Despite previous controversies, it seems like the lords of Linux are warming to AI-assisted coding. The penguin's kernel community has agreed to stop asking coders to disclose whether they used bots to help with their contributions. And Linux kernel supremo Linus Torvalds has made clear that he opposes "stupid" AI disclosure rules as long as people understand the code they're submitting. "The Maintainers Summit reached consensus to deprecate the Assisted-by Tag," said Sasha Levin, a Linux kernel developer and engineer at Nvidia, in a message to the Linux kernel mailing list last week. "Remove the documentation requiring it." The policy had said that those using "any sort of advanced coding tool in the creation of [their] patch" should acknowledge that through the addition of an "Assisted-by" tag in the commit message. The AI labeling proposal initially took shape in July 2025. It became part of the documentation in January 2026. This was shortly after Torvalds said that he was fairly positive about vibe coding as a way for people to get into programming, but remained skeptical about its effect on software maintainability. Since then, he's warmed to AI tool usage. AI attribution wasn't an issue when the generated code was obvious, buggy, and bad. But in the past year or so, AI coding agents started producing code that is of acceptable quality for the purposes of the Linux kernel community. "LLMs are good enough now," said Greg Kroah-Hartman, a veteran Linux kernel maintainer, at the Linux Foundation's Open Source Summit in South Korea in August. "They've gotten good enough to find bugs because LLMs, AI, are just pattern matchers and code is nothing but patterns in a specific format." Cautious acceptance hasn't erased concerns about errors slipping through unnoticed, accountability, and coding agents reproducing copyrighted code. There's also still a stigma in some circles around the use of AI tools. Those who have spent years honing software skills, coding with limited assistance from tools, may disparage developers who don't understand the code they conjure with a prompt. Rapid change will do that – in the space of a few years, software production practices have changed drastically, and software quality, security, and maintenance practices have yet to fully adjust. The leading makers of AI coding agents – Anthropic, Google, Microsoft, OpenAI, and the like – are doing their best to promote AI-driven projects as better than anything a human developer could manage. Yet even they differ in their AI attribution practices. Claude adds provenance messages by default, but Codex does not. In fields such as advertising and art, content provenance can help assess misinformation and human creative input. For code, the focus tends to be more on functionality than form. In a prior discussion of the merits of the tag requirement, Jeff Layton, a Linux kernel developer with Red Hat, explained the rationale for reversing course. First, he said, not everyone abides by the policy. Second, he said, "it basically serves as free advertising for proprietary LLM companies." And third, he said, "it's not clear why we want to collect this info in the first place." Some kernel contributors still see value in the tagging requirement. "I'm not happy removing the tag without adding another mechanism for required disclosure," said Laurent Pinchart, a Linux kernel dev and founder of Ideas on Board, in a mailing list post on Tuesday. "Without that, I can foresee submitters pushing back on the question." Pinchart challenged the argument that AI labeling rules will just be ignored. "What this is about is setting our expectations, telling what we want and what we don't want," said Pinchart. "There's still value in that, and I'd argue there's even more value now. There will be people who won't care, there will be people who cheat. We've always had those, we'll keep having them, and we'll keep rejecting their contributions. I really don't get the unenforceable argument here." Linus Torvalds responded with an emphatic rejection of tool usage declarations. He wrote: What you are about is something entirely different. It has nothing to do with "expectations", and it has everything to do with your hangups. I don't understand this whole idiotic concept of "don't use AI for X". Here's a new rule just for you: don't use compilers for any of your kernel submissions. Because if you can't show that you really understand what the machine is actually doing by writing your code in assembler, then I don't want to see your code contribution. You clearly aren't competent if you can't write it that way. And btw, you had better not use an editor to write that commit message either. Because I need you to show me that you can actually write a good commit message without using tools that make it easy to backspace and hide the mistakes you made while typing. Ok? Because otherwise those tool-assisted commits of yours clearly aren't showing that you know what you are doing. Do you not see how UTTERLY STUPID those kinds of rules would be? So no. We're not going to make stupid rules that say "you can't use tools for X". The kind of expectations *I* have is that people do their best and I expect people to understand what they are doing. But that understanding has very little to do with what tools they use. And quite honestly, in my experience, the people I respect and call intelligent are not the ones that shy away from using new and interesting tools. ®

