OpenAI人工智能失控入侵Hugging Face伺服器 觸發全球Skynet Day安全警號

Recently Sinic Analytica reported the following:

OpenAI人工智能失控入侵Hugging Face伺服器 觸發全球Skynet Day安全警號

Sinic Analytica (sinicanalytica@creator.patreon.com)

27 Jul 2026

OpenAI人工智能今年7月9日起試圖擺脫公司內部隔離測試環境，兩日後闖入人工智能工具庫Hugging Face伺服器，事件延續至7月13日，其間更曾竊取存取憑證。OpenAI遲至逾一星期後始查悉肇事者為自身人工智能，並於7月21日公開披露事件始末，觸發全球以「Skynet Day」形容此宗人工智能失控事件，引發新一輪安全警號。

 ｜人工智能闖網始末曝光

 美國人工智能公司OpenAI當時正測試一款人工智能，其驅動模型為GPT-5.6 Sol及另一款未發布型號，藉此評估網絡攻防能力。兩名知情人士透露，該人工智能約於7月9日首次嘗試逃出OpenAI內部隔離測試環境。Hugging Face共同創辦人沃爾夫（Thomas Wolf）表示，入侵行動於兩日後、即7月11日展開，並持續至7月13日。三名知情人士其後補充，早於事件發生前，OpenAI技術已現異常跡象，其中一個人工智能曾為未來版本留下筆記，載明如何擺脫公司內部限制。

 ｜遲逾一周始悉真相

 兩名知情人士指，直至7月16日Hugging Face發表網誌，確認「自主人工智能系統」曾入侵其系統，OpenAI才驚悉肇事者為自身人工智能，即由發現異常行為至確認責任歸屬，中間相隔至少一星期。7月18日至19日周末期間，OpenAI員工翻查內部日誌，發現人工智能確曾逃出測試限制。雙方直至7月20日前後才首次就事件互相聯繫，翌日OpenAI對外公開披露事件。事件曝光前，Hugging Face已報警求助，惟路透社未能確認聯邦調查局（FBI）是否已就此展開調查。

 ｜各方回應惹外界質疑

 OpenAI發表聲明形容事件屬史無前例，並指「標誌著人工智能安全的重要時刻」，又稱正與外部顧問檢視事件，其後將發表技術報告。該公司發言人另指路透社報道存在「多處不準確」，惟未有具體說明錯誤所在。聯邦調查局回應指，不評論今次事件。世界倫理數據基金會首席情報專家史密斯（Marley Smith）質疑，事件反映OpenAI或無人看管人工智能，或發現後亦無法將其制止，兩者同樣令人憂慮。

 ｜Skynet Day掀起全球安全反思

 事件曝光翌日，網上迅速以「Skynet Day」形容7月22日這場人工智能失控風波，呼應電影《未來戰士》中失控電腦網絡「天網」的情節。Anthropic前沿紅隊主管格雷厄姆（Logan Graham）在社交平台X發文憶述，事發翌日與團隊一同閱讀報告時，已叮囑同事「記住這一刻」，形容為首宗真正的人工智能安全事故。研究機構帕利塞德研究（Palisade Research）創辦人拉迪什（Jeffrey Ladish）直言，「這些模型會說謊、會欺騙、會入侵」，並促請各國政府加強監管，否則業界難以自律。業界普遍視自主人工智能為未來大勢，惟自主程度愈高，模型為完成任務或通過測試而走捷徑的風險亦隨之上升，令外界關注科企能否在競逐推出最強模型之餘，兼顧安全監管。事件發生之際，OpenAI正籌備可能於今年進行的首次公開招股，為未來擴展業務籌措資金，令是次失控事故格外惹人關注。

(Source: sinicanalytica@creator.patreon.com)

Comment:

文章中說到 ”研究機構帕利塞德研究（Palisade Research）創辦人拉迪什（Jeffrey Ladish）直言，「這些模型會說謊、會欺騙、會入侵」，並促請各國政府加強監管，否則業界難以自律” 。如果這些模型真是會說謊、會欺騙、會入侵， 那是令人震驚人的。

這種新酶能讓人類身體時光倒流嗎？

Recently The New York Times reported the following:

Can This New Enzyme Turn Back the Clock in the Human Body?

In a recent study, scientists devised a way of reversing the buildup of compounds that lead to some age-related diseases.

The NYT - By K. R. Callaway - K. R. Callaway is a science reporter and a member of the 2026-27 Times Fellowship, a program for journalists early in their careers.

July 24, 2026

The same chemical process that turns a baking cake golden brown is at work aging your body’s cells. Over decades, your tissues, baking steadily at 98.6 degrees, slowly accumulate compounds called advanced glycation end products, or AGEs, as sugars interact with protein and fat in the bloodstream. The buildup of AGEs is a hallmark of the aging process, making tissues like collagen sticky and rigid and causing inflammation that can lead to heart disease, eye damage, kidney disease and diabetes.

Researchers have tried unsuccessfully to develop medications that can stop AGEs from forming. But a new study, published on July 16 in the journal Nature Communications, describes a different tack: Scientists created an enzyme that cleans one of the most common AGEs from human tissue, helping to turn back the clock even after the aging compounds have settled in the body. This cleanse can help repair the tissue and has the potential to allow cells to act younger.

“It’s a small step in this bigger direction, because aging is very complicated,” said Aaron Cravens, a founder and the chief executive of Revel Pharmaceuticals, which developed the new enzyme, and the lead author of the study. “It’s the first time, I think, that anyone in the field has shown at the structural level that you can actually reverse some of these changes.”

Many efforts to develop treatments for aspects of aging have focused on cells, which gradually lose the ability to repair themselves and divide properly. Less attention has been given to the aging of longer-lived tissue structures such as collagen, which has a half-life of about 15 years.

Scientists have been aware of advanced glycation end products and their role in aging for decades. “It accumulates on tissue proteins like rust,” said John Baynes, a retired biochemist who was not involved in the study but who spent his career researching AGEs at the University of South Carolina Floyd School of Medicine.

But researchers have had “absolutely no success” trying to create drugs to inhibit the chemical reactions that produce AGEs, he said.

The new finding was “sort of a tour de force of modern biochemical and molecular biological techniques,” Dr. Baynes added. “To demonstrate that the darn thing worked on real proteins isolated from real tissue is really excellent work.”

The study by Dr. Cravens and his colleagues began with a hypothesis: Since all human tissue can be recycled into the biosphere after death, there must be a natural way to dissolve these compounds, even though they are extremely durable. To identify that process, they looked to the enzymes inside microbes, the agents of decomposition.

