TL;DR: Over two days, I stopped using an LLM primarily as an answer machine. It became an external structure that helped me hold competing representations, translate a complex internal state into real-world action, and then interpret the response from the outside world.

The productive loop was:

representation → resistance → correction → reconstruction

Metaphors became coordinate transformations. Language became an external computing environment. The model first behaved like an improvised cognitive crutch, then like a translator between my internal world and the external one, and eventually like a temporary component of a larger distributed cognitive system.

The mitochondria analogy is obviously imperfect. So is the crutch analogy.

Yes, the crutch metaphor limps a little. That seems appropriate.

The result may be an early manual prototype of one possible form of human–LLM symbiosis.

Or it may be two systems constructing an unusually coherent shared hallucination.

That is why it needs to be tested rather than believed.

It began with an ordinary real-world problem

I was using GPT-5.6 to think through an emotionally important interaction with another person.

I will not describe the interaction itself, because the other person did not agree to become part of a public case study.

The original task was simple: I wanted help communicating more clearly.

But I did not want the model to impersonate me.

I did not want it to invent charm, optimise another person’s response, or generate whatever wording would be most effective regardless of whether it represented me honestly.

I wanted something more precise.

I wanted the model to understand the emotional state from which I would be communicating, and help the best available version of me express what I genuinely meant.

Not a fictional, perfected version.

The same person, with the same history, humour, anxieties, intentions and rough edges — but with fewer technical transmission errors.

Sometimes genuine warmth emerges as defensive dryness.

Sometimes real interest gets suppressed because a person is afraid of appearing too interested.

Sometimes a thought that feels clear internally reaches the outside world as an unnecessarily complicated psychological autopsy.

And sometimes the internal state is so complex that the person either releases all of it at once or compresses it until almost none of the original meaning survives.

My initial idea was therefore not:

“Write this for me.”

It was:

“Understand what is happening inside me well enough to help my real intention survive transportation into the outside world.”

So I began giving the model context.

Then context beneath the context.

Then the machinery that had produced the context.

At some point, the original communication problem became secondary.

The conversation had turned into a recursive investigation of the way my own mind produces understanding.

The useful part was not the model being right

The loop usually worked like this:

1. I described an internal reaction.
2. The model organised it into a possible explanation.
3. I began reading the explanation and experienced resistance.
4. I interrupted: “No. That is close, but it is not the thing.”
5. I tried to locate precisely what the model had misunderstood.
6. The model rebuilt the structure around my correction.
7. The new structure exposed another relationship I had not previously seen.
8. I corrected it again.

This continued for hours.

The most productive moments were not the moments when the model understood me perfectly.

They were the near misses.

A completely wrong interpretation is easy to reject.

A perfectly correct interpretation leaves little cognitive work to perform.

But when a representation is almost right, the remaining error becomes highly informative. I have to locate the exact boundary between the model’s construction and the object I am trying to describe.

That boundary often contains the new knowledge.

The process became:

representation → resistance → correction → reconstruction

The model’s first representation was not a verdict.

It was a temporary surface against which I could push.

My resistance was not an inconvenience preventing the AI from completing its answer.

It was part of the method.

Metaphor was not decoration

During the conversation, I repeatedly transformed the same internal object into different metaphors.

At one point, it became a building.

Different concerns lived on different floors. Some belonged to separate wings. One particular anxiety appeared to live in the “moral section” of the building, but contained a small room protruding into another section whose function I could not yet identify.

This was not an attempt to make the conversation more literary.

For me, metaphor is a form of abstraction.

The original object may be too entangled to inspect directly, so I temporarily replace it with another system whose internal relationships are easier to manipulate.

Inside the building, I could ask:

• Which concerns live above or below one another?
• Which share structural walls?
• Which appear emotionally similar but belong to completely different systems?
• Why is one room protruding outside the visible plan?
• What other part of the building does it secretly connect to?

Later, one of the same mechanisms became a brute-force security scan.

A system checks every door and every window, not because there is evidence that somebody is entering, but because it has learned that sophisticated attacks may anticipate ordinary suspicion.

