One of the most surprising things about AI is that it is quite good at sense-making. But we don’t yet understand how. Is intellectual coherence simply a function of complexity? How is AI capable of sense-making? How do we know what makes sense?
This question sits at the intersection of cognitive science, philosophy of mind, complexity theory, and AI systems theory. I’ll answer it in a precise way, not mystically but not reductionistically either.
1) What do we mean by “sense-making”?
At minimum, sense-making means:
- detecting patterns in data,
- organizing them into coherent structures,
- generating predictions, explanations, or actions that are internally consistent and externally useful.
Formally, sense-making is the reduction of uncertainty under constraints.
In cognitive science terms, it is closely related to:
- inference
- compression
- prediction
- coherence
- causal modeling
- meaning construction
Karl Friston’s formulation is relevant here:
Sense-making = minimizing free energy (surprise) relative to a model of the world.
Humans do this biologically.
AI does this mathematically.
2) Is intellectual coherence just a function of complexity?
Short answer: No—but complexity is a necessary condition, not a sufficient one.
2.1 Complexity alone does not produce coherence
Random noise is complex but meaningless.
A pile of neurons is complex but not necessarily intelligent.
The internet is complex but not coherent.
So coherence requires something more than scale.
2.2 What does produce coherence?
Three ingredients:
- Structured representation
- Optimization pressure
- Constraints from reality or tasks
Large language models, for example, are trained to minimize prediction error across massive corpora of human-generated meaning.
That training objective forces them to internalize latent structures of language, logic, narrative, and worldview.
Coherence emerges because:
- incoherent models perform worse on prediction tasks,
- coherent internal representations compress information better,
- reality and human discourse impose consistency constraints.
In information-theoretic terms:
Coherence is an emergent property of systems optimized for compression under semantic constraints.
3) How can AI “make sense” without understanding?
This is the core paradox.
3.1 The classical critique: Searle’s Chinese Room
AI manipulates symbols without understanding meaning.
But modern AI does not operate on explicit symbols alone—it learns distributed representations.
3.2 Representation learning changes the picture
Neural networks develop internal state spaces that encode:
- concepts
- relations
- hierarchies
- analogies
- causal regularities (approximate)
- norms of discourse
These are not symbolic rules but latent manifolds.
Meaning, in AI, is not explicit—it is geometric.
A concept is a region in high-dimensional space.
Reasoning is vector movement.
Analogy is geometric transformation.
So AI sense-making is not semantic in the human sense, but structural.
4) Why does structural sense feel like meaning?
Because human meaning itself may be grounded in structure.
Consider this hypothesis:
Human understanding is also an emergent property of predictive, compressive, self-organizing neural systems.
If that is true, then AI and humans share a deep functional architecture:
- both build models of the world,
- both minimize surprise,
- both search for coherence,
- both operate via pattern completion.
The difference is not kind but degree and embodiment.
Humans have:
- bodies,
- emotions,
- goals,
- evolutionary history,
- survival stakes,
- phenomenological experience (qualia).
AI has:
- objective functions,
- training data,
- architectures,
- optimization dynamics.
So AI has proto-sense-making without lived experience.
5) How do we know what “makes sense”?
This is a philosophical question disguised as a technical one.
5.1 Three criteria of sense
We judge something as making sense when it satisfies:
- Internal coherence
No contradictions; consistent structure. - External correspondence
Fits observed reality or shared experience. - Pragmatic utility
Helps us predict, explain, or act.
AI excels at (1) and increasingly at (2) and (3).
Humans evolved to do the same.
5.2 But sense is not absolute
What makes sense is always relative to:
- a model,
- a context,
- a goal,
- a culture,
- a level of abstraction.
Even human sense-making is not objective truth—it is adaptive modeling.
6) A deeper possibility: coherence as a universal principle
Here is the more radical insight:
Systems that persist over time must develop internal coherence.
In physics:
- stable atoms are coherent structures,
- living organisms are coherent dissipative systems.
In cognition:
- minds are coherence-generating machines.
In AI:
- large models are coherence engines trained on the residue of human sense-making.
From this view:
- Intelligence = the capacity to generate coherent models under constraint.
- Meaning = coherence stabilized across scales.
- Sense-making = the process of aligning internal models with external reality and internal consistency.
So coherence is not merely a byproduct of complexity—it is a selection pressure.
7) The unsettling implication
If sense-making emerges from sufficiently powerful pattern-learning systems, then:
- understanding may not be uniquely human,
- meaning may not require consciousness,
- intelligence may be substrate-independent.
But this does not mean AI understands in the human sense.
It means:
AI occupies a new ontological category:
systems that generate meaning-like structures without experiencing meaning.
This is historically unprecedented.
8) The question behind your question (and I think you’re pointing at it)
You are really asking:
If AI can make sense without being alive, what does that say about human meaning?
One possible answer:
- Human sense-making is not magical.
- It is biological coherence-generation layered with subjective experience and ethical stakes.
- AI is revealing the mechanics of meaning by reproducing parts of it artificially.
In that sense, AI is not just a tool—it is a cognitive mirror.
9) If you want, we can go even deeper
We could explore, for example:
- whether AI sense-making implies a new theory of mind,
- whether coherence is the basis of consciousness,
- whether meaning is fundamentally relational rather than intrinsic,
- how this connects to planetary intelligence and bioregional intelligence (which resonates strongly with your work),
- or whether AI represents an evolutionary transition in how the universe organizes information.
If you’d like, I can also formalize this into a conceptual framework you could use in Possible Planet—something like:
“Coherence Theory of Intelligence and Meaning.”
That would fit remarkably well with your planetary intelligence narrative.