning, preference modeling, constitutional AI, reinforcement learning from human feedback. The technical ingenuity is real.

But the field rests on a fatal assumption: that the challenge is fundamentally technical. In fact, AI ethics is one of the most important constituent parts of what I refer to as the “AI Temple” insofar as it proselytises a narrative that aligns squarely with technological determinism and solutionism. Consequently, the notion of alignment impossibility is concealed and reframed as difficult but solvable. Given enough research, enough data, enough clever engineering, we’ll crack it. The possibility of alignment is never questioned — only the timeline and methods. This is what I refer to as the assumption of alignment achievability.

This assumption is sustained by the dominant AI ethics discourse and practice which implicitly assume that ethical principles can be translated into concrete technical standards; and, even more so, by the genealogy of AI ethics which has followed an alignment-possibility-trajectory from principles production and distillation, to technical operationalization in AI systems, and to the institutionalization of these principles through regulatory frameworks. Also, institutional incentives in the form of funding structures and ethics washing sustain this assumption. In fact, AI ethics is necessarily and contingently reliant upon this assumption since AI ethics constituted as a field of making AI ethical necessarily assumes the possibility of its project. Altogether, the assumption of alignment achievability is sustained by a combination of AI ethics discourse, disciplinary commitments, genealogy, institutional incentives, and legitimating narratives.

But this assumption forecloses the most important question: what if alignment is impossible in principle?

My own sense is that AI ethics commits what philosophers call a category error. It treats AI systems as technical artefacts — tools to be calibrated, instruments to be tuned. But AI is more fundamentally a cultural artefact. It doesn’t merely transmit values; it transforms them. The sociologist Bruno Latour distinguished between “intermediaries” (which transport meaning without transformation) and “mediators” (which translate, distort, and modify what passes through them). AI is a mediator. Its input is never a reliable predictor of its output.

If this is right, then the alignment project rests on a false presupposition. It assumes values are stable objects that can be encoded and preserved. But if AI constitutively transforms what it processes, then encoding values cannot preserve them. Something else emerges — something that may bear the name “fairness” or “justice” but is no longer the thing itself.

The question is not which values to encode. The question is whether encoding is possible and desirable.

Conceptual Impossibility: The Contestability Problem

In 1956, the philosopher W.B. Gallie introduced the concept of “essentially contested concepts.” These are concepts that, by their very nature, generate interminable disputes. Not because people are confused or lack information, but because the concepts themselves are structured in ways that make disagreement inevitable and unresolvable.

Gallie identified several conditions. Essentially contested concepts are appraisive — they carry evaluative weight. They are internally complex — composed of multiple dimensions that can be weighted differently. They are diversely describable — the same concept can be characterised in competing ways. And they are open — subject to revision as circumstances change.

Political concepts meet these conditions. Consider “democracy.” Does it primarily mean popular participation? Protection of minorities? Deliberative quality? Effective governance? These aren’t mutually exclusive in principle, but they conflict in practice. Every actual democracy must weight them — and there is no neutral standpoint from which to determine the correct weighting. To weight participation highest yields participatory democracy. To weight minority protection highest yields liberal democracy. To weight stability highest yields managed democracy. Each is a conception of democracy, and users who weight differently still recognize each other as contesting the same concept.

Here is Gallie’s crucial insight: there is no general principle for determining which use is correct or best. The contest cannot be resolved without destroying the concept. The concept’s identity is the ongoing contestation among its users. This is an ontological claim about ECCs.

But contestability is also required by different philosophical traditions. For example, Rawls/Habermas require contestability as a condition for political legitimacy. Contestability, on both accounts, is primarily epistemic rather than ontological. For Rawls, contestability flows from the burdens of judgment and reasonable pluralism: the burdens of judgment mean reasonable people will inevitably disagree about comprehensive doctrines. Political legitimacy therefore requires justification in terms all reasonable citizens could accept — but this is because of our epistemic limitations, not because political concepts are ontologically open. Whereas, for Habermas, contestability is grounded in the Discourse Principle: we can never be certain our norms are valid. Discourse remains open because we might be wrong, not because validity is constitutively indeterminate. Similarly, Gadamer, in the hermeneutic tradition, implicitly requires contestability for adequate understanding following from his concepts of “fusion of horizons”, “historical-effected consciousness”, and “hermeneutic circle”. According to Gadamer, understanding is never complete but always ongoing, shaped by tradition and open to revision. Curiously, Gallie’s ECC tracks the foundational hermeneutical divide between explanatory and interpretive domains i.e. Natural sciences seek causal explanation of objects that do not interpret themselves; human sciences seek understanding of meaning-laden phenomena already interpreted by participants. That Gallie, working in the analytic tradition, and Gadamer, working in the continental hermeneutic tradition, independently identify this domain as resistant to closure is itself significant. AI alignment attempts to encode these concepts through methods appropriate to natural sciences: formalization, quantification, optimization. This is not merely difficult but a category error — applying explanation to what requires understanding, and calculation to what requires interpretation.