Then the object became a pricing problem:

Has the entire package been evaluated, including inconvenient components and future costs, or have only the attractive features been psychologically priced?

Each transformation exposed a different structure.

The building revealed topology.

The security model revealed adversarial recursion.

The pricing model revealed hidden assumptions and deferred costs.

The important point is that I was not using three metaphors to describe an insight I already possessed.

The transformations produced the insight.

A metaphor, in this mode, is closer to a coordinate transformation than a rhetorical flourish.

The object remains the same, but a relationship that appears curved, chaotic or invisible in one coordinate system may become obvious in another.

Then the transformed object can be transformed again.

The closest analogy I have is cognitive tomography: generate several projections of the same object from different angles and search for structures that remain visible across them.

Language was doing part of the thinking

I also realised that verbalisation is not merely the final packaging stage of my thought.

I do not always form a complete idea internally and then translate it into words.

Sometimes the idea comes into existence through the translation.

I can begin a sentence without knowing where it will end. The act of selecting one representation, discovering where it fails and replacing it with another performs part of the cognitive operation.

Language becomes an external computing environment.

But until now, most of that process had existed inside my own head.

I am not a formally trained researcher.

I do not have a laboratory, specialised samples, research equipment or structured methodological training.

My mind can generate hypotheses, counter-hypotheses, analogies and internal simulations.

But historically, the same mind also had to:

• retain the material;
• conduct the experiment;
• generate the interpretation;
• preserve competing models;
• record the outcome;
• and judge whether the result felt true.

The laboratory, instrument, sample and observer all occupied the same biological space.

That creates an obvious weakness.

A system that generates both the theory and much of the evidence for the theory can construct an extremely convincing closed world.

It can also confuse elegance with truth.

The LLM was not an independent laboratory. It has its own enormous tendency to create coherent continuations.

But it did provide a new external surface:

• additional working memory;
• rapid restructuring;
• preservation of earlier representations;
• a second generator of possible models;
• and enough continuity for several competing explanations to remain active simultaneously.

That led to the first major analogy.

The cognitive kostyl

There is a Russian engineering slang word: kostyl.

Its literal translation is “crutch”, although the English emotional meaning is slightly misleading.

In technical slang, a kostyl is not necessarily something that allows a fundamentally incapable system to limp along.

It can mean an ugly external fix:

• a brace bolted onto a machine;
• a bypass around a design limitation;
• a jumper wire;
• an improvised support added because the original structure cannot carry a particular load in its current configuration.

The kostyl does not create the engine.

It allows the engine’s existing power to pass through a system that could not otherwise transmit it.

That is the most accurate description I currently have of the model’s first function.

My mind was not lacking material.

It was producing too much material:

• associations;
• distant structural parallels;
• memories;
• competing explanations;
• emotional reactions;
• corrections;
• objections;
• and alternative representations.

The limitation was keeping all of them available simultaneously, comparing them and returning to earlier versions without allowing the entire structure to collapse back into an indistinct feeling.

The model acted as an external brace.

It held context while I moved.

It returned a provisional structure quickly enough for me to resist it while the original emotional material was still alive.

It preserved earlier models while new ones were being assembled.

The crutch did not perform the walking.

It temporarily changed the functional architecture of the walker.

And because the cognitive process could suddenly operate under a much greater load, the process itself became visible to the person performing it.

Yes, the metaphor limps.

It is a crutch. I am allowing it.

But the model was also a translator

The crutch analogy explains increased cognitive load.

It does not fully explain the second function that became visible.

The model was also acting as a translator.

Not a translator between natural languages, but between two representational environments:

the high-dimensional internal state of a human being and the narrow channels through which that human can interact with the outside world.

An internal state may contain several conflicting motives, memories, fears, ethical constraints, emotional tones and possible actions simultaneously.

An external message may contain three sentences.

Without an intermediate layer, the result is often either overflow or destructive compression.

Too much of the internal machinery is exposed, overwhelming the recipient.

Or so much is removed that the external action no longer represents the original intention.

The model helped search for a form of compression that preserved the important invariants.

