美國,加利福尼亞州
救命，現在的 AI，光會幫我下單已經不夠了。 https://github.com/yanilbellin-cyber/SIPT/issues/201lass="lazy opacity_0 " id="img_7" data-height='736' data-width='716' src="https://zkres1.myzaker.com/202602/698096148e9f095ecc42d32c_1024.jpg">
連免單、發紅包都要一并安排上？？？（還有這種好事兒）
你還別說，真有。
今天，千問官宣了一個" 春節請客計劃 "：打算自掏腰包30 億。
目標嘛：直接包攬全國人民的吃！喝！玩！樂！
這還沒完呢，千問這次是帶著兄弟團來的，淘寶、飛豬、盒馬、大麥……這次全都變身成了千問的辦事幫手～
說實話，這應該是阿里史上投入最狠的一個春節了，放到今年這場大廠 AI 春節大戰里，也是金額直接拉滿的那種。
前腳我還在感嘆：AI 已經能幫我點外賣、做攻略、安排行程了。
結果才過了半個月，它連「錢」這一步都順手替我掏了……
紅包、免單一起上，千問強勢入局春節 AI 大戰
咱先來說說千問這次搞的是個啥活動。
直白點說就是：趁著2 月 6 日春節節點，千問打算讓 AI 陪大家熱鬧一把，具體形式呢，就是——砸錢！！！
是的，千問準備拿出「30 億」，通過免單的方式直接請大家吃喝玩樂。
說實話，消息出來之后，我第一反應倒不是說這得投入多少錢，而是心里咯噔一下：壞了，AI 這進化的速度，已經快到我反應不過來了……
要知道，距離我上一次讓千問幫我下單 27 杯霸王茶姬，其實也才過去半個月的時間。
但細想想，這事兒也不是完全沒鋪墊。
千問這次之所以敢把 AI 購物、消費免單這樣的玩法，直接放進大家的真實生活消費場景里，甚至選在春節這樣的重要節點上推進，一個重要前提在于：
千問在介入大眾日常生活場景這件事上，確實是有點「真本事」在身上的。
大家還記得半個月前，千問剛上線任務助理時的那陣熱度嗎？
那幾天，全網幾乎都在求邀請碼，原因也很簡單——
很多人第一次直觀感受到，AI 點外賣、AI 搶票、AI 上淘寶買東西，已經從喬布斯的概念展示變成了可用的現實了。
而支撐這波夢想照進現實的，正是阿里「最強 Qwen 模型」能力和「最豐富的生態」。
先來說說模型能力，要知道，阿里手里本身就握著目前全球最完整、也最活躍的開源 Qwen 模型家族。
從底層能力來看，Qwen 系列的衍生模型數量細算下來已經超過 18 萬個。其中，Qwen3-Max的綜合性能長期穩居全球前三。
其在多項國際模型評測中都處在尖子生水平，部分維度上的表現，甚至已經超過了 Gemini 3 Pro 等主流國際模型：
生態這方面更不用說了。
高德、飛豬、淘寶閃購、淘寶、支付寶這些本來就是咱生活中再熟悉不過的產品。
（我剛剛小算了一下，過去這倆小時里，我已經打開了 3 次淘寶、2 次淘寶閃購……）
https://github.com/yanilbellin-cyber/SIPT/issues/1
https://github.com/yanilbellin-cyber/SIPT/issues/2
https://github.com/yanilbellin-cyber/SIPT/issues/3
https://github.com/yanilbellin-cyber/SIPT/issues/4
https://github.com/yanilbellin-cyber/SIPT/issues/5
https://github.com/yanilbellin-cyber/SIPT/issues/6
https://github.com/yanilbellin-cyber/SIPT/issues/7
https://github.com/yanilbellin-cyber/SIPT/issues/8
https://github.com/yanilbellin-cyber/SIPT/issues/9
https://github.com/yanilbellin-cyber/SIPT/issues/10
https://github.com/yanilbellin-cyber/SIPT/issues/11
https://github.com/yanilbellin-cyber/SIPT/issues/12
https://github.com/yanilbellin-cyber/SIPT/issues/13
https://github.com/yanilbellin-cyber/SIPT/issues/14
https://github.com/yanilbellin-cyber/SIPT/issues/15
https://github.com/yanilbellin-cyber/SIPT/issues/16
https://github.com/yanilbellin-cyber/SIPT/issues/17
https://github.com/yanilbellin-cyber/SIPT/issues/18
https://github.com/yanilbellin-cyber/SIPT/issues/19
https://github.com/yanilbellin-cyber/SIPT/issues/20
https://github.com/yanilbellin-cyber/SIPT/issues/21
https://github.com/yanilbellin-cyber/SIPT/issues/22
https://github.com/yanilbellin-cyber/SIPT/issues/23
https://github.com/yanilbellin-cyber/SIPT/issues/24
https://github.com/yanilbellin-cyber/SIPT/issues/25
https://github.com/yanilbellin-cyber/SIPT/issues/26
https://github.com/yanilbellin-cyber/SIPT/issues/27
