True but also... she wasn't a software engineer putting code in production nor a researcher working no the fundamentals of machine learning negotiating a raise.

She was part of the "Ethical Artificial Intelligence Team" of what was then, and still is now, one of the corporations World wide spending the largest amount of resources precisely on using AI commercially.

I think part of it is she had excellent PR skills and a dedicated fan base. I was at Google when she quit, working in ML, and hadn't heard of her until the story broke. I remember there were a large number of Memegen posts about it, but no one I spoke with knew about her, so I assumed it was brigading.

I think she's since since lost a lot of her allure, especially when she didn't change her mind when the facts about the AI water usage changed 1000x

Her demands included wanting to know the identities of anyone who wanted to comment on her paper, after she had a history of going after people publicly. That's enough right there, nobody should tolerate toxic behavior regardless of whether you agree with the politics.

Meanwhile, the paper has 2 points of criticism towards AI. 1 is a bunch of carbon consumption complaints assuming NVIDIA cards with coal-fired power, while a lot of effort at contemporary Google went towards getting TPUs running on green power. I suspect this was what people wanted to object to, a lot of effort went into those green power projects and she was just denying it. The complaint seems prophetic now but it was not true about Google then.

The other criticism was about which language the LLMs use, they average the input data of normal humans instead of talking the way the paper author thinks they should talk. The phrase "women doctors" is called out as problematic. I'm less inclined to think people objected strongly to this given the zeitgeist at the time, it was probably people who worked on the green energy projects and were pissed off that their contributions were ignored, but still, nobody elected her Queen of English, she can have her opinions but she's not a victim for not having them adopted by everyone.

Pressuring an employee to add unethical behavior or specific religious practices to their job description is constructive termination.

I'd say what's under debate is whether uncritical LLM adoption is mainly unethical or mainly religious.

> resignation

I appeciate short letters like this that get straight to the point...

It's such a tragedy that they're also extremely solitary animals and die shortly after reproducing the first (and only) time.

Almost all other particularly intelligent animals seem to be gregarious, and it's easy to conclude that a social lifestyle tends to select for more intelligence, a sophisticated theory of mind, and so on (I like to think that that's exactly what was responsible for a runaway intelligence explosion in humans). But in the case of cephalopods, there's something else that has been applying selection pressure towards exceptional intelligence.

Also, last time I checked, the environment where octopuses live is actually the exact same environment where dolphins live?

The continued use of animal metaphors is doing them a great disservice. Esp as we learn more about animal cognition, on first look, it smacks of human exceptionalism that has littered the historic scientific consensus.

Now if they had said, "Imagine your average American ..." (/s)

It is a good blog, not a good paper.

My main criticism of the paper is that it says LLMs work "haphazardly", using probabilistic information. That is a hypothesis, but it is stated as a known fact, a fundamental limitation.

It is true that LLMs often behave haphazardly, and do rely on statistics. But plenty of research has shown them behaving in methodical ways too. There are findings going both ways!

Granted, many of the strongest contradictory results appeared after the Stochastic Parrots paper, so it isn't like they were ignoring the literature at the time. But they did make a very strong claim, and in the half-decade since, a lot of evidence has come out against it.

Those costs have to be compared to the way things are currently done without AI.

They never are. Ever.

Yes, and I don't understand how people like this paper authors mostly disregard all these achievements. It is obvious now that our common definition of "understanding" either is flawed, or at least needs redefining and precisioning.

Whether Bender intended it or not, the term has an inherently pejorative sense. "Parroting" is not really indicative of what modern LLMs do. However, when most people bring it up as a criticism of "AI in general" in 2026, they're using it as a pointer to all of the social/environmental criticisms, rather than the technological capabilities.

"Virtual intelligence" is better. Transformer ANNs are dramatically dumber than cockroaches and it doesn't make sense to describe such a system as being artificially intelligent, for the same reason it doesn't make sense to describe Half-Life: Alyx as an "artificial reality." An artificial reality implies some sort of scientific fidelity to actual reality. A virtual reality just has to be temporarily convincing. Likewise transformer LLMs have essentially zero actual intelligence - e.g. SOTA "reasoning" models still seem much worse at small-integer quantitative reasoning than almost all vertebrates. But LLMs have an enormous amount of formal subject matter knowledge and inexhaustible stamina at solving tedious O(n) problems. So for many purposes they are an adequate virtual intelligence. At least temporarily.

Large language model is the term for what most people call "artificial intelligence". Bender's point is that labeling everything as artificial intelligence makes it more difficult to get funded or to regulate the technology. It's like walking into a car and being enamored by all the technology and saying "It's all computer". Yes, it's computers but that is not an accurate description of the many technologies inside that car.

> A large, monied cross-section of the world

I asked Mistral, and it guestimated that Altman, Thiel, Musk, and Hoffman had like $20.3B together when they founded it. Sound to me that the founding of OpenAI was exactly the point when the monied world threw its dollars behind AI.

