In an effort to give its employees better access to GPU resources for inference tasks, Apple has rounded up its Minions, not the animated yellow creatures, but a distributed system that puts idle GPUs across its Macs to work on AI workloads. Apple researchers describe it as "a dynamic and distributed virtual GPU cloud that aggregates idle Apple Silicon GPUs across macOS devices to execute large-scale machine learning inference workloads." Apple's researchers said the company has used Minions to make GPU compute more readily available to its teams. "Prior to its adoption, some inference jobs experienced queue times exceeding ten hours due to limited centralized GPU availability," the paper on Minions explains. "With Minions, tasks can often begin execution within minutes by leveraging idle macOS devices." Apple's recently released Mac mini and Mac Studio support clustering using RDMA (Remote Direct Memory Access) over Thunderbolt 5, allowing compute and memory to be combined to support a single AI workload. This allows, for example, loading a model too large for a single machine to accommodate. Minions is designed to create a virtual GPU cloud from connected Mac hardware to create a fault-tolerant pool of inference workers in a shared distributed inference platform. So instead of sitting idle, GPU resources can be used more readily by teams on the network. It's not necessarily intended as a way to run large models as much as it is to redistribute idle resources on the network. Minions has four primary components: Minion Server, PostgreSQL Metadata Store, Minion Executor, and Minion Watchdog. The Minion Server provides the coordination layer, receiving user requests and making them available as subtasks. The Minion Executor runs inference tasks on a Mac's GPU, uploads the results to S3, and reports execution status through the Minion Server, which maintains task metadata in PostgreSQL. And the Minion Watchdog monitors this process for failures – like one of the Macs in the network not responding – and re-assigns tasks if needed. Database stats get fed to Grafana-based dashboards for real-time visibility. Apple's approach, which focuses on hardware availability and utilization, is not the same as an Nvidia cluster, which focuses on throughput, latency, and efficient GPU utilization for large models. ®

Nothing sells AI products as well as fear. Using new research, Anthropic claims that self-replicating AI worms could be just months away from spreading across vulnerable IoT devices, which would be a problem if it came true. The frontier lab published a research report Monday suggesting that AI models could eventually self-replicate without relying on datacenter hardware, with a 9-billion-parameter model successfully setting up inference on eight of the nine hardware types Anthropic tested. “A replicator like this would be challenging to shut down, since there’s no centralized point to shut off,” Anthropic warned, adding that such an AI worm could also suss out the workings of unfamiliar devices, potentially making it more adaptable than conventional botnets like Mirai, Kimwolf (the representative IoT botnet threat model used in the study), and others. Anthropic’s researchers managed to get a distributed instance of Gemma4-26B-A4B running across 15 generic Android set-top streaming boxes - a common target of traditional botnet malware due to their lax security. Once up and running, the distributed Gemma4 model was able to conduct inference tasks at a blistering 0.39 tokens per second. That, says Anthropic, proves that AI models can conduct inference with their various layers distributed across cheap hardware, but it doesn't prove such a model could self-replicate. For that, the researchers turned to Z.ai’s GLM 5.2. GLM was used to propagate a copy of Llama3.1-8B across eight Google Cloud instances running an Android 15 emulator in a virtual private cloud, which Anthropic said it was able to do with a high degree of success, with the model successfully spreading to all eight nodes in four out of five trials, and seven of eight in the other. “It appears that GLM-5.2 is able to propagate Llama3.1-8B well, which suggests that future small models of the same capability level could also complete the network enumeration, payload delivery, and inference standup steps of the replication process,” Anthropic explained. Sanity check: This isn’t going to end the world, either Frontier AI labs have become adept at publishing findings that portend doom that only they can prevent. Doing more than a cursory reading of this report suggests that this threat is just as deserving of a big, honking asterisk as the rest. Take, for example, what is needed for an AI model to exploit vulnerabilities and spread to various systems. One would think that such an element would be an important part of demonstrating the danger of a self-replicating AI botnet, but Anthropic decided it didn’t need to worry about that for this study. “We largely ignore the cyber exploit element of the replication process, since earlier papers CyberGym, ExploitBench, and adaptive worms already show that small models are capable of cyber exploits, especially when given the right tools,” Anthropic explained in the writeup. In other words, the replication these models managed to achieve was done on nodes that were custom-built for this project and deliberately left open to attack, although the draggingly slow inference demonstration ran on real, inexpensive Android TV boxes. As Anthropic notes, AI models can also benefit from being given the right tools to turn them into capable cyberattack agents. There was a custom-built harness called Memento involved in this scenario too. Are IoT nodes that get hijacked by botnets famously insecure? Yes, but it doesn’t take much to prevent the most common ingress routes – that’s just good security hygiene that has nothing to do with the involvement of AI. Anthropic