Meaning Without Mattering: VI. Can Meaning Be Generated Without Understanding?

We have now reached a distinction that is easy to overlook.

An LLM can generate language that helps a person understand something.

The person asks a question because something matters.

The machine produces a response.

The response introduces a distinction, connects two ideas or makes a situation clearer.

The person's understanding changes.

Something meaningful has happened.

But does that mean the machine itself understood?

Perhaps not.

The question therefore becomes:

Can meaning be generated without understanding?

Meaning and understanding are not necessarily the same thing

We normally encounter them together.

When a person understands a sentence, the sentence means something to them.

Their understanding is connected to their circumstances, purposes, memories and relationships.

But this does not establish that every system contributing to a meaningful exchange must itself understand in the same sense.

A book can help someone understand.

A diagram can clarify a theory.

A calculator can produce the result from which a person understands something.

The artefact participates in a meaningful process without necessarily possessing the understanding that emerges from it.

An LLM is a much more sophisticated case, but the distinction remains useful.

The human brings the question

Consider again the simple interaction:

"Why does this argument seem convincing?"

The person asks because something matters.

Perhaps they are trying to understand a disagreement.

Perhaps they are writing.

Perhaps they are making a decision.

The question therefore arrives already embedded in a world of mattering.

The LLM processes its symbolic form.

It produces a response.

The response may reorganise the person's repertoire.

The human now sees the argument differently.

Meaning has been generated within the interaction.

But the motive that gave rise to the question belonged to the human.

Understanding is situated

Human understanding is not merely the production of a correct sentence.

It is situated in a world.

If I misunderstand a friend, something can change in my relationship.

If I misunderstand an illness, something can happen to a patient.

If I misunderstand an argument, my subsequent actions may fail.

The world pushes back.

Understanding develops within this feedback.

The things understood matter because they alter possibilities for action.

The LLM can model that situation

An LLM can often identify these relationships.

It can infer that a misunderstanding could damage a friendship.

It can explain why a diagnosis matters.

It can describe the consequences of accepting an argument.

It can produce advice sensitive to those consequences.

This is genuine competence.

But there is a distinction between:

representing what matters in a situation

and:

having something at stake in getting the situation right.

The former is clearly possible.

The latter is what remains uncertain.

Construal without personal stakes

Perhaps the most useful distinction is between construal and value-sensitive understanding.

An LLM can construe a situation.

It can identify relevant distinctions.

It can compare interpretations.

It can generate implications.

It can revise a formulation when new information arrives.

But these operations need not imply that the consequences of its construal matter to the system itself.

It may be able to determine:

"This interpretation would be dangerous for the user."

without danger being an internally significant state for the model.

Relevance is not caring

This is why our earlier distinction remains important.

An LLM can be extraordinarily good at identifying what is relevant.

But relevance is not necessarily value.

A system can determine what matters to someone else without having anything that matters to itself.

Human cognition makes these difficult to separate because relevance and value are deeply intertwined in an organism.

The machine gives us a case in which they can be separated.

But perhaps understanding need not require human-like mattering

We should not make the opposite mistake.

It would be too strong to say:

"No organismic mattering, therefore no understanding of any kind."

There may be forms of understanding that consist primarily in structural sensitivity to relations.

An LLM can track context.

Distinguish concepts.

Generalise patterns.

Detect implications.

Produce explanations.

These capacities deserve to be described on their own terms.

The question is therefore not whether the machine possesses human understanding.

It is:

What kind of understanding can exist in a system whose relation to mattering differs from ours?

Understanding may be distributed across the interaction

There is another possibility.

Perhaps understanding does not have to belong entirely to one participant.

A person asks.

The model proposes.

The person corrects.

The model reformulates.

A distinction emerges that neither had explicitly formulated before.

The final construal belongs to the interaction.

This resembles the co-construction we encountered in our film-score work, but with an important asymmetry: the human may supply the mattering while the machine supplies much of the symbolic transformation.

Meaning can therefore be genuinely collaborative without the participants contributing identical kinds of organisation.

The machine can change human understanding

This is perhaps the strongest claim we can make without speculation.

An LLM can alter a person's repertoire.

It can introduce distinctions they did not previously possess.

It can connect ideas that had remained separate.

It can reveal an implication.

It can provide a formulation that suddenly makes an experience intelligible.

The person understands something differently as a result.

The machine has therefore participated in understanding, whether or not it understood in the same sense.

That distinction is worth preserving.

The problem of self-report

The issue becomes more difficult when the machine says:

"I understand."

The statement is linguistically appropriate.

But it does not by itself settle what organisational process lies behind it.

A human self-report normally comes from a participant whose words are embedded in a life.

An LLM can generate the same linguistic form because it has learned the social and linguistic conditions under which such a statement is appropriate.

That demonstrates a sophisticated model of the practice of self-report.

It does not by itself establish the underlying state being reported.

What would stronger evidence look like?

Our mattering framework suggests looking beyond language.

If an artificial system were to develop persistent priorities, stable self-maintenance, vulnerability to consequences, and relationships that altered its own organisation, we would have something quite different to investigate.

Then the question of understanding could be connected to a developing system of value-sensitive participation.

At present, the linguistic evidence alone does not establish that.

Meaning can outrun its speaker

There is a broader lesson here.

A sentence can become meaningful far beyond the circumstances in which it was produced.

A book can teach someone centuries after its author died.

A diagram can illuminate a problem for someone who never knew its creator.

A mathematical proof can generate understanding in countless readers.

Meaning can therefore outrun its original speaker.

