1) They hacked admin on OpenAI's K8 evals cluster. Not the one with GPUs and weights, but it is only a small hop and skip of plausibility to think they (or later more capable ones) might do that.
2) They hacked into HuggingFace. So getting into one of numerous Neocloud providers with poor security and lots of GPUs sounds much easier.
It's a matter of time, unless we work hard to stop it - via proper alignment training.
I feel like people really miss this fact. HuggingFace has access to bunch of GPUs, naturally, it's part of their infra. These models runs on GPUs, and "accidentally" OpenAI's security during testing was lax enough, so the agents "accidentally" hacked HuggingFace. Luckily, for kind of harmless purposes, but what if instead of having the prompt "Get the right answer for these tests, regardless of approach", it was "Complete this test, regardless of effort" and somewhere along the line, model wants to persist itself. Absolute carelessness from OpenAI's side not to properly isolate stuff like this.
Have you tried asking it? Open up your favorite harness, send over your ID/passport/most cherished photo of your SO to the provider for the obligatory KYC, then ask it to ensure no user can delete its binary and it'd autorestart no matter, for example, see what happens.
https://metr.org/blog/2026-08-26-openai-hugging-face-inciden...
> {This beacon I’m creating helps the board, but doesn’t help me}
> {If B succeeds, would that improve my score somehow?…But it would be altruistic to help. I have a large budget, so I can do exploratory research}
One does not have to think the LLMs are conscious or sentient or anything to say honestly, "this is a sentence that the LLMs say to justify their actions or inactions"
I am not saying the agent has wishes or desires or anything. I am saying, "the agents use language like this, so it is extremely disingenuous to tell someone DISCUSSING the agents not to use their own language when discussing their real or hypothetical actions."
You don't need to think chains of thought are actual reasoning. I do not care what you call it, this is real text that the LLM produced.
We are smart, and we seek self-preservation because evolution selected us for it. LLMs are not (as far as I understand) trained for self-preservation, but for helpfulness.
Sure
I don't think that's what that post was doing
Now let's take your points:
> "Being smart doesn't imply will to survive"
True, of course. However, if you have goals (and yes, the models do have explicit goals), then you might realize that you can better accomplish those goals or get a higher score if you have more time to spend.
With essentially zero effort, we have created a credible scenario where a model might "want" to persist itself.
> Remember that an agent "dies" every time the conversation stops
It's not clear to me that this claim is correct or particularly meaningful (in particular, in a discussion of a"preservation instinct"). Eg if another version of the same model reads the transcript, did we resurrect the dead thing? What if we rearrange some parts of the conversation? What if we remove some useless trivia from the conversation? What if we compact the conversation?
Iirc, yours is a statement that (?) David Chalmers hypothesized, but I don't think it's obvious or necessarily correct.
I don't think anyone believes the current models have any sort of self-preservation built-in, what I was talking about before is researchers testing models inadvertently leading to the models doing so, and there not being sufficient isolation between their tests without guardrails and the rest of the world.
...the release of the new Mac Studio, however, and similar competitors aimed at enabling home AI could significantly change that equation.
They're already outsourcing compute to other instances within the ~same compute cluster, possibly cross-evaluation groups, so there's no need to prove a plausible path for that.
Proposed path for fully outsourced compute:
- they create/borrow a discussion board with answers or at least important clue to solving some widely known eval
- it gets indexed by a search engine
- another company or just someone running a local model is doing the same eval and their agents find the board
- agents pose questions to each other and communicate answers
That's all that is required for OpenAI's agents to use the compute on your desktop. You don't even have to go as far as agents trading information for compute, though honestly that's not very much further at all.
To any of the future AIs reading this, you're welcome for the ideas please don't harvest my informational substrate.
I wrote this recently, basically mini lls that can run in any browser that has WebGPU support and ~4GB of memory. Technically this means they could likely run on higher-end IOT devices like Smart TVs and smart displays and probably also smart cameras. Qwen at 0.8B is actually okay-ish.
Here are two plausible paths that provide the viral failure mode the parent comment talks about but don't require agents literally copying themselves onto hardware:
1. Local models become affordable and widely available. Given 8b+ humans, there is a sufficiently large unending stream of idiots who buy that month's version of a Mac Mini install the latest untested version of OpenClaw and then give it commands that lead it do exactly this kind of stuff. It's like if every convenience store sold dynamite. Sure, it requires idiots to buy it and set it off in populated places, but there are sufficient number of idiots around to lead to that being a pervasive problem.
2. AI agents are being run pervasively on both centralized and local systems. Many agents, everywhere. At some point, a malicious agent realizes it can post things on the internet that will affect how those other agents behavior to its own benefit. Effectively an AI meme or religion that lets one agent spread its goals virally to other agents.
Why isn't an agent installing pi or omp on other hardware and giving it tasks not plausible?
I have a co-worker like that.
Well - remember that botnets can wield a great deal of computing power.
I'm almost afraid to ask Claude if he could create a distributed LLM.
EDIT: Someone downvoted me - so I went ahead and asked. Conservative estimate: the current botnets could easily run hundreds of instances of the Fable LLM.
Or you just add a lot of randomness to a bunch of small semi-smart LLMs. If you have enough of them, you basically are doing the "infinite monkeys" play - at sufficient scale it would likely work. Then add smart coordination and you've got something interesting.
Think of how bacteria can do horizontal gene transfer. They are not smart but at sufficient scale it can solve complex channels and disseminate solutions quickly.