Why Coherence May Be the Next Scaling Law in AI
For much of the past decade, AI progress was explained by a remarkably simple idea:
Scale.
More compute.
More data.
More parameters.
Larger models consistently produced more capable systems.
The scaling laws became one of the defining principles of modern artificial intelligence.
They weren’t wrong.
But they may have been incomplete.
Because another optimisation target is beginning to emerge.
Not simply larger intelligence.
More coherent intelligence.
The Next Phase
The first generation of frontier models proved that intelligence scales through compute.
The next generation appears to be asking a different question.
How do we make intelligence dependable?
As AI moves beyond answering questions into recommending actions, coordinating workflows and executing tasks, raw capability is no longer enough.
The system must also:
preserve context
maintain intent
reason consistently
integrate new information without unnecessary contradiction
explain why conclusions change
Intelligence becomes valuable when it becomes reliable.
Coherence Creates Trust
Imagine two AI systems with identical knowledge.
Both achieve similar benchmark scores.
One regularly loses context.
Contradicts itself.
Forgets previous objectives.
Changes recommendations without explanation.
The other remains internally consistent.
Carries goals across interactions.
Updates its reasoning transparently as new information arrives.
Most people would trust the second system.
Not because it knows more.
Because it behaves more coherently.
Trust emerges from consistency.
From Retrieval to Resolution
Traditional search optimised retrieval.
Find relevant documents.
Present possibilities.
Let the user decide.
AI-mediated discovery changes the optimisation problem.
The system now attempts to understand intent.
Resolve ambiguity.
Reduce uncertainty.
Recommend a pathway.
Increasingly, execute that pathway.
Once AI participates in decision-making rather than information retrieval, coherence becomes infrastructure.
Without coherence, dependable resolution is impossible.
Coherence Compresses Uncertainty
Every contradiction increases entropy.
Every forgotten preference introduces friction.
Every inconsistent recommendation reduces confidence.
Coherent systems move in the opposite direction.
They compress uncertainty.
Preserve intent.
Stabilise behaviour.
The result is not simply a better interaction.
It is a more predictable system.
And predictability is the foundation of trust.
Beyond Bigger Models
This may explain why the industry’s focus is broadening beyond parameter counts alone.
Today’s leading systems increasingly invest in:
memory
reasoning loops
reflection
model routing
specialised models
tool use
persistent context
These developments don’t primarily make models larger.
They make systems more coherent.
The optimisation target shifts.
From generating intelligence…
to organising intelligence.
A Different Kind of Scaling
The first phase of AI demonstrated that intelligence scales through compute.
The next phase may demonstrate that intelligence scales through coherence.
As successful reasoning pathways are reused…
As context persists…
As uncertainty falls…
The system doesn’t simply become larger.
It becomes more dependable.
The Infrastructure Layer
Perhaps the next frontier won’t be measured solely by parameters, benchmark scores or raw compute.
It may be measured by something far more practical.
Can the system repeatedly produce trusted outcomes?
Because once AI begins acting on our behalf, coherence is no longer an emergent property.
It becomes infrastructure.
The first scaling law helped explain how intelligence grows.
The next may explain how intelligence becomes trusted.
And that may prove to be the more important optimisation problem.