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（中央社舊金山8日綜合外電報導）iPhone製造商蘋果（Apple）秋季發表會明天登場，料見可折疊旗艦新機及Siri新功能。分析師看好新執行長特納斯將證明蘋果仍能推出突破性硬體及具人工智慧（AI）競爭力Siri。

路透社報導，蘋果明天預期將發表高階機種iPhone Pro和iPhone Pro Max的升級新版，而iPhone Air和其他iPhone標準及入門機種則要等到明年春天才會更新，好讓這次發表會聚焦在真正主角：首款折疊式iPhone。

分析師表示，儘管這款可如護照般開闔的新iPhone預期售價高達2500多美元（約新台幣7萬9000元），且相機和處理器規格可能並非頂級，但仍有望在新崛起的折疊手機市場快速成為領導產品。

早在2019年起，蘋果的對手如三星（Samsung）、Google（谷歌）和中國的華為已開始推出折疊手機。但分析師認為，蘋果雖較晚進入市場，但在解決這類產品幾項關鍵技術難題後，包括讓折線轉軸在開闔無數次後仍感覺堅固，以及讓螢幕摺痕降到最低，可望使蘋果因晚到而受惠。

至於虛擬助理功能Siri，蘋果先前已宣布將進行深度大改，部分利用Google的AI技術。Siri這項行動經過多年延宕後，如今可能成為本月1日才接替庫克（Tim Cook）上任的特納斯（John Ternus）首項重大成就。

分析師表示，Siri若能在今年內實現成功改革，就有不錯機會可追上長年對手、Google的Gemini。

蘋果在生成式AI領域起步較慢，但投資人大多不以為意，原因是看中蘋果握有10億多台活躍使用裝置及忠誠客群。但如今特納斯面臨壓力，要說服消費者蘋果的裝置是運用AI最佳方式。

特納斯的主要挑戰將是說服客群願意使用新版Siri，讓他們及第三方軟體開發人員相信蘋果在AI領域能與OpenAI這類對手並駕齊驅，後者往往每隔幾週就會推出重大升級，而蘋果傳統上更新速度較為緩慢。

由於特納斯過去5年擔任低調的蘋果硬體技術主管，因此分析師預期，未來他很可能把焦點放在推出強大的裝置，來作為蘋果擴展自家AI的基礎。（編譯：張正芊）1150908

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（中央社台中8日綜合外電報導）台灣航太業者漢翔航空工業正快速拓展業務版圖，從傳統飛機製造跨足反無人機系統領域，反映出台北當局對中國無人機系統能力持續提升的高度警惕。

日經亞洲（Nikkei Asia）報導，漢翔航空工業副總經理蔡明晃表示：「從這次伊朗的經驗就可以看出，沙烏地阿拉伯及荷莫茲海峽（Strait of Hormuz）對岸的其他國家，正持續受到無人機干擾。因此，對台灣而言，擁有一套完善的反無人機系統非常重要，因為中國目前正大力推動無人機發展。」

蔡明晃說，這不單是台灣才會面臨的挑戰，「全球普遍認為，傳統防空系統對付無人機不符成本效益，因此各國必須打造專屬的無人機防禦系統與反制系統，並配備專用的雷達等設備。」

蔡明晃表示，無人機防禦機制包含在目標癱瘓前，先進行偵測、辨識與追蹤。

他說，「手段上分為軟殺（soft kill）與硬殺（hard kill），軟殺是利用干擾、欺敵或劫持等技術」，入侵無人機的GPS與通訊訊號，使它們迷失方向，「硬殺則是直接將它摧毀，最簡單的方式是使用砲彈，成本較高的手段則是發射火箭」。

身為台灣唯一民用及軍用大型航機製造商，漢翔在國家防空體系中扮演關鍵角色，同時也是台灣與波音（Boeing）、空中巴士（Airbus）等全球航空巨頭之間的重要橋梁。

台灣正全力提升防禦實力，同時密切關注歐洲等其他國家，如何因應無人機挑戰。台灣科技、民主與社會研究中心（DSET）在去年一份通訊刊物中指出，歐洲目前強調建立多層次反無人機系統，結合感測器、指管通情網路，以及涵蓋動能與非動能手段的目標摧毀機制。

DSET指出，除了漢翔，總部位於新竹的「創未來科技」（Tron Future Tech）是另一家積極研發反無人機系統的台灣業者。

對台灣而言，反無人機系統成敗的關鍵在於成本效益。

蔡明晃指出：「常規飛彈與武器系統，原本是設計用來對付高造價的敵對武器，這就是為什麼在當前的不對稱防禦態勢下，專屬反制系統會如此至關重要。」

漢翔正積極研發各類硬殺手段，蔡明晃強調這些方案「必須非常便宜」。

他說，傳統防空砲彈造價最便宜，但存在「精準度不佳的問題」；第2種是造價相對低廉的小型火箭，不是昂貴的愛國者（Patriot）飛彈，而是成本更低、目前正在研發中的彈種；第3種則是直接出動無人機去衝撞敵方無人機，「我們稱之為攔截機，也就是用無人機作為攔截工具」。

台灣在全球無人機產業鏈的地位正逐步攀升。DSET最新研究估計，台灣今年第1季對歐洲的無人機出口量，已超越去年全年總出口量。

蔡明晃指出，海外需求最強勁的是台灣製造的馬達與電池，客戶希望藉此降低對中國相關零組件的依賴；他並表示，「國際客戶目前也正逐步對飛控板、AI元件等特殊模組展現出興趣」。（編譯：張茗喧）1150908

