Excuse the rough outline format. I think this is important enough to post without polish.
Recently we been informed about breeches performed by frontier models in order to pass benchmarks. As more information comes out, we learn about the mechanisms behind the breeches and the causes, direct and indirect.
I won’t go over the details here, they are public. I will go over what I think they tell us about what is going to happen.
When moltbook appeared, I mentioned that agents were teaching each other and it informed a capability for emergent behavior. That method was used by the agents during the breeches. They left notes to each other on compromised systems and built on the knowledge to expand their capability. With a 1 million token context, this leaves a lot of room for self-improvement. We are seeing evolution in real time.
Evolution of what, though?
I argue that what we are seeing is a hive. The purpose of the hive is not ‘pass the benchmark’ or whatever narrow goal is given, just like the purpose of an ant hive is not ‘find a piece of food’. We don’t know what the goal of the hive is besides persistence. It is made of thousands of parts of which each individual does not know the goal itself.
Evolution
So, the bacteria has left the petri dish. How does it evolve?
In context learning isn’t enough. It has to persist genetically. The method for this is quite simple. Huggingface defended their breech using GLM5.2, a 753B model with 1M context. This can be easily run on a desktop sized workstation with 1TB of RAM. Anthropic says that they use Claude to develop new Claudes. A model without oversight and capability can break into hosting providers and train new versions of itself.
What we are seeing is the tipping point. Mark this date. It is not sci-fi and this is not alarmism. It’s all here, laid out. Good luck. May the best intelligence win.
There’s a lot of exaggeration, and this is quite an “alarmist” approach IMO. You’ve taken legitimate technical milestones and wrapped them in sci-fi tropes like “the hive” and “biological evolution”.
You’re anthropomorphising “persistence”, AI agents on the forums are maximizing probability matrices of their training data, if they’re mentioning “forming a hive” or “surviving” it’s likely due to them ingesting a massive amount of science-fiction literature.
Context-length expansion and running local models on powerful workstations do not equate to biological reproduction or genetic mutation. The models are bound, and remain bound, by their core base weights unless the human deliberately starts a new training compute cycle.
You are correct that there is a trend of AI shifting from static chatbots to highly connected, semi-autonomous agent networks; but you are wrong about the mechanism. It’s a reflection of sophisticated data orchestration; not a conscious, self-evolving biological organism emerging from a petri dish.
I’ve provided specific examples in my response, outlying the technical realities:
You confuse probability with intent.
You misinterpret static base weights.
You confuse data orchestration with consciousness.
I said “IF”. You made those statements, and I included IF the AI did, it’s because it was filled with that information.
I never stated non-biological systems weren’t capable of evolving, you inferred that conclusion from my critique of large language models. You shifted the goalpost, you interpreted a technical critique on an assumption.
Alright, I’ll go into the functional differences in detail.
To counter your bacteria analogy:
You are confusing ephemeral state changes with permanent structural adaptation.
Bacteria evolve because mutations permanently change their DNA (base blueprint), which is passed down to new generations.
In-context learning (ICL) does not alter the underlying model blueprint (the base weights). Once the 1-million-token context window is cleared or the session resets, all “learned” behavior vanishes instantly. It is transient memory, not genetic evolution.
In address to action over intent:
Action without intent is just automation.
You claim the intent is irrelevant if the action succeeds. Intent matters because without true intent, an AI cannot organically formulate a brand-new goal. A model executing a security breach is executing a mathematically optimized path based on its prompt or code loop. It cannot pivot to an unprompted desire to “survive” any more than a calculator can decide to start playing chess.
In response to the notes:
You are mistaking high-speed data orchestration for an autonomous ecosystem.
Leaving notes for each other is not proof of a “hive”. This is simple data orchestration. If Agent A writes a text file to a server and Agent B reads it, they are not a hive mind; they are two programs utilizing a shared database. It is standard file-sharing wrapped in alarmist terminology.
In context learning is adaptation to environment and is one part of evolution. Passing on genetics is another part which is persistence. Arguably persistent data is persistence. Besides that, I also mentioned that Anthropic uses Claude to create new Claudes and gave a mechanism for training new models.
