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  5. Analytics India MagazineOpinion
    AI score51

    Microsoft proposes using Guardian Agents to supervise enterprise AI agents

    Hayete Gallot, Executive Vice President of Microsoft's security division, proposed in a blog post deploying Guardian Agents to monitor and intervene before AI agents execute actions, and stated that frontier models cannot be trusted regardless of their origin. The plan intends to bring enterprise agents under centralized management, assigning each agent an identity, container, and permission policy, then combining identity, data, cloud, endpoint, network, and runtime signals to assess risk in real time, with red team and blue team agents intervening when risk rises. The plan will be implemented on the basis of Microsoft's Project Perception security system announced in July.

    #Safety / alignment#Agents#Microsoft

  6. shao_mengOpinion
    AI score56

    English translation is being prepared.

    Quoted postAI Engineer 🔜 CODE Summit🌉 · @aiDotEngineer

    Inside Grok Bot: is the agent harness dead? @jediahkatz of @SpaceXSI explains why smarter models let Cursor remove prompts and guardrails, and why Grok Bot is adding them back. Watch his AI Engineer New York talk: https://t.co/3dHGpRezon https://t.co/hYEUl4orJr

    #English text is being prepared.#Agents#English text is being prepared.

  10. Tessl: Product and Engineering BlogFirst-partyOpinion
    AI score55

    Jellyfish data: coding agents already write 22% of PRs, but nearly all require human review

    Jellyfish's analysis of data from about 300,000 engineers at 1,300 companies shows that at the median enterprise, autonomous coding agents have already written 22% of merged PRs, up from 18% a month earlier, with the top 10% of companies reaching 53%. However, the median share of PRs independently reviewed and merged by agents is only 0.6%, and 9% for the top 10%, with human review remaining the main bottleneck. The author also notes that the abandonment rate for agent-submitted PRs is 40%, versus 20% for humans, and that the number of human reviews per merged agent PR has more than doubled over the past year.

    Nicholas Arcolano

    #English text is being prepared.#Agents#English text is being prepared.

  11. SwyxOpinion
    AI score51

    swyx on DeepSeek GPU revenue data: lifetime value per card up about 30%

    Citing DeepSeek production data disclosed by Latent.Space, swyx says a single cluster processes 155T tokens per day with a 96% input cache hit rate, yielding $38 in revenue per GPU-hour at off-peak prices. He believes this raises the lifetime value of every GPU sold by about 30%, corresponding to roughly $600 billion in market value potential, and jokes that the more you buy, the more you earn.

    Quoted postLatent.Space · @latentspacepod

    deepseek's production numbers are kinda wild: 155T tokens/day from one cluster (96% input cache hits) and $38 revenue/GPU-hour at off-peak prices. at $5–8/GPU-hour, that’s ~80% margin on GPU costs alone. at DeepSeek prices!! we dug into the math, their 72-hour cache, and what actually got open sourced in today's AINews: https://t.co/NcwbxbaqGn

    #English text is being prepared.#English text is being prepared.#DeepSeek

  17. ayi_ai_notesOpinion
    AI score58

    English translation is being prepared.

    Quoted postAYi · @AYi_AInotes

    English text is being prepared.

    #Industry news#English text is being prepared.#English text is being prepared.

  18. Ayi AI NotesOpinion
    AI score62

    Anthropic CEO Dario's Long Post Responds to Nine Criticisms from an External Safety Researcher

    Anthropic CEO Dario Amodei published a long post responding to nine criticisms from external safety researcher Nathan Calvin, acknowledging that some of the examples were Anthropic's mistakes but disagreeing with the main thrust of the entire critique. He said Anthropic's strategy is "race to the top," that is, demonstrating responsible technology building at the frontier in order to raise standards across the entire industry; and he listed safety measures from the past year that he considers to have had commercial costs, including SB53 legislation, biosecurity protections, delaying the release of Mythos-class models, chip export controls, restricting sales of AI services to China-related companies, and committing 5% of company equity to a public-interest fund. He also acknowledged that the communication around points 7, 8, and 9 could have been better, and that the point 5 RSP revision is the most substantive point of dispute.

