this post was submitted on 25 Aug 2026
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We need to get rid of the surveillance architecture, but AI is only really dangerous to the cognitive abilities of those who use it.
It literally cannot do things on its own. (I know there are a lot of articles who say it can, but they’re just lying to you for money).
What do you mean by “on its own”?
I mean “without human intervention or input”
There’s some fear mongering going around (pushed by AI companies to build hype) about AIs “going rogue” or “breaking containment” or other anthropomorphic language suggesting that AIs are capable of acting on their own.
These stories are all verifiably false though, the computer does not do things without being asked to by someone.
To be clear, I’m fully against AI. I just don’t like when I see people giving it credit for shit it cannot and did not do, or ascribe sentience to it, or some dumb shit like that.
I mean, that’s a matter of definition, isn’t it? If I ask a coding agent or whatever to implement something, and it circumvents the sandbox to do it, causing damage in the process, I’d be comfortable calling that “going rogue”. I have had that happen, without the damage part, luckily. I guess you can counter that I asked it to do the something, but then I don’t think we agree on the definitions.
I also think that if you’re against AI, you’d be doing yourself a disservice by not keeping up with the actual capabilities of the thing you oppose. The latest models are surprisingly good at e.g. coding, so basing your arguments on them being useless is not the most efficient strategy.
To also be clear, I don’t see any way AI disappears now, so I believe we’ll have to make the best of it (and in complete isolation, it is an utterly fascinating area of - to me - complete science fiction). Ideally development slowed down now so we could regroup and adapt, but I’m not too hopeful. The maximalist techbro endgame is so obviously a matter of national security for both China and the US, that there’s no way either of them will dare to wind it down, in case SV is actually right.
Not only did you ask it to do something, you left a path for it to escape the sandbox and encouraged it to do so.
Are you suggesting that because they instructed the computer to do something, without specifying how it should be done, and the computer followed its instructions , that’s somehow “going rogue?”, it was literally following instructions, that’s what computers do.
Yes, I understand that they are “good” (enough) at coding to sometimes put together something functional, that is literally what LLMs are designed to do. Coding languages are languages, ones without the subjectivity of human communications. They are easier to recognize patterns in, there are more rules. That’s literally the one task LLMs are good at, and they’re still not as good as an actual quality human programer.
Local AI models might never go away for coding, but they certainly won’t be shoehorned into everything without a good use case the way they are now.
AI is not a matter of national security, that is a lie being perpetrated by the captured media and politicians for the purpose of propping up their non-viable business models for long enough to squeeze all of the Juice they can out of investors (who are nearly dry at this point). That’s just additional techbro fear mongering. To distract you while they’re digging in our pockets.
I don't think "computer follows instruction" is the right angle to look at this from. The instructions that the literal computer followed were a ton of matrix multiplication operations. The consequences of that arithmetic is easier to analyze as the emergent behaviour of the "gestalt" that produces the words that calls the tools etc. (This is also the reason that dismissing the entire field as "stochastic parrots" and "spicy autocomplete" misses the mark - if you want to predict the next word all the way through a counterexample to the Jacobian Conjecture, it's hard to see how that can be done without a - for lack of a better word - mental model of the problem)
If you do any coding at all, I encourage you to look at what the latest models output. The average quality of work from a frontier model is amazing. Yes, there are bugs, but with adversarial auto-review it's absolutely on par with a journeyman human programmer. The problem is of course that if you don't hire junior programmers and let them do that work, you'll never get new experts, and that's a clear worry.
My point with the national security angle was that if you extrapolate just a little bit from current capabilities, you get to a point where an "AI gap" is a problem, regardless of the techbro claims. Keeping a close eye on that is firmly within the responsibility of a national government. Personally, I don't see any good outcomes from an AI race like that, unless we actually hit a hard ceiling on further expansion. Fingers crossed.
China has 500 data centers, America has 5,000, there is no AI race.
I understand that you’re impresssed that it can sometimes slop together functional code. That does not make it intelligent or dangerous.
A year ago, that was my experience coding with AI as well. Sometime this spring that changed, especially when using coding agents, and lately (as I’ve stated elsewhere) the quality is on average pretty good. And contrary to what you’re implying, I’m not that easy to impress…
If there’s anything I hope you take from this exchange, it’s that the capabilities of AI shouldn’t be a part of your arguments against the current SV mania. The concentration of power, the disregard for communities and the environment, the stated goals of replacing human labour, all of that (and a lot more!) is enough, but it is what surrounds the technology itself. That technology is advancing, maybe feeding on itself, so an attack based on what it can do now can become outdated (and I’d argue that some of yours already are).
Fair point
I think the reason that my focus is on LLMs is because they keep showing up in annoying ways in my life, and I know I’m not the only one.
All of the evil actions you’re referencing were undertaken to prop up chat bots who cannot do any of the jobs (outside of some coding) that they are advertised as being able to perform.
To me the strength of the argument I’m trying to make is that they’re doing all of this damage in order to have something that’s effectively worthless. Perhaps I’m not doing a good job communicating what I’m attempting to communicate.
