That’s very interesting.

I agree that it’s very possible, likely even, that the Christian seven deadly sins were heavily influenced by virtue ethics, the golden mean, etc. But the statement “the seven deadly sins predates Christianity” doesn’t feel right imo.

While yes, the concept of virtues, identifying and espousing them definitely predate Christianity, the “seven deadly sins” is a very specific thing. While similar, they aren’t the same so you cant really say that the concept of the seven deadly sins existed specifically before it was developed and spread by Christianity.

I don’t think you’re trying to say that the seven deadly sins specifically predate Christianity, just that the concept of identifying negative and detrimental traits and behaviours do. But it’s easy to think you are conflating those at a glance. (Or maybe you are, I don’t actually know.)

Do you have a proper substantive source for the seven deadly sins predating Christianity?

I’m legitimately curious here, because I can’t find anything that supports that claim.

Are you unfamiliar with the concept of an implication? Within the common cultural context of the English speaking internet, the implication of that statement is very clear. And just making that implication, is in of itself a judgement.

For example: “you would look good in some cement shoes” without cultural context, that doesn’t imply very much. With the cultural context/understanding that many of us have, that’s a death threat.

Because male nurses are uncommon and in that specific circumstance female is the default that most people assume.

If your only measure of success is wealth, that’s kind of sad.

If your only measure of success is wealth, that’s kind of sad.

We’re not speaking Icelandic right now. Should I randomly put Japanese katakana in english sentences just because it’s テchnically phonetically コrrect?

Your inability to find humor in something does not mean that there is no humor.

The issue isn’t the comic, it’s you.

They obviously meant patriotic citizens of the countries in question

You can’t think of an actual argument with substance, so you’re just going to try and paint me as someone hateful. How embarrassing for you.

You keep changing the question.

You asked why people dislike AI. I gave you reasons. Now you’re arguing whether AI users should be legally responsible for misuse. That’s a completely different conversation.

And your gasoline analogy falls apart because gasoline isn’t designed to generate persuasive text, images, voices, or code at scale. AI is. The concern isn’t that it’s “a tool” it’s that it’s a tool that dramatically lowers the cost and effort required to produce plagiarism, scams, misinformation, and deepfakes. Society has always treated technologies differently when they massively change capability and scale. That’s why we have regulations for cars that don’t apply to horses, and aviation laws that don’t apply to bicycles.

As for the “fuck_ai” crowd: congratulations, you’ve found some extremists on the internet. They aren’t representative of everyone who criticizes AI. You asked why people dislike it, not whether literally every critic wants it banned.

The irony is that you’ve spent this entire discussion asking for reasons people hate AI, then dismissed every reason as either “humans do it too,” “it’s already illegal,” or “those people are morons.” You didn’t come looking for reasons you came looking for excuses to ignore them.

Every one of your rebuttals boils down to “humans do it too,” as if that settles anything. It doesn’t. Humans can lie, plagiarize, scam, and spread misinformation. AI makes all of those faster, cheaper, and infinitely more scalable. That’s the entire point, and you keep pretending scale is irrelevant because acknowledging it wrecks your argument.

Your “Pandora’s Box” rant is just a straw man. Nobody said AI should be abandoned because of its social impacts. You asked why people dislike AI beyond data centers. You were given several reasons. Instead of addressing them, you built a ridiculous caricature so you could dunk on an argument nobody made.

At this point, you’re not refuting the criticisms, you’ve just downgraded every objection to “humans do it too.” By that logic, we’d never regulate anything. Humans steal, so why have laws? Individual humans pollute, so why regulate industrial pollution? Humans commit fraud, so why care when AI lets fraud happen at industrial scale?

At this point, you’re not rebutting the criticisms, you’ve replaced them with a lazy thought-terminating cliché. “Humans do it too” is what people say when they don’t have an answer but still want to sound like they won.

“My GPU runs on solar” is a great answer… to a question nobody asked. Hosting locally cuts server energy use. It doesn’t magically erase the training data issues, copyright, scams, misinformation, hallucinations, or the broader social impacts. You’re arguing against one criticism while pretending you’ve answered them all.

a person who is excessively proper or modest in speech, conduct, dress, etc.

That definition in no way describes the word prude as being pejorative.

And one person’s personal anecdotes also doesn’t really prove that it’s an insult.

Did you not actually read my comment? It was about how the original person was using a bad source to prove their point, which you are also doing.

And to reiterate the very first thing I said, I do in fact believe that the word prude is pejorative. But that is beside the point I’ve been trying to make. Which is: if you’re trying to make a point using sources, make sure they are actually good ones.

Here’s an actual source that backs up your point. Of course that’s only one source when every other dictionary definition I looked up didn’t say either way whether it was insulting.

Just prefacing this by saying that I agree that prude is more pejorative than not.

But, in that section of the Wikipedia article you pointed out, there’s 3 [citation needed]‘s and the citations that are there, are for: two thesaurus’, a book of first names, and a paper on translating indecent language.

None of these actually supports your point, because we don’t know who wrote that section or why, and they provided no proper sources for their opinions. This is one of the cases where Wikipedia is a bad source when it usually pretty solid.

Then why did you say it?

The question was about any shows with a mixed gender group of friends with no romantic engagements between them.

