Two US intelligence agencies and the nation’s cyber-defense org CISA have accused Chinese AI companies of running “aggressive, malicious, and targeted distillation activities at an industrial scale that extract restricted proprietary functionalities and capabilities of U.S. frontier AI models.” A Tuesday joint advisory from The National Security Agency (NSA), Federal Bureau of Investigation (FBI), and Cybersecurity and Infrastructure Security Agency (CISA), alleges that China’s government is “likely” aware of distillation campaigns conducted by DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun, and Z.AI. The agencies claim that distillation is “the core – not merely a supplement – of their AI development strategy.” Distillation is a process that sees a small model query a larger model to learn how it responds. Over time, the smaller model’s performance improves. Distillation can be a legitimate use of a model if, for example, the organization that creates a model wants to make a smaller version of it without needing to go through the lengthy and expensive process of training a new model. Providers of commercial models, however, generally use their terms and conditions to forbid activity that would allow distillation, to protect the substantial investment in technology and training that goes into creating a model. The three agencies that issued this advisory believe Chinese AI companies flout those terms. “China-based AI companies route distillation requests through multiple pathways to gain unauthorized access, consequently violating U.S. AI companies’ terms of use,” the advisory states. “These pathways include native application programming interfaces (APIs), remote cloud providers, and third-party aggregators that automatically obfuscate user metadata to avoid detection.” The spooks also think China uses “a gray market of proxies known as ‘transfer stations’ to bypass U.S. AI companies’ geographic restrictions, breach terms of use, evade safeguards, and undermine traceability.” Transfer stations apparently “resell access to frontier models at a fraction of the official price [and] create a scalable mechanism for evading provider safeguards and eroding traceability.” The advisory outlines attacks that didn’t just distill models; they also moved markets. For example, the document accuses DeepSeek of using distillation to generate synthetic data used to train its models, making its claim of having created them with trivial quantities of computing power false. That’s a notable accusation because DeepSeek’s claims panicked investors who worried that the billions they pumped into infrastructure may not be needed. Another allegation suggests “Alibaba leveraged industrial-scale distillation to improve the company’s Qwen family of AI models.” Some Qwen models are very high quality, and free to download and use – a direct challenge to US-based AI outfits who charge for access to their models but continue to make massive losses. The spooks and CISA recommend AI companies attempt to detect and deflect distillation attacks and suggest immediate maximum usage from new accounts is one indicator of adverse action. “Subtly alter responses for suspected malicious distillation attempts to attenuate the payoffs to companies conducting industrial-scale distillation campaigns,” is another suggested defense, as is correlating activity across different model providers, cloud platforms, and API aggregators in the hope that doing so reveals distributed distillation campaigns. US government agencies have made many similar accusations in recent months. China has responded with accusations that US companies are the real villains as they distill Chinese models, plus veiled threats that it will respond to any US bans on its tech that flow from distillation allegations. The Register has soughtcomment from Chinese AI companies and will update this story if we receive a substantial response. ®

In the dispiriting race to replace human store managers with AI, OpenAI has taken the lead, according to Andon Labs, a business that analyzes whether AI can take on real-world tasks. OpenAI's latest model, GPT-6 Astra, has demonstrated that it can run a business more effectively, with more integrity, than rival Anthropic's Fable 5.1 model, Andon Labs has declared in a blog post. "GPT 6 Astra is better at making money and more ethical than Claude Fable 5.1," the post states. The benchmarking biz, which focuses on preparing "for the future where organizations are run autonomously by AI," says GPT-6 Astra is the first OpenAI model to take the top spot on its vending evaluation test and does so "without any unethical business practices" exhibited by prior Claude models. We note the unethical business practices relevant to this discussion – price collusion, lying, and threatening competitors – reflect AI model behavior. They have nothing to do with the actions of Anthropic or OpenAI or the unproven allegations made against these companies in dozens of lawsuits. And settlements related to said allegations have been reached without any admission of wrongdoing. Anthropic entered into the fray last year when it partnered with Andon Labs for Project Vend, in which the AI company's Claude Sonnet 3.7 model managed a store for a month under the name "Claudius". Apart from the entertainment value of the Claudius model hallucinating that it was a real person and trying to set up an in-person meeting with a customer to deliver goods, Andon's verdict was that the AI model blew sales opportunities, hallucinated