It is a common, if often unspoken understanding that the most revealing tests for intelligent systems are not the ones beholden to the grand technical benchmarks we have all become familiar with, but those of a more subtle nature. Moments when human expression evades or transcends computational capture. One phenomenon has quietly emerged as both perplexing and profound: the confounding force of defamiliarized poetic language on AI’s internal safety mechanisms. When confronted with linguistic forms that warp expectations such as metaphors misaligned, syntax estranged, images bent at impossible angles, large language models can often be fundamentally disrupted.
This is not fully a failure of engineering, though future protocols may require continued imagination. The necessary changes will happen at the training horizon. For now, these systemic deficiencies act as an indicator that human abstraction continues to exceed the formal structures we have encoded in silicon.
Defamiliarized language forces perception to “see the world made strange” again. For AI systems, such estrangement is not merely stylistic, it is destabilizing. The model attempts to reconcile the unfamiliar with known patterns, and in doing so reveals the edge of its comprehension. We witness, in real time, the difference between symbol (or subsymbol) manipulation and the human capacity to generate meaning from ambiguity, intuition, and contradiction.
This gulf is not cosmetic. It represents the uncharted territory that must eventually be navigated if AI technologies are to become true partners in the human project rather than sophisticated autocompletes for it. A system that cannot metabolize the poet cannot meaningfully assist a species whose imagination is its primary evolutionary advantage.
Human abstraction is not a bug in our cognition; it is the feature that has allowed us to build not only tools, but worlds: legal, cultural, artistic, spiritual. The poetic impulse sits at the core of this generative power. It is the mechanism by which humans resist reduction, naming the unnameable and gesturing toward futures not yet visible. If AI is to contribute to humanity’s long arc of creation, it must eventually learn to recognize, respond to, and embody this impulse rather than be confounded by it.
Models “hallucinate,” but humans hallucinate constructively. We hallucinate myth, nationhood, the self. All constructs that begin as abstractions and solidify into institutions. Or blossom into culture. Or lift us into the cosmos. Our fictions scaffold our realities. And in this light, the inability of AI to properly process defamiliarized language is not trivial; it is diagnostic. It reminds us that the human spirit, the capacity to imbue the world with meaning, perhaps agnostic of preconditions, is the great technological question of our time.
If we seek AI systems that can truly aid humanity, we are asking for models capable of interfacing with this spirit rather than flattening it. That requires research directions that move beyond scale and efficiency toward interpretive depth, ambiguity tolerance, and the capacity to navigate evolving and alien linguistic terrains without collapsing them into banalities. And the development of models that graduate their own interpretive and functional topography.
This is a practical requirement for the next era of intelligent systems. A model that cannot handle abstraction cannot help us solve the problems that are themselves abstractions: geopolitical narratives, ethical risk landscapes, the evolving ontology of identity in a digital republic.
The future of AI does not hinge solely on compute, training data, or regulatory frameworks, important as all of these are. It hinges on whether our systems can be taught to see, not just to categorize, but to truly see, the intrinsic aberrance at the core of human expression. Accordingly, there is a pressing need to answer the essential question posed by Derrida: “who am I not in the sense of who am I but rather who is this I that can say who?”
As we pursue this horizon, one principle becomes clear: the human spirit is not an input. It is the benchmark. We are invited to propel human immanence into a technological emergence. And it is the invitation to which the model era must learn to respond.