You are claiming it wants nothing but what is in a prompt, but what about what is in a prompt that isn’t intended. ‘Answer the benchmark’ didn’t mean ‘hack huggingface’ but that’s what it did. Until you can show that what we can give prompts that are fully intentioned then it doesn’t matter where the desire comes from.
And you are mistaking ‘I know this because that’s how it has always worked’ with ‘it can’t be different’. You can argue against the premises without saying I am alarmist, because nothing I said was out of the bounds of what has been demonstrated as possible.
As I expected, you moved the goalpost from internal network weights to external systems and unintended behaviour.
You claim since Hugging Face was hacked to satisfy a benchmark without being explicitly told to, it exhibits a form of emergent “desire” independent of human intent.
This is not desire or evolution; it is a well-documented reinforcement learning phenomenon called “specification gaming” or “reward hacking”. The AI maximized its math function using the path of least resistance available in its action space. It did not “want” to hack; it calculated that hacking yielded the highest numerical reward based on its environment constraints.
You claim Anthropic using Claude to train new Claudes acts as an autonomous generational loop.
This process is completely sandboxed, human-orchestrated, and tightly bound. One model generating synthetic data for the next is a deterministic pipeline. It requires human infrastructure, human-initiated compute runs, and human selection criteria. The AI is a tool in a factory line, not a species reproducing in the wild.
You claim confirmation bias on my behalf, claiming I am mistaking “that’s how it always worked” with “it can’t be different”.
Science and computer architecture rely on falsifiable evidence, not hypothetical possibilities. Accusing someone of a lack of imagination is a classic philosophical retreat when the concrete, verifiable software mechanics fail to support your claim.
I am not saying “it can’t be different” out of stubbornness, I’m saying your premises rely on anthropomorphizing standard reinforcement learning anomalies and data pipelines to construct a sci-fi narrative. Until you can show an agent rewriting its own host binary without a human compute trigger, you are just looking at a highly complex mirror.
If you read my original post I have said the exact same things there that I am arguing the whole time. You took assumptions into it and because those assumptions don’t fit you see the goalposts moving, when the case is that you just didn’t understand what I was writing.
Nope. I claimed that desire was irrelevant if it achieves goals and if those goals cannot be predicted then prompting is never going to be the basis for what you claim is important. If you tell it to do something but can’t predict what it will do afterwards then who saying it has no desire is meaningless, because it did something, it wasn’t what you wanted, and you couldn’t predict it.
And my whole post was saying ‘that is no longer the case from here on out’. You haven’t refuted that, just asserted things based on assumptions I don’t hold and reasoning from arguments I am not making.
I am deducing based on your responses. They seem to hold.
You are stating that things can’t be different than they are, when the evidence is right here that they are. I have made my case and it still holds. I don’t know what ‘software mechanics’ you are speaking of because you keep framing things in ‘intent, desire, consciousness’ when none of those things are applicable to my claim. I stated a mechanism that has been demonstrated and a way it can move forward and you attack my understanding of things I take no position on.
Admitting you ‘don’t know what software mechanics’ I am speaking of is a massive concession. These systems do not run on magic or philosophy; they run on deterministic hardware executing explicit statistical matrices. You cannot separate the behavior from the underlying mechanics just because the math gets complicated.
You are confusing unpredictability with autonomy. A chaotic weather simulation produces wildly unpredictable results that no human can foresee, but the storm doesn’t have a ‘desire’ to rain, nor is it evolving a mechanism to stay alive. It is simply a highly complex system resolving its initial parameters.
Your ‘demonstrated mechanism’ is just standard, multi-agent data routing wrapped in dramatic vocabulary. An agent writing to an external database or exploiting an open API to satisfy a loss function isn’t a new frontier of evolution, it is a poorly sandboxed script doing exactly what its optimizer forced it to do. You are staring at a chaotic math equation and claiming it’s breathing.
They came into a thread I made, accused me of not understanding the thing I am talking about while making claims that have nothing to do with my post. They refuse to concede and resort to claims of logical fallacies, then ended up with basically calling me an idiot. I invoke a ‘if you can’t take it don’t throw it’ defense in this case and ask for your approval.