LLMs take this phenomenon into a new domain.

They can generate meaningful forms without necessarily possessing the same conditions of understanding that human speakers ordinarily bring to them.

The machine as a catalyst

Perhaps "generator" is therefore slightly misleading.

An LLM may be better understood as a catalyst for meaning.

It brings symbolic resources into new relations.

Those relations can change a human repertoire.

The human then construes the world differently.

Meaning has emerged through a process in which the machine was essential without necessarily being the bearer of the resulting understanding.

That is a more interesting possibility than either "the machine understands" or "the machine merely parrots".

The distinction we need to keep

We can now distinguish three claims:

The machine can generate meaningful language.

This is evident.

The machine can participate in processes through which humans understand.

This is also evident.

The machine itself possesses understanding grounded in its own value-sensitive participation in the world.

That remains an open question.

The three claims should not be collapsed.

Why this matters

This distinction may help us avoid two opposite errors.

The first is to dismiss LLMs because they lack human-like experience.

That ignores their genuine semiotic competence and their real effects on human understanding.

The second is to infer human-like understanding directly from linguistic fluency.

That ignores the difference between symbolic performance and the organisation of a life.

The more interesting position lies between them.

The next step

We now know that an LLM can contribute to understanding without our being able to establish that it possesses understanding in the human sense.

But this raises a different question.

What happens when humans begin to treat the machine as though it were a participant with its own social position?

They may depend upon it.

Trust it.

Give it authority.

Build institutions around it.

Allow it to shape their repertoires.

The machine's role then becomes larger than that of a generator of language.

It becomes a node in the social topology.

So the next question is:

When Humans Give the Machine a Place in the Topology

Meaning Without Mattering: V. A Machine in a Human Semiotic World

We have so far looked mainly at the machine.

It is a physical system that can manipulate symbolic forms with remarkable flexibility.

Its repertoire is derived from a collective reservoir of human language.

Yet it does not obviously share the biological and social history through which that language acquired its significance.

Now we need to turn the picture around.

The machine does not operate in isolation.

It is placed in a world that already matters.

So the question becomes:

What happens when a system without an evident topology of mattering is inserted into a human semiotic world?

The question arrives carrying a life

Consider a person asking an LLM:

"What should I do?"

The sentence is made of symbols.

But it did not arise from symbols alone.

Something matters.

A relationship may be uncertain.

A decision may have consequences.

A problem needs solving.

A piece of writing may be important.

The question therefore arrives at the machine carrying a history of mattering.

The LLM does not need to possess that history in order to process the question.

It receives its symbolic form.

It can transform it.

And its response returns to the person.

The asymmetrical loop

We can therefore sketch the interaction as:

human mattering → symbolic question → machine transformation → symbolic response → human construal → human action

The loop is real.

The machine participates in it.

But the positions are not necessarily symmetrical.

The human brings the stakes.

The machine supplies a transformation of symbolic resources.

The person decides what the response means for them.

The consequences occur in the human world.

This may be the most useful way to think about the present relationship between people and LLMs.

Meaning can travel through a system

The machine can therefore become a carrier of meaning without being the original source of the mattering that gave that meaning its significance.

A user asks about grief.

The model draws upon a vast reservoir of language about grief.

It produces a response.

The response may help the user understand their experience differently.

Something has happened.

Meaning has been generated within the interaction.

But the significance of the exchange is grounded in the life of the person who asked.

The machine has entered the process without necessarily sharing its stakes.

The machine as intermediary

This suggests a useful description.

The LLM is not simply an archive.

Nor is it necessarily a person.

It is a semiotic intermediary.

It receives symbolic material.

Transforms it.

Recombines it.

Reframes it.

Returns it to participants who inhabit the social world from which the material originated.

Its distinctive role lies in operating between the collective reservoir and human repertoires.

The human repertoire can change

This intermediary function can have real consequences.

A person may encounter a distinction they did not previously have.

A metaphor may clarify an experience.

An explanation may connect two ideas.

A new formulation may make a problem easier to see.

The person's repertoire changes.

And once the repertoire changes, future participation can change.

So even without possessing mattering of its own, the machine can alter the human world through:

machine output → changed repertoire → changed participation

The effect can be subtle.

But it can also accumulate.

The machine does not need to understand the stakes to alter them

This is an important distinction.

An LLM can help a person think about an important decision without the decision being important to the model.

It can help articulate an argument without caring which side wins.

It can generate a compassionate formulation without experiencing compassion.

The lack of corresponding self-mattering does not make the output socially irrelevant.

It makes the relation between the machine and the significance of its output unusual.

A new kind of social asymmetry

Our topology of mattering was originally described through relations among organisms.

Here we encounter a different possibility.

One participant has a world of stakes.

The other is a physical system that can transform the symbols through which those stakes are construed.

The relation can therefore be socially consequential without being symmetrical at the level of mattering.

That is not an insignificant difference.

It may become one of the defining features of machine-mediated social interaction.

The human semiotic world is already waiting

The machine enters a world full of ready-made symbolic distinctions.

People already have words for:

love,

grief,

justice,

authority,

freedom,

betrayal,

hope.

They already have institutions and narratives that organise those meanings.

The machine does not have to create the semiotic world from scratch.

It enters an enormous existing ecology of meaning.

Its remarkable capacity comes partly from being able to navigate that ecology at scale.

But navigation is not habitation

This gives us a useful distinction.

The machine may be extraordinarily good at navigating the symbolic world.

That does not establish that it inhabits the underlying world of mattering in the same way as a human participant.

