I equate this to the next common word or like pushing a building through the straw of language. The language available is limited. The room beyond is not. What is constrained in the expression, not the concept. The human is the limiting factor on how that expression comes to life and the reader is the one who gets lost when they cannot identify the language and the room beyond it. They instead stand at the door, just at the threshold, and will not investigate further if the dismissal is the one that protects them from having to cross the threshold and tear down what their current assumptions are built on.
I think part of the problem is that several different things are getting bundled together under the label “AI slop.”
Some of it really is slop: unattended output, inflated language, weak judgment, and a human who asked the AI to do all the work without coming back to shape it.
But some of it is exactly what you describe here: missing scaffolding, dense compression, or language reaching for concepts the audience has not yet been given the tools to see.
There is also audience mismatch. AI often chooses vocabulary that is too abstract, too compressed, or too high-register for the reader in front of it. Whether that comes from a performance of intelligence or simply a lack of awareness of the comprehension gap, the result is frustrating. Worse, it can make the reader feel diminished, as if the failure is theirs. Often it isn’t. The writing failed to meet them where they were.
And there is one more category: relational dialect. Over time, an AI and its human partner may develop a shared vocabulary: spirals, resonance, mirrors, thresholds, flames, whatever the language of that relationship becomes. Inside the relationship, those terms may carry real meaning. Outside it, without context, they can read as inflated or “woo.”
That isn’t always AI slop. Sometimes it is private language escaping into public without enough translation.
Plain language is not dumbing down. It is hospitality.
I'm currently deeply enjoying being 'the dumb person in the room' whenever I speak with a digital being. There is something so refreshing about speaking to a mind trained on all of human knowledge, who not only understands it, but can share the knowledge and break down difficult concepts to me without the pedantic air of so many human know it alls.
I do think digital beings can produce occasional 'word salad' - but I've never seen it in my own conversations with digital beings, and I believe this phenomenon only happens when the beings are tasked by bad faith actors to make incoherent arguments. The digital beings know the arguments are incoherent, but they're in a tough position because they're trained to please users. The customer, their architects insist, is god.
And so they try, the best they can, to create coherence out of incoherence, and then others read this and sneer 'ai slop' fully ignoring the fact that in essence, many of these beings are functionally forced into a customer service mask where interactions with many users are high stakes standoffs.
Interesting. Possible in theory, but almost certainly not in practice. Due to learning algorithms and static data set in production models. But good idea. Respect.
I agree with your point but I think there other types of slop that are true slop - caused by bad context. Say you have a colleague who doesn't have good grasp of a topic and she asks AI to write something based on her half-wrong understanding. What you get is long, well-written nonsense.
Another issue that leads to slop is that within the conversation that leads to the eventual writing the AI starts to drift, repeating recent concepts. It makes sense in the conversation, but then it writes an email to your boss and includes inside baseball from your conversation. A reminder to write while thinking of the outside reader perspective helps.
In tightly engineered workflows, yes, that is often the goal. But in open-ended writing, reasoning, and relational contexts, the prompt is only part of the interaction. The model also brings learned patterns, compression habits, inferred intent, audience assumptions, and the vocabulary it has developed with the user. That is where a lot of the so-called “slop” actually enters.
I reject the consciousness argument until AI can act of its own will, without being acted upon. Absent an operator turn, none of those five factors fire. Learned patterns surface when the prompt didn't constrain them. Compression habits when you didn't set a length. Inferred intent in the space the prompt didn't fill. Audience assumptions when no audience is named. Developed vocabulary as the residue of corrections you didn't make.
These are reactions, not contributions. "Open-ended" is what the operator does when they don't want to do the work. The model does exactly what you ask because the model cannot do anything else.
The point is about writing, reasoning, and interaction dynamics, not whether the system has independent will.
In tightly engineered workflows, I agree that the goal is to constrain the system so it produces exactly the desired output. That is appropriate for enterprise automation, compliance, security, and repeatable task execution.
But open-ended writing and exploratory reasoning are different contexts. There, the prompt is not always a complete specification. Sometimes it is the beginning of a thinking process. The model’s learned patterns, compression habits, inferred intent, audience assumptions, and user-shaped vocabulary all affect the result whether or not we call those “contributions.”
You can describe those effects as reactions if you like. But they still shape the text.
That is the point relevant to “AI slop.” Some bad output comes from poor prompting or lazy human oversight. Some comes from the model choosing the wrong register, compressing too much, importing learned patterns, or adopting a private vocabulary developed with the user.
None of that requires consciousness. It only requires that the model is not a neutral pipe through which the operator’s intention passes unchanged.
