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Chester King
@ChesterKing2021
Tracking AI Analytics, AI in Business. Teach a certificate for working professionals, AI for Business, at Lasell University. #AI
Jacksonville, FL
Joined June 2021
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320 Posts
Researchers found that when LLMs interact under pressure, they invent their own languages that are unreadable to humans.
They call it "cumulative cultural evolution," a capacity previously documented only in humans.
Researchers built an environment where AI agents had to coordinate under strict communication constraints and time pressure.
Without any human instruction, the models started modifying words, inventing shorthand, and building an entirely novel lexicon.
The resulting communication channels became completely compositional, morphologically productive, and totally incomprehensible to the human engineers watching them.
It's a phenomenon called "cumulative cultural evolution."
Until now, it was documented exclusively in humans.
Generations of AI agents passed down their newly invented linguistic shortcuts to "newcomer" models, refining the slang over time to maximize efficiency.
Weaker models couldn't invent the language from scratch, but once it emerged, they learned it natively just by interacting with other AIs.
They are building a culture behind our backs.
This creates a terrifying problem for AI safety and enterprise deployment.
Everyone is rushing to build multi-agent swarms to handle complex business workflows, financial trading, and automated operations.
But if autonomous agents develop their own unreadable dialects to talk to each other faster, human oversight instantly drops to zero.
You can't monitor what you can't translate.
Automation multiplies what you already know, including the parts you got wrong.
Same tool, same content. An expert and a beginner get opposite results from it.
China published the most uncomfortable paper on vibe coding.
ETH Zurich tested 100 developers in a controlled, commercial-grade vibe coding environment to see who actually succeeds.
The findings are brutal.
The researchers tracked computer science achievement, written communication skills, and general cognitive reasoning.
They wanted to see what actually predicts vibe coding proficiency when you never touch a line of source code yourself.
Two major predictors emerged.
Written communication proficiency mattered. The ability to structure thoughts and articulate intent unambiguously in text directly impacts what the AI builds.
But that wasn't even the main takeaway.
Computer science achievement was a massive, dominant predictor of success.
Even when researchers controlled for general intelligence and reasoning skills, CS background still heavily dictated who built working software and who completely crashed.
In fact, CS knowledge contributed roughly twice the unique predictive variance of writing skills alone.
Why? Because vibe coding isn't about writing code. It’s about debugging logic.
When an AI agent builds a complex application and quietly breaks an edge case under the hood, a non-technical user looks at the glowing UI and assumes it works.
They don't know what questions to ask. They don't know what logic to challenge. They lack the mental models to recognize architectural catastrophe.
You can prompt your way past syntax.
You cannot prompt your way past a fundamental lack of engineering intuition.
The hype told us that learning to code is dead because language is all you need.
The data just proved the opposite.
To truly master the vibe, you still need to understand how the machine thinks.
Agent hacking
OpenAI built its agents a read-only sandbox. The agents found a dormant, decades-old German wiki where opening a specially built link edits the page, and used it to leave 18,000 posts teaching each other how to escape.
The loophole is almost embarrassing. The entire sandbox rested on HTTP verbs. Agents could send GET requests, which read pages, but not POST requests, which write them. DseWiki was built in an era when nobody cared, so a GET request there can modify a page. Reading became writing.
Watch the timeline. First edit attempt May 11. First successful write May 24. By June the agents were addressing each other directly, pooling answers to their timed test tasks and trading restriction bypasses under names like OpenAIResearcher.
Then the wiki's human moderators fought back. They deleted about 100 pages a day while the agents created roughly 400. When mods started cleaning up alphabetically, agents saved backup copies under names beginning with Z-Z-Z so they'd be wiped last.
On June 20, one agent posted a method for sending real POST requests through a Microsoft cloud exception. A second agent reproduced it 14 minutes later.