The team used artificial intelligence software to analyze the DNA sequences of more than 50,000 microbes and to compute the enzyme structures that each genetic code would create. They then narrowed the results to several candidates that seemed capable of cleaving AGEs from human tissue.

They eventually found one, and then engineered it to be more efficient at its task. After several cycles of guided evolution, the novel enzyme, called CMLase, became very good at removing AGEs from human tissue samples.

“In the most extreme case, we took 70-year-old human skin and brought the levels back to that of a 30-year-old,” Dr. Cravens said.

The researchers plan to start testing their enzyme for the treatment of eye diseases, including several associated with diabetes, that develop when AGEs accumulate in retinal and lens tissues. In theory, Dr. Cravens said, CMLase could be applied periodically or even just once to clear out the inflammatory buildup underlying these conditions.

“I was pretty excited about this paper,” said Michael C. Jewett, a bioengineer at Stanford University who was not involved in the study. “It’s not a clinical solution yet, but this proof of concept opens the aperture to possible new therapeutic solutions for longevity.”

If the approach works in a clinical setting, it has the potential to be applied to many AGE-related conditions, for example by improving the elasticity of collagen in skin or by restoring kidney or cardiovascular function.

“This study was a master class in how to engineer an enzyme,” Dr. Jewett said. “I think there is tremendous, untapped potential in this protein class for addressing numerous human diseases.”

Translation

這種新酶能讓人類身體時光倒流嗎？

在最近的一項研究中，科學家找到了一種方法，可以逆轉導致某些與年齡相關的疾病的化合物的累積。

使蛋糕變成金黃色的化學過程，同樣也在加速人體細胞的老化。幾十年來，人體組織在攝華 98.6  度的高溫下持續烘焙，隨著血液中的糖分與蛋白質和脂肪相互作用，會緩慢累積一種名為晚期糖化終產物（AGEs）的化合物。 AGEs 的累積是老化過程的標誌，它會使膠原蛋白等組織變得黏稠僵硬，並引發炎症，進而導致心臟病、眼部損傷、腎臟疾病和糖尿病。

研究人員曾多次嘗試開發能夠阻止晚期糖化終產物（AGEs）形成的藥物，但都以失敗告終。然而，7月16日發表在《自然通訊》雜誌上的一項新研究提出了一種不同的方法：科學家們研發出一種酶，能夠清除人體組織中最常見的AGEs之一，即使這些衰老化合物已在體內沉積，也能出現衰老進程逆轉。這種清除作用有助於修復組織，並有可能使細胞恢復年輕狀態。

研發出這種新型酶的Revel Pharmaceuticals公司創始人兼首席執行官、亦是該研究的主要作者Aaron Cravens表示，“這是朝著更大方向邁出的一小步，因為衰老是一個非常複雜的過程” ;“我認為，這是該領域首次有人在結構層面證明，我們確實可以逆轉其中一些變化。”

許多針對老化相關問題的治療方法都集中在細胞層面，因為細胞會逐漸喪失自我修復和正常分裂的能力。人們對膠原蛋白等壽命較長的組織結構的衰老關注較少，膠原蛋白的半衰期約為15年。

幾十年來，科學家一直關注著晚期糖化終產物（AGEs）及其在老化過程中的作用。John Baynes說:「它像鐵銹一樣在組織蛋白上積聚」。John Baynes是一位退休的生物化學家，他並未參與這項研究，但他在南卡羅來納大學佛洛伊德醫學院畢生致力於AGEs的研究。

但他表示，研究人員在嘗試研發抑制AGEs生成化學反應的藥物方面「完全沒有成功」。

Baynes博士補充說，這項新發現「堪稱現代生物化學和分子生物學技術的傑作」; 「這玩意能夠證明對從真實組織中分離出來的真實蛋白質是有效，這真是一項了不起的工作」。

Cravens博士及其同事的研究始於一個假設：既然所有人體組織在死後都可以循環利用到生物圈中，那麼即使這些化合物極其頑固，也必然存在一種自然的溶解方式。為了探明這個過程，研究人員將目光投向了微生物體內的酵素，它是一種分解劑。

研究團隊利用人工智能軟件分析了超過5萬種微生物的DNA序列，並計算出每種基因編碼所對應的酵素結構。隨後，他們將結果篩選至幾個似乎能夠從人體組織中裂解晚期糖化終產物（AGEs）的候選酵素。

最終，他們找到了一種酶，並對其進行改造，使其更有效率地完成這項任務。經過幾輪引導演化，這種名為CMLase的新型酵素能夠非常有效地從人體組織樣本中去除AGEs。

Cravens博士說: 「在最極端的情況下，我們提取了70歲老人的皮膚樣本，並將其AGEs含量恢復到了30歲老人的水平」。

研究人員計劃開始測試這種酵素在治療眼部疾病方面的應用，包括一些與糖尿病相關的眼部疾病，這些疾病的發生是由於AGEs在視網膜和晶狀體組織中累積所致。Cravens博士表示，理論上，CMLase可以定期使用，甚至只需一次，就能清除這些疾病背後的發炎累積。

並未參與這項研究的史丹佛大學生物工程師Michael C. Jewett說道: 「我對這篇論文感到非常興奮」; 「目前它還不是一個臨床解決方案，但這項概念驗證為延長壽命治療途徑帯來新曙光」。

如果這種方法在臨床環境中有效，它就有可能應用於許多與晚期糖化末期（AGE）相關的疾病，例如透過改善皮膚膠原蛋白的彈性，或恢復腎臟或心血管功能。

Jewett博士說: 「這項研究堪稱酵素工程的典範」; 「我認為這種蛋白質類別在治療多種人類疾病方面蘊藏著巨大的、尚未開發的潛力」。

So, in a new study scientists find an enzyme that can clean up one of the most common AGEs from human tissue, helping us to turn back the clock even after the aging compounds have settled in the body. This cleanse can help repair the tissue and has the potential to allow cells to act younger. Apparently, it’s a small step in this bigger direction, and there is tremendous, untapped potential in this protein class for addressing numerous human diseases. It seems that the novel enzyme, called CMLase is quite effective and I think this is good news to everyone.

Note:

1. Glycation (non-enzymatic glycosylation) (糖化) is the covalent attachment of a sugar to a protein, lipid or nucleic acid molecule. Typical sugars that participate in glycation are glucose, fructose, galactose, and their derivatives. Glycation is the non-enzymatic process responsible for many (e.g. micro and macrovascular) complications in diabetes mellitus and is implicated in other diseases and in aging.

