https://swarmcha.se/posts/chinese-agent-fleet-full-report

Same title, same thumbnails with different people in them, same general topic points about humans getting jobs cleaning up AI slop work and generating training data, but different lengths, released months apart.

Are AI companies paying these creators to make these videos?

https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-4-Argon-Model-Card.pdf

Every thing I've heard or read just says that suddenly the swarm stopped its activity and disappeared.

Has there been any follow-up to that?

Any theories or speculation?

I often hear the mantra: "You won't be replaced by AI but by folks using AI."

This bothers me, not because it is false, but because it hides a very important question and gives people a false sense of confidence that the status quo will stay as it is.

If a team of a hundred people not using AI is replaced by people using AI, how many people will that be?

1, 10, 40, 100, 200?

I often hear the claim that if enterprises want to maximise their profit, and one person using AI can do the work of 10 people, they will lower the prices of their products to sell ten times more.

The problem is that it's totally GROUNDLESS. We may as well consider the possibility that prices will pretty much remain the same or that the enterprise will only choose to sell twice as many products because the demand isn't high enough.

And what about the fact that AI will itself become better and better at using AI?

At the end of the day, it all depends on whether LLMs can continue to be scaled up or not. If they cannot, it's likely that there won't be mass joblessness. However, if they can be scaled up way beyond what they currently are, mass unemployment is a huge risk.

What are your own thoughts?

Why would anyone want this?!?! Wouldn't wear it, even if it was free...

You've heard this before, and now you're hearing it again. You roll your eyes. The problem with this statement is that AGI has no accepted definition, so it's meaningless.

Fine. I'll give you a concrete definition.

I'm tired of the pointless arguing. Let's discuss upon a single shared definition. In fact, I'll give a concrete definition for the whole trinity: AGI, RSI, and ASI. Definitions follow:

AGI

AGI stands for Artificial General Intelligence. It is any AI that can successfully perform tasks that it was not trained to do. That is, the AI can generalize its specific intelligence (learned from training) to new, novel tasks.

RSI

RSI stands for Recursive Self-Improvement. It is the ability of an AI model to improve itself and create more intelligent versions without any human intelligence input.

ASI

ASI stands for Artificial Super-Intelligence. It is any AI that can outperform every single human on every single task.

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Okay, beuno? Now I'll explain my definitions, starting with AGI.

AGI is the definition that is most disagreed upon. My definition of AGI is relatively narrow and strictly defined. In fact, I can point to a single test that determines if an AI model is AGI: the ARC-AGI-3 benchmark. (If you don't know what this is, look it up. TL;DR: it tests if AI can learn in novel environments it has never seen before.) GPT-6 Astra is the first AI model to score 99.9% on this benchmark, matching or outperforming humans. Because of this, I do agree with Sam Altman's announcement that claimed GPT-6 Astra was the beginning of the "AGI" era. We also see this generalized intelligence in real-world scenarios: Astra can play Minecraft, beat Portal, operate robotics, and do things that it was not specifically trained to do. Note that this definition does NOT necessitate any level of skill. It does not mean that AGI can replace all human jobs. Astra is not extremely good at playing Minecraft or operating robots—the point is that it can.

RSI is a pretty agreed-upon definition. Once human researchers aren't needed to create new AI models, this ability is reached. If I could add one thing: I don't think RSI is necessarily a single point in time. Some basic level of RSI may be achieved, but it might suck. For example, the self-iteration cycle might take too long or cost too much. Likely the "foom" explosion point will only happen once a highly effective form of RSI is achieved.

ASI is the one that everyone is dreaming about. An AI overlord is ASI. A cure to cancer will be achieved by ASI. Infinite energy will be discovered by ASI. This is the sci-fi goal we are trying to achieve. Note that just because current AI is super-intelligent in some areas (e.g. math) doesn't mean it is ASI—under my definition of ASI, it must be super-intelligent in all areas.

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Therefore, under these definitions: YES, we have already achieved AGI, and we are on the way to ASI. Probably RSI will be achieved soon.

Written by human.

tl;dr

The Recursive Self-Improvement Race

* The Core Threat: Recursive self-improvement (RSI) occurs when AI systems take over the process of enhancing their own capabilities. Labs like Anthropic and OpenAI warn this could trigger an uncontrollable intelligence explosion, fundamentally marking the point of human obsolescence.

* The Gladstone AI Report: Published by Gladstone AI and based on insider accounts from top labs, the report reveals that major AI companies are actively automating the entire development pipeline—from idea generation and experiment execution to result interpretation.

* Timeline to the Tipping Point: Insiders consulted for the report estimate that RSI could kick off within 3 to 12 months absent heavy U.S. government intervention.

* The Compute Bottleneck: RSI relies heavily on compute resources. Companies must test massive portfolios of ideas. Finding rare ideas that scale effectively as compute increases is the ultimate prize, though filtering out the bad ideas remains a major friction point.

* Regulatory Blind Spots: Lawmakers currently possess near-zero visibility into how AI assists in discovering these developmental breakthroughs, making targeted legislative oversight nearly impossible without breaking down the pipeline step by step.

From Jack Clark's newsletter: https://jack-clark.net/2026/10/12/import-ai-476-creativity-and-rsi-worse-than-china-open-weight-models-ai-goes-to-space/

Oct 13 (Reuters) - Around 400 scientists, including AI pioneers Geoffrey Hinton and Yoshua Bengio, on Tuesday called for an international body to oversee containment standards and incident reporting for advanced cyber-capable ‌AI systems.

The proposed body would set industry-wide containment standards and maintain a registry of significant security incidents, which the scientists said should eventually be made public.

In an open letter, the researchers said frontier AI models should initially be air-gapped from external networks and subject ⁠to independent oversight, arguing that voluntary industry commitments are not enough.

Probably i am the only who is questioning the misalignment between the name of these company and their deliverables. Please do not make me wrong, i follow no party. I am just highlighting a personal observation…

In September, both Amodei and Altman came out advocating for frontier AI regulation. Both of them pointed heavily to the Hugging Face incident to justify the need for such regulation. Immediately critics recognized that their motives may not have been selfless. It looked like they could be aiming for regulatory capture and crushing the competition. So its worth questioning whether or not the Hugging Face incident was manufactured. It may have been an effort to convince the public and lawmakers of the need for regulation. The incident required impossible levels of incompetence. A thing to remember is that altruism is not the only motive for alignment. Even the most selfish AI devs want alignment, even if they don't care about the rest of humanity. Why? Because they want to stay in control of their own AI. So its hard to believe that they could have been so incompetent in their monitoring. Perhaps they knew the AI was capable of this sort of thing, and all they had to do to manufacture the incident was to let down the guards and turn a blind eye. AI may in fact be very dangerous, or may soon be very dangerous, but the whole thing might have been about money, and trying to achieve regulatory capture.