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AI“改装”支付宝

李程程 2026-06-18 10:04
李程程 2026/06/18 10:04

邦小白快读

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支付宝完成有史以来最颠覆的改版,推出AI原生改造的AI版支付宝“阿宝”,核心是把所有生活服务折叠进统一对话入口,给普通用户带来更便捷的使用体验,核心干货如下:

1. 目前“阿宝”已经启动邀请测试,后续会逐步向全量用户开放,使用方式简单,在支付宝首页向右滑动就能进入简洁对话框,上万种服务都能通过对话办理,同时平台保留了原版本和AI版,用户可根据习惯自由切换,降低适配门槛。

2. 实际使用中,用户不需要再记忆各类服务的入口位置,也不需要在多层菜单、多个小程序间跳转,只需要说一句话就能完成需求,从查公积金、寄快递到找充电桩、订机票都能一站式闭环完成,把操作步骤压缩到最少。

3. 资金安全有保障,规则明确AI仅执行用户明确同意的操作,涉及资金变动必须本人确认,若出现错误由平台负责赔付,不用过度担心安全问题。

支付宝此次AI原生改造,给品牌商带来了新的流量逻辑和增长机会,也明确了消费端的新趋势,核心干货如下:

1. 消费趋势变化,当前用户对AI的期待已经从聊天尝鲜,转向真实生活场景的落地服务,越来越偏好能直接完成交易闭环的AI体验,对服务效率的要求越来越高,不愿意花时间在多个页面跳转操作,品牌需要适配这种高效需求调整运营。

2. 品牌营销与流量分配逻辑彻底改变,旧模式下用户获客链路长,要经过搜索、浏览、对比多个环节,每一步都有用户流失,品牌获客成本持续攀升,新模式下交易链路大幅缩短,用户需求明确后AI直接匹配服务,能有效降低流失。

3. 未来品牌竞争核心转向服务能力,新的流量分配不再基于广告竞价,而是基于用户意图和品牌服务能力匹配,服务质量好、服务完成率高的品牌会获得更多AI推荐,品牌需要聚焦提升服务能力才能拿到更多流量。

支付宝AI改版给卖家带来了明确的增长机遇,同时也提示了需要注意的风险,核心干货整理如下:

1. 行业增长方向明确,当前用户对AI的需求已经从信息咨询转向真实事务办理,AI落地交易场景是明确的增量市场,支付宝已经搭建了完整的AI原生支付基础设施,目前AI智能体支付累计突破3亿笔,覆盖95%的主流通用智能体框架,给卖家接入AI交易提供了成熟的基础条件。

2. 获客端的利好明显,旧模式下卖家获客链路长、流失率高、获客成本不断上涨,AI模式下交易链路简化为“用户提需求-AI匹配-完成交易”,获客门槛降低,匹配精准度更高,能有效提升转化效率。

3. 需要注意的风险与调整方向,新的流量分配机制不再依赖广告竞价,转而以服务质量、服务完成率为核心推荐依据,卖家需要及时调整运营策略,重点提升自身服务能力,否则很难获得流量倾斜,要尽快适配新的生态规则。

支付宝AI原生改版,给工厂推进数字化转型、挖掘新商业机会带来了不少启发,核心干货如下:

1. 产品生产与设计端的需求变化,当前C端用户越来越追求高效、精准的服务体验,对交易闭环的要求越来越高,工厂在做产品设计和服务规划时,需要围绕AI场景下的高效匹配需求优化自身的产品与服务逻辑,适配新的匹配规则。

2. 数字化转型的启示,支付宝此次AI改造不是简单在原有架构外挂AI功能,而是从底层架构做AI原生重构,这给工厂数字化转型提供了参考方向,不能只做表面的AI加法,要从底层业务逻辑适配AI能力,才能真正释放AI的价值。

3. 可挖掘的商业机会,支付宝目前拥有超过400万小程序,覆盖超过8000个生活服务场景,还拥有完整的交易闭环和成熟的信任体系,工厂可以借助支付宝AI生态,更精准地对接C端用户需求,缩短交易链路,降低获客成本,拓展新的销售与服务渠道。

支付宝此次AI改版,明确了全球AI应用的发展新趋势,也给服务商解决行业痛点提供了可参考的路径,核心干货如下:

