广告
加载中

阿里巴巴紧急调整 内部有大事发生

李松月 2026-06-10 09:22
李松月 2026/06/10 09:22

邦小白快读

EN
全文速览

本文核心披露了阿里巴巴针对AI业务的新一轮重大组织架构调整,透露出阿里已经将AI升级为集团级核心战略,核心干货如下:

1.本次核心调整内容:合并通义大模型事业部与未来生活实验室成立Token Foundry事业部,由集团CEO吴泳铭直接管辖;同时任命通义大模型搭建者周靖人为首席科学家,牵头成立AI未来研究院专注前沿AI探索,将多模态相关项目纳入新事业部体系。

2.调整的核心基础:阿里AI已经跨过纯投入阶段,正式进入商业化回报周期,目前通义Qwen-3.7-Max大模型位列全球第五、国产第一,编程能力排名全球第二,阿里云AI相关收入连续11个季度三位数增长,已经形成完整收入闭环。

3.未来布局方向:阿里围绕AI智能体方向提前布局下一代AI技术,持续加大算力和人才投入,目标是把AI能力打造为AGI时代像水电煤一样的通用基础设施。

阿里巴巴本次AI战略升级,给品牌商带来了多方面的机会和参考,核心干货如下:

1.行业趋势层面:当前AI已经从技术概念落地进入商业化发展阶段,AI从聊天工具向具备行动能力的智能代理演化是确定的行业方向,阿里在多模态生成、AI视频生成领域已经进入全球第一梯队,技术成熟度足够支撑品牌商用。

2.落地成本层面:阿里AI已经形成收入闭环,MaaS平台客户数同比增长8倍,未来阿里会把AI能力打造成普惠的基础设施,品牌商不需要投入高额成本自建大模型,就能低成本获取AI能力,可用于用户需求洞察、个性化营销内容生成、产品研发设计等多个场景。

3.机会提示:当前AI商业化落地场景已经明确,品牌商可以依托阿里的AI基础设施推进自身数字化和智能化升级,抓住AI带来的效率提升和业务增长机会。

阿里巴巴本次AI战略升级,给各类卖家带来了明确的增长机会和方向指引,核心干货如下:

1.市场机会层面:阿里已经把AI升级为集团最高级别的核心战略,持续加大资源投入,未来会逐步开放更多AI基础能力,卖家可以依托阿里大模型能力,开发AI营销内容生成、智能客服、智能选品、用户运营等多种产品和服务,低成本落地AI相关业务。

2.需求层面:阿里AI已经进入商业化回报周期,阿里云AI收入占云外部收入比例已经突破30%,未来一年预计突破50%,MaaS平台客户数同比增长8倍,说明市场对AI相关服务的需求已经进入爆发期,相关赛道卖家可以抓住需求窗口快速布局。

3.风险提示:AI行业技术迭代速度极快,卖家需要紧跟头部平台的技术方向,提前布局围绕智能体、多模态等新方向的业务,避免沿用传统节奏错过技术窗口。

阿里巴巴本次AI战略调整,给工厂推进数字化转型、挖掘新商业机会带来了多方面启示,核心干货如下:

1.产品研发层面:阿里的大模型、多模态生成技术已经成熟,达到全球第一梯队水平,工厂可以借助阿里开放的AI能力优化产品设计流程,通过AI快速生成设计方案、匹配用户需求,大幅降低产品研发设计的成本和周期。

2.商业机会层面:阿里的目标是把AI能力打造成AGI时代的普惠基础设施,未来会向实体产业开放更多AI能力,工厂既可以依托AI能力升级生产流程,推进智能化生产,也可以结合自身产业优势开发AI相关周边产品,切入新赛道。

3.转型启示:阿里为适配AI发展,调整组织架构消除部门墙、缩短管理链路,推出3-5人超级小组的灵活创新机制,这种模式对工厂推进数字化转型也有借鉴意义,工厂可以通过灵活的小团队试错加快数字化落地速度。

阿里巴巴本次AI战略调整,透露出AI行业的最新发展趋势,给AI相关服务商指明了方向,核心干货如下:

