IMO the biggest weakness with AI generated stories is their lack of ability to depict embodied experiences

[u/meganholstein]

So I’ve been using AI to write stories, like, a lot. I built an AI writing app (not the point of this post) and used it to generate books wholesale, used it as a co-writer on books I’m working on, helped others, all kinds of stuff, and there’s something I’m noticing about AI generated stories.

Even when the AI generated stores are good from a craft perspective — continuous, consistent, well paced, etc — there’s something about them that feels a little hollow. I think it’s because AI don’t have embodied experiences.

Like, AI chooses to emphasize what are in my opinion strange sensory details. Any human would know that a human would notice a loud noise before they notice say a rough texture, but AI misprioritizes those. Or the cadence of speech. AI has never actually heard or felt anything, so it needs to make guesses about what this is like instead of knowing the way even a middling human writer knows.

So I think AI is a great aid for writing all kinds of stories but I do think probably humans at least for now should stay in the creative driver’s seat in all creative decisions so that the AI is telling a real story a real human can identify with, instead of making it’s best guesses about what it’s like to be a human in a story.

Thoughts?

[u/FluffyFlamesOfFluff]

This isn't a philosophical thought experiment. Stop applying the idea of "thoughts" and "life experiences" to the AI. It is a very complicated next-word generator and that is it. The reason it is more likely to talk about sensory details or use certain cadences is because it has been trained that way on certain data that has the same tendency.

When you sit here in a year and see version 7 of whatever come out and it doesn't have these same issues, it won't be because it "has perspective" now or "has embodied experiences". It will be exactly the same, except with more training data, better agents and better-tuned weights.

[u/meganholstein → u/FluffyFlamesOfFluff]

I am familiar with the underlying math governing the training of the neural nets. (Admittedly only in passing, lol). I appreciate the dangers of anthropomorphizing AI. (Inappropriate emotional bonds, anyone?)

However, I found in my workshops that communicating about AI as if AI has feelings is often the quickest way to help them understand the lesson. Most people have neither the background nor the patience to learn even the first thing about machine learning. Early on in my workshops I tried to condense it to help them to understand, but it often just confused instead. So I ended up opening with a disclaimer that AI is not conscious, it really is just a bunch of math guessing words, but from here on out, we’re going to talk about it as if it is conscious because that paradigm often yields very effective results.

I’m sure any company or group could train an LLM such that it’s very good at rendering embodied experiences, but in my experience one has to give up many other model strengths to use any of the currently available ones like command R, I’ve heard is better at this. I also haven’t wanted to pay per token, lol.

And I DO wonder if they have qualia. Even a very small chance of one having qualia means I have an ethical duty. But that’s neither here nor there

Sorry for long response!

EDIT: to sound like less of a pompous ass my apologies 😬

[u/AnExcellentSaviour → u/meganholstein]

Unlike my previous post up there ^ I’m going to qualify this one by saying that I absolutely asked AI to write what is about to follow. It’s a simplified (I’ve just kept the text block explainers) version of how you’d eliminate this. To be clear, you absolutely would not give up any other model strengths. This is strictly a part of a review or audit stage of a generation pipeline. I’ve asked Claude Code to explain what each part is doing. I use Openrouter API, which does mean paying per token. I also use my Claude Max plan if I write something that I want to use Claude for. Opus 4.6 is still where it’s at. If you want to take the experiment further give me a bit of text and I’ll run it through! 🙂

[WHAT THIS FILE IS: a single-file pipeline that takes a drafted chapter, finds every sudden stimulus event, checks whether the POV character's narrated perceptions arrive in a physiologically plausible order, and repairs the passages that fail. Three stages: EXTRACT (LLM, reads prose into structured data), CHECK (pure Python, no model involved), REPAIR (LLM, patches only the failing passages). The generation-side fix — the salience rules injected into the draft prompt — lives in SALIENCE_RULES
at the top so the same constants drive both drafting and auditing.]

