據報道，Meta 正在建立由人工智慧驅動的搜尋引擎來與 Google 和 Bing 競爭

據報道，Meta（納斯達克股票代碼：META）正在開發一款由人工智慧驅動的搜尋引擎，他們希望該引擎能夠與Google（納斯達克股票代碼：GOOGL）和必應（Bing）相媲美。

Meta (NASDAQ:META) is reportedly building an AI-driven search engine that they hope will rival Google (NASDAQ:GOOGL) and Bing.

據報道，Meta（納斯達克股票代碼：META）正在開發一款由人工智慧驅動的搜尋引擎，他們希望該引擎能夠與Google（納斯達克股票代碼：GOOGL）和必應（Bing）相媲美。

Similar to Google’s AI Overview, Meta’s search engine would deliver AI-generated summaries of current events through the Meta AI chatbot. The company’s team has allegedly been working on this project for over eight months, with the goal of reducing Meta’s reliance on Google, which it currently uses as its primary search reference for Meta AI.

與Google的人工智慧概述類似，Meta 的搜尋引擎將透過 Meta AI 聊天機器人提供人工智慧生成的當前事件摘要。據稱，該公司的團隊已經在這個項目上工作了八個多月，目標是減少 Meta 對谷歌的依賴，目前 Meta AI 的主要搜尋參考是Google。

While AI is widely touted as a game-changer for everything from workplace productivity to societal efficiency, in reality, most consumers primarily use AI chatbots as glorified search engines. This effectively turns these AI chatbots into alternative search engines, saving users time from piecing together answers from multiple sources as they need to do in conventional search engines.

儘管人工智慧被廣泛吹捧為從工作場所生產力到社會效率等各個方面的遊戲規則改變者，但實際上，大多數消費者主要將人工智慧聊天機器人用作美化的搜尋引擎。這有效地將這些人工智慧聊天機器人轉變為替代搜尋引擎，從而節省了用戶在傳統搜尋引擎中需要從多個來源拼湊答案的時間。

When these chatbots became popular, traditional search engines actually started losing market share to these up-and-coming AI chatbots that were answering search queries more efficiently for their users.

當這些聊天機器人變得流行時，傳統搜尋引擎實際上開始失去市場份額，而這些新興的人工智慧聊天機器人正在為用戶更有效地回答搜尋查詢。

Leading search providers like Google quickly noticed this shift and infused AI into their search processes, allowing users to receive AI-generated summaries without leaving the traditional search interface—and apparently, it’s paying off.

像Google這樣的領先搜尋提供者很快就注意到了這一轉變，並將人工智慧融入他們的搜尋過程中，允許用戶在不離開傳統搜尋介面的情況下接收人工智慧產生的摘要——顯然，它正在得到回報。

In its recent Q3 earnings call, Google reported a 35% year-over-year revenue increase to $11.4 billion, attributing its AI infrastructure and generative AI features as driving forces for revenue growth. Even though these AI tools don’t directly generate revenue, their presence enhances the user experience and attracts more users, ultimately driving revenue through channels like ad placements.

在最近的第三季財報電話會議中，Google報告營收年增 35%，達到 114 億美元，將其人工智慧基礎設施和生成式人工智慧功能視為營收成長的驅動力。儘管這些人工智慧工具不會直接產生收入，但它們的存在增強了用戶體驗並吸引了更多用戶，最終透過廣告投放等管道增加收入。

Meta, whose revenue model heavily relies on advertising, could likely achieve similar results by building its own AI-powered search platform.

Meta 的收入模式嚴重依賴廣告，它很可能透過建立自己的人工智慧搜尋平台來實現類似的結果。

OpenAI eyes in-house chip production

OpenAI著眼於內部晶片生產

Amid a global shortage of high-performance chips, OpenAI is reportedly working to design and produce its own specialized chips for AI processing, reducing its reliance on third-party suppliers like NVIDIA (NASDAQ:NVDA) and AMD (NASDAQ:AMD).

據報導，在全球高性能晶片短缺的情況下，OpenAI 正在致力於設計和生產自己的人工智慧處理專用晶片，以減少對NVIDIA（納斯達克股票代碼：NVDA）和AMD（納斯達克股票程式碼：AMD）等第三方供應商的依賴。

OpenAI is working with semiconductor designer, developer, manufacturer, and supplier Broadcom to design these custom chips that will be used primarily for inference rather than training. Inference is the process in which an AI model, having already been trained on vast amounts of data, analyzes new data and makes predictions, which ultimately manifest themselves as outputs.