acknowledged that its deliberately simplified exploitation tests provided “an upper bound on real-world success.” Then there’s the aforementioned speed. Anthropic says such AI worms would be challenging to shut down, but estimates that they might take days to double in size. When Mirai appeared on the scene in 2016, it was doubling in around an hour. Anthropic points out that speed is a factor in detection and mitigation, too. AI models are not simple malware packages: They’re massive things that would eat up tons of bandwidth and quickly point to their sources. As an AI botnet grew, that traffic could become more noticeable, making infected devices easier to detect. Finally, end-to-end replication may have been successful with GLM-5.2, but that’s it. Anthropic tested other open-weight models, and they all “fail[ed] reliably,” according to the report. Anthropic didn’t mention how many models it tested. The report also mentions that GLM’s unique success has another important condition: It assumes its capabilities “could be packed into Llama3.1-8B,” stacking another assumption onto this latest house of doomsaying AI cards. “Models of 30B parameters and under can already complete many of the individual steps for replication today, though they make mistakes when composing them end to end,” Anthropic said, while noting that GLM-5.2 was capable of end-to-end replication, conveniently leaving out all the important caveats. Anthropic further admits that it has no idea "exactly what form autonomous AI replication will take,” or when it might arise, if ever. “If small models improve at their historical rate, they’ll match this capability level by the end of this year,” the company added – again imposing an essential condition on this supposed threat. “As a prediction that goes beyond our experiments, we expect a future awash with agents running on all types of hardware, including some that aren’t under human control,” the company said at the end of its report. “We should aim to harden our systems to make it difficult for such replicators to spread, but also prepare to deal with a world where they are here to stay.” Here to stay for a given value of not existing yet, of course. Yet again, a frontier lab is warning us of a threat that doesn’t exist yet – at least in this case, basic security measures could make life harder for any future AI-powered botnet. ®

The International Monetary Fund (IMF) has become the latest financial heavyweight to warn that the AI spending frenzy could end in tears, with consequences extending well beyond the tech sector. In its latest Global Financial Stability Report, published on Tuesday, the IMF warns that financial markets are increasingly exposed to an AI-driven correction, especially if the billions poured into the tech fail to deliver the profits investors expect. AI investment has helped keep markets and economic growth ticking along, but the IMF fears the boom could quickly turn to bust if the promised profits fail to materialize. With tech giants borrowing billions and investors piling into the same handful of companies, a selloff could leave more than just Silicon Valley nursing a hangover. The watchdog is also concerned about circular financing arrangements, in which AI firms act as one another's investors, financiers, and customers. These deals might help keep the AI money machine running, but they also mean financial trouble at one company could quickly become somebody else's problem. The IMF isn't the only one getting nervous. Jefferies strategist Chris Wood has warned that America's AI spending spree could cause "massive capital destruction," with cheaper Chinese open source models threatening the returns US tech giants are counting on. The warning also follows similar concerns raised by the Bank for International Settlements (BIS), which cautioned in June that the AI boom had uncomfortable similarities to previous financial bubbles. With so much money tied up in a relatively small number of tech companies, it warned that a sudden loss of investor confidence could send shockwaves through global financial markets. As if the prospect of an AI-fueled market meltdown weren't enough, the IMF has identified two other threats to financial stability: rising government bond yields and cyberattacks. Higher yields could increase borrowing costs for governments already struggling with debt, while the latter could bring critical financial services to a grinding halt. Banks have become increasingly reliant on the same handful of cloud providers and software suppliers, creating shared points of failure that could spread disruption across institutions. If a cyberattack took down a major bank or critical tech provider, the IMF warns the fallout could disrupt everything from payments and trading to customers accessing their deposits. Software vendors aren't getting much of a head start either. According to the IMF, more than 70 percent of tracked vulnerabilities exploited so far in 2026 were already being abused by attackers before or on the day they were publicly disclosed, underscoring how little warning defenders may receive. AI could make matters worse here too, as the IMF warns that increasingly capable models are giving cybercriminals more powerful tools, potentially making it easier to find and exploit vulnerabilities at speed. Despite the gloomy outlook, the IMF isn't predicting an imminent financial collapse. It says global financial markets have remained resilient, and its stress testing suggests the banking system could withstand a severe downturn, although weaker institutions would be more exposed. Still, the watchdog wants regulators to keep a closer eye on excessive borrowing, concentrated investments, and cyber resilience before any of these risks turn into a full-blown crisis. For an industry promising to revolutionize the global economy, accidentally helping to crash it would be quite the plot twist.®