Great -- another "submarines can't swim" person.
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.
There at least is not a large contingent of people going around trying to say there is no such thing as swimming beyond what submarines can do...
Anyone calls anything "AI" and I think it is fair to accept that other people trace the line somewhere else.
It's a pointless naming exercise, no better than me arguing that I'm going to stop calling it quicksort because sometimes it's not quick.
It's widely called this, it's exactly in line with how the field would use it. You can have your own definitions, it just makes talking to other people harder because you're refusing to accept what certain words mean to others - perhaps a fun problem given the overall complaint about LLMs not understanding the meaning of words.
By that definition, SQL query planners, compiler optimizers, Google Maps routing algorithms, chess playing algorithms, and so on were all "AI". (In fact, I'm pretty sure SQLite's website refers to their query planner as an "AI" somewhere; by classical definitions this is correct.)
But does an SQL query planner "understand" databases? Does Stockfish "understand" chess? Does Google Maps "understand" roads? I doubt even most AI proponents would say "yes". The computer does the searching and evaluation, but the models and evaluation functions are developed by humans, and stripped down to their bare essentials.
But that definition a machine that understands the words it produces is AI
Imagine you use an ARIMA model to forecast demand for your business or the economy or whatever. It's easy to say it doesn't have a 'world model' in the sense that it doesn't predict things that are obvious only if you understand what the variables _mean_ implicitly. But in what way is it different from an LLM?
I think Stallman is in the same camp as Sutton https://www.dwarkesh.com/p/richard-sutton
On topics with "complicated disagreements", an important way of moving forward is to find small things where we can move forward.
There are a large number of otherwise intelligent people who think that "LLMs work by predicting the next word; therefore LLMs cannot think" is a valid proposition; and since the antecedent is undoubtedly true, they think the consequent is undoubtedly true, and they can therefore stop thinking.
If I can do one thing, it would be to show people that this proposition is not true: a system which did think would do better at the "predict the next word" task than a system which did not think.
You have to come up with some other way to determine if a system is thinking or not.
Old man yells at cloud _computing_
And it's certainly not a boon for freedom and openness.
Don't get me wrong I think they are remarkable but I still prefer to call it LLM rather than AI.
Acting Intelligent, works for me.
Any test you can device for this, ChatGPT would reliably pass if the medium was text, while a good fraction of humans might actually fail. It does a pretty good job if the medium was audio.
Video, and in person remains slightly out of reach for now. But I doubt we are not going to get there eventually.
> 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.
What does that mean? "Others have called such tools AI" is argumentum ad populum and a fallacious argument.
> Ignoring large and varied debates as to what these words mean.
Lacking evidence of ChatGPT knowing or understanding things, that is the null hypothesis.
He's famously a curmudgeon, not lazy. How would you expect him to respond?
> Totally ignoring the history of the field.
This criticism is so vague it becomes meaningless. No-one can respond to it because we don't know what you're citing exactly, but you're obviously right that the field is broad, older than most realise, and well-developed philosophically.
> Ignoring large and varied debates as to what these words mean.
Stallman's wider point (and I think it's safe to say this, considering it's one that he's been making for 40+ years) would be that debating the epistemology of closed-source flagship models is fruitless because... they're closed source.
Whether or not he's correct on the epistemology of LLMs is another discussion. I agree with him. They're language models, explicitly, and embracing them without skepticism in your work is more or less a form of gambling. Their undeniable usefulness in some scenarios is more an indictment of the drudgery and simplicity of many people's work in a service economy than conclusive evidence of 'reasoning' ability. We are the only categorically self-aware & sapient intelligence, insofar as we can prove that we think and reason (and I don't think I need to cite this).
Mundane for Dec 2025.
Its a mistake to expect too much from it now though or treat it as some sort of financial cost-cutting panacea. And its a mistake being played right now by millions, spending trillions that may end up in financial crash when reality checks back that will make 2008 crisis look like a children's game.
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.
Except that LLMs have no mechanism for transparent reasoning and also have no idea about what they don't know and will go to great lengths to generate fake citations to convince you that it is correct.
Isn't that a good definition of what bullshit is?
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 is what RMS is flagging, though not very substantiated.
What you're talking about is "The Singularity", where a computer is so powerful it can self-advance unassisted until the entire planet is paperclips. There is no one claiming that ChatGPT has reached or surpassed that point.
Human-like intelligence is a much lower bar. It's easy to find arguments that ChatGPT doesn't show it (mainly it being incapable of learning actively, and with there being many ways to show it doesn't really understand what it's saying either), but a Human cannot create ChatGPT 2.0 on request, so it follows to reason a human-like intelligence doesn't necessarily have to be able to do so either.
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.
I think now: What do I think he's wrong about now, that in the future will be revealed I am wrong? I heavily use LLMs...
So many times I've thought he was insane and wrong about issues but time shows that he is a prophet operating according to certain principles and seeing the future correctly. Me in the past was living in an era where these predictions were literally insane. "TVs spying on you? Pfft conspiracy nonsense"
I'm considering doing a "is Stallman right?" website which detects what's the ${majority_view) and ${current_thing) from HN posts and states RMSs opinion about it. But answering my own question, it's very hard to detect what I think is wrong if I believe it to be right!
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.
I'm not sure if these models are trained using unsupervised learning and are capable of training themselves to some degree, but even if so, the learning process of gradient descent is very inefficient, so by the commonly understood definition of intelligence (the ability to figure out and unfamiliar situation), the intelligence of an inference only model is zero. Models that do test time training might be intelligent to some degree, but I wager their current intelligence is marginal at best.
This reads like more a petulant rant than a cogent and insightful analysis of those issues.
eatitraw•53m ago
Seems 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?
csmantle•51m ago
GNU grep respects user arguments and input files to the dot. It is not probabilistic.
kubafu•49m ago
xorcist•44m ago
Rygian•49m ago
An LLM operates a probabilistic process, and provides output which is statistically aligned with a model. Given an input sufficiently different from the training samples, the output is going to be wildly off of any intended result. There is no algorithm.
oulipo2•44m ago
IanCal•42m ago
eptcyka•47m ago
eatitraw•38m ago
croes•41m ago
bjourne•9m ago