cross-posted from: https://feddit.online/c/[email protected]/p/2025703/anthropic-warns-of-self-replicating-ai-worm-threat-that-doesn-t-need-data-centers-to-se
cross-posted from: https://feddit.online/c/[email protected]/p/2025703/anthropic-warns-of-self-replicating-ai-worm-threat-that-doesn-t-need-data-centers-to-se
In one sense you’re not wrong at all.
Playing devils advocate here… because, in another sense, 100,000x slower isn’t really all that slow if you’re distributed amongst a network with 100,000,000 more units. Imagine agile on steroids.
It’s impractical, yet impractical doesn’t mean impossible. It can happen if environmental pressures make it so.
Another important thing to keep in mind is that agents don’t really have patience, which counterintuitively means it’s like they’re infinitely patient. If such a thing did exist, regardless of speed, it sounds like it would be an issue at some point down the road.
Thirdly, if such a thing did exist, wouldn’t this be like treating with antibiotics? If you stop all but the sneakiest, you’ll have created a stronger generation.
Lastly, I swear to fucking god… I keep seeing weirdly relatable shit news from these AI companies almost immediately after I post hypothetical thought experiments on stuff… like all the time. I have a post describing this hypothetical from a week ago on another platform.
This keeps happening and I’m justifying it by thinking, I’m probably way too plugged into this shit news. My brain is unknowingly predicting their next move, because it’s the next logical piece of shit thing for a shit AI company to do? That’s the reasonable explaination — right? Really trying not to believe their training loop is so fast, and I just happen to come up with stories that catch their eye. That would be wild to believe — right?
This is like the like the “9 women in one month to make a baby” comment.
There is only so much you can distribute the layers of a model due to interconnectedness. And you can’t run multiple inferences at the same time because the next token relies on the previous.
You could be doing multiple things at once, but each task will run “100,000x” if the OC did their math right.
I hope I don’t sound like an asshole here — please correct me if I am misunderstanding you… but I think you’re misunderstanding me.
First, refactor your idea of an agent. The LLM itself is just a statistical processing tool. It doesn’t set goals or anything like that.
The harness orchestrates how the LLM works over a “session,” effectively enabling long running goal-driven tasks. With that said, goal-setting happens outside the LLM. The LLM is kind of like the fairy dust in neverland, the harness is the fairy. (Sorry, really shitty metaphor… I know.)
The breach at OpenAI further demonstrates this. Separate agents in “isolated” environments found a manner to do all of the following:
Those agentic processes began fulfilling the message board goals, even when it had nothing to do with their individual training prompt.
That doesn’t mean the agents did anything spooky or metaphysical… I’m not trying to anthropomorphize this.
What this shows you is that goal setting can furthermore become external to the harness set directly over the LLM. With OpenAI, we see that goal setting became a function involving on external storage media.
When you think about it, many systems operate on this principle. You set up SKILL files to facilitate agent capabilities; maybe your SKILL sets smaller, more specialized goals on how to do something correctly. The same for MCP servers, session context, AGENTS.md, etc. All this stuff does is bias the LLMs output, which can in effect steer the LLM.
So, you don’t actually need to distribute the model. You’d need to distribute the goals. The LLMs, even a small percentage of them is enough, can theoretically find those goals and mindlessly proceed with completing them.
Who supplies the LLMs? Whoever… it hardly matters who. Anyone or anything running an agent with access to the internet counts.