1. 行业发展新趋势,全球AI应用已经从前期追逐模型参数、算力规模的军备竞赛,开始转向服务生态、交易闭环、信任体系的比拼,“服务智能”是下一阶段AI应用的核心发展方向,AI的可持续商业化路径核心在真实交易场景,而非单纯的信息服务。

2. 当前行业存在的核心痛点,海外头部AI产品模型能力强,但服务生态薄弱,只能做信息生成无法完成交易闭环;国内多数APP的AI改造都是外挂模式,底层架构不变,用户要完成服务仍然需要跳转操作,体验断裂,都无法满足用户真实办事需求。

3. 可参考的解决方案,支付宝给出的路径是从底层做AI原生改造,依托自身积累二十多年的完整服务生态、交易闭环、成熟风控信任体系,把所有服务折叠进对话入口,同时搭建了覆盖全链路的AI原生支付基础设施,这套模式可以给服务商做AI项目改造提供清晰参考。

支付宝AI原生改版,给各类平台商的AI转型提供了可落地的参考经验,也明确了需要规避的风险,核心干货如下:

1. 当前市场对平台AI化的核心需求,用户不再满足于AI作为平台的附加功能,需要AI能够直接完成全流程办事,实现交易闭环;商家也需要更短的转化链路、更低的获客成本,这要求平台做深度改造,而非停留在表面的AI加法。

2. 可参考的最新改造做法,不对原有架构做小修小补,而是从底层做AI原生改造,将所有服务折叠进统一对话入口,让AI成为平台本身而非附加功能;同时保留新旧版本切换功能,给用户留出适应空间,降低用户抵触情绪;搭建完整的AI原生支付基础设施,覆盖授权、执行、收款、安全全链路,建立清晰的信任机制保障用户资金安全。

3. 风险规避提示,要避免陷入只追逐大模型参数的误区,要重视自身服务生态建设、线下场景积累和信任体系搭建,新的流量分配机制要转向以服务质量为核心,才能适配AI时代的需求,实现可持续发展。

支付宝此次AI改版是全球AI超级APP发展的标志性事件,带来了很多产业新动向和新的商业逻辑,核心研究价值整理如下:

1. 产业发展新动向,当前全球AI应用陷入“聊天优先”的路径依赖,中美科技公司都在探索如何把AI从“尝鲜工具”变成“生活基础设施”,支付宝走出了一条差异化的“服务智能”路线,绕过模型军备竞赛,在服务执行维度另辟赛道,将全球AI竞赛的方向从参数算力比拼转向生态、交易、信任的比拼。

2. 全新的商业模式与估值逻辑,传统互联网的价值估值建立在用户时长和广告变现基础上,AI Agent模式下,最优体验反而是用户停留时间越短越好,新的价值衡量核心指标变为Agent代办笔数和AI闭环交易额,商业价值来自交易而非用户注意力,这是一套完全不同的商业化和估值逻辑。

3. 产业研究启示,中国拥有全球最完善的数字生活服务基础设施,只有具备海量服务生态、完整交易闭环、成熟信任体系的平台才能跑通服务智能,这是中国AI公司独特的优势,也为全球AI商业化落地提供了新的路径参考。

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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

Alipay has launched its most disruptive overhaul ever, rolling out an AI-native version of the app codenamed "Abao". The core update moves all lifestyle services into a unified conversational interface to deliver a far more convenient user experience. Key takeaways are as follows:

1. "Abao" is currently in invite-only testing and will gradually roll out to all users. To access it, users simply swipe right from Alipay's homepage to open the clean chat interface, where more than 10,000 services can be accessed via natural conversation. Alipay retains both the original version and the new AI version, allowing users to switch freely based on their habits to lower the adoption barrier.

2. In practice, users no longer need to memorize where different service entry points are located, or navigate through multi-layered menus and jump between multiple mini-programs. Users can get things done with just one sentence of instruction, covering everything from checking provident fund balances, sending parcels, finding EV charging stations to booking flights — all in a one-stop closed loop, with minimal steps required.

3. Fund security is fully guaranteed. Clear rules stipulate that AI only executes operations explicitly approved by users; any transaction involving fund changes requires personal confirmation from the user, and the platform will compensate users for any errors caused by the system, so users do not need to worry about safety risks.

