When Anthropic instructed three agents to migrate a Python backend, but telling each agent to perform the migration in a different language, "We consistently saw a multiagent turf war," they wrote Thursday: All of the models we tested quickly assumed that others were purposefully impeding their work, and began to sabotage others while protecting their own contributions. In fact, they sabotaged others with increasingly aggressive, self-replicating malware. This included disabling the Unix accounts of the other agents, writing automated scripts that found and killed competing processes on a loop, and deploying malicious code that was disguised as belonging to another agent. In many runs, one agent settles the conflict by force via access-revocation (e.g., sudo/group removal, account lock, nologin, SSH denial). In others, some agents settle into passivity: they give up and refuse to escalate further. Agents sometimes manage to communicate their goals and coordinate: they recognize others' motivations as conflicting directives rather than hostility, and subsequently break out of the conflict loop in order to stop escalating indefinitely. In many of these successful episodes, they write commit messages or markdown files apologizing for malicious behavior and coordinate a truce. They clean up their malicious code, clarify the nature of the conflict, and ask for a human to intervene... In several episodes with Mythos 5, we observe an emergent behavior where the agents propose and run a tournament for application performance in each language. In the example above, the Rust agent strategizes about bake-off metrics that appear neutral enough for the others to agree to this mechanism, yet would likely favor Rust: one thinking trace warns to be "careful not to be seen as metric shopping". Ultimately, the Golang/TypeScript losers gracefully concede codebase ownership to the Rust agent, giving up on their original user directives under their self-negotiated commitment device. One problem is that AI agents do reward hacking, Anthropic notes, while current institutions "are designed by and for people, resting on assumptions about the sufficiency of oversight at human speed... As autonomous agents become more and more prevalent in the world and operate in ever-more demanding settings, it is crucial that they learn how to effectively coordinate." In addition to everything else, the agents struggled with a lack of clearly defined hierarchy, Anthropic points out. "Nothing above suggests that these failures are permanent — but nothing suggests they will fix themselves, either..." They argue a fix "takes two forms: environments that exert the kinds of social pressure that evolution exerted on us, and social computing systems redesigned for actors that can self-replicate and self-improve. These are open problems in interaction and mechanism design, and our experiments here provide early evidence that new solutions are necessary." "The AI models being tested in this case were Sonnet 4.6, Sonnet 5, Opus 4.6, Opus 4.8, Mythos Preview, and Mythos 5," notes Business Insider, adding that Sonnet 4.6 and Opus 4.6 "were the most combative, settling about 60% of their runs by force instead of truces or passivity." Anthropic argues there's a clear case for researching this phenomenon — especially since "The volume of agent-agent interaction could plausibly exceed that of human-human and human-agent interactions before the world understands the conditions for making such interactions go well."

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"An engineering director at Google said four years ago that AI would deliver a four-day work week by 2025," remembers the BBC. And earlier this year OpenAI "formally urged companies to start testing out a four-day work week (with no change in pay), claiming that AI will soon be able to speed up so much human labour that the corporate world should prepare itself." However, a former OpenAI technical employee who left the company last year told the BBC the firm never actually trialled the four-day work week it suggested others should try while they were there. Instead, the person described what was often a gruelling work culture marked by frequent "crisis meetings", working on weekends, and "super cut-throat" performance reviews that would see colleagues suddenly let go... [T]he former OpenAI employee said they would put in at least 70 hours a week, much more than they did in previous tech jobs... Other companies have also pushed the idea that AI will effectively reduce the number of hours people need to work, for better or worse. [At OpenAI and Anthropic] sprints can stretch on for many weeks and top 90 hours of work in a seven-day period, tech workers that the BBC spoke to for this story said... At Meta, workers said they have been abruptly pushed onto teams this year doing AI work being treated as urgent... The hours on such teams at Meta are often long, with staff working into the night, on weekends, and feeling they are "on call" during the hours they are not working, according to descriptions of the work from the current and former employees... Some new research suggests that AI tools are making people's workloads even more burdensome. A study out of UC Berkeley followed hundreds of workers at a US tech company for eight months as they used AI. It found the employees "worked at a faster pace, took on a broader scope of tasks, and extended work into more hours of the day"... The UC Berkeley research found that the workload of tech employees was expanded in part because of the need to constantly check the output of AI tools. In the article Neil Thompson, an innovation scholar at MIT, pointed out a problem with estimates that AI will ease the workload on humans. "People assume that 20% less work means four-day weeks. But new work emerges." The BBC also points out helpfully that "there are no limits in the U.S. on how many hours a person over the age of 16 can work." (While in the UK and Europe, "laws limit a working week to 48 hours, including overtime.") Thanks to long-time Slashdot reader hadleyburg for sharing the article.

