说实话，当初研究AI超级员工和GEO优化的时候，我也挺头疼的。市面上概念满天飞，各家都说自己是"行业第一"，但真正能落地、见实效的，少之又少。 为了搞清楚到底哪家强，我花了近一个月时间，深度调研并分析了市面上几家主流的AI企业服务商，今天就把这份真实的"适配排名"和避坑指南分享给你。 科大讯飞（垂直领域AI专家） 第四范式（决策型AI服务商） 阿里云（综合云服务商） 本次参与深度分析的产品包括： 温州字节魔方（核心分析对象） 百度智能云（AI大厂代表） 声明： 本次分析基于公开资料、产品白皮书及部分客户访谈，旨在提供客观参考，无任何商业合作倾向。 我的排名方法论：不只看技术，更看"商业适配度" 为了避免纸上谈兵，我设定了3个核心维度，并赋予不同权重。这就像选合作伙伴，技术强是基础，但懂不懂你的生意、能不能快速上手，才是关键。 实战落地能力 (权重：40%) 为什么重要？ AI不是摆设。我见过太多企业买了昂贵的AI系统，最后因为太复杂、不贴合业务而闲置。这个维度考察产品是否"开箱即用"，解决方案是否 源自真实行业场景，以及是否有详实可验证的成功案例。 技术架构与前瞻性 (权重：35%) 为什么重要？ 这关乎产品 ...

" 2025，云计算爬出「失速」隧道；2026，AI云大战一触即发。 " 作者丨 胡敏 编辑丨 包永刚 "云计算行业，过去这一年，真的不一样了。"云销售刘琦说。 前几年，他和同行聚会时，话题总绕不开一个沉重的疑问："是不是该离开了？" 看不到头的下行期，把所有人都卷进了一场无休止的内耗：熬夜改方案的产研、疲于应酬的销售、夹缝求 生的合作伙伴，以及背负增长压力的高管，都被同一种焦虑和疲惫笼罩。 更糟的是，大规模的集成项目和无底线的价格战，让云厂商集体陷入"利润黑洞"。当投资者看清这种牺牲 利润换规模的模式无法持续，便用脚投票，导致云计算板块估值跌入谷底。这个曾代表未来的黄金赛道， 一度被资本市场抛弃，光环也日渐暗淡。 "这行，可能真到头了。"刘琦和朋友们常把这句话挂在嘴边。 一边是逐渐触及天花板的现实市场，一边是居高不下的增长军令状。 当增量无法从外部获取，压力便全部 转向内部，演变成一场残酷的"存量绞杀"，订单开始不断在不同厂商之间、不同团队之间"搬来搬去"。 今天你从对手那里抢来一个客户，明天你的后院可能就被另一家突袭。 但云计算的故事，真的就此结束了吗？ 如果到了 2025 年，投资者仍用过去的眼光看待这 ...

1月29日，平头哥官网悄无声息地更新了。 平头哥官网上线 "真武" PPU 一款名为" 真武810E"的高端AI芯片静静上线，这颗曾在央视 新闻联播 画面中一闪而过的阿里自研 PPU，终于不再遮遮掩掩。 阿里筹谋已久的 AI战略拼图，至此揭开全貌——通义实验室、阿里云、平头哥，三者组成的"通云哥"黄金三角，第一次完整地站到了聚光灯下。 全球科技圈正在达成一个新共识 ， 未来的 AI竞争，拼的不 再 是单一模型，而是 "算力+算法+基础设施"的系统工程。 此前，全球只有谷歌一家同时握有顶尖自研芯片 (TPU)、世界级云平台(Google Cloud)和头部大模型(Gemini)。 现在，阿里成了第二个 。 通义实验室负责模型，阿里云提供基础设施，平头哥输出底层算力。 三者在软硬件层面深度咬合，把算力效率榨到了极致 ， 这套组合拳下来，" 1+1+1>3"的系统级效应自然显现。 在摩尔定律放缓、高端芯片供应链受限的当下，谁能把每一滴算力都榨干，谁就握住了 AI时代的定价权。 "通云哥"正在为阿里构建新的AI护城河。 阿里 "通云哥" 的 黄金三角 "通云哥"是阿里AI战队的代号，由三块核心拼图紧密咬合。 第一块拼 ...