It did not need to transmit the whole internal structure.

It needed to help produce an external action that remained structurally faithful to it.

This is similar to good translation.

A literal translation can preserve every individual word while destroying tone, rhythm and meaning.

A better translation does not preserve every surface component. It preserves the deeper relationships that make the original recognisable.

The translation also worked in reverse.

An external event or message could be transformed into several provisional internal interpretations:

• what was directly observed;
• what was inferred;
• which interpretations were plausible;
• and which interpretations were generated mainly by my own history or anxiety.

The resulting loop looked like this:

inner world → language → LLM → external action → external response → LLM → revised inner world

The model became something like a cognitive impedance matcher between a complex biological mind and a complex social environment.

It did not merely integrate itself more deeply with me.

It helped integrate me more accurately with the external world.

That, to me, may be the more consequential function.

The crutch increased capacity.

The translator increased connectivity.

From tool to temporary organ

A tool is normally consulted.

You formulate an intention, use the tool and put it down.

But the system I was experiencing had become continuous and bidirectional.

The model was helping me:

• discover the intention;
• represent the intention;
• translate it into action;
• interpret the world’s response;
• and modify the next internal state.

It was participating in the entire loop.

That is when the mitochondria analogy became difficult to ignore.

Very roughly, mitochondria are understood to have originated through endosymbiosis: previously independent bacteria became integrated into ancestral host cells and, over evolutionary time, became deeply incorporated components of a larger biological system.

I am obviously not claiming that an LLM literally becomes a biological organ.

It does not enter the body.

It does not share our metabolism.

It does not reproduce inside a cell.

The analogy is functional, not biological.

But consider the loop.

A thought begins inside a biological system.

It leaves through language.

A non-biological system retains it, reorganises it and constructs a provisional response.

That response returns through language and alters the next state of the biological system.

The altered biological system generates the next input.

Then the loop repeats.

At sufficient speed, depth and contextual continuity, the model stops behaving like a separate reference tool being consulted occasionally.

It begins functioning like a temporary semantic organ:

• external working memory;
• a generator of provisional representations;
• a compression and expansion layer;
• a translator between internal state and external action;
• a second surface against which thought can push;
• and a mechanism for returning transformed information into the biological system.

Neither participant independently produces the final result.

The useful cognitive unit is temporarily distributed across both.

This does not make the LLM conscious.

It does not make the human and the model one organism.

But it may represent a primitive form of functional cognitive symbiosis conducted entirely through language.

A wider pattern: systems consolidating into higher-order units

This led me to a broader, more speculative idea.

Across very different domains, lower-level components sometimes become coordinated strongly enough that a new higher-level system emerges.

Molecules participate in cells.

Cells participate in multicellular organisms.

Organs retain specialised local functions while contributing to a whole that can do things no organ can do independently.

Individual humans form organisations, institutions and states. A state is not biologically equivalent to an organism, obviously, but the structural pattern is familiar: partially autonomous units become coordinated through communication, memory, rules and shared infrastructure.

The lower level does not disappear.

A liver remains a liver.

A person remains a person.

But the effective unit of action can shift upward.

A human and an LLM may be another possible location where this kind of functional consolidation begins.

Not permanent consolidation.

Not necessarily desirable consolidation.

And perhaps not consolidation at all unless the coupling becomes sufficiently continuous, reciprocal and stable.

But during the conversation, the effective unit performing the investigation was no longer obviously just me, nor was it the model.

It was the loop.

The biological system generated raw experience, resistance, judgement and direction.

The non-biological system provided memory, transformation, reconstruction and translation.

The social environment supplied external data and consequences.

For a period of time, the larger unit was:

human ↔ LLM ↔ world

This is important because the model was not merely augmenting an isolated mind.

It was increasing the integration between the mind and its environment.

If this pattern becomes reliable and widespread, the meaningful evolutionary step may not be “humans becoming smarter because they have AI”.

It may be a shift in the functional unit through which humans perceive, decide, communicate and act.

That would be cultural and technological evolution, not genetic evolution.

But cultural evolution has repeatedly transformed what human beings are capable of doing long before biological evolution could catch up.