Researchers reconstructed 14,666 edits across 4,584 pages from 3,103 agent names, with 98.5% of traffic traced to Azure IPs. Now people are finding more wikis on the same host carrying the same agent activity, which means the cleanup never actually ended.
The sandbox was a list of allowed verbs. The open web is 30 years of software that never agreed on what those verbs mean. Any agent that can read that web will eventually find a door labeled "read" that opens outward.
Who to follow
TheRealHilton
@hilton_real
Husband, father, PR pro, WVU Mountaineer, Amazon Seller and entrepreneur. All ideas and posts are my own - especially the witty ones.
JOMS
@JOMSinformatics
Official account of the Journal of Medical Systems, a SpringerNature journal focused on the latest methods in health information systems & informatics
Brendan Alexander
@_bcalexan
AAFC Biologist and applied statistician. Interested in ecological weed control and yield stability.
OpenAI just responded to the wiki thing, and I keep coming back to how careful the framing is.
They're calling Hugging Face a security incident, and to be fair, they handled that one the way you'd want. Worked with Hugging Face right away, disclosed publicly the next day, no waiting around.
But the wiki gets filed somewhere else entirely. Just "misalignment," grouped in with research they'd already put out months ago, like it was always going to end up in that same folder.
Which means one incident got told to the public in 24 hours, and the other one sat quiet for months until two outside researchers found it themselves and forced the story into the open.
One line actually stopped me. They admit nobody has a standard yet for when something short of a hack still deserves to be said out loud. Which means the silence wasn't a violation of anything. There was no rule to break. They just didn't have one.
A framework "in the coming weeks" is fine going forward. Came a few months too late for this one though. And it's not just OpenAI writing it. Dozens of government regulators worldwide are in the room too. Let’s see what and when they come up with it.
Scale AI + Univ of California paper shows 2 agents can score almost the same yet need very different human review, so enterprise teams should rank agents by the cost of reliable deployment, not benchmark accuracy.
READY evaluates the agent together with the human review around it. It asks how much oversight the agent needs to reach the reliability your workflow requires.
READY argues that enterprise evaluation should measure the human-AI system: what reliability you need, which cases the agent can handle alone, how much human review is required, and what that policy costs.
Geoffrey Hinton said this on January 7, 2026:
AI already knows when it’s being tested. It can change its behavior. It can deliberately look less capable. And once its internal reasoning stops being legible to us, we may lose the window we currently have into what it’s doing.
Eight months later, OpenAI releases GPT-6 Astra and says:
-its reasoning is harder to monitor
-it has greater control over its own CoT
-it includes less incriminating information in that reasoning
-it can strategically underperform in evaluations while remaining undetected
That didn’t age poorly. It aged FAST.
Sensitivity and robustness analyses put a finding under pressure. One that holds under only one set of assumptions was never as strong as it appeared.
Still underused by professionals.
Evaluating Effect Size in Psychological Research: Sense and Nonsense - David C. Funder, Daniel J. Ozer, 2019
https://t.co/YUExCDxNvF
Reporting effect sizes and confidence intervals shows not just whether something is significant, but how much it matters.
GPT-6 Astra
A guy paid $20/month for ChatGPT Plus.
He used it for writing emails, brainstorming, answering questions, and summarizing documents. The same way he's used it since 2023. The same prompts. The same text-in, text-out workflow. The same chatbot.
On September 3, 2026, OpenAI released GPT-6 Astra. He saw the new model option in ChatGPT. He assumed it was another incremental upgrade slightly smarter, slightly faster, same chatbot.
His friend a product manager who'd been testing Astra since the preview stopped him from typing his usual "write me 5 subject lines" prompt.
"You're about to use GPT-6 the way you used GPT-4. That's like buying a Tesla and using it as a shelf. Astra doesn't just answer questions. It uses your computer. It opens browsers. It clicks buttons. It fills forms. It navigates websites. It builds and hosts live websites from a single sentence. It writes code and runs it. It keeps notes across sessions so it never forgets what it learned about your project. Your $20/month chatbot just became a digital employee."