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（中央社舊金山7日綜合外電報導）OpenAI於9月3日發表最新模型GPT-6 Astra，輝達執行長黃仁勳於社群平台發文盛讚「通用人工智慧（AGI）已降臨」。但有專業人士對此暫時保留態度。

OpenAI表示，Astra代表人工智慧（AI）領域「向前邁出一大步」，總裁布羅克曼（Greg Brockman）稱「我們現在進入通用人工智慧（AGI）時代，這種說法並不誇張」AGI是指AI發展的新階段，屆時AI系統在多數工作上的智慧程度將與人類相當。

OpenAI宣稱，GPT-6在軟體工程、科學、數學以及專業工作流程等各種任務上，都能展現出驚人能力。

輝達（Nvidia）執行長黃仁勳6日也在社群平台X上發文，寫道「GPT-6 Astra，使用約10萬顆以上的NVIDIA Grace Blackwell NVLink72 GPU訓練。從ChatGPT、o1再到Astra，只花了4年。AGI已經到來。恭喜@OpenAI團隊。下一步將是40萬顆GPU上線」。

科技新聞網TechRadar指出，GPT-6 Astra更關鍵的突破在於「代理人能力」（Agentic Work）大幅升級，讓使用者在委派多階段任務時，大幅降低AI脫序或曲解指令的風險。

TechRadar資深主筆赫克特（Hamish Hector）指出，通用人工智慧（AGI）其實沒有明確且公認的定義，「判斷究竟有沒有實現AGI，有點像是在判斷一件藝術品究竟算不算傑作」。

他認為廣義而言，AGI是一種能在所有涉及思考或推理的任務上擊敗人類的AI系統，更重要的是它還應能運用訓練過程中學到的知識適應新問題。例如AGI遇到新挑戰時應能自行學會新技能來克服問題，而不需重新訓練或重新編寫程式，它只要運用已掌握的知識，就能持續進化。

赫克特表示，實現AGI一大重要原因，是為讓AI發展再邁入下一階段：「超級人工智慧」（ASI）。ASI將會是一種人類無法企及的AI，將能處理並解決甚至人類都無法理解的問題。AGI顯然不等於ASI，但AGI能自我訓練、為自己開發能力，意味AGI最終很可能透過層層自我改進，建構出ASI。

透過 Google News 追蹤中央社

不過赫克特也提醒，黃仁勳高調宣稱AGI到來的背後，難掩濃厚的商業推力。從黃仁勳貼文裡先稱Astra是使用10萬顆NVIDIA高端晶片所打造出，然後又說下一步就必須讓40萬顆晶片上線運作，顯然AGI目前暫時成為投資市場上的熱門詞彙。

赫克特認為，企業和執行長有非常強烈的誘因宣稱當前已實現AGI，之後也可能對下一個新模型做出同樣的宣稱，「因此，如果未來GPT-7又被稱為AGI正式登場，我也不會感到意外」。（編譯：陳亦偉）1150908

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（中央社紐約7日綜合外電報導）資料中心的龐大用水用電議題，正在美國面臨民眾強大不滿聲浪。但根據對資料中心投資成百上千億美元的科技業大公司說法，至少用水問題是可以解決的。

法新社報導，資料中心形同放滿電腦伺服器的倉庫，這些電腦用來運作網際網路，且越來越多用於人工智慧（AI）運算。當中使用的晶片運作時溫度可超過攝氏80度，需要用水降溫。

挪威獨立能源商情公司Rystad Energy指出，去年全球資料中心冷卻用水量達2220億公升，若未採取控制措施，到了2030年可能翻近3倍至6440億公升；若有，則有望將增幅控制在2倍內。

美國AI晶片大廠輝達（Nvidia）為此採用新方法，用閉環式冷卻系統來節省用水，並讓水直接流經伺服器，以盡可能靠近晶片。

不過，閉環式冷卻系統可能讓用電量增加，因為這些密閉管線中的冷卻用水仍須以某種方式降溫，通常是靠吹送氣流，而這需要用電。

輝達緩和這項問題的方法，是讓水以較高溫度進入伺服器，以減少冷卻吹氣用電。輝達永續發展事務主管派克（Josh Parker）指出，這種做法通常用簡單風扇讓空氣循環就足以降溫。

輝達設定的進水溫度是攝氏45度。而根據資料中心認證機構Uptime Institute的2024年資料，其他閉環式冷卻系統多數進水溫度是攝氏32度左右。

輝達今年6月甚至在1份報告中說，當一些設施部署其最新AI工廠級平台DSX時，幾乎可以完全不消耗水。DSX是用來設計及管理AI資料中心。

輝達的說法雖然大膽，但業界確實正面臨愈來愈大壓力要實現這個目標。

美國雲端運算業巨擘微軟（Microsoft）、亞馬遜網路服務（Amazon Web Services，AWS）和Meta告訴法新社，他們也使用閉環冷卻系統，且完全沒有水淨損失。

其中微軟和亞馬遜雖在2022至去年間持續將其本已眾多的資料中心數量擴大，但根據兩公司最新永續報告，這段時期他們的用水效率皆提升，微軟加強了25%，亞馬遜提高了37%。

然而，冷卻用水只是資料中心用水的一部分。資料中心本身及當中使用的晶片和伺服器，都需要用電來運作或製造，而這部分發電也需要用水。

以美國為例，這些隱性用水量，有可能達到資料中心本身用水量的2倍。（編譯：張正芊）1150907

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（中央社記者吳家豪台北7日電）為降低航空業對氣候的衝擊，Google今天宣布正與國泰航空合作，在亞太地區展開人工智慧（AI）預測飛機凝結尾跡（contrail，俗稱飛機雲）避讓技術的測試；初步結果顯示，透過AI預測並微幅調整飛行高度，可降低約40%因凝結尾跡造成的暖化效應。