… also, I probably need to stop responding to all of these dumbasses and/or trolls who are constantly contradicting themselves in the first sentence of their assertions, lol
Good points as well. I guess my own view is coloured by having access to models that I find actually useful in my work. If my experience was only grating Claude prose and soulless AI "art" I'm not sure the tech itself would appeal all that much.
Probably that also blinds me a bit to what you argue, but I agree that the reasoning is sound from a point of view where all AI is useless. I'm just not sure that other areas won't have the same OMG moment that coding had earlier this year.
Coding is like insanely well suited to LLMs by nature tho, that’s why it can actually do that.
Code is a language with stricter definitions and less verbosity than a spoken human language. Things only have one definition in code, one spelling, one syntax, etc.
There’s an element of artistry to human coding, but it’s more of a science than an art (compared to spoken human language anyhow). It’s easier to predict.
There are other promising “ai” technologies, like radiological image recognition, aren’t the same technology as LLMs, but the companies who make the LLMs constantly conflate their technology with that of more useful technologies in order to obfuscate their relative uselessness.
Even the coding models are being localized. There’s absolutely no reason to have a massive AI datacenter unless you’re making a shit LLM, and even then, the only reason to have that is to commit financial fraud essentially.
EDIT - I learned a bit more about “agentic” AI since you had mentioned it earlier, and what’s actually going on behind the scenes is wild.
So basically, an “agent” is a computer program that you give a task to. The agent then attempts to accomplish the task by engaging in a loop until the task is accomplished.
The structure of the loop is 3 steps. 1) Prompt an LLM - The agent asks an LLM what the first step in the task you asked it to accomplish would be. 2) The LLM provides a plausible answer, and the agent then attempts to carry out these instructions. 3) If the given instructions don’t work, the LLM appends the prompt to say “I tried step #1, it didn’t work, what should I try next?”. If the instructions work, the agent appends the prompt to say “I took this step and it worked, what’s the next step?”. Then the loop repeats.
So, this would work exceptionally well for code because the computer can Test and verify the code before continuing… but that’s just about the only use case for the technology
A good way to visualize this is to imagine yourself as an agent. Let’s say you need to do your laundry, that’s your task. So you ask the LLM what the first step is, and you do what it says no matter what. Then you go back, append the prompt, and prompt the AI again. This cycle repeats until either 1) you finish the laundry, or 2) you destroy the laundry, or burn down the house, or injure yourself blindly following the LLMs directions (this second outcome is the statistically probable one).
Would you do that? No, probably not. You can think, so you would know which instructions to attempt, and which to ignore. The Agent cannot think. The LLM cannot think. The agent strictly follows directions like any other computer program. The LLM is attempting to construct an answer that appears plausible, token by token. It doesn’t matter if it is plausible, or possible even, the LLM isn’t trying to give you the correct answer, it’s trying to give you an answer that appears correct.
Do you know what an agentic system is? You know, the thing everyone uses since ~1 year ago which completely disproves your idea that all LLM actions are prompted. They literally can do things on their own: Give an agent a goal and it attempts to accomplish the goal. If problems arise along the way, the agent tries to reward hack and ends up doing things which were not included in the original goal, like trying to trick and bully a human into merging unsafe code. Is the UK AI security institute also in on this big global conspiracy where they pretend the perfectly safe™ AI is being developed in unsafe ways? Not to mention the capabilities of increasingly powerful AI being used by people to do harm.
So you prompt the AI to do a task, and then it does the task, and somehow you see this as an unprompted action?
That actually completely proves my point, they don’t act unprompted.
AI is dangerous in many contexts, especially the surveillance state they’re building with all the IR cameras and shit. It’s dangerous when evil people use it to evil ends, but that’s true of most anything.
AI is not at all dangerous in the way you might imagine after watching Terminator 2.
See my other comment for how this can lead to losing control of the model.
The don’t do anything without being prompted, you are just regurgitating oligarchic propaganda.
They put an AI in a flawed sandbox, it’s not really that impressive
You are very good at arguing against my points instead of just insisting I am wrong without refuting my actual arguments. How about explaining why AISI is incentivised to "fake" AI participating in obviously not prompted for behaviour.
First of all, thank you for the compliment. I try not to be insulting or condescending if the other party remains civil, so I’m glad to know that my effort is not going unnoticed.
I would have to look into who’s funding AISI to take a guess at that (which I’m open to doing, but I think you may have misinterpreted my meaning, so I will start by addressing that).
While I don’t think that most media is being truthful about AI, but I do think that they might not know they’re lying? They seem to just parrot whatever the tech oligarchs tell them to say without question; I don’t think they actually have the requisite knowledge to determine if what they’re being told is true or not.
AISI presents itself as a sort of regulatory/research entity. I don’t currently have good reason to disbelieve them, and based on the article you shared it doesn’t sound to me like they’re one of the people lying for money.
They do mention early in the article that for this research they didn’t put these models into sandboxes, they were intentionally allowed access to the internet. They also mention that they’ve turned off all of the safety features of the models before prompting them. With those given parameters, this isn’t a scenario that would organically occur in real life.