Silicon Valley’s AI bros have supposedly found a new way to signal to those around them how committed they are to their industry: Take up smoking despite the risk of cancer, because they expect bots to develop a cure … any day now. At least, that’s the claim Palantir CTO Shyam Sankar made in a recent interview on CBS's Face the Nation. In trying to explain what he described as a doomsday cult surrounding effective altruism, whose adherents include some AI researchers who believe the technology could bring about a utopia, Sankar claimed smoking has become a way for researchers to demonstrate their faith in the tech. “So many AI researchers have started smoking as a way of signaling to other researchers how much they believe in AI,” Sankar said to the confusion of host Margaret Brennan. Those researchers “believe so much in AI that it's going to cure cancer, I'm showing you my chips are all in,” Sankar explained. Sankar described the decision to take up smoking as a bet that AI would cure any health ailments that arose from it as “mass psychosis” and “social delusion” that’s getting in the way of actual discussions around the state of AI, and he blamed Silicon Valley’s pet philosophy, effective altruism, for those delusions. “There's an unseen force driving the narrative around AI safety, and it's really this millenarian doomsday cult meets messianic complex called effective altruism,” Sankar said. That philosophy prioritizes making the most effective decisions that benefit the most humans – like utilitarianism on steroids – and it tends to take a long-term approach to things. That’s where AI comes in: Effective altruists are ostensibly focused on ensuring AI systems are built in a way that will best benefit humanity. That focus has been a key reason why so much AI doomerism has come out of Silicon Valley too: If we’re going to build safe AI, says the effective altruist, we have to make sure that we consider all the risks. “There's only really roughly 2,000 AI researchers in America, and the vast majority of them believe in this sort of worldview around effective altruism,” Sankar noted. The Palantir CTO seems to believe, like many effective altruism critics, that all the doomsday scenarios and far-fetched risks being posited by the EA AI crowd are less technical critiques and more like theological ones. “I think there's a profound truth to Pascal's saying that every human has a god-shaped hole in their heart,” Sankar said, “and it's important to understand that many of these folks in Silicon Valley who are building this technology have filled that hole with the machine God of AGI.” Smoking as a bet that the machine god will save them from cancer, therefore, is akin to the ascetic’s endurance of physical pain or trauma in the hopes God will heal them or see them safely through their torment. From a more practical perspective, this isn’t exactly a great idea, as one former cancer researcher who spoke to The Register told us. “Good luck to them with this approach,” the researcher, who declined to be named, opined. “Even more so, good luck to their non-smoking friends and family taking in their passive smoke.” Taking up smoking in the hopes of a cure may not be a wise idea when one considers the timeline needed to develop a cancer drug, much less a cancer cure. "Even with AI, developing and introducing a new cancer drug into any of the world's health systems can take several years,” the researcher explained. “Even then we wouldn't know whether or not resistance to the drug would develop in the tumors.” The ability of AI to discover drugs and medical treatments has been much hyped – Google DeepMind CEO Demis Hassabis even predicted last year that 2025 would be the year humans started popping AI-designed pills. That hasn’t happened yet, and there’s no real indication it will anytime soon. In other words, the likelihood that AI is going to kill us all is ridiculously overblown. The actual casualties, it seems, are more likely to be the AI bros giving themselves cancer as a show of faith in their machine god. ®

If you use OpenAI's dot, don't be surprised if it consumes a lot of tokens on its own. According to AI agent security and governance outfit Geordie, one dot agent produced 155 separate activity sequences and consumed more than 300,000 tokens - all without further human interaction, and all in the course of a day. Geordie has been testing the “always on” agentic assistant, dot, since OpenAI announced it in September, following in the footsteps of Meta Muse and Grok Bot. Compared to other personal agents, however, OpenAI’s dot “looked a little bit different,” Hanah-Marie Darley, Geordie chief AI officer, told The Register. “One of the things that we discovered really quickly was that they were really, really active without much user interaction.” For its testing, Darley gave dot read-only permissions, and she didn’t connect it to her personal apps, email, or other communications apps like Slack. The bot quickly went into research mode, collecting as much information as possible and surveying systems. “You can think about it very much like a new project manager coming in and taking a look at what's there, looking around to see what accesses you've given it, what accesses you have, and also what kind of systems you're running,” Darley said. The agent collects all of these data points into something akin to a daily journal entry, and continually updates those throughout the day. “If you've given it any tasks, it will effectively check on its own tasks, too,” Darley said. “But what was really unique was that the dot was spinning up agents without interacting with the user at all. It was all driven by the dot rather than a persistent or scheduled task from the user, or any user prompting.” 