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Why Training on More Data Cannot Produce AGI
For more than a decade, progress in artificial intelligence has followed a remarkably consistent pattern: bigger models, more data, better results. Each wave of scaling has delivered real gains, sometimes dramatic ones. It is therefore natural to assume that AGI is simply the continuation of this curve, that if we scale far enough, general intelligence will eventually emerge.
But this assumption rests on something that is rarely examined.
Scaling has proven extraordinarily good at refining intelligence.
It has never shown that it can originate it.
This distinction matters more than it appears.
The assumption no one stopped to prove
The dominant belief can be summarized simply:
Given enough data, compute, and optimization, general intelligence will emerge.
This idea has survived not because it was structurally validated, but because it kept working, within a specific domain. Scaling has repeatedly succeeded at tasks where the underlying structure already existed and merely needed to be captured more precisely.
Language, vision, programming syntax, and many forms of reasoning fall into this category. They are densely structured domains with vast historical traces. Scaling thrives there.
But AGI is not defined by competence within known structures.
It is defined by the ability to operate before those structures are known.
That difference is easy to miss and costly to ignore.
What large-scale training actually does
Despite the mystique around modern models, their core operation is conceptually simple.
They do not build internal world models from first principles.
They perform statistical compression over historical patterns.
Given sufficient data, they learn which continuations are likely, which associations co-occur, and which transformations preserve coherence inside an already-established structure. This is extraordinarily powerful, but it has a boundary.
Large-scale training assumes that:
- the structure already exists in the data,
- the data sufficiently samples that structure,
- and refinement, not invention, is the task.
In other words, training excels at filling in structures. It does not bring new ones into existence.
This is the point at which confusion often arises.
Information refines structures. It does not create them.
The real bottleneck is structure generation, not knowledge
AGI is not the ability to know everything.
It is the ability to figure something out when almost nothing is known.
At its core, general intelligence requires the capacity to:
- reconstruct a system’s causal skeleton from minimal cues,
- test whether that skeleton is coherent,
- and discard it when it fails.
This is not a data coverage problem.
It is a structure generation problem.
No amount of additional examples can guarantee this capability, because examples only constrain hypotheses that already exist. They cannot force a system to propose the right kind of structure in the first place.
Scaling improves certainty inside a hypothesis space.
AGI requires the ability to create the hypothesis space itself.
Why scaling looks like a wall, not a slowdown
When people say that scaling is “slowing down,” they are slightly misreading what is happening.
The issue is not diminishing returns in the usual sense.
The issue is directional saturation.
As datasets grow and models stabilize, the learned structures become more internally consistent and more confidently reinforced. Paradoxically, this makes structural deviation harder, not easier.
The system becomes extremely good at saying things that fit.
It becomes extremely bad at asking whether the structure itself is wrong.
This is why progress begins to feel flat, not because models are failing, but because they are becoming complete within the wrong layer.
Scaling doesn’t fail by getting worse.
It fails by becoming structurally complete.
Hallucination is not a bug, it is a symptom
Much discussion around AI “hallucination” treats it as an implementation flaw or an alignment failure. Structurally, it is neither.
Hallucination occurs when a system trained to operate on probability is forced to respond in a space where no coherent structure is available. The model is still doing exactly what it was trained to do: produce the most likely continuation.
The problem is not that it lies.
The problem is that probability is being asked to substitute for structure.
When structure is missing, probability fills the gap.
This is not a failure of scale. It is a boundary of the approach.
Humans do not acquire core intelligence by seeing more examples
The contrast becomes clearer when we look at how humans acquire certain kinds of understanding.
People do not learn to drive by watching millions of driving episodes.
Engineers do not understand new machines by memorizing datasets.
Reverse engineers can often reconstruct a system’s logic after a single exposure.
These abilities do not come from statistical accumulation.
They come from structural closure, the ability to infer how parts must relate in order for a system to function at all.
Some of the most important insights humans make occur before there is enough data to train on.
That is not an accident. It is a different kind of intelligence.
Why AGI cannot emerge from scaling alone
None of this diminishes the achievements of large-scale models. Scaling has been transformative, and it will continue to be valuable.
But its limits are structural, not incidental.
A system that only refines existing structures, even perfectly, will never become general. Without the ability to generate, reject, and stabilize new structures under uncertainty, it will remain confined to interpolation, no matter how large it grows.
AGI will not arrive by extending what we already know ever more precisely.
It will arrive when systems can operate before knowledge is available.
AGI will not emerge from scaling what we already have.
It will emerge from systems that can generate what has never been seen.
For readers interested in a formal framework that addresses this structural gap, see the Structural Reasoning Theory (SRT) white paper, which explores how intelligence can arise from structure-first reasoning rather than data-first optimization.
Further Reading
This essay presents a structural perspective rather than a formal theory. Readers interested in precise definitions and SRT may refer to: