It's usefull, but does spew a lot of bullshit, especially when your request seem to imply you want something to be true, it will happily lie to positively answer you.
> ChatGPT is not "intelligence", so please don't call it "AI". I define "intelligence" as being capable of knowing or understanding, at least within some domain.
Great -- another "submarines can't swim" person. [EDIT2: Apparently this is not his position, although it's only clear in a different page he links to. See below.]
By this definition nothing is AI. Quite an ignorant stance for someone who used to work at an AI laboratory.
ETA:
> Please join me in spreading the word that people should not trust systems that mindlessly play with words to be correct in what those words mean.
Please join me in spreading the counterargument to this: The best way to predict a physical system is to have an accurate model of a physical system; the best way to predict what a human would write next is to have a model of the human mind.
"They work by predicting the next word" does not prove that they are not thinking.
EDIT2, con't: So, he clarifies his stance elsewhere [1]. His position appears to be:
1. Systems -- including both "classical AI" systems like chess and machine learning / deep learning systems -- can be said to have semantic understanding, even if they are not 100% correct, if there has been some effort to "validate" the output: to correlate it to reality.
2. ChatGPT and other LLMs have had no effort to validate their output
3. Therefore, ChatGPT and other LLMs have no semantic understanding.
#2 is not stated so explicitly. However, he actually goes into quite a bit of detail to emphasize the validation part in #1, going so far as to describe completely inaccurate systems still count as "attempted artificial intelligence" because they "purport to understand". So the only way #3 makes any sense is for #2 to be presented as stated.
And, #2 is simply and clearly false. All the AI labs go to great lengths to increase the correlation between the output of their AI and the truth ("reduce hallucination"); and have been making steady progress.
So to state it forwards:
1. According to [1], a system's output can reflect "real knowledge" and a "semantic understanding" -- and thus qualify as "AI" -- if someone "validate[s] the system by comparing its judgment against [ground truth]".
2. ChatGPT, Claude, and others have had significant effort put into them to validate them against ground truth.
3. So, ChatGPT has semantic understanding, and is thus AI.
He's not talking about intelligence though, he's saying it has no knowledge or understanding, whereas something like a decision tree or neural net object recognition model does.
That's what I'm thinking every time I hear or have to use the term "AI". It is not intelligent, but everyone is so used to call it so. LLM is much better.
> ChatGPT is not "intelligence", so please don't call it "AI".
Totally ignoring the history of the field.
> ChatGPT cannot know or understand anything
Ignoring large and varied debates as to what these words mean.
From the link about bullshit generators
> There are systems which use machine learning to recognize specific important patterns in data. Their output can reflect real knowledge (even if not with perfect accuracy)—for instance, whether an image of tissue from an organism shows a certain medical condition, whether an insect is a bee-eating Asian hornet, whether a toddler may be at risk of becoming autistic, or how well a certain art work matches some artist's style and habits. Scientists validate the system by comparing its judgment against experimental tests. That justifies referring to these systems as “artificial intelligence.”
Feels absurd to say LLMs don't learn patterns in data and that the output of them hasn't been compared experimentally.
We've seen this take a thousand times and it doesn't get more interesting to hear it again.
I prefer using LLM. But many people will ask what is an LLM and then I use AI and they get it. Unfortunate.
At the same time, LLMs are not a bullshit generator. They do not know the meaning of what they generate but the output is important to us. It is like saying a cooker knows the egg is being boiled. I care about the egg, cooker can do its job without knowing what an egg is. Still very valuable.
Totally agree with the platform approach. More models should be available to be run own own hardware. At least 3rd party cloud provider hardware. But Chinese models have dominated this now.
ChatGPT may not last long unless they figure out something, given the "code red" situation is already in their company.
I have most sympathy for the ideals of free software, but I don't think prominently displaying "What's bad about:", include ChatGPT, and not make a modicum of effort to sketch out a basic argument, is making any service to anyone. It's barely worth a tweet, which would excuse it as a random blurb of barely coherent thought spurred by the moment. There are a number of serious problems with LLMs; the very poor analogies with neurobiology and anthropomorphization do poison the public discourse to a point where most arguments don't even mean anything. The article itself present LLMs as bullshitters, which is clearly another anthropomorphization, so I don't see how this really addresses these problems.
Whats bad about: RMS Not making a decent argument make your position look unserious
The objection that is generally made to RMS is that he is 'radically' pro-freedom rather than be willing to compromise to get 'better results'. This is something that makes sense, and that he is a beacon for. It seems such argument weaken even this perspective.
