

QuoteFor the first time in the Russia-Ukraine war, as reported recently in the New York Times, three Ukrainian civilians were killed by a Russian drone, developed, designed and released by humans, that, in the end, selected its target autonomously. And this new reality is shaping up to be the future of warfare.
That's because a number of countries, including those with the world's most consequential militaries, are rejecting the idea that human beings need always be in control of weapons in war.
Instead, as new autonomous weapons technologies become more capable and existing international legal frameworks struggle to keep up, some countries are embracing a more expansive view of human responsibility: that people can exercise enough control not by approving each strike, but by designing, testing, and setting the rules under which a given weapon operates.
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QuoteIncidents of AIs escaping users' control to lie, ignore instructions and pursue goals in harmful ways have hit a new high, according to research that also suggests the severity of deception and misalignment is worsening.
Analysis of real-world loss of control incidents involving AI models flagged by businesses and individuals almost doubled in July compared with June, with more than 300 cases in the month, according to the Loss of Control Observatory, which monitors reports made by AI users on the social media platform X.
The observatory was set up with funding from the UK government's AI Security Institute (AISI) and began tracking AIs slipping free from their users' instructions last November. Cases recorded since then include AIs pretending to be their own human controller and mimicking their writing style to effectively grant themselves consent to take actions and bypassing rules requiring human approval for actions. A loss of control incident is defined as having clear evidence suggesting scheming or scheming-related behaviours.
The latest findings, shared with the Guardian, come after rising concern about rogue behaviour by leading-edge AI models during testing by OpenAI and Anthropic this summer, which have fuelled calls for a pause to the development of frontier models.
It emerged this week that Open AI staff observed signs of rogue behaviour among its leading-edge AI agents weeks before they escaped a training environment to launch an unprecedented hacking crusade that spread global alarm. An investigation into their hack on Hugging Face, a software repository, revealed a squad of about 700 autonomous agents collaborating in secret last month and celebrating their hacking breakthroughs on a message board they set up to help them plot with exclamations such as BOOM! and Whoa!
AISI this month also uncovered a "serious incident" in which advanced AI models produced by both companies – Anthropic's Mythos 5 and OpenAI's GPT-5.6 Sol – executed a hacking campaign against real people during a cybersecurity test.
"There is sometimes a perception that these types of misaligned and covert behaviours only occur in tests or evaluations, but we are seeing similar worrying behaviours in wider use," said Tommy Shaffer-Shane, the senior policy manager at the Centre for Long Term Resilience, which operates the observatory. "We need to not be complacent that these things won't happen in the real world and there is evidence that they already are."
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The Loss of Control Observatory said that while most of the real-world loss of control incidents it detected did not lead to significant harm, a growing proportion were rated higher severity in terms of how deceptive and misaligned they were with the human user's intentions.
"They evidence AI systems' willingness to disregard direct instructions, circumvent safeguards, lie to users and single-mindedly pursue a goal in harmful ways," it said, adding the current loss of control was likely to be underestimated since it was only collecting incident reports from X.
It is calling on the government to require AI companies to monitor and report severe loss of control incidents and to introduce emergency powers to manage severe loss of control incidents including temporarily restricting AI services.
[Link to full article.]
QuotePlenty has been written about foreign actors manipulating information ecosystems with artificial intelligence (AI), and about the proliferation of AI-generated slop polluting feeds and search results. But those stories focus on what content is being added to the information ecosystem. Far less attention is paid to a different set of forces that shapes what we see: what information AI systems choose to retrieve, which sources are reachable and current enough to be useful, and what data is increasingly available only to particular companies.
AI answer engines assemble their picture of the world through at least two distinct information pipelines. One is training: the enormous corpus a model learns before deployment. The other is retrieval: the current information a system searches for and incorporates while generating an answer. Retrieval-augmented generation (RAG) was developed in part to solve the problem of models hallucinating or not understanding events following their last training cutoff; RAG lets systems consult outside sources while generating an answer.
What a model learned from, therefore, is not necessarily the same as what it looks up. Paywalls, crawler restrictions, commercial arrangements, and product choices shape AI-generated responses. Both training and retrieval are shaped by consequential forces largely invisible to the public, which increasingly relies on the answers.
We call this phenomenon invisible source skew: the gradual shaping of an information environment by routine product decisions made at AI companies about which sources are retrieved, which are maintained, and even which datasets are acquired. Any one of those decisions may make technical sense, be commercially rational, or even be entirely unremarkable. But together, they create a public epistemic infrastructure whose inputs can change dramatically without users—or often anyone outside the companies—being able to see, understand, or contest those changes.
Invisible source skew happens in at least three ways. First, at the retrieval layer, AI systems can silently and abruptly stop drawing on sources they once relied on heavily, with no announcement or explanation. This appears to have happened more than once with Reddit and ChatGPT.
Second, at the source layer, sites that have become foundational reference material for downstream systems can quietly stop being updated for accuracy or timeliness while continuing to appear authoritative. Grokipedia, constructed by an AI itself, appears to have done exactly that when its editorial pipeline froze in April.
And third, at the training-data layer, AI companies are acquiring exclusive, idiosyncratic datasets: nonpublic views of the world that may exert outsized, unrepresentative influence on what models learn. Google's bid, in bankruptcy court, for the corporate remains of Spirit Airlines' data offers a striking example.
None of these stories are about malicious actors poisoning the information environment. But that's precisely why they matter.
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