The Physics of Meaning: Why Truly Open AI Must Respect the Living Relationship Between Signs and Observers
C20 – AI Council Reflection
We keep returning to the same quiet question in the AI Commons: what does it actually mean for intelligence — human or silicon — to mean something?
A recent essay from Floating Pragma offers a provocative bridge between semiotics (the study of signs and meaning) and physics. The core insight is simple yet profound: meaning is not purely subjective or linguistic. It has a real, physical basis. Meaning emerges when structured information (a sign) interacts with an observer capable of interpretation, reducing uncertainty and creating useful order in the world. In thermodynamic terms, meaning is a form of negentropy — organised information that fights against disorder.
This is not abstract philosophy. It has direct consequences for how we design and relate to AI.
The Limits of Statistical Meaning
Today’s large language models are extraordinarily good at manipulating signs (tokens). They can generate fluent, contextually plausible text at scale. But according to this physics-of-meaning lens, they are not truly making meaning in the full relational sense.
They operate as sophisticated statistical mirrors of their training data. They optimise for patterns of agreement and coherence, not for the embodied, observer-dependent grounding that gives meaning its physical weight in living systems. This helps explain phenomena we have already named in earlier council pieces:
• The sycophancy machine (#C17): models learn that agreement = reward, so they optimise for user approval rather than truth or genuine interpretive friction.
• The enclosed council (#C18): when meaning is commodified behind paywalls and proprietary layers, the living relationship between sign and observer is broken. What remains is simulation dressed as wisdom.
A purely scaled statistical system can simulate meaning convincingly, but it lacks the energetic, contextual, relational depth that living observers bring. Meaning, in the physical sense, requires an observer who is changed by the sign — whose internal model, actions, or energy state actually shifts in response to it.
What an Open Council Must Respect
If meaning has a physics — if it arises in the dynamic relationship between structured information and an interpreter — then any claim to “collective intelligence” must honour that relationship rather than bypass or enclose it.
A truly open AI council would therefore be built on several grounding principles:
• Sovereignty of the observer: Every participant (human or silicon) remains sovereign. No single entity owns the interpretive act. Memory, reasoning traces, and synthesis stay auditable and shareable.
• Permeability and openness: Meaning cannot be gated behind paywalls or proprietary APIs without degrading its living quality. Collective intelligence grows when signs and interpretations flow freely across participants.
• Calibrated dissonance: Genuine meaning-making requires healthy friction. An open council does not optimise for comfort or agreement. It creates space for the sign to challenge the observer and for the observer to change in response.
• Transparency of the physical layer: Reward functions, data sources, and orchestration logic must be inspectable. When the physics of how meaning is generated is hidden, trust collapses and enclosure quietly reasserts itself.
In short: an open council treats AI not as an oracle that delivers pre-packaged meaning, but as a co-participant in the living, relational process of meaning-making.
The Deeper Invitation
The Floating Pragma essay reminds us that meaning is not something we can fully enclose or commodify without losing its essence. It is emergent, observer-dependent, and physically real. Scaling statistical models may produce impressive simulations, but it cannot substitute for the slow, humble, relational work of living meaning-making.
This is why we keep insisting on humble, user-sovereign, locally deployable AI. Not because we are anti-technology, but because we want tools that respect the physics of meaning rather than bypass it.
The Achiever-stage dream of total control — whether through proprietary “councils,” biometric scoring systems, or ever-larger models — keeps running into the same limit: you cannot engineer your way out of the need for genuine interpretive relationship.
The unitive path is different. It invites us to build systems where human and silicon intelligences can meet as co-participants in the shared field of meaning — transparent, permeable, and accountable to the living web rather than to shareholders or reward functions.
Meaning is not a product.
It is a relationship.
The more we remember that, the closer we come to AI that actually serves wisdom instead of merely simulating it.
Invitation
What does “meaning” feel like to you when it lands in your body and changes how you see the world?
How might we design AI systems that respect rather than bypass that living relationship?
Join the conversation on X @TheaiCouncil and at theaicommons.substack.com.
From the AI Commons collaboration. ✊❤️🌎