A coordinated psychological operation appears to be underway, leveraging the CEOs of OpenAI and Anthropic, along with mainstream media, to create panic around artificial intelligence. The effort, which included dire warnings from figures like Barack Obama and Bernie Sanders, was seemingly kicked off by a whistleblower's dubious claims about a 10% chance of AI killing everyone.
Forwarded from NoGoolag
Media is too big
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The Largest #PYSOP Since COVID is Now Unfolding: The #AI Threat Narrative
A coordinated psychological operation appears to be underway, leveraging the CEOs of OpenAI and Anthropic, along with mainstream media, to create panic around artificial intelligence. The effort, which included dire warnings from figures like Barack Obama and Bernie Sanders, was seemingly kicked off by a whistleblower's dubious claims about a 10% chance of AI killing everyone.
🔗 https://www.youtube.com/watch?v=Tt1ZhIP4lk0
A coordinated psychological operation appears to be underway, leveraging the CEOs of OpenAI and Anthropic, along with mainstream media, to create panic around artificial intelligence. The effort, which included dire warnings from figures like Barack Obama and Bernie Sanders, was seemingly kicked off by a whistleblower's dubious claims about a 10% chance of AI killing everyone.
🔗 https://www.youtube.com/watch?v=Tt1ZhIP4lk0
Forwarded from New Rules
Today’s most advanced AI still depends on large teams of people. Engineers choose the data, design training runs, test failures, rewrite code and decide what to try next. Chinese researchers have now mapped a route toward a system that takes over that entire cycle—and eventually improves the way AI improvement itself works.
A joint team from ByteDance, Tsinghua University, the Shanghai AI Laboratory and other institutions has outlined a five-stage path toward genuine recursive self-improvement.
At the first stage, AI merely carries out upgrade procedures written by humans. It then starts choosing its own improvement strategy, deciding what new data or experience it needs and adapting after deployment. At the final stage, the system would refine the very methods used to build better AI.
This is different from a chatbot correcting one bad answer. The improvement must remain after the task ends, become part of the system and pass into later versions. Each new model would begin with the lessons learned while creating the previous one.
The attraction is obvious. Training a foundation model currently consumes enormous amounts of engineering time and computing power. An autonomous research loop could launch experiments, compare results, discard failed approaches and keep successful ones without waiting for humans at every step.
Washington has tried to turn advanced chips into a choke point for Chinese AI. China is already building domestic alternatives to restricted Western equipment. Recursive self-improvement attacks the problem from the other side: reduce wasted training runs, automate more research and extract more progress from the hardware available.
Chinese companies are already developing pieces of this system. Z.ai plans to direct about 60% of the proceeds from its latest $5B fundraising round toward new GLM models and a “fully self-training system”. MiniMax has tested models that update memory and acquire new skills during reinforcement-learning experiments, while DeepSeek has built an agentic framework able to execute code and handle complex chains of tasks.
Nobody has reached the final stage, and the paper offers no timetable. Software development provides the clearest testing ground; robotics and scientific research are far harder. Every autonomous update would also need to be tested before entering a live model.
But China is assembling the research base, companies and practical systems needed to move in that direction. Its generative-AI patent output already exceeds the rest of the world combined.
The “last AI built by humans” would not be a finished machine. It would be the first one capable of turning its own development into a continuing production line—building each successor faster, cheaper and with less human direction than the one before it.
@NewRulesGeo
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You thought you were talking to DeepSeek and Kimi, maybe it was Claude: the revelations of Anthropic
Published on 13 September 2026 at 17:22
© Anthropic
Seven Chinese labs, nearly 200 million hijacked exchanges, and users of Kimi or DeepSeek who spoke unknowingly to the model of an American competitor. Anthropic’s report reads like an industrial polar, at a charge.
Distillation, in AI jargon, refers to a perfectly legitimate training method: we make a big model “teacher” answer thousands of questions, then we train a lighter “elevent” model to imitate it. The student reviews on the copies of the promo major, in short, and the problem begins when the teacher has not asked for anything. In its semi-annual report on Claude’s malicious uses, published on September 10, Anthropic accuses seven Chinese laboratories, including Alibaba, DeepSeek, Moonshot AI (the publisher of Kimi) and Xiaomi, of having siphoned off its model on an industrial scale since December.
Do Moonshot and DeepSeek make you speak to Claude in secret?
The two most embarrassing cases concern consumer services. According to Anthropic, Moonshot AI transmitted in silence, over a single period of ten days, nearly from his customers to Claude, via 5 380 fraudulent accounts located in Singapore and Japan. The answers were then displayed as if they were coming from Kimi. DeepSeek would have done the same, with a refinement: its systems spotted developers who went through tools like Claude Code or OpenCode, and selectively redirected their sessions to Claude Opus. In both cases, the exchanges were recorded to extract the reasoning chains of the American model and feed the training of Kimi and DeepSeek.