OpenAI 正在與半導體設計師、開發商、製造商和供應商 Broadcom 合作設計這些客製化晶片，這些晶片將主要用於推理而不是訓練。推理是人工智慧模型經過大量資料訓練後，分析新資料並做出預測的過程，最終表現為輸出。

One reason OpenAI may be pursuing in-house chip production is to mitigate operating costs and navigate ongoing supply chain challenges. The chip shortage has hit AI companies particularly hard, with high demand for GPUs and other specialized hardware driving up prices and making it difficult for companies to secure the hardware they need. By designing and manufacturing chips internally, OpenAI would gain greater control over its supply chain, similar to how Apple (NASDAQ:AAPL) did when it began designing its own processors for iPhones and Macs.

OpenAI 尋求內部晶片生產的原因之一是降低營運成本並應對持續的供應鏈挑戰。晶片短缺對人工智慧公司的打擊尤其嚴重，對 GPU 和其他專用硬體的高需求推高了價格，並使公司難以獲得所需的硬體。透過內部設計和製造晶片，OpenAI 將對其供應鏈獲得更大的控制權，類似於蘋果（NASDAQ：AAPL）開始為 iPhone 和 Mac 設計自己的處理器時的做法。

Bringing chip production in-house could also lead to significant long-term cost savings for OpenAI. As the company moves toward vertical integration, it could decrease dependency on external suppliers, who have raised prices due to the high demand and limited supply of AI hardware.

將晶片生產引入內部還可以為 OpenAI 帶來顯著的長期成本節省。隨著公司走向垂直整合，它可能會減少對外部供應商的依賴，由於人工智慧硬體的高需求和有限供應，外部供應商提高了價格。

Elon Musk’s xAI pursues new fundraising round

馬斯克 (Elon Musk) 的 xAI 正在進行新一輪融資

On the heels of OpenAI’s $6.6 billion funding round at a $157 billion valuation, Elon Musk’s AI company, xAI, has announced that it is in discussions for another funding round that would push xAI’s valuation over $40 billion. This follows a recent $6 billion raise that valued the company at $24 billion just months earlier, underscoring the fact that AI remains a hot sector for investment in what is otherwise a venture capital drought.

繼 OpenAI 以 1570 億美元估值完成 66 億美元融資後，馬斯克 (Elon Musk) 的人工智慧公司 xAI 宣布正在討論另一輪融資，這將使 xAI 的估值超過 400 億美元。幾個月前，該公司剛完成了 60 億美元的融資，估值達到 240 億美元，這突顯了這樣一個事實：在風險資本匱乏的情況下，人工智慧仍然是投資的熱門領域。

These major fundraising rounds highlight the unusually large capital needs in artificial intelligence, particularly for companies developing and scaling advanced models. AI development requires substantial investments in computing infrastructure, with some companies spending multiple billions on data centers and GPUs to support training and inference for their AI models. Even top AI companies like OpenAI have acknowledged that profitability remains elusive, with OpenAI not expecting to turn a profit until at least 2029.

這些主要融資輪凸顯了人工智慧領域異常龐大的資本需求，特別是對於開發和擴展先進模型的公司而言。人工智慧開發需要對運算基礎設施進行大量投資，一些公司在資料中心和 GPU 上花費數十億美元，以支援其人工智慧模型的訓練和推理。即使像 OpenAI 這樣的頂級人工智慧公司也承認，獲利能力仍然難以捉摸，OpenAI 預計至少要到 2029 年才能獲利。

This high-cost environment reflects the unique economics of AI. Companies need high-performance computing environments, vast datasets, and specialized hardware to train and run large language models and other advanced AI systems. However, these items are very expensive. For companies like xAI and OpenAI to remain competitive and continue innovating, they have ever-increasing capital needs to sustain growth and scale their businesses, which is why most AI companies raise money at every chance they can.

這種高成本環境反映了人工智慧獨特的經濟學。公司需要高效能運算環境、大量資料集和專用硬體來訓練和運行大型語言模型和其他高階人工智慧系統。然而，這些物品非常昂貴。對於像 xAI 和 OpenAI 這樣的公司來說，為了保持競爭力並持續創新，他們需要不斷增加的資本來維持成長和擴大業務，這就是為什麼大多數人工智慧公司會抓住一切機會籌集資金。

US tightens AI restrictions on China

美國收緊對中國人工智慧的限制

This week, the U.S. Department of the Treasury issued a final rule restricting American investments in China, Hong Kong, and Macau, specifically within areas like AI, semiconductors, microelectronics, and quantum information technologies. The rule prohibits U.S. companies and investors from engaging in certain transactions with entities based in these regions.

本週，美國財政部發布了一項最終規則，限制美國在中國、香港和澳門的投資，特別是在人工智慧、半導體、微電子和量子資訊技術等領域。該規則禁止美國公司和投資者與這些地區的實體進行某些交易。

“This Final Rule takes targeted and concrete measures to ensure that U.S. investment is

「該最終規則採取了有針對性的具體措施，以確保美國投資