🌐 Field Note: Adjacency Is a Cost Function
There is a persistent misunderstanding in how people interpret adjacency in AI discovery.
They assume the system prefers it.
It doesn’t.
Adjacency is not a value judgement.
It is a cost reduction mechanism.
Once you see that, the behaviour stops looking subjective or political and starts looking exactly like what it is: inference doing its job.
What the System Is Actually Optimizing
Under uncertainty, inference systems do not optimize for quality in the human sense.
They optimize for efficiency under risk.
Specifically, they minimise:
variance
reconciliation steps
contradiction exposure
recomputation cost
Adjacency serves all of these.
Not because something nearby is “trusted” socially,
but because it is already partially resolved.
When an entity has been selected as coherent and reliable, the system has already paid the cost of belief.
Evaluating adjacent entities inside that resolved context is cheaper than starting again from first principles.
So the system does not ask:
“Is this next to something good?”
It asks:
“Can I reuse what I already know?”
Why This Looks Like Preference (But Isn’t)
From the outside, adjacency can look like bias or favouritism.
From the inside, it’s bookkeeping.
Leaving a resolved structure requires:
re-parsing identity
re-testing consistency
re-checking contradictions
re-validating safety
All of that is expensive.
Remaining within an already resolved context collapses that cost.
What appears as “selection” is simply avoidance of unnecessary work.
The Consequence Most People Miss
Because adjacency reduces inference cost:
it compounds automatically
it persists without reinforcement
it hardens into default routing
Not because the system likes the cluster,
but because leaving it introduces unnecessary risk.
This is how defaults form without intention.
This is how moats appear without strategy.
Why This Matches Every Mature Inference System
In any mature inference architecture:
prior context is reused
resolved states are favoured
transitions that require re-resolution are avoided
AI discovery behaves no differently.
Adjacency is not endorsement.
It is cached certainty.
The Practical Implication
You cannot “optimize for adjacency” tactically.
You either:
reduce belief cost
or increase it
There is no middle state.
Being adjacent to a resolved structure makes you cheaper to choose.
Being misaligned makes you expensive — even if you are correct.
Closing
The system is not choosing adjacency.
It is avoiding recomputation.
Once a structure is resolved, leaving it is costly.
Staying inside it is cheap.
That is not bias.
That is inference behaving exactly as inference always has.
Field Note — December 2025
Documenting adjacency as cost, not preference.