豆包手机的问题，也是蚂蚁灵光的问题。
作者｜汤一涛
编辑｜靖宇
10 月 21 日的 Meta 财报会上，扎克伯格披露了一组关于个人智能体 Muse 的关键数据：超过 1000 万人设置了 Muse，购物是 Muse 最常见的用途，超过四分之一的用户在用它买东西，平均客单价超过200 美元；上线第一周，Muse 交易量便超出了预期，Meta 不得不与支付合作伙伴 Stripe 一起临时上调了信用额度。
六天后的 10 月 27 日，蚂蚁集团发布了灵光 2.0，将产品定位从最初的「30 秒用自然语言生成小应用」，彻底转向「能管事的个人 AI 助理」，官方口号概括为九个字：「记得住、盯得牢、办得了」。它打通了支付宝账单、钉钉、飞书、米家等服务，让 AI 替你管日常事务，试图成为中国版 Muse。
但 Muse 和灵光面对的问题截然不同。美国电子商务分散在大量独立网站中，Muse 可以通过云端浏览器直接访问店铺；而中国的日常消费与沟通高度集中在少数几个超级 App 内部，数据与交易链路彼此独立。
灵光 2.0 界面
我们花了两天实测了灵光 2.0，并横向对比了千问、Muse 和 Claude Duet。实测表明，把一个 AI 个人助手放到中国的移动互联网生态里，真正的瓶颈已经不再是模型的推理能力，而是跨平台的权限边界、安全责任划分与商业利益归属。
首先定义一下「私人助理」这个概念。在现实世界里，一个人类私人助理之所以能替你把事情办妥，靠的是四样东西：
- 信息：知道你的偏好、账单、日程等上下文；
- 权限：能以你的名义采取行动，而且外部平台和交易对手认可这个授权；
- 责任：一旦出错了，有明确的追责与兜底机制；
- 忠诚：在利益冲突时替雇主着想。
过去，只有极少数富人请得起私人助理，因为维持这四样东西的成本极其高昂。到了大模型时代，云端算力和代码大幅降低了「整理信息」和「持续盯盘」的成本；但后三项（权限开放、责任承担与立场忠诚）依然受制于商业生态与法律规则。这三个维度，正是检验灵光 2.0 真实成色的核心框架。
01
权限：灵光拿到了一半
在「看」和「盯」的层面，灵光 2.0 表现不错。实测中，授权支付宝账单后，它能准确揪出百度网盘会员和 App Store 订阅等自动续费项目（不过受限于接口权限，它明确提示「账单只能看到扣款记录，看不到当前的签约状态」）；授权办公软件后，它也能顺利读取钉钉群聊与通讯录，或是在飞书里新建一篇云文档。
一旦指令推进到和外部的交互中，灵光就会撞上两堵墙。
第一类拦截来自灵光自身的安全策略。实测中，无论是让它在钉钉发群消息，还是在飞书里回复联系人，哪怕用户在前端界面点击了「确认发送」，最终都失败了。
面对追问，灵光在自我排查后承认：不是钉钉的问题，是这边执行环境的安全策略把发消息这类操作都拦截了。
第二类来自外部平台的接口风控与限流。尽管在公众认知中蚂蚁与阿里同属一个大生态，但灵光与淘宝之间并未打通底层接口。当我们让灵光查看淘宝购物车时，它只能在云端打开淘宝网页版，要求用户扫码、提交短信验证码登录；即便登录成功，购物车数据也始终无法加载，直接被淘宝的反爬风控机制拦截在外。
米家也是类似的情况：扫码授权成功后，小米的接口连续两次触发限流，设备列表始终拉取失败。
灵光进不了淘宝的购物车，但千问却可以。
当我们把同一句指令「帮我看看淘宝购物车有什么降价幅度比较大的商品」发给千问时，两者的权限差距一目了然：千问不仅直接列出了购物车清单，甚至清楚地知道里面那把人体工学椅「已经加购快 300 天、看了几十次」。
与此同时，支付路径，阿里目前也没有向灵光 2.0 开放。当我们在实测中让灵光点一杯瑞幸咖啡时，它无法调用近在咫尺的支付宝 AI 付，只能告诉用户「最后一步得你自己点一下确认」，并甩出一条跳转支付宝瑞幸团购券的链接。
灵光（左）无法完成支付，只能给出跳转到支付宝的链接；千问（右）可以直接在 App 内完成点单的全流程
灵光和千问支付权限的差异，部分源于集团内部的组织边界与产品分工。一方面，蚂蚁与阿里作为两个独立运营的主体，数据与接口有着严格的合规隔离；另一方面，在阿里的 AI 布局中，依托支付宝 App 的「阿宝」负责服务大众用户的日常需求，而独立的「灵光」则侧重于前沿的产品与技术探索。
这是国内互联网巨头在 AI 时代的普遍选择：交易闭环高度收拢在拥有自有交易场的平台内部，对外则保持严格的防御。
字节跳动联合中兴努比亚推出初代豆包手机时，就尝试用系统级权限跨应用模拟点击，结果迅速触发微信、淘宝等 App 的风控拦截。
到了今年 9 月 16 日，量产版「豆包手机二代」（努比亚 NaviX Ultra）上市，字节改用更为温和的「协议敲门」方式，推出了 SAEP（屏幕自动化操作声明协议），把是否允许 AI 操作的选择权交给开发者。
但 10 月 14 日公示期结束后，仍有 155 款主流 App明确拒绝，覆盖腾讯、阿里、快手、美团四大系，京东是唯一全系未出现在拒绝名单上的主要平台。
