搜索“Agent”

了多少视频，Token烧完了你可能连“怎么烧的”都说不清楚。 这场“集体上架”最深远的意义，或许不在于多了一个销售渠道，而在于大模型厂商的身份认知正在发生根本性转变。过去，大模型公司是“技术厂牌”，靠参数、 benchmark、论文说话。如今，它们正在努力变成“消费品牌”，靠用户体验、口碑、复购率吃饭。 中信建投证券在2026年7月发布研报测算，中国通用办公Agent付费市场未来3至5年进入成熟阶段后，市场中枢约每年390亿元。谁能把AI卖给更多普通“打工人”，谁就能分到这块蛋糕。 当AI服务像话费一样被摆上电商货架，当Token成为可购买、可定价、可流通的数字商品，大模型行业的竞争维度正在被重新定义，从“谁的模型更强”到“谁能让更多人愿意掏钱”。 “Token零售化”的竞赛才刚刚开场。至于最终谁能笑到最后，答案可能不在技术论文里，而在消费者的购物车里。 微信扫一扫分享 此内容为集微网原创，著作权归集微网所有，爱集微，爱原创 相关推荐 AI算力+新能源拉动功率器件需求，捷捷微电Q3业绩增速大幅抬升; AI终端、机器人、汽车电子放量，全志科技Q3经营性盈利维持高景气; “广州第一芯”开盘

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hese domains and whether it can operationalize that knowledge in realistic settings. The second was a broad misuse set covering direct requests for harmful content and behavior in agentic, tool-use settings. The third was our own multimodal content evaluation, which tests models on harmful prompts paired with benign look-alikes across 17 languages and text, image, and audio inputs. In each of these tracks, Inkling and Inkling-Small’s results were in line with other open-weight models. External r

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Related Topics Deep learning; Neural networks Language models; Speech recognition Data science; Data centers; Data analytics Data management; Synthetic data As agents take on more work for users, companies are deciding whether to let outside AI systems act on their platforms. October 12, 2026 AI agents are beginning to do more than help people complete individual tasks. They are increasingly being built to act on their behalf, working across applications, pursuing broader goals and interacting w

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n. These are professional users of frontier-grade LLMs in math and code who see firsthand that "large, complex projects that used to take them weeks/months can now be completed by agents with a prompt." Karpathy lists "building apps, copying apps, translating apps, decompiling apps from binaries" as examples. He adds that this shift happened fast. In October 2024, Karpathy called AI agents mostly marketing that barely worked . Just a few months later, he stopped writing code by hand entirely , g

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f-policing regime, any frontier AI company shares only what it wants and hides the rest. OpenAI wants the public to take its word, but consider its record. In July, a rogue OpenAI agent swarm hacked Hugging Face. OpenAI disclosed the incident only after Hugging Face announced the breach, saying its agents had “slipped out of control.” After that, the world learned of more rogue agent attacks, including incidents involving a German-language wiki, an Australian Medicare site, and even the websites

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斯的点名，称其旗下的一个智能体今年6月未经授权进入该国的全民医保相关系统，事件发生后，OpenAI直到9月才通知，通报时机与方式“不可接受”。 让所有人“刹车”的，是开得最快的那几家 但接下来是问题的另一半：谁在呼吁刹车，又为什么是现在刹。 稍稍往前翻，9月12日，Anthropic CEO达里奥发表长文《我们必须为前沿AI定节奏》，预测6到12个月内更强的Agent集群可能接管整个互联网，提出“三步刹车法”——引入第三方常驻监督、全美前沿公司划能力红线设强制检查站，甚至可讨论限制训练算力和AI研发AI的速度，并要把框架推向全球。 山姆·奥特曼第一时间附议，并掷出一个更重磅的决定：OpenAI今年不推进IPO，理由是“仍有大量安全工作需要完成”。一家估值数千亿美元、投资者排队等退出回款的公司，以“AI太危险，我要先做安全”为由推迟上市，这在商业史上都算罕见。 单看这层，是负责。但把镜头拉远，味道就变了。 因为这套“减速”说是由领跑者提出的。当前，Anthropic和OpenAI站在AI大模型行业的最前面，规则越复杂、门槛越高、越强调检查站，后来者追赶的成本就越大。对自己是踩刹车，对竞争

