8月3日消息，据techcrunch报道，苹果公司首席执行官蒂姆·库克于日前罕见召开一小时全员大会，向数 万名员工传达目标：苹果"必须"在人工智能领域取得胜利，并将为此投入"不设上限"的资源。 库克援引公司历史表示，苹果并非首次后发制人。"我们很少是第一个——Mac之前有PC，iPhone之前 有智能手机，iPad之前有平板电脑，但我们都重新定义了现代标准。"他同时强调，"人工智能的影响将 超越智能手机与互联网，因此苹果必须这么做，苹果也必将这么做，不这样做我们就会落后。" 外媒称，为兑现承诺，苹果管理层披露两项关键举措：一是自研芯片，下一代Apple Silicon将把AI加速 器规模翻倍，成为所有终端设备的核心引擎。二是Siri重构，原混合架构因质量未达标已被放弃，项目 交由Vision Pro负责人迈克·罗克韦尔团队重新设计端到端大语言模型架构，预计2025年发布"远超预 期"的重大升级。 此外，库克透露苹果已就收购或深度合作评估多家AI初创公司，包括Perplexity与Mistral，并继续与 OpenAI、Anthropic保持技术对话，以加速AI功能落地。 ...

来源：环球网 【环球网科技综合报道】8月3日消息，据techcrunch报道，苹果公司首席执行官蒂姆·库克于日前罕见召 开一小时全员大会，向数万名员工传达目标：苹果"必须"在人工智能领域取得胜利，并将为此投入"不 设上限"的资源。 库克援引公司历史表示，苹果并非首次后发制人。"我们很少是第一个——Mac之前有PC，iPhone之前 有智能手机，iPad之前有平板电脑，但我们都重新定义了现代标准。"他同时强调，"人工智能的影响将 超越智能手机与互联网，因此苹果必须这么做，苹果也必将这么做，不这样做我们就会落后。" 外媒称，为兑现承诺，苹果管理层披露两项关键举措：一是自研芯片，下一代Apple Silicon将把AI加速 器规模翻倍，成为所有终端设备的核心引擎。二是Siri重构，原混合架构因质量未达标已被放弃，项目 交由Vision Pro负责人迈克·罗克韦尔团队重新设计端到端大语言模型架构，预计2025年发布"远超预 期"的重大升级。 此外，库克透露苹果已就收购或深度合作评估多家AI初创公司，包括Perplexity与Mistral，并继续与 OpenAI、Anthropic保持技术对话，以加速AI功能落地。（ ...

Core Insights - Apple is considering the use of generative artificial intelligence technology to accelerate its custom chip design process [1][4] - The company has gained significant experience in chip design since the launch of the A4 chip in 2010, which is now widely used across various devices including Mac desktops and Vision Pro headsets [4] - Apple emphasizes the importance of using advanced tools in chip design, particularly the latest design software from Electronic Design Automation (EDA) firms [4] - Major EDA companies like Cadence Design Systems and Synopsys are integrating AI capabilities into their products, which could enhance design efficiency [4] - Apple made a decisive move in 2020 to transition its Mac product line from Intel chips to its own Apple Silicon without any backup plans, showcasing its commitment to innovation [4]

【 科技新报 】据《彭博社》记者Mark Gurman 最新报导指出，苹果正计划在2027年推出一款"几乎全 玻璃、曲面设计 " 的iPhone，而该年份恰逢iPhone问世20周年。 这项说法呼应了《The Information》上周末的报导，该报指出 " 2027年的某款iPhone将把前镜头隐藏于 屏幕之下，实现真正的全屏幕边到边显示 " 。而去年底《The Elec》也曾报导，苹果正与显示器供应 商合作开发无边框iPhone，但并非如三星或vivo先前推出的那种侧边下弯曲面屏幕。 不过Gurman所说的 " 几乎全玻璃、曲面 " iPhone的设计听起来更令人好奇，毕竟目前的iPhone 15 Pro 就已具备大量玻璃覆盖，只有镜头周围与钛金属边框为金属材质，最接近的概念或许是苹果过去揭露 的多项专利设计，其中一项来自2019年的专利，描述了一款以玻璃环绕机身、形成"连续环状外壳 " 的 手机。 除了变革性的iPhone设计外，Gurman认为2027年将是苹果 " 产品大爆发 " 的一年，包括： 此外，Gurman表示，到2027年，苹果也可能推出由大型语言模型驱动的全新Siri，并投入研发自家伺 ...