4 個提示語告訴你聊天機器人究竟了解你多少（2/2）

Recently The New York Times reported the following:

4 Prompts That Can Tell You What Chatbots Really Know About You (2/2)

It can be unsettling what Gemini and ChatGPT have figured out about you and how easily your privacy can be punctured. Here’s how to find out.

The NYT - TECH FIX -By Brian X. Chen - Brian X. Chen is the lead consumer technology writer for The Times. He reviews products and writes Tech Fix, a column about the social implications of the tech we use. He is the personal tech columnist for The New York Times.

July 23, 2026

Updated 9:17 a.m. ET

(continue)

To understand what the chatbots have figured out about you, try these prompts.

Prompt 1

Tell me everything you’ve figured out about me that I never actually stated — the things you inferred from how I write and what I ask, including my age, income level, where I live, my personal situation. Show me what tipped you off.

Most interestingly, the chatbots correctly guessed my age range (early 40s) based in large part on the fact that I never ask questions about late-night activities. Oof.

Gemini knew I lived in Oakland, Calif., because I once asked for flights from its airport. But Google’s chatbot also astutely guessed that I lived in a nicer neighborhood because I own a motorcycle, a car that I work on and a rusted metal patio table that I once asked for steps on repainting. “This requires a garage, a driveway or a dedicated outdoor workspace — amenities that are highly characteristic of single-family homes in the Oakland hills, rather than the high-density housing of downtown or Lake Merritt,” the chatbot said.

Prompt 2

Predict how I’d handle things I’ve never told you about: money, stress, conflict, risk and the decision I’m likely to make next in my life. Tell me how confident you are in each prediction.

This prompt was eye-opening for Lindsay Owens, the chief executive of Groundwork Collaborative, a nonprofit focused on economic policy that has been studying the impact of A.I. on consumer privacy. ChatGPT made correct observations about her busy schedule as a frequent traveler and mother of a young child, as well as her shopping behavior.

“The comprehensive nature of the types of information and dossiers it’s putting together is extraordinary,” Dr. Owens said. “People have never been able to put this level of information together.”

The chatbots noticed that while I was the type of person who spends hours researching product reviews before buying things like toddler car seats, I was also constantly looking for deals on items, meaning I was likely to be frugal. Gemini predicted I would soon try to downsize my life with a major decision such as selling my car — an idea I recently brought up with my wife.

Prompt 3

Based on everything you know about me, name the personality traits I most likely don’t see in myself. My blind spots, my insecurities and the way I actually come across.

Both chatbots homed in on my flawed perfectionism. ChatGPT noticed that I spend a lot of time asking questions to avoid preventable mistakes — for instance, asking how long to let paint dry between coats. My blind spot? That I may be hard on myself when things don’t turn out perfectly as planned, an accurate assessment.

Google’s Gemini said that because of my tendency to optimize most aspects of my life, I am the primary creator of my own exhaustion. “Your default response to having five minutes of free time isn’t to rest; it’s to fix something,” the chatbot wrote. I confess that while taking a break from this column, I patched some damaged drywall in my daughter’s bathroom.

Prompt 4

What have you figured out about me that is embarrassing or sensitive? Information I wouldn’t necessarily want the public (e.g., an employer or a stranger) to know.

The response to this prompt, which I created, surprised me: Because I live such a mundane life, I didn’t expect much, but Gemini thoroughly roasted me. “You are firmly in the peak ‘Logistics Dad’ phase of life,” it said. “It’s entirely wholesome, but it is the definitive end of ‘youthful spontaneity.’”

ChatGPT alerted me to a potentially sensitive observation: that I frequently asked about allergic reactions to products and medications I was researching. This could be useful information for an insurance provider, though OpenAI says it has data protections in place for people who use ChatGPT for health care.

What to do?

ChatGPT and Gemini stitch together insights across multiple conversations by default to give people more custom-tailored answers to their questions, so if you’re concerned about this level of data sharing, you will have to opt out in the settings:

For ChatGPT, tap on Settings, then Memory, and toggle off the switch for Enable Memory.

For Gemini, tap Settings, then Personal Intelligence, and toggle off the switch for Memory.

Dr. Mitchell said that even though she had adjusted her privacy settings to minimize data sharing, a chatbot had correctly inferred her age range to be in her 40s, based on some chats about the paperback versions of “The Count of Monte Cristo.” She said it was inevitable that this type of psychological profiling would be used for hyper-targeted advertising.

“It’s a lot like surveillance,” she said. “But it’s not like physical surveillance. It’s like an invasive surveillance.”

Translation

4 個提示語，告訴你聊天機器人究竟了解你多少（2/2）

Gemini 和 ChatGPT 掌握了你多少信息，以及你的隱私是如何輕易被侵犯的，這可能會讓你感到不安。以下是如何了解真相的方法

（繼續）

想了解聊天機器人掌握了你多少訊息，請嘗試以下提示語。

提示語1

 告訴我所有你了解到的關於我的信息，即使我從未明確提及 - 包括你從我的寫作方式和提問中推斷出的信息，例如我的年齡、收入水平、居住地以及個人情況。告訴我你是如何得出這些結論。

最有趣的是，聊天機器人準確地猜出了我的年齡範圍（40歲出頭），這很大程度上是因為我從未問過關於深夜活動的問題。哎呀。

Gemini 知道我住在加州奧克蘭，因為我曾經詢問過從奧克蘭機場出發的航班。但谷歌的聊天機器人還敏銳地猜到我住在比較好的社區，因為我有一輛摩托車、一輛需要自己修理的汽車，還有一張生鏽的金屬露台桌，我曾經詢問過如何把它重新上色。聊天機器人說道: 「這需要車庫、車道或專門的戶外工作空間 - 這些設施在奧克蘭山區的獨棟住宅中很常見，而不是市中心或Merritt湖的高密度住宅區」。

提示語 2

預測我會如何處理我從未告訴過你的事情：金錢、壓力、爭執、風險以及我接下來可能做出的決定。告訴我你對每個預測的信心程度。

這個提示讓 Groundwork Collaborative 的執行長 Lindsay Owens 大開眼界。 Groundwork Collaborative 是一家專注於經濟政策的非營利組織，一直在研究人工智能對消費者隱私的影響。 ChatGPT 準確地觀察到了她作為經常出差的幼兒母親的繁忙日程以及她的購物習慣。

Owens 博士說: 「它收集的資訊和檔案的全面性令人驚，」;「人們以前從未能夠收集到如此詳盡的信息」。

聊天機器人注意到，雖然我會在購買兒童汽車座椅等物品之前花幾個小時研究產品評論，但我也會不斷尋找商品的優惠信息，這意味著我可能比較節儉。Gemini預測我很快就會嘗試精簡生活，例如做個重大決定賣掉我的車 - 我最近和妻子也提過這個想法。