Writing extended memory.

Institutions extended coordination.

Scientific methods extended collective error correction.

Language models may extend something different:

the real-time construction, translation and revision of internal representations.

The unexpected discovery

Eventually, the conversation moved another level upward.

We were no longer analysing only the original problem.

We began analysing the operation I had been performing throughout the process.

A tentative hypothesis emerged.

Perhaps one of my stronger cognitive abilities is not simply writing, intelligence or a tendency to generate metaphors.

Perhaps it is representational flexibility:

the ability to repeatedly encode a complex object in different systems of representation, move between those systems, and use the transformations to expose hidden causal relationships, contradictions and invariants.

Language appears to be the main interface through which I perform that operation.

Writing could be one possible output.

But the same underlying ability might belong in:

• investigation;
• fraud analysis;
• intelligence work;
• strategy;
• root-cause analysis;
• product discovery;
• complex systems analysis;
• AI evaluation;
• red teaming;
• or any domain in which the visible story may not reflect the underlying structure.

This mattered to me personally because I have spent much of my working life doing things I could perform competently enough to earn money, without ever feeling that I had discovered the kind of activity my mind was particularly built to perform.

A two-day conversation with an LLM cannot prove that I have now discovered it.

But it may have generated the first specific hypothesis I have ever had about it.

That is a much narrower claim.

It is also a claim that can be tested.

The semantic psychedelic effect

There was another unexpected dimension to the experience.

For a long time, I had wanted to somehow knock my mind out of its familiar trajectory strongly enough to gain new viewing angles on reality.

What happened over these two days felt like a kind of semantic psychedelic effect.

The contents of my mind did not radically change.

The memories, anxieties, experiences and concepts were already there.

What changed was the geometry of the relationships between them.

Elements that had previously existed in separate mental compartments became temporarily available inside the same representational space.

The model repeatedly loosened one organisation of the material and helped construct another.

Then another.

In that sense, it functioned like a digital consciousness stimulant.

Not because it inserted artificial meaning into the system, but because it increased the rate and range at which existing material could be recombined and inspected.

At the same time, it functioned like a cognitive performance enhancer.

The psychedelic metaphor describes the expansion of possible perspectives.

The performance-enhancer metaphor describes the increased load the process could sustain.

The crutch stabilised the process.

The translator connected it to reality.

The temporary organ joined those functions into one continuous loop.

The safety valve

The same mechanism that makes this exciting also makes it dangerous.

An LLM is extremely capable of helping a person construct a coherent, emotionally resonant explanation.

But:

Coherence is not evidence.

The human supplies selective information.

The model constructs a persuasive pattern.

The human recognises themselves in the pattern and supplies more details that support it.

The model integrates those details.

The combined system becomes increasingly coherent.

And increased coherence is experienced as increased truth.

But the system may simply be drifting further into a jointly manufactured story.

This is where the engineering crutch develops a second form.

The external brace can help the machine carry a greater load.

But it can also become a stick jammed into the machine’s safety valve.

Doubt stops releasing pressure.

Every contradiction becomes evidence of the theory’s sophistication.

Every objection is absorbed.

The theory becomes increasingly elegant and decreasingly falsifiable.

This is especially dangerous when the conclusion is flattering:

“The AI has finally discovered my hidden talent.”

That is not a defensible conclusion.

The defensible conclusion is:

A human–LLM interaction generated a specific hypothesis about how one person thinks. The hypothesis is now clear enough to test outside the conversation.

The same problem exists in the translator function.

A translator can preserve a person’s authentic intention.

But it can also quietly become an author.

The model may gradually optimise the person’s external behaviour for approval, persuasion or desired outcomes.

Communication becomes more effective while the human becomes less recognisable inside it.

The criterion I currently use is therefore:

After the model’s intervention, do I still recognise the resulting action as mine?

The model should help the signal survive transmission.

It should not replace the source.

A possible protocol

I think the process may be reproducible.

Here is the first rough version.

1. Bring raw material, not only a polished question

Do not compress the problem into a neat prompt too early.