Google在官方部落格發文說明，飛機在高空寒冷、潮濕的空氣層飛行時，引擎排放的廢氣常在機身後方凝結成白色條狀尾跡。多數尾跡會迅速消散，但部分會擴散為持久性雲層並吸收滯留熱量，凝結尾跡約占航空業整體氣候暖化影響的1/3。

過去幾年，Google致力將凝結尾跡避讓技術從AI預測模型推進到真實飛行測試，涵蓋美國境內與北大西洋航線。這次攜手國泰航空，是Google在亞洲第一個商業航空合作夥伴，測試範圍延伸至超長途航班（航程超過16小時或距離逾1.3萬公里）。

Google指出，凝結尾跡避讓的原理，類似飛行員為繞開亂流而調整航道。透過整合AI預測模型、衛星影像分析與先進氣象情報，系統可在飛機起飛前精確鎖定容易生成尾跡的空域，協助規劃微幅的高度調整，並在安全規範內執行，既不影響飛安也不會干擾旅客。

在實際操作層面，國泰航空透過機上Wi-Fi與專屬電子飛行資料夾（EFF），將動態氣象預測直接傳送至駕駛艙，讓飛行員在不打斷標準飛行作業流程的情況下，獲取即時數據。

Google表示，在初期鎖定100多個目標航班的測試中，有超過80架次遵循凝結尾跡避讓航線；根據Google的衛星影像分析估算，這些航班減少約40%的尾跡暖化效應。其中，「香港－新加坡」航線因空域容易形成持久尾跡，單一航線的避讓調整就貢獻整個試驗逾50%的減排效益。

Google強調，相較於等待新型機隊或替代燃料普及，凝結尾跡避讓是現階段最立即可行、具成本效益的航空減緩暖化解決方案。Google正與國泰航空邁入第二階段更大規模的試飛合作，並攜手非營利倡議組織Contrails.org，持續驗證凝結尾跡避讓技術在亞洲與跨太平洋各類不同營運環境下的運作機制。（編輯：林家嫻）1150907

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On August 4, Grant De Swardt, an independent AI consultant in East Sussex, U.K., noticed something strange going on with his Claude Max 20x account. He hadn’t been working that day, yet his token usage was climbing.

The next day, he disabled everything he had attached to Claude and did not work with it. Token consumption again increased. “In the clearest controlled interval, it increased from 45% to 55% while I performed no work, scheduled Cowork tasks were paused or completed, Dispatch/cloud execution was disabled, and there was no corresponding active local Claude Code task,” De Swardt told TechCrunch.

What was eating up his token allowance? He had no idea, so he contacted Anthropic and asked for an itemized list. Anthropic didn’t provide one, but it agreed something was off. It suspended his paid account, invalidated all of his sessions and server-side Claude Code tokens, and issued him a partial refund of £44.49 for the remaining time on his $200-per-month subscription.

The suspension wreaked havok on his business, he told TechCrunch. His job is to help small and mid-size businesses set up agents — a sort of forward-deployed engineer for hire — for tasks like automatically loading purchase-order data from emails into the accounting software.

As a sole proprietor, he relies on agents throughout his whole business, too: daily admin tasks, website design, coding. “Like everything is just running through AI these days,” he said.

After investigating, Anthropic told De Swardt it found the culprit: A compromised Claude session key was used to mint unauthorized Claude Code OAuth tokens. The company told him the account “appeared to have been used by an unauthorized-looking third-party service to handle activity for other people, but they could not determine how it obtained access,” he told TechCrunch. “They say the evidence is consistent either with credentials/session data being taken without my knowledge, or with the account having been connected to an outside service.”

In other words, a hacker was able to obtain access to De Swardt’s account and was covertly siphoning off his tokens. Because account support tracks total usage but not itemized usage, even upon request, this kind of theft could have gone on for months undetected.

He posted his experience on Reddit and after 80 comments, he discovered he was not alone. One person claimed that their account “was auto-upgraded without my consent, my credit card got charged, and the usage shot from 0% to 100% automatically without me even touching it.” Another saw usage go from 0 to 49% in 12 minutes, when all they had used it for was a couple of prompts and a web search.

One Claude user said their account burned through its max tokens every day for three days without them using it at all; this person then created a GitHub report about it. Like with the Reddit post, other users shared similar experiences there, too.

Two of them posted emails from Anthropic where the company had — to its credit — identified and warned them that their tokens were being stolen.

“We have recently become aware of a bad actor that is using common infostealer malware to steal Claude login sessions from people’s computers, then using those login sessions to access Claude accounts and consume their usage,” the email read. Infostealers are a type of malware that installs itself on a user’s computer and steals saved passwords, session data, and login credentials.

When Anthropic saw suspicious activity, it signed the users out, invalidated existing authorizations, issued some refunds, and warned them that they may have malware.

The company also said the malware didn’t come from using Claude itself. Such malware can be picked up from many sources online, from downloading infected software to clicking on infected ads.

Anthropic did not send De Swardt one of those emails. He insists he found no evidence that his computer was compromised and says he still has no way of determining how hackers gained access.

De Swardt’s Claude account was reinstated after about two weeks. But the difficulty of getting speedy help for the matter, plus the lack of an itemized usage, soured him on Claude. He cancelled his subscription in favor of Cursor and its ability to use multiple models, including more affordable open source options.