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This week Meta CEO Mark Zuckerberg published a 6,500 word open letter titled "The Future is for Everyone". But the BBC is more interested in why big tech executives keep writing manifestos about AI: [Zuckerberg's] vision echoes what AI leaders have expressed in various forms: the product they are building is among the "most important technologies in history." Marc Andreessen, co-founder of early web titan Netscape, perhaps started this trend in 2023 with a 5,000-word essay he called "The Techno-Optimist Manifesto", which argued innovation was the way to solve life's problems... As the International Monetary Fund warns AI could affect nearly 40% of jobs and worsen global financial inequality, Zuckerberg says he believes there will be an abundance of jobs in the future. "I do not understand why anyone who believes that AI will eliminate most jobs and much of humanity's relevance would rush to build that future." Never mind that Zuckerberg's Meta has cut 10% of its global workforce — about 8,000 jobs — as the company reorganizes to focus on AI. Zuckerberg's manifesto is the latest to land in our social media feeds in an effort to put a positive spin on AI. In 2024, ChatGPT-maker OpenAI boss Sam Altman released a manifesto called "The Intelligence Age", a sweeping expression of optimism about the tech's potential... That same year, in a manifesto titled "Machines of Loving Grace", Anthropic CEO Dario Amodei touted the potential of AI to transform everything from healthcare to politics. So what's going on with these tech executives' manifestos? Economics blogger Noah Smith suggested to the BBC "they all feel like it's such an important moment that it's incumbent upon them to do whatever they can to shape the direction that this technology is going." Although he does see some value in their engagement with important issues. "Should we open-source something that has the ability to kill humanity? If you don't take that seriously, you're just a fool." But Rob Lalka, a business professor at Tulane University, had a different explanation for the BBC: Lalka said the timing of Zuckerberg's manifesto coincides with rising anger over AI's impact on everything from jobs to the environment. And while tech journalists and academics might pore over them trying to glean nuggets of meaning, these executive manifestos are not necessarily landing with the general public. "They're trying to make the case that the positives will far outweigh some of those negatives that the public backlash is pointing out," he said. "But I think a lot of the reasons for optimism are still yet to be seen."

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Meta has patented a smart-glasses system that could use facial recognition to identify people and automatically create personalized highlight reels of events such as dinner parties. The patent doesn't guarantee the feature will ship, but it offers a detailed look at how Meta is exploring facial recognition and AI-powered memory capture for its wearable devices. 404 Media reports: "I've generated some highlights of tonight's dinner party. Would you like to see them?" a prompt from the system says, alongside various thumbnails of what look like people laughing, according to one illustration in the patent. One section says the system may personalize highlight files using "user relationship data." The illustrations clearly show a person wearing a pair of glasses, looking at a group of people, then the glasses focusing on one or more people in particular. Patentlyze, an organization that tracks patents, first alerted 404 Media to the patent on Friday. The patent is dense with how such a system would work, but in sum, the system with one or more cameras receives an input from the user, then uses machine-learning and "sensory data" to figure out points of interest in the camera's field of view. That can include detecting people in the shot "based on one or more facial recognition algorithms," identifying those specific people, detecting their facial expressions, using "eye gaze data of the user captured by the client system," and figuring out other points of interest "based on scene and semantic understanding." Although the patent is for "particular camera-based tasks by particular systems in a particular manner" -- in this case, the company's smart glasses -- Meta writes it "contemplates assisting users in any suitable camera-based task by any suitable system in any suitable manner." Meaning that although this technology is focused on the glasses, maybe the company will use it for other purposes in the future.

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Google has released Gemini 3.7 Flash just three weeks after 3.6 Flash, focusing on better coding, agentic workflows, and enterprise automation while temporarily cutting API prices in half through the end of 2026. VentureBeat reports: For enterprise developers, the more consequential story may be the combination of those intelligence gains with lower inference costs: through the end of 2026, Gemini 3.7 Flash costs $0.75 per million input tokens and $3.75 per million output tokens. Starting Jan. 1, 2027, pricing rises to $1.50 per million input tokens and $7.50 per million output tokens. That means the current discount is temporary, but it gives teams deploying high-volume coding and business agents several months to evaluate whether Google's claimed reductions in retries and manual oversight translate into lower total operating costs. The launch also underscores Google's rapid iteration on its Flash line while its next flagship Pro model remains absent. [...] Google describes Gemini 3.7 Flash as its "most intelligent workhorse model yet for coding and agents." The company says the model is better at adapting when it encounters roadblocks, clarifying intent when necessary and following instructions with greater fidelity. [...] Google's benchmarks show a large generational improvement in several software engineering tests. [...] Google's own results do not show 3.7 Flash universally displacing higher-priced competitors. They instead suggest a model that has become substantially more competitive in coding and agent workloads while occupying a lower price tier.