Someone said IT crowd, you then brought up one person attempting to roofie someone. Based on the available information, this very strongly implies that you are considering this a “romantic entanglement”.

If you didn’t think that was a romantic entanglement, why the fuck would you bring it up?

Do… do you consider attempted sexual assault/rape a “romantic entanglement”?

What the fuck.

Here, I plugged our entire conversation into chatgpt.

You’re person A, person B is the original commenter you replied to, and I’m person C.

Person A and Person C are mostly talking past each other.

Where Person A is right

Person A is making a practical claim: “In ordinary use, ChatGPT seems highly reliable, and critics often exaggerate how often it gives wrong answers.”

That’s a reasonable observation.

Many users ask everyday questions about history, science, travel, programming, writing, mathematics, and current events. Modern models are substantially more accurate than both:

random people on social media,

random Google search results clicked without expertise,

and many traditional question-answering sources.

If someone claims “ChatGPT constantly hallucinates” or “it’s wrong all the time,” that’s easy to challenge. Modern models clearly perform far better than that.

Person A is also correct that:

Nobody in the thread produced a simple “gotcha” question that reliably causes failure. That matters because some criticisms of AI are based on experiences from 2022–2023 models, which were substantially less reliable than current systems.

Where Person C is right

Person C is making a different claim:

“You cannot estimate a 99.9% accuracy rate from personal impressions.”

This is also correct.

Suppose you ask 100 questions and all appear correct.

That does not justify concluding the true error rate is 0.1%.

Why?

Because: you may not know enough to verify every answer,

you may be unconsciously selecting easy questions,

many errors are subtle rather than obvious, and a small sample size tells you little about rare failures.

This is exactly why benchmark testing exists. If somebody claimed:

“Commercial aircraft engines fail only once every 100,000 flights because I’ve flown 20 times and never seen one fail,” we would reject that reasoning.

Person C is essentially pointing out a sampling problem.

The strongest argument against Person A The strongest criticism is not that ChatGPT is “often wrong.”

It’s that:

Humans are surprisingly bad at detecting when ChatGPT is wrong.

Many AI errors look plausible.

For example, if I invent:

a fake court case,

a fake academic paper,

a fake quote,

a nonexistent historical anecdote,

the answer can sound extremely convincing.

A casual user may never discover the mistake. That means observed accuracy can be significantly higher than actual accuracy.

What experiment would actually settle this? Person A asks a fair question:

“How much evidence would convince you?” A proper experiment would look something like:

Construct a large test set (thousands of questions).

Cover many domains:

history

law

medicine

physics

mathematics

current events

geography

programming

Define correct answers beforehand.

Blindly score responses.

Measure:

correctness,

completeness,

confidence calibration.

This is basically what AI benchmark researchers do.

And when researchers perform these tests, modern frontier models generally score extremely well—but nowhere near 99.9% across all domains.

Performance varies dramatically by task. Can someone produce a question that reliably causes errors?

This is harder than Person A assumes. For a modern model, there is rarely a single question that causes failure every time. A better challenge would be categories such as:

obscure local regulations,

highly specialized legal questions,

recent events,

nonexistent citations,

invented books or papers,

long chains of reasoning,

edge-case programming problems,

ambiguous historical claims.

Models are much more likely to fail there. But that doesn’t mean they fail on every attempt.

Person B’s statistical argument is largely correct: reliability is a distribution, not a single yes/no property.

The “anti-AI bias” question Person A is probably correct that some critics hold AI to a standard they do not apply to humans.

If a person:

answers 95% correctly,

speaks fluently on thousands of topics,

writes code,

explains science,

summarizes books,

we’d consider them exceptionally knowledgeable.

Many critics compare AI not against average humans but against:

experts,

reference works,

or idealized notions of truth.

However, the opposite mistake also exists. Some AI enthusiasts compare ChatGPT against:

random Reddit comments,

random Google searches,

uninformed acquaintances.

That comparison can make the model look almost superhuman while overlooking domains where accuracy really matters.

Overall verdict

If I had to judge the arguments:

Person C has the stronger methodological argument. You cannot infer 99–99.9% accuracy from personal experience.

Person A has the stronger practical argument.

Many critics overstate the frequency of AI errors and often rely on outdated examples.

Person B is probably closest to the core issue.

The meaningful question isn’t “Can someone find one wrong answer?” but “What is the model’s error rate across different classes of tasks?”

So if this were a debate, I’d say:

Person A is probably correct that modern ChatGPT is much more reliable than many critics claim.

Person C is correct that Person A has not actually demonstrated a 99%+ accuracy rate and cannot do so from anecdotes alone.

Those positions are compatible rather than contradictory.

Do with that as you will.

The scientific method exists for a reason. If you want an accurate idea of the accuracy of LLMs, then the best way is by applying the scientific method to it.

Until you’ve done that, you’re just basing your conclusions on conjecture, anecdotal evidence and vibes, with nothing actually substantive or empirical backing it up.

You seem to be positing that it’s giving results to the tune of 99% to 99.9% accuracy based entirely on vibes.

If you actually want to know, you will have to do thousands upon thousands of prompts, across hundreds of topics that you can accurately fact check, before you can say with any sort of confidence whether it’s that accurate or not.

Your sample size is orders of magnitudes too small for you to reasonably have an accurate accuracy rate.