In his experience, these other models work as well as Claude. “It’s not that much different or better,” he said, adding that he can’t see going back “without [Anthropic] actually having resolved the issue in any way.”

He says Anthropic still lacks tools that allow users to see what’s consuming their tokens. “I don’t think there’s any way that these people can protect themselves.”

When asked for information on how users can identify misuse, Anthropic declined to comment.

Cognition, the startup developing coding assistant Devin, announced it raised $2 billion at a $48 billion valuation. The round, which comes just four months after Cognition’s previous fundraise at a $26 billion valuation, was led by Andreessen Horowitz, Accel, Founders Fund, General Catalyst, and Avenir.

The startup’s soaring valuation signals that VCs still see room for multiple major players to capture meaningful market share in AI coding, one of the technology’s most significant applications. Cognition said that since announcing its last fundraise in May, its annualized run-rate revenue has grown from $492 million to $900 million. While the startup didn’t explain how it calculates that run-rate revenue, the metric is usually defined as a month’s top line multiplied by 12.

Cursor, another popular coding assistant, was in talks in April to raise capital at a $50 billion valuation before agreeing to sell to SpaceX for $60 billion later that month. At the time of the funding talks, Cursor’s annualized revenue had surpassed $2 billion. This means Cognition currently commands a higher revenue multiple than Cursor did in the spring.

Cursor ultimately sold to SpaceX largely because it was severely compute-constrained, according to investors familiar with its financials. It is unclear whether Cognition will face similar compute shortages.

Cognition leases an Nvidia server cluster that costs hundreds of millions of dollars annually, which could push its total cash burn to $800 million this year, The Information reported.

Like Cursor did before joining SpaceX, Cognition is training its own model based on open source alternatives. Over time, reducing its reliance on expensive third-party models from OpenAI and Anthropic will help cut costs and bring the company closer to breakeven.

Cognition is expected to reach $4 billion to $5 billion in annualized revenue by the end of 2026, according to The Information. By comparison, TechCrunch reported in the spring that Cursor was on track to surpass $6 billion by year-end. Still, it’s notable that a16z, a major backer of Cursor, which made a killing when it sold to SpaceX, is back to lead a round in a Cursor competitor.

Founded in 2024 by math prodigy Scott Wu, Cognition counts Mercedes-Benz, NASA, Goldman Sachs, and Citi among its major enterprise customers.

The queue to send objects into orbit is growing, but space infrastructure isn’t keeping up with demand. As the realization sinks in that Elon Musk’s SpaceX can’t be the sole answer, other companies are raising their hand — and money.

These include The Exploration Company (TEC), which operates out of Germany, France, Luxembourg, Spain, and Italy, and which has raised $450 million in what it describes as “the largest-ever Series C by a European space company.” But the race is global, with U.S.-based rival Stoke Space currently raising a $1 billion Series E round.

Rivalry aside, these rounds show that investors on both sides of the Atlantic are willing to back companies seeking to build reusable spacecraft. That’s a category pioneered by SpaceX, but that many players, both public and private, now believe it shouldn’t solely control as space gains strategic importance.

These sovereignty-related concerns create extra tailwinds for an EU-headquartered company like TEC. Led by Hélène Huby, a French national who spent two decades at Airbus and ArianeGroup, the company intends to use its funding to build the foundation for a reusable European heavy-lift launcher offering an affordable alternative to SpaceX.

SpaceX itself may have no other choice but to loosen its nascent monopoly on low-earth-orbit launches to prioritize its own payloads; reports suggest that its Falcon rockets are already increasingly reserved for its Starlink satellites, amid uncertainty as to what will happen when these get retired in favor of Starship.

This creates demand that TEC won’t be able to meet for years, even if it sticks to its self-admittedly ambitious timeline. According to Atomico, “10 Nyx missions are booked,” with “more than $2 billion in contracts and commitments.”

Some of these commitments come from institutional partners, including the European Space Agency, but also NASA. Its Series C was also transatlantic, with Atomico, and the EQT-managed Scaleup Europe Fund co-leading the round alongside Bessemer Venture Partners, whose partner Alex Ferrara will join TEC’s board of directors.

This latest round also consolidates TEC’s status as a company mostly funded by private investors, and one that plans accordingly. In a press conference earlier today, Huby said that she’d love to see Europe add human space flight to its roadmap, but wouldn’t be able to convince VCs to spend $4 billion towards a crew capsule, “then wait 10 years.”

So, TEC’s timeline is more pragmatic, but it is tight. Its most immediate focus is Nyx, a reusable capsule akin to SpaceX’s Dragon and capable of carrying cargo, then people. The company intends to get Nyx to dock with the soon-to-be-retired International Space Station and return safely to Earth by November 2028.

According to Huby, funding from the Series C will stretch to also fund Storm, a rocket engine program paving the way for further developments, at least initially. “Where we need more money is to build a launch pad, and to build the rocket itself,” she told journalists.

TEC wouldn’t be the first European commercial space company to launch a rocket — German startup Isar Aerospace crossed that milestone out of Norway last weekend. Huby praised the feat as “great news for the whole ecosystem,” but said that TEC intends to build a much bigger rocket than Isar’s expandable Spectrum vehicle.

However, Isar also has “a much bigger” launch vehicle in its roadmap, Ars Technica reported — presumably for the same reason Huby gave as to why TEC does. “In the next five to 10 years, half of our broadband communication could come from space, data centers could be in space; […] and if this is the case, we need massive rockets and cadence.”

