Core Viewpoint - The discussion around VLA (Vision-Language-Action model) is intensifying, with contrasting opinions on its short-term feasibility and potential impact on the automotive industry [2][12]. Group 1: VLA Technology and Development - The Li Auto i8 is the first vehicle to feature the VLA driver model, positioning it as a key selling point [2]. - Bosch's president for intelligent driving in China, Wu Yongqiao, expressed skepticism about the short-term implementation of VLA, citing challenges in multi-modal data acquisition and training [2][12]. - VLA is seen as an "intelligent enhanced version" of end-to-end systems, aiming for a more human-like driving experience [2][5]. Group 2: Comparison of Driving Technologies - There are two main types of end-to-end technology: modular end-to-end and one-stage end-to-end, with the latter being more advanced and efficient [3][4]. - The one-stage end-to-end model simplifies the process by directly mapping sensor data to control commands, reducing information loss between modules [3][4]. - VLA is expected to outperform traditional end-to-end models by integrating multi-modal capabilities and enhancing decision-making in complex scenarios [5][6]. Group 3: Challenges and Requirements for VLA - The successful implementation of VLA relies on breakthroughs in three key areas: cross-modal feature alignment, world model construction, and dynamic knowledge base integration [7][8]. - Current automotive chips are not designed for AI large models, leading to performance limitations in real-time decision-making [9][11]. - The industry is experiencing a "chip power battle," with companies like Tesla and Li Auto developing their own high-performance AI chips to meet VLA's requirements [11][12]. Group 4: Future Outlook and Timeline - Some industry experts believe 2025 could be a pivotal year for VLA technology, while others suggest it may take 3-5 years for widespread adoption [12][13]. - Initial applications of VLA are expected to be in controlled environments, with broader capabilities emerging as chip technology advances [14]. - Long-term projections indicate that advancements in AI chip technology and multi-modal alignment could lead to significant breakthroughs in VLA deployment by 2030 [14][15].

Core Insights - The article discusses the integration of three major technological trends: Artificial Intelligence (AI), Embodied Intelligence, and Intelligent Driving, highlighting their interconnectedness and the underlying industry chains [2][3][20]. Group 1: Artificial Intelligence - AI is defined as the capability of machines to simulate human intelligence behaviors, including perception, thinking, learning, and decision-making. IDC predicts that by 2028, China's AI investment will exceed $100 billion, with a compound annual growth rate of 35.2% [7][8]. - The AI industry chain includes components such as AI chips, servers, sensors, machine learning frameworks, and data services, emphasizing the importance of chips as the core of the industry [9][8]. Group 2: Embodied Intelligence - Embodied Intelligence refers to intelligent agents with physical bodies that interact with the physical world, accumulating knowledge and skills through perception and control. Its applications span various sectors, including industrial manufacturing, healthcare, and education [13][14]. - The industry chain for Embodied Intelligence includes upstream core technology development, key components, system integrators, and downstream applications, showcasing a comprehensive view of the sector [14]. Group 3: Intelligent Driving - Intelligent Driving is described as an advanced driving technology that combines AI, autonomous driving, vehicle sensors, and internet technologies to enhance driving experiences. The ultimate goal is fully autonomous driving [17][18]. - The industry chain for Intelligent Driving encompasses core technology and hardware supply, system integration, and application scenarios, with significant representation from companies in the field during the upcoming expo [18][20]. Group 4: Event Overview - The 2025 China International Industrial Expo will feature three main exhibition areas focusing on "Secrets of Computing Power," "AI's Rebellion," and "Intelligent Driving Disassembly," showcasing advancements in semiconductor independence, AI-enabled industrial software, and digital transformation in manufacturing [24][23]. - The event will also host industry summits on topics like industrial internet and integrated circuits, aiming to empower high-quality development in the electronic information industry [24].

特斯拉销量又又又下滑了！ 今年1月起，特斯拉创始人埃隆·马斯克在美国新任总统唐纳德·特朗普设立的所谓政府效率部（下 称"DOGE"）扮演削减政府机构的领导角色，为此，特斯拉遭受了一轮又一轮的抵制行动。这直接导致 特斯拉今年一季度全球销量同比下滑13%。 而在与特朗普闹掰了之后，马斯克成立美国党，并宣布明年参加竞选。特朗普政府则在马斯克成立美国 党之前，就颁布了对电动汽车发展极为不利的"大而美"法案（《税收与支出法案》）。显然，与执政党 的对立，对特斯拉又是一种伤害。 7月24日，特斯拉又交出了一份令人失望的财报：二季度，特斯拉在全球总共交付38.4万辆车，同比下 降13.5%；营业收入225亿美元，同比降幅从一季度的9%下滑到12%；自由现金流由一季度6.6亿美元下 降至1.5亿美元。净利润同比下降23%，但降幅相比一季度的39%收窄，为13.9亿美元。 汽车业务萎缩 马斯克的错？ "特斯拉销量持续下滑，营收、利润双降，主要原因还是马斯克参政带来的负面影响。"全联车商投资管 理（北京）有限公司总裁曹鹤在接受《华夏时报》记者采访时如是说。 在马斯克参与DOGE时，就有不少特斯拉的忠实粉丝呼吁：马斯克，分一点爱给 ...

