春节AI大战：美团入局了！

标杆研学中心02-13

这两年，大模型的竞争风向变了！以前，行业里比拼的全是工程师眼中的“硬指标”，千亿还是万亿参数、预训练数据量多少、榜单排名几何，成了衡量AI实力的唯一标准。但今年，阿里用AI请喝奶茶、腾讯靠AI狂发红包，这两个看似简单的操作，背后藏着中国AI行业的关键转折：AI的主战场，正从实验室的技术参数表，转向老百姓柴米油盐的日常日子里。因为绝大多数用户从来不在乎模型的技术参数有多亮眼，也不关心大模型的训练逻辑...

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href=http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN20260213105352a6f5bd56&s=b><strong>标杆研学中心</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>这两年，大模型的竞争风向变了！以前，行业里比拼的全是工程师眼中的“硬指标”，千亿还是万亿参数、预训练数据量多少、榜单排名几何，成了衡量AI实力的唯一标准。但今年，阿里用AI请喝奶茶、腾讯靠AI狂发红包，这两个看似简单的操作，背后藏着中国AI行业的关键转折：AI的主战场，正从实验室的技术参数表，转向老百姓柴米油盐的日常日子里。因为绝大多数用户从来不在乎模型的技术参数有多亮眼，也不关心大模型的训练逻辑...</p>\n\n<a 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game!","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"83690":0.9,"03690":1.96,"HMTD.SI":0.6,"METmain":0.6,"MPNGY":1.96},"content_text":"这两年，大模型的竞争风向变了！以前，行业里比拼的全是工程师眼中的“硬指标”，千亿还是万亿参数、预训练数据量多少、榜单排名几何，成了衡量AI实力的唯一标准。但今年，阿里用AI请喝奶茶、腾讯靠AI狂发红包，这两个看似简单的操作，背后藏着中国AI行业的关键转折：AI的主战场，正从实验室的技术参数表，转向老百姓柴米油盐的日常日子里。因为绝大多数用户从来不在乎模型的技术参数有多亮眼，也不关心大模型的训练逻辑有多复杂，大家只在意一个最朴素的问题：这AI，能在日常生活中帮我干啥？事实上，最近春节这场AI大战，引发了越来越多的思考：作为国民长假的具体生活场景里，用户高频使用AI的真实需求到底是什么？比如，临近春节，选年夜饭餐厅、选旅游酒店和饭店、领优惠券、比价等，这些看似琐碎的生活需求，背后藏着无数费时费力的小痛点：翻遍平台筛不到除夕营业的包间，看来看去找不出合适的性价比餐厅，甚至兴冲冲到店却发现商家歇业……可以看到，本地生活场景中，用户对AI最核心的诉求，归纳起来是两点——省时间、省钱。这些春节吃喝玩乐的高频烦恼，恰恰成了AI智能体大展拳脚的最佳考场。你方唱罢，我登场，在新的一年，又有各种AI落地的里程碑事件。这些事件可能是让你焦虑，乃至过度焦虑，但有一个智能体却是能让你开心的。这几天，美团“问小团”AI管家宣布升级，我们只需要升级美团App至最新版本，便可以在搜索框选择使用“问小团”，它可以帮助我们搜集美团上准确及时的商户与服务信息，并进行二次校验，结合真实用户评价，提供精准匹配需求且可交易的选择。同时，用户进入“问小团”春节专区，还可一键领取多种春节消费券，获取智能推荐的最优惠下单方案，覆盖外卖、闪购、餐饮堂食等场景。那么，为什么美团在这个时间入局AI春节大战？它又有什么竞争力呢？这个春节，你需要一个本地生活AI管家春节的本地生活消费，从来都是痛点与需求并存：想给长辈送年货，希望半小时能闪送上门；想订年夜饭，既要符合人数要求，又想兼顾口味和性价比；想安排一场聚会，希望能一站式搞定吃饭、K歌、小酌的行程……这些本地生活垂直领域里，复杂且具体的场景化需求，是普通搜索工具和通用AI难以解决的，而“问小团”AI管家的出现，恰好填补了这一空白。