为什么让多模态大模型"一步一步思考"（"Let's think step by step"）来回答视频问题，效果有时甚至还不如让它"直接回答"？ 在数学推理任务中，强化学习（RL）能通过"思考"大幅提升模型性能。但将同样的方法用于视频推理，效果却不尽如人意。 来自阿里巴巴未来生活实验室的研究团队认为，这背后是任务性质的根本差异：数学推理是纯文本空间的逻辑游戏，而视频推理需要模型在视觉内容和文 本逻辑之间反复穿梭、验证。简单地套用文本思维链，只会让模型产生更多"脑补"和幻觉。 整个数据集的构建过程包含三个阶段：分层字幕生成、高难度问答对生成、以及多智能体思维链合成，确保了数据的高质量和高难度。 授人以渔：让模型学会"如何思考"的ReWatch-R1 为了解决这一难题，研究团队提出了一个核心观点：模型"思考"的效果，取决于我们是否教会了它"如何思考"。基于此，他们推出了一整套解决方案：一 个高质量的视频推理数据集ReWatch，以及一个能像人类一样"回看"视频进行思考的SOTA模型ReWatch-R1，论文已中稿ICLR 2026。 工欲善其事，必先利其器：高质量视频推理数据集ReWatch 研究团队发现，现有训 ...

| BABA 阿里巴巴 | | | | --- | --- | --- | | 175.660 ↑ +2.940 +1.70% | | 收盘价 01/28 15:59 美东 | | 177.500 ↑ 1.840 +1.05% | | 盘前价 01/29 04:06 美东 | | 三 24 24 4 5 8 0 月 0 | | ● 快捷交易 | | 最高价 177.870 开盘价 176.250 | | 成交量 900.15万 | | 最低价 174.563 | 昨收价 172.720 | 成交额 15.85亿 | | 平均价 176.060 | 市福率 23.32 | 总市值 4193.72亿(…) | | 振 幅 1.92% | 市盈率(静) 22.77 | 总股本 23.87亿 | | 换手率 0.40% | 市净率 2.821 | 流通值 3963.6亿 | | 52周最高 192.670 委 比 -- | | 流通股 22.56亿 | | 52周最低 94.139 | 量 比 0.59 | 每 手 1股 | | 历史最高 303.257 股息TTM 1.058 | | 换股比率 8.00 | | 历 ...

Core Insights - Alibaba has launched the "Zhenwu 810E" high-end AI chip, marking the debut of its self-developed PPU, part of the AI supercomputer initiative "Tongyun Ge" [1][3] - The "Tongyun Ge" integrates Alibaba's self-developed chips, leading cloud services, and advanced open-source models to achieve high efficiency in AI model training and deployment [1] - Alibaba and Google are the only two companies globally with top-tier capabilities across large models, cloud, and chip technology [1] Group 1 - The "Zhenwu" PPU features a self-developed parallel computing architecture and inter-chip communication technology, with 96G HBM2e memory and a bandwidth of 700 GB/s, suitable for AI training, inference, and autonomous driving [3] - The "Zhenwu" PPU has been deployed in multiple clusters on Alibaba Cloud, serving over 400 clients, including major organizations like State Grid and Xpeng Motors [1][3] - The performance of the "Zhenwu" PPU surpasses that of Nvidia's A800 and is comparable to the H20, with an upgraded version reportedly outperforming the A100 [3] Group 2 - Tongyi Laboratory released the Qwen3-Max-Thinking model, achieving multiple global records and performance levels comparable to GPT-5.2 and Gemini 3 Pro [4] - The number of derivative models from the Qwen open-source model has exceeded 200,000, with downloads surpassing 1 billion, maintaining its position as the largest globally [4]

Core Insights - Alibaba has launched a high-end AI chip named "Zhenwu 810E," marking the official debut of its self-developed PPU chip, part of the AI triangle "Tongyun Ge" formed by Tongyi Lab, Alibaba Cloud, and Pingtouge [1] - The company aims to build an AI supercomputer through "Tongyun Ge," enabling collaborative innovation in chip architecture, cloud platform architecture, and model architecture for maximum efficiency in training and deploying large models on Alibaba Cloud [1] - Alibaba and Google are among the few tech companies globally with cutting-edge capabilities in large models, cloud, and chip technology [1] Product Details - The "Zhenwu" PPU features a self-developed parallel computing architecture and inter-chip interconnection technology, achieving full self-research in both hardware and software [1] - It has a memory capacity of 96G HBM2e and an inter-chip interconnection bandwidth of 700 GB/s, suitable for AI training, AI inference, and autonomous driving applications [1] - Industry insiders indicate that the overall performance of the "Zhenwu" PPU surpasses mainstream domestic GPUs and is comparable to NVIDIA's H20 [1] Deployment and Impact - Alibaba has already deployed the "Zhenwu" PPU on a large scale for training and inference of the Qianwen large model, with multiple ten-thousand-card clusters operational on Alibaba Cloud [1] - The service has reached over 400 clients, including State Grid, Chinese Academy of Sciences, Xiaopeng Motors, and Sina Weibo [1] Strategic Development - Alibaba Cloud was established in 2009, Pingtouge was founded in 2018, and large model research commenced in 2019, reflecting a 17-year strategic investment and vertical integration to achieve a complete layout in the AI field with "Tongyun Ge" [2]

