The quiet drift beneath the surface of AI fluency
Elin Nguyen - January, 2026
The story we tell ourselves about intelligence
For most of the past few years, the story we’ve told ourselves about artificial intelligence has been comfortingly simple. When systems hallucinate facts, contradict themselves, or offer confident nonsense, it’s because they aren’t good enough yet. They need more data, more parameters, better alignment. Intelligence, we assume, is the missing ingredient. Once it scales far enough, reliability will naturally follow. It’s a reassuring belief. It lets progress feel inevitable. And it has just enough truth in it to survive scrutiny.
A problem that doesn’t look like a failure
But there is a quieter problem hiding underneath the noise of benchmarks and product demos—one that doesn’t show up as an obvious error, and doesn’t announce itself as a failure. It shows up instead as a subtle instability in meaning itself. And once you notice it, it becomes hard to unsee.
When fluency starts to frame reality
In recent years, AI systems have become fluent in almost every domain we point them at. They draft contracts, summarize medical literature, advise executives, and increasingly frame the decisions humans go on to make. Even when a person retains final authority, the model often draws the map first: what matters, what doesn’t, what the problem even is. That framing power is easy to underestimate, because it arrives wrapped in clarity and confidence. And yet, confidence can be a remarkably poor proxy for coherence. To understand why, it helps to step away from language entirely.
This isn’t hallucination but disagreement about reality
Much of the debate about AI reliability has been tangled up in words—ambiguous prompts, vague instructions, open-ended questions. So to remove excuses, I turned to a different kind of test: ARC-style reasoning tasks. These are the industry’s own benchmark for abstraction and intelligence. They are non-linguistic, fully observable, symbolic puzzles. Humans agree almost instantly on what they are looking at. There is no hidden context to infer, no rhetorical trick to navigate. The expectation going in was modest. Different models might solve the puzzle better or worse. Some might fail, others might succeed. What I did not expect was that they would disagree on something more basic. Given identical inputs and identical instructions, frontier models quietly constructed different internal versions of the task itself. Not different strategies, but different objects. Different assumptions about structure. Different dimensions. Different boundaries around what counted as the thing being reasoned about. Each model then proceeded to reason quite competently—just not about the same problem.
Different synonyms are one thing, different ontologies another
This is what I’ve come to call interpretation drift: the instability of a system’s internal task representation under fixed conditions. It is not a failure of logic in the usual sense. It happens before reasoning begins. And it cannot be explained away as randomness or sampling noise, because it shows up in the very geometry of the task—what exists, how big it is, how parts relate to one another. Once you see this, a number of familiar AI failure modes snap into focus. Hallucinations are no longer just wrong facts. They are the natural outcome of reasoning over an object that was never anchored in the first place. Inconsistencies are not mere lapses of memory; they are symptoms of a task representation that shifts between prompts. Unreliability is not an isolated defect, but the downstream consequence of unstable meaning.
When evaluation itself begins to slip
Most importantly, evaluation itself starts to break down. When models disagree about what the task is, assessing correctness becomes an act of interpretation layered on top of interpretation. Humans are forced to reconstruct mental models from fluent explanations, even when those explanations describe structures that never existed. The ground truth recedes, replaced by competing, internally coherent stories.
Why mitigation is not the same as stability
None of this is an argument that AI systems are useless, or that progress has been illusory. On the contrary, the industry has become quite good at managing the visible symptoms. Retrieval keeps facts straighter. Structured prompting narrows the space of answers. Human oversight catches what slips through. These mitigations work, and they matter. But they also reveal something important. If intelligence alone were enough to deliver stability, we wouldn’t need so many layers designed to compensate for its absence.
The fault line AI must not cross
There is a boundary here, and it has less to do with how smart a system is than with what we ask it to hold. Authority—real authority—assumes shared objects, stable meaning, and traceable responsibility. It assumes that when multiple agents participate in a decision, they are at least reasoning about the same thing. That assumption holds for humans not because we are flawless, but because our institutions—laws, procedures, accountability structures—exist precisely to keep meaning from drifting too far. AI systems do not have that kind of anchor by default. They reconstruct meaning dynamically, probabilistically, and silently. As long as interpretation drift remains non-zero—and today it demonstrably is—these systems can assist decisions, inform judgment, and expand human capability. What they cannot safely do is carry autonomous authority in high-stakes domains where stability is not optional.
This is not a verdict on the future of AI. It is a recognition of the present. The mistake would be to treat this boundary as a temporary embarrassment, something scaling will eventually wash away. The more productive response is to see it for what it is: a design constraint.
From intuition to evidence
In recent work Empirical Evidence of Interpretation Drift in ARC-Style Reasoning using these ARC-style reasoning tasks—the field’s own benchmark for abstraction and intelligence—I tested whether frontier models, given identical inputs and instructions, would at least converge on the same task object before reasoning began. They did not. Models diverged not only in solutions, but in what they believed the task was: its structure, its dimensionality, even the objects they treated as real.
This matters for governance because it removes a long-standing ambiguity. Concerns about hallucinations, inconsistency, or overconfidence are often waved away as surface defects, destined to disappear as models scale. The ARC results show something more fundamental: instability appears upstream of reasoning, at the level where meaning itself is formed. For policymakers and safety practitioners, this provides a concrete reference point—an empirical artifact that grounds what has otherwise remained a largely philosophical concern.
The question that remains
Once you name it, the question shifts. It is no longer just “How do we make models smarter?” but “Where should meaning live when decisions matter?” Should it remain inside systems optimized for prediction, or should it be enforced structurally, outside the model, in ways that make drift visible and accountable?
What must not be allowed to float
Complex systems don’t stay coherent by accident. They stay coherent because someone, somewhere, decided that certain things were not allowed to float. Seeing that clearly is not pessimism. It is how progress becomes durable. And perhaps that is the real opportunity here—not to chase ever more fluent intelligence, but to learn how to build systems that know, reliably, what they are talking about before they decide what to do.
Disclaimer
This article was written with the assistance of AI used as a mechanical text-editing tool. Large language models do not possess intent, understanding, perspective, or authority, and cannot generate meaning independent of human direction. Therefore, all framing, arguments, judgments, and responsibility for the claims here rest entirely with the human author.
In a follow-up piece, I will examine a quieter danger now emerging: the reflex to reject AI-assisted text altogether—and how that reaction risks confusing accountability with authorship, turning fear itself into a quiet form of authority transfer.