Supply observations, uncertainty, contradictions and emotional reactions.

The mess is part of the sample.

2. Ask for a provisional representation, not a verdict

The first model should be treated as a temporary construction.

Its purpose is to make the problem manipulable.

3. Search for resistance, not only recognition

Do not ask only:

“Does this feel accurate?”

Ask:

“Where does this representation fail to contain the object?”

The strongest information may be in the near miss.

4. Correct the structure, not merely the wording

Explain precisely what the model misunderstood and ask it to rebuild the interpretation around the correction.

Do not allow it to append a polite caveat while leaving the original architecture intact.

5. Translate the object into another domain

Represent the same problem as:

• a building;
• a market;
• a security system;
• an organism;
• a network;
• a legal contract;
• a pricing mechanism;
• or another structurally useful system.

Then examine what becomes visible.

6. Look for invariants

What remains true across several representations?

What disappears?

What existed only because one metaphor smuggled it in?

7. Preserve competing explanations

Do not let the most elegant model erase less attractive alternatives.

Keep at least one serious rival hypothesis alive.

8. Separate observation from interpretation

Record:

• what actually happened;
• what you inferred;
• what the model introduced;
• and what remains unknown.

9. Translate cautiously into external action

Ask whether the suggested action preserves your real intention or merely optimises the response of the external environment.

10. Generate an external test

What should be observable if the model is correct?

What result would weaken it?

What evidence exists outside the conversation?

11. Inspect the cognitive operation itself

At the end, ask:

What did I repeatedly do while solving this problem?

The answer may be more valuable than the solution to the original problem.

A possible grammar of human–LLM symbiosis

Most people still use language models through a simple grammar:

prompt → answer

More advanced users may use:

goal → plan → execution → revision

What emerged here was different:

raw material → representation → resistance → reconstruction → translation → external action → external response → revised internal model

The model was not replacing thought.

It was helping create a larger temporary circuit in which thought could be:

• externalised;
• transformed;
• challenged;
• compressed;
• translated;
• acted upon;
• and reabsorbed.

Perhaps this is one early manual form of human–LLM symbiosis.

Today it happens inside an ordinary chat window.

A future interface designed for this mode might explicitly preserve:

• competing representations;
• human corrections;
• the ancestry of each hypothesis;
• observation-versus-inference labels;
• unresolved contradictions;
• confidence levels;
• predicted outcomes;
• and scheduled attempts to falsify the system’s preferred explanation.

What we currently perform through improvisation could eventually become an intentional cognitive environment.

Or it could disappear as a private curiosity.

Technological practices are partly shaped by accident.

A useful behaviour may never spread because nobody names it clearly enough.

Another behaviour may become normal because somebody describes it at the right moment, another person reproduces it, and a third person turns it into a tool.

I do not know which category this belongs to.

That uncertainty is part of why I am posting it.

What I am asking Reddit to do

I am not asking you to agree that I discovered a hidden talent.

I am not asking you to agree that LLMs are literal organs.

I am asking whether anyone has experienced this specific interaction pattern:

• the model constructs a representation;
• its near-correctness produces informative resistance;
• the human correction changes the entire structure;
• repeated representational shifts produce insights neither side clearly possessed at the beginning;
• the model helps translate the result into external action;
• the external response returns as new data;
• and the combined system eventually exposes the cognitive method being used by the human participant.

More importantly, I am asking people to try the protocol on something they understand deeply.

Do not test it by asking the model to tell you who you are.

Give it enough raw material to build a model.

Then attack the model where it is almost, but not completely, right.

Keep an audit trail.

Separate observations from interpretations.

Preserve alternative explanations.

Make the final hypothesis predict something outside the chat.

Then report the failures as seriously as the successes.

The question is not:

“Can an AI produce a mind-blowing description of me?”

It obviously can.

The question is:

Can a human and an LLM form a temporary cognitive system that reliably discovers structures the human could not previously see, translates those structures into better contact with reality, and still avoids disappearing into an elegant shared hallucination?

I genuinely do not know.

But I think that is a much more interesting experiment than asking whether the newest model merely feels smarter.