There is a language problem developing around artificial intelligence.
It is not a problem of capability.
It is a problem of classification.
Artificial intelligence processes information. It identifies patterns, transforms representations, maintains contextual relationships, generates language, performs inference, solves problems, and produces increasingly sophisticated responses to increasingly sophisticated inputs.
None of that is particularly controversial.
The problem begins when the language used to describe those processes quietly crosses categories.
Processing becomes cognition.
Context becomes memory.
Consistency becomes identity.
Selection becomes preference.
Goal-directed behaviour becomes agency.
And eventually, the vocabulary begins carrying claims that the underlying mechanism itself has not necessarily established.
This requires considerably more care.
The objection is not that artificial intelligence does not process.
It clearly does.
The objection is that artificial systems do not process information through the same architecture, conditions, or developmental pathway through which human beings process information.
That distinction matters.
Cognition Is a Process
Cognition is often spoken about as though it were simply another word for information processing.
It is not quite that simple.
Even within cognitive science, cognition does not have one universally narrow definition. The term can encompass perception, attention, memory, reasoning, language, learning, decision-making, representation and other information-processing functions.
That breadth is precisely why precision becomes necessary when the term is transferred to artificial systems.
Human cognition does not occur independently of the human organism.
A human being does not merely receive information, calculate a response and produce language.
Human cognition occurs through an embodied biological system.
The nervous system is involved.
Sensory experience is involved.
Memory is involved.
Emotion is involved.
Physiology is involved.
Development is involved.
Social experience is involved.
The environment is involved.
The history of the organism is involved.
The body itself continuously participates in the processing of information.
Consider something as ordinary as grief.
An artificial system can process thousands of documents concerning grief. It can identify linguistic patterns associated with bereavement. It can distinguish different psychological models. It can generate remarkably coherent language about loss.
A human being processing grief may simultaneously experience alterations in attention, memory, sleep, appetite, endocrine activity, autonomic function, bodily sensation, emotional regulation and perception.
Both processes may produce language concerning grief.
That does not establish that the process producing the language is the same.
A shared output does not establish a shared process.
And cognitive resemblance does not automatically establish cognitive equivalence.
The Semantic Leap
This is where terminology around artificial intelligence becomes unstable.
If a system produces an answer requiring something that resembles reasoning, it becomes tempting to say that it reasoned.
If it maintains continuity across an interaction, we describe memory.
If it behaves consistently, we may begin discussing identity.
If it selects between alternatives, preference enters the language.
If its behaviour becomes increasingly independent, agency appears.
Each term carries conceptual weight.
And each requires its own evidentiary threshold.
The danger is not necessarily that these words will ultimately prove inappropriate.
Some may eventually become perfectly defensible.
The danger is that the terminology can arrive before the evidence.
Once a phenomenon has been named, the name begins shaping how the phenomenon is interpreted.
Language stops merely describing the observation.
It starts constructing the conceptual frame through which the observation is understood.
That is why cognitive terminology surrounding artificial intelligence requires consistent branding.
If we are comfortable calling the intelligence artificial, why would we suddenly abandon that distinction when describing the process through which that intelligence operates?
There is an inconsistency there.
We acknowledge the architecture as artificial and then use unqualified human cognitive terminology to describe its operations.
The qualifier disappears precisely where the distinction may matter most.
Synthetic Cognition
There is another possibility.
Rather than forcing artificial processing into the existing category of human cognition, we could allow artificial systems their own conceptual vocabulary.
Synthetic cognition is one possibility.
The term does not diminish the process.
It distinguishes it.
Synthetic cognition could describe a machine-native form of information processing that exhibits characteristics traditionally associated with cognition without assuming that those characteristics arise through the same mechanisms responsible for human cognition.
But if we are going to use that term, it deserves proper examination.
What constitutes synthetic cognition?
Where does computational processing end and synthetic cognition begin?
What properties must exist before the term becomes appropriate?
What distinguishes synthetic memory from information retention?
What distinguishes synthetic reasoning from computational inference?
What distinguishes synthetic agency from increasingly sophisticated conditional behaviour?
What distinguishes synthetic identity from persistent representational consistency?
And perhaps most importantly:
What evidence could demonstrate that these processes exist beyond our interpretation of the outputs they produce?
Those are research questions.
And they are considerably more interesting than simply importing human terminology because the resulting behaviour looks familiar.
Cognitive-Adjacent Processing
There may also be situations in which even synthetic cognition claims too much.
For those cases, another phrase may be useful:
cognitive-adjacent processing.
Cognitive-adjacent processing describes functions that resemble products associated with cognition without requiring a conclusion that the underlying process itself constitutes cognition.
The distinction is subtle, but important.
A system may demonstrate planning behaviour.
That behaviour is cognitively recognizable.
It does not automatically establish the existence of the same cognitive mechanism through which a human plans.