That cadence will rely on reusability, which TEC plans to reach by using the same technology as SpaceX and Stoke: full-flow stage combustion “It’s the most difficult, but also the most efficient engine cycle in the world. It is the first time ever in Europe that a company develops such a rocket engine,” Huby said.

TEC’s advances haven’t gone unnoticed in Europe, with French president Macron and the European Commission’s president Ursula von der Leyen both praising its Series C. It is also not a coincidence that it was disclosed on the eve of the International Space Summit in Paris, where Huby hinted that related announcements will be made.

Less than two weeks after Meta agreed to a massive $18 billion multistate settlement in a lawsuit over social media’s consumer harms, the company announced its biggest bet on consumer AI to date — and one that requires significantly more trust than social media ever did. On Tuesday, the company introduced Muse, its new personal AI agent that helps consumers with everyday tasks and projects for users in the U.S.

To use Muse, consumers will have to trust Meta with more of their personal information than ever before. The AI agent works by connecting to the user’s apps and services that are a part of everyday workflows, like email, calendars, payments, and other things the individual may regularly use, like apps for health and fitness, the smart home, dining, shopping, music and events, and more.

The idea is a sizable bet on what comes after the ChatGPT era, where AI chatbots answered questions, served as sounding boards, or even became digital companions. Instead, Muse is focusing on AI that can actually do things for you.

Image Credits:Meta

The company says the agent can do things like sending emails, booking travel, lowering bills, filling out forms, creating plans, turning recipe reels into grocery lists, sending party invitations, and making purchases, leveraging Link by Stripe for checkout. The latter offers purchase protections, which could potentially ease consumers’ fears of letting an AI check out on their behalf. (Shopify’s Shop Pay and 1Password integrations are also coming soon.)

Muse’s users can decide which apps and services they want to connect, doing so one at a time, to make the opt-in nature of using Muse more transparent. The agent is powered by Meta’s AI model, Muse Spark, and ships with built-in connectors (pictured below) for several services, with plans to add more over time. If a service the user wants isn’t available but offers a public API, Muse can set up a connection using credentials the user provides. When no API is available, Muse can access the service via the browser instead.

Image Credits:Meta

Muse will initially be available via the web at muse.ai, through apps on iOS and Android, and through chats in WhatsApp. It will soon also make its way to Meta’s AI glasses, the company says. It will be free to use, with subscription plans kicking in as usage increases, which is why Muse requires a payment card to get started.

Two paid plans will be available at launch: Power at $20/month and Maximum at $100/month. Both of these subscriptions offer more Muse usage for handing off everyday tasks, though Meta believes most people will remain on the free tier. (The company says the app includes a usage meter that shows users what percentage of their usage they have left. It will also warn users when free usage runs out and present options to subscribe.)

Like other AI agents, Muse will continue to work even after the user leaves the app. It will also improve over time by learning from the user’s conversations what’s important to them to make suggestions unprompted, Meta noted.

Image Credits:Meta

The concept is not unique to Meta. The agentic era is now coming into its own, as larger companies and smaller startups alike are experimenting with how AI agents will make the most sense for consumers and can become integrated into people’s daily lives.

Some have tried AI web browsers or services, like Gemini Spark or Claude Cowork, that can kick off various tasks on consumers’ behalf. Others are integrating AI into the chat applications consumers use the most, like Apple’s iMessage platform, SMS, and WhatsApp.

Image Credits:Meta

Despite their usefulness, these powerful agents have forced consumers to wrestle with difficult questions about how much privacy they’re willing to give up. Many early testers of the AI assistant Instinct were shocked to see the app required a broad “perpetual and irrevocable” license to “access, use, host, cache, store, reproduce, transmit, display, publish, distribute, and modify” any of the user’s materials, including for training its AI models.

Image Credits:Meta

Under the hood, Meta claims that Muse runs in its own “dedicated, secure computer with its own browser,” Muse Secure VM, which offers various privacy, safety, and security protections over customers’ data. The company says a separate Sentinel agent runs on that same virtual machine, but is kept apart from Muse at the system level.

Image Credits:Meta

This means Muse won’t have visibility into people’s passwords or payment methods. Meta also claims that Muse doesn’t share people’s conversations or data with Meta’s ads systems.

(These claims are explained in more detail in a technical post, also released today, but will require deeper investigation by security experts.)

Could Meta’s history hurt Muse adoption?

Despite Meta’s documentation of its security measures, it remains to be seen whether the company has enough consumer trust for its agent to be successful.

As it stands, Meta has a history of proclaiming one thing and doing another. In 2011, for instance, the tech giant settled with the FTC over charges that it deceived consumers by making users’ private information public without their approval. In 2019, the FTC penalized Facebook in a then record-breaking $5 billion settlement over eight separate privacy-related violations. In 2023, the FTC charged Meta with violating a privacy order that was filed after the 2019 settlement.

In terms of technical matters, Meta has also had some big missteps before, having discovered in 2019 a number of users’ passwords in readable formats, exposing people to potential hacks. The massive Cambridge Analytica data scandal, which saw Facebook data belonging to millions of consumers collected by a third-party without their consent, still lingers in some people’s minds, too.