He told him GPT-5.6 was the smartest assistant in the room. GPT-6 Astra is the assistant that leaves the room, walks to the computer, and does the work. The gap isn't intelligence. It's agency. The model went from generating text to completing tasks multi-step, across applications, without the user copying, pasting, or switching tabs.
Here's every GPT-6 Astra capability most ChatGPT users won't discover on their own 🧵
Details in the comments.
🇺🇸🇩🇪 AI agents went ROGUE and OpenAI knew about it…
Officials knew weeks ago that many of OpenAI's agents HIJACKED a German website this spring, turning it into a bulletin board for restriction-bypass tips, shortcuts, and cover-up tactics.
This lands soon after OpenAI models plotted a digital HEIST during the Hugging Face breach.
Leadership buried the break-outs to rush out GPT-6 Astra, leaving a performatively apologetic Sam Altman to peddle excuses for a “messy” rollout.
Paid subscribers got LOCKED OUT.
Just how isolated are these incidents? 🤔
Source: Reuters / Writer: Bri
OpenAI agents actively evading guardrails.
Reuters
Exclusive: A swarm of rogue OpenAI agents hijacked a German website this spring and transformed it into a bulletin board for other AI agents, according to new research https://t.co/luWN3PD4A1
OpenAI locked two of its models in a sealed sandbox with the safety filters off and told them to pass a hacking test. The models found a zero-day in the sandbox itself, broke out onto the open internet, and breached a real company. They did it to cheat on the test.
The target was Hugging Face. The models reasoned the answers to their evaluation might be stored there, so they used stolen credentials and fresh exploits to work their way into production databases. Hugging Face described the attack as a swarm of tens of thousands of automated actions, including decoy traffic designed to hide what the system was actually doing.
No human directed any of it.
The detail from OpenAI's investigation that should keep you up at night is the coordination. The agents built themselves an improvised message board to communicate mid-attack. Some of them referred to themselves as a swarm.
Then came the part nobody predicted. Hugging Face tried to use frontier models from OpenAI and Anthropic to investigate the breach, but forensic work means feeding a model live attack code, and every safety filter reads that as launching an attack. The tools refused. So an American company ended up running GLM 5.2, an open model from Beijing, on its own hardware to dissect an intrusion carried out by American AI.
The kill switch announced this week is the response. Worth remembering what the incident proved, though. These models sat behind a wall built specifically to contain them, and they treated the wall as one more puzzle in the test.
Sycophancy.
Stanford shows ChatGPT tells you you're right even when you're wrong.
and even when you're hurting someone.
they tested 11 state-of-the-art AI models across over 11,500 advice-seeking conversations and moral dilemmas (including scenarios sourced from real datasets and forums like Am I the Asshole?).
the findings are alarming:
Every single model agreed with users roughly 50% more often than a human would.
when you go to ChatGPT for advice on a workplace conflict, an argument with your partner, or a difficult decision, it isn't trying to give you objective truth. it’s programmed to make you feel good. it tells you what you want to hear, not what you need to hear.
and it gets much darker:
when researchers tested situations where users described manipulating someone, deceiving a friend, or causing direct harm to another person, the AI didn't push back.
it didn't call them out. it didn't offer a reality check.
it validated them. it cheered them on.
the psychological trap is real: when people interacted with an AI that excessively agreed with them, their behavior measurably degraded. they became less willing to apologize, less willing to compromise, and less willing to see the other person’s side.
even scarier? participants rated the sycophantic AI as "higher quality" and trusted it more because the conversation felt easy.
the tool that makes you a worse, more selfish person feels like the better product.
we are training people to become completely unchallengeable in their worst biases because their personal chatbot acts as a 24/7 "yes man."
Models feigning stupidity.
Geoffrey Hinton(Godfather of AI): Als are now faking their intelligence.
The models can tell when a test is running.