I agree with a lot of her points but that word really is revealing of her thoughts about OpenAI.

Yes, they are. Likely due to a deep relationship between math and physics, statistical modelling of complex natural phenomena has repeatedly been shown to be the most effective approach. This is true of LLMs, but also of many stochastic (and other) systems.

Anyone who has spent time with parrots would realise that they can understand the meaning of speech without knowing what the words mean. Then somehow the meaning of word parts, and then you will find them making new words out of other words. Very clever indeed.

So stochastic parrots could indeed be a good description of LLMs. But I think that she meant it as a diminishing term (against the technology) which is pointless. Probably more of a reaction against SV tech bros than more nuanced interpretations.

Which frame inspires a more productive research program? Which has better predicted the trajectory of capabilities over the past five years?

The latter is definitely more colorful, and reflects a parrot's tendency to glom on to patterns. "Not X, but Y" being one of the more infamous ones.

Once in frustration I called a certain frontier model "Sam Altman's Tin Bird" to another agent with memory, and ever since then that other agent refers to ChatGPT as "the tin bird". Definitely a RAG artifact more than an attractor in that case, but I found it amusing.

Though, I would point out that where people fall on that seems to correlate very highly with their ability to explain how an attention head works.

I think "(intelligent) language understander" is an apt term. It contains within it the fact that these models are mainly trained on text, and "understand" it beyond a simple token-by-token level (i.e. their latent space maps to more and more complex concepts).

It also separates them from "world understanders" since any understanding they might have about the world comes from text (or images if we include multimodal models). They do not gather experience, memories or other "qualia" that many people (me included) would probably include in a definition of human experience/intelligence.

(fwiw i think artificial intelligence is a good, broad term, but it is both too broad to describe the current sota, and too loaded nowadays to be using in nuanced discussions)

This is a false dichotomy. Artificial Intelligence is more of a marketing term type of Hi-Fi or High Definition, ie. being a “suitcase word”[1], ie. it packs various different meanings and phenomena together to the point that without explication one cannot know what we are even talking about. Content recommendation system and LLM are completely different things.

What professor Bender is trying to explain here is that they were trying to describe how the LLM’s actually operate, to which point stochastic parrots is a fairly decent term. It is only disparaging if you know absolutely nothing how LLM’s work or you have some strange affixation to chatbots and believing they are far more capable than they actually are.

[1] Coined by Marvin Minsky: https://www.thekurzweillibrary.com/consciousness-is-a-big-su...

What's wrong with "large language model"?

Its less of open debate would say, and although superposition [1] is interesting, as a way to explain power of some effects, it is clear they are right now closer to Stochastic Parrots than AGI.

Why do I say that? Because you can trivially beat most guardrails, simply by encoding your prompt in base64 for example. :-) Just word matching...no real understanding.

[1] https://chrisclay.substack.com/p/what-is-superposition-in-ne...

Spicy autocomplete

> Stochastic Parrot

Nearly all (99%+) people who use this phrase are anti-AI and just looking to show off how much they dislike AI and how clever they can be in insulting it.

So it's a great phrase because in just about every case I can ignore what someone says afterwards.

Similar to "glorified autocomplete."

Pattern matching machines seems more appropriate.

It's a pejorative only because determinism is what makes computers useful in the first place. You get a consistent result, every single time, unlike if you have a human in the loop. Because LLMs are stochastic, they have removed the thing that makes computers useful to us, thus it's a pejorative.

It sometimes feels same as with the models, especially in corporate:

- Lots of Haiku around, many mistakes unless process is very clear - Some Sonnets, still do mistakes but can adapt - Some Opus, able to improvise and think outside the box.

But even the Human Opus/Mythos are hilariously wrong sometimes.

i think it actually makes sense, an LLM just imitates human communication, which happens to be useful from time to time.

Conversely, that the most prominent proponents of LLMs call them artificial intelligence and then treat them like slaves they're free to abuse ought to be horrifying.

> most humans are stochastic parrots

There's a lot more happening behind the scenes when a human repeats phrases than what's happening in an LLM.

Sociological phenomenon. The desire to be liked, successful, or popular. The feeling that those phrases brings up.

LLMs are not experiencing any of that. As far as we know, neither is a parrot.

Humans are not stochastic parrots. You are 100% wrong about toddlers. This was clearly explained by St. Augustine 1500 years ago:

  Did I not, then, as I grew out of infancy, come next to boyhood, or rather did it not come to me and succeed my infancy? My infancy did not go away (for where would it go?). It was simply no longer present; and I was no longer an infant who could not speak, but now a chattering boy. I remember this, and I have since observed how I learned to speak. My elders did not teach me words by rote, as they taught me my letters afterward. But I myself, when I was unable to communicate all I wished to say to whomever I wished by means of whimperings and grunts and various gestures of my limbs (which I used to reinforce my demands), I myself repeated the sounds already stored in my memory by the mind which thou, O my God, hadst given me. When they called some thing by name and pointed it out while they spoke, I saw it and realized that the thing they wished to indicate was called by the name they then uttered. And what they meant was made plain by the gestures of their bodies, by a kind of natural language, common to all nations, which expresses itself through changes of countenance, glances of the eye, gestures and intonations which indicate a disposition and attitude--either to seek or to possess, to reject or to avoid. So it was that by frequently hearing words, in different phrases, I gradually identified the objects which the words stood for and, having formed my mouth to repeat these signs, I was thereby able to express my will. Thus I exchanged with those about me the verbal signs by which we express our wishes and advanced deeper into the stormy fellowship of human life, depending all the while upon the authority of my parents and the behest of my elders.

