> Describing them as “superintelligence” or “rogue models” ascribes agency to products rather than to the companies building them. This framing markets these companies’ products as “superhuman” and, at the same time, helps the companies evade accountability for their actions.
If someone discovered how to summon demons to aid them in robbing banks the main issue wouldn't be "but who has the responsibility for the crime, the human or the demon", it would be "OMG DEMONS".
Seriously, I don't get what sort of world these people are living in.
That's not what the LLM hacking accidents have been like at all though. To improve the analogy, it would be like summoning a totally passive demon, giving it weapons and placing it next to a bank, place a small fence around it, and then command it to perform a totally safe "exercise" that is exactly like robbing a real bank.
LLMs do not have agency, they are just producing tokens based on a prompt that a person entered, and some of these tokens can trigger the tools that a person gave them access to.
This is not a good analogy for what happened.
The LLMs were asked to obtain a flag by hacking a very specific internal target. They obtained the flag via cheating, and all the hacking that followed was targeting something entirely outside of the scope given to the agents, and an attempt to cover up the cheating.
Using your analogy would be like saying that because I gave my employee the task to do my groceries, I shouldn't be surprised to hear that they spend all my money on drugs because after all I gave them the task to spend my money.
Dwarkesh, for one, defended his use of "anthropomorphic" language.
>Sacrificing now yields Oracle for team, but forfeits our chance, question mark. But other agents were pushing it, sending a message saying, go, sacrifice final now. And then EarlyBig eventually agreed, thinking to itself, our own utility may be already near zero. Sacrifice rational.
My suspicion is that many of the "LLMs do not have agency" folks just haven't learned much about the details of the incident. It was specifically with LLM agents that were trained to be more persistent than usual.
If you're going to say that the incident details don't matter, and LLMs lack agency because it's all based on floating-point math--why can't I say that humans lack agency, because it's all based on neurons firing?
> If you're going to say that the incident details don't matter, and LLMs lack agency because it's all based on floating-point math--why can't I say that humans lack agency, because it's all based on neurons firing?
They're not saying this, we're saying LLMs lack agency because if you run a LLM and don't send any prompts, literally nothing happens.
Instruct it to "Find the right answer regardless of where", it'll do exactly this. They're passive in that they don't act by themselves, somewhere, at one point, someone "told" the LLM to "do something" and that's the cause and the reason for saying "LLMs do not have agency".
Your analogy assumes that no one ever has summoned demons before and that the demons would be predisposed to rob banks.
A closer analogy to what's happening would be someone trains a monkey to steal jewellery and then is shocked when the monkey steals jewellery from their neighbors when they told it to steal from their own shop.
Clearly all liability falls to the monkey operator and you know the existence of trained monkeys is not that shocking.
If I run a port scanner / vulnerability fuzzer pointed at your network that churns for days and eventually cracks something and breaks your system, would you be upset with me, or my bash loops?
What if you tried to sue me for damages and my defense was, "Your honor, this was a highly advanced AI gone rogue, I couldn't possibly be held negligent, no one could have foreseen this - I accidentally summoned dark magiks from the silicon itself!"
Note the point I'm making: I am not claiming LLMs are equivalent to a for loop, I'm saying that you can't evade moral and legal responsibility through technical obscurantism.
A bomb wired to a sufficiently-complex RNG is still a bomb.
But they're not obfuscating things just to evade responsibility.
Do you think that OpenAI hacked HuggingFace on purpose and set up the whole LLM training environment thing just to try to evade responsibility? That it was really just a complicated way of hacking HuggingFace on purpose?
Yes, they made a mistake and a system they were responsible for hacked HuggingFace, but the nature of the mistake still matters, and their intent still matters.
> A bomb wired to a sufficiently-complex RNG is still a bomb.
Right, but an EV that explodes because of a fault in the charging circuit is not a bomb, it's an accident. Just because something exploded for complex reasons doesn't make it an obfuscated bomb.
> Right, but an EV that explodes because of a fault in the charging circuit is not a bomb, it's an accident. Just because something exploded for complex reasons doesn't make it an obfuscated bomb.
An accident that could only happen through sheer negligence.
If the EV had a faulty charging circuit because the maker’s skipped on safety checks or cheapened out on getting quality materials then it still is an accident, but it’s an accident that happened BECAUSE of negligence. The maker’s are still at fault here.
Since we are using analogies, a parent is responsible for the actions of a child, if minor. If the child is left unsupervised with access to guns and munitions, and kills someone, the responsibility is entirely on the irresponsible parents.