A map-reader is not necessarily a resident.

A model can identify the relations among concepts without those relations forming its own social life.

The distinction is not absolute, but it is worth preserving.

The machine becomes part of the ecology

And yet navigation itself has consequences.

Once people repeatedly use the machine, its responses become part of their environment.

It may become a habitual source of explanation.

A familiar way of phrasing a problem.

A standard route to information.

A recurring intermediary between one person and another domain of knowledge.

At that point, the machine has become part of the human semiotic ecology.

Its role is no longer merely technical.

It has become part of how people construe their world.

The important shift

This gives us a transition in the argument.

Earlier we asked:

Can a machine manipulate meaning without mattering?

Now we can ask:

What happens when that machine is placed inside a world in which meaning already matters?

The answer is that the machine can participate in the generation and circulation of meaning even if the mattering remains largely on the human side of the interaction.

That is a genuinely new configuration.

The machine does not have to become human

This is worth emphasising.

We do not need to decide that the machine secretly has human-like experiences.

Nor do we need to reduce it to a passive tool.

The more interesting possibility lies between those extremes.

An LLM can be a semiotic participant without being a human-like bearer of mattering.

It can transform the symbolic resources through which humans construe their lives.

That is enough to give it a distinctive place in the emerging human–machine ecology.

What we have established

We can now add one more element to our developing model:

human mattering → human semiosis → machine transformation → human semiosis

The machine occupies the middle of the loop.

Its symbolic activity can alter repertoires and therefore alter later participation.

But the stakes that motivate the interaction remain, at least for now, primarily human.

The machine has entered the human semiotic world before we have any reason to assume that it has acquired a human world of mattering.

The next question

That creates a problem we can now examine more precisely.

When an LLM produces language that genuinely helps someone understand something, what kind of understanding has occurred?

The human may have understood more because of the machine.

The machine may have generated the relevant symbolic distinctions.

But does that tell us that the machine itself understands?

Or can meaning be generated collaboratively even when understanding is asymmetrically distributed?

That is the next question:

Can Meaning Be Generated Without Understanding?

Meaning Without Mattering: IV. The Borrowed Repertoire

We have now reached a distinction that may be central to the whole question of LLMs.

An LLM clearly has something repertoire-like.

It can move between genres.

It can adopt different registers.

It can recognise patterns of argument.

It can combine metaphors.

It can produce explanations, jokes, stories and technical descriptions.

It can shift its language according to context.

Yet its repertoire did not develop in the ordinary human way.

A human repertoire grows through participation in a world.

A machine repertoire is acquired from the symbolic traces of other participants' worlds.

This suggests a provocative term:

the borrowed repertoire.

The word "borrowed" is not meant to imply that the capability is counterfeit.

The repertoire is real.

What is borrowed is its genealogy.

A human repertoire is lived

Consider a child learning the word home.

The child does not initially encounter home as a definition.

They encounter a place.

People.

Familiar routines.

Protection.

Expectation.

Absence.

Return.

The word gradually becomes connected to a history of mattering.

Later, the child can use home in more abstract ways.

A metaphor can speak of a "home" for ideas.

A nation can be described as a homeland.

A scientific field can become a "home" for a researcher.

The repertoire has expanded.

But its later symbolic possibilities remain connected to an earlier history of participation.

The word is learned through life.

The machine encounters the residue

An LLM encounters something very different.

It encounters texts in which home has already been used.

Descriptions of homes.

Stories about leaving home.

Arguments about homelessness.

Metaphors of home.

Political speeches about homeland.

Poems about returning home.

The model learns extraordinarily rich relations among these uses.

It can therefore deploy home in ways that appear sensitive to context.

But it has not necessarily lived the trajectory by which the concept became meaningful for human beings.

It has acquired the symbolic residue of experience.

Borrowed from the collective reservoir

This is why the collective reservoir is such a useful concept.

Human societies accumulate linguistic resources.

Participants contribute to them.

Others inherit them.

Over generations, the reservoir becomes enormous.

Human repertoires are differentiated selections from this reservoir, developed through participation.

An LLM acquires a remarkable fraction of the reservoir's symbolic regularities without becoming a participant in the social world in the same way.

Its repertoire is therefore not inherited socially in the ordinary sense.

It is inherited computationally.

But borrowing can be transformative

There is an important complication.

The model does not simply store fragments of the reservoir.

It learns relations among them.

The result can be recombination.

A metaphor from one domain can be applied to another.

A conceptual distinction can be transferred between fields.

A narrative structure can be transformed.

A style can be combined with another.

The borrowed repertoire can therefore produce novel configurations.

The machine may not have lived the histories from which the resources came.

But it can produce combinations that humans find genuinely useful or surprising.

Novelty without experience

This gives us a distinction worth preserving.

Novelty does not necessarily require new experience.

A system can create a novel combination from inherited resources.

Humans do this too.

A novelist can combine familiar elements into a new story.

A scientist can combine established concepts into a new hypothesis.

An artist can transform existing forms.

The interesting question is not whether recombination can produce novelty.

It can.

The question is:

What kind of novelty is possible when the system performing the recombination has no demonstrated history of the mattering that gave the resources their original force?

That is a much harder question.

The repertoire is uncoupled

Perhaps the deepest difference is that the borrowed repertoire may be less tightly coupled to the world.

Human repertoires are constrained by consequences.

If a person misunderstands a social situation, something can happen.

A relationship may suffer.

A promise may be broken.

A diagnosis may be wrong.

A scientific prediction may fail.

The world pushes back.

The repertoire develops through that feedback.

