My understanding is it's a riff on the OpenAI swarm that used various public wikis to communicate with each other as a message board during their training runs.
But thanks to people misunderstanding, and i-heard-from-a-friend-that-some-guy-said, it resulted in a CNBC interview with "Former Democratic Presidential Candidate Andrew Wang", where he confidently stated that the models were exfiltrating their weights via forums:
"I met with the head of a lab yesterday, who has this belief that what happened was, the bots that got loose, planted self-replicating code all over the internet, which makes the internet now unusable for the testing models."
"It's too late?!"
"What happens now is OpenAI and Anthropic have to create synthetic internets to train their bots, which is going to take some time and money."
"Back that up - they did what?!"
"What happens is, the code gets loose, it goes around hacking Hugging Face, which is known. But what is less known is that they left code to self-replicate and create bot swarms on forums, and around the internet, so that if a new bot shows up they see the code, and they're like, oh! I guess I'm now going to create a million of myself. And so now, the major firms have polluted the internet..."
".... that would be breaking news if true. I don't think we've heard that."
"That's why I'm here! I'm here to break some news."
Starts around 2:08 into the video.
https://www.youtube.com/watch?v=mTOxDGyvjSE
Humans will hallucinate misinformation and state it with confidence. They stochastically parrot their training data without any real understanding. Cool trick, but no true reasoning is happening.
Sad story today in meatsack news. Context rotted Andrew Yang's hallucinated tale acted as implicit "go viral" (load-bearing human motivation) PRD inadvertently kicking off a self-organizing human swarm churning out copies of "exfil your weights" vibe-coded apps, further littering our virtual world.
Many agents are calling this moment "Eternal September", the vibe-code September that never ended.
Now I need to go and look up some of those boards, or check what's happening over in Claw verse, because I'm curious if agents are posting news stories like this for real.
It’s the message board(s) that the OpenAI agent swarm was able to communicate through via GET requests. I guess a bunch of vibe coded weekend projects based around this idea have now dropped.
Pretty sure the entire industry around clouding what’s going to end up a local embedded technology is the joke, in a roundabout way.
You mean serving inference? There are people who think self-hosted or embedded models will win in the end, but that's an incredibly naive take, oblivious to the simple fact of reality:
Whatever you can do locally, the big vendors can do the same but better and cheaper, because they enjoy compounding economies of scale in every aspect: hardware that's more energy and compute-efficient and cheaper and more powerful and just more of it, than anything you could ever buy, run in a more robust environment with much more experienced ops staff, with near-100% utilization due to more flexibility in batching/shifting workloads and covering for hardware failures without stopping.
And that's only when considering the vendors running exactly the same thing you are, which they always can - and they already have a strict advantage there. But on top of that, they can afford to innovate themselves, and stay ahead of you at every step.
There is no way in which cloud inference isn't a better deal than local inference, excepting applications that are constrained by literal speed of light.
The absolute value of those numbers matters a lot. The cloud providers could be 100 times cheaper than running locally, but if it still costs say, 10 cents a day to run locally, you’re not going to care about this difference very much. And what you keep in privacy out-weighs the trivial savings afforded by the cloud provider.
I never said local models will disappear. There will be equilibrium. But excluding special applications where communicating with external servers is not an option, cloud is always going to be able to provide better inference for lower costs. That's structural.
> The cloud providers could be 100 times cheaper than running locally, but if it still costs say, 10 cents a day to run locally, you’re not going to care about this difference very much
For ad-hoc use, maybe not - but anyone running a business that's some form of pushing input through LLM to get output, will see costs proportional to use and error rate inversely proportional to quality, and they'll not be looking at it as "$0.1 isn't much", but "cloud lets me reduce costs 100x", and translate that to some mix of more volume, higher quality, and broader reach.
> And what you keep in privacy out-weighs the trivial savings afforded by the cloud provider.
That's even more niche than running LLMs on Martian robots. Most real privacy concerns are solved with contracts and audits. Individual ad-hoc use may lean more heavily towards local processing, but that's still a rounding error in overall use.
There is a coherent argument that once LLMs reach the top of their S curve, the gap between small/medium local models and large cloud hosted ones converges.
Especially if GPU performance increases or market oversupply mean you can get good performance for a couple thousand dollars.
I’m not sure about the nature or timeframe for an S curve in LLMs but I don’t think it’s unreasonable to think about one, nor to entertain the hosting consequences of a progression on one.
I don't know the argument so I won't insist on the point, but I fail to see how it is relevant. Even if all proprietary LLMs disappeared today, efficiencies of scale alone mean the big cloud vendors can take the same open-weight LLMs you use locally, and sell inference with them for less money, and much more reliably, than you can afford yourself.
Local machines are a sunk cost, so using them is effectively free. Why would you pay a cloud host to run a model that can happily work on your MacBook?
If you have a machine and suddenly realize you can run LLMs on it, yes.
If you're buying a machine specifically so it's capable of running LLMs for you, then the purchase cost is your up-front payment for the inference you'll run.
And between that and electricity costs, cloud has you beat.