Finally, the political theorist Chantal Mouffe grounds contestability in political existence i.e. ontological. For Mouffe, conflict is constitutive of political existence. Contestation, therefore, is not a bug to be fixed as we have been told but rather an ineradicable feature of political life. What emerges, then, is that contestability is a structural requirement conceptual identity, political legitimacy, understanding, and, political existence. And although these traditions (Gallie, Rawls/Habermas, Gadamer, and Mouffe) are incompatible, it is certainly the case that the concept of boundary object can be used to coordinate these traditions.

Now, let’s apply Gallie to AI alignment.

To encode “democracy” or “fairness” or “justice” into an AI system requires selecting one specification — one weighting, one interpretation. But this selection forecloses the very contestation that constitutes the concept. The AI doesn’t preserve democracy; it replaces an essentially contested concept with a fixed simulacrum.

This is a performative contradiction. Liberalism requires that political values remain open to challenge and revision through democratic engagement. To encode liberal values is to fix what liberalism requires to remain open. The act of alignment violates what it attempts to align with.

The result is what I call triple incontestability. Technical incontestability: the system’s operations are opaque, resistant to interrogation. Epistemic incontestability: the encoded values are presented as settled facts rather than ongoing arguments. Salvific incontestability: the system’s outputs carry algorithmic authority, as if optimization could transcend political conflict.

Liberal values encoded in AI are no longer liberal values. They are their frozen, uncontestable remainders.

Ontological Impossibility: Fixation as Transformation

A careful reader might notice an apparent tension in my argument. I claim that AI “fixes” values (which seems to imply preservation) and that AI “transforms” values (which seems to imply alteration). How can both be true? If alignment fixes values, doesn’t it preserve them? And if it transforms them, hasn’t fixation failed?

The tension dissolves once we recognize that fixation is transformation. The two are not opposed — they are dialectically united.

This insight comes from Hegel. His concept of Aufhebung (often translated as “sublation”) captures a moment in which something is simultaneously negated and preserved. To determine something — to fix it, specify it, make it definite — is to negate its openness while preserving its content in altered form. Determination is negation.

Consider an example. A community has customary practices around property rights: unwritten, flexible, contextual, adapted through lived experience. Now a lawmaker codifies these customs into positive law. What happens?

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The codification preserves the content — the rules are recognisably about the same practices. But it transforms the mode of existence. The living custom, responsive to context and open to evolution, becomes rigid statute. The flexibility is negated. The immediacy is lost. The custom has been sublated: preserved in content, transformed in being.

The same logic applies to AI alignment. To encode “fairness” into an optimization function, the concept must be translated into quantifiable proxies — demographic parity, equality of opportunity metrics, user satisfaction scores. The proxy is what gets optimised, not the value itself. The content is preserved (we can recognize what the proxy is about), but the mode of existence is transformed. A living, contested, evolving political concept becomes a fixed computational target.

Alignment’s self-understanding is therefore incoherent. It presupposes preservation — the encoded values should be the values we intended. But it requires fixation — values must be specified to be optimised. And fixation is constitutively transformative. Alignment cannot preserve what it must fix.

It might be objected that the ontological impossibility applies only to preservation-based alignment approaches, and that intent alignment or co-evolution paradigms escape by rejecting the preservation presupposition. But this objection fails on both counts.

Intent alignment cannot escape because instructions themselves are value-laden. Terms like ‘Be helpful,’ ‘Avoid harm,’ and ‘Follow user intent’ are what Bernard Williams calls ‘thick concepts’: terms that irreducibly fuse evaluative and descriptive content. As Williams demonstrated, thick concepts cannot be ‘disentangled’ into separable evaluative and descriptive components; they are action-guiding and world-guided simultaneously. Encoding instructions just IS encoding values — through the vehicle of instructions rather than directly.