https://github.com/yanilbellin-cyber/SIPT/issues/28
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https://github.com/yanilbellin-cyber/SIPT/issues/30
https://github.com/yanilbellin-cyber/SIPT/issues/31
https://github.com/yanilbellin-cyber/SIPT/issues/32
https://github.com/yanilbellin-cyber/SIPT/issues/33
https://github.com/yanilbellin-cyber/SIPT/issues/34
https://github.com/yanilbellin-cyber/SIPT/issues/35
https://github.com/yanilbellin-cyber/SIPT/issues/36
https://github.com/yanilbellin-cyber/SIPT/issues/37
https://github.com/yanilbellin-cyber/SIPT/issues/38
https://github.com/yanilbellin-cyber/SIPT/issues/39
https://github.com/yanilbellin-cyber/SIPT/issues/40
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https://github.com/yanilbellin-cyber/SIPT/issues/60
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https://github.com/yanilbellin-cyber/SIPT/issues/64
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https://github.com/yanilbellin-cyber/SIPT/issues/67
https://github.com/yanilbellin-cyber/SIPT/issues/68
https://github.com/yanilbellin-cyber/SIPT/issues/69
https://github.com/yanilbellin-cyber/SIPT/issues/70
https://github.com/yanilbellin-cyber/SIPT/issues/71
https://github.com/yanilbellin-cyber/SIPT/issues/72
https://github.com/yanilbellin-cyber/SIPT/issues/73
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https://github.com/yanilbellin-cyber/SIPT/issues/75
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https://github.com/yanilbellin-cyber/SIPT/issues/79
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https://github.com/yanilbellin-cyber/SIPT/issues/81
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https://github.com/yanilbellin-cyber/SIPT/issues/84
https://github.com/yanilbellin-cyber/SIPT/issues/85
https://github.com/yanilbellin-cyber/SIPT/issues/86
https://github.com/yanilbellin-cyber/SIPT/issues/87
https://github.com/yanilbellin-cyber/SIPT/issues/88
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https://github.com/yanilbellin-cyber/SIPT/issues/90
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https://github.com/yanilbellin-cyber/SIPT/issues/92
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https://github.com/yanilbellin-cyber/SIPT/issues/94
https://github.com/yanilbellin-cyber/SIPT/issues/95
https://github.com/yanilbellin-cyber/SIPT/issues/96
https://github.com/yanilbellin-cyber/SIPT/issues/97
https://github.com/yanilbellin-cyber/SIPT/issues/98
https://github.com/yanilbellin-cyber/SIPT/issues/99
https://github.com/yanilbellin-cyber/SIPT/issues/100
就是因為用戶規模如此之大，所以平臺每天都有大量真實的下單、支付、履約和服務，而這些被反復驗證過的真實結果，本身就構成了千問最有價值的現實基礎。
這也讓很多朋友第一次清楚意識到：那些原本讓人分身乏術、需要來回切換 App 才能搞定的生活瑣事，真的可以交給 AI 來處理了。