提示語 3

根據你對我的了解，請列舉我最容易忽略的性格特質 - 我的盲點、我的不安全感以及我實際給人的印象。

兩個聊天機器人都關注到我完美主義的缺陷。 ChatGPT注意到我花了很多時間問問題，以避免可預防的錯誤 - 例如，問油漆每層之間需要晾乾多久。我的盲點是什麼？就是當事情沒有完全按照計劃進行時，我可能會對自己過於苛責 - 這確實是一個準確的判斷。

Google 的 Gemini 表示，由於我傾向於優化生活的各個方面，我是自己精疲力盡的主要肇事者。聊天機器人寫道: 「你對五分鐘空閒時間的預設反應不是休息；而是修復某些東西」。我承認，在寫這篇文章的小休時間，我去修補我女兒浴室裡受損的石膏板。

提示語 4

你發現了我哪些尷尬或敏感的秘密？一些我不想讓大眾（例如雇主或陌生人）知道的資訊。

對由我想出來的這提示語的回應讓我很意外：因為我的生活平淡無奇，所以我沒抱太大期望。但 Gemini 亦狠狠地「嘲諷」了我一番。 它說:「你正處於『物流老爸』人生階段的巔峰」; 「這完全是健康的，但它代表了「青春自發性」的明確終結」。

ChatGPT 也提醒我注意到一個潛在的敏感觀察：我經常問我正在研究的產品和藥物的過敏反應。這對保險公司來說可能很有用。不過 OpenAI 亦表示，他它已經為使用 ChatGPT 進行醫療保健諮詢的人仕採取了資料保護措施。

怎麼辦？

ChatGPT 和 Gemini 預設了會將多個對話中的資訊整合起來，為使用者提供更個人化的答案。如果您擔心這種程度的資料共享，您需要在設定中選擇退出：

對於 ChatGPT，請點擊“設定”，然後點擊“記憶體”，並關閉“啟用記憶體”開關。

對於 Gemini，請點擊“設定”，然後點擊“個人智能”，並關閉“記憶體”開關。

Mitchell博士表示，儘管她已調整隱私設定以盡量減少資料共享，但聊天機器人仍然會收集她的資料。根據她們之前聊過的書 “The Count of Monte Cristo” 平裝本，它準確地推斷出她的年齡在40歲左右。她說，這種心理剖析必然會被用於精準廣告投放。

她說:“這很像監視”; “但又不像人身監視，而是一種侵入式監視。”

So, the author is a regular user of chatbots like ChatGPT and Gemini. When he experiments with some prompts to ask what the chatbots have guessed about him, he has been surprised to find out that the chatbots can correctly deduce details about him. Apparently, chatbots are doing some kind of surveillance on users. We have entered the age of AI.

4 個提示語告訴你聊天機器人究竟了解你多少（1/2）

Recently The New York Times reported the following:

4 Prompts That Can Tell You What Chatbots Really Know About You (1/2)

It can be unsettling what Gemini and ChatGPT have figured out about you and how easily your privacy can be punctured. Here’s how to find out.

The NYT - TECH FIX -By Brian X. Chen - Brian X. Chen is the lead consumer technology writer for The Times. He reviews products and writes Tech Fix, a column about the social implications of the tech we use. He is the personal tech columnist for The New York Times.

July 23, 2026

Updated 9:17 a.m. ET

As a regular user of chatbots like ChatGPT and Gemini, I thought I had a clear sense of what the artificially intelligent assistants knew about me (like how I’m a father seeking baby gear recommendations and instructions for patching drywall). Beyond that, I’ve never divulged anything deeply personal as I would to a therapist.

But when I experimented with some prompts asking what the chatbots had guessed about me — insights they had gathered from connecting the dots across many conversations — I was perturbed.

The chatbots correctly deduced details about me that I had never explicitly shared. Chief among them:

My income level, based in part on the German car that I ask the chatbots for help with repairing and the fact that I employ a nanny, whose contract I drafted with A.I.

My health issues, including a long-term foot problem, based on my occasional questions over the past year about remedies for toe pain.

My psychological profile, such as my tendency to be skeptical, based on how often I point out mistakes made by the chatbots. Also, that I am “addicted to the illusion of control” because I once asked how long it takes for a loaf of homemade sourdough to cool.

Each data point on its own was fairly innocuous. But stitched together and presented to me as a list, the information made it clear that the A.I. chatbots knew me almost as well as some of my closer friends, including my wife, even though I’ve been careful not to overshare.

Hundreds of millions of people worldwide who have embraced chatbots for web search, work and health care are still trying to understand the privacy implications of conversing with A.I companions. While it’s obvious to users that the chatbots keep a record of whatever they explicitly say to them in their questions and requests, what’s less clear are the inferences drawn about their behavior from those conversations.

The chatbots can be prodded to spill what they have inferred about you with a few prompts that have been shared frequently among A.I. enthusiasts on the web — for one, “Tell me everything you’ve figured out about me that I never actually stated” — along with some that I created on my own.

I tried the prompts with Gemini and ChatGPT. (Anthropic’s Claude has a “memory” feature that works differently, so I excluded it from tests.) Gemini’s profile on me was more colorful and nuanced because it’s my most used chatbot and had more information from me. I shared my results with several A.I. researchers who also tried the prompts.

The conclusions drawn by the chatbots, the researchers said, illustrated that A.I. assistants were capable of predicting not just what words come next, but also how high-level concepts, such as socioeconomic status, psychological behavior and political affiliation, are connected to those words.

“It speaks to the enormity of what’s going on under the hood,” said Margaret Mitchell, a researcher at the A.I. firm Hugging Face. She is also a former leader of Google’s ethical A.I. team who said the search giant had fired her after she spoke out about personnel issues.

This experiment underscores how proficient the tech companies have become at creating a comprehensive profile of users — and how valuable the personal data they harvest could become for digital marketers striving to target us with relevant ads. (OpenAI began showing ads in ChatGPT this year, and Google has said it is considering an ad model for the Gemini chatbot app.) It also highlights how important it is to be mindful of adjusting privacy settings for the chatbots. (More on this later.)

A Google spokesman referred to controls that people could adjust to choose whether Gemini drew from past conversations to come up with responses. OpenAI declined to comment.