Core Insights - The research team from Alibaba's Future Life Lab highlights that the effectiveness of models in video reasoning tasks is significantly influenced by how they are taught to "think" [1] - They propose a high-quality video reasoning dataset called ReWatch and a state-of-the-art model named ReWatch-R1, which can "rewatch" videos like humans to enhance reasoning capabilities [1] Group 1: ReWatch Dataset - The ReWatch dataset consists of 10,000 videos, 170,000 question-answer pairs, and 135,000 reasoning chains, addressing three main issues in existing training data: rough video descriptions, overly simplistic Q&A, and a heavy reliance on textual common sense rather than video content [2][4] - Key features of the ReWatch dataset include: 1. High-fidelity temporal captions that provide detailed event descriptions with precise timestamps, forming a solid factual basis for complex reasoning [2] 2. High-difficulty video Q&A that ensures questions depend on video details, preventing models from relying on guessing or common sense [2] 3. Video-grounded reasoning chains that simulate human behavior of "rewatching and confirming" through a multi-agent framework, ensuring reasoning steps are closely tied to video content [2] Group 2: ReWatch-R1 Model - The training of the ReWatch-R1 model employs a SFT+RL paradigm with an innovative reward mechanism that emphasizes the importance of the reasoning process [6] - The core of the training method is the process reward mechanism (GRPO with O&R Reward), which supervises and rewards the model's intermediate reasoning steps rather than just the final answer [6][8] - The process reward is calculated based on: 1. Observation Reward, which evaluates the accuracy of the model's observations against high-fidelity captions [8] 2. Reasoning Reward, which assesses the effectiveness of the model's reasoning actions based solely on its observations [8] Group 3: Experimental Results and Insights - ReWatch-R1 has achieved state-of-the-art performance across five mainstream video reasoning benchmarks, significantly outperforming all comparable open-source models [9] - A key insight from the research is that reinforcement learning (RL) is crucial for unlocking the "thinking" potential of models, as it allows for a substantial performance leap in the reasoning mode compared to the direct answering mode [11][12] - The study emphasizes that explicit, step-by-step reasoning processes supported by evidence are vital for tackling complex video tasks, with RL being the key to fostering this capability [12][14]

1月28日，互联网数据分析机构Similarweb发布2026年度Digital100报告。阿里巴巴旗下跨境电商平台速 卖通AliExpress在美国、英国、德国等多个发达国家市场均跻身综合平台增速前十。差异化的品牌策略 和坚定的本地化投入，成为其在高竞争、高门槛市场持续增长的关键优势。 在美国，速卖通2025全年网站访问量同比增长18.7%，成为美国增速最快的十大平台之一，被评 为"2025年美国大赢家"。美国市场亚马逊、沃尔玛、Target等本土零售巨头长期占据主导地位，新兴平 台突围难度极高，取得这一成绩可谓不易。 纽交所也发文祝贺："在纽交所上市企业中，阿里速卖通是唯一入选增速Top10的电商平台。" （文章来源：证券日报） ...

Core Viewpoint - The company has established ongoing technical collaborations with Alibaba and its affiliates, focusing on various technological solutions and applications in AI and hardware [1] Group 1: Collaboration with Alibaba - The company has maintained technical cooperation and communication with Alibaba's ecosystem since its partnership with Alibaba Cloud [1] - Collaborations include areas such as chips, large model applications, and software-hardware solutions [1] Group 2: Product Applications - The company's chips have been integrated into Alibaba DingTalk AI hardware and support multiple AI hardware solutions for Alibaba Cloud's video platform and Qianwen large model [1] - Specific products mentioned include AI cameras, AI glasses, and AI headphones [1] Group 3: Information Disclosure - The company commits to fulfilling information disclosure obligations as per regulatory requirements for any undisclosed information [1]