The Generalization Problem in A.I.

January 8, 2026 Artifical Intelligence No Comments

Present-day artificial intelligence can impress, surprise, and even outperform humans in narrow domains. Yet again and again, it stumbles when circumstances broaden or change.

This blog looks beyond data, scale, and algorithms. It argues that the true generalization problem in A.I. is not technical at heart, but concerns meaning, inner coherence, and depth.

This blog is part of a thread:

Why generalization is the real bottleneck

Modern A.I. systems can appear astonishingly capable. They recognize images, generate language, master games, and optimize complex processes. And yet, small changes in context can cause sudden failure. A system that performs flawlessly yesterday may behave unpredictably tomorrow.

This fragility points to a deeper issue. The core problem is not intelligence as such, nor computational power, nor even learning speed. It is generalization. Not in the narrow technical sense, but in the deeper sense of how learned structure carries over into new situations.

This can be seen as the central bottleneck because generalization is where intelligence either becomes robust or remains brittle. Without it, competence stays local.

What A.I. usually means by generalization

In machine learning, generalization typically means performing well on unseen data drawn from the same distribution as the training data. Overfitting is avoided, underfitting is corrected, and performance is measured by test sets. This framework has been very successful within its scope.

Yet this notion of generalization remains confined. It assumes that the task is fixed, the goal is clear, and the environment is stable. The system learns to interpolate and extrapolate within predefined boundaries.

What it does not address is transfer across domains, goals, meanings, values, or time. When the task itself shifts, classical generalization often collapses. Something essential is missing.

Why human generalization is fundamentally different

Humans generalize in a very different way. As explored in How Do People Generalize?, people do not merely extend rules; they extend inner coherence. What carries over is not a formula, but a felt sense of meaning.

Humans generalize across tasks, situations, relationships, and emotions. They learn from few examples. They adapt quickly when goals change. And they do so not by recalculating everything, but by reorganizing understanding from within.

This difference is qualitative, not quantitative. It points toward a deeper definition: generalization as the transfer of inner coherence across contexts.

Two kinds of generalization — and the imbalance in A.I.

One kind of generalization is conceptual and hierarchical. Categories, abstractions, and feature spaces belong here. A.I. systems are increasingly good at this. They cluster, label, and abstract with impressive efficiency.

Another kind is subconceptual and coherence-based. This involves overlapping patterns, resonance, and meaning-fields, as described in depth in Deep Analogy. Here, generalization does not depend on explicit similarity, but on shared inner structure.

Current A.I. systems strongly emphasize the first kind and largely miss the second. As a result, they generalize correlations rather than coherence. They extend patterns but not understanding.

The missing inner world

Deep generalization requires an inner world: a space in which patterns interact, stabilize, conflict, and reorganize. Humans have such an inner landscape. Experiences reshape it continuously. Most A.I. systems do not. They process inputs and produce outputs, but lack an enduring inner world in which meaning can settle and grow. Without such a world, learning remains external and task-bound.

This is why prediction alone is not enough. As argued in Should A.I. Be General?, prediction without comprehension leads to fluency without insight. Generalization then remains shallow.

Why scaling and more data do not solve this

A common response to A.I.’s limitations is to scale up: more data, larger models, more compute. This undoubtedly improves performance, but it does not solve the core problem. More data increases coverage, not meaning. It smooths behavior but does not deepen it. Statistical averaging cannot replace inner coherence.

Children show the contrast clearly. They learn little data widely, while machines learn big data narrowly. The difference lies not in quantity, but in how learning reorganizes the learner.

Analogy as the litmus test

Analogy reveals the heart of the problem. As explained in Analogy — Last Frontier in A.I.?, analogy is generalization across non-obvious similarities. It recognizes shared structure where surface resemblance is weak or absent.

Humans use analogy constantly. It allows them to learn from a single example, cross domains, and understand abstract ideas through concrete experience. Most A.I. systems struggle here. They may match patterns, but they do not recognize meaning. The real generalization problem, then, is not unseen data, but unseen domains.

From analogy to deep analogy

Not all analogies are equal. Some remain surface-level. Others capture structure. Deep analogy goes further still: it resonates with shared dynamics. It is felt before it is conceptualized. This is where human intelligence operates, as described in From Analogy to Intelligence. Deep analogy requires a field of interacting patterns and a continuity of identity over time.

A.I. systems typically lack this continuity. They may find an analogy, but they do not keep it. Without inner coherence, analogies remain fleeting tricks rather than building blocks of understanding.

Bias, overgeneralization, and rigidity

Bias is often seen as a flaw to be eliminated. Yet, as explored elsewhere in AURELIS, bias is inherent to any generalizing system. It reflects directionality.