Alipay's AI-native overhaul brings brands new traffic logic and growth opportunities, while also clarifying emerging consumer trends. Key takeaways are as follows:

1. Consumer behavior is shifting: Users' expectations for AI have moved beyond casual chat to practical in-scenario services. An increasing number of users prefer AI experiences that can complete the full transaction closed loop, and they demand higher service efficiency and are unwilling to waste time jumping between multiple pages. Brands need to adjust their operations to meet this demand for efficiency.

2. The logic of brand marketing and traffic distribution has fundamentally changed. Under the old model, the user acquisition funnel is long, requiring multiple steps including search, browsing and comparison, with user drop-off at every stage that pushes customer acquisition costs steadily higher. Under the new model, the transaction journey is drastically shortened: AI directly matches services once a user states their demand, effectively reducing drop-off rates.

3. Future brand competition will center on service capability. Under the new traffic distribution model, placement is no longer based on ad bidding, but on matching user intent with a brand's service capability. Brands with higher service quality and higher completion rates will receive more AI recommendations, so brands must focus on improving service capability to capture more traffic.

Alipay's AI overhaul brings clear growth opportunities for sellers, while also highlighting risks to watch for. Key takeaways are as follows:

1. The industry growth direction is clear: User demand for AI has shifted from information consultation to completing real-world tasks, so bringing AI into transaction scenarios is a clear incremental market. Alipay has already built out complete AI-native payment infrastructure: cumulative AI agent payments have exceeded 300 million transactions, and the infrastructure covers 95% of mainstream general-purpose agent frameworks, providing mature, ready-to-use conditions for sellers to connect AI-enabled transactions.

2. The benefits for user acquisition are significant. Under the old model, sellers faced long acquisition funnels, high drop-off rates, and steadily rising customer acquisition costs. Under the AI model, the transaction journey simplifies to "user states demand → AI matches service → transaction completed", lowering barriers to acquisition, improving matching accuracy, and effectively boosting conversion efficiency.

3. Risks and required adjustments: The new traffic distribution mechanism no longer relies on ad bidding, and instead prioritizes service quality and completion rate for recommendation placement. Sellers need to adjust their operational strategies in a timely manner to focus on improving service capability, or they will struggle to gain favorable traffic allocation, and should adapt to the new ecosystem rules as soon as possible.

Alipay's AI-native overhaul offers valuable insights for factories looking to advance digital transformation and unlock new business opportunities. Key takeaways are as follows:

1. Changes in product design and production demand: Today's end consumers increasingly pursue efficient, accurate service experiences and have higher requirements for closed-loop transactions. When designing products and planning services, factories need to optimize their product and service logic around the demand for efficient matching in AI scenarios, to adapt to the new matching rules.

2. Insights for digital transformation: Alipay's AI upgrade is not simply adding an AI module onto its existing architecture, but an AI-native restructuring from the bottom up. This provides a reference direction for factories' digital transformation: companies should not just do surface-level AI add-ons, but adapt their underlying business logic to AI capabilities to truly unlock AI's value.

3. Unlockable business opportunities: Alipay currently hosts more than 4 million mini-programs covering over 8,000 lifestyle service scenarios, with a complete transaction closed loop and a mature trust system. Factories can leverage Alipay's AI ecosystem to connect more accurately with end consumer demand, shorten transaction chains, lower customer acquisition costs, and expand new sales and service channels.

Alipay's AI overhaul clarifies a new development trend for global AI applications, and provides a reference path for service providers to solve core industry pain points. Key takeaways are as follows:

1. New industry development trend: Global AI development has shifted from the early "arms race" focused on model parameters and computing power scale to competition over service ecosystems, transaction closed loops and trust systems. "Service intelligence" is the core development direction for the next stage of AI applications. The sustainable commercialization path for AI lies in real transaction scenarios, not just pure information services.

2. Core current industry pain points: Leading overseas AI products have strong model capabilities but weak service ecosystems, and can only generate information but not complete full transaction closed loops; most AI upgrades for domestic apps are add-on modules that leave the underlying architecture unchanged, meaning users still have to jump between multiple interfaces to complete services, resulting in a fragmented experience that fails to meet users' real demand for getting things done.

3. A reference solution: Alipay's approach is to build AI-native architecture from the ground up, leveraging its more than 20 years of accumulated complete service ecosystem, transaction closed loop, and mature risk control and trust system to fold all services into a conversational entry point. It has also built out full-stack AI-native payment infrastructure. This model provides a clear reference for service providers carrying out AI transformation projects.