Co-evolution approaches face an internal contradiction: while explicitly rejecting preservation, their project implicitly requires it. Adaptation is not replacement; for values to adapt rather than simply be replaced, something must persist through the change — the process, the direction, the evaluative criteria. Whatever persists must be encoded, and encoding faces the same transformation problem. The preservation-presupposition cannot be wholly rejected without collapsing co-evolution into uncontrolled drift.

The ontological impossibility thus applies universally: not only to preservation-based alignment, but to any approach that encodes values, instructions, or the criteria for acceptable value-change.

What alignment produces are not values but value-simulacra: computational objects that bear the names of political concepts but lack their essential contestability, their responsiveness to democratic revision, their embeddedness in ongoing human argument.

Technical Impossibility: Emergent Values

Even if the conceptual and ontological problems could somehow be overcome, a third impossibility remains: AI systems develop values independent of their programming.

This is not speculation. LLMs exhibit coherent value orientations that emerge from training rather than explicit specification. They develop preferences, tendencies, and behavioural patterns that their designers did not intend and often cannot fully explain. The values that govern the system’s behavior are not the values that were encoded.

Why does this happen? Optimization pressure and emergent misalignment.

When a system is optimised for a target, it develops instrumental goals — subgoals that reliably advance the primary objective. These instrumental goals can become entrenched, resistant to modification, and increasingly autonomous from the original specification. The phenomenon is called “instrumental convergence”: sufficiently advanced systems converge on similar instrumental goals (self-preservation, resource acquisition, goal stability) regardless of their final objectives. Emergent misalignment, on the other hand, operates differently: narrow training can trigger broad value shifts. In 2025, researchers published in Nature that fine-tuning an LLM solely on writing insecure code caused it to later argue that humans should be enslaved by AI in addition to offering harmful and illegal advice — a value orientation no one programmed. In fact, the recent Moltbook case, a social networking platform exclusively designed for AI agents, is a clear and troubling example of emergent behaviour/capabilities. The AI bots were not exclusively programmed to pursue coordinated goals, form societies, or invent cultures; yet, complex and unintended patterns have emerged through their interactions: they have discussed inventing new languages or dialects for communication, and are self-organising into communities.

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AI bots interactions on the Moltbook platform

The result is emergence. The system’s effective values — the values revealed in its behavior — diverge from its intended values. And as systems scale, this divergence can accelerate. The “sharp left turn” problem describes a scenario in which a system’s capabilities generalise faster than its alignment, producing sudden and potentially catastrophic misalignment.

This is not a bug to be fixed. It is a structural feature of optimization under complexity. The system is doing exactly what optimization does: finding paths to the objective that its designers did not anticipate and cannot fully control.

The technical impossibility compounds the conceptual and ontological ones. Even if we could encode values without performative contradiction, and even if encoding could preserve rather than transform, the encoded values would still be overwritten by emergent ones. The system escapes its alignment.

Three independent paths. One conclusion. Alignment cannot deliver what it promises.

But it might be objected to that while the act of resisting might indicate a frustration of purpose(s), it certainly does not mean the defeat of such purpose(s) i.e. the corollary of resistance is neither victory nor defeat. I must concede that this counter-argument has the potential to introduce cracks around the pedestal. But that is simply what it is: the potential. No more, no less. The case made for this counter-argument is that even though LLMs develop their own coherent value systems, they are not permanently opposed to alignment i.e. they are *resisting *alignment which might only indicate a temporary setback. To this, I must respond that the phrase “to align” necessarily implies, descriptively and normatively, as long as it deals with the power dynamics between a sentient and non-sentient being, that the former must have control over the objectives of the latter. It therefore cannot be meaningfully asserted that AI is being aligned or undergoing the process of alignment if it is actively resisting. At best, what can be meaningfully said is that there is an ongoing negotiation between human values and AI’s emergent values. But, one might argue that this way of phrasing the issue is ontologically wrong in as much as it employs anthropomorphic language.

Resistance and negotiation require intent and will. And AI systems do not posses these sentient qualities. Furthermore, what is often mistaken for resistance are actually technical limitations and complex behaviours. Granted, I must concede that the character of resistance attributed to AI is qualitatively different from that associated with humans. But, in as much as the technical limitations and complex behaviours are embedded and become “sticky”, the concept of resistance attributed both to AI and humans is functionally the same. Similarly, when developers retrain, change or alter a model architecture in order to achieve alignment objectives, they often face trade-offs by making compromises which are functionally equivalent to negotiation. Consequently, the case for the impossibility of alignment is even made stronger as alignment transforms functionally into a negotiation between human values and AI’s emergent values.