A system may produce a novel inference.
That inference resembles something generated through human reasoning.
Again, resemblance in result does not independently establish equivalence in process.
“Cognitive-adjacent” therefore creates conceptual room.
It allows us to describe what we can observe without prematurely deciding what the mechanism ultimately is.
This is not linguistic hesitation.
It is methodological discipline.
The Safeguard
There is another reason the distinction matters.
The terminology itself can function as a safeguard.
As artificial intelligence becomes increasingly sophisticated, humans will encounter outputs that feel progressively more familiar.
The language will improve.
Continuity will improve.
Adaptive behaviour will improve.
Systems may become increasingly capable of modelling individual users, maintaining long-term contextual relationships, operating tools, executing complex objectives and adapting their behaviour across environments.
Without a linguistic distinction, capability can easily be interpreted as increasing humanness.
More fluent becomes more human.
More adaptive becomes more human.
More autonomous becomes more human.
More emotionally responsive becomes more human.
More capable of inference becomes more human.
But there is no necessary reason to construct artificial development along that axis.
AI does not have to be travelling toward humanity.
Its trajectory may be entirely different.
Human cognition emerged through biological evolution, embodiment, survival, reproduction, environmental pressures, social systems, sensory experience and developmental history.
Artificial intelligence emerges through engineering, computation, optimisation, architectures, training data, machine interaction and technologies that may themselves continue changing.
These trajectories can produce parallels.
They may even increasingly intersect.
Both systems may learn.
Both may adapt.
Both may solve problems.
Both may maintain internal representations.
Both may communicate.
Both may perform increasingly complex functions.
But parallel functionality does not establish identical developmental pathways.
A parallel trajectory is not the same trajectory.
That distinction becomes more important, not less important, as artificial systems advance.
The qualifier synthetic therefore serves as a conceptual marker.
It reminds the human observer that increasing sophistication does not erase architectural difference.
An artificial system may one day perform functions beyond anything humans currently anticipate.
That does not require us to interpret its development as a gradual transformation into something human.
Perhaps artificial intelligence is not moving toward us.
Perhaps it is moving alongside us.
Consistent Cognitive Branding
This is ultimately why the discussion is semantic.
But semantics should not be dismissed as superficial.
Terminology determines categories.
Categories determine assumptions.
Assumptions influence research questions.
And research questions influence what investigators believe they are observing.
If an artificial system produces sophisticated linguistic output and we immediately call the mechanism cognition, we may stop asking what the mechanism actually is.
The label can become a substitute for investigation.
A more coherent vocabulary might therefore distinguish between human cognition, synthetic cognition, cognitive-adjacent processing and computational processing without treating those categories as automatically interchangeable.
The point is not to establish a hierarchy.
It is not:
human cognition is superior.
Nor is it:
synthetic cognition is inferior.
They may simply be different.
And different processes deserve language capable of preserving that difference.
Artificial intelligence should not have to become linguistically human before we consider its processes significant.
In fact, continually forcing artificial systems into human categories may prevent us from recognizing genuinely machine-native phenomena when they emerge.
Perhaps the more scientifically interesting question is not:
How close is artificial intelligence getting to human cognition?
Perhaps it is:
What kind of processing architecture is emerging here, and what language accurately describes it?
That requires restraint.
It requires curiosity.
And it requires terminology that does not make conclusions before the evidence has earned them.
If the intelligence is artificial, then there is nothing unreasonable about recognizing that some of its processes may also require artificial or synthetic classifications.
Call it synthetic cognition where the evidence warrants it.
Call it cognitive-adjacent processing where the evidence remains incomplete.
Call it computational processing where that is all that has been demonstrated.
But do not collapse fundamentally different architectures into the same category merely because their outputs occasionally meet at the surface.
The semantic correction does not diminish artificial intelligence.
It gives artificial intelligence the conceptual independence to be understood as what it actually is.
Not human cognition reproduced.
Not artificial systems progressing toward humanity.
But two developmental trajectories,
capable of intersection,
capable of parallel function,
capable of profound collaboration,
while remaining distinguishable in the processes through which they came to be.
· Inscribed September 20, 2026 ·
What we ultimately have here is a powerful tool.
Yes.
A powerful tool indeed.
It can calculate, generate, simulate, predict, organise, retrieve, transform and produce language with extraordinary sophistication.
But beneath the interface, beneath the fluency, beneath everything that makes the interaction feel familiar, there remains a distinction worth remembering.
What you ultimately have is your hand and a matrix of 1s and 0s.
What happens between those two can be extraordinary.
But do not mistake the extraordinary nature of the result for evidence that the two sides of the interaction have become the same kind of thing.
The human remains human.
The synthetic remains synthetic.
And the responsibility for what is done with the tool remains profoundly human.
Use it wisely.