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大模型 computerworld.com ·

ChatGPT、Claude和Grok同时宕机,企业需制定备份计划

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近日,OpenAI的ChatGPT、Anthropic的Claude和SpaceXAI的Grok几乎同时发生长时间宕机,分别持续约2小时、4小时和3.5小时。三家公司均确认问题并修复。事件凸显了企业过度依赖生成式AI工作流而忽视宕机风险的隐患,尤其是AI代理承担更多自动化任务时,企业可能面临严重暴露。

AI推荐理由 报道了ChatGPT、Claude和Grok同时发生长时间宕机事件,对依赖AI的企业有警示意义。来源为Computerworld,可信度中等。信息具体,但缺乏深入分析,时效性一般。

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在人工智能时代,企业正面临一个令人不安的新问题:当代理式助手宕机时会发生什么? 这在周四成为了一个非常真实的场景,OpenAI的ChatGPT、Anthropic的Claude和SpaceXAI的Grok几乎同时且有些神秘地经历了重大、长时间的宕机。 从美国东部时间早上开始,几个ChatGPT模型在约两小时内宕机,Claude模型宕机了四个多小时,Grok模型宕机了近三个半小时。三家公司都承认了“严重”问题并应用了修复。 当用户在论坛上抱怨,IT团队争相让服务恢复时,这一事件揭示了组织在未考虑大规模宕机潜在且不可避免的影响的情况下,多么仓促地采用了生成式AI工作流。 技术分析师和记者Carmi Levy表示,AI代理正越来越多地接管自动化和更广泛的工作流,当AI急刹车时,企业可能会发现自己“暴露在令人不安的境地”。这种情况应该“为IT领导者敲响警钟,他们大多忽视了如果这些日益关键的平台突然宕机将付出的代价。风险不再是假设性的。” 数小时宕机影响核心服务 ChatGPT在OpenAI备受期待的GPT-6 Astra发布当天宕机,这款新前沿模型据称接近人工通用智能(AGI),更接近其创造超越人类的自主系统的目标。 OpenAI宕机发生在周四上午11点左右(美国东部时间),影响了多项服务,包括搜索、文件上传、代理、GPT、语音模式、图像生成、ChatGPT工作、合规API、深度研究、ChatGPT Atlas以及其他连接器和应用。在某些情况下,用户无法登录,对话无法加载,界面在尝试发送消息时返回错误。OpenAI的Codex服务,包括Web、API、命令行界面(CLI)和VS代码扩展,也受到影响。 OpenAI在下午12:55(美国东部时间)修复了问题,并建议Codex远程控制用户重新配对移动设备。 Claude在上午7:37(美国东部时间)左右开始宕机,Anthropic在接下来的几个小时内承认了“详尽列表”的受影响模型,错误率升高:Mythos和Fable 5.1和5、Sonnet 5以及Opus 5、4.8和4.6。 问题在上午11:27(美国东部时间)得到解决。此次事件发生前一天,Claude刚刚经历了约27分钟的宕机,也是由于Sonnet 5请求错误率升高。 与此同时,Grok在上午9:30(美国东部时间)左右开始出现问题。Grok Web、Build、API、Office/Workspace插件、Android和X都受到影响。服务在下午1:08(美国东部时间)恢复到“健康”流量。 “多个不同提供商同时经历宕机是一个奇怪的情况,”Info-Tech Research Group的首席研究总监Brian Jackson指出。他推测,这可能与公共基础设施有关,如内容分发网络(CDN)层、域名系统(DNS)或共享云基础设施。 宕机规划的理由 Levy指出,就在几个月前,典型企业中AI的使用范围仅限于员工使用聊天机器人回答基本问题或起草简单电子邮件。大规模AI平台宕机发生时,对整体组织生产力的影响相对较小。“但情况正在变化,而且变化很快,”他说。 Levy指出,组织现在必须更好地了解代理式AI对日常工作流的影响,以及一旦员工将控制权交给自动化、基于云的工具,这些工具在多大程度上干扰他们完成复杂任务的能力。 在像周四这样的事件中,员工可能会回退到传统手动工作流,例如以老式方式更新电子表格或整理报告。但他们也可能意识到,在依赖AI代理完成如此多工作后,他们变得过于依赖自动化,他们的“认知技能可能不如以前敏锐了,”Levy说。 他说,代理式AI日益普及应促使组织重新审视其灾难恢复和业务连续性计划,并评估潜在服务中断对生产力的影响。虽然像Google Workplace和Microsoft 365这样的基于云的生产力平台提供了有限程度的“离线模式”功能,使用本地存储的数据,文档可以同步到Dropbox或Google Docs for Desktop中的硬盘,但代理式AI平台提供的离线变通方法较少,至少在目前形式下如此。 Levy说,组织应更详细地记录工作流,并制定情景计划,以应对AI平台长时间中断时的近期恢复方案。他们还需要更好的培训,以确保员工长期保持手动技能,并能够在服务中断时投入使用,因为企业越依赖代理完成关键任务,“并为了生产力而将人类排除在循环之外,”员工在不可避免的服务中断期间就越不可能重新介入,他指出。 “完全有可能让本意良好的组织过度依赖AI自动化,”Levy说。“太多组织即将因为没有制定备用计划而学到一些惨痛教训。” Info-Tech的Jackson还建议为LLM采用模块化架构;企业应将模型视为“可以热插拔替换的通用商品”。那可能是另一个云服务提供商(希望没有同时经历宕机)或自托管选项,如开放权重模型。 “在这种场景下,当你的首选提供商可能不可用时,你有一个后备方案可以提供相同的智能层,即使它只是权宜之计,”Jackson说。 人工智能企业应用生成式AI安全
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Enterprises are facing a disturbing new question in the age of AI: What happens when agentic assistants go dark? This became a very real scenario on Thursday, as OpenAI’s ChatGPT, Anthropic’s Claude, and SpaceXAI’s Grok near-simultaneously, and somewhat mysteriously, experienced significant, prolonged outages. Beginning in the morning, Eastern time, several ChatGPT models went down over a roughly two hour period, Claude models over a four-hour span, and Grok models for a near three-and-a-half hour duration. All three companies acknowledged the “elevated” issues and applied fixes. As users grumbled in forums and IT teams scrambled to get them back online, the incident revealed how hastily some organizations have adopted generative AI workflows without considering the potential, and inevitable, impact of widespread outages. AI agents are increasingly taking over automated and wider-scale workflows, and enterprises could find themselves “uncomfortably exposed” when AI hits the brakes, said technology analyst and journalist Carmi Levy. The situation should “serve as a wakeup call to IT leaders who have largely ignored what it’ll cost them if these increasingly critical platforms suddenly go dark. The risk is no longer hypothetical.” Hours-long outages impact core services ChatGPT went down on the same day as OpenAI’s anticipated launch of GPT-6 Astra, the new frontier model that the company says approximates artificial general intelligence (AGI) and gets nearer to its goal of creating autonomous systems that outperform humans. The OpenAI outage occurred around 11 a.m. ET on Thursday and impacted a slew of services, including search, file uploads, agents, GPTs, voice mode, image generation, ChatGPT work, Compliance API, Deep Research, ChatGPT Atlas, and other