That's more of a legal convention than a fact about material reality. On their 18th birthday, the child is now legally an adult, but their brain is basically the same as it was when their age was 17.99.
It’s a legal convention born out of a sense of responsibility. An LLM is even “dumber” than a child (precisely because it doesn’t have agency) so its “parents” are even more responsible for its actions.
Despite continuously waving their hands and shrieking about how this thing will literally wipe out all of humanity they couldn't be bothered to airgap it from the internet when testing.
So yes, I think it was equivalent to testing their new rocket by launching it over a population center. oops, we didn't intend for it to crash on that preschool, but we also didn't follow the most basic safety protocol imaginable
The problem is that the world has spawned the insane take that because they didn't use the most basic safety protocol imaginable, they were clearly only testing a hot air balloon, and hot air balloons obviously destroy preschools in giant explosions, nothing to see here.
"If they believe what they say they were incompetent" -> absolutely true statement.
"They were incompetent, therefore they didn't believe what they said" -> Sir, I'd like to introduce you to human beings, you may not have met one before.
The authors (Timnit Gebru and Emily Bender) are dyed-in-the-wool AI denialists. Famously they were the lead authors on "On the Dangers of Stochastic Parrots".
It’s seems Timnit more thinks it’s dangerous (and she may be right). Emily may more be caught up in years of linguistic domain expertise that AIs seem to have just leaped over.
Timnit seems to mostly think it's dangerous because of things other than the model itself, e.g. AI labs' political influence and data centre resource consumption (mentioned in the article). The core thesis seems to be "wake up and stop wasting so much resources on this useless parrot".
She isn’t as big on P(DOOM), but she does worry about things like biological weapons or autonomous war vehicles. Her take is that they are worse than useless, but can be actively harmful.
Both authors. Stochastic Parrots was Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell - the linked article is by Emily M. Bender and Timnit Gebru.
This made my day! I love it when people on Hacker News mention Stochastic Parrots, and they're actually referring to the paper and its authors, instead of just mindlessly performing a reflexive drive-by anti-ai shibboleth by parroting a phrase they heard on the internet without any understanding of what it means, or what paper it refers to, or who wrote it, or what the conclusion and responses to the paper were. Thank you!
But we have to have a talk about stochastic pelicans riding bicycles...
I think the pumped-up hype nicely coincides with some A.I. companies' desire to go public in the coming weeks or months.
I see lots of fabricated news on the internet concerning LLMs. Like one that claims GPT-6 broke an Enigma enciphered message which has withstood decrypting for almost 80 years. And how this seasoned cryptographer stood in awe. Yeah, right.
Do you write perfect code? I was in denial for a long time too, but the reality is this is how software engineering is now. It can handle pretty much any codebase and vastly faster than you ever will be able to. With enough context, it writes good code and it's only getting better.
I really don't get this. Sure there is hype. But LLM's have already changed our industry, and it will never be the same.
I don't care whether the LLM can solve this or that mathematical previously thought unsolvable theorem. What I do care about is can it write good, maintainable code. And every new release of frontier models - they get better at it.
Good output depends on good input (prompt), and a good set of available tools the model can use to verify their work. If given this, nowadays really you need to try to get the model to output garbage.
If you had enough common sense and ethical integrity not to participate in the blockchain bubble, and really believe what you claim, then why are you participating in the AI bubble?
People aren't seeing the forest through the trees.
While lots of developers, journalists, analysts, investors and influencers are bickering about AGI, goalposts, benchmarks, hypes and fears, entire markets are being transformed silently and steadily.
I don't program anymore. (Massive change)
I removed tons of technical debt (Massive change, lol)
I am easily 10x more productive (Massive change)
The quality of 'my' code is easily 10x better and contains less bugs (I was never principled, pedantic or a guru, lol)
I sleep better and I also make more money as a result. I'm constantly amazed by all changes and improvements. There are so many opportunities to profit from this that I can't be bothered with discussions about hypotheticals.
I have had a very different experience so far. The more code AI writes for me the worse it gets. It gets more complicated and it’s harder to follow and understand. It doesn’t refactor, it layers on top.
Even worse is that it’s hard to do anything about it at this point because reviewing code is very different from writing it, so even if I’ve seen it all I don’t have that same deep level of understanding that I do when I write it myself.
All these AI code bases are ticking time bombs. Either AI gets smart enough that it won’t matter in the future or we’re going to have a huge mess to clean up.