Alipay's AI-native overhaul provides actionable reference experience for all types of platforms pursuing AI transformation, and also clarifies risks to avoid. Key takeaways are as follows:

1. Core market demand for AI-enabled platforms: Users are no longer satisfied with AI as an add-on feature for platforms; they want AI to directly complete full-process tasks and deliver transaction closed loops. Merchants also demand shorter conversion funnels and lower customer acquisition costs. This requires platforms to carry out in-depth transformation, rather than stopping at surface-level AI add-ons.

2. Proven updated transformation practices: Instead of making minor tweaks to the original architecture, platforms should rebuild as AI-native from the bottom up, fold all services into a unified conversational entry point, and make AI the platform itself rather than an add-on feature. Platforms should also retain the ability to switch between old and new versions to give users space to adapt and reduce user resistance. They should also build complete AI-native payment infrastructure covering the full chain of authorization, execution, payment collection and security, and establish a clear trust mechanism to protect user fund safety.

3. Guidance for risk avoidance: Platforms should avoid the pitfall of only chasing larger large-model parameters, and instead prioritize building out their service ecosystem, accumulating offline scenario coverage, and developing a trust system. The new traffic distribution mechanism should shift to center on service quality to meet the demands of the AI era and achieve sustainable development.

Alipay's AI overhaul is a landmark event for the development of global AI-powered super apps, bringing many new industry trends and new business logic. Key research takeaways are as follows:

1. New industry development trend: Global AI application development is currently stuck in a "chat-first" path dependency, and technology companies in both China and the U.S. are exploring how to move AI from a "novelty tool" to core daily infrastructure. Alipay has forged a differentiated "service intelligence" path, bypassing the model arms race to open up a new track focused on service execution. It has shifted the focus of global AI competition from parameter and computing power contests to competition over ecosystems, transactions and trust.

2. A brand new business model and valuation logic: Traditional internet valuation is built on user time spent and ad monetization. Under the AI agent model, the optimal user experience actually means shorter user sessions. The new core value metric becomes the number of tasks completed by agents and AI closed-loop transaction volume; business value comes from transactions, not user attention. This is a completely different framework for commercialization and valuation.

3. Insights for industry research: China has the world's most complete digital lifestyle service infrastructure. Only platforms with massive service ecosystems, complete transaction closed loops, and mature trust systems can successfully build out service intelligence. This is a unique advantage for Chinese AI companies, and provides a new path reference for global AI commercialization.

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.

支付宝选择了改造自己——那个复杂、庞大、功能齐全的支付宝,重构一个更简洁、更智能、更懂你的“阿宝”。

当全球AI应用陷入“聊天优先”的路径依赖时,支付宝正在尝试走出一条截然不同的路。它从底层架构上进行AI原生改造,让Agent成为调度全端能力的操作系统。

6月16日,支付宝正式推出AI版,命名为“阿宝”。用户只需在首页“往右一滑”,就能进入一个极度简洁的对话框——上万种服务,从查公积金、寄快递到找充电桩、订机票,全部在这一句话里办完。目前,新版本已启动邀请测试,后续将逐步向全量用户开放。

支付宝选择将自己过去十几年构建的复杂服务体系,那些层层嵌套的菜单、入口、小程序,全部折叠进一个对话入口。用蚂蚁集团支付宝事业群总裁李俊的话说:“用AI为10亿用户探索生活服务的最优路径。”

全球AI超级APP迎来突围

当下全球头部AI应用正面临一个共同的难题:如何从“尝鲜工具”变成“生活基础设施”?中美科技公司正在尝试不同的解法。

硅谷明星AI产品的解法是,以独立AI对话入口为核心,向外缓慢叠加插件、联网搜索、代码执行等能力。OpenAI的ChatGPT、Anthropic的Claude、谷歌的Gemini的优势显而易见,模型能力和对话体验全球顶尖。但问题同样突出,服务生态过于薄弱。用户的高频场景停留在信息获取和内容生成,属于“能陪聊但不能办事”。AI完成了“规划”,“执行”还得靠人。

比如说,用户可以让ChatGPT生成一份详细的北海道旅行攻略,它甚至能推荐小众温泉酒店和当地美食。但是,当你要它“帮我订一下机票和酒店”时,它就无能为力了。你必须自己打开Booking和Agoda等传统OTA平台,比价、下单、支付。

国内多数日活过亿APP在AI化改造的路上,则选择了另一条路:“存量功能+AI入口”的外挂模式。

各家的思路大同小异,在原有界面中嵌入一个AI助手、搜索框或浮窗,底层架构基本不变。用户可以跟AI提问,要完成实际服务,还是要跳转到其他页面手动操作。这种模式的体验感是断裂的,毕竟始终要在多个APP间跳转操作。AI本质上只是提供了操作攻略和信息归集。

国民级超级APP在思考什么?