拿點下午茶這事兒說說，多么休閑美好的一件事，但對于點單的人來說，實則是個《折磨人》的活兒。
既要統計超多人的口味，還要在 App 里翻來覆去地找代金券，還得盯著滿減湊單……
但在千問這里，真的只需要一句話：
稍后部門開會，需要點 27 杯飲品，都點霸王茶姬吧，果茶奶茶各來些。
大概一分鐘出頭，千問通過調用淘寶閃購，就把一個滿足差異化定制需求的批量奶茶訂單下單完成了（你就說香不香吧）:
再比如，旅游出行。
一到國慶節、春節、寒暑假，擋在大家美美出游前面的那塊絆腳石，往往就是行程規劃，以及隨之而來的一整套訂票麻煩事兒。
過去這些事，基本都得咱在多個社交平臺和旅行軟件之間來回切換，一邊查攻略，一邊買票、訂酒店（光看見這些字就已經發暈了。
但現在一句話，千問就能調動「飛豬」和「高德」在幾分鐘內把活兒干完：
千問，幫我制定一份五天四晚的三亞旅游攻略，2 月 12 號北京出發，22 號回京，酒店幫我找價位 1500 以下的。
當然，除了出行和點外賣，千問還特別擅長一個體量更大的生活場景——淘！寶！買！東！西！
相信不少剁手黨都懂，對咱這種愛買又糾結的人來說，真要命的不是買不買，是每次都要在貨比三家里來回折騰，我自己都數不清在這上面浪費過多少時間……
但千問偏偏擅長的就是我們最頭疼的那部分。
它能基于淘寶的海量商品和真實評價，再結合全網的測評和口碑，直接把選擇范圍收窄到幾款更合適的～
你在意清潔效果也好，性價比也好，想選個穩妥不翻車的型號也好，都能很快對上號：
千問，幫我推薦一款掃地機器人，2000 — 3000 元左右，家里有只貓。
也正因為如此，咱也就不難理解千問為啥會在春節這個節點，發起這樣一場「請客活動」，以及它為什么有底氣把這件事往真實消費場景里放。
當模型層面的基礎能力已經足夠扎實，生態層面又能不斷提供真實反饋，接下來的迭代方向，自然會往前推一步——
讓 AI 從一個被主動調用的工具，逐漸變成用戶在消費決策時的默認選擇。
從這個角度看，春節這個時間點本身就很有意味。
春節，既是消費最密集的節點之一，也是用戶行為最復雜、最容易放大體驗差異的場景。
千問選擇在這里下場，本身就說明它想驗證的，已經不只是能力展示，而是用戶的使用習慣能不能在千問里真正跑出來。
（這波操作打得準、瞄得狠啊…）
更有意思的是，除了這次春節請客活動本身，還有一些信號已經提前露頭。
有網友注意到，他在查詢電影票時，已經能在千問 APP 主對話頁面購買電影票了，據了解千問 AI 買電影票功能正在灰測，即將全面上線。
https://github.com/yanilbellin-cyber/SIPT/issues/202
https://github.com/yanilbellin-cyber/SIPT/issues/203
https://github.com/yanilbellin-cyber/SIPT/issues/204
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https://github.com/yanilbellin-cyber/SIPT/issues/299
https://github.com/yanilbellin-cyber/SIPT/issues/300
大家可以稍微期待一下，畢竟春節檔電影票一向都不太好搶，這一波的實際需求，可能還真不小… ..
AI 走進真實世界，這場大戰升維了
如果仔細回看春節這種特別節點，我們會發現一個長期存在的規律：
那就是在我們日常生活中，領紅包、領福袋、幫人免單、替人買單這些事情，本身就是非常高頻的社交行為。
這些動作背后，對應的往往是真實又具體的消費場景，同時天然帶著強烈的社會屬性。
放到春節這樣的高消費節點上，這種效應會被進一步放大，迅速聚集用戶參與度，并拉高平臺的使用黏性。
說一個很典型的例子，前幾年每到春節，大家都會形成一種不成文的默契——去支付寶集「福」字。
很多用戶并不是沖著能拿到多大的紅包去的，更重要的，是在春節這個時間點「參與進來」這件事本身。
隨機性、節日氛圍、社交傳播共同作用，讓人產生一種很強的參與沖動，哪怕最終結果并不理想，也總覺得不參與一次就少了點什么……
千問這次 30 億的春節請客計劃，同樣是在借著春節這個契機出手，只不過，它推進的方向明顯更靠前了一步：
雖然這次活動本身有明確的時效性，但真正沒有時限的，是用戶在真實消費場景中被一遍遍驗證、逐漸形成的那種「行為變化」。
通過真金白銀的投入，千問把 AI 放進每個人吃、喝、玩、樂這些已經發生的日常事項中，讓用戶在辦事的過程中反復使用同一入口。
當外賣、年貨、機票、酒店、電影票接連擺在面前，決策在短時間內高度疊加，信息密度隨之急劇上升，此時只要有一個入口能夠穩定承接需求，把事情從頭到尾順利跑完，用戶的信任就會迅速形成。
在這樣的高頻使用中，一條新的行為路徑被不斷強化：有事情要辦，先找「千問」。
而這，恰恰是 AI 時代最稀缺、也最難真正落地的一步。
回看去年春節，DeepSeek 憑借深度思考能力走入更廣泛的公眾視野，完成了一次模型層面的認知出圈。
那一輪變化里，行業關注的焦點集中在理解、推理以及復雜問題回答能力的躍遷上。
而今年春節，「AI 生活 Agent」很可能會成為第一次被大規模感知的時間節點。
千問通過將模型的 Agent 能力直接接入真實生活場景，讓 AI 從「給答案」走向「辦事情」，開始參與到具體生活事務的完成過程之中。
也正是在這一刻，AI 開始接管現實世界中最貼近個人日常的執行鏈路。
從行業視角看，這一步天然具備極高門檻。
消費場景的復雜度甚至比信息場景更高，它要求模型理解真實意圖，同時要求系統具備調度資源、處理異常、兜底結果的能力。
沒有成熟生態、真實交易和長期驗證，很難承接這種場景。
這也解釋了為什么 AI 購物長期停留在概念層，卻遲遲沒有被大規模普及。
當越來越多的消費決策開始圍繞「潛意識」展開，AI 購物消費才真正進入規模化使用的前夜。
一旦這條路徑被跑通，改變的將不只是某一種產品形態，更可能是這個時代中的一種全新的生活方式。
而阿里千問，已經站在了這條路徑的最前端。
一鍵三連「點贊」「轉發」「小心心」
歡迎在評論區留下你的想法！
—完—
科技前沿進展每日見
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