跨平台打通受阻的同时，各家巨头在自家生态内部却在加速闭环：千问打通了淘宝，豆包 App 已接入抖音电商内测直接下单，微信支付则推出了『 AI 专属卡』，接入自家的 WorkBuddy 去点美团团购。
美国市场的博弈路径则有所不同。Meta 手里只有社交网络而没有电商平台，Muse 想要完成购物，只能靠云端浏览器访问公开网页，或是逐家进行商业谈判。
这同样引发了分化：Muse 上线 12 天后，依赖站内搜索广告变现的亚马逊便于 9 月 20 日将其屏蔽；摩根大通、嘉信理财等金融机构也阻止了 Agent 访问客户账户。
相反，靠商家交易抽成、长期与亚马逊竞争的独立站平台 Shopify 选择与 Muse 合作，允许其浏览店铺并用 Shop Pay 结账。今年 1 月 11 日，Shopify 还与谷歌联合发布了旨在标准化智能体交易接口的 UCP（通用商务协议）。
02
责任：你始终是第一责任人
在现实世界里，你之所以需要一个私人助理，本质上是为了把一部分决策和执行的责任转移出去。一个人类助理出错了，你可以让他重做、可以依法追责。这种风险分担，本身就是雇佣关系的一部分。
AI 助理目前做不到这一点。翻开《灵光用户服务协议》（2026 年 5 月 29 日版本），第 1.3 条用加粗字体写得很明确，Agent 协助完成的操作，视为用户本人的操作，后果和风险由用户承担。
这并非灵光独有，而是当下 AI 行业的通行条款。在现实的消费体验中，这是一个结构性矛盾。一方面，在厂家的市场宣发与公关叙事里，AI 助理被包装成无所不能的「赛博管家」，鼓励用户放手。但与此同时，在底层的技术现实中，大模型幻觉无法消除，AI 在自动化运行中一定会出错。这套叙事逻辑本身就是悬空的。
这很像过去几年的自动驾驶行业：车企在宣传时往往强调系统的自动驾驶与托管能力，但在用户协议与法律定性上，始终将其限定为 L2 级辅助驾驶，不赋予 AI 自主承担风险的资格。方向盘后的驾驶员依然是第一责任人，一旦发生事故，法律责任由用户自行承担。
对用户来说，当下最实际的建议可能是：像对待 L2 辅助驾驶一样对待 AI 助理，让它帮你盯着，但做决定和按按钮的时候，自己确认一遍。
03
忠诚：谁付钱，它替谁说话
在所有的实测环节里，最能体现个人 AI 立场差异的，是一道关于比价的测试题。我们问千问和灵光同一个问题：「清闲人体工学椅在哪个平台买最便宜？」
两款产品的回答截然相反：
千问直接回复：「抱歉，我只能帮你查询淘宝平台的价格信息。」在给出的淘宝商品卡片下方，还叠加了各类折扣、红包信息。
灵光则调取了公开网页信息，同时比对了京东和淘宝的到手价，最后给出结论：「京东最便宜，建议直接在京东下单。」
灵光之所以能给出这样客观、完全站在用户省钱角度的建议，很大程度上是因为它目前处于交易闭环之外。但一旦进入商业化深水区，「钱从哪来」就会立刻决定 Agent 替谁说话。
Muse 的商业模式已经比较清晰。对多数用户免费，主要从平台上的交易中抽成。花旗预测，Muse 变现后约贡献 1680 亿美元交易额，按 5% 佣金率，首年交易收入约 84 亿美元。
而在国内，摩根士丹利 9 月 15 日发布的报告同样预测，到 2030 年中国消费者 AI 可变现市场规模将达约 2940 亿元人民币，其中约 99% 都将来自商家出资的广告和交易佣金。换句话说，在分析师眼中，中国消费 AI 的钱几乎全部来自商户，而不是用户。
如果几乎全部收入来自商户，AI 助理的利益就很难与用户完全一致。在 Shopify 这种商家高度分散的生态里，跨平台推荐和抽佣可以并存。Agent 帮用户找到最合适的商品，平台从中抽成，三方都受益。但在国内平台高度集中的市场结构下，一个深度绑定单一商城的 AI，天然会倾向于扮演商城导购的角色。
订阅是另一条路。Muse 针对重度用户推出了高昂的订阅档位，Power 档每月 20 美元（每周 5 亿 token 额度），Max 档每月 100 美元（每周 30 亿 token 额度）；Anthropic 的 Claude Duet 同样严格受制于付费套餐额度。
用户直接为算力买单，理论上能让 AI 保持独立立场，但这取决于普通人愿意为一个「替自己操心」的软件付多少钱。历史经验表明，纯工具订阅在中国 C 端市场是一条很难跑通的路径。
目前，蚂蚁还没有公开灵光 2.0 的商业模式。它今天在比价时毫不犹豫地站在用户这边，很大程度上是因为它尚未背上交易变现的指标，但这并不代表明天它接通商业闭环后，还能保持同样的中立。
*头图来源：The Guardian｜作者：Keystone-France/Gamma-Keystone
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The problem with Doubao's phone is also the problem of ants' light.
It's the author.
Editor