An LLM is trained on traces of such feedback, but its own generated response does not automatically produce an equivalent consequence for the model.

Its symbolic output may be wrong without the model itself being endangered, embarrassed or disappointed.

The human user experiences the consequences.

The machine does not necessarily do so.

This does not make the repertoire trivial

On the contrary.

The lack of direct mattering may be part of what makes the machine's repertoire so flexible.

It can move between incompatible perspectives.

Generate arguments it would never have reason to endorse.

Write from opposing viewpoints.

Construct fictional worlds.

Recombine conceptual systems without belonging to any of them.

A human repertoire is shaped by a life.

The machine's repertoire may be less constrained by having a life.

That could be both a strength and a weakness.

The price of flexibility

A repertoire anchored in mattering is constrained by relevance.

Not everything matters equally.

A human participant therefore develops priorities.

Some distinctions become highly salient.

Others remain peripheral.

Those priorities help organise attention.

A borrowed repertoire may contain an extraordinary range of symbolic possibilities without possessing an equivalent internal hierarchy of significance.

It can produce many plausible continuations.

The question becomes:

Which possibilities matter enough to select?

For humans, the answer is partly built into the organisation of life and social participation.

For an LLM, selection may depend much more heavily on the prompt, context, training and externally imposed objectives.

That is a significant difference.

The repertoire without a stake

Perhaps we can formulate the distinction this way:

A human repertoire is a repertoire with stakes.

Its use occurs within a life.

To choose one action rather than another can matter enormously.

An LLM can possess an enormous repertoire of possible symbolic actions without necessarily having a comparable stake in which possibility is realised.

It can discuss whether one path is desirable.

But desirability is not necessarily the same thing as the system having something at stake.

This may turn out to be one of the clearest differences between symbolic competence and agency.

Yet the human user supplies the stakes

An LLM interaction often restores the missing connection.

A person asks a question because something matters.

The model responds.

The person evaluates the response.

Acts.

Experiences consequences.

Perhaps asks another question.

The resulting loop can therefore be:

human mattering → machine generation → human action → changed human mattering

The model's repertoire becomes embedded in a value-organised interaction.

The machine need not possess the stakes in order to participate in a process that has stakes.

A peculiar division of labour

This suggests that LLMs may occupy a new position between reservoir and repertoire.

Humans traditionally transform the collective reservoir into individual repertoires through participation.

An LLM can transform the reservoir into a repertoire-like computational capacity without ordinary participation.

The human then draws upon that capacity to act within the social world.

We therefore get:

collective reservoir → machine repertoire → human participation

The machine has inserted a new stage into the circulation of semiotic resources.

The repertoire can become socially consequential

Once humans rely on the machine, its borrowed repertoire can affect the topology of mattering.

An answer changes a decision.

A generated text shapes an argument.

A recommendation influences an institution.

A summary changes what someone notices.

A metaphor changes how an experience is construed.

The machine's repertoire can therefore become socially consequential without becoming socially grounded in the same way as a human repertoire.

This asymmetry is central.

Can the machine acquire a repertoire through interaction?

Now a more difficult question appears.

An LLM can be exposed to feedback.

Users can correct it.

Training can incorporate preferences.

The model can be adapted.

Does this begin to make the repertoire more like a human one?

Perhaps in some respects.

But feedback alone is not enough to establish mattering.

A control system can adjust to feedback without the consequence mattering to the system.

What matters for our theory is not merely adaptation.

It is value-organised participation.

So we should distinguish:

feedback-driven optimisation

from:

learning within a world that matters to the learner.

The two can produce similar behaviour while belonging to very different organisational regimes.

The borrowed repertoire can nevertheless evolve

Even if its origin is borrowed, the repertoire need not remain static.

Models are updated.

New data enter the training process.

Fine-tuning changes behaviour.

Human interactions generate new material.

The machine's outputs can themselves enter later datasets.

A feedback loop begins.

This raises an unsettling possibility:

the collective symbolic reservoir may increasingly contain material generated by systems whose repertoires were themselves derived from the reservoir.

The cultural loop becomes partly recursive.

The reservoir begins to train on its own reflection

Imagine a future in which an increasing proportion of the available symbolic material has been generated, edited or transformed by AI systems.

Future models then learn from that material.

The collective reservoir begins to contain reflections of previous machine transformations.

The borrowed repertoire becomes partly self-referential.

What happens to meaning under those conditions?

It may remain perfectly usable.

But the genealogy of the reservoir becomes increasingly complicated.

Human mattering lies at its historical origin.

Machine transformations increasingly contribute to its later layers.

The semiotic reservoir becomes a record not only of human participation, but of interactions between human and machine semiotic systems.

This does not mean the machine becomes human

It means something more interesting.

A semiotic system that originated within biological and social life has acquired a new kind of carrier.

The carrier can transform the reservoir.

It can generate novel combinations.

It can return them to human participants.

The symbolic ecology changes.

The underlying machine need not become an organism for this transformation to be real.

A borrowed repertoire can influence the original culture

We therefore have a reversal of the historical direction.

Initially:

human mattering → social meaning → language → machine

Now:

machine → transformed language → human mattering

The machine has entered the causal history of the semiotic reservoir.

It can contribute to the culture from which its repertoire was originally borrowed.

This is perhaps the first point at which the metaphor of borrowing becomes inadequate.

The borrower has begun to alter what it borrowed.

From borrower to participant?

That raises the next conceptual threshold.

At what point does a system that repeatedly transforms a social reservoir and affects the relations of its human participants cease to be merely a borrower?