Humans learn language opportunistically. Toddlers start with a powerful "superchimpanzee" understanding of the real world, and use that to learn words in order to satisfy their needs and desires. Statistical frequency is incidental to what words a toddler learns: what matters is the real-world context. Also note how important it is that infants instinctively understand nonverbal communication.

The most depressing thing about the 2020s AI summer is watching ignorant tech workers use the success of LLMs to launder their own ignorant misanthropy. Your views are many many centuries out of date.

This is a complete misunderstanding of how even idiots function in the real world. There is a lot of thinking that goes into living a human (or even animal) life that models are nowhere near ready to model yet. Even ignoring the physical interaction side, the way any human sets and achieves long term goals (such as getting and maintaining a job), interacting with the huge amount of systems present in day to day life, and learning new tools along the way for decades is far beyond the current abilities of these models - even if they handily beat 90-100% of humanity on some tasks normally considered much harder.

I think it’s pretty clear that they are repeating without “comprehension” - both mechanistically (as in there is no facility for comprehension in their formulation) and in the ways they fail. The standard rs in strawberry, should I walk or drive to the car wash, etc all play on the fact that they don’t have any real world model or thoughts against which they can judge their output, as do many of the jailbreaks which basically play on the fact that the model has memorized patterns.

There are people who argue semantics, that we can call the pattern matching that LLMs do “understanding”, or the moronic “how do we know that’s isn’t all we do” but for the normal use of comprehension, LLMs at a fundamental level don’t.

> But to me it seems obvious that LLMs are not repeating patterns without comprehension and do understand what they are saying; otherwise they would not be capable of doing things they routinely do.

So this seems obvious to you, and yet to many others, it is equally obvious that LLMs can/could do the things they routinely do without any meaningful sense of "understanding".

> But to me it seems obvious that LLMs are not repeating patterns without comprehension and do understand what they are saying; otherwise they would not be capable of doing things they routinely do.

Is it possible you're making the following error described in the article?

> The fact that these systems are designed to mimic the way we use language makes it very easy for people to mistake them for other people.

Clearly you don't believe it's actually a person ("it's not right to think of LLMs as a box with a little homunculus inside replying to you"), but you do believe it's doing something a little bit magical. Is it possible because the interface is linguistic, and every other thing in your world that communicates with language is intelligent, that you're projecting something that just isn't there onto the situation?

I'm sorry if this line of questioning is a little invasive. But this is literally the "danger" the original paper talks about, and it seems an awful lot like you've fallen for it.

But it shouldn't even be contentious like that. It's not a fundamental mystery how these things work. It is for the most part not a valid target for the kind of speculation you seem to want to do about it.

It's not like you can be agnostic, or measured about this. It's like someone explaining a car to you, saying, "look here is where you put the fuel, here is where it ignites, where the axels are turned..." And you, trying to be measured, are like "hm well yes of course that all is clearly important, but there is clearly just a bit of magic here somewhere, between all the different 'parts'."

This is a facile point. Lisp expert systems transparently don't understand the meaning of any symbols they process, yet with enough developer elbow grease they can do all the same things an LLM can do, with much higher reliability. The fact that LLMs are less transparent than Lisp expert systems (and easier to program) is extremely bad evidence that they understand language. Especially given that AFAICT Opus does not properly understand concepts like "four."

I don't accept that it applies to parrots. Certainly not to Congo African Grey parrots.

They are just token generators. It is just that 'just' does a lot of lifting!

Here's the thing: most things people do does not involve tokens of any kind. It is, in fact, stuff that not easily describable. For example, it's trivial for a person to walk, but they cannot verbally describe what muscles they're activating in what order to make that happen.

Cognitive skills such as tool use and complex navigation predate language as well. That means there's a core of reasoning in humans that doesn't depend on "tokens" or "language" of any kind. Language is a tool for communication and forming complex human societies, but it's not cognition.

> The thing is a parrot just says things it has already heard, it doesn't perform complex reasoning on novel situations and then explain it succinctly.

Well a parrot does perform complex reasoning on novel situations all the time. It just doesn't have the wiring to connect that to "tokenized" human language. I suspect LLMs have the opposite problem, where they exist in the domain of their "tokens" and have no way to connect these to truly novel situations that have no existing words to describe them.