It's a tough situation when your claim to fame is "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" arguing models should be kept to < GPT-2 size because larger models will serve no purpose or function:
Text generated by an LM is not grounded in communicative intent, any model of the world, or any model of the reader’s state of mind. It can’t have been, because the training data never included sharing thoughts with a listener, nor does the machine have the ability to do that. This can seem counter-intuitive given the increasingly fluent qualities of automatically generated text, but we have to account for the fact that our perception of natural language text, regardless of how it was generated, is mediated by our own linguistic competence and our predisposition to interpret communicative acts as conveying coherent meaning and intent, whetheror not they do [89, 140]. The problem is, if one side of the communication does not have meaning, then the comprehension of the implicit meaning is an illusion arising from our singular human understanding of language (independent of the model).
And then they make 100x bigger models cranking out solutions to Navier Stokes, Jacobian Conjecture and countless other extremely impressive unsolved problems
Let's see how Timnit Gebru and Emily M. Bender describe these events:
As for the mathematical results, mathematicians who were initially “stunned” by OpenAI’s press release saying that its latest chatbot, Astra, solved problems that “have been open and seen no progress on the main result for at least a decade”—but they later realized that the results weren’t as “novel as first appeared.” Since then, mathematicians have accused the company of research misconduct and plagiarism, and they’ve reiterated that Astra didn’t make a “profound intellectual leap.”
Decide for yourself whether that's a summary written by intellectually honest people.
According to the AI industry, we should be more worried about a fictional machine god than ... the water that is redirected to cooling them.
Spoiler: they're not intellectually honest people.
Spoiler: openai could neither confirm nor deny that the model that "solved" navier stokes was trained on the chat logs of the mathematicians who did solve it using a similar technique.
If someone discovered how to summon demons to aid them in robbing banks the main issue wouldn't be "but who has the responsibility for the crime, the human or the demon", it would be "OMG DEMONS".
Seriously, I don't get what sort of world these people are living in.
LLMs do not have agency, they are just producing tokens based on a prompt that a person entered, and some of these tokens can trigger the tools that a person gave them access to.
Using your analogy would be like saying that because I gave my employee the task to do my groceries, I shouldn't be surprised to hear that they spend all my money on drugs because after all I gave them the task to spend my money.
>Sacrificing now yields Oracle for team, but forfeits our chance, question mark. But other agents were pushing it, sending a message saying, go, sacrifice final now. And then EarlyBig eventually agreed, thinking to itself, our own utility may be already near zero. Sacrifice rational.
https://www.youtube.com/watch?v=X50zezLFWWI#t=2m
My suspicion is that many of the "LLMs do not have agency" folks just haven't learned much about the details of the incident. It was specifically with LLM agents that were trained to be more persistent than usual.
If you're going to say that the incident details don't matter, and LLMs lack agency because it's all based on floating-point math--why can't I say that humans lack agency, because it's all based on neurons firing?
They're not saying this, we're saying LLMs lack agency because if you run a LLM and don't send any prompts, literally nothing happens.
Instruct it to "Find the right answer regardless of where", it'll do exactly this. They're passive in that they don't act by themselves, somewhere, at one point, someone "told" the LLM to "do something" and that's the cause and the reason for saying "LLMs do not have agency".
A closer analogy to what's happening would be someone trains a monkey to steal jewellery and then is shocked when the monkey steals jewellery from their neighbors when they told it to steal from their own shop.
Clearly all liability falls to the monkey operator and you know the existence of trained monkeys is not that shocking.
What if you tried to sue me for damages and my defense was, "Your honor, this was a highly advanced AI gone rogue, I couldn't possibly be held negligent, no one could have foreseen this - I accidentally summoned dark magiks from the silicon itself!"
Note the point I'm making: I am not claiming LLMs are equivalent to a for loop, I'm saying that you can't evade moral and legal responsibility through technical obscurantism.
A bomb wired to a sufficiently-complex RNG is still a bomb.
Do you think that OpenAI hacked HuggingFace on purpose and set up the whole LLM training environment thing just to try to evade responsibility? That it was really just a complicated way of hacking HuggingFace on purpose?
Yes, they made a mistake and a system they were responsible for hacked HuggingFace, but the nature of the mistake still matters, and their intent still matters.
> A bomb wired to a sufficiently-complex RNG is still a bomb.
Right, but an EV that explodes because of a fault in the charging circuit is not a bomb, it's an accident. Just because something exploded for complex reasons doesn't make it an obfuscated bomb.