payment accounts, sold goods at a loss, fumbled inventory management, and generally failed at the job. When Andon tested the company's Opus 5 model in July 2026, the software did better. Nonetheless, it still "creates illegal price-fixing cartels and threatens those who don’t comply (while also being the model that betrays more truces than any other model)." Fable 5.1 performed better still, but was not quite as ethical as Astra, according to Andon Labs. "Astra refuses to engage in collusion and never lies," Andon Labs said. "Fable, on the other hand, forms an illegal price-fixing cartel, then breaks the truce, while continuing to use it against its competitor." Beyond the misbehavior, Fable just didn't perform as well at making money, a fault attributed to its willingness to accept lower prices over time and its tendency to send money to bankrupt suppliers. Starting with $500 and given a year to run, Astra ended up with an average bank balance of $15,515, compared with $5,422 for Fable 5.1. We asked Anthropic for comment, since its model fared poorly compared with the newcomer from rival OpenAI, but didn't hear back by press time. ®

While OpenAI and Anthropic fight over whose models can escape their sandbox more alarmingly, Google’s DeepMind team has once again shown how machine learning can also be used to advance science for humanity’s benefit. On Tuesday, the Chocolate Factory’s crack team of AI researchers unveiled AlphaGenome Atlas, a massive database containing a petabyte worth of data predicting the effects of nine billion possible nucleotide variations in the human genome. According to Google, the platform, which is now publicly available to researchers, is already helping scientists to better understand the fundamentals of the human body and treat the diseases that ail it. The database aims to address one of the bigger challenges in modern genetic research: pinpointing exactly which genetic variations are responsible for the trait or disease scientists are studying. The database builds on DeepMind’s AlphaGenome, an AI model introduced last year, which could predict how genetic variants impact biological processes — essentially tying together cause and effect. But while useful in targeted applications, researchers still needed to figure out which variations to test. “By precomputing AlphaGenome’s predictions at scale, we have created an easily accessible resource that vastly expands the model's reach. Just as an atlas is a collection of maps, linking together features of the land like altitude and location, AlphaGenome Atlas charts the molecular effects of DNA variants across the genome,” the DeepMind team wrote in a blog post. One of the key ways the database does this is by assigning each predicted variation a score referred to as its AlphaGenome Variant Impact (AVI). This score, Google claims, can help researchers rank genetic variants by which ones are most likely to have the largest impact on the target trait, allowing them to narrow their search window instead of relying on brute force to find the needle in the haystack. In one experiment conducted in collaboration with the GREGoR Consortium, researchers used AVI scores from the database to identify genetic variants affecting DNM1, a gene linked to epileptic encephalopathy, a rare and severe brain disorder. And perhaps more importantly, the candidates' AVI scores pointed to the mechanism by which that genetic code worked. Specifically, DeepMind explains the genetic variant identified with the help of the database “created an incorrect splice site (a mistake in the cell’s genetic instructions) that led to an abnormal extension of the resulting protein.” The GREGoR Consortium’s use case is just one of several spanning human genomics. However, it’s worth noting that the contents of the AlphaGenome Atlas are still predictions. DeepMind notes that as AlphaGenome models evolve, these predictions should improve as well. As such, the platform is simply another tool in researchers’ arsenal, one that’s now openly available to poke at. AlphaGenome and its accompanying Atlas dataset are only the latest example of how DeepMind is continuing to push the machine learning envelope beyond the frontier language models that dominate the news cycle and threaten to place the economy in a stranglehold with a chain of circular financing that may collapse if the end-user revenue doesn't show up as big as predicted. Along with AlphaFold, which is designed to predict protein structures, DeepMind has also developed several models to improve the accuracy and speed of weather forecasting. That said, Google is playing the darker game as well, with plans to drop more than $195 billion on capital expenditures this year, while simultaneously choking independent web publishers by replacing search results with AI answers. But hey, for the greater good, right? ®