Core Viewpoint - Apple is transitioning to in-house modem chips, starting with the C1 modem in the iPhone 16e, aiming to replace Qualcomm and eventually control all network functionalities internally [1][3]. Group 1: C1 Modem Introduction - The C1 modem debuted in the iPhone 16e, focusing on efficiency but lacking support for 5G millimeter wave and all wavelengths of existing Qualcomm modems [3]. - The C1 modem is described as the most power-efficient modem on an iPhone, providing fast and reliable 5G connectivity [3]. Group 2: Future Modem Developments - The C2 modem, codenamed Ganymede, is expected to launch in the iPhone 18 series in 2026, featuring mmWave support and download speeds of 6 Gbps [3]. - The C3 modem, codenamed Prometheus, is planned for release with the iPhone 19 series in 2027, aiming to surpass Qualcomm's performance and support next-generation satellite networks [4]. Group 3: Network Chip Developments - Apple is also working on a new network chip, codenamed Proxima, to replace Broadcom's chips, expected to debut in HomePod mini and Apple TV updates later this year [6]. - This new chip will support Wi-Fi 6E and is anticipated to enhance connectivity between Apple devices while reducing costs [6]. Group 4: Integration Plans - After the modem transition, Apple is considering integrating its cellular modem into the main Apple Silicon chip, which could bring cost and efficiency benefits [7].

Core Viewpoint - Apple's decision to purchase approximately $1 billion worth of NVIDIA's GB300 NVL72 GPU cluster servers marks a significant shift in its AI strategy, acknowledging the advantages of the GPU ecosystem and generative AI paradigm over its self-developed chips [1][3][21]. Group 1: Apple's Shift to NVIDIA - Apple has historically relied on its self-developed chips, achieving great success with its Apple Silicon series in mobile and edge computing [3]. - The recent order for NVIDIA's GPUs indicates Apple's recognition of the GPU ecosystem's superiority in the generative AI space, driven by urgent market demands for high-performance computing [3][4]. - Analysts suggest that Apple plans to order around 250 NVL72 servers, with each server costing between $3.7 million and $4 million, totaling nearly $1 billion [3]. Group 2: Implications of Siri's Performance - Siri's declining competitiveness against rivals like Google Assistant and Alexa has prompted Apple to reassess its AI hardware strategy [4][5]. - The anticipated updates to Siri have been delayed, reflecting the challenges Apple faces in enhancing its AI capabilities [4][5]. Group 3: Generative AI and Market Dynamics - The rise of generative AI has redefined user expectations for intelligent assistants, shifting from simple command execution to intelligent collaboration [5]. - Apple's investment in NVIDIA GPUs is speculated to support the development of an Apple LLM, enhance Siri, and integrate AI into various applications [5][6]. Group 4: GPU vs. ASIC - The choice of NVIDIA's GPUs over self-developed ASICs highlights the critical importance of time and performance in the AI race, with NVIDIA's established ecosystem providing immediate solutions [8][16]. - NVIDIA's GPUs have become the de facto standard for training large language models (LLMs), showcasing their performance and ecosystem maturity [8][11]. - The high cost of NVIDIA GPUs, which have surged to $90,000 each, reflects their dominant market position, with NVIDIA reporting a revenue of $39.3 billion and a gross margin exceeding 70% [8][9]. Group 5: Future Outlook - Despite the current preference for NVIDIA GPUs, Apple may still pursue a hybrid strategy, utilizing NVIDIA for model training while relying on its own chips for inference [6][19]. - The ongoing competition between ASICs and GPUs suggests that while ASICs may face challenges now, they are not entirely out of the picture for future applications [19][21].