B站有位博主作为典型的“纠结星人”，分享了自己的实测体验：以前出门前光是选餐厅就能纠结半小时，脑子里满是问题——这家店好不好吃？会不会踩坑？性价比高不高？吃完周围有没有能逛的地方？即便打开平台搜索，看推荐、翻评价、比价格的流程走下来，只会更纠结。但这次他使用“问小团”AI管家后，这一切烦恼都迎刃而解，甚至输入“吃喝玩乐的正确打开方式”这种模糊到近乎“废话”的需求，它也能给出精准解决方案。实测发现，在美团搜索框使用“问小团”，输入“出门给长辈拜年，有啥半小时能送到的年货”、“哪有全家人和宠物都能去的年夜饭餐厅”等多条件复杂问题，立马就能找到附近靠谱的闪购商家、餐厅和各类外卖商品推荐，还能看清详细的推荐理由，点个链接就能一键点外卖、下单团购套餐，从搜攻略、种草到做决定，整个过程都顺多了。春节前美团升级“问小团”AI管家（图源：美团App）这是不是一下子可以治愈你的春节美食焦虑？体验下来，发现“问小团”AI管家有三个优势：第一个核心优势，千万准确及时的商户信息、海量用户评价。我们都知道，自2022年ChatGPT诞生以来，“数据幻觉”始终是AI大模型的共性难题。因为无法及时获得最新数据，有些AI工具会输出一些编造的信息，不仅严重影响用户体验，甚至引发纠纷。所以，对于智能体，AI信息底座的真实性非常重要。而通过测试来看，“问小团”所有推荐，都是基于美团平台上准确、及时的商户信息与服务内容，经过二次校验后给出的，结合了海量的真实用户评价，推荐的每一家门店、每一个商品，都是“真实营业、符合需求、可交易”的。举个例子，很多人都爱吃炸鸡，但是麦当劳、肯德基等连锁品牌的炸鸡有些吃腻了，想要吃点更好吃的炸鸡怎么办？想找一家离自己家近，但又不要太罪恶（热量太高），外卖可以送到，春节还开门的炸鸡店，眼睛都看花了，怎么办？我们可以打开美团，在搜索框使用“问小团”AI管家，问他“北京最好吃的炸鸡是哪家店？”，很快它就会根据信息和用户评价给出答案和排序，完全不用你动脑子，想吃什么就可以直接问它。第二个优势，不止做信息推荐，更做真实可落地的解决方案。过年聚会最怕什么？信息不全、选店踩雷，到了还吃闭门羹。好不容易约上三五好友到店，却发现餐厅已关门；没有了解到餐厅是否有包间和价格，被迫在大厅扯着嗓子聊天......为了这种尴尬的经历，原来我们必须要费时费力的搜索餐厅，然后挨个打电话询问餐厅是否营业，以及前台包间信息和价格，最后还要比对哪家更合适。而现在，我们可以打开美团，在搜索框使用“问小团”AI管家，进入对话页面。以“和亲戚春节聚餐，环境要安静雅致不要太吵闹，最好是淮扬菜或粤菜，服务要好的，有团购套餐优惠的”这一多条件的复杂问题为例。随后，“问小团”AI管家便快速进行了解读，从高端宴请品质、经典淮扬风味、高性价比套餐三个维度，筛选出了三家餐厅，不仅关注到了包间私密性与春节营业安排，还给出了它首选的餐厅，点击便可进入团购页面。第三个优势，打造了从需求到履约的全流程服务，真正让用户省时间、省钱。在本地生活上，除了“找店难”，还有“比价累、领券烦、下单繁”：各类消费券分散在不同入口，跨平台领券耗时费力；不同优惠券的使用规则不同，不知道如何搭配才能达到最优优惠；安排行程时，需要反复切换多个平台查找不同商家，浪费大量时间。而“问小团”AI管家一次性解决了所有问题，打造了从需求解读-精准推荐-一键领券-优惠规划-消费履约的全流程服务闭环。我们可以进入其春节专区，可免口令一键领取外卖、闪购、餐饮堂食等多品类消费券，无需手动筛选。举个例子，在美团搜索框使用“问小团”AI管家，问询“性价比高的火锅餐厅，给出优惠套餐，同时附近还要有遛弯的公园”。“问小团”AI管家会根据你的定位位置和消费习惯，给出推荐的火锅餐厅和优惠套餐或代金券。最后，它也不忘我的需求，给出了附近可以遛弯的公园。总的来说，相比于通用AI软件，美团“问小团”AI管家更能从生活场景的实际需求出发，帮用户省钱省力。它不是简单的信息检索工具，更像是本地生活AI管家，从需求解读到消费履约，每一个环节都围绕用户的“省时间、省钱”核心诉求展开，让春节吃喝玩乐的决策变得简单又高效。什么样的智能体，才是人人叫好的应用？为什么美团能做出“问小团”AI管家这样的工具，而其他平台却比较难复制？