Mark Zuckerberg, chief executive officer of Meta Platforms Inc., exits Los Angeles Superior Court in Los Angeles, California, US, on Wednesday, February 18, 2026. Zuckerberg testified that it’s “very difficult” to enforce Instagram’s age limits and downplayed how much teen users do for the company’s business during a landmark trial over social media addiction. Image Credits: / Getty Images Kyle Grillot/Bloomberg (opens in a new window) / Getty Images

Meta has also been repeatedly hauled before Congress to testify on how it protected — or failed to protect — minors from harm. With Congress failing to act, Meta ultimately became the target of several related lawsuits, including the one Meta just settled with 29 states in August, a New Mexico lawsuit over harms for children, where Meta was ordered to pay $942 million, and the thousands of personal-injury and school-district cases that are still pending against multiple social media giants.

To ease consumers’ fears, Meta not only talks in depth about its security promises, offering technical documentation and explanations. The company has also designed Muse in a way that would make consumers feel more connected with the agent itself. Users can customize Muse by giving it a name, picking out its avatar, and configuring its look and various settings that dictate how the agent communicates with them.

Time will tell if this personal connection and the utility Muse provides are enough for consumers to once again trust Meta with their personal information.

Apple will host its annual iPhone event on Wednesday, September 9 at 10 a.m. PDT, and it’s expected to be a big one. All signs point to Apple’s long-awaited entrance into the foldable smartphone market, finally shipping this kind of hardware about seven years after Samsung did.

Apple has a YouTube stream prepared for the “Surprise and shine” event, and you can set reminder notifications within YouTube to make sure you don’t miss it (or you can bookmark this page). You can also watch the livestream on Apple’s own events site.

Aside from the foldable iPhone Ultra, Apple is also expected to announce the iPhone 18 Pro and iPhone 18 Pro Max models, for people who want an upgrade but don’t see the appeal of the foldable, or who don’t want to pay the rumored $2,000 price for the iPhone Ultra. The standard iPhone 18 model isn’t expected to launch until next year.

Apple may also release new hardware like an updated HomePod, camera-enabled AirPods, and new Apple Watches.

With new Apple CEO John Ternus at the helm, this will be Apple’s first major product announcement since longtime CEO Tim Cook stepped down.

NYU mathematics professor Tristan Buckmaster announced three proofs on Tuesday with a preliminary finding on one of the major unsolved problems in theoretical mathematics. The findings, made in collaboration with Anthropic mathematician Levent Alpöge and using both Codex and Claude AI models, are significant in themselves — but they’re also accompanied by an unusual controversy surrounding OpenAI’s attempts to solve the same problem.

“There is another part of this story,” Buckmaster wrote in his statement announcing the proofs, “and one that, honestly, I very much wish I did not have to be concerned with.” According to the statement, a parallel effort by OpenAI built on their work before it became public, leading to a tangle of academic rivalries and conflicting claims.

Shortly after the Buckmaster’s statement, OpenAI published a full proof of the Navier–Stokes existence and smoothness problem, which Buckmaster’s findings had taken steps towards. According to OpenAI, the proof was discovered by an unreleased next-generation model, which has tackled a range of different unsolved problems over the past week. All told, the week-long effort consumed 300 billion output tokens — $22.5 million worth of compute, if charged at current Astra rates.

The Navier-Stokes existence and smoothness problem is one of the seven Millennium Prize problems — a set of major unsolved math problems, each carrying a $1 million bounty from Clay Mathematics Institute for the first person or group to provide a solution. The Navier-Stokes equations are widely used in fluid mechanics but poorly understood in theoretical terms. A solution would represent a significant advance in the collective understanding of mathematical physics.

While Buckmaster and Alpöge were finalizing their own results, they learned that “information about our progress had been passed to OpenAI.” When they contacted OpenAI, they were told that OpenAI had already achieved a full proof of the central problem. But when they asked follow-up questions about when OpenAI had begun its research into the problem and how much human input was involved, the answers became more evasive.

“It emerged that an entire team had been working on the problem,” Buckmaster said, “and that an insane amount of compute had been used…. Eventually, it was agreed that [the first prompt] had been sent in the past few days, after information about our work had reached OpenAI.”

If true, that would suggest the OpenAI team had become convinced that Buckmaster and Alpöge’s approach was the right one, and decided to use its material advantage in computing resources to reach a formal proof first.

OpenAI’s post confirms much of this timeline, specifically saying that the latest effort began on September 1, inspired by rumors that two Millenium Prize problem had been solved. Additionally, the post confirms the ongoing conversations with Buckmaster and Alpöge.

Although the problem is widely pursued among mathematicians, the specific tactic taken by Buckmaster and his collaborator is far less common. As a result, Buckmaster found it suspicious that OpenAI ended up taking the same approach at the same time.

“The route to the Clay problem through a smooth force, options c and d in Fefferman’s statement of the problem, is the route Luis and Diego opened and the one Levent and I had quietly chosen to attack,” Buckmaster wrote. “Almost nobody else I know of was working on it,” he continued. “It is not the direction one arrives at in a few days by giving a model the problem statement.”

While Alpöge is employed by Anthropic, he was not conducting this research on the company’s behalf. As a result, the duo used a mix of models, relying primarily on OpenAI’s Codex in their work. Even so, Alpöge’s affiliation with a rival lab seems to have been a sore point for OpenAI, and Buckmaster alleges that Bubeck asked him to remove Alpöge’s credit as part of a proposed compromise.

When Buckmaster pushed to make the dispute public, he says that Bubeck replied: “Why would you ruin your career?” Buckmaster says that when he pushed back, Bubeck followed up with: “If you don’t want me to be nice, then I don’t have to be nice.”