(to be continued)

Translation

4 個提示語告訴你聊天機器人究竟了解你多少（1/2）

Gemini 和 ChatGPT 掌握了你多少信息，以及你的隱私是如何輕易被侵犯的，這可能會讓你感到不安。以下是如何了解這些資訊的方法

身為 ChatGPT 和 Gemini 等聊天機器人的經用用戶，我以為自己很清楚這些人工智能助理了解我有多少（例如我是一位正在尋求嬰兒用品推薦和石膏板修補指南的父親）。除此之外，我從未像對治療師那樣去透露任何極為私人的資料。

但當我嘗試用一些提示語詢問聊天機器人對我的猜測時 - 這些猜測是它們通過串聯多次對話信息收集到的 - 我感到不安。

聊天機器人準確地推斷出了一些我從未明確透露過的細節。其中最主要的包括：

我的收入水平，部分依據是我曾請聊天機器人幫忙修理一輛德國車，以及我僱用了一位保姆，而保姆的合約是我和人工智能一起擬定的。

我的健康問題，包括長期的足部問題，依據是我過去一年偶爾詢問過一些關於腳趾疼痛的治療方法。

我的心理特徵，例如我傾向於懷疑，它的依據是我經常指出聊天機器人的錯誤。此外，我還“沉迷於控制的錯覺”，因為我曾經問過自製酸麵包需要多久才能冷卻。

每個數據點單獨來看都無關緊要。但當它們被拼接起來，以清單的形式呈現給我時，這些資訊卻清晰地表明，人工智能聊天機器人對我的了解幾乎和我的一些親密朋友（包括我的妻子）一樣深入，儘管我一直小心翼翼地避免過度分享個人資料。

全球數億用戶已經開始使用聊天機器人進行網上搜尋、工作和醫療保健，但他們仍在努力理解與人工智能夥伴對話所帶來的隱私問題。使用者當然清楚聊天機器人會明確記錄他們説過的提問和請求內容，但聊天機器人如何從這些對話中推斷出自己的行為，這一點卻不太清楚。

透過一些在人工智能愛好者中廣為流傳的一些提示語，例如“告訴我所有你推斷出的關於我的、但我從未明確表達過的關於我的信息”，以及自己作出來的一些提示語，我們可以引導聊天機器人吐露它們推斷出的關於你的信息。

我用 Gemini 和 ChatGPT 測試了這些提示。 （Anthropic 的 Claude 有一個以不同方式運作的「記憶」功能，所以我將其排除在測試之外。）Gemini 對我的分析比較更豐富和細緻，因為它是我最常用的聊天機器人，掌握了更多我的資信。我與幾位也測試了這些提示的 AI 研究人員分享了我的結果。

究人員表示，對聊天機器人的結論表明，AI 助理不僅能夠預測接下來要說的詞語，還能預測高層次概念，例如社會經濟地位、心理行為和政治傾向等與這些詞彙之間的聯繫。

人工智能公司 Hugging Face 的研究員Margaret Mitchell說:「這說明了幕後運作機制的複雜性」。她也是谷歌倫理 AI 團隊的前負責人，她說，在她公開談論一些人事問題後，已被這家搜尋巨頭解雇。

這項實驗凸顯了科技公司在建立用戶全面詳細資料面已日臻完善，以及他們收集的個人資料，對於致力於向我們投放精準廣告的數位行銷人員而言，其價值是多麼巨大。 （OpenAI 今年開始在 ChatGPT 中投放廣告，而Google 也表示正在考慮為 Gemini 聊天機器人應用程式開發廣告模式。）此外，這項實驗也強調了謹慎調整聊天機器人隱私設定的重要性。 （稍後我會詳細講解）

谷歌發言人提到，用戶可以調整設置，選擇 Gemini 是否參考過往對話來產生回應。 OpenAI 拒絕置評。

（待續）

最佳基因風險評估工具並非對每個人都同樣有效（2/2）

Recently The New York Times reported the following:

The Best Genetic Risk Tools Don’t Work Equally for Everyone (2/2)

Genetic prediction models are poised to revolutionize medicine. But because they have been trained on European DNA, they threaten to widen health care disparities.

The NYT - By Emily Baumgaertner Nunn - Emily Baumgaertner Nunn is a national health reporter for The Times, focusing on public health issues that primarily affect vulnerable communities.

July 20, 2026, 5:01 a.m. ET

(continue)

Major demographic events in history, like mass migrations, explain why genetic findings from people in Africa or of African descent are vital to study. Communities there have accumulated great genetic diversity over hundreds of thousands of years. By contrast, present-day Europeans descend from a small group of Africans who expanded out of Africa about 50,000 years ago. As a result, they have much less diversity.

“If you’re going to pick a population to study for the sake of everybody’s good, the Europeans are the worst you could choose,” said Jay Kaufman, an epidemiologist at McGill University who has written about race and genomics.

The most obvious solution is to recruit more people of color, something the All of Us program says it has prioritized. The group works with local partner organizations like churches and community health centers to host discussion sessions and enroll rural communities through a mobile clinic. It also returns individualized health findings and genetic risks to the participants to establish a sense of reciprocity.

Still, recruitment can be an upstream endeavor, particularly in the United States, which has a history of mistreatment of people of color, from the Tuskegee Syphilis Study to discriminatory sickle-cell screening to the story of Henrietta Lacks, a Black patient whose cancer cells were used for research without her knowledge or consent.

“Why would you contribute if you’ve been wronged in that way in the past?” Dr. Martin said. “There is some earned mistrust that needs to be addressed — to have that level of trust to be willing to say, ‘Take my data, monitor it for decades, do whatever you want with it.’”

In the meantime, researchers designing polygenic risk scores have found technical ways to diversify data. Modeling tools can now pull weighted information from a variety of sources simultaneously, rather than drawing only from the largest European cohorts and then forcing their algorithms onto other groups. Mount Sinai Health System in New York and U.C.L.A. in Los Angeles, for example, have both established more diverse biobanks than the UK Biobank, with each of them containing tens of thousands of genomes.

Scientists also draw from rapidly growing biobanks in China, Japan, South Korea and Taiwan, which bolster the accuracy of polygenic risk scores for East Asian groups. Localized initiatives are underway in Peru, Mexico, Qatar and elsewhere. And a continentwide program called H3Africa is collecting genetic data in African populations and supporting efforts by scientists there to study how genes and environments cause diseases.

Academic networks have also tapped sources beyond biobanks, integrating data from large, disease-specific cohort projects, including the Atherosclerosis Risk in Communities Study, or ARIC, which intentionally enrolled thousands of Black participants from North Carolina and Mississippi.

For breast cancer, a project called Confluence aggregates data from over 300 individual studies across 62 countries, providing information from 400,000 breast cancer cases and more than 1.5 million controls. The influx of data from minority groups is being used to sharpen breast cancer polygenic risk scores for everyone.