connectors and apps. In some cases, users were prevented from logging in, conversations failed to load, and the interface returned errors when attempting to send messages. OpenAI’s Codex services, including web, API, command line interface (CLI), and VS code extension, were also impacted. OpenAI fixed the issue by 12:55 p.m. ET, and advised Codex remote control users to re-pair their mobile devices. Claude began to go dark around 7:37 a.m. ET, with Anthropic acknowledging an “exhaustive list” of impacted models with elevated errors over the next few hours: Mythos and Fable 5.1 and 5, Sonnet 5, and Opus 5, 4.8, and 4.6. The issue was resolved by 11:27 a.m. ET. The incident followed a roughly 27-minute outage just the day before, also due to elevated errors on requests in Sonnet 5. Grok, meanwhile, began experiencing issues around 9:30 a.m. ET. Grok Web, Build, API, Office/Workspace plugins, Android, and X were all impacted. The services returned to “healthy” traffic at 1:08 p.m. ET. “It’s a curious scenario for multiple different providers to experience outages at the same time,” noted Brian Jackson, a principal research director at Info-Tech Research Group. It could be related to a common infrastructure such as a content delivery network (CDN) layer, domain name system (DNS), or shared cloud infrastructure, he theorized. A case for outage planning Just a few months ago, the extent of AI use within the typical enterprise was limited to employees using chatbots to get answers to basic questions or to draft simple email messages, Levy noted. Large-scale AI platform outages, when they occurred, had relatively little impact on overall organizational productivity. “But things are changing, and quickly,” he said. Organizations must now have a better understanding of the impact agentic AI has on day-to-day workflows, and the degree to which they disrupt employees’ ability to complete complex tasks once they’ve handed the reins over to automated, cloud-based tools, Levy noted. In incidents like Thursday’s, employees may fall back on traditional manual workflows, such as updating spreadsheets or pulling reports together the old-fashioned way. But they might also realize that, after relying on AI agents to do so much work on their behalf, they’ve become too dependent on automation, and their “cognitive skills may not be as sharp as they once were,” Levy said. The growing prevalence of agentic AI should prompt organizations to revisit their disaster recovery and business continuity plans and assess the productivity impact of potential service outages, he said. While cloud-based productivity platforms like Google Workplace and Microsoft 365 offer limited degrees of “offline mode” functionality using locally-stored data, and documents can be synchronized to hard drives in Dropbox or Google Docs for Desktop, agentic AI platforms offer up fewer offline workarounds, at least in their current form. Organizations should document workflows in greater detail and scenario-plan what near-term recovery might look like in the event of an extended AI platform outage, Levy said. They also need better training to ensure employees maintain their manual skills over time and are equipped to press them into service in the event of a service outage, because the more enterprises lean on agents to complete critical tasks, “and pull humans out of the loop in the interest of productivity,” the less able employees will be to step back in during inevitable service interruptions, he pointed out. “It is entirely possible for otherwise well-meaning organizations to be over-reliant on AI automation,” Levy said. “Too many organizations are about to learn some hard lessons about not having a backup plan in place.” Info-Tech’s Jackson also recommends a modular architecture for LLMs; enterprises should view the model as a “commodity that can be hot-swapped with an alternative.” That might be another cloud service provider (which hopefully isn’t experiencing a concurrent outage) or a self-hosted option like an open-weights model. “In a scenario like this, when your first choice provider might not be available, you have a fallback that can supply that same intelligence layer, even if it’s only a stopgap solution,” said Jackson. Artificial IntelligenceEnterprise ApplicationsGenerative AISecurity

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