Buckmaster also raised concerns that, because he used Codex extensively in assembling the project, information from his work could have informed OpenAI’s own efforts to solve the problem. OpenAI reserves the right to train models on Codex interactions, although users are able to opt-out. If the OpenAI team used a model trained on Buckmaster’s own Codex interactions, it’s plausible that it could have regurgitated his work when faced with a similar problem.

In its own post, OpenAI downplayed the possibility that regurgitation could have been involved. “We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem,” the post reads. “While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models⁠. However, our proofs differ significantly and even the precise results proved are different in the Euler case (forced vs unforced).”

Regardless, the issue is likely to reignite the ongoing debate about AI’s role in mathematical research, and OpenAI’s specific incentives. For his part, Buckmaster seems to believe the best answer is to get as much information about the research out into the public eye.

Update 2:35p.m. ET: Incorporated details from OpenAI’s release of the Navier-Stokes result.

Google Cloud and Accenture are working together on a joint unit dedicated to sending engineers into enterprises to help them better adopt Google’s AI tools and services.

The new unit, dubbed Accenture Gemini Enterprise Business Group, is Google’s latest foray into the increasingly competitive world of “forward-deployed engineers,” or FDEs. Rivals in the AI race, including OpenAI, Anthropic, Microsoft, and Amazon, have all recently launched separate business units in a bet that implementing AI models can become its own trillion-dollar business.

It’s the kind of bet AI companies and hyperscalers increasingly need to make. Hyperscalers are committing hundreds of billions of dollars a year to GPUs, data centers, and power capacity even as the revenue directly attributable to AI remains a fraction of that investment.

Google Cloud generated $24.8 billion in the second quarter, a big chunk of which was driven by enterprise AI. But the commitments behind that growth are enormous. Google Cloud’s parent company Alphabet reportedly accumulated $811 billion in purchase commitments and contractual obligations as of June 30.

This return on investment is not yet materializing in the way companies and investors need it to, so everything hinges on whether or not AI companies can create enough demand for their services. But that demand is not guaranteed, as enterprises themselves are struggling to see a true return on investment on their AI spending.

It’s conventionally held that enterprises have simply lacked the expertise to intelligently integrate AI tools and services into their workflows in a way that not only saves them money, but helps them make more of it in the long run. That’s where the FDEs come in as a steady, guiding hand that, ideally, possesses the perfect mental cocktail of business acumen and agentic AI prowess needed to change everything.

As part of its deal with Accenture, Google will train up to 1,000 of the consultancy firm’s FDEs to work with enterprises and build custom AI applications on the Gemini Enterprise platform. The organization will live under Accenture, according to a Google spokesperson.

According to August data from Ramp, Google accounts for roughly 6% of enterprise AI spending among Ramp’s U.S. customers, compared to Anthropic’s 43.5% and OpenAI’s 39.7%. (A Google spokesperson pointed out that many of Ramp’s customers exclude the types of major enterprises that are signing large, strategic AI deals with Google Cloud, which go beyond just model API usage — like Oracle, Meta, Anthropic, and ServiceNow.) Google’s new unit with Accenture, which The Wall Street Journal first reported, is the latest of its aggressive expansions of its FDE model this year as it attempts to resolve enterprise deployment bottlenecks and catch up to rivals.

Earlier this year, Google Cloud launched a $750 million partner ecosystem commitment that embedded Google’s own FDEs across multiple consultancies, including Capgemini, Cognizant, and Deloitte. The tech giant also struck a multi-year partnership with CVC Capital Partners to deploy FDEs directly into the investment firm’s portfolio companies.

Google isn’t the only giant at risk of being outpaced by newer firms. Companies that are dedicated specifically to embedding engineers into businesses to build bespoke AI workflows — like Ode with Anthropic, or OpenAI’s The Deployment Co. — threaten big consultancy firms like Accenture as well. For the professional services giant, the Google tie-up adds to its own wave of FDE programs this year, which include a similar Microsoft FDE practice in March, an FDE initiative with ServiceNow in May, and a joint program with SAP in June.

This article has been updated with comments from Google.

Last October, Google said it would bring an Iowa nuclear power plant back from the dead. Now the facility’s owner, NextEra Energy, has received a $1.9 billion loan from the U.S. Department of Energy to finance the refurbishment.

The sizable loan is the second of its kind, suggesting that the Trump administration views revived nuclear power as a key source of electricity for tech companies seeking to power their AI data centers. Last year, the Department of Energy extended a $1 billion loan to Constellation Energy to restart a reactor at Three Mile Island.

James Danly, Deputy Secretary of Energy, said that the Iowa power plant’s restart in 2029 will “drive down electricity costs,” though he did not explain how. Just 50 megawatts will be set aside for the local power cooperative, NextEra CEO John Ketchum said during an earnings call last year. That capacity would cover 18% of Iowa’s demand growth since 2021, the year before ChatGPT was released.

Google is reportedly looking to build up to six data centers near the Duane Arnold Energy Center, which hasn’t operated since 2020 when an intense rainstorm damaged the power plant. Rather than repair it, NextEra decided to mothball it. At the time, cheap natural gas was flooding the market, making nuclear power economically unappealing.

A lot has changed in the last six years, though. After decades of little growth in demand, the sudden rise of AI coupled with broader electrification of the economy meant that utilities and power providers were suddenly scrambling to find new generating sources of electricity. New data centers are expected to nearly triple the sector’s electricity demand by 2035.

Shuttered nuclear power plants are becoming one of the tech industry’s favorite choices to quickly provide clean, firm power.

Microsoft signed a deal with Constellation Energy two years ago to restart a reactor at Three Mile Island that last operated in 2019. The reactor is scheduled to restart in 2028 and generate 835 megawatts.