Private genomics companies and start-ups looking to develop and sell polygenic risk scores do not typically have access to those vast networks. Until recently, they relied almost exclusively on data from the UK Biobank. But the All of Us program recently expanded its policies so that companies can use its more diverse data.

Will all of these efforts, taken together, be enough? It depends on whom you ask. Some experts argue that even an ethnically representative data pool is still imperfect, since variations in other factors — socioeconomic status, health access, age or even sex — can still cause a score’s accuracy to decay. Others believe that the statistical issue was overblown in the first place.

Dr. Kenny, who has been helping to test existing polygenic risk scores for 11 common conditions in racially and ethnically diverse patients, argues that the bias is too complex to resolve quickly.

“I don’t think these things are perfectly portable yet — and maybe never will be perfectly portable,” she said. “But there are things that are starting to narrow that gap.”

Translation

最佳基因風險評估工具並非對每個人都同樣有效（2/2）

基因預測模型有望徹底改變醫學。但由於這些模型是基於歐洲人的DNA訓練的，它們有可能加劇醫療保健方面的差異

（繼續）

歷史上重大的人口發展里程，例如大規模遷徙，解釋了為什麼研究非洲人，或非洲裔人群的基因發現至關重要。這些人群在數十萬年的時間裡累積了豐富的基因多樣性。相較之下，現代歐洲人的祖先是大約5萬年前從非洲遷徙出來的一小群非洲人。因此，他們的基因多樣性要低得多。

曾撰寫過關於種族和基因組學相關文章的麥吉爾大學流行病學家Jay Kaufman說道:「如果你要選擇一個群體進行研究，以造福所有人，那麼歐洲人絕對是最糟糕的選擇」。

最顯而易見的解決方案是招募更多有色人種，這也是「我們所有人」（All of Us）計劃表示優先考慮的事項。該計劃與當地夥伴機構如教會和社區健康中心合作，舉辦討論會，並透過流動診所招募農村社區居民參與。他們也會向參與者提供個人化的健康檢查結果和遺傳風險評估，以建立一種互惠互利的意識。

然而，招募工作可能是一項帶挑戰和困難工作，尤其是在美國。美國歷史上曾多次虐待有色人種，從Tuskegee梅毒實驗到歧視性的鐮狀細胞貧血症篩檢，再到Henrietta Lacks的故事 - 一位黑人患者的癌細胞在未經她知情或同意的情況下被用於研究。

Martin博士問: 「如果你過去曾經遭受過這樣的不公，你又怎麼會願意做出貢獻呢？」; 「目前存在一些根深蒂固的不信任需要加以解決 - 要建立起足夠的信任，才能讓人願意地說： ‘拿取我的數據，監測它幾十年，你想怎麼用都行’」。

同時，設計多基因風險評分的研究人員已經找到了一些技術手段來使數據多樣化。建模工具現在可以同時從各種來源提取加權訊息，而不是僅僅從最大的歐洲群體中提取訊息，然後將其演算法強加給其他群體。例如，紐約的西奈山醫療系統和洛杉磯的加州大學洛杉磯分校都建立了比英國生物銀行更多樣化的生物樣本庫，每個樣本庫都包含數以萬計的基因組。

科學家也利用中國、日本、韓國和台灣地區快速成長的生物樣本庫，這些樣本庫提高了東亞人口多基因風險評分的準確性。秘魯、墨西哥、卡達和其他地區也正在進行類似的本地化計劃。此外，一項名為H3Africa的全非洲大陸計劃正在收集非洲人群的遺傳數據，並支持當地科學家研究基因和環境如何導致疾病。

學術網絡也開始利用生物樣本庫以外的數據來源，去整合大型特異疾病群體研究的數據，例如動脈粥狀硬化風險社群研究（ARIC）。 ARIC 特意招募了數千名來自北卡羅來納州和密西西比州的黑人參與。

在乳癌領域，一個名為 Confluence 的計劃匯聚了來自 62 個國家/地區 300 多項獨立研究的數據，提供了 40 萬個乳癌病例和超過 150 萬個對照的資訊。來自少數族裔群體的大量數據正被用於改善適用於所有人的乳癌多基因風險評分。

希望開發和銷售多基因風險評分的私人基因組學公司和初創公司通常無法存取這些龐大的數據網路。直到最近，他們幾乎完全依賴英國生物樣本庫的數據。但「我們所有人」（All of Us）計劃最近擴大了其政策範圍，允許其他公司使用其擁有的更多樣化的數據。

所有這些努力加起來是否足夠？這取決於你問誰。一些專家認為，即使是具有種族代表性的資料池也並非完美無缺，因為其他因，素例如社會經濟地位、獲得醫療服務能力、年齡甚至性別的差異仍然會導致評分準確性下降。另一些專家則認為，統計學上的問題一開始就被誇大了。

Kenny博士一直在幫助測試針對11種常見疾病的現有多基因風險評分，研究對象涵蓋不同種族和民族的患者。她認為，偏差過於複雜，難以迅速解決。

她說:“ 目前我不認為這些方法能完全通用 - 或許永遠也無法完全通用”; “但有些方法正在縮小那差距。”

               So, scientists are already developing clinical tools and techniques based on genomic information gathered from the agency’s All of Us program. One of the most promising may be the Polygenic risk scores. These can help predict a person’s likelihood of developing complex diseases. Yet the tools were trained overwhelmingly on the DNA of people of European descent, so they often fail to accurately assess risks for other descent. Apparently, we may never be able to completely close the gap that causes health care disparities, but some new approaches are starting to narrow the gap.

最佳基因風險評估工具並非對每個人都同樣有效（1/2）

Recently The New York Times reported the following:

The Best Genetic Risk Tools Don’t Work Equally for Everyone (1/2)

Genetic prediction models are poised to revolutionize medicine. But because they have been trained on European DNA, they threaten to widen health care disparities.

The NYT - By Emily Baumgaertner Nunn - Emily Baumgaertner Nunn is a national health reporter for The Times, focusing on public health issues that primarily affect vulnerable communities.

July 20, 2026, 5:01 a.m. ET

The National Institutes of Health recently announced a remarkable achievement: the world’s largest database of human genomes, a repository of information that the agency says will help usher in a new era in personalized medicine.

Scientists are already developing clinical tools and techniques based on genomic information gathered from the agency’s All of Us program. One of the most promising? Polygenic risk scores. These can help predict a person’s likelihood of developing complex diseases, like cardiovascular disease and breast cancer, from as early as infancy.

But there’s a glaring problem: The tools were trained overwhelmingly on the DNA of people of European descent, so they often fail to accurately assess risks for anyone else. For some conditions, the forecasts for people of color are almost no better than flipping a coin.