1.行业发展趋势:当前AI已经从概念验证阶段正式进入商业化落地阶段,市场对AI的需求增长具备高度确定性,未来三到五年行业会持续高速增长;AI的发展方向已经从问答工具逐步向具备行动能力的智能代理演化,Token作为AI基础能力会成为新时代的基础设施。

2.技术方向层面:当前头部大厂已经开始布局下一代AI技术,包括AGI、智能体、多模态、世界模型、视频生成等方向都是未来的核心赛道,服务商可以围绕这些方向提前布局配套服务。

3.市场机会层面:当前各类企业都有AI升级降本提效的需求,但是缺乏足够的技术能力,服务商可以依托阿里开放的大模型基础设施,针对不同行业开发定制化AI落地解决方案,满足企业需求,抓住行业增长红利。

阿里巴巴围绕AI进行的多轮组织重构和战略布局,给各类平台商布局AI业务提供了成熟的参考,核心干货如下:

1.组织架构调整参考:阿里原来的AI力量分散在多个部门,存在部门墙问题,通过两次组织整合,将核心AI业务统一收归集团CEO直接管辖,缩短管理链路、提升资源集中度,这种调整方式适合业务复杂的大型平台商参考,解决AI资源分散的问题。

2.业务布局路径参考:阿里明确了AI基础设施化的核心方向,围绕Token的创造、输送、应用搭建整体体系,同时兼顾短期商业化落地和长期下一代技术储备,既通过MaaS模式实现收入增长,也成立专门研究院探索前沿技术,这种平衡路径值得平台商参考。

3.风险规避参考:AI行业技术迭代快,平台商可以参考阿里的超级小组创新机制,给小规模年轻创新团队更高自由度,没有短期KPI压力,避免大组织反应缓慢错过技术窗口,同时要尽快推动AI商业化落地形成收入闭环,降低长期投入的风险。

阿里巴巴本次AI组织和战略调整,反映了国内头部互联网大厂AI产业发展的最新动向,具备很高的研究价值,核心干货如下:

1.产业发展新动向:当前国内头部大模型已经进入全球竞争序列,在编程、多模态生成等部分关键能力已经达到全球领先水平,AI产业已经整体从纯投入阶段进入商业化回报阶段,阿里作为头部企业已经形成AI收入闭环,验证了AI商业化的可行性,打破了此前AI只有投入没有收入的质疑。

2.组织战略新变化:为了适配AI快速迭代的特征,头部大厂正在持续进行组织重构,从原来的分散布局逐步转为集团级统一整合,由最高决策层直接牵头核心AI业务,同时分设前沿技术研究院和商业化生产事业部,兼顾长期探索和短期变现,还建立了灵活的小团队创新机制适配行业节奏。

3.商业模式新方向:头部大厂正在探索AI基础设施化的全新商业模式,将AI基础能力以MaaS的形式对外开放,做成类似水电煤的公共服务获取规模化收入,这是AI领域值得深入研究的全新商业模式方向。

返回默认

声明:快读内容全程由AI生成,请注意甄别信息。如您发现问题,请发送邮件至 run@ebrun.com 。

我是 品牌商 卖家 工厂 服务商 平台商 研究者 帮我再读一遍。

Quick Summary

This article discloses a major new organizational restructuring of Alibaba's AI business, revealing that the company has elevated AI to a core group-level strategy. Key takeaways are as follows:

1. Core restructuring details: Alibaba merged its Tongyi large model division and Future Life Lab to form the new Token Foundry division, which reports directly to Group CEO Daniel Zhang. Meanwhile, Zhou Jingren, the lead developer of the Tongyi large model, has been appointed chief scientist to lead the newly established AI Future Institute focused on cutting-edge AI research, and all multimodal AI projects have been integrated into the new division structure.

2. Foundation for the adjustment: Alibaba's AI business has moved beyond the pure R&D investment phase and officially entered a commercial monetization cycle. Its Qwen-3.7-Max large model ranks 5th globally and 1st among all Chinese-developed large models, with its coding capability ranking 2nd globally. Alibaba Cloud's AI-related revenue has grown at a triple-digit rate for 11 consecutive quarters, forming a complete closed revenue loop.