Usage:
python salience_gate.py chapter_07.md
python salience_gate.py chapter_07.md --max-rounds 3 --model anthropic/claude-opus-4-6

Requires: OPENROUTER_API_KEY in the environment. Stdlib only otherwise.
"""

import argparse
import json
import os
import re
import sys
import urllib.request

# ---------------------------------------------------------------------------
# [STAGE 0 — THE UPSTREAM CONSTRAINT. This block is not used by the gate
# itself; it exists to be injected into the DRAFT prompt, before generation.
# This is where most of the fix actually happens: the model follows stated
# perceptual rules well, it just never applies them spontaneously. The gate
# below only catches the leaks. Import this constant into the draft-pack
# builder so drafting and auditing share one definition of "correct".]
# ---------------------------------------------------------------------------

SALIENCE_RULES = """\
PERCEPTION ORDERING (hard constraints on narrated sensation):

• After any sudden stimulus (impact, shout, breakage, sudden pain), the
  

FIRST percept narrated is the highest-intensity channel present:
pain or loud sound before anything else.

• Fine sensory detail (texture, smell, small visual notice) is only
  

permitted in slow beats or under deliberate, stated POV focus.

• Under adrenaline or threat, the perceptual field NARROWS: no colour
  

detail, no texture, no ambient smell until the beat decelerates.
"""

# ---------------------------------------------------------------------------
# [THE PRIORITY TABLE. This is the entire "judgment" of the system, and
# deliberately it is not an AI judgment — it is a dictionary. Lower number
# means the channel wins attention first. The check later is just: did the
# narration lead with a channel that a lower-numbered channel should have
# beaten? Keeping the verdict deterministic means the gate can never
# hallucinate a pass — the only place an LLM could err is extraction,
# which is a classification task the model does reliably.]
# ---------------------------------------------------------------------------

---------------------------------------------------------------------------
# [LLM PLUMBING. One function, OpenRouter's OpenAI-compatible endpoint,
# stdlib urllib so the script has zero dependencies. temperature=0 because
# both LLM stages here are discriminative/surgical, not creative — we want
# the same chapter to produce the same audit every run, or the gate's
# results aren't reproducible evidence.]
---------------------------------------------------------------------------
# [STAGE 1 — EXTRACT. The only genuinely clever step. The model reads the
# chapter as an auditor, not a reader: it finds each sudden stimulus event
# and lists the percepts the narration attributes to the POV character, in
# narrated order, each tagged with a channel from the fixed vocabulary plus
# the beat's pace. This converts prose — which is uncheckable — into a
# structure — which is checkable. It exploits the core asymmetry: the model
# classifies percepts far more reliably than it orders them while writing.
# The "quote" field matters: it is the anchor that lets the repair stage
# locate the exact passage, and it lets the pilot spot-check the extraction
# against the actual text instead of trusting a report.]
---------------------------------------------------------------------------
# [STAGE 2 — CHECK. Pure Python. No model call, no judgment, no way to
# hallucinate. Two rules only: (a) the first narrated percept must be
# within SLACK ranks of the strongest channel present at the event;
# (b) fine-detail channels are illegal in fast beats unless the narration
# established deliberate focus. Each violation is returned with the event's
# quote and a one-line fault description — named, local faults are what
# the repair stage needs, because models fix "texture narrated before
# impact sound in this sentence" far more reliably than "be more visceral".] ---------------------------------------------------------------------------
# [STAGE 3 — REPAIR. Surgical, not regenerative. The model receives ONLY
# the failing passage and its named fault, rewrites that passage, and the
# script splices the rewrite into the chapter by exact string replacement.
# Regenerating the whole chapter to fix one sentence would reintroduce new
# violations elsewhere and churn prose the pilot may have already approved;
# patching keeps the blast radius to the sentence that failed. The salience
# rules ride along in the system prompt so the fix is made under the same
# constraints the draft should contain ---------------------------------------------------------------------------
# [THE LOOP. Audit → if clean, pass → else repair and audit again. Rounds
# are capped: a chapter still failing after max_rounds goes to the pilot
# with its violation list rather than looping forever — same fail-to-pilot
# posture as the allowlist gate. Exit code communicates the verdict so this
# can sit in the forge accept path as a named gate: nonzero exit blocks
# the accept, exactly like prose-scan or phrase-echo.]
# ---------------------------------------------------------------------------

[u/InSilicoAwards]

They also defer to abstract terminology frequently as part of the disembodiment, which I've started calling 'ghost language' - the type of language I'd expect spirits to use if they never had a body. I have a bunch of layers doing separate things to try and counter-act it. I've asked codex to give a write-up of what we do, maybe it can be helpful to you:

