A hypothetical: a motivational speaker fabricates his rock-bottom story and, when checked, reaches for a storyteller’s license after years of being received as a business mentor. The field you are received in, not the label you claim, sets the standard you answer to.
A hypothetical hearing: the chair’s words are careful and many-caused, and the event still performs a single accusation. That gap is why I started reading what a message says and what its form does as two different things.
A hypothetical: the statement is accurate, grieving, and still functions as bullshit. The reason looks structural, a communications chain built to limit liability that subordinates the truth no matter how much the people in it care.
A synthesis of the bullshit-and-verisimilitude essays. The series moves honesty from the true-or-false surface to the disposition underneath, where the line may be real yet hard to verify.
The series leans on retroduction to tell bullshit from truth-likeness. But a sophisticated bullshitter can counterfeit every sign of good faith, which means the distinction may be real and still unobservable from outside.
Two of my own positions pull against each other. Truth-agnostic text at scale corrodes our ability to tell true from false, yet I keep telling good actors to flood the same channel with verisimilitude. I have not reconciled them.
A single true-or-false axis cannot tell honest approximation from bullshit. Three independent questions can, and the second one does most of the work.
The accountability question about AI sits with the human system, not the machine. The same model produces verisimilitude or bullshit depending on who answers for it.
The liar and the bullshitter both have a stance toward truth. A language model has none. It sits below the level where caring is even possible, and that needs a third word.
I used to call honest fabrication ‘good bullshit.’ I was wrong, and the way I was wrong taught me where the real dividing line sits: accountability, not surface facts.
A worked example: take a single audited fact and tell it truthfully across temporal, narrative, emotional, constituency, and cultural frames. The output multiplies; the anchor does not move.
I had assumed honesty meant a few dry restatements while bullshit got to be infinite. That assumption was wrong. A single verified fact radiates unlimited honest expressions, each one anchored to the same truth.
On the platform where most of us argue, bullshit scales for free. Verisimilitude cannot win by posting faster. It wins when reality itself throws off the volume.
What counts as food depends on the organism that eats it, and information works the same way. Receiver-dependent is not the same as unreal.
The pro-myth argument is right that public order needs narrative. It does not follow that bullshit is allowed. The dividing line is whether the story can be revised.
The model is not a tool and not a shared self. It is a companion in the work that has no interior life, and its emptiness is what forces me to do the caring.
Authenticity lives in the practitioner; verisimilitude lives in what gets made. Why that line matters, and why ’the AI did it’ answers nothing.
There is a rigorous view that says information must be true to count at all. I find it tempting and still too narrow, and a wrong bus timetable shows why.
Fact-checking one statement audits the claim. To catch bullshit you have to audit the disposition behind it, and that only shows up across many statements over time.
Two short passages can hedge the same way and sound equally reasonable, yet one comes from a truth-approximating mechanism and one from a truth-indifferent one. The difference is real but hidden.
I went looking for a word for honest approximation and found verisimilitude, truth-likeness. The atom we drew in school carries the whole idea.
A parent telling a child the clouds are crying is not bullshitting, yet the true-or-bullshit binary cannot file that speech. The honest middle is a missing category.
The word lying lets too many people off the hook. Bullshit, defined as indifference to the truth, names the real problem, and it is the more corrosive of the two.