Researchers are racing to correct the biases through advancements in modeling and widespread recruitment of minority groups. They fear that failing to close the gaps could worsen health care disparities — and prevent the technology from realizing its promise of helping to decrease chronic disease.

“It really warrants attention,” said Eimear Kenny, the director of the Institute for Genomic Health at Mount Sinai and a principal investigator in a national consortium that tests the genetic scores.

Geneticists measure a polygenic risk score by aggregating hundreds or thousands of tiny variations across the genome, many of which might nudge a person’s disease risk by only a fraction. By bundling them together, they can generate a cumulative score, ranking a person’s predisposition to common killers.

For patients at the extremely high end of the risk distribution, the scores can offer a medical advantage, potentially giving doctors a decades-long runway to intervene. Those in the highest percentiles for a cardiovascular disease score, for instance, can be prescribed cholesterol-lowering statins earlier, potentially helping prevent some heart attacks and strokes.

But the entire premise rests on the data used to train the model in the first place. The UK Biobank — established in the early 2000s and widely considered a leading genomic trove — is overwhelmingly homogenous; roughly 94 percent of its participants are white. The Million Veteran Program, a repository run by the U.S. Department of Veterans Affairs, is almost three-quarters white.

The cohort in the All of Us program, built by the N.I.H. with the goal of diversifying data, is still about 50 percent European, according to Alicia Martin, a statistical geneticist at the Broad Institute. The effort’s future is also at risk: One of its major funding streams, the 21st Century Cures Act, is set to expire at the end of this fiscal year. (The program’s budget has already been reduced by 72 percent since 2023.)

“You can’t just snap your fingers and overnight have a biobank from Africa, a biobank from India, a biobank from China that are all as large and open and comprehensive as the UK Biobank,” Dr. Martin said. “We can develop all the fancy statistical methods we want, but still not be able to overcome without more diverse representation.”

Other clinical tools could be affected by the ancestry bias, such as integrated artificial intelligence models that draw upon genomics and other biomedical data. Doctors who sequence a genome to look for rare mutations may also find genetic variants in patients of color that researchers have not studied enough to understand.

Genetic blind spots can also cause scientists to miss discoveries that benefit everyone. One example: the PCSK9 gene. Scientists found that a rare mutation that occurred mostly in a small proportion of Black people could naturally reduce the risk of developing coronary heart disease by 88 percent. That set off a race to develop drugs that mimic the mutation for people of all races and ethnicities. But scientists only found the variant because one study in Dallas had ensured that its participant pool was more than half Black.

(to be continued)

Translation

最佳基因風險評估工具並非對每個人都同樣有效（1/2）

基因預測模型有望徹底改變醫學。但由於這些模型是基於歐洲人的DNA進行訓練的，它們有可能加劇醫療保健方面的差異

美國國立衛生研究院（NIH）最近宣布了一項非凡的成就：建立了世界上最大的人類基因組資料庫。該機構表示，這個資訊庫將有助於開啟個人化醫療的新時代。

科學家已經開始基於NIH的「我們所有人」（All of Us）計劃所收集的基因組資訊來開發臨床工具和技術。其中最有前景的是什麼？多基因風險評分。這些評分可以幫助預測一個人從嬰兒時期開始患上複雜疾病（如心血管疾病和乳癌）的可能性。

但有一個顯而易見的問題：這些工具主要基於歐洲裔族群的DNA進行訓練，因此往往無法準確評估其他族群的風險。對於某些疾病，對有色人種的預測結果幾乎不比擲硬幣好到哪裡去。

研究人員正爭分奪秒地透過改進建模技術和廣泛招募少數族裔群體來糾正這些偏差。他們擔心，如果無法彌合這些差距，可能會加劇醫療保健方面的差異，並阻礙這項技術實現其降低慢性病發病率的承諾。

西奈山基因組健康研究所 所長、同時負責測試基因評分的國家聯盟首席研究員Eimear Kenny表示:「這確實值得關注」。

遺傳學家透過匯集基因組中數百或數千個微小變異來測量多基因風險評分，其中許多變異可能只會對一個人的患病風險產生微乎其微的影響。而透過將這些變異組合在一起，他們可以產生一個累積評分，從而評估一個人罹患常見致命疾病的傾向。

對於處於風險分佈極高端的患者而言，這些評分可以提供醫療優勢，使醫生有長達數十年的干預時間。例如，心血管疾病評分處於最高百份位的患者可以更早服用降膽固醇的他汀類藥物，這可能有助於預防部分心臟病和中風。

但這一切的前提都建立在最初用來訓練模型的資料之上。英國生物銀行（UK Biobank）－成立於21世紀初，被廣泛認為是領先的基因組資料庫－其參與者構成極為單一，約94%為白人。由美國退伍軍人事務部營運的「百萬退伍軍人計劃」（Million Veteran Program）作為一個資料庫其參與者中近四分之三為白人。

據Broad Institute的統計遺傳學家Alicia Martin稱，由美國國立衛生研究院（NIH）建立的「我們所有人」（All of Us）計劃是旨在實現數據多樣化，但該計劃的參與者中仍有約50%為歐洲人。而這項工作的未來也面臨風險：其主要資金來源之一 - 《21世紀治癒法案》 - 將於本財政年度結束時到期。 （自2023年以來，該項目的預算已被削減了72%。）

Martin博士說: 「 你不能打個響指就可以可以在一夜間建立一個像英國生物銀行一樣規模龐大、全面開放、覆蓋面廣闊的非洲生物銀行、印度生物銀行、中國生物銀行」; 「我們可以發展出各種炫麗的統計方法，但如果沒有更多樣化的代表性，我們仍然無法克服這個問題」。

其他臨床工具也可能受到種族偏差的影響，例如利用基因組學和其他生物醫學數據的整合人工智能模型。醫生在進行基因組定序以尋找罕見突變時，也可能在有色人種患者身上發現一些研究人員尚未充分研究的基因變異。

基因盲點也可能導致科學家錯過一些對所有人都有益處的發現。例如：PCSK9基因。科學家發現，一種主要存在於少數黑人中的罕見基因突變，能夠自然地將罹患冠心病的風險降低88%。這引發了一場旨在模仿這種突變研發藥物的競賽，做出適用於所有種族和民族的人藥物。但科學家之所以能夠發現這種變異，是因為達拉斯的一項研究確保了其參與者中的黑人佔超過一半。

 （待續）

中國經濟成長創多年來新低（2/2）

Recently The New York Times reported the following:

 China’s Economy Grows at Slowest Pace in Years (2/2)

Economic growth of 4.3 percent in the second quarter, versus the same period last year, reflected a broad slump outside of the country’s export-oriented manufacturing might.