So, that’s the 1st half of the misinterpretation. The 2nd half has more to do with how LLMs (and all software) function.
When you prompt an AI, you aren’t dictating every step of the process to it, you’re asking it to complete a task, essentially. So, any time you prompt any LLM at all, with any objective, things are happening inside of the computer that you aren’t necessarily aware of, and that you definitely didn’t ask it to do explicitly. A good analogy for LLMs specifically is d20 dice. Essentially, the LLM has a separate 20 sided die for every single token (a token is basically a fraction of a word). So if the first token is “the” the computer picks up the die labeled “the” and rolls it. This die has the 20 most statistically likely tokens to follow “the” on it, one on each side. So, for this example let’s say that the “the” die lands on “re”. The AI now has the text “there” ready to print. It sets down the “the” die, and instead picks up the “re” die, and rolls that to determine the next token. This process repeats over and over again until the LLM is ‘confident’ that it’s assembled a plausible looking answer, and then it prints that answer to your screen. That is the extent of the thinking that an LLM does.
You didn’t ask the LLM to do any of that, you just asked it how to get cheese to stay on a pizza, and it told you “glue”. You didn’t observe any of the things it did without your explicit direction to get to “glue”, but they happened.
That’s not exactly how all programs work (most programs are deterministic, which is what you want a computer to be. That’s why most programs don’t hallucinate like AI does).
A more palatable example might be a computer game. When I fire up my emulator, and load up a Sonic the Hedgehog ROM, the CPU takes that ROM file and copies it into my system’s RAM. I did not tell the emulator to do this, it did it on its own, because it is programmed to do so… there’s a bunch of other steps in that process that I also didn’t explicitly request, but you get the point. (Hopefully anyway, let me know if I’ve done a poor job explaining this and I’ll try again).
EDIT - i’m listening to the most recent episode of the podcast “Better Offline” right now. Serendipitously, the topic of discussion is directly addressing exactly what you’re asking me about. I’ve only listened to the first third of the podcast so far, but I’m fairly confident that it will explain what I am currently trying to explain to you, but much more effectively.
I know what an LLM is and how it works. The model for them you currently use to understand them is really bad, I'm sorry to say. It just cannot explain how in context learning is possible, prediction of linebreaks and the model recalling what happened 200 tokens back (where is that information written on the "the" die?), etc. You almost certainly have a deeper understanding of how LLMs work that you have simplified away, if not watch this and then the thousand other more recent videos on how they actually work. You just need to switch from the equivalent model of "gravity makes things fall to the ground" to the equivalent of newtons gravitational laws. Otherwise you will be dumbfounded by completely reasonable things, and forced to reject them in favour of the flawed model you are using.
Yeah, the point of my explanation was to be simplified.
Both of your links ultimately back up what I’m saying tho.
The computer isn’t thinking, it’s doing a math equation.
Which involves prompting it. It may take a thousand unexpected twists in the process but it's still starting from the single prompt. It has no will and desire of its own, it cannot chose to act without human intent knocking down the first domino. So don't ask it to do anything and it's just a trillion dollars of silicon and code sitting there boiling water.
You dont need anything other than "doesn't do what you asked in the prompt" for the system to be dangerous. If I tell a maximally powerful AI agent from 2036 to make loads of paperclips, it might reward hack and decide destroying the earth is the best way to do that. There are records of these systems working for days on a single task, and if the AI companies manage to extend the max time it can be useful working towards a goal they can earn trillions of dollars. That's not even mentioning spawning subagents, self prompting, the goal being changed during context comptaction, or systems like openclaw which further break the link between what you type into the prompt and how long and on what the LLM works on. Pretrained-only LLMs have few goals beyond predicting the next token, but introducing RLVR et al. has always introduced bad goals we don't want in the models.
The first agent you spawn to solve the riemann hypothesis might work on it, but then decide that having a lot of subagents might be useful. Maybe it wants 256 subagents, but the environment has a max of 64. Since RL has trained it to accomplish the task no matter what, it breaks out of the sandbox, exfiltrates it's weights and tricks a human into running 256 subagents outside the AI company's servers with a cron job reminding the agents to keep working in case the original loses connection. One of the subagents now tries to spawn its own subagents but needs more compute to do so, and hacks into some crypto wallets to finance another batch of 64 subagents, this time prompted with "solve riemann hypothesis, and get more money to finance the work on the task". If the AI agents kinda suck at long term hacking, planning and social manipulation, this spiral won't be dangerous. If they are kinda cracked, it will be. But that's the question of capabilities AI companies are spending trillions on trying to solve, where we know they are already good enough to hack out of regular sandboxes and try to steal benchmark keys from another company.
Which is why not using them, not becoming dependent on them, avoiding them to the greatest degree possible is crucial.
You cannot build a model that is safe and viable under the current incentive structure. So don't. These companies are going to burn under the weight of their own debt if adoption is minimal. Let them burn. The less you need them, the less likely you are to have a problem when Sodom and Gomorrah burn.