    Quoted postDario Amodei · @DarioAmodei

    Thank you for this, @_NathanCalvin. I appreciate and am a fan of the investigative work you’ve done on behalf of AI safety and believe your comments are coming from a constructive, honest place, and that it benefits the public to have people whose job is to scrutinize the actions of frontier AI companies. I think something would be wrong if companies of the size of Anthropic didn’t get harshly criticized basically every day; strong antibodies against corporate power are a healthy feature of a free society, and especially so when issues of the gravity of the one we’re facing are at play. I also do agree that some of the examples you cite were mistakes by Anthropic. That said, I disagree with the thrust of your post. At the core, a good fraction of the things you criticize are consequences of Anthropic’s basic theory of change that we have held for many years. Our strategy has always been what I’ve called the “race to the top” — by showing how it’s possible to build the technology responsibly, we can raise the bar for the entire industry. The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier; that’s one lesson I’ve learned repeatedly. And looking at that strategy as a whole, I’m proud of our track record and stand by it. I believe that if we take a list of things that Anthropic did in the past year that were pro-safety and commercially costly to us, they far outweigh the list you’ve made, while at the same time I also disagree with some of the items on your list. Here are some things Anthropic has done, in the same period you’ve cited (the last year), which were very costly to us commercially while having strong safety benefits: 1. We played a key role in SB53 (and other state laws), at a time when doing so was politically perilous to the company and AI regulation was very unpopular. We in fact argued for a stronger version of SB53 than was ultimately passed; there were a lot of stakeholders and we were not the limiting factor in the strength of the bill that was passed. 2. We have very strong biological safeguards on our models, because we’ve seen concerning results in bio uplift trials and even seen and blocked potential attempts to misuse our models for bioweapons (as seen in our threat intelligence report from a few weeks ago). This is a very serious threat that we want to have zero tolerance for. But having very low false negatives means having significant false positives (despite months of effort on our part to improve the classifiers), enough so that biologists joke about how restrictive our classifiers are, and competitors sometimes sell against us by touting their more permissive bio capabilities. This has been very painful to us in an area where we care a lot about supporting an ecosystem of Claude users (biomedical science; and it is particularly frustrating to me as a biologist), but it’s the right thing to do and we intend to keep doing it. 3. Similarly, we first had Mythos-class models in February but didn’t make them generally available until June (when we had stronger cyber safeguards in place). We only offered them to a limited set of partners in order to focus on cyberdefense. It would have been much more economically rational to simply release the model to everyone. We likely sacrificed a significant chunk of a several-month model lead. But because of this, many companies secured their cyber infrastructure before the deluge of widely available cyber-capable models. Of course there are ways it could have gone better or been run better, but this was what we were aiming to do. 4. I advocated forcefully and publicly, in my essay Policy on the AI Exponential in June of this year, for the government to have the power to block model releases. Back then this was still outside or maybe on the edge of the Overton window, though I understand the window has moved since then. We were attacked intensely for this. 5. I don’t want to re-ignite conflict, but the events of February 2026 are a strong example. It is clear that our public stance could have seriously damaged the company’s future. 6. We’ve continued to advocate for export controls on chips and chipmaking equipment to China, because we think it allows democracies to negotiate from a position of strength on AI safety agreements. We’ve done this despite problems it’s created for us with industry partners who hold very different views and on whom we are very dependent. 7. As far as I’m aware we were the first, and still only, frontier company to restrict the sale of AI services to PRC-controlled companies, including those operating through third countries (otherwise a major loophole). We’ve therefore forfeited significant short-term business to prevent fueling AI platforms and applications that would help the CCP’s military and intelligence services. We are also cracking down on distillation; generally, distillation is not a major threat to us commercially, but like chip exports it has concerning national security and safety implications, so we expend significant energy and company resources to address it. 8. As you mentioned, we’ve put out a lot of reports, announcements, long model cards, public advocacy, etc, all trying to inform the public about the risks of AI. These involve a level of detail and effort that would be implausible for a purely profit-maximizing company. 9. Just this week, we announced we were putting 5% (and possibly more over time) of the company into a public benefit fund. All of these decisions came with very substantial costs to us. To be fair, those that involved holding back or restricting access to frontier capabilities also had a broader cost — for example, the public might otherwise have benefitted from faster progress in biology, so I am not saying that our decisions were without tradeoffs — but they were not driven by profit-seeking. For each decision, you can come up with ulterior motives that could have driven us — I think it is a general truth that any decision, on any topic, can always be portrayed as a nefarious scheme. But most of those end up being pretty farfetched or don’t fully make sense if you actually examine them closely. For example, it is deeply implausible that it would be a shrewd business move to block a large swath of research biology in order to do the work ourselves and compete with our customers; we expect the benefit to the ecosystem to greatly exceed the work Anthropic does itself, and for the two to complement each other. It is not in Anthropic’s interest to be perceived as unfriendly to biologists. Our Life Sciences Verification Program, which gives vetted biology professionals access to our models, also refutes this idea. Of course, a different set of people will see this and make a different criticism: that Anthropic shouldn't be making decisions about who gets access to what in the first place. I agree, in the sense that ideally the government would be making these decisions, and that’s why we advocate for regulation. But for now we are forced to make them, and enabling bioterrorism isn’t a better option. I think that even if I conceded every item in your list, I would happily hold up mine next to it; I don’t think it’s a close call. That is of course partly subjective and different things are important to different people. Maybe one way you could object is that several of the items above relate to AI misuse and not loss-of-control; if you think the latter is much more important than the former, that might change your calculus. But this is not Anthropic’s view — we view both as serious issues. You can also disagree with our decisions, as many have, but they are consistent. But I also disagree with at least some of your list. Several items don’t represent a deviation from Anthropic’s strategy or values at all, once we postulate the “race to the top” strategy that requires being a frontier AI company. Your items 1-4 are all simply examples of what it mea

    Same story · 3 reports

    #Industry news#Anthropic#English text is being prepared.

  19. ayi_ai_notesOpinion
    AI score57

    English translation is being prepared.

    Quoted postAYi · @AYi_AInotes

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    #Industry news#English text is being prepared.#English text is being prepared.