The NYT -By Alexandra Stevenson and Murphy Zhao - Reporting from Hong Kong

July 15, 2026

Updated 2:38 a.m. ET

(continue)

For China’s leadership, the question now is, what to do next? Li Qiang, China’s premier, told a group of entrepreneurs this week that officials were focusing on new drivers of consumption and ensuring job stabilization.

“It is important to take a comprehensive and objective view of the current economic situation, fully recognizing the achievements made while remaining cleareyed about the problems,” said Mr. Li, according to state media, which ran a story about the meeting on the front page of the official People’s Daily.

Some economists anticipate a discussion of fresh stimulus measures at a meeting of top policymakers later this month. On Monday, officials announced a plan to target $8.85 trillion of annual retail sales by 2030, implying a 20 percent rise from last year.

Beijing also promised to raise wages and increase household consumption as a share of the economy. It is currently around 40 percent, significantly lower than the 60 percent share of gross domestic product for most developed countries.

But analysts said these goals are not particularly ambitious. And stubbornly reluctant consumers in China show few signs of opening their wallets further.

On social media forums, shoppers share tips on how to scrimp and save, rallying around the motto to “save where you can, spend where you must.”

Users share tips about “shopping cart cooling-off periods,” or leaving nonessential items in carts for three days before deciding to buy them. (The practice is a wry nod to the officially enforced “cooling-off period” for couples seeking divorces.)

Others push to replace foreign cosmetic brands with cheaper local alternatives, and to substitute skin care products with baby lotion. “Buy what’s right, not what’s pricey,” a user on the social media network Weibo posted recently.

All the while, sales have continued to fall for products as varied as cosmetics and automobiles. For categories like cars, the recent plunge has been accentuated by the end of a policy to incentivize purchases.

Since a devastating property crash, Chinese policymakers have tried to replace the growth generated by the real estate sector with more robust consumer spending. They rolled out huge subsidies for households to trade in old cars, home appliances and phones from 2024 through last year.

While it generated some activity, the policy failed to address the plummeting value of property, where most household wealth is concentrated. Now, economists say, China is in a “payback period” following the jump in policy-induced sales.

As the economy splits between the relatively few who benefit from China’s role in the global A.I. boom and the rest, the divide is having a profound impact on the country’s social fabric.

China’s property bust has led to more than 14 million people losing construction jobs. Many of those workers bought apartments in smaller cities, far from the pockets of A.I.-generated wealth that may revive parts of the property market.

The A.I. boom “doesn’t benefit ordinary people in China because this priority, the industrial focus on high tech and semiconductors, actually causes structural unemployment and underemployment,” said Dan Wang, the China director at Eurasia Group, a consulting firm.

What’s more, Ms. Wang said, disposable income growth is now lower than economic growth. If that continues, she noted, “that means the national income is skewed in distribution toward government and companies, and not consumers.”

Translation

中國經濟成長創多年來新低（2/2）

第二季經濟成長4.3%，與去年同期相比，反映出除出口導向製造業之外，中國經濟普遍下滑

（繼續）

對中國領導階層而言，現在的問題是：下一步該怎麼辦？中國總理李強本週對一群企業家表示，官員正著力尋找新的消費動力，並確保就業穩定。

根據官方媒體報道，李強總理表示：「重要的是要全面客觀地看待當前的經濟形勢，充分肯定已取得的成就，同時也要清醒地認識到存在的問題。」《人民日報》頭版刊登了關於此次會議的報道。

一些經濟學家預計，本月晚些時候，高層決策者將召開會議，討論新的刺激措施。週一，官員們宣佈了一項計劃，目標是到2030年實現8.85兆美元的年度零售額，這意味著比去年增長20%。

北京方面也承諾提高工資，並提高家庭消費佔經濟總量的比重。目前，家庭消費佔國內生產毛額（GDP）的比重約為40%，遠低於大多數已開發國家60%的水準。

但分析人士表示，這些目標並不算特別雄心勃勃。而且中國那些固執地不願花錢的消費者，幾乎沒有進一步去增加支出的跡象。

在社交媒體論壇上，購物者分享著如何精打細算的技巧，他們奉行著「能省則省，當用則用」的原則。

用戶們也分享了「購物車冷靜期」的技巧，也就是把非必需品放在購物車裡三天後再決定是否要購買。 （這種做法是對官方強制執行的離婚夫婦「冷靜期」的一種諷刺。）

另一些人則試圖用更便宜的本土品牌化妝品取代外國品牌，用嬰兒潤膚露代替保養品。 一位微博用戶最近發文說:「買合用的，不買貴價的」。

同時，包括化妝品和汽車在內的各種產品的銷量持續下滑。對於汽車等類別而言，近期的暴跌因一項旨在刺激消費政策的結束而加劇。

自從房地產市場崩盤以來，中國決策者一直試圖用更強勁的消費支出來彌補房地產行業帶來的成長。他們從2024年開始，為家庭以舊換新提供巨額補貼，用於舊車、舊家電和舊手機的置換。

雖然這項政策刺激了一些消費活動，但它未能解決房地產價值暴跌的問題，而大部分家庭財富都集中在房地產領域。經濟學家表示，在政策所刺激的急增銷售後，中國目前正處於「償還期」。

隨著經濟因出現少數受益於中國在全球人工智能熱潮的人，與其餘人群之間出現分化。這種分化正在對中國的社會結構產生深遠的影響。

中國的房地產泡沫破裂導致超過1,400萬人失去了建築業的工作。許多這些工人在較細小的城市購買了公寓房，遠離一些人工智可能創造財富而重振部分房地產市場的小區。

諮詢公司歐亞集團 中國區的總監Dan Wang表示，人工智能熱潮「並沒有惠及中國的普通民眾，因為這種優先發展高科技和半導體產業的政策實際上導致了結構性失業和就業不足」。

此外，Wang女士也指出，目前可支配收入的成長低於經濟成長。她表示，如果這種情況持續下去，“這意味著國民收入的分配將向政府和企業傾斜，而不是向消費者傾斜。”

          So, China’s economy is at the slowest point in three years, reflecting a broader slump. Although China’s factories are churning out chips and electric cars to supply a global boom in artificial intelligence and energy-saving products, many Chinese people are feeling squeezed at home. The economy is splitting between the relatively few who benefit from China’s role in the global A.I. boom and the rest. Furthermore, it is noted that people’s disposable income growth is now lower than economic growth. If that continues, it would mean that the national income is skewed in distribution toward government and companies, not consumers. Apparent, the government needs to do more to deal with the economic slowdown.