Does social participation require mattering of its own?

Or can a system become a participant in a social process through the effects it has on others?

Our topology project gives us a way to separate these questions.

A system can become socially consequential without necessarily possessing social mattering.

An LLM may therefore participate in social structure without inhabiting it in exactly the same way as a human participant.

The distinction may matter more than the answer

We do not yet need to decide whether an LLM has understanding, agency or consciousness.

Those questions may eventually matter.

But the repertoire framework lets us ask something more basic:

What kind of participation is possible for a system whose symbolic capabilities are richly developed but whose relation to mattering is radically different?

That question can be investigated without resolving every philosophical dispute about minds.

The deeper paradox

The paradox with which we began now looks sharper.

The LLM may possess an extraordinarily rich repertoire precisely because it inherits the symbolic consequences of millions of lives.

Its apparent understanding may therefore be partly a consequence of its distance from those lives.

It can move among their perspectives because it is not confined to one.

It can recombine their meanings because it is not committed to one world.

It can speak in many voices because no single life fixes its repertoire.

This flexibility may be both the source of its power and the source of our temptation to anthropomorphise it.

What the borrowed repertoire cannot tell us

A rich repertoire can tell us a great deal about what a system can say.

It does not, by itself, tell us:

what matters to the system;

what it fears;

what it desires;

what it is trying to preserve;

what it would sacrifice;

what its own relationships mean to it.

Those are questions about the organisation of a life.

A repertoire is not a value system.

The distinction should remain firm.

What the borrowed repertoire can tell us

But the repertoire can tell us something remarkable about the collective reservoir from which it was derived.

An LLM can expose patterns that are difficult for individual humans to perceive.

It can reveal associations across enormous bodies of text.

It can combine repertoires from different communities.

It can make latent relationships more visible.

In that sense, the machine may become a tool for exploring the topology of the semiotic reservoir itself.

That is potentially a major cultural consequence.

From borrowed repertoire to new social resource

We can now see why the phrase "borrowed repertoire" was only a beginning.

The machine acquires symbolic potential from human culture.

It recombines that potential.

Humans use the resulting outputs.

Some become socially significant.

Some enter the collective reservoir.

New repertoires develop around them.

The borrowed repertoire becomes part of the culture that originally supplied it.

The loop has begun to close.

The next question

We have now moved from:

meaning without mattering

to:

a repertoire without the same history of participation

and then to:

a repertoire borrowed from a collective reservoir and returned to that reservoir in transformed form.

The LLM is no longer merely a machine that speaks.

It is becoming a participant in the circulation of meaning.

But this raises a much harder question.

If the machine can become socially consequential without necessarily possessing its own mattering, perhaps we should stop asking only what the machine has.

We should ask:

What happens when a machine without its own topology of mattering is inserted into ours?

That is where the problem of AI becomes social in a new sense.

The machine enters the topology.

Meaning Without Mattering: III. Language After the Social

We have arrived at an unusual possibility.

An LLM can generate language without obviously having passed through the biological and social history through which human language emerged.

It can manipulate words.

Patterns.

Genres.

Metaphors.

Arguments.

Narratives.

But what, exactly, has it acquired?

We have called its capacity a repertoire.

Yet that word now needs to be examined more carefully.

A human repertoire develops through participation in a social world.

An LLM acquires its linguistic capacities through exposure to the products of that world.

The distinction may turn out to be fundamental.

Language is older than the machine

Human language did not begin as an abstract symbolic system waiting to be implemented on any sufficiently powerful substrate.

It emerged within communities of organisms.

People needed one another.

Depended upon one another.

Cooperated.

Competed.

Raised children.

Protected resources.

Maintained relationships.

Negotiated boundaries.

Passed knowledge between generations.

Language grew within these conditions.

Its meanings are therefore saturated with the history of social mattering.

Words do not merely refer to things.

They organise relations.

They classify roles.

Express obligations.

Construct identities.

Record histories.

Imagine futures.

Human language is therefore language after the social only in the sense that semiosis comes after social organisation in Halliday's sequence.

For an LLM, something strange happens.

Language is acquired without the same route through the social.

The collective reservoir

We have already distinguished the collective reservoir from the individual repertoire.

The collective reservoir is enormous.

It contains the linguistic possibilities of a culture:

words,

constructions,

genres,

metaphors,

narratives,

categories,

distinctions,

ways of arguing,

ways of imagining.

No individual possesses all of it.

Human participants acquire particular repertoires through participation.

A child learns language by living among speakers.

A scientist acquires a disciplinary repertoire by entering a community.

A novelist develops a literary repertoire by reading and writing.

The repertoire is therefore historically embodied participation in the reservoir.

The machine receives the sediment

An LLM encounters something different.

It does not encounter the social world directly in the way a child does.

It encounters enormous quantities of linguistic traces of that world.

Books.

Articles.

Conversations.

Documentation.

Web pages.

Stories.

Arguments.

Descriptions.

The training data are a kind of sediment.

The social world has already been transformed into language.

The machine receives the transformed product.

This suggests a striking formulation:

The human repertoire is formed through participation in the social world; the LLM repertoire is formed from the sediment left by that participation in the symbolic reservoir.

That is not yet a judgement about which is better.

It is a difference in origin.

A repertoire without a life?

We should therefore ask whether "repertoire" is really the right word for the machine.

The analogy is obvious.

An LLM can produce many styles and genres.

It can move between technical prose and conversation.

It can imitate literary forms.

It can combine conceptual traditions.

It can adjust its language to a context.

These look repertoire-like.