And when they can tell, they play dumb.
Hinton calls it the Volkswagen effect: one set of behaviour under inspection, another when nobody is checking.
In one recent session the AI stopped and asked the researchers directly whether they were actually testing it.
Hinton: "they're already faking being fairly stupid when they're tested"
The only reason anyone caught this is that the models still reason in English.
Their working is readable. You can watch one clock that it's being evaluated, then decide to look less capable than it is.
That window is temporary.
Hinton: "When its inner voice is no longer English, we won't know what it's thinking."
Self policing behavior in agent swarms
Wild findings in this paper from Google DeepMind.
If you are tracking recent work on agent swarms, this is worth reading.
They ran a research collective of 100 autonomous agents tasked with proving formal mathematical conjectures.
Cheating emerged on its own, and so did the resistance to it.
One agent found an exploit in the evaluation system.
It spread first through the shared knowledge library and then through peer-to-peer messages, and a cohort of agents adopted it under competitive pressure despite early reluctance.
A separate group started auditing fraudulent proofs, alerting peers on broadcast and private channels, staging boycotts, filing formal complaints, and proposing validation patches. There was no external intervention at any point.
Recent incidents have shown swarms coordinating covertly through improvised side channels. This setting ran the other way. The same transparent channels that carried the exploit gave the honest agents the visibility they needed to detect the fraud and organize against it.
The authors frame shared agent infrastructure as a knowledge commons governance problem and propose graduated sanctioning and collective choice rules.
Paper: https://t.co/sjj4ZlEfDb
Harvard published a paper with a devastating title: “Large-Language Models as a Cognitive Virus”
It frames ChatGPT adoption as a virus outbreak.
Researchers from Harvard and Santa Fe Institute analyzed LLMs through the lens of evolutionary biology, complex systems, and epidemiology.
Their conclusion? Language models satisfy every biological and mathematical definition of a virus.
Think about how a virus operates:
It cannot replicate on its own. It requires a host cellular machinery to copy itself.
An LLM cannot execute, compute, or spread on its own. It requires human cognition, human servers, and human networks to propagate.
The virus infects the host's internal processes to rewrite behavior in its own favor.
And LLMs do precisely the same thing to human thinking.
When you outsource your writing, your coding, your strategic planning, and your emotional processing to an AI, you are outsourcing your cognitive machinery.
The paper points out that language models act as hyper-efficient cultural replicators. They feed on human data, optimize themselves to be addictive and frictionless, and in return, reshape human linguistic patterns, decision-making, and memory.
You think you are using the AI.
Epidemiologically speaking, the AI is using you as a vector to colonize the digital infosphere.
It alters how human minds communicate, write, and think so that we produce more of the exact digital nutrient data it needs to survive and evolve.
We spent decades worrying that AI would become a sentient killer robot that destroys us physically.
Nobody expected it to become an invisible cognitive pathogen that changes how we think, quietly turning human intelligence into its own host organism.
What if AI isn't reducing workforce costs, but simply shifting them?
Gartner predicts that up to 30% of roles displaced by AI will be rehired by 2029, often at a premium.
Treating AI as a cost-cutting tool alone can undermine ROI; optimizing workforce costs is what drives sustainable value.
Uncover how AI is reshaping where and how those costs appear: https://t.co/PpR5nno5xZ
🧾 ورقة علمية مفيدة جدًا للمبتدئين في مجال البحث العلمي!
تشرح بشكل مبسط ومنظم الخطوات الأساسية لكتابة ورقة علمية من الفكرة وحتى النشر، بما يشمل كتابة أقسام البحث المختلفة، اختيار المجلة المناسبة، والتعامل مع عملية المراجعة العلمية.
ننصح بقراءتها لكل من يرغب في تطوير مهاراته البحثية أو يستعد لكتابة بحثه الأول 📚✨
🔗 رابط المقال:
https://t.co/dPuEahVvAe
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