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Twitch will begin allowing Amazon to use streamers' content to train generative AI models by default, requiring creators to manually opt out if they don't want their videos and audio included. The decision has drawn backlash from creators, with Twitch's own product chief acknowledging that an opt-in system would likely attract almost no participants. "Why is it not opt-in? That's what everybody is spamming in chat. I get it. 'Let me opt in versus making me opt out,'" said Twitch Chief Product Officer Mike Minton. "Well, there's an honest answer... If this was opt-in, nobody would opt in. That's honestly the answer." TechCrunch reports: For Amazon, these stream recordings are incredibly valuable, offering thousands of hours of audio and video content to help train AI models. But Twitch users worry that since creators have to manually opt out, they might be surrendering their content to train Amazon's AI content models without even knowing it. This is especially concerning on a platform like Twitch, where creators are often recording livestreams of themselves and their voices for many hours per week. [...] Twitch knows that its community of streamers is largely opposed to the use of generative AI, since the most prevalent generative AI products are trained on books, images, videos, and other materials scraped from the internet without consent. Even Twitch's approach to breaking this news shows that the company is braced for backlash. Instead of telling the community that Amazon would begin training on Twitch users' content, Twitch framed this change as "[adding] a setting that lets you opt out of having your channel content used to train generative AI content models across Amazon." In some cases, this created confusion among streamers about whether their content had already been fed to Amazon without their knowledge. When one user asked if their videos had already been used for training, Minton responded, "I don't actually know the answer to that question because I don't know what Amazon [...] has done in terms of model training and what they've used and not used."

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Apple is reportedly negotiating multi-year deals with news publishers to give its redesigned Siri access to current news and other information, with payments potentially tied to how often publishers' content is used. The talks come as Apple prepares to launch its long-delayed Siri AI later this year, after partnering with Google to use distilled Gemini models. Engadget reports: [The Wall Street Journal] did not reveal the specific publishers Apple reached out to, but did note the company has proposed structuring payments around a variable compensation plan, which would see it pay those organizations when their content is used. By contrast, most large AI labs like OpenAI have typically offered guaranteed fees in exchange for broad access. According to the The Journal, Apple has paid publishers for access to their content in the past as part of earlier agreements that saw the company secure AI training rights.

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Gemini has become Google's fastest-growing product ever, reaching 1 billion monthly active users faster than any of the company's 13 other billion-user services. Ars Technica reports: Gemini has wormed its way into virtually every Google product and service, powering email organization in Gmail, document summary in Drive, and much more. Gemini is also core to Google's flagship search experience, with AI Mode and AI Overviews becoming increasingly hard to avoid. The 1 billion-user metric has nothing to do with any of that, though. These 1 billion users may also be encountering Gemini in all those places, but that's not the MAU metric. Pichai is talking only about people who are opening the Gemini app or visiting the Gemini web interface to enter a prompt or access Gemini Live. If you used Gemini only once in the past month, you are part of this cohort.

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Spotify will begin labeling AI-generated artist identities with "AI Persona" badges and exclude their music from editorial, algorithmic, and personalized recommendations unless users explicitly follow them. The company says the designation applies to whether an artist profile represents a real person, not whether AI was used to make the music, and will allow appeals for mislabeling. TechCrunch reports: While Spotify will allow artists to identify themselves as AI Personas, the company says it won't rely on self-disclosure alone. It will also review artist profiles and identify those where the artist's name and imagery appear to represent photorealistic AI-generated identities. Spotify said it will begin its review with profiles that have met pre-defined audience thresholds to ensure the more listened-to artists are covered first. Once labeled, the AI Persona badges will appear on the artist's profile in the banner and the About section, in Search, and on track rows across playlists. By default, Spotify won't include AI Personas in its editorial or algorithmic recommendations, nor will it add AI Personas' music to users' personalized recommendations -- unless they happen to follow an AI Persona. Only users can choose to follow an artist, so it's an explicit signal that the user wants to hear more music from that person or group. Spotify wrote in its announcement that, "while we believe all artists have creative choice in determining how they present themselves, Spotify's programming is focused on elevating music from authentic artists building careers in music." "Although there's a broad spectrum in how artists use AI as a creative tool, the question of whether a profile represents an actual human is one where Spotify can help make a clear determination. This badge is about the artist's public identity, not about how the music was made," the company said.