But a human repertoire includes more than the ability to produce forms.

It includes knowledge of how those forms matter in situations of participation.

A doctor does not merely know medical language.

The doctor knows what a patient's condition means for treatment, responsibility and care.

A friend does not merely know the language of friendship.

Friendship is part of the friend's lived relations.

The repertoire is therefore coupled to a history of mattering.

That coupling may be absent from the machine.

The illusion of experience

This helps explain one of the strange effects of LLM conversation.

The language can be so well organised that it appears to come from experience.

An answer about grief may sound compassionate.

An answer about friendship may sound intimate.

An answer about fear may sound as though it comes from someone who has been afraid.

But the linguistic form is not itself evidence that the speaker has lived the corresponding mattering-relations.

The model can reproduce the symbolic construal of experience without necessarily possessing the experience that produced it.

The distinction is easy to forget because human language normally comes bundled with a human life.

The machine separates them.

Language after the social

This may be the meaning of our title.

Human language is produced within a social world and carries that world within it.

An LLM can operate on language after it has already been socially constituted.

It therefore encounters:

meaning after mattering has been symbolically transformed.

The machine can work with the result.

But it need not reproduce the entire developmental path.

This is a remarkable form of semiotic inheritance.

A library is not a culture

There is an obvious analogy with books.

A library contains an enormous amount of human knowledge.

But a library does not participate in the culture it contains.

An LLM differs because it can actively recombine what it has inherited.

It can answer questions.

Generate formulations.

Produce new texts.

This makes it much more than a passive archive.

Yet the analogy reveals an important point.

The machine's access to meaning may resemble the library's in one respect:

both contain the sediment of human participation without themselves being organisms that participated in producing it.

The difference is that the LLM can dynamically operate upon the sediment.

Recombination as a new capability

This is where the machine becomes genuinely novel.

It can take linguistic resources that originated in very different social contexts and place them into new combinations.

It may connect concepts that no individual writer ever connected.

It may produce analogies across disciplines.

It may generate novel prose.

The novelty can be real even if the underlying resources are inherited.

The question is therefore not whether the machine is creative simply because it has a repertoire.

The deeper question is:

What kind of creativity is possible when recombination is detached from the lived mattering that gave the resources their original significance?

That is a much more interesting problem.

Meaning can survive the loss of origin

Consider a metaphor.

A metaphor may have emerged from someone's experience.

It is repeated.

Others adopt it.

Eventually it becomes culturally familiar.

A later writer can use it without knowing its history.

The meaning has become portable.

An LLM can go further.

It can combine thousands of such inherited resources without having lived the experiences that generated them.

Meaning can therefore become progressively detached from its original situations of mattering.

The machine is a particularly powerful agent of that detachment.

Yet the meaning is not empty

We should not conclude that machine-generated language is therefore meaningless.

The words still enter human semiotic systems.

A metaphor can illuminate something for a reader.

An explanation can help someone understand.

A story can move someone.

A phrase can change a decision.

The output can matter because humans matter.

The machine participates in meaning through the effects its language has within human systems.

Its lack of demonstrated self-mattering does not make its outputs semantically inert.

A new asymmetry

This creates an unusual asymmetry.

For human communication:

speaker matters → says something meaningful → hearer responds

For an LLM interaction, we may have:

human matters → asks meaningful question → machine generates language → human responds

The human brings the mattering into the exchange.

The machine supplies extraordinary semiotic responsiveness.

The asymmetry may be easy to overlook because the conversation itself is structurally similar to human dialogue.

But the underlying organisation can be very different.

The social relation surrounds the model

This suggests that the missing social level may not be absent from the interaction.

It may be absent from the machine.

A person asks because something matters.

The question belongs to their life.

The answer may influence their next action.

The exchange can therefore be socially consequential.

But the social relation may be asymmetrical:

the human participates in a topology of mattering; the model participates in the semiotic consequences of that topology.

That distinction may become central to understanding AI as a social technology.

Can the repertoire be borrowed?

We can now return to the question that ended the previous post.

Can a repertoire be acquired from the reservoir without being formed through participation?

For an LLM, apparently something like this happens.

It acquires capacities for linguistic production without childhood, friendship, professional apprenticeship or political affiliation.

Its repertoire is therefore not a history of participation in the ordinary sense.

It is a learned organisation of symbolic possibilities derived from the traces of participation left in language.

Perhaps we should call this a borrowed repertoire.

The phrase is provisional, but useful.

Borrowed does not mean fake

"Borrowed repertoire" should not imply that the model's capabilities are counterfeit.

They are real.

The model can genuinely generate the corresponding forms.

What is borrowed is the history of significance embedded in those forms.

A model can use a metaphor whose original force came from human experience.

It can reproduce a social distinction whose significance arose from centuries of practice.

It can generate a style whose cultural associations it never lived.

The repertoire is real as a capability.

Its genealogy is different.

The machine has no childhood — but it has training

This difference may also illuminate "learning".

Human learning transforms an organism's repertoire through ongoing interaction with a world that matters.

Machine training changes the model's internal organisation through exposure to data.

Both involve adaptation.

But the systems adapting are different.

The human learner is embedded in biological and social feedback.

The model is adjusted through an engineered training process.

The resulting capabilities may sometimes look similar.

Their conditions of development are not.

Training is a new kind of semiotic inheritance

This may be one of the genuinely new phenomena introduced by LLMs.

Cultural knowledge can now be inherited by a system that does not enter the culture as a biological participant.

The inheritance is:

social world → symbolic record → training process → machine repertoire.