3. Future strategic direction: Alibaba is making early布局 in next-generation AI centered around AI agents, continues to ramp up investment in computing power and talent, and aims to build its AI capabilities into a universal infrastructure as essential as water, electricity and coal in the AGI era.

Alibaba's latest AI strategy upgrade brings multiple opportunities and insights for brands. Key takeaways are as follows:

1. Industry trend outlook: AI has now transitioned from a technological concept to commercial development, and its evolution from simple chat tools to action-capable intelligent agents is a clear industry direction. Alibaba already ranks among the global top tier in multimodal generation and AI video generation, with sufficient technical maturity to support brand applications.

2. Lower implementation costs: Alibaba's AI business has already formed a closed revenue loop, and the number of customers on its MaaS (Model as a Service) platform has grown 8x year-over-year. As Alibaba builds AI capabilities into accessible public infrastructure, brands can acquire enterprise-grade AI capabilities at low cost without the high capital expenditure of building and maintaining their own large models. These capabilities can be applied across multiple scenarios, including consumer demand insight, personalized marketing content generation, and product R&D design.

3. Opportunity outlook: Commercial application scenarios for AI are now clearly defined. Brands can leverage Alibaba's AI infrastructure to advance their own digital and intelligent transformation, and capture opportunities for efficiency gains and business growth driven by AI.

Alibaba's AI strategy upgrade provides clear growth opportunities and directional guidance for all types of sellers. Key takeaways are as follows:

1. Market opportunities: Alibaba has elevated AI to its highest-level core corporate strategy and will continue to increase resource investment, gradually opening up more fundamental AI capabilities. Sellers can leverage Alibaba's large model capabilities to build a range of AI-powered products and services including AI marketing content generation, intelligent customer service, smart product selection and user operation, and deploy AI-related business at low cost.

2. Demand dynamics: Alibaba's AI business has entered the commercial monetization cycle; AI revenue now accounts for more than 30% of Alibaba Cloud's non-cloud revenue, and is projected to exceed 50% next year. With 8x year-over-year growth in MaaS platform customers, the market demand for AI-related services has entered an explosive growth phase, creating a window of opportunity for sellers in related tracks to scale quickly.

3. Risk warning: Technological iteration in the AI industry is extremely fast. Sellers need to align with the technical direction of leading platforms, proactively布局 businesses around emerging areas such as AI agents and multimodal AI, and avoid missing the technology window by sticking to traditional development timelines.

Alibaba's AI organizational restructuring provides multiple insights for factories pursuing digital transformation and exploring new business opportunities. Key takeaways are as follows:

1. Product R&D optimization: Alibaba's large model and multimodal generation technologies are already mature, ranking among the global top tier. Factories can leverage Alibaba's open AI capabilities to optimize product design workflows: AI can rapidly generate design proposals and match them with consumer demand, significantly cutting product R&D costs and shortening development cycles.

2. New business opportunities: Alibaba's goal is to build AI capabilities into accessible public infrastructure for the AGI era, and will gradually open up more AI capabilities to the physical industry. Factories can not only leverage AI capabilities to upgrade production processes and enable smart manufacturing, but also develop AI-related peripheral products by combining their own industrial advantages to enter new growth tracks.

3. Transformation insights: To adapt to AI development, Alibaba adjusted its organizational structure to break down silos, shorten management chains, and introduced a flexible innovation mechanism of 3-5 person "super teams". This model offers a useful reference for factories pursuing digital transformation, as flexible small-team trial and error can accelerate the pace of digital implementation.

Alibaba's AI organizational restructuring reveals the latest trends in the AI industry and points out clear strategic directions for AI-related service providers. Key takeaways are as follows:

1. Industry development trends: AI has now officially transitioned from the proof-of-concept phase to commercial implementation, with high certainty in market demand growth. The industry will maintain rapid growth over the next three to five years. AI development is evolving from Q&A chat tools to action-capable intelligent agents, and Token-based fundamental AI capabilities will become the core infrastructure of the new era.