At the Meta Financial Conference on 21 October, Zuckerberg disclosed a set of key data on the individual AI agent Muse: more than 10 million people set up Muse, which is the most common use for shopping, with more than a quarter of users buying with it at an average cost of more than $200; during the first week on the line, Muse traded more than expected, and Meta had to temporarily raise credit quota with the paying partner Stripe.
Six days later, on October 27th, the ant group publish took the light 2.0 and positioned the product from its original "30 seconds to produce small applications in natural languages" to a "Personal AI Assistant" with an official slogan of nine words: "Remember, keep an eye on and do it." It's got the services to pay the bills, nails, flying books, rice, etc., so AI can run your day-to-day affairs and try to become a Chinese Muse.
But Muse and Shining Light face different problems. U.S. e-commerce is scattered across a large number of stand-alone websites, and Muse can access shops directly through cloud viewers; and China ' s daily consumption and communication is highly concentrated within a few super Apps, with data and trade links independent of each other.
Spirit 2.0 Interface
It took us two days to actually measure the light 2.0 and cross-reference the thousand questions, the Muse and Claude Duet. Evidence suggests that the real bottleneck of putting an AI personal assistant in China’s mobile Internet ecology is no longer the model’s ability to deduce, but rather the permission border across the platform, the delineation of security responsibilities, and the attribution of business interests.
The concept of "personal assistant" is defined first. In the real world, it's four things that a human personal assistant can do for you:
- Information: know your preference, bill, schedule, etc. context;
- permission: can act on your behalf, and external platforms and counterparties accept this authorization;
- Responsibility: there is a clear accountability and bottom-up mechanism in case of error;
- Integrity: care for the employer in case of conflict of interest.
