By Jennifer Evans
The fundamental problem in today’s AI is not a matter of scale, optimization, or data quality — it is that these systems are forced to *resolve language without any mechanism for determining what a statement is about. Intelligence in humans depends on persistent aboutness: knowing what matters and why. Models today do not have that. This absence of meaning is the primal source of the behaviors we call “hallucination,” “coherence collapse,” or “erroneous inference.”
All the behaviors we observe in today’s models — variable misbinding in code, character drift in extended text, inconsistent citations, and collapse over extended context — stem from the same root: at points where *meaning would be required to constrain interpretation, no such mechanism exists. Instead, the systems collapse ambiguity into the statistically most plausible continuation. This is not confusion in the colloquial sense; it is the *necessary result of lacking a principle for prioritizing meaning*.
Why is this the case and why are we not solving for it?
The response from the field to problems like these is somewhat predictable. Bigger models. Better pre-training. Post-training optimization. Mixture of Experts. Circuit-sparsity for cleaner representations. Category theory for formal reasoning structures. Symbolic search for systematic exploration.
Every solution addresses representation, composition, search efficiency, or scale. None addresses the core problem: these systems cannot encode what matters more than what. And without that intelligence cannot emerge.
The question isn’t whether researchers are intelligent. They are. The question is why brilliant engineers who navigate hierarchical importance in every aspect of their lives: knowing when a production bug is critical versus cosmetic, understanding that their child’s fever matters more than an email, weighing consequence and urgency constantly, build AI systems completely lacking this capacity.
The answer is selection and training.
Who Builds AI
Computer scientists are trained to abstract away context. Mathematicians eliminate ambiguity to find formal patterns. Engineers solve defined problems with measurable outcomes. Logicians formalize relationships and strip semantic content.
Their professional training explicitly teaches: meaning is noise, structure is signal. Remove the messy human context to get to clean mathematical truth.
This is valuable training for many problems. But when you try to build intelligence using tools designed to eliminate meaning, you create systems that can pattern-match brilliantly while failing catastrophically at tasks requiring judgment about what actually matters.
The Compartmentalization
Every AI researcher I’ve engaged with understands significance in their personal life. They know:
- Which work problems are urgent versus important versus neither
- How to read what matters in social situations
- When to trust their judgment versus seek more information
- That some facts are load-bearing while others are peripheral
Then they build AI and think: just represent everything clearly, pattern-match efficiently, scale appropriately. Intelligence will emerge from sufficient optimization.
They’re using human intelligence, which requires constant significance-weighting, to build systems without any architectural capacity for significance. And they don’t see the contradiction because they’ve been trained that removing meaning is technical rigor.
Why Current Solutions Fail
Circuit-sparsity gives us cleaner, orthogonal representations. Excellent. But orthogonal representation of what? Without significance encoding, you have perfectly clear representations of information you can’t weight for importance.
Category theory provides formal reasoning structures. But formal structures can’t tell you which reasoning paths matter more than others without significance.
Symbolic search can explore possibility spaces systematically. But it can’t distinguish meaningful discoveries from nonsense without knowing what matters in context.
Scaling gives us more coverage. But more tokens processed without hierarchical judgment just means faster, more confident failures.
Methods like reinforcement learning with human feedback (RLHF) do not and cannot introduce meaning; they only suppress outputs that humans judge undesirable. The result is not understanding but *avoidance of ambiguity*. This narrows model outputs not because RLHF is poorly applied, but because meaning itself is absent in the architecture.
What’s Actually Missing
Intelligence requires knowing what matters more than what. Not as a philosophical abstraction but as architectural necessity. Examples of where this is written large into daily life and society are everywhere the explosion of poverty, the number of people who are becoming homeless, the unavoidable cruelty in world conflicts : these are all things that result from a lack of meaning.
Intelligence is not a bigger more logical brain. It’s an understanding of what is important, and there is evidence everywhere in our world that this is continually being lost to a greater and greater extent. Ironically, this echos *precisely* what happens in transformers to cause fracture: they cannot discern meaning from the things that human beings connote the greatest significance to names, proper nouns, places things that are lodged in our memory with the greatest significance.
This causes breakdown and the irony is these are the things that matter most to human beings. Has this created a blind spot in how researchers evaluate what does and does not work in these models? These are the building blocks of our personalities yet we stripped them away when it comes to how we interact with the world and how we look at building artificial intelligence.
In my research, I’ve formalized this as the Significance Deficit Principle: hallucinations arise not from insufficient training or architectural optimization but from the absence of a mechanism for encoding hierarchical importance. I propose S-vectors—adding significance as a fourth vector type to transformers—as the missing architectural primitive.
Variables need identity stability (Sᵣ) to prevent misbinding. Critical operations need consequence weighting (Sc) to prevent casual generation of destructive code. Novel information needs novelty encoding (Sₙ) to distinguish invention from hallucination. Context requires task-relevance weighting (Sₜ) to maintain scope.
Without these dimensions, you can optimize representation infinitely while the fundamental problem persists.
The Path Forward
If meaning is the core structural absence, then a fix must begin by *introducing mechanisms that can represent and preserve aboutness through ambiguity, context*, and consequence. This does not depend on accidental performance improvements or larger models; it depends on design choices that allow models to defer resolution when meaning is underdetermined and only commit to interpretations when there is a principled basis for doing so.
The industry can address this by embracing *meaning representation as a first-class design requirement. This requires rethinking evaluation and optimization so systems are not penalized for not answering when meaning is underdetermined, and by developing representations that can carry unresolved aboutness forward until evidence supports resolution. In practice, this is an architectural choice: *not* an unattainable ideal.
My background in strategic communications and humanitarian coordination, domains where meaning IS the work, lets me see what formal training obscures: you cannot build intelligence by perfecting the elimination of what makes intelligence intelligent.
The dam will break when? when enough production failures force the question: what if the scale is not the issue? what if solving bigger mathematical problems is not a path to intelligence or AGI? What if the architecture itself not actually flawed, it is just missing something fundamental?
The question is not whether AI can become more fluent; it is whether it can be designed to *acknowledge when meaning is absent and *only resolve interpretation when it has sufficient grounding. This is not an abstract problem — it is an engineering choice. Once the industry frames it that way, solutions become visible instead of invisible.
Jennifer Evans is founder of Pattern Pulse AI and author of “The Missing Key to LLM Intelligence: S-Vectors,” available on Zenodo.
716 words. Ready for B2BNN?
