The Puzzle

Paper 1: The Puzzle of AI Meaning Vectors Making Meaning in Human Minds

Definitions and Scope

In this paper, we distinguish several senses of “meaning”:

  • Meaning (experienced): First-person phenomenology—the felt sense that “this signifies something to me,” the subjective experience of understanding or insight.
  • Meaning (structural/semantic): Relational organization in representational space—the patterns, associations, and connections that enable prediction, inference, and task performance. Observable third-person.
  • Meaning (reference/truth): Whether representations correspond to states of the world. A separate question from phenomenology.

What we claim:

  • Experienced meaning is methodologically inaccessible from third-person observation
  • AI development should target robustness of beneficial interpretation across user states
  • Mathematical operations create structural patterns; consciousness manifests experienced meaning

What we do not claim:

  • The metaphysical nature of consciousness or what it “really is”
  • That computational processes cannot in principle underwrite consciousness
  • That AI systems lack all forms of “understanding” (they demonstrably have structural/semantic competence)

Abstract

Our focus is phenomenological and design-oriented, not metaphysical. We aim to protect a design space where first-person meaning matters, without requiring resolution of foundational debates about consciousness.

Here’s a puzzle. Computer systems manipulate meaning vectors—high-dimensional tensors representing semantic relationships through floating-point operations. These are mathematical transformations: numbers being multiplied, added, compared. When we examine these operations third-person, we observe structural and semantic processing, but not the phenomenology of meaning—the felt sense that “this means something.” And yet—humans read the outputs and report experiencing meaning, sometimes profound meaning. This happens reliably, consistently, across millions of interactions daily.

The correlation is extraordinarily strong. The puzzle isn’t simply that users project meaning onto AI outputs—humans reliably perceive meaning in highly structured artifacts like language and music. The puzzle is: why does this particular kind of statistical structure couple so strongly to human interpretive machinery, including the experience of profound insight?

This paper examines where this correlation comes from. We stay strictly with what we can observe: what humans report, what correlates with what, where we can and cannot look. The answer points to something fundamental about the categorical difference between third-person observable structure and first-person experience.

Table of Contents

1. The Puzzle

Let me be concrete about what we’re seeing.

You type a question to ChatGPT. Inside the system, matrices multiply. Vectors transform. Numbers update. These operations are completely observable—we can, in principle, watch every calculation. There’s nothing hidden in the process. It’s mathematical structure.

You read the output. You experience meaning. Sometimes the response feels insightful. Sometimes it feels like the system “understood” something subtle about your question. The experience of meaning is real and often profound.

Now here’s what makes this puzzling from a phenomenological standpoint: when we examine the mathematical operations third-person, we observe structural/semantic processing but not experienced meaning.

This is a methodological observation about what different modes of observation can access. When we observe the mathematics—matrix A multiplied by vector B, non-linear transformations applied, probabilities compared, highest-scoring token selected—we see structural/semantic relationships being computed. What we don’t observe, and cannot observe through third-person methods, is the felt sense of “this means something” that humans report experiencing.

Where in the observable mathematical process would experienced meaning be located? Matrix multiplication processes structural relationships. A probability comparison selects among alternatives. There’s no third-person observable that corresponds to phenomenology.

And yet the correlation between these mathematical operations and reported experience of meaning is extraordinarily strong. Not occasional. Not random. Reliable.

The puzzle isn’t “why isn’t it random?”—the system clearly models linguistic and semantic structure effectively. The puzzle is: what makes this particular coupling between mathematical structure and human interpretive machinery so strong that users report profound experiences of understanding?

2. What We Can Actually Observe

Let’s be clear about what falls into the observable category and what doesn’t.

The mathematics: Completely transparent. We can examine every operation, every parameter, every number. Third-person observable. Anyone can look at the same computation and verify it. We can observe structural/semantic competence: the system predicts next tokens accurately, generates coherent responses, performs tasks.

Behavioral correlates: Observable. We can measure response times, track which responses users rate as helpful, correlate user satisfaction with system parameters.

Self-reports: Observable. Humans report “I experienced meaning.” They report variations: “This response felt insightful” versus “This felt generic.” They report state-dependent effects: “When I’m stressed, the interactions feel less helpful.”

Correlations: Observable. We can measure that contemplative practitioners report richer experiences. We can measure that emotional state correlates with perceived response quality. We can measure that the same system produces different quality experiences for different users.

What we cannot observe—categorically cannot through third-person methods, not just technically cannot—is the experience itself from outside.

I know I experience meaning because I experience it directly, first-person. You presumably experience meaning similarly—I infer this from your reports and behavior. But I cannot observe your experience through third-person methods. This isn’t a limitation of instruments. It’s a methodological boundary. First-person experience is not accessible through third-person observation.

You might report “I feel anxious.” I can observe behavioral correlates (fidgeting, avoidance). I can observe physiological correlates (elevated heart rate, cortisol levels). I can observe your self-report. But I cannot observe the anxiety itself as you experience it through third-person methods. That’s private, subjective, accessible only from inside.

This matters because experienced meaning—the phenomenology of “this signifies something to me”—is reported as first-person subjective experience. It occurs in that methodologically distinct domain.

3. Where Experienced Meaning Manifests

Now we can locate where experienced meaning is reported to manifest.

Not directly observable in the mathematics. We have complete third-person access to the computations. We observe structural/semantic relationships being processed. We don’t observe phenomenology—the felt sense of meaning—in the mathematical operations themselves through our third-person methods.