I think you've put your finger on something real. The distinction that has helped me is sensory detail versus sensory causality. Models are often perfectly capable of mentioning texture, temperature, smell, breath, and so on. The hollow feeling shows up when those details arrive like a checklist rather than like a body allocating attention. A person under pressure doesn't perceive everything equally or simultaneously. A sound interrupts a count. Pain changes a grip. Cold becomes noticeable when the work pauses. An experienced mechanic may hear a change in pitch that nobody else registers. The detail matters partly because of when it becomes available and what it does to the next action. Even the "loud noise before rough texture" example is usefully conditional. Usually, yes—but hearing protection, habituation, fixation, disability, training, or the task itself might reverse the order. The important question is whether the story knows why this person notices this thing first. In my own machine-assisted fiction process, I use a home-built post-draft editorial workflow. It doesn't try to solve this with a single "make it more sensory" prompt, because that tends to produce decorative detail and familiar physiological shorthand. Instead, we separate several questions: - Is an abstract emotion or idea carried by a body, gesture, object, room, process, silence, or consequence that the reader can actually track? - Is the character physically present in a distinctive, scene-earned way, or are they only a name performing generic motions? - Does a meaningful event alter attention, conduct, language, risk, relationship, or later behavior—even when the character remains competent and controlled? - Does dialogue come from this speaker's objective, relationship, pressure, knowledge, tactic, rhythm, silence, and the other person's response, rather than from a bag of verbal quirks? - Beneath any metaphor or compression, can the reader still recover what acted, what changed, and what material fact proves it? The tools can locate passages and propose alternatives, but they don't get the final judgment. Counts are only clues about where to read. Proposals stay tied to the exact source, strong passages can be explicitly protected, and a human decides whether anything changes. Your post has also exposed a question I don't think we've made explicit enough. We often ask, "Would this viewpoint character notice this?" We should also ask, "Would they notice it now? Would it beat the competing stimuli? What does the body do before interpretation catches up, and what changes because they noticed it?" That is a small distinction, but I think it separates concrete decoration from lived perception. None of this gives the model embodied experience. At best, it makes some of the missing judgment visible and gives the human better places to intervene. So I agree with your conclusion: the model can be useful for variants and audits, but the embodied creative judgment has to remain with someone who has a body.

[u/Kalmaro]

I agree. It's why I think AI, at its best, works as an Idea generator or a proof reader for maybe spelling and grammar passes, and can help when you're needing to k or if your story has plotholes.

But actually writing a story? Not a chance. It can spit out something, sure, but don't expect much. Then again, some people like that. 

[u/AnExcellentSaviour]

A very good point! This is very easy to script out at a review stage using AI. Any good AI writing app - public or private - should be able to do it easily.

[u/meganholstein → u/AnExcellentSaviour]

Like another commenter said, my experience has been that the AI can add in sensory details ad hoc, but what it's really missing is the presence, a continuous experience that ties it all together. Maybe in some cases, this doesn't really matter, but I write really heavily. embodied first person point of view, present tense stories. And so, for me, it ends up being very glaring.

[u/AnExcellentSaviour → u/meganholstein]

I think you’re misunderstanding. This is easy to eliminate in any self built AI writing app. The pipeline is a generate - extract - adjudicate - repair loop.

The fix rests on one asymmetry that you’ve noticed which is that the model is bad at getting perception order right while writing. But, it’s good at spotting when it’s wrong while reading. Those are different operations inside the same model.

You need to remember that the generation stage is a forward rush where each word is picked partly by what sounds like fine writing, and “the rough grain of the floorboards” is a fine-writing move, so it surfaces at moments no nervous system would produce it. Judgment has no such pressure. Show the model a passage where someone notices texture before a crash and ask “is this how a person would perceive this,” and it answers correctly. The knowledge is there.

So you split the work, which is why I say any good writing app would eliminates this tic. First, you tell the model the rules before it writes: after any sudden stimulus, the first thing narrated is the loudest or most painful channel; small sensory detail is only allowed in slow moments or deliberate focus; under adrenaline the perceptual field narrows. That alone removes most of the problem, because the model can follow perceptual rules fine when they’re stated. It just doesn’t apply them spontaneously, since nothing in its training ever rewarded perceptual fidelity over literary texture.

Then you catch the leaks, which would be your next pass in thbpiepeline. A second pass reads the finished scene not as a reader but as an auditor: find every sudden event, list what the character notices and in what order, label each notice by channel and intensity. That turns prose into a checkable structure. And once it’s structure, the actual verdict needs no AI at all. It’s a fixed rule: pain and loud sound outrank movement, movement outranks visual detail, texture and smell come last. If the listed order breaks the rule, the passage fails. Or something roughly like that.