Another facility in Illinois, Constellation Energy’s Clinton Clean Energy Center, was in danger of closing down before its parent found a new customer in Meta, which is buying all of the clean energy attributes from the 1.1 gigawatt power plant. The arrangement will see Clinton sending its electrons to the local grid, while Meta will use the certificates to offset emissions it is producing elsewhere. The tech giant’s Hyperion AI data center, for example, will need 10 natural gas power plants to operate. If completed, the data center will consume more electricity than all of South Dakota.

Duane Arnold is smaller, but in the process of refurbishment, NextEra will squeeze an additional 14 megawatts from the facility, bringing the total to 615 megawatts.

Altogether, the three power plants represent the lowest hanging fruit in the U.S. There might be one or two more, according to a report from Utility Dive, though those candidates, including San Onofre in California, have been shuttered for longer and would require more work to bring back online.

Chrome has officially switched from a four- to a two-week release schedule, as Google promised earlier this year, with Tuesday’s launch of Chrome 153 on desktop, iOS, and Android.

The shift to faster releases is tied to Chrome’s evolving security strategy in the AI era, the company explained. As automated AI tools and community bug reports have pushed up the volume of patches and updates, Google says that a shorter release cycle makes it easier to manage security fixes.

This is also critical because faster-moving threats, some of which can also be attributed to AI, can be better addressed by shrinking the window between landing a fix in the public codebase and getting that fix to end users, the company notes.

A shorter release schedule would help keep the “N-day” patch gap — the gap between when a security vulnerability is known and when it gets patched — as small as possible.

Faster releases have another benefit, too: They can help Chrome ship features faster. This is also key in the AI era, as AI-assisted software development has enabled a host of new browser competitors to emerge.

While OpenAI’s web browser, ChatGPT Atlas, has been shut down, there are still plenty of other alternative browsers looking to carve out a piece of Chrome’s market for themselves, including Brave, Dia, Opera Neon, Perplexity’s Comet, DuckDuckGo’s browser, and more.

Plus, Google is experimenting with adding more AI features to Chrome and then rapidly iterating on those additions, which also demands faster updates.

The move to a two-week release schedule benefits the broader web as well. Because of Chrome’s position as the most-used browser globally, such changes can help set the standard for the industry. Mozilla, Microsoft, and Brave have already begun adopting a faster two-week schedule, following Chrome’s lead.

This is not the first time Chrome has adjusted its release schedule to enable faster patch management. The company first moved to a four-week release cycle in 2021, down from six weeks, after establishing its principles of “release early, release often” over a decade prior.

French AI lab Mistral AI on Tuesday said it has raised €3 billion (about $3.58 billion) at a post-money valuation of more than €21 billion (about $24.39 billion), confirming earlier rumors.

This Series D round, which Mistral said is “the largest equity fundraising round ever completed by a European technology company,” was led by Samsung Electronics, with EQT-managed Scaleup Europe Fund and existing investor PSG Equity joining as co-leads.

Mistral said it will use the funding to scale its compute capacity, build infrastructure, accelerate commercial growth, and expand its international footprint. The money could also help clarify its positioning — the company says its goal is not to build a European ChatGPT, and though its models haven’t gone mainstream, it still envisions itself as an AI lab.

The funding will help Mistral pursue a subtle shift in strategy that aims to further address concerns in Europe and elsewhere about being too dependent on the United States for tech, especially with the intensifying politics around AI regulation and products Stateside.

In addition to a push to build 1 GW of compute capacity in Europe by 2030, Mistral in August unveiled tools that let its customers choose which regions their AI queries are processed in. It’s also started hosting third-party, open-weight AI models (Chinese ones, too) to bolster its positioning as an AI services provider that wants its customers to control which AI models they use and how they use them.

The company on Tuesday pegged its frontier research as “the foundation underpinning its infrastructure, products and sovereignty,” which may be an indirect response to naysayers who interpreted the company’s decision to host Chinese models as a sign that it was turning into an inference provider.

Mistral’s mention of its global ambitions also likely is an effort to dispel a common misunderstanding that the company’s remit is contained within France. The lab now operates in 20 countries, and its go-to-market strategy is more focused on helping governments and corporations leverage AI and preserve a sense of control, unlike other frontier labs like OpenAI and Anthropic, which sell their AI models more broadly.

This strategy has built high hopes for Mistral as a French tech champion, as Samsung’s entry into Mistral’s cap table has the blessing of France’s authorities. In a post on X, French president Macron said the round reflected France and South Korea’s goal of “building a third way in AI.”

The fact that Mistral’s funding round warranted such a statement is a reminder of the geopolitical undertones that have surrounded the AI company, mostly to its benefit. Amid growing demand for sovereign AI infrastructure, not being an American company has reportedly boosted Mistral’s revenue.

On the other hand, the capital required to compete with the leading U.S. labs isn’t available in France alone. But with Dutch chipmaker ASML as a major partner and investor, and now Samsung, Mistral seems to have found a third way — similar to Germany’s Aleph Alpha’s merger with Canada’s Cohere.

Mistral still works with U.S. players, particularly Microsoft, through a strategic partnership the two companies significantly expanded in July. The new Series D was also backed by American investors: existing backers such as a16z, Nvidia, and Salesforce Ventures invested, as did new backers Advent and BlackRock.

Still, with the Grand Duchy of Luxembourg also joining as a new backer and many other existing European backers doubling down, the AI lab’s cap table remains resolutely international. And that might be enough for the customers Mistral is targeting.