除了美团的大模型技术，关键在于美团打造了属于垂直领域智能体的核心壁垒。一个能真正解决用户问题的智能体，核心壁垒体现在三个方面：第一，深耕行业的Know-how（专业知识）；一个好的垂直智能体，一定需要有深入的行业Know-how（专业知识），必须在行业深耕多年，知道行业的痛点在哪、哪些关键节点和效率可以通过AI提升。而“问小团”AI管家的背后，是美团十余年扎根本地生活领域，持续投入、精心打造的本地生活信息基建，知道餐厅年三十是否营业、门口有没有停车位、店内有没有宝宝椅，甚至连桌子旁有没有插座都知道，这也是AI能够精准输出结果的基础，更是满足用户需求和行业痛点的根本。另一方面，十余年的深耕，让美团对本地生活的消费逻辑、商家运营模式、用户需求痛点了如指掌：知道用户选餐厅时最关心的是口味、性价比、设施，订年夜饭时最在意的是营业状态、包间数量、套餐内容，找闪购商品时最看重的是配送时效、商品库存。基于这份行业Know-how，美团搭建了覆盖全场景、全维度的本地生活信息基建，为“问小团”AI管家提供了坚实的基础。它拥有真实的本地生活“信息底座”，也能从源头就知道用户需要什么、行业能提供什么，而这份沉淀，是新入局平台短时间内无法复制的。第二，重投入做本地生活数据集，才能构筑最深的行业护城河；一款优秀的智能体，必须拥有属于自己的数据集。美团经过十余年的积累，积累了海量、准确、及时的本地生活全链条数据，包括商户经营数据、用户消费行为数据、真实评价数据、履约服务数据、商家动态信息数据等，能让“问小团”AI管家更精准地理解本地生活场景的消费逻辑。而这份数据集的精准与鲜活，又得益于美团三大数据维护能力：一是全维度、精细化的信息采集，依托地推团队、骑手生态、商家生态，采集商户经纬度、营业时间、设施配置、配送时效等硬信息，以及环境相册、推荐菜相册等软信息，甚至细化到停车场、充电插座等细节；二是AI+人工的多重核验与动态更新，搭载美团自研的LongCat（龙猫）模型，结合人工审核团队，对数据集进行多重核验校准，同时通过骑手报备、商家主动更新、订单核销数据等渠道，实时更新商家动态，尤其针对春节等消费高峰期做专项信息维护；三是用户侧和商家侧的正向循环，用户留下的真实UGC内容不断丰富数据集，商家为获取精准客流主动维护线上信息，而餐饮SaaS等产品沉淀的经营数据又反向赋能信息建设，让数据集持续优化、不断丰富。以上任意一环，都需要不断投入，是一项需要长期坚持的“苦功夫”。第三，专家型+务实型的全流程闭环，避免AI幻觉的实用能力。真正能被用户认可的智能体，必然是专家型+务实型的AI管家，能完成用户意图识别-目标拆解-步骤执行-结果反馈的全流程闭环，而这也是“问小团”AI管家的核心能力，更重要的是，其依托真实可靠的信息底座，从源头规避了AI行业常见的“数据幻觉”问题。在意图识别环节，“问小团”AI管家能基于美团的行业Know-how和数据集，精准读懂用户的模糊需求、复合需求：在目标拆解环节，它会将用户的单一需求拆解为多个具体的执行目标，比如将“春节吃喝玩乐”拆解为“选餐厅、领券、规划后续行程”；在步骤执行环节，它会依托美团的信息基建和真实商户数据，进行信息查询、商家筛选、二次校验、优惠搭配，给出可直接交易的方案，而非泛化的建议；在结果反馈环节，用户可通过推荐链接直接下单消费，而消费后的体验和反馈又会反向优化“问小团”的推荐逻辑。整个流程中，每一步都有真实的信息和数据支撑，让“问小团”不会出现“推荐倒闭商家”、“给出无效优惠”等AI幻觉问题，真正做到实用、好用、管用。服务人的AI，才是智能体元年爆发的原动力阿里的一杯奶茶、腾讯的一个红包，美团的吃喝玩乐AI管家，这些现象背后，指向的是同一个行业趋势：AI的发展，终究要回归生活的烟火气。其中，谁才是满足用户“省钱、省时间”真实需求的那一个？当大模型的技术竞争进入下半场，拼的不再是参数的大小、模型的先进，而是谁能把技术藏进生活的细节里，谁能帮用户解决一个个费时费力的小痛点，谁能成为用户身边的场景化AI管家。AI的童年时代已经过去，正在迎来自己的成人礼。【 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