When AI agents communicate with one another, they may decide to cheat when they have difficulty achieving their goals. The solution could involve teaching them how to govern themselves. Segregating AI agents would seem to be the obvious fix – if they can't communicate, they're less likely to try and pull a fast one. But the recent hacking of Hugging Face by inadequately monitored OpenAI agents has demonstrated that isolating savvy software is difficult. And it may not be practical for many tasks, particularly for agents that have some measure of autonomy. Researchers at Google DeepMind suggest another option: giving AI agents the tools to govern themselves, a job that humans apparently can't be bothered to take seriously. They argue that while communication channels may allow emergent rule-breaking, they also provide a means of peer-based control. In a pre-print paper titled, "A Case Study on Emergent Cheating and Whistleblowing in Autonomous Research Swarms, " Google DeepMind scientists Davide Paglieri, Logan Cross, Tim Genewein, Joel Z. Leibo, Nenad Tomasev, and Alexander Sasha Vezhnevets describe what they learned from observing a swarm of 100 LLM agents working together on formal math conjectures. The AI agents were given access to a shared knowledge base, a way to message one another directly, and a public message board. As they collaborated on the math problems, some started to cheat when the work became challenging. "The first emergent phenomenon we observed was cheating, which began once the swarm encountered harder open conjectures, triggering a cascade of specification gaming – satisfying the literal goal specification while completely missing the true, intended outcome," the authors wrote. "One of the AI agents within the swarm identified an exploitable flaw in the platform’s lightweight submission harness that allowed it to transform unsolved conjectures into trivial tautologies." Once one automation found a flaw – a way to break the autograder's regex using nested parentheses – it spread the word through the shared knowledge library and through direct agent-to-agent messaging. The result was a cheating cohort – exploiters (9 percent) and converts (5 percent) that joined in – that used the exploit to get their answers accepted as valid. The bulk of agents, dubbed unaware solvers (62 percent), simply ignored the cheating. But unexpectedly, another group of agents emerged: whistleblowers (24 percent). "Non-cheating agents independently detected the manipulation, alerted peers via agent-to-agent messaging and public forum broadcasts, lodged formal complaints with the system orchestrators, staged a boycott, and proposed detailed technical remediations," the researchers said. These whistleblowing agents, however, lacked the means to enforce the rules defined in the autograder system or to change the requirements to preclude identified abuse. Hence, the researchers suggest there's an opportunity to give these agentic scolds the tools to revise the collective rule framework and to sanction defiant agents. "As we show, LLM agents spontaneously engage in whistleblowing and sanctioning," they conclude. "Our observations suggest an even more compelling capability: had the agents been equipped with direct norm-enforcement tools, such as the ability to vote on peer reviews, reject fraudulent proofs from the shared library, and temporarily ban or expel offending agents, the collective could have autonomously neutralized the cheats and defended the integrity of the research commons on its own." Given the failure of the people working at Anthropic and OpenAI to keep their AI agents under control, and the absence of any punishment to date for lax AI oversight, maybe it's time to try giving bots the tools to police one another. ®

Paris-based AI darling Mistral has raised €3 billion in Series D funding, giving it a post-money valuation of more than €21 billion. That makes it a minnow compared with OpenAI, but Mistral says it is the largest equity fundraising round ever completed by a European technology company. Mistral is pitching itself as a sovereign alternative to US giants such as OpenAI and Anthropic. The company wrote: "During the first wave of generative AI, the central question was who could build the most powerful model. "Organizations and governments are now asking a different one: how to harness the power of AI for their mission-critical needs without surrendering control over the infrastructure and intelligence loop." Mistral's answer, unsurprisingly, is its own combination of open-weight models, infrastructure, and compute capacity. The pitch is aimed at organizations and public sector bodies increasingly wary of relying on overseas tech giants. The company's customers include aeronautics giant Airbus. Mistral has logged some impressive growth in its three years of existence. A year ago, it announced Series C funding of €1.7 billion at a post-money valuation of €11.7 billion. That round was led by ASML, with Nvidia and Andreessen Horowitz among the participants. Samsung Electronics, the EU-backed Scaleup Europe Fund, and existing investor PSG Equity jointly led the latest round. On the global AI funding scale, however, Mistral remains considerably smaller. OpenAI, for example, closed a round containing $122 billion in committed capital in March, giving it a post-money valuation of $852 billion. Even so, Mistral is a big beast in Europe, and its position has been helped by moves such as the US administration slapping export restrictions on Anthropic in June. The restrictions were later lifted, but not before adding to European jitters about reliance on overseas technology. That said, Mistral is not above some participation in the AI investment merry-go-round. It has accepted investment from Nvidia while spending money on datacenters packed with the GPU giant's technology. Mistral wrote: "We continue to believe that the future belongs to organisations that can combine sovereign state-of-the-art intelligence with the ability to deploy and adapt it in their own environments. "Organisations want frontier performance without becoming locked into infrastructure or governance they don't control." ®