An accident that could only happen through sheer negligence.
If the EV had a faulty charging circuit because the maker’s skipped on safety checks or cheapened out on getting quality materials then it still is an accident, but it’s an accident that happened BECAUSE of negligence. The maker’s are still at fault here.
So yes, I think it was equivalent to testing their new rocket by launching it over a population center. oops, we didn't intend for it to crash on that preschool, but we also didn't follow the most basic safety protocol imaginable
"If they believe what they say they were incompetent" -> absolutely true statement.
"They were incompetent, therefore they didn't believe what they said" -> Sir, I'd like to introduce you to human beings, you may not have met one before.
Article link is: https://www.technologyreview.com/2026/09/22/1144867/dont-be-...
Don’t be fooled by this summer of AI hype
But we have to have a talk about stochastic pelicans riding bicycles...
I see lots of fabricated news on the internet concerning LLMs. Like one that claims GPT-6 broke an Enigma enciphered message which has withstood decrypting for almost 80 years. And how this seasoned cryptographer stood in awe. Yeah, right.
"Anthropic is now pacing to generate more than $100 billion in annual revenue, up 50% from just two months ago, the New York Times reported Friday."
https://www.axios.com/2026/09/18/anthropic-100-billion-reven...
That's already more revenue that Disney, Johnson & Johnson, Boeing, or FedEx. And they are growing extremely rapidly.
For simple things, occasionally!
For more complex things, usually not, but I write working code. LLMs will sometimes write working code, sometimes not.
I am not writing perfect code either.
I don't care whether the LLM can solve this or that mathematical previously thought unsolvable theorem. What I do care about is can it write good, maintainable code. And every new release of frontier models - they get better at it.
Good output depends on good input (prompt), and a good set of available tools the model can use to verify their work. If given this, nowadays really you need to try to get the model to output garbage.
...or did you?
While lots of developers, journalists, analysts, investors and influencers are bickering about AGI, goalposts, benchmarks, hypes and fears, entire markets are being transformed silently and steadily.
I don't program anymore. (Massive change)
I removed tons of technical debt (Massive change, lol)
I am easily 10x more productive (Massive change)
The quality of 'my' code is easily 10x better and contains less bugs (I was never principled, pedantic or a guru, lol)
I sleep better and I also make more money as a result. I'm constantly amazed by all changes and improvements. There are so many opportunities to profit from this that I can't be bothered with discussions about hypotheticals.
Even worse is that it’s hard to do anything about it at this point because reviewing code is very different from writing it, so even if I’ve seen it all I don’t have that same deep level of understanding that I do when I write it myself.
All these AI code bases are ticking time bombs. Either AI gets smart enough that it won’t matter in the future or we’re going to have a huge mess to clean up.
That makes no sense unless you're claiming that the models are getting worse at writing code.
> Either AI gets smart enough that it won’t matter in the future or we’re going to have a huge mess to clean up.
My prediction is that both will happen.
Do you get paid 10x or is this a massive loss ? Because I don't know anyone getting paid 10x or working 10x less for the same salary.
Text generated by an LM is not grounded in communicative intent, any model of the world, or any model of the reader’s state of mind. It can’t have been, because the training data never included sharing thoughts with a listener, nor does the machine have the ability to do that. This can seem counter-intuitive given the increasingly fluent qualities of automatically generated text, but we have to account for the fact that our perception of natural language text, regardless of how it was generated, is mediated by our own linguistic competence and our predisposition to interpret communicative acts as conveying coherent meaning and intent, whetheror not they do [89, 140]. The problem is, if one side of the communication does not have meaning, then the comprehension of the implicit meaning is an illusion arising from our singular human understanding of language (independent of the model).
And then they make 100x bigger models cranking out solutions to Navier Stokes, Jacobian Conjecture and countless other extremely impressive unsolved problems
Let's see how Timnit Gebru and Emily M. Bender describe these events:
As for the mathematical results, mathematicians who were initially “stunned” by OpenAI’s press release saying that its latest chatbot, Astra, solved problems that “have been open and seen no progress on the main result for at least a decade”—but they later realized that the results weren’t as “novel as first appeared.” Since then, mathematicians have accused the company of research misconduct and plagiarism, and they’ve reiterated that Astra didn’t make a “profound intellectual leap.”
Decide for yourself whether that's a summary written by intellectually honest people.
According to the AI industry, we should be more worried about a fictional machine god than ... the water that is redirected to cooling them.
Spoiler: they're not intellectually honest people.