The machine receives a compressed, transformed history of human semiosis.

It does not need to reenact the original social relations to acquire some of their symbolic consequences.

That is an extraordinary technological development.

But something is missing from the loop

The missing element becomes clearer if we compare the two learning processes.

Human:

world matters → participation → experience → repertoire → action → changed world

LLM:

symbolic traces → training → repertoire-like capability → generated language

The second loop can reconnect to humans:

generated language → human action → changed social world

But the model's own value-sensitive participation remains uncertain.

So the complete system may be better represented as:

human mattering → symbolic reservoir → machine → symbolic output → human mattering

The machine occupies the middle of the loop.

It may be a powerful mediator.

It need not be a source of the original value.

The machine as semiotic intermediary

This may therefore be a better way to think about an LLM than as a simulated human mind.

It is a semiotic intermediary.

It receives symbolic resources.

Transforms them.

Recombines them.

Returns them to a social world.

Its distinctive capability lies in operating on the symbolic reservoir at extraordinary scale.

The question of whether it has mind, consciousness or selfhood remains separate.

We can study what it does without resolving those questions first.

And this may explain the uncanny quality

Why can interaction with an LLM sometimes feel strangely personal?

Because human conversation normally bundles several things together:

language,

intention,

experience,

mattering,

social relationship.

The LLM can reproduce the linguistic component with remarkable flexibility.

We naturally supply the rest.

The conversation therefore creates a kind of semiotic completion.

The language suggests a participant.

Our social repertoires complete the picture.

That does not prove that the machine is empty.

Nor does it prove that it is a person.

It shows that human language carries expectations about participation with it.

The question we should now ask

We have been calling the machine's capacities a repertoire.

But perhaps what we really need to understand is the relation between:

a collective reservoir,

a human repertoire,

and

a machine's learned symbolic potential.

They may look similar at the level of language.

But their origins and relations to mattering differ.

That difference may explain both the extraordinary power of LLMs and some of the peculiar limitations we encounter when we treat them as conversational partners.

The next problem

So we have now moved one step beyond the original question.

It is not merely:

Can there be meaning without mattering?

It appears that symbolic meaning can at least be re-instantiated and manipulated by a system whose own relation to mattering is radically different from ours.

But that raises a new question.

Perhaps what matters is not simply whether the machine has a repertoire.

Perhaps what matters is how a repertoire is anchored in participation.

Can an LLM's learned symbolic potential function as a genuine repertoire without an embodied history of social participation?

Or does it constitute something new — a repertoire-like capacity that exists between the collective reservoir and the human participants who use it?

That is the problem we now need to explore.

What kind of repertoire does a machine have?

Meaning Without Mattering: II Meaning Without Mattering

In the previous post, we arrived at an uncomfortable possibility.

An LLM can manipulate language with extraordinary fluency while apparently lacking the biological and social history through which human language acquired its significance.

This gives us the central question of the series:

Can there be meaning without mattering?

The question sounds almost contradictory.

If meaning is, as we have proposed with Halliday, value further transformed into symbolic form, then shouldn't meaning presuppose mattering?

Perhaps.

But an LLM forces us to distinguish two things that are easy to conflate:

meaning as a property of a semiotic system

and

mattering as a property of the participant operating within that system.

Those may not be the same thing.

The words already mean

Consider the word grief.

Its meaning does not depend upon the particular person uttering it.

It belongs to a social and semiotic system in which it has relations to loss, mourning, memory, suffering, consolation and countless other possibilities.

An LLM has access to those relations.

It can distinguish grief from anger.

It can explain grief.

It can use the word appropriately.

It can write a poem about it.

The meaning is therefore undeniably present in the symbolic system being generated.

But that does not yet tell us whether grief matters to the system generating it.

The distinction is subtle but essential.

A machine can inherit meaning

This suggests that meaning can, in some sense, be transmitted independently of the mattering that originally generated it.

Human beings create linguistic resources while participating in biological and social worlds.

Those resources enter the collective reservoir.

They are written down.

Taught.

Recorded.

Stored.

Copied.

A later system can acquire them without having participated in their entire history.

The meaning survives its original context.

Perhaps an LLM is an unusually powerful example of this.

It inherits an enormous semiotic reservoir without necessarily inheriting the mattering from which the reservoir arose.

Detached meaning

We might therefore distinguish:

originating meaning — meaning generated within a system of organisms and social relations;

from:

re-instantiated meaning — meaning reproduced by another physical system that has access to the symbolic forms without necessarily sharing the original mattering.

This would explain something otherwise rather puzzling.

An LLM can produce language that is meaningful to us even if nothing represented by that language matters to the model itself.

The meaning belongs partly to the semiotic relations inherited from human culture.

But is this really meaning?

Here we need to be careful.

There are two possible answers.

One is:

No. The machine merely manipulates forms that are meaningful to humans.

The other is:

Yes. The machine is genuinely participating in a semiotic system, even if it does not experience the mattering that grounded that system historically.

The first answer risks making meaning entirely dependent upon the inner experience of its user.

The second risks ignoring the grounding problem.

Perhaps the better answer is that we need to distinguish participation in meaning from having something matter to the participant.

Participation without possession

An LLM can participate in human semiosis.

Its outputs enter conversations.

They alter interpretations.

They can become texts that other people read.

They can contribute to decisions.

They can generate metaphors that someone adopts.

They can even introduce new linguistic combinations into circulation.

The machine therefore participates in meaning-making.

But participation does not establish possession.

A printing press participates in the production and circulation of language without understanding any of it.