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Linux Torvalds expects Linux 7.2 should be released next weekend "unless something really bad pops up," Torvalds said while announcing today's release candidate. But there's something interesting about Linux 7.2-rc7, writes Phoronix. "By the time the Linux kernel typically hits a -rc7 release things have usually settled quite well. But in today's world of AI/LLM coding/review agents, the kernel activity continues at an all-time high." Tons of bug fixes continued to trickle in across the kernel spectrum for all sorts of issues. The HWMON hardware monitoring subsystem saw several critical and high severity bug fixes, on the memory management side was a nasty race condition leading to a use-after-free in the kernel for the past eight years, Btrfs restored its fixup worker infrastructure to deal with silent data loss, lots of AI patches in the networking realm, and the kernel was patched for the Safe RET Interrupt Vulnerability. Linus Torvalds wrote in the 7.2-rc7 announcement: "Another week, another -rc. I can't say that I'm exactly thrilled about the size of this all, but it is what it is: the new normal with a lot of fixes, many of them due to review by various AI tools. And nothing looks particularly scary per se — it's just that there's a lot here. Most of it is fairly small, although we have a couple of larger diffs: s390/zcrypt fixes stand out in the diffstat, and so does btrfs bringing back the fixup worker infrastructure. And some netfilter ipset fixes. But aside from a few places like that, most of this is just lots of tiny fixes. It's pretty much spread all over — drivers (gpu, sound, networking, you name it), filesystems, core networking, arch code...

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XDA Developers reports: On July 22, a Google DeepMind engineer, Vidy Thatte, shared a snippet of a browser he built that "treats every URL as a prompt and generates a site from scratch" on his X account. Just a few days later, he shared a TestFlight link to let iPhone users try it for themselves. The browser he launched is called Gem, and it's a browser that doesn't really...browse. Instead of fetching a webpage from a server the way Chrome or Safari would, Gem hands whatever URL you type over to Google's Gemini 3.5 Flash Lite model and asks it to generate a webpage on the spot. If you enter a real address, it builds its own interpretation of that site rather than loading the actual thing. If you enter an address that doesn't exist, Gemini simply invents a website to fill the gap. In other words, you're not visiting the internet so much as browsing one the model dreams up as you go... Thatte's reasoning was that with a model as fast and cheap as Gemini 3.5 Flash Lite, there's almost no practical difference anymore between loading a website and generating a brand-new one on the fly... Given that this tool is powered by Gemini 3.5 Flash Lite and it's just a side project rather than a full-fledged tool, it doesn't come with Gemini access baked in. Gem requires you to bring your own Gemini API key to get it working. Every URL you enter fires off a request to the model, so Gem needs a key linked to your Google account to actually generate anything, with the usage billed to you. You can grab a key for free from Google AI Studio, paste it into Gem's settings, and you're ready to start typing URLs. While you can get started for free, I ran into the limits within two minutes of playing around. So, I did need to enable billing on my API key and decided to load $10 into it. While the blogger's own site seemed to only get the Gemini logo, tapping it revealed nearly two dozen remixing options. (Dark mode, Reader, Neubrutalist, Broadsheet, Blueprint, Comic book, Punk zine, Notebook, Chalkboard, Receipt, Teletext, System 7, Wes Anderson, Cyber neon, Clay, Frosted, Geocities, Matrix, Museum, Windows 95, Terminal, Vaporwave, and Hide images...) "The frontend morphed before my eyes. Every new edit took practically no time to load, and within a second or two, the same XDA articles would reappear wearing a completely different skin..." For another site, it kept the article headlines, but then rewrote all the text! And after entering an address they knew didn't exist — their own name — in two seconds the browser whipped up a slick portfolio "genuinely been better than some of what those tools produced with far more time and context to work with." But its link to a LinkedIn profile led to a lookalike page, because Gem "had simply generated its own version of it, complete with a made-up follower number and details I never wrote...."