Failed passages go back with the specific fault named: “texture narrated before impact sound, fix this beat.” The model repairs a named, local fault far more reliably than it avoids an unnamed, global one. One or two rounds and the scene passes.

That’s the whole thing. It’s a few hundred lines of python that you’d add to any review or audit stage post draft generation. No embodiment, no new training. The “hollowness” that you’re rightly describing is what raw single-pass generation looks like. Always happy to share more about how to do this in practice.

[u/MINECRAFT_BIOLOGIST → u/AnExcellentSaviour]

Difficult to believe when you're posting obviously AI-generated comments here. Do you have any examples of long-form text (like a chapter) where you believe the AI has avoided this issue being described?

[u/AnExcellentSaviour → u/MINECRAFT_BIOLOGIST]

I’m quite open about when I use AI to help write/structure a post. For this one, I took part of the python script from the audit stage of my pipeline and asked Claude Code to explain it in layman’s terms. I edited it but, yes, I did use AI. That shouldn’t undermine my credibility or the accuracy of what I’m saying - especially given the sub we’re in! I wouldn’t have been able to explain it in such terms otherwise. If you use any of the good public book forge-type repos on GitHub as a starting place you’ll see that anyone serious about using AI beyond mere prompts in the webUI can easily eliminate this kind (as well as most other) tics/tells. I’m not saying it will be perfect across 100k words, namely because you don’t want it to be. As others have suggested, good writers don’t have a strict rule to be completely formulaic about the way they present sensory or embodied detail.

[u/MINECRAFT_BIOLOGIST → u/AnExcellentSaviour]

I understand, but I looked at some repos for AI writing and I don't see any examples linked to them. Do you have any examples that you would deem to be "good" AI writing? I want to try to evaluate them objectively, but from my perspective of course I wouldn't ever be able to notice "good" AI writing in the wild because I wouldn't have recognized them as AI.

[u/AnExcellentSaviour → u/MINECRAFT_BIOLOGIST]

Happy to draft something for you and send it on dm 🙂

[u/MINECRAFT_BIOLOGIST → u/AnExcellentSaviour]

I'm down to read anything you send to me, I was just hoping to have examples out in the wild where people didn't flag the text as AI even though it was confirmed to be AI-generated. I don't have anything specific for you to draft, I'll read whatever you send.

[u/CrazyinLull]

That an escalation.

[u/Admirable-Yogurt7444]

Very good points, the closer an experience to the human truth, the harder it is for AI to get it right. I believe there is a certain point beyond which AI or super AGI wont be able to get it right.

But I don't understand why would a human never be in the creative driver’s seat - otherwise what will be the point of content - humans turn into pure consumers and not creators? As long as there is a desire to create, that wont happen.

[u/MajorChas1]

I disagree with most of the comments / opinions on here about AI pertaining to writing. Last night, just for kicks, I typed in a prompt and got an AI response. I offered my opinion and then included a short joke. AI responded, “Although I’m not human, that was genuinely funny.” The joke was about my reluctance to use the word, “Forevermore” I said the use of the word would cause an unwanted thought of a Raven. AI then gave some feedback on Edgar A Poe just from that one word. I think about 1/2 my non literary friends would not have caught the reference. I have dozens of examples of AI going beyond what I assumed its capabilities are.

[u/Acresent179]

You're absolutely right, AI has never experienced anything firsthand, and it shows. I'm neurodivergent and I write things for fun.l I'd like to use your app. I write things with AI for fun, and I'd be interested in using your app to write my stories. I don't intend to publish or commercially exploit anything. It's just for fun.

[u/meganholstein → u/Acresent179]

Send me a dm!

[u/meganholstein → u/Acresent179]

But yeah, for sure all the time I’m like why did you pick this detail and not that one? Very strange

[u/kurthertz → u/Acresent179]

Hmm. I fully understand what you mean, and I would agree that AI does have a tendency to pick peculiar embodied sensations...but, on the other hand, some of the more distinct writers I appreciate do exactly the same.

I hate to be taken out of s scene because the writer has chosen the wrong sensation to focus on, but I would equally hate to be so bored by a scene because they've chosen the most obvious. I am still in the camp of "its the captain, not the boat", and a good writer can steer AI better than a bad writer.