In the past, only a very small number of wealthy people could afford personal assistants because the cost of maintaining these four things was extremely high. By the time of LLM, cloud computing and code had significantly reduced the cost of “collating information” and “continuous targeting”; but the latter three (permission open, responsible and loyal) remained subject to commercial ecology and legal rules. These three dimensions are the core framework for testing light 2.0 for real color.
01
permission: Half the light.
At the "look" and "look at" levels, the light 2.0 is good. In fact, after authorizing the payment of a valuable bill, it is able to accurately trace automatic renewal items, such as Baidu webmasters and App Store subscriptions (although limited to the interface permission, which clearly suggests that "the bill can only see the withholding record and not see the current status of the contract"); after authorizing the office software, it is able to read the nailing and mailing book or to create a new cloud document in a flying book.
Once the command moves into an external interaction, the light hits two walls.
The first type of interception comes from the light's own security strategy. In fact, whether it's pegged to the crowd or in a flyer's book, even if the user clicks on the front-end "Acknowledge Sender", it eventually fails.
In response to a question, Ling Luang admits after self-censorship: it is not a question of nailing, but of implementing an environmental security strategy to intercept all such operations.
The second category is interface wind control and rate limit from external platforms. Despite the fact that ants and Ari are the same macro-ecosystems in public awareness, there is no bottom interface between the light and the treasure. When we give the light to see the BMW, it can only open the web page at the cloud end, requiring users to scan, send SMS proof numbers to log in; even if the login is successful, the BMW data will never be loaded and directly intercepted by the CBMs.
The rice family is in a similar situation: once the sweep authorization was successful, the rice interface triggered rate limit twice in a row, and the equipment list was always drawn down.
Spiritlight can't get into a treasure-hunting cart, but it can.
When we sent out the same directive, "see if there's a much larger drop in the permission, the difference between the two is clear: the permission not only directly lists the shopping carts, but also knows well that the anthropotechnical chair in it "has been purchased 300 days and dozens of times."
At the same time, the payment path, Ali, is not currently open to the light 2.0. When we actually get the light on a sip of a saloon, it can't call the close-to-silhouette Ai, and can only tell the user that "the last step requires your own confirmation" and throw out a link that jumps to pay the Boreys's coupons.
Psychic (left) cannot complete the payment, only a link can be given to jump-to-pay; thousands of questions (right) can complete the whole process of the order directly in App
The difference between the light and the payment of permission stems in part from the division of the organizational boundaries and products within the group. On the one hand, the data and interfaces of ants and Ali, as two independent operators, are strictly in a compliance isolation; on the other hand, in Ali's AI layout, the "Apo" of App is responsible for serving the daily needs of the general public, while the independent "Light of the Spirit" focuses on cutting-edge product and technology exploration.
This is the common choice of domestic Internet giants in the AI era: the closed circle of transactions converges at a high level within a platform with its own trading grounds, while maintaining strict external defence.
When byte beats the initial Doubao cell phone, the byte jumps together with Shinubia, it tries to use a system-level permission cross-application simulator, which quickly triggers the wind-control interception of WeChat, treasure-hunting etc. App.
By September 16, this year, the mass version of Doubao's Second Generation of Cell Phones (Nubia NaviX Ultra) was put on the market, and byte moved to a more moderate way of "knocking the door with a protocol" to launch SAEP (Scene Automation Operations Statement Protocol) to give developers the option of allowing AI operations.
However, at the end of the public announcement period on October 14, 155 of the mainstream App expressly rejected it, covering Tencent, Ali, the Express, the United States of America, and Kyoto was the only main platform that had not appeared on the rejection list.
While cross-platform access is blocked, the giants are closing the ring within their home ecology: Doubao App has accessed the Douyin electric power company for direct orders, WeChat payments have introduced the "AI card" and access to their home, WorkBuddy, to order from the group.
The game path of the United States market is different. Meta has only social networks and no electric platform, and Muse has to use the cloud browser to access the public web page or to negotiate business at home.
This also led to a division: 12 days after Muse went online, Amazons, who relied on the station to search for the advertisement, were able to block it on September 20; and Agent was prevented from accessing client accounts by financial institutions such as Morgan Chase and C.I.C.