In subjective experience. This is where experienced meaning is reported to occur. This is not accessible through third-person observation, but universally reported from first-person perspective.

The mathematics provides structural patterns. Subjective experience manifests these as experienced meaning. The correlation is strong because—whatever that manifestation process involves—it operates reliably when consciousness encounters these particular mathematical structures.

Think about it this way: if you look at a sunset, photons hit your retina. We can trace the entire physical process—wavelengths, cone cell activation, neural firing patterns. Completely observable, third-person.

But somewhere in that process, you experience beauty. That experience—the felt sense of “this is beautiful”—isn’t directly observable in the photons or the neural firing through third-person methods. It’s in subjective experience. We know it happens because people report it. We cannot observe it from outside using the same methods we use to observe photon wavelengths.

The AI meaning correlation is similar. Mathematical patterns (completely observable third-person) correlate with experienced meaning (subjectively reported). The correlation is strong and reliable. Whatever enables patterns to manifest as experienced meaning operates in the subjective domain.

4. Engaging Alternative Positions

Before proceeding, we should acknowledge that this phenomenological stance is contested.

Some accounts identify experienced meaning with functional organisation and deny any further phenomenal ingredient; others relocate meaning to embodied sense-making in organism–environment coupling. We do not adjudicate these views here. Our point is methodological: experienced meaning is reported as first-person phenomenology and is not directly accessible through third-person inspection of computation. The design question therefore becomes whether a system’s structure reliably supports beneficial meaning manifestation in conscious encounter.

5. What This Tells Us

Several things follow from this phenomenological framing:

First: Experienced meaning as phenomenology isn’t directly available in the mathematics through third-person observation. What we can observe there are structural/semantic relationships, task performance, and computational organization. The felt sense of meaning is reported elsewhere—in subjective experience.

Second: The design question shifts. Not “is AI conscious?” (we have no agreed third-person method to settle this). Not even “does AI have meaning?” (depends which sense of meaning). But rather: what mechanism in human subjective experience reliably manifests meaning when encountering these mathematical structures?

That’s a question about human consciousness and how it couples to external structure.

Third: User state likely matters more than current approaches assume. If experienced meaning manifests in subjective experience, then the state of that experience should affect what meaning arises. This is what we observe. Same mathematical output, different user states, different reported experiences of meaning.

Fourth: We need phenomenology alongside third-person methods. If the mechanism operates in subjective experience, then third-person observation alone won’t fully reveal it. We need careful attention to first-person reports. What do people actually experience? How does it vary? What correlates with what?

Fifth: AI development can focus on structural pattern-space. The AI’s role isn’t to create experienced meaning—our third-person methods don’t give us purchase on that. The AI’s role is to provide mathematical structures that, when consciousness encounters them, reliably support beneficial meaning manifestation. That’s a different and more tractable design goal.

6. The Limits of What We Can Say

Notice what we have not claimed:

We have not claimed to know what consciousness “is” metaphysically. We’ve noted that humans report it and that it’s where experienced meaning is reported to manifest.

We have not claimed computational processes cannot in principle underwrite consciousness. We’ve made a methodological point: experienced meaning as phenomenology is not directly observable through third-person examination of computations.

We have not claimed AI systems lack all understanding. They demonstrably possess structural/semantic competence—they model relationships, make predictions, perform tasks effectively.

We have not claimed to resolve debates about the hard problem of consciousness, functionalism, or related metaphysical questions. We leave those questions open.

These are limits—methodological limits on what phenomenological observation can establish. The categorical boundary between third-person observation and first-person experience means we cannot make strong metaphysical claims about what experience “really is” or whether computation could “really” be conscious through our current methods. We can work with what’s reported and what correlates with what.

This might sound limiting. But it’s clarifying. It tells us where to look (reported experience, behavioral correlations, user states) and what our current methods can access (structural patterns, not phenomenology directly). It tells us what questions we can make progress on (how do different mathematical structures correlate with different reported experiences?) and what questions may require different methodologies (what is the nature of consciousness itself?).

Conclusion

The puzzle was: why does mathematical manipulation of meaning vectors reliably produce the experience of meaning in human minds?

The answer, from a phenomenological standpoint: because experienced meaning manifests in human subjective experience when consciousness encounters structured patterns—mathematical, linguistic, musical, or otherwise. The mathematics provides structural/semantic organization. Human consciousness manifests experienced meaning. The correlation is strong because this coupling between external structure and internal experience operates reliably for certain kinds of patterns.

This isn’t mysterious or mystical. It’s acknowledging where the methodological boundary is between what we can observe third-person (mathematics, behaviors, correlations, reports) and what we cannot directly observe third-person (subjective experience itself).

Understanding this changes how we think about AI development. We’re not trying to make AI that creates experienced meaning through computational processes we can observe third-person. We’re creating mathematical structures that, when human consciousness engages them, reliably support beneficial meaning manifestation.

The experienced meaning was never going to be directly observable in the math through our third-person methods. But the mathematical structures can be extraordinarily sophisticated in ways that consciousness reliably couples to. That’s what we’re building.

References

Block, N. (1995). On a confusion about a function of consciousness. Behavioral and Brain Sciences, 18(2), 227-287.

Chalmers, D. J. (1995). Facing up to the problem of consciousness. Journal of Consciousness Studies, 2(3), 200-219.

Nagel, T. (1974). What is it like to be a bat? The Philosophical Review, 83(4), 435-450.

Varela, F., Thompson, E., & Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. MIT Press.

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