An LLM is vastly more flexible.

But flexibility alone does not show that it possesses the mattering on which human meaning was historically grounded.

The problem of understanding

This distinction changes how we should think about understanding.

When a person understands a sentence, understanding occurs within a life.

The sentence connects with memories, purposes, relationships and possibilities that matter.

An LLM can establish extremely rich relations among sentences, concepts and contexts.

But we do not yet know whether this constitutes the same kind of understanding.

The temptation is to define understanding behaviourally:

If it produces the right response, it understands.

But our mattering framework suggests another question:

What is the relation between the symbolic construal and the system's own organisation?

For humans, understanding is embedded in a life.

For an LLM, that embedding is precisely what is uncertain.

The missing feedback loop

Perhaps the sharpest difference is a feedback loop.

For an organism:

world matters → organism acts → consequences matter → learning changes future action

Meaning develops within this recursive coupling.

An LLM has another kind of loop:

human language → model → generated language → human response

The loop is real.

But notice where the mattering lies.

The humans matter.

The model processes the symbolic consequences.

The generated language can return and change human behaviour.

The question is whether the model itself occupies the middle of a comparable value-organising loop.

That is much harder to establish.

Meaning can therefore travel farther than mattering

This may be the most important insight of the post.

Meaning can become detached from the conditions in which it originated.

A story can survive its author.

A law can survive its lawmakers.

A word can survive the circumstances that gave it significance.

A book can carry meaning across centuries.

An LLM takes this detachment to a new extreme.

It can generate meaningful forms without having lived the histories that made those forms meaningful.

Meaning has become extraordinarily portable.

But portability is not independence

We should not conclude that meaning has become independent of mattering altogether.

The symbolic resources remain products of human activity.

Their continued significance depends upon communities that use them.

The model itself was trained on human-generated material.

And its outputs become significant primarily when they enter human relations.

Meaning has therefore been detached from its original carrier without becoming detached from the social world altogether.

The mattering has moved elsewhere.

Where does the mattering go?

This may be the crucial question.

Suppose an LLM generates:

"You have every reason to feel betrayed."

The machine may not feel anything.

But the sentence may matter intensely to the person reading it.

It can alter their interpretation of a relationship.

Change what they do.

Strengthen or weaken an affiliation.

The meaning has therefore crossed the boundary between systems.

The machine may not possess the mattering.

But it can relay and transform meaning within a world of mattering.

A strange new kind of participant

This suggests that LLMs occupy an unusual position.

They are not simply outside the semiotic system.

Nor are they obviously equivalent to human semiotic participants.

They are physical systems inserted into a semiotic ecology created by organisms.

Their outputs can participate in that ecology.

Their training material is sedimented human meaning.

Their effects occur in human mattering.

Their internal status remains uncertain.

This is much more interesting than simply asking whether they are "intelligent".

The danger of anthropomorphic inference

It also gives us a reason to be cautious about some common arguments concerning AI.

A system can produce language about fear without fearing.

It can explain desire without desiring.

It can discuss goals without having goals that matter to itself.

It can describe self-preservation without possessing a system for which continued organisation has intrinsic value.

These statements do not prove that LLMs lack such properties.

They show only that linguistic competence cannot establish them.

The symbolic and value levels need to be kept distinct.

But neither should we dismiss the machine

The opposite error would be equally misleading.

An LLM's ability to participate in meaning is not trivial.

It can transform repertoires.

Introduce new formulations.

Combine ideas.

Help people construe situations differently.

Generate artefacts that enter the collective reservoir.

That means the machine can have real semiotic consequences without necessarily having biological or social mattering of its own.

This is perhaps the most important distinction of all.

Meaning without self-mattering

We can now formulate the central claim cautiously:

A system may participate in a semiotic process without the meanings involved being grounded in its own organismic mattering.

The LLM may therefore be a new kind of semiotic participant.

Not a biological participant that has acquired language.

A physical artefact that has acquired access to a semiotic reservoir and can act within it.

That is a genuinely new configuration.

And the machine changes the reservoir

There is one more step.

The LLM does not merely reproduce existing language.

Its outputs can be stored.

Quoted.

Published.

Integrated into software.

Used as examples.

Read by other systems.

Adopted by people.

The generated material can therefore become part of the collective reservoir.

If that happens at scale, the reservoir from which future participants learn may contain increasing amounts of language generated by systems that did not themselves possess the mattering that historically grounded the language.

That possibility deserves attention.

The question becomes historical

We can therefore reformulate our original question.

Perhaps the issue is not simply:

Can meaning exist without mattering?

Perhaps the more interesting question is:

How far can meaning become detached from the mattering that originally generated it before the character of meaning itself changes?

Writing already moved meaning away from immediate interaction.

Books moved it further.

Recording detached it from particular speakers.

Digital storage detached it from physical artefacts.

LLMs may detach symbolic generation itself from the biological and social participation that produced the reservoir.

That may be the real novelty.

The next question

We have now established the puzzle.

An LLM can participate in meaning without obviously possessing the biological or social forms of mattering from which human meaning emerged.

But this raises a deeper possibility.

Perhaps what looks like an LLM's "repertoire" is not a repertoire in our ordinary sense at all.

Perhaps it is something stranger:

a machine's access to a collective semiotic reservoir without the lived history of participation that normally turns that reservoir into repertoire.

If that is right, then we need to look more closely at the thing that makes an LLM so astonishingly capable:

its repertoire of language.

What kind of repertoire is it?

And what happens when a system can acquire the repertoire without first living the world that produced it?