2. Technology direction: Leading tech giants have already begun布局 next-generation AI technologies. Areas including AGI, AI agents, multimodal AI, world models, and video generation are the core growth tracks for the future, and service providers can proactively build supporting services around these directions.

3. Market opportunities: All types of enterprises now have demand for AI upgrades to cut costs and improve efficiency, but most lack sufficient in-house technical capabilities. Service providers can leverage Alibaba's open large model infrastructure to develop customized AI implementation solutions for different industries to meet enterprise demand and capture the industry growth dividend.

Alibaba's repeated organizational restructuring and strategic布局 around AI offers a mature reference for all types of platform operators looking to build their own AI businesses. Key takeaways are as follows:

1. Organizational restructuring reference: Alibaba's AI capabilities were originally scattered across multiple departments, hindered by siloed working. After two rounds of integration, core AI business is now unified under the direct oversight of the group CEO, shortening management chains and concentrating resources. This restructuring model is well-suited for large, complex platforms to solve the problem of scattered AI resources.

2. Business布局 path reference: Alibaba has clearly positioned AI infrastructure as its core strategic direction, building an end-to-end system around the creation, distribution and application of Token capabilities. It balances near-term commercial implementation with long-term next-generation technology储备: it drives revenue growth through a MaaS business model, while establishing a dedicated research institute to explore cutting-edge technology. This balanced approach is a valuable reference for platform operators.

3. Risk mitigation reference: Given the fast pace of technological iteration in AI, platform operators can adopt Alibaba's "super team" innovation mechanism, granting small, young innovation teams greater autonomy and freeing them from short-term KPI pressure, which avoids the slow response common in large organizations that leads to missed technology windows. Platforms should also push for early commercial implementation of AI to form a closed revenue loop and reduce the risk of long-term R&D investment.

Alibaba's recent AI organizational and strategic adjustments reflect the latest developments in AI among China's leading internet giants, carrying high research value. Key insights are as follows:

1. New industry development dynamics: Leading Chinese large models now compete on the global stage, with key capabilities including coding and multimodal generation already reaching globally leading levels. The overall Chinese AI industry has transitioned from the pure investment phase to the commercial monetization phase. As a leading player, Alibaba has already built a closed AI revenue loop, validating the commercial viability of AI and dispelling previous doubts that AI would never generate returns to match its investment costs.

2. New organizational and strategic changes: To adapt to the fast iteration pace of AI, leading Chinese tech giants are continuously restructuring their organizations, shifting from a scattered布局 to group-level unified integration, with top decision-makers directly leading core AI business. They also separate cutting-edge AI research from commercial development divisions to balance long-term exploration and short-term monetization, and build flexible small-team innovation mechanisms to match the industry's pace.

3. New business model direction: Leading giants are exploring a brand-new business model of AI infrastructure, opening up fundamental AI capabilities to external parties via a MaaS model to build a public utility similar to water, electricity and coal that generates scalable revenue. This is an entirely new business model direction in the AI sector that warrants in-depth research.

Disclaimer: The "Quick Summary" content is entirely generated by AI. Please exercise discretion when interpreting the information. For issues or corrections, please email run@ebrun.com .

I am a Brand Seller Factory Service Provider Marketplace Seller Researcher Read it again.

火车跑得快,全靠车头带。

出品 | 电商之家 作者 |李松月

在互联网行业,有一句话很现实:资源往哪里倾斜,公司未来就在哪里。

过去几年,AI已经从一个新技术概念,变成科技公司争夺未来的核心赛道。为了抢占先机,不少企业都在不断调整组织架构,把最重要的人才、资金和技术资源集中到AI业务上。

如今,随着AI竞争进入新阶段,国内互联网巨头们也在加快调整步伐。

日前,阿里巴巴宣布合并通义大模型事业部与未来生活实验室,正式成立Token Foundry事业部,由集团CEO吴泳铭直接负责。

与此同时,一批AI核心业务也被重新整合。其中,周靖人将担任阿里巴巴首席科学家,并牵头成立阿里巴巴AI未来研究院,专注于前沿AI科技探索;郑波团队所负责的Happy Horse、Happy Oyster等项目,则被纳入Token Foundry事业部体系。