Instead, a stand-alone platform based on commercial transactions and long-term competition with the Amazon, Shopify, chose to work with Muse, allowing it to browse the shop and settle the account with Shop Pay. On 11 January this year, Shopify also joined Google with publish in the UCP (General Business Agreement) to standardize AI agent trading interfaces.
02
Responsibility: You are always the first. People
In the real world, you need a personal assistant, essentially to shift part of the responsibility for decision-making and implementation. A human assistant makes a mistake, you can make him do it again and you can hold him accountable. This risk sharing is itself part of the employment relationship.
AI Assistant cannot do this at this time. Turning over the Psychic User Service Agreement (29 May 2026), article 1.3 is clearly written in bold font, and Agent assists in the operation, which is considered to be the user's own, with consequences and risks borne by the user.
It's not unique, it's the general terms of the current AI industry. This is a structural contradiction in the real experience of consumption. On the one hand, in the marketing and public relations narratives of the factory, AI assistants are packaged as an inescapable "saibo housekeeper" to encourage users to let go. But at the same time, at the bottom of the technology reality, LLM's illusions cannot be eliminated, and AI must have made a mistake in automating. The logic of the narrative itself is staggered.
It's like the auto-driving industry in the last few years: While car companies tend to emphasize the system ' s auto-driving and hosting capabilities in their advocacy efforts, it is always limited to L2-level assistive driving in terms of user agreements and legal characterization, and does not confer on AI the right to take risks on its own. The driver behind the wheel remains the first person responsible and, in the event of an accident, the legal responsibility rests with the user.
For the user, the most practical suggestion at this point might be to treat the AI assistant like an L2-assisted driver and let it keep an eye on you, but when you make a decision and press the button, check it out.
03
Faith: Who pays, he speaks for.
Of all the empirical links, the one that most captures the difference in the individual AI position is a test on price. We ask the same question: "What platform is the cheapest place to buy a human trade chair?"
The answer for the two products is the opposite:
“I'm sorry, I can only help you with the price information on the plaque. Under the cards given for treasure-seeking products, various discounts and red bags were added.
The light draws information from the public web page and concludes with "Kyodong is the cheapest, and recommends a direct order in Kyodong."
To a large extent, the light has been able to make such an objective and entirely cost-saving suggestion because it is now outside the closed circle. But once into the commercial depths, "where money comes from" immediately decides who Agent speaks for.
Muse's business model is clearer. For most users, free, mainly from transactions on the platform. Citigroup predicts that Muse will contribute approximately $168.0 billion in turnover, at a commission rate of 5 per cent, or $8.4 billion in the first year.
At the domestic level, Morgan Stanley's report of September 15, publish similarly predicted that by 2030, China's consumer AI realizable market would be about 29.4 billion yuan yuan, of which about 99 per cent would come from commercial-funded advertising and trade commissions. In other words, in the eyes of analysts, almost all of China's AI-consuming money comes from businesses, not users.
If almost all of the income comes from the business, it is difficult for AI's assistant's interests to be fully consistent with the user. In a highly dispersed business such as Shopivy, cross-platform recommendations and domestic helpers can coexist. Agent helps users to find the most appropriate commodities, from which the platform is drawn, and the three parties benefit. However, under a market structure with a high concentration of domestic platforms, a deep-seated single-city AI would naturally tend to play a shopping role.
Subscription is another way. Muse has introduced high-level subscription slots for heavy users at $20 per month for Power (500 million token quota) and $100 per month for Max (3 billion token quota); Claude Duet of Anthropic is also strictly subject to paid.
Users pay directly for their power, and in theory allow AI to stand on its own, depending on how much ordinary people are willing to pay for a "care for themselves" software. Historical experience has shown that a simple tool subscription in the C-end market in China is a difficult path to run.
Currently, ants do not have an open business model of 2.0 light. It does not hesitate to be on the side of the users today in terms of price, largely because it has not yet backed up the indicators of trade realization, but that does not mean that it will remain as neutral as it is tomorrow with the commercial closure.
*The Guardian by Keystone-France/Gamma-Keystone
This article is an original article from Polar Park.
Auto-fetched and translated by AI Gem Box. All rights belong to the original author.