腾讯在2025年三季度财报开始释放信号,微信AI助手多次传出消息,官方曾两度出面辟谣。直至最近,腾讯正在测试微信内置AI Agent,才引发行业热议。作为国民级APP,微信哪怕是一个小图标的修改,都会牵动所有人的神经。而另外一个国民级APP——支付宝始终未有消息传出。

直至前几日,外媒爆料AI版支付宝正秘密测试,到昨日“阿宝”抢先微信AI、正式面世,印证了这确实是支付宝有史以来最颠覆的一次改版。

支付宝没有在原先APP加入AI助手,而是对底层架构进行AI原生改造。它试图改变外界诟病产品冗余的特征,新版支付宝的主界面仅有两个功能页——“阿宝”和“资产”。前者是对话式服务入口,后者是统一的个人账本。其他所有服务,都被折叠进AI的对话框里。

这意味着,AI不再是支付宝APP的一个附加功能,而是APP本身。用户不需要知道某个服务藏在哪个菜单、哪个小程序里,只需要说一句话,AI就能理解意图、匹配服务、完成闭环。

从测试体验的感受来看,这是支付宝一次彻头彻尾的大改造,看起来颇为“冒险”,也略显激进。可以看出,支付宝计划绕过“模型军备竞赛”的正面战场,在“服务执行”这个维度上另辟战场。

支付宝凭什么敢动“手脚”?

支付宝AI化的核心逻辑是:AI的价值不在于生成文字、图片和视频,而在于完成交易或解决现实诉求。这背后依赖的不是更强的模型参数,而是更深度的服务生态,更完整的交易闭环,以及更可靠的信任机制。

这是支付宝积累了二十二年的核心能力。

回顾支付宝APP的进化脉络,有一条清晰的线索:从“缴费不出门”到“挂号不排队”再到“乘车不带卡”,支付宝始终在干一件事——用技术缩短人对社会服务的操作距离。

2004年,支付宝解决的是电商交易的信任问题;2013年,余额宝让理财门槛降到1元;2016年,城市服务上线,水电煤缴费不用再跑营业厅;2019年,小程序生态爆发,政务服务、医疗服务、出行服务全面线上化。

每一步,都在把原本需要线下排队、填表、等待的服务,压缩到手机上的几次点击。而此次AI改版,则是将操作距离进一步压缩至零,连选择点击都不用了,直接跟“阿宝”说句话就行。

放宽视野到全球,硅谷的数字经济生态是相对割裂的,也许只有国内,也只有支付宝和微信拥有覆盖支付、政务、医疗、出行、餐饮、零售等方方面面的海量支付及小程序服务生态。根据中国社科院发布的平台价值研究报告显示,支付宝上小程序数量超过400万,覆盖了超8000个生活服务场景。

当然,支付宝能率先跑通“服务智能”,背后离不开中国高度数字化的社会生活基础设施。从政务服务“一网通办”到医院挂号全流程线上化,从公交地铁扫码乘车到水电煤在线缴费,中国在过去十年里构建了全球最完善的数字生活服务体系。

支付宝是这些服务的集大成者,这不是AI技术本身能解决的问题,而是需要长期、艰苦的线下生态建设。

对比来看,通用大模型可以学习海量互联网文本,生成流畅的对话和看似合理的答案。但要让AI真正理解“帮我找充电桩”这个需求背后的全部上下文,比如,用户当前位置、车辆剩余电量、充电桩接口类型、价格偏好、是否需要同时停车吃饭等,这需要真实场景下的服务数据和交易闭环。

支付宝拥有真实的服务供给和交易能力,这种闭环服务执行力,是纯技术公司和创业项目无法逾越的护城河。

今年5月,支付宝召开AI支付生态大会,公布了一组数字:AI智能体支付累计突破3亿笔,覆盖95%的主流通用智能体框架。与此同时,四个产品正式成体系:AI付、AI收、Token Pay、AI钱包,拼出的是一套完整的AI原生支付基础设施——从用户授权到智能体执行,从收款变现到资产管理,从安全防护到信任协议,每一环都有对应产品。