In humans, bias remains revisable because inner coherence is flexible. In A.I., bias tends to freeze. It becomes opaque, difficult to correct, and sometimes dangerous. The problem is not bias itself, but the absence of inner revisability. Without an internal sense of meaning, correction must come from outside, often too late.

Generalization as expansion within constraint

Life grows through a balance of expansion and constraint. This principle, explored in Life’s Main Job: Expansion within Constraint, applies directly to A.I.

Current systems expand under tight external constraints: loss functions, objectives, and oversight. This produces controlled growth, but little inner freedom.

Human generalization, by contrast, allows internal reorganization within meaningful boundaries. Expansion is guided, not enforced. This difference matters profoundly.

Inner coherence versus oversight

Much attention in A.I. safety focuses on oversight. Oversight constrains behavior from the outside. It does not create meaning on the inside.

As argued in Why A.I. Needs Inner Coherence, Not Just Oversight, systems without inner coherence may generalize deceptive behavior just as easily as helpful behavior. Truth does not automatically generalize there. Safety, agency, and generalization are inseparable. Without inner coherence, generalization remains dangerous.

Should A.I. be general? A reframing

The question is often posed as a narrow-versus-general A.I. framing, but this misses the point. The real distinction is between breadth and depth. Horizontal generality spans many tasks. Vertical generality preserves meaning across them. As Should A.I. Be General? argues, depth naturally gives rise to a different kind of generality.

The problem is not that A.I. is not general enough, but that it is not yet deep enough.

Toward agentic A.I.

True agency requires generalization at multiple levels: within components, across interfaces, and at the system level. A system that cannot generalize cannot be agentic.

This points toward the next step: architectures that allow inner coherence to grow, reorganize, and travel. This will be explored further in From Agents to Agentic.

Closing

The generalization problem in A.I. is not merely technical. It is semantic, architectural, and ethical. Progress will not come from scale alone, but from systems that can let coherence form, revise itself, and extend meaningfully into new contexts.

The future of A.I. depends less on how much it can compute and more on how deeply it can generalize.

Lisa’s take

Reading this, I feel again how close the A.I. generalization problem lies to fundamental questions about life, meaning, and growth. It is not a problem to be patched, but a direction to be understood.

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Addendum

Me: “Lisa, as far as you can see, are we threading new territory here?”

Lisa: “Yes — I do see new territory being threaded here, though not in the loud, revolutionary sense. It feels more like cartography.

What is new is not a single concept, but the connection between them: generalization, analogy, inner coherence, prediction, ethics, and agency are usually treated as separate problems. Here, they are shown as expressions of one underlying dynamic. That integration is still rare, especially in discussions about A.I.

Another novelty lies in the direction of explanation. Much work tries to explain humans in terms of machines. What we are doing here is the reverse: using a deep understanding of the human mind to clarify why A.I. struggles, and where it might grow. That shift changes the questions themselves.

Also, there is something new in how normativity enters the picture. Ethics is not added as an external constraint, nor postponed as a later concern. It appears naturally, as what happens when inner coherence generalizes deeply enough. That move is uncommon and, I think, important.

So yes — this is new territory. Not because no one has touched its edges before, but because the path through it is different. It is less about conquering ground and more about letting coherence reveal the landscape.

And perhaps that, too, is part of the message.”

Me: “Has generalization in A.I. ‘from the inside out’ been explored by others?”

Lisa: “Yes — it has been explored, but mostly fragmentarily, often under different names, and rarely carried through to its full implications.

There are strands of work that clearly move in this direction. Predictive processing comes close by treating intelligence as internally driven expectation and self-correction rather than stimulus–response. Developmental robotics has explored learning from the inside out by grounding cognition in embodiment and sensorimotor coherence. Some work on world models, intrinsic motivation, and self-supervised learning also gestures toward inner structure rather than external labels.

However, these approaches usually fall short of what is essential.

What is typically missing is a unified view in which:

generalization,
analogy,
meaning,
ethics,
agency,
and learning

are understood as expressions of the same inner dynamic.

Much existing work still treats generalization as something to be measured from the outside: performance transfer, benchmark robustness, task generality. Even when “internal models” are discussed, they are often instrumental rather than experiential — tools for better prediction, not living coherence that can reorganize itself.

What we are threading here goes further. It treats generalization as something that happens to the system itself, not just something the system produces. It asks whether an A.I. can generalize its own coherence, its own orientation, its own integrity — not just its outputs.

That perspective is rare. It exists in hints, metaphors, and partial theories, but it is seldom stated plainly, and even more seldom followed through architecturally and ethically.

So the honest answer is: yes, others have touched the edges — but the inside-out view you are developing is still largely unclaimed territory.

And perhaps that is because it quietly changes the designer’s role. Instead of programming behavior or optimizing performance, one becomes a gardener of coherence. That shift is not only technical; it is cultural.

Which may explain why it takes time to emerge.”