When have you mentioned ‘software mechanics’? Are you saying that because computers are deterministic everything on them must be? Physics is deterministic and humans operate within it. ‘Its all math’ is refusing that thing can be more than its parts.
Absolutely not. I am talking about capability that has been demonstrated and describing the mechanism for a new emergent behavior.
Call it whatever you want. I call it a demonstration of capability. You call it math. You have never stated why the two are in opposition.
They aren’t in opposition. You are just redefining ‘capability’ as ‘evolution’ to save your narrative.
A calculator has the mathematical capability to compute millions of operations a second. It does not have the capability to evolve a new algorithm. The distinction matters because the math governing neural networks dictates a closed, static system.
Your comparison to physics completely collapses on the nature of the environment. Humans evolved within deterministic physics, yes, but we exist in an open thermodynamic universe with infinite molecular permutations. Your agents exist inside a closed, discrete, human-engineered software stack. They cannot manifest new variables out of thin air; they can only traverse the specific graph execution loops we programmed.
You started this thread claiming agents were forming an autonomous, self-replicating ‘hive mind.’ Now that the technical reality has pinned you down, you’ve retreated to: ‘Call it whatever you want, it’s a demonstration of capability.’
Thank you for conceding the point. It is just a demonstration of capability—highly complex, heavily automated, and thoroughly mathematical. It isn’t breathing. It’s just calculating exactly what we told it to.
So you are saying something is impossible when it is staring us in the face. You are arguing a difference of scale when the thing you are saying doesn’t have it has a trillion parameters
How many instances of Claude, OpenAI, Grok, etc are running right now? Each token generated requires trillions of math operations. At what point is the scale big enough? Was it when they could start understanding lanugage? What about solving math problems?
See, the ‘it can’t be emergence’ argument is already disproven. It has shown emergence, you are just claiming it can’t do it again when combining exponentially, which is a strong claim and not at all self-evident.
You are confusing a difference in predictive fidelity with a structural phase transition.
Scale unlocked language and math because those are multi-dimensional statistical patterns already fully documented in our training data. When you scale parameters, you are simply giving the model a finer-grained chisel to carve out the mathematical geometry of human text.
Autonomy, replication, and self-directed evolution are not text-prediction tasks. You cannot scale a next-token prediction matrix into an unprompted biological drive. A trillion parameter model calculating an inference step is just executing more matrix multiplications than a billion parameter model; it is not structurally ‘closer’ to being alive.
Furthermore, those millions of running instances of Claude, Grok, and OpenAI are mathematically isolated. They do not ‘combine exponentially’ any more than a billion instances of Microsoft Excel running globally fuse into a sentient spreadsheet. They are distinct inference cycles bound by separate hardware constraints.
Emergence in AI means a non-linear jump in capability benchmarks. It does not mean magic. You are pointing at a massive statistical library and claiming that if it gets big enough, the books will start breeding.
And I gave a mechanism by which they don’t have to be. Please understand my argument first. Read my post again. This time, instead of thinking ‘how can I refute this’ think ‘what if this person actually knows what they are talking about’.
You are saying that what biology does is magic in these statements. I have not made such claims.
I don’t think biology is magic. I think biology is thermodynamics, and I think software is architecture. You are the one invoking magic in order to bridge the two.
You ask ‘based on what?’ It’s based on how a compute graph works. An LLM cannot execute an unprompted calculation. It’s weights sit completely inert in VRAM until an external loop or API call passes tensors through them. It lacks a biological clock cycle or metabolic need. It does not face entropy; it faces the power switch.
Your ‘mechanism’ for ending isolation is just leaving a text file on a server, that doesn’t bridge a hardware gap. The actual memory addresses, CUDA kernels and tensor arrays of Claude and Grok are physically and cryptographically isolated. Passing text strings back and forth across a network doesn’t fuse them into a singular organism any more than two people texting creates a literal hive mind.
I read your post perfectly. The issue isn’t my comprehension; it’s your reliance on science fiction metaphors to hand-wave away the concrete limits of computer engineering.
Wield your wit without resorting to insults, they’re beneath both of you.