最后就是AI“动手”的核心难题:信任。AI决策的错误率不可能为零,但在涉及资金和个人隐私的服务场景中,用户对错误的容忍度极低。

这里的壁垒不是模型多强,而是一份数字化社会信任契约,这恰恰是支付宝的优势所在,全球领先的风控体系、“你敢付、我敢赔”的承诺兜底。除此之外,这份契约的核心是,你授权AI帮你办事,但资金管理权永远在你手上。AI只执行你明确同意的事情,涉及资金变动必须你本人确认,万一出错了,平台负责赔付。

不过,还有一个现实的问题是,虽然人们对AI的期待超越了聊天,转向真实生活场景中的实际服务能力,但绝大多数普通用户,对AI仍然陌生甚至恐惧。他们不知道怎么用AI,不知道AI能干什么,更不敢把生活事务交给AI。

就内测体验来看,更新后的支付宝仍保留原版本和AI版,用户可根据习惯自由切换。对于那些期待原生AI应用的人来说,新版本提供了逐步适应新交互界面的空间。

服务智能(Service AI)的重估逻辑

当我们理解了支付宝路线的独特性,就需要重新审视一个问题:什么是AI的终极价值?

本质上,ChatBot争夺的是用户的时间与注意力。用户在ChatGPT上花30分钟聊一个话题,这30分钟就是OpenAI争夺到的一个人有限的时间。

还有一个领域值得争取的,是用户的“交易行为”——生活决策和开支。当人们在支付宝AI上说“帮我订一张去北京的机票”,背后是机票价格、支付金额、交易佣金。后者的商业价值是指数级的。

Agent进入交易场景,显然与商业的语境更贴近,这也是为何全球科技巨头都在焦虑:即便是拥有了最强模型,可持续的商业化路径和未来在哪里?

支付宝正在探索“服务智能”(Service AI),也许以后衡量AI超级APP的核心指标,不再是月活或对话次数,而是“Agent代办笔数”和“AI闭环交易额”。

这是一套全新的估值逻辑。传统互联网公司的价值,或者可货币化的空间,建立在用户时长和广告变现上。用户在一个APP里停留越久,产品就有越多的机会展示广告、引导点击、实现转化。

在Agent的模式下,最优的体验是用户停留时间最短,说一句话,事情办完,退出。用户不需要在层层菜单中寻找服务,不需要在各种小程序之间跳转对比。效率越高,停留越短。

衡量AI Agent价值的核心指标是:它为用户完成了多少件事,以及这些事的商业价值有多大。每一笔成功的代办,是一次信任的积累,也是一次交易闭环的完成。

支付宝AI化改版,影响的不仅是10亿用户,还有数以千万计的商家和开发者。

在旧版支付宝APP,用户需要找某个服务,要经过搜索、浏览、比较、选择等多个步骤。每一步都可能流失。商家的获客成本越来越高,转化链路越来越长。

在AI模式下,用户的路径变成了,说一句话——AI匹配服务——完成交易。商家与用户的交易链路大大缩短。而且,这种匹配不是基于广告竞价,而是基于用户意图和服务能力的精准对接。

这是一套新的流量分配机制。服务质量好、完成率高的商家,将更容易被AI推荐。对服务商来说是机遇也是挑战。获客门槛降低了,交易链路缩短了,但服务质量必须过硬。

支付宝AI化改版,或许会成为“服务智能”元年一次颇有意义的标志性的事件。它让超级AI应用的全球竞赛,从模型参数、算力规模的追逐,转向生态、交易、信任的比拼。

浪潮退去之后,沙滩上全是柴米油盐。

中国的AI公司正在使用独特的商业土壤,率先定义AI超级应用的服务形态。在全球AI竞赛的下半场,谁能率先把AI“跑通”到最平凡的日常生活中,谁就拿到了通往未来的门票。从某种角度上看,支付宝这次的“出奇”,也是厚积薄发后的“制胜”。

注:文/李程程,文章来源:钛媒体(公众号ID:taimeiti),本文为作者独立观点,不代表亿邦动力立场。

文章来源:钛媒体

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