This seems to be a complaint against general use of "Artificial Intelligence" term: none of it is "real intelligence" as we don't really have a definition for that.
I never really considered this too deeply, because I've never studied "Agentic AI" before (except for natural language processing). Stallman is making a really good point. ChatGPT doesn't solve the intelligence problem. If ChatGPT was actually able to do that it would be able to make ChatGPT 2.0 on request.
> ChatGPT cannot know or understand anything, so it is not intelligence. It does not know what its output means. It has no idea that words can mean anything.
This argument does a great job anthropomorphizing ChatGPT while trying to discredit it.
The part of this rant I agree with is "Doing your own computing via software running on someone else's server inherently trashes your computing freedom."
It's sad that these AI advancements are being largely made on software you can not easily run or develop on your own.
At ZetaCrush (zetacrush.com) we have seen benchmark results that align with Richard's view. For many of our benchmark tests, all leading models score 0/100
I think we can call LLMs artificial intelligence. They don't represent real intelligence. LLMs lack real-life experience, and so they cannot verify any information or claim by experiencing it with their own senses. However, "artificial intelligence" is a good name. Just as artificial grass is not real grass, it still makes sense to include "grass" in its name.
This should be a badge of honor, a rite of passage for companies: when they become big and important enough for humanity, RMS will write a negative <company>.html page on his website.
What the world calls an LLM is just a call-and-response architecture.
In the labs they’ve surely just turned them on full time to see what would happen. It must have looked like intelligence when it was allowed to run unbounded.
Separate the product from the technology and the tech starts to get a lot closer to looking intelligent.
LLM is a model. So, it fits under "all models are wrong, some are useful". Of course, it can produce wrong results. But it can also help with mechanistic tasks.
And you can run some models locally. What does he think of open-weight models - there is no source code to be published. Closest thing - the training data - needs so many resources to turn into weights that it's next to useless.
Unfortunate that he starts with the thinking argument because it will be nitpicked to death, while bullshit and computing freedom arguments are much stronger and to me personally irrefutably true.
For those who will take “bullshit” as an argument of taste I strongly suggest taking a look at the referenced work and ultimately Frankfurt’s, to see that this is actually a pretty technical one. It is not merely the systems’ own disregard to truth but also its making the user care about the truthiness less, in the name of rhetoric and information ergonomics. It is akin to the sophists, except in this case chatbots couldn’t be non-sophists even they “wanted” to because they can only mimic relevance, and the political goal they seem to “care” about is merely making other use them more - for the time being.
Computing freedom argument likewise feels deceptively about taste but I believe harsh material consequences are yet to be experienced widely. For example I was experiencing a regression I can swear to be deliberate on gemini-3 coding capabilities after an initial launch boost, but I realized if someone went “citation needed” there is absolutely no way for me to prove this. It is not even a matter of having versioning information or output non-determinism, it could even degrade its own performance deterministically based on input - benchmark tests vs a tech reporter’s account vs its own slop from a week past from a nobody-like-me’s account - there is absolutely no way for me to know it nor make it known. It is a right I waived away the moment I clicked “AI can be wrong” TOS. Regardless of how much money I invest I can’t even buy a guarantee on the degree of average aggregate wrongness it will keep performing at, or even knowledge thereof, while being fully accountable for the consequences. Regression to depending on closed-everything mainframes is not a computing model I want to be in yet cannot seem to escape due to competitive or organizational pressures.
44 comments
[ 3.5 ms ] story [ 51.4 ms ] threadSeems unnecessary harsh. ChatGPT is a useful tool even if limited.
GNU grep also generates output ”with indifference to the truth”. Should I call grep a “bullshit generator” too?
Great -- another "submarines can't swim" person. [EDIT2: Apparently this is not his position, although it's only clear in a different page he links to. See below.]
By this definition nothing is AI. Quite an ignorant stance for someone who used to work at an AI laboratory.
ETA:
> Please join me in spreading the word that people should not trust systems that mindlessly play with words to be correct in what those words mean.
Please join me in spreading the counterargument to this: The best way to predict a physical system is to have an accurate model of a physical system; the best way to predict what a human would write next is to have a model of the human mind.
"They work by predicting the next word" does not prove that they are not thinking.
EDIT2, con't: So, he clarifies his stance elsewhere [1]. His position appears to be:
1. Systems -- including both "classical AI" systems like chess and machine learning / deep learning systems -- can be said to have semantic understanding, even if they are not 100% correct, if there has been some effort to "validate" the output: to correlate it to reality.