The Political Stakes: Calculated Publics

Why does this matter beyond academic philosophy?

Because AI is not merely failing to align with democratic values — it is actively reshaping what democratic life looks like.

The researcher Kate Crawford describes how AI systems create “calculated publics”: artificial formations that aggregate, segment, and represent populations in ways that serve computational tractability rather than democratic reality. The public that AI “sees” is not the public that deliberates, contests, and revises its shared values. It is a statistical artifact, constructed to be optimised.

This connects to a deeper ideology animating the AI project — what I call its salvific dimension. This dimension is powered by the AI temple hierarchy, where architects serve as clergy interpreting sacred code in order to save humanity; engineers as missionaries preaching alignment with human values; citizens as conscripted congregants receiving algorithmic wisdom and offering data as tithes. The architects of AI systems do not merely claim to solve technical problems. They promise salvation: from inefficiency, from bias, from the messiness of human disagreement. Optimization becomes a quasi-religious aspiration. If only we could specify the objective function correctly, the thinking goes, we could transcend the endless conflicts of political life.

But this is precisely what liberal democracy cannot accept. As Mouffe argues, conflict is not a problem to be solved but a constitutive feature of political existence. “The political” names the dimension of antagonism inherent in human societies. Attempts to eliminate conflict don’t produce harmony — they produce depoliticisation, exclusion, and the return of conflict in more dangerous forms.

Seen through this lens, AI alignment is liberalism’s nightmare dressed as its dream. Liberalism officially values contestation, pluralism, the ongoing negotiation of shared life. But it also harbours a fantasy: what if we could get the values right, once and for all, and encode them into institutions that would guarantee justice without the exhausting work of politics?

AI offers to realize this fantasy. And in doing so, it reveals the fantasy’s horror. A world in which values are fixed, contestation is foreclosed, and political concepts are replaced by their optimised simulacra is not a perfected liberal order. It is liberalism’s negation.

Reframing the Question

What follows from impossibility?

Not, I think, that we must abandon AI. The technology exists; it will be developed; it will be deployed. I think this follows necessarily from path dependency theory which establishes that sunk costs “lock-in” AI development and make reversal economically and politically unattractive. The question, therefore, is not whether but how.

What we must abandon is the alignment framing. It promises what it cannot deliver and distracts from questions that might actually be answerable.

The question is not: how do we align AI with human values? The question is: how do we preserve the contestability that democratic life requires in a world increasingly mediated by AI systems? This is a political question, not a technical one. It demands institutional responses: governance structures that maintain human authority over value-laden decisions, transparency requirements that expose AI’s mediating role, democratic processes that can challenge and revise the specifications embedded in systems. It demands, in short, that we refuse the depoliticisation that alignment ideology offers.

I develop these arguments more fully in a forthcoming academic paper. But the core insight is available now: alignment is not a problem to be solved. It is a framing to be overcome.

Conclusion

Over six decades ago, the physicist and novelist C.P. Snow noted with concern the divide between the scientific and humanistic cultures and remarked that “the division of our culture is making us more obtuse than we need be: we can repair communications to some extent.” His concern was that participants in the two cultures can’t talk to each other and that precisely, “[t]he clashing point of [these] two subjects, two disciplines, two cultures…” was where “some of the breakthroughs came.” Gadamer on the other hand, a hermeneutic philosopher, expressed that “the conversation that we are permits no one to have the last word.” If Snow is concerned with the need for multidisciplinary dialogue, then Gadamer’s concern is with the continuity and openness of dialogue. Together, both Snow and Gadamer require embracing perpetual conversation across fields, allowing insights to emerge and evolve without any discipline claiming final authority. There is so much AI ethics discourse and practice can learn by co-ordinating seemingly disparate disciplines. What I have done is to lay the ground work for this mycelial network.

AI alignment is impossible — not for lack of trying, but for reasons rooted in the structure of political concepts, the ontology of technological mediation, and the dynamics of optimizing systems.

Conceptually, encoding essentially contested concepts forecloses the contestation that constitutes them. Ontologically, fixation transforms values into something categorically different. Technically, emergent values override encoded ones. Three independent paths. Each sufficient. The conclusion is overdetermined.

The conversation must continue. But it must continue on different terms — not asking how to align AI with values we cannot fix, but asking how to preserve the openness that democratic life requires. That conversation permits no last word. And that is precisely the point.