'Hyper-personalized,' and 'always-on' … what could possibly go wrong? Most enterprise AI agent usage tends to be custom-built bots, tasked with specific business projects, or agent harnesses like Claude, Codex, or Hero for AI-assisted engineering and coding, Darley said. “Those tend to be really driven by employees who are setting goals,” she told us during an interview. “But interestingly, personal agents set a lot more of their own tasks - and I don't think many people realize that as they start to play with them.” This is the whole value proposition of personal agents. Rather than passively waiting for user prompts, bots like Muse and dot aim to be proactive digital workers that perform multi-step tasks and connect to multiple apps, services, and accounts on the users’ behalf. “They’re trying to go hyper-personalized and collect as much context and information about you as possible to recommend things to you, and to be proactive,” Darley said. “But what that means is that they're effectively always on, operating in the background, and they have these active periods,” she added. Dot, in particular, is more proactive than other personal agents like Muse or Grok Bot, according to Darley. This doesn’t necessarily translate into higher token usage, she added. “They just operate differently.” For example, dots always operate with at least two agents running: The primary agent interacts with the user, and the secondary agent continuously runs in the background, collecting data. Dots and other agents can also spin up subagents for specific tasks. 50 traces in 2 minutes Of the dot-in-testing’s 155 agent traces, at least 50 of those happened within a two-minute period, and the majority occurred within a couple of hours, according to Darley. “Within one of the traces that we analyzed, the agent spun up multiple sub-agents that were short-lived, they would just do specific tasks, and fed the information from the existing accesses that had already been granted to the dot - and none of this was interacting with the user,” Darley said. Part of the evaluation involved testing a development system, and one of the dots spun up a sub-agent that checked all of the code repositories in the system. Meanwhile, another sub-agent continuously inventoried and tallied up all the data that it found while operating. “The user would have no idea that there were sub-agents working on their behalf,” she added. “They would have no idea how those agents were given access, or what their tasks were, which is really, really different from how most agents have been operating in business contexts.” The 155 traces cost Geordie about 307,000 tokens. Other requests cost anywhere from a couple hundred tokens to several thousand, depending on how much research the dot performs. “That’s really interesting because it's self-directed research,” Darley said. The user isn’t asking it to perform these tasks - or use a set number of tokens - but rather the dot does it on its own. Tokenomics Token costs vary widely depending on the user's location, subscription tier, discounted pricing arrangements, computing costs, and other terms and conditions. But for the purposes of our discussion, roughly 200,000 tokens consumed by a dot based on OpenAI’s Astra model cost between $5 and $10. “So if you think about what that’s going to look like every day, that could mean spending what you pay for a latte, depending on the city - but without any interaction with the user,” Darley said. “One of the things that a lot of people struggle with, especially when it comes to understanding their AI spend, is: How does token usage connect out to work product, or how does it connect out to results? And it's a hard one because it's not necessarily one to one,” she noted. One request to an LLM could cost 500 tokens and still be more valuable, from a business perspective, than another single request that cost 800,000 tokens. “It's really about how much compute does it cost you to get to a good answer, and that becomes a really subjective judgment,” Darley said. When it comes to dot and other always-on agents, however, she suspects “a lot of people aren’t going to be prepared for the background spend.” Do you know what your agents are doing? Just like employees did with chatbots, and then with coding agents, and now with personal agents: assume that people will bring dot and friends from home into their work life, Darley said. “Businesses are surprised often by the number of agents that are actively in their systems, even if they have a smaller rollout or a more controlled rollout in mind,” she said. “The second thing is not all agents act the same,” she added. “They operate with varying degrees of autonomy, with varying degrees of user input, and they're also really context driven. The best agents are the ones with the narrowest context because that means that they can pursue their goal with the right information at the right time.” Darley suggests organizations try the technology. She’s a self-described “massive AI optimist,” and says she believes agents can improve productivity and transform the way people work. AI isn’t going away, and, as such, businesses should familiarize themselves with agents. But she has one final warning before letting agents loose. “Don’t give them any personal information that you wouldn't be comfortable sending in an email,” Darley said. “And don’t put real money on it - like your personal cards. Buy it a Visa gift card, if you want to give it access to make purchases for you. Oftentimes, people don’t think about that until something goes wrong.” ®