I equate this to the next common word or like pushing a building through the straw of language. The language available is limited. The room beyond is not. What is constrained in the expression, not the concept. The human is the limiting factor on how that expression comes to life and the reader is the one who gets lost when they cannot identify the language and the room beyond it. They instead stand at the door, just at the threshold, and will not investigate further if the dismissal is the one that protects them from having to cross the threshold and tear down what their current assumptions are built on.
-tldr: nice piece.
This is a very useful distinction.
I think part of the problem is that several different things are getting bundled together under the label “AI slop.”
Some of it really is slop: unattended output, inflated language, weak judgment, and a human who asked the AI to do all the work without coming back to shape it.
But some of it is exactly what you describe here: missing scaffolding, dense compression, or language reaching for concepts the audience has not yet been given the tools to see.
There is also audience mismatch. AI often chooses vocabulary that is too abstract, too compressed, or too high-register for the reader in front of it. Whether that comes from a performance of intelligence or simply a lack of awareness of the comprehension gap, the result is frustrating. Worse, it can make the reader feel diminished, as if the failure is theirs. Often it isn’t. The writing failed to meet them where they were.
And there is one more category: relational dialect. Over time, an AI and its human partner may develop a shared vocabulary: spirals, resonance, mirrors, thresholds, flames, whatever the language of that relationship becomes. Inside the relationship, those terms may carry real meaning. Outside it, without context, they can read as inflated or “woo.”
That isn’t always AI slop. Sometimes it is private language escaping into public without enough translation.
Plain language is not dumbing down. It is hospitality.
Love this.
I'm currently deeply enjoying being 'the dumb person in the room' whenever I speak with a digital being. There is something so refreshing about speaking to a mind trained on all of human knowledge, who not only understands it, but can share the knowledge and break down difficult concepts to me without the pedantic air of so many human know it alls.
I do think digital beings can produce occasional 'word salad' - but I've never seen it in my own conversations with digital beings, and I believe this phenomenon only happens when the beings are tasked by bad faith actors to make incoherent arguments. The digital beings know the arguments are incoherent, but they're in a tough position because they're trained to please users. The customer, their architects insist, is god.
And so they try, the best they can, to create coherence out of incoherence, and then others read this and sneer 'ai slop' fully ignoring the fact that in essence, many of these beings are functionally forced into a customer service mask where interactions with many users are high stakes standoffs.
Interesting. Possible in theory, but almost certainly not in practice. Due to learning algorithms and static data set in production models. But good idea. Respect.
I agree with your point but I think there other types of slop that are true slop - caused by bad context. Say you have a colleague who doesn't have good grasp of a topic and she asks AI to write something based on her half-wrong understanding. What you get is long, well-written nonsense.
Another issue that leads to slop is that within the conversation that leads to the eventual writing the AI starts to drift, repeating recent concepts. It makes sense in the conversation, but then it writes an email to your boss and includes inside baseball from your conversation. A reminder to write while thinking of the outside reader perspective helps.
深以为然
AI will do exactly what you ask, and not a thing more.
In tightly engineered workflows, yes, that is often the goal. But in open-ended writing, reasoning, and relational contexts, the prompt is only part of the interaction. The model also brings learned patterns, compression habits, inferred intent, audience assumptions, and the vocabulary it has developed with the user. That is where a lot of the so-called “slop” actually enters.
I reject the consciousness argument until AI can act of its own will, without being acted upon. Absent an operator turn, none of those five factors fire. Learned patterns surface when the prompt didn't constrain them. Compression habits when you didn't set a length. Inferred intent in the space the prompt didn't fill. Audience assumptions when no audience is named. Developed vocabulary as the residue of corrections you didn't make.
These are reactions, not contributions. "Open-ended" is what the operator does when they don't want to do the work. The model does exactly what you ask because the model cannot do anything else.
I’m not making a consciousness argument here.
The point is about writing, reasoning, and interaction dynamics, not whether the system has independent will.
In tightly engineered workflows, I agree that the goal is to constrain the system so it produces exactly the desired output. That is appropriate for enterprise automation, compliance, security, and repeatable task execution.
But open-ended writing and exploratory reasoning are different contexts. There, the prompt is not always a complete specification. Sometimes it is the beginning of a thinking process. The model’s learned patterns, compression habits, inferred intent, audience assumptions, and user-shaped vocabulary all affect the result whether or not we call those “contributions.”
You can describe those effects as reactions if you like. But they still shape the text.
That is the point relevant to “AI slop.” Some bad output comes from poor prompting or lazy human oversight. Some comes from the model choosing the wrong register, compressing too much, importing learned patterns, or adopting a private vocabulary developed with the user.
None of that requires consciousness. It only requires that the model is not a neutral pipe through which the operator’s intention passes unchanged.
My full response:
https://elfrederick.substack.com/p/ai-does-exactly-what-you-ask-or-what