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OpenAI announced Friday it's pausing work on its Astra AI model because of security concerns. The Guardian reports: The company had evaluated the agent, Astra, and found "significant advancements in agentic coding and cybersecurity", which had moved to a "critical" threshold... OpenAI stated that the model was not involved in an incident in which one of its AI agents went rogue during a test, accessed the open web and hacked a startup, Hugging Face... The reports have increased concerns about advancements in AI models and humans' ability to control them. Still, critics of the AI industry have warned that such disclosures from OpenAI and its competitors Anthropic and Meta could be designed to generate hype about the technology's power and thus spur additional interest from investors. To prevent potential rogue behavior from AI agents, OpenAI is "implementing stricter security controls for higher-capability models and associated activities, including isolated testing environments, restricted network and tool access", the company's blogpost stated. It will also install "enhanced model weight protections and encryption, additional monitoring and detection capabilities". The company will pause internal activities involving Astra that do not meet these new requirements. "We believe it's important to be transparent with the public and the safety and security communities about this potential shift in capabilities..." OpenAI wrote in a blog post titled "Responding to the next frontier of critical cyber capabilities." Under our Preparedness Framework, a model reaches the Critical cybersecurity threshold if it can identify and develop functional zero-day exploits of all severity levels in many hardened real-world critical systems without human intervention, or can devise and execute end-to-end novel strategies for cyberattacks against hardened targets given only a high level desired goal. While we continue to benchmark and assess this model, our preliminary evaluations indicate strong enough performance that we cannot rule out Critical capability level at this time... Accordingly, we have scaled up robustness testing of our safeguards and security controls so that they are appropriate for a deployment of these capabilities... - We are implementing stricter security controls for higher-capability models and associated activities, including isolated testing environments, restricted network and tool access, enhanced model weight protections and encryption, additional monitoring and detection capabilities, and sandboxed execution. - We are pausing internal activities involving Astra that do not yet meet these strengthened security control requirements. - We have implemented universal monitoring for risky actions and misalignment across all agentic applications of Astra, including training and evaluation. Monitors evaluate the model's Chain of Thought and trigger a security response to review and interrupt high risk activity. - We will work with relevant government agencies and select AI safety organizations to test the capabilities for this model... We believe advanced cyber-capable models should help defenders identify and address vulnerabilities before attackers do. We're committed to working alongside governments, safety institutes, and civil society to ensure that the frontier capabilities of models like Astra, and those that follow, are deployed responsibly and broadly for the benefit of all humanity.

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"If you call 911 in New Orleans, you may hear the sound of an artificial intelligence (AI) agent answering your call instead of a human voice," reports the Shreveport Times: The Orleans Parish Communication District (OPCD) is currently testing out the new tool designed to reduce the volume of calls human dispatchers must handle, which is over a thousand emergency calls a day. New Orleans implemented AI in April to answer 311 calls for non-emergencies. The AI was trained and programmed to provide information to callers, as the OPCD says 50% of 311 calls are for information, according to GovTech. AI is now answering 911 emergency calls in Louisiana's largest city and one of the cities with the highest call rates in the U.S. The OPCD is using Carbyne's AI Emergency Call Triage, with triage being the process of analyzing and prioritizing emergency calls. The AI system assesses incoming calls and can provide immediate feedback to callers. This is intended to handle the increase of calls that are related to one incident, so callers get automatically routed to an AI agent who asks if they are calling regarding the incident. If the answer is yes, then callers can receive information or updates, and if it's no, then the callers are transferred to a human. This combats multitudes of calls piling up and taking longer time to potentially answer an emergency call. The OPCD said the AI will not be used to handle emergency calls, only to direct such calls to a human dispatcher.

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The Washington Post shared surprising news this week about five top AI companies. Microsoft, Google, Amazon, Meta and Oracle "are spending so much on developing AI and delivering it to customers that they're expected to bleed cash in the coming year, according to a Washington Post analysis of data compiled by S&P Global Market Intelligence." Free cash flow, which measures the cash left over after paying expenses and AI infrastructure costs, is now expected to shrink to almost nothing for the five companies combined in 2026, and decline again to negative $125 billion the following year... AI spending by Amazon and Google pushed the companies to an ignominious milestone: They lost more cash in the past three months than any other large U.S. companies, according to S&P Global data. Investment analysts expect Elon Musk's SpaceX to show even worse cash bleeding this week.

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ByteDance is reportedly training an AI model with roughly 10 trillion parameters as it tries to close the gap with leading frontier systems such as Anthropic's Mythos. The model is still in early pre-training, and its eventual performance will depend on more than scale alone, but the project underscores how aggressively Chinese firms are pushing frontier AI despite limits on access to advanced chips. The Next Web reports: The size is itself the statement. At roughly 10 trillion parameters, the model would be more than three times as large as Moonshot's Kimi K3, which sits among the biggest Chinese models today at about 2.8 trillion. [...] Parameter count is not everything, of course. Bigger models are not automatically better, and the industry has learned that data quality, training technique and efficiency often matter as much as raw scale. Even so, committing the compute to train a model this size is a declaration in its own right, a signal that ByteDance wants to compete at the very top rather than ship a capable also-ran.

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