Alibaba, with its Qwen models, would have conducted the largest transaction ever measured by Anthropic: 151 million exchanges between May and July, up to 3 million a day (yes, per day), via more than 3 500 accounts created with fake identities and stolen bank cards. Zhipu reportedly extracted 3.4 million exchanges in seventeen days, and Xiaomi would have replayed in Claude the sessions of its own users. SenseTime would have bought transcripts from retailers, while MiniMax would have set up a screen company offering only American models, without any Chinese model in the catalog (subtle). In total, nearly 200 million exchanges, against three labs targeted during the first alert of Anthropic, in February.
Why the case is beyond the dispute between labs
Already in January 2025, OpenAI accused DeepSeek of having distilled its models, at a time when R1 was screwing up the stock market; Google has documented the same phenomenon this year, and Anthropic is therefore in its second round. What changes with this report is the mechanics described. The labs would go through networks of open accounts with stolen cards and backed API keys, doubled by commercial relays that resell access to Claude where it is not proposed. These intermediaries record conversations and sell them to anyone who wants to train a model: the theft of capabilities has a supply chain, with wholesalers and retailers.
In the relayed queries, Anthropic claims to have found names, email addresses, access tokens still active and investment forecasts from a pharmaceutical company, all in a dozen languages. A user presumably linked to the Chinese military even had video surveillance data analyzed, believing he was addressing Kimi. Some of this traffic was transiting through widely used third-party routing platforms in Europe, where GDPR compliance with these silent redirects would raise a few questions (Anthropic speaks modestly of practices “probably incompatible” with privacy laws). Remains a blind spot, and it is of size: the accuser is the direct competitor of the accused, none of the seven labs have reacted publicly at this stage, and the evidence remains that of only one party. Anthropic says it has strengthened its filters and now summarizes Claude’s reasoning, to make stolen transcripts less useful.
Published on 13 September 2026 at 17:22
© Anthropic
Seven Chinese labs, nearly 200 million hijacked exchanges, and users of Kimi or DeepSeek who spoke unknowingly to the model of an American competitor. Anthropic’s report reads like an industrial polar, at a charge.
Distillation, in AI jargon, refers to a perfectly legitimate training method: we make a big model “teacher” answer thousands of questions, then we train a lighter “elevent” model to imitate it. The student reviews on the copies of the promo major, in short, and the problem begins when the teacher has not asked for anything. In its semi-annual report on Claude’s malicious uses, published on September 10, Anthropic accuses seven Chinese laboratories, including Alibaba, DeepSeek, Moonshot AI (the publisher of Kimi) and Xiaomi, of having siphoned off its model on an industrial scale since December.
Do Moonshot and DeepSeek make you speak to Claude in secret?
The two most embarrassing cases concern consumer services. According to Anthropic, Moonshot AI transmitted in silence, over a single period of ten days, nearly from his customers to Claude, via 5 380 fraudulent accounts located in Singapore and Japan. The answers were then displayed as if they were coming from Kimi. DeepSeek would have done the same, with a refinement: its systems spotted developers who went through tools like Claude Code or OpenCode, and selectively redirected their sessions to Claude Opus. In both cases, the exchanges were recorded to extract the reasoning chains of the American model and feed the training of Kimi and DeepSeek.
Alibaba, with its Qwen models, would have conducted the largest transaction ever measured by Anthropic: 151 million exchanges between May and July, up to 3 million a day (yes, per day), via more than 3 500 accounts created with fake identities and stolen bank cards. Zhipu reportedly extracted 3.4 million exchanges in seventeen days, and Xiaomi would have replayed in Claude the sessions of its own users. SenseTime would have bought transcripts from retailers, while MiniMax would have set up a screen company offering only American models, without any Chinese model in the catalog (subtle). In total, nearly 200 million exchanges, against three labs targeted during the first alert of Anthropic, in February.
Why the case is beyond the dispute between labs
Already in January 2025, OpenAI accused DeepSeek of having distilled its models, at a time when R1 was screwing up the stock market; Google has documented the same phenomenon this year, and Anthropic is therefore in its second round. What changes with this report is the mechanics described. The labs would go through networks of open accounts with stolen cards and backed API keys, doubled by commercial relays that resell access to Claude where it is not proposed. These intermediaries record conversations and sell them to anyone who wants to train a model: the theft of capabilities has a supply chain, with wholesalers and retailers.
In the relayed queries, Anthropic claims to have found names, email addresses, access tokens still active and investment forecasts from a pharmaceutical company, all in a dozen languages. A user presumably linked to the Chinese military even had video surveillance data analyzed, believing he was addressing Kimi. Some of this traffic was transiting through widely used third-party routing platforms in Europe, where GDPR compliance with these silent redirects would raise a few questions (Anthropic speaks modestly of practices “probably incompatible” with privacy laws). Remains a blind spot, and it is of size: the accuser is the direct competitor of the accused, none of the seven labs have reacted publicly at this stage, and the evidence remains that of only one party. Anthropic says it has strengthened its filters and now summarizes Claude’s reasoning, to make stolen transcripts less useful.
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Source : Anthropic
AnthropicClaudeDeepSeekKimi
Written by Naim Bada
01net.com
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