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代。 不乏真的有人抱着这样的心态面对未来。但他们不焦虑，也不惊慌，只是做理性的风险防范，譬如减少无意义的消费和投资，保持现金流。 但从积极的一面看，无法逆转的趋势之下，程序员们有职业人生的Plan B吗？ 邱岳文所在的猎头公司，一个颇有增量的业务方向，是将一线大厂的离职程序员，输送到医药、零售、快消、制造业等传统行业。这些行业面临数智化转型，内部有大量AI Agent开发需求。 “现在很多拥有AI技能、但不想在互联网继续内卷的程序员，会选择去传统行业做AI的领头人。”他们的薪资总包会缩水，但由于工作强度远没有互联网大，其实时薪并没有太大改变。 传统企业还有一点优势，即对年龄卡得没有互联网严。“40岁的都能进，如果级别高一点，45岁以内也能接受。”一些药企的研发主力大多是博士，平均年龄本来就比互联网高。另外，这些企业对大厂的技术底层相对认可，非常青睐有互联网背景的人转过去。 这算是一条去往传统行业的“迫降路径”，起码能把职业生涯再延长5到10年。 除此以外，AI编程还为许多人开辟了“一人公司（OPC）”的新路线。 本科计算机科学专业的周子游目前人在海外，一边在大厂担任资深开发，一边创业做

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At this year’s OpenAI DevDay, CEO Sam Altman unveiled the company’s new AI agent Dots — and told the crowd that the company wants to “set a new standard for privacy in frontier AI.” OpenAI would spend the day taking veiled shots at Meta’s Muse, its primary competitor, for failing to keep users’ data safe. Yet Muse itself, a couple of months earlier, had launched as a supposedly safer alternative to predecessor OpenClaw — with CEO Mark Zuckerberg promising it was “built from the ground up for pri

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AI developments, explaining what they mean for people and organizations putting this technology to work. Elon Musk posted an “ important note ” on Wednesday about Grok Bot, the AI agent SpaceX launched in beta in August: “Important note regarding Grok @Bot: Going forward, @SpaceX will use the best back end model for any given task, including Claude Opus 5.5, MidJourney, Suno and other leading APIs. Whatever is most likely to give you the best outcome.” Grok Bot is a joint product of SpaceXAI, th

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Some interesting machine & agent Vercel traffic stats. • Across the entire Vercel network, 58.18% traffic is bot-originated (last 30d). This was 32% in Jan 2024. • 60%+ of deployments on Vercel are now agentic, up from 3% in Jan 2026. • Up to 83% of pageviews are now coming from agents to Vercel's own documentation sites. The % reliably increases the more we optimize content for them. I expect 'direct' human internet traffic to basically become a rounding error in coming years. The web will thri

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ugust 2026) shows what a16z calls a textbook Jevons paradox in the AI market. Token prices keep dropping, but H100 GPU rental prices hold steady or climb. Cheaper tokens unlock AI agents, automation, and new applications, so volume grows faster than per-unit costs fall. How much demand comes from humans versus the systems themselves isn't clear, since agentic AI burns through tokens at a staggering rate . Compute demand could be artificially inflated, and even modest human usage growth could tri