2. ChatGPT and other LLMs have had no effort to validate their output
3. Therefore, ChatGPT and other LLMs have no semantic understanding.
#2 is not stated so explicitly. However, he actually goes into quite a bit of detail to emphasize the validation part in #1, going so far as to describe completely inaccurate systems still count as "attempted artificial intelligence" because they "purport to understand". So the only way #3 makes any sense is for #2 to be presented as stated.
And, #2 is simply and clearly false. All the AI labs go to great lengths to increase the correlation between the output of their AI and the truth ("reduce hallucination"); and have been making steady progress.
So to state it forwards:
1. According to [1], a system's output can reflect "real knowledge" and a "semantic understanding" -- and thus qualify as "AI" -- if someone "validate[s] the system by comparing its judgment against [ground truth]".
2. ChatGPT, Claude, and others have had significant effort put into them to validate them against ground truth.
3. So, ChatGPT has semantic understanding, and is thus AI.
[1] https://www.gnu.org/philosophy/words-to-avoid.html#Artificia...
And it's certainly not a boon for freedom and openness.
> ChatGPT is not "intelligence", so please don't call it "AI".
Totally ignoring the history of the field.
> ChatGPT cannot know or understand anything
Ignoring large and varied debates as to what these words mean.
From the link about bullshit generators
> There are systems which use machine learning to recognize specific important patterns in data. Their output can reflect real knowledge (even if not with perfect accuracy)—for instance, whether an image of tissue from an organism shows a certain medical condition, whether an insect is a bee-eating Asian hornet, whether a toddler may be at risk of becoming autistic, or how well a certain art work matches some artist's style and habits. Scientists validate the system by comparing its judgment against experimental tests. That justifies referring to these systems as “artificial intelligence.”
Feels absurd to say LLMs don't learn patterns in data and that the output of them hasn't been compared experimentally.
We've seen this take a thousand times and it doesn't get more interesting to hear it again.
Mundane for Dec 2025.
At the same time, LLMs are not a bullshit generator. They do not know the meaning of what they generate but the output is important to us. It is like saying a cooker knows the egg is being boiled. I care about the egg, cooker can do its job without knowing what an egg is. Still very valuable.
Totally agree with the platform approach. More models should be available to be run own own hardware. At least 3rd party cloud provider hardware. But Chinese models have dominated this now.
ChatGPT may not last long unless they figure out something, given the "code red" situation is already in their company.
Whats bad about: RMS Not making a decent argument make your position look unserious
The objection that is generally made to RMS is that he is 'radically' pro-freedom rather than be willing to compromise to get 'better results'. This is something that makes sense, and that he is a beacon for. It seems such argument weaken even this perspective.
This argument does a great job anthropomorphizing ChatGPT while trying to discredit it.
The part of this rant I agree with is "Doing your own computing via software running on someone else's server inherently trashes your computing freedom."
It's sad that these AI advancements are being largely made on software you can not easily run or develop on your own.
In the labs they’ve surely just turned them on full time to see what would happen. It must have looked like intelligence when it was allowed to run unbounded.
Separate the product from the technology and the tech starts to get a lot closer to looking intelligent.
And you can run some models locally. What does he think of open-weight models - there is no source code to be published. Closest thing - the training data - needs so many resources to turn into weights that it's next to useless.
For those who will take “bullshit” as an argument of taste I strongly suggest taking a look at the referenced work and ultimately Frankfurt’s, to see that this is actually a pretty technical one. It is not merely the systems’ own disregard to truth but also its making the user care about the truthiness less, in the name of rhetoric and information ergonomics. It is akin to the sophists, except in this case chatbots couldn’t be non-sophists even they “wanted” to because they can only mimic relevance, and the political goal they seem to “care” about is merely making other use them more - for the time being.
Computing freedom argument likewise feels deceptively about taste but I believe harsh material consequences are yet to be experienced widely. For example I was experiencing a regression I can swear to be deliberate on gemini-3 coding capabilities after an initial launch boost, but I realized if someone went “citation needed” there is absolutely no way for me to prove this. It is not even a matter of having versioning information or output non-determinism, it could even degrade its own performance deterministically based on input - benchmark tests vs a tech reporter’s account vs its own slop from a week past from a nobody-like-me’s account - there is absolutely no way for me to know it nor make it known. It is a right I waived away the moment I clicked “AI can be wrong” TOS. Regardless of how much money I invest I can’t even buy a guarantee on the degree of average aggregate wrongness it will keep performing at, or even knowledge thereof, while being fully accountable for the consequences. Regression to depending on closed-everything mainframes is not a computing model I want to be in yet cannot seem to escape due to competitive or organizational pressures.