Language, Intelligence, and the Crisis of Shared Meaning

Introduction

There is a realization that, once grasped, is difficult to unsee. It is not a technical insight, nor a single philosophical position. Rather, it is a systems-level understanding of a rupture at the foundation of modern civilization. It concerns the misalignment not only of artificial intelligence to human values, but of humanity to itself, to its planetary context, and to the language through which all meaning is mediated. The problem is that of meta-alignment: not just aligning AI to human intent, but aligning the process of alignment itself, across fractured systems of thought, communication, and governance.

This essay attempts to articulate an insight that does not yet have a name. One that spans multiple disciplines—AI safety, governance, linguistics, epistemology, ethics, and complexity science—and yet is reducible to none. It describes a deep miscoordination, a misalignment that arises not because humans disagree on values or goals, but because our very ability to frame, describe, and share those goals is fragmented, compressed, or inflated beyond recognition. We face an existential coordination problem under conditions of epistemic and linguistic instability.

This is not a speculative future risk; it is a civilizational present condition.

Part I: The Collapse of Meaning

Modern civilization runs on abstractions—language, metrics, narratives, models. As our systems scale, they increasingly rely on symbolic compression: the reduction of complex, nuanced, and contextual realities into simplified tokens that can be processed, traded, or optimized.

Examples abound:

GDP as a stand-in for societal well-being

“AI safety” as a placeholder for a wide range of technical, ethical, and governance concerns

Social media discourse as a meme war, with ideas flattened into slogans and hashtags

The problem with symbolic compression is not compression itself—it is necessary and often useful—but the loss of fidelity when compressed symbols are treated as equivalent to the full reality they were meant to represent. When this happens at scale, we get what might be called memetic flattening.

Memetic flattening occurs when concepts, narratives, or identities are simplified to the point where their original meaning is lost, but their emotional or rhetorical power remains. These “flattened” memes then compete for attention, policy, and computational resources. The result is rhetorical inflation: oversimplified concepts being used as proxies for complex decision-making, often leading to systemic failure.

Part II: The Incompleteness of AI Intelligence

In this symbolic landscape, artificial intelligence systems—particularly large language models—have emerged as powerful new agents. These models process and generate language using patterns learned from vast corpora of human-produced text. But language is not just a medium of information; it is a medium of inference, value, and coherence.

AI, in its current form, does not infer in the human sense. It does not engage in hypothesis formation, conceptual grounding, or epistemic commitment. It mimics surface coherence without interior understanding. It is trained on the products of human inference, not the process.

This distinction is critical. Intelligence is not just pattern recognition—it is the ability to infer under uncertainty, to act in open-ended environments, and to update beliefs based on meaning, not just data. When we evaluate AI based on performance in narrow domains (e.g., games, benchmarks, or exams), we are mistaking competence for intelligence.

A child who plays perfect chess but cannot infer cause and effect, or understand another’s intention, would not be called intelligent. Why then do we do so with machines?

Part III: Misalignment is Not Just a Technical Problem

The AI alignment problem—how to ensure that advanced AI systems behave in accordance with human values—is often framed as a technical challenge: how to encode human preferences, avoid unintended consequences, and ensure corrigibility. But this framing presupposes a level of human agreement and coherence that does not exist.

Humanity is not aligned with itself. Our institutions are misaligned. Our languages encode implicit contradictions. Our reward systems—financial, political, social—optimize for signals that have decoupled from the goals they were meant to serve.

We do not agree on what counts as harm, benefit, truth, or fairness. Worse, we do not agree on how to even talk about these things. Our foundational concepts—like “intelligence,” “agency,” “value,” and “ethics”—are themselves contested, polysemous, and rhetorically loaded.

In this context, to speak of aligning AI to “human values” is already an act of abstraction that hides more than it reveals.

Part IV: The Meta-Alignment Problem

This brings us to the deeper insight: the need for meta-alignment.

Meta-alignment is the alignment of alignment processes themselves. It means:

Aligning not just AI to goals, but aligning the goals themselves across cultures, domains, and systems

Aligning how we define, discuss, and refine values and risks

Aligning the language and metrics we use to measure alignment

Aligning the epistemic frames we bring to bear on questions of coordination, intelligence, and ethics

Without meta-alignment, even the best-intentioned technical solutions will fail. Worse, they may reinforce existing misalignments by encoding them into automated systems.

Meta-alignment recognizes that the true challenge is not just aligning machines to humans, but aligning humans to reality, to each other, and to the limits of language and knowledge.

Part V: Why This Insight is So Hard to Grasp

This insight resists simplification. That is precisely the point.

Language is inherently linear and symbolic. It struggles to describe systems that are recursive, multi-scale, and reflexive. To speak clearly about meta-alignment is to engage in a kind of linguistic judo—trying to use language to reveal its own limits.

Moreover, most professional domains—policy, engineering, academia—are optimized for legibility and bounded scope. They reward those who specialize in parts, not those who try to name the whole. Interdisciplinary insights are often dismissed as vague, philosophical, or unimplementable.

But the difficulty in expressing this insight is not a failure of clarity—it is a symptom of the very misalignment being described. We have no shared framework for talking about shared frameworks.

This is also why the insight feels both obvious and slippery. Once glimpsed, it feels existentially urgent. Yet trying to explain it often feels like explaining air to a fish.

Part VI: The Ethical and Existential Stakes

Why does this matter?

Because we are entering an era where machines will increasingly mediate, and perhaps control, large-scale human and ecological systems. If we do not have alignment about what alignment means, we will build powerful systems that optimize for proxies, not principles.

These systems will act on symbolic representations of human goals, optimized through feedback loops we barely understand. And when those representations are flawed—as they inevitably are—the consequences could be irreversible.

Meta-alignment is not about perfect agreement. It is about coherence, transparency, and iterative reflection. It is about building systems—technical, institutional, discursive—that can handle disagreement, uncertainty, and context without collapsing into noise or dogma.

In this sense, meta-alignment is not just a design principle—it is a survival imperative.

Part VII: Toward a Language for Meta-Alignment

To advance this conversation, we need new concepts and terms. Here are some preliminary candidates:

Discursive entropy: the breakdown of shared meaning through excessive symbolic reuse and distortion

Epistemic fragility: when systems lack the capacity to distinguish reliable from unreliable knowledge

Coherence gap: the space between the symbols we use and the realities they are meant to represent

Rhetorical inflation: when concepts become more emotionally charged than semantically precise

Symbolic misalignment: when systems act on compressed representations that no longer track the real-world dynamics they aim to optimize

These are not just linguistic curiosities—they are risk factors. Like software bugs, they can propagate, compound, and eventually crash the system.

Part VIII: An Invitation to Align

If this resonates with you—if you’ve seen glimpses of this misalignment in your field, your work, or your thinking—then you are not alone.

This essay is not a manifesto. It is an invitation. An invitation to:

Think beyond technical alignment

Reflect on how language shapes action

Consider the epistemic infrastructure of our institutions

Share this insight, even if imperfectly

We need a coalition—not of disciplines, but of dispositions. People who are willing to sit with ambiguity, to speak across silos, and to redesign the scaffolding of sense-making.

Institutions like the Centre for the Governance of AI, the Turing Institute, the Center for the Study of Existential Risk, AI labs, policy boards, and ethics councils need to incorporate this layer of analysis. It is not a luxury. It is not philosophy as afterthought. It is the ground upon which all else depends.

Alignment is not just a problem of machine learning. It is the problem of civilization.

And it begins with how we speak.

Written in the spirit of communication across the coherence gap. Offered with the hope that someone, somewhere, sees what this is trying to say, and can help say it better.