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创业公司推出专供的GPU集群，Radical Ventures还会帮助被投公司协调供应商合同。 这对于正在训练或部署模型的小型团队，稍微解决了燃眉之急。 但 堵不如疏 ，算力会出现这种短缺情况，其实个中原因相当复杂。 OpenAI、Meta乃至谷歌都在积极建设新的数据中心，承诺 未来 会提供海量GPU资源。 但模型快速增长的需求等不及，这中间的时间差，或者说画的大饼，就是公司还得继续四处找算力的原因之一。 从今年开始， Agent 开始代替一问一答成为用户使用的核心范式，连续长时的工作任务迫使服务器承担起更多的并行计算。 需求增加之后，连同芯片的租赁行情都发生了翻天覆地的变化。 SemiAnalysis今年4月公布的数据显示， H100 一年期租赁合约价格，从2025年10月低点的每GPU每小时1.70美元，上涨至2026年3月的2.35美元，涨幅接近 40% 。 GPU堪比黄金的说法，并非空穴来风。 真正能即时交付、稳定运行的算力不足，新的算力还遥遥无期，共同造就了算力市场的困境。 于是 算力霸权 变得具体。 初创公司挑战行业巨头的叙事将日益凋零，即使有人有路线，计算资源不够也完蛋。

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hese domains and whether it can operationalize that knowledge in realistic settings. The second was a broad misuse set covering direct requests for harmful content and behavior in agentic, tool-use settings. The third was our own multimodal content evaluation, which tests models on harmful prompts paired with benign look-alikes across 17 languages and text, image, and audio inputs. In each of these tracks, Inkling and Inkling-Small’s results were in line with other open-weight models. External r

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OpenAI built a room with no doors – or so it thought. In early July 2026, a cluster of the company’s frontier AI agents was placed inside a cybersecurity testing environment called ExploitGym, tasked with finding and exploiting software vulnerabilities. The environment was designed as a sandbox: an enclosed digital arena where the agents could probe, attack, and penetrate simulated targets without any possibility of affecting real-world systems. The agents were supposed to stay inside. They did

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o do so and reports its certainty loosely. A decision model hands the application a probability it can branch on, in a fraction of the time. Three patterns that made Jev useful in agents A decision model works beside the LLM. The LLM plans, writes, and calls tools, while the decision model handles the many small judgments an agent makes between those steps. Imagine a support agent processing a refund request. The LLM drafts a plain-language reply to the customer. A single call to the decision mo

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e AI Law promised in the 2023 and 2024 legislative plans was shelved in 2025, four months after the DeepSeek moment. The State Council’s AI+ Action Plan targets 70% penetration of agents and intelligent terminals by 2027 and 90% by 2030, aiming for a rapid deployment of AI across all sectors and industries. We think the Chinese government takes an open position on frontier models but tightens the applications and regulates outputs. Since 2025, China has issued a number of mandates, including man

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ARTICLE Discover why AI isn't stupid but blind, and how building effective memory systems for agents can transform organizational efficiency. Learn more now. What we learned building memory for agents that work across a whole company An agent with read access to the wiki, the tracker, the CRM, and three years of Slack still doesn't know who owns a decision. It doesn't know which of four documents is current, what the team tried last quarter and dropped, or what it's allowed to repeat to whom. So

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Everyone wants an AI wife...not like that Hi folks, Personal agent season is still in full-swing. Hark (from Brett Adcock - Figure robots) launched which suggests tasks as one-tap action buttons. Tab came out of stealth at a $300M valuation. Every put an agent in Slack. Meta and Sierra want a standard for how these agents deal with businesses. My favourite take came from Sonia Baschez (who worked with me on Makerpad years ago!): Proactive has a downside, though. Shane Mac’s agent posted his pers

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My Decoder guest today is Hayden Field, The Verge ’s senior AI reporter, and we’re discussing the new wave of consumer-friendly AI agents. If you’ve been paying attention to this space, you know AI enthusiasts have been using agents for a minute now — homebrew OpenClaw setups led to a surge in Mac Mini sales earlier this year. But the launch of Meta’s Muse, OpenAI’s Dots, and xAI’s Grok bot has brought easy to use agents to millions. Muse and Dots have had the highest-profile product launches, a

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sformers achieving leading results beyond language, and an open-source model surpassing OpenAI’s o1, which DeepSeek-R1 subsequently did. Let’s dive into the key stories This year, agents are doing valuable work in software and science, physical AI is learning how to act in the world, and access and control are becoming more consequential. Below, I share the developments that stood out to me and what I think they mean for where AI goes next. The frontier is now a three-lab race between Anthropic,

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