这一轮调整,表面上看是组织架构变化,但本质上,反映的是阿里AI战略已经进入一个全新阶段。

因为在互联网大厂体系中,“由CEO亲自挂帅”本身就意味着极高战略等级。

尤其是在阿里这样业务复杂、组织庞大的公司内部,任何一个事业群如果直接由集团CEO管理,往往意味着它已经不再只是某个部门的创新项目,而是上升为集团级核心战略。

事实上,这种变化并不是突然发生的。

如果回看阿里过去一年的动作,会发现其AI战略一直在持续加速,而且方向越来越清晰。

其中最关键的人物之一,就是周靖人。

作为通义大模型从零到一的关键搭建者,周靖人过去几年一直是阿里AI体系的重要核心人物。

去年,他刚刚进入阿里巴巴合伙人名单,而如今被任命为阿里首席科学家,则进一步确认了其在阿里AI体系中的战略地位。

“首席科学家”并不仅仅是一个头衔。

在阿里技术体系中,这是最高学术职位之一,意味着其不仅负责具体研发方向,更承担着集团级技术路线的制定职责。

此次周靖人牵头成立AI未来研究院,也说明阿里开始进一步强化“长期技术投入”。

随着AI竞争进入深水区,行业已经不再只是拼产品速度,而开始拼“下一代能力储备”。

尤其是在AGI、Agent、多模态以及世界模型等方向逐渐升温的背景下,大厂必须提前布局未来技术路线。

而周靖人的角色,更像是阿里AI体系中的“技术总设计师”。

与此同时,阿里在模型与产品侧,也已经开始逐渐跑出成绩。

上个月底,阿里正式发布Qwen-3.7旗舰模型。在权威第三方评测平台Artificial Analysis的全球大模型总榜中,Qwen3.7-Max得分56.6分,位列全球第五、国产第一。

过去很长一段时间里,外界对国内大模型的讨论,更多集中在“能否追上海外”。但进入2026年之后,头部国产模型已经开始在部分关键能力上进入全球竞争序列。

编程能力就是最典型的例子。在Code Arena榜单中,Qwen3.7-Max得分1541,仅次于Claude系列,在大模型厂商中排名全球第二,在开发者社区和行业客户中获得了较高认可。

而编程能力之所以重要,还因为它是当前AI商业化落地最明确的场景之一。

无论是AI编程助手、Agent开发,还是企业级自动化系统,代码生成能力都会直接决定AI的实际生产效率。因此,编程能力强弱,也逐渐成为衡量模型产业价值的重要指标。

因此Qwen系列持续提升的模型能力,也开始反映到商业层面。

在上个月发布的股东信中,阿里首次明确披露:AI业务已经跨过纯投入阶段,正式进入商业化回报周期。

这其实是一个非常重要的信号。

过去,大量公司持续投入数十亿甚至数百亿美元建设模型与算力,但真正形成规模化收入的企业并不多。

而阿里首次明确表示AI业务开始进入回报阶段,也意味着其AI投入已经开始形成实际收入闭环。

也正是在这样的背景下,Token Foundry事业部成立了。

Token Foundry事业部的命名本身就有深意。

“Token”是大语言模型中最基础的计量单位,被称为AGI时代的“水电煤”;“Foundry”意为“铸造厂”。

合起来,就是“铸造Token的工厂”。

从组织逻辑来看,这个事业部的作用非常明确:它并不仅仅负责研发,而是要成为阿里AI体系中的“生产中枢”。

事实上,在Token Foundry成立之前,阿里已经进行过一次更大规模的AI组织整合。

2026年3月16日,阿里正式成立Alibaba Token Hub(ATH)事业群,同样由吴泳铭亲自挂帅。

ATH的成立,被外界视为阿里面向AI Agent时代的重要战略集结。

而“Token Hub”这个名字本身,就已经透露出阿里的核心思路。

在大语言模型中,Token是最基础的计量单位。吴泳铭在内部信中提出,要实现“创造Token、输送Token、应用Token”三大目标。

其背后逻辑,其实是将Token视为AGI时代的“水电煤”。

换句话说,阿里希望未来的AI能力,像今天的电力、云服务一样,成为一种基础设施。

而ATH,本质上就是围绕这一目标,对内部AI资源进行的一次大整合。

过去,阿里的AI力量长期分散于达摩院、阿里云以及各业务线中。虽然技术储备雄厚,但组织层面存在明显“部门墙”。

而ATH成立之后,通义实验室、MaaS业务线、千问事业部、悟空事业部以及AI创新事业部等核心板块,被统一纳入同一组织框架。

这实际上是在缩短管理链路、提高资源集中度。

而Token Foundry,则是在ATH基础上的进一步深化。

值得注意的是,郑波团队的加入,也说明阿里正在进一步强化多模态方向。

此前,Happy Horse在海外权威AI评测平台Artificial Analysis的Video Arena榜单上,以匿名形式同时拿下文生视频与图生视频双赛道第一。

其中,图生视频赛道Elo得分达到1411分,刷新榜单历史纪录。这一成绩,意味着阿里在AI视频生成领域已经进入全球第一梯队。

而Happy Horse所属的,正是ATH创新事业部。

此次纳入Token Foundry,也说明阿里开始把视频生成、多模态等方向,进一步纳入统一AI生产体系。

阿里正在做的,其实是提前为下一阶段AI形态搭建基础。

除了组织升级之外,阿里近期在技术侧的动作同样密集。

4月,阿里正式成立集团技术委员会,由吴泳铭亲自担任组长,成员包括周靖人、吴泽明以及李飞飞。

与此同时,原通义实验室升级为通义大模型事业部。

技术委员会成立前后,阿里连续发布多款新模型。

其中包括Qwen3.5-Omni全模态模型、Wan2.7-Image视觉生成模型以及Qwen3.6-Plus等产品。

这些模型有一个共同方向:“模型即智能体”。

这也是当前全球AI行业的重要趋势之一。

过去,大模型更多是“问答工具”;而现在,越来越多公司开始尝试让模型具备行动能力、任务执行能力与长期记忆能力。

换句话说,AI正在从“聊天机器人”逐渐向“智能代理”演化。

而阿里的布局,也明显围绕这一方向展开。

与此同时,阿里还开始强化内部创新机制。例如,在创新事业部内部推出“超级小组”机制。

这些小组规模通常只有3至5人,大量由95后、00后年轻工程师组成,并被赋予更高自由度进行新技术探索。

这些团队不仅能够直接与决策层沟通,还拥有更灵活的Token消耗权限,并且没有短期量化KPI压力。

其核心目的,其实就是提高创新效率。

AI行业变化速度极快,如果仍然沿用传统互联网产品迭代节奏,很容易错过技术窗口。

而技术委员会的存在,则是在集团层面负责整体技术方向协调。

更重要的是,这一切变化,建立在阿里AI商业化开始兑现的基础上。

2026年Q4,阿里云AI相关产品收入达89.71亿元,连续11个季度三位数增长,占云外部收入比例首次突破30%。

吴泳铭预计,未来一年这一比例将突破50%。

MaaS平台“百炼”客户数同比增长8倍,AI模型与应用服务ARR预计年底突破300亿元。

相比2022年,阿里未来算力中心规模将达到十倍以上增长。在他看来,未来三到五年AI需求增长是确定性的,投资回报同样具有高度确定性。

阿里正以前所未有的力度押注AI基础设施,这已是长期战略。

从ATH到技术委员会,再到Token Foundry,阿里正稳步推进AI时代的组织重构。

正如吴泳铭所言,AI投资的回报是可预期的,而阿里正在用实实在在的投入,为这一确定性押下重注。

注:文/李松月,文章来源:电商之家(公众号ID:iechome),本文为作者独立观点,不代表亿邦动力立场。

文章来源:电商之家

广告
微信
朋友圈

这么好看,分享一下?

朋友圈 分享

APP内打开

+1
+1
微信好友 朋友圈 新浪微博 QQ空间
关闭
收藏成功
发送
/140 0