Core Insights - The excitement surrounding Tesla's Robotaxi has evolved into a more complex understanding of its real-world implications and challenges as the initial hype has cooled down [3][5]. Group 1: Disruptive Potential of Robotaxi - The concept of Mobility as a Service (MaaS) suggests that the value of cars will shift from horsepower and range to the service value they can generate daily, potentially transforming millions of Tesla owners' vehicles into a decentralized transportation network [5]. - Tesla's "pure vision" approach, relying solely on cameras and neural networks, contrasts with competitors like Waymo that use expensive lidar and high-definition maps, offering the potential for low marginal costs and rapid global scalability if successful [5]. - The average usage of a private car is less than 1.5 hours per day, while Robotaxi could increase this to 16 hours, redefining cars from consumer goods to production assets and altering valuation logic across the automotive industry and urban environments [5]. Group 2: Key Challenges for Decision Makers - Questions regarding the technological route of FSD V12's "end-to-end AI" remain, particularly its performance in extreme weather and ambiguous traffic scenarios, as current tests still require safety drivers and remote control [6][8]. - The business model poses challenges in balancing a self-operated fleet with private car participation, including liability, insurance, and maintenance complexities, especially in competition with established players like Waymo [8]. - The large-scale deployment of Robotaxi will challenge urban charging networks and data centers, necessitating a redesign of insurance pricing and claims processes for autonomous driving, while also impacting suppliers of chips and sensors [8]. Group 3: Internal Insights and Industry Perspective - The company emphasizes the importance of firsthand experience from industry insiders to navigate the uncertainties and opportunities presented by Robotaxi, advocating for direct engagement with experts in the field [9]. - By connecting with top professionals from leading companies, stakeholders can gain valuable insights into the challenges and breakthroughs encountered in real-world testing and commercialization [9]. - The company has access to over 30,000 industry experts, providing a robust network for informed decision-making and strategic planning in the evolving landscape of autonomous vehicles [9]. Conclusion - The introduction of Tesla's Robotaxi is expected to create significant long-term industry ripples, urging stakeholders to actively engage and leverage insights from top experts to seize emerging opportunities [29].

前言 6月22日，特斯拉的Robotaxi，也就是无人驾驶出租车，在德州奥斯丁正式开始试运营。这是个划时代 的里程碑。这意味着，特斯拉正式开始颠覆汽车乃至整个交通运输行业。 接下来，我在直播和短视频里都聊了这部分内容，咱们一起感受下这一历史性的时刻。 （偷偷放一张 Robotaxi美照 云体验Robotaxi 一边带大家云体验特斯拉Robotaxi，一边给大家做一些讲解。 这次狼真的来啦！ 这回真的来了，这不是喊狼来了，是狼真的来了。特斯拉从2015年就开始搞这个自动驾驶，大家都知 道，马斯克以前好几次说快出来了，结果都跳票了。但这事你不能光看跳票，你得看它背后的技术路 径。新的技术路径啊，如果选对了，它就是一路往上，选错了，一开始进展很快，但后面就平了。马斯 克之前选错过几次，他们团队也不是傻子，对吧？不断试错、纠偏，最后终于在2024年初找到了正确路 线——就是这个端到端学习，以FSD V12为标志。 这次上线，时间点是6月22号。上线之后你看得出来，特斯拉团队是非常谨慎的。一共不到20辆车，还 采取了邀请制，不是谁都能打车，要有人邀请，还得再带一个18岁以上的成年人一起乘车。车上是有安 全员的，但不是坐 ...

Core Insights - The article highlights the rapid advancements in the information technology sector, emphasizing ten key IT technologies that will shape digital transformation over the next decade [1] Group 1: Generative AI - Generative AI has evolved from text generation to multimodal capabilities, enabling the creation of videos, 3D models, and code [2] - Microsoft's AutoGen framework allows AI agents to autonomously break down tasks, enhancing efficiency in development processes [2] - Ethical risks are increasing, prompting OpenAI to introduce a framework for AI behavior guidelines [2] Group 2: Quantum Computing - IBM's 1121-Qubit quantum processor achieves a 1000x speedup in drug molecule simulations, while Google's quantum error correction reduces error rates to 0.1% [6] - Morgan Stanley applies quantum algorithms to optimize investment portfolio risk assessments, reducing errors by 47% [6] - Commercialization of quantum computing faces engineering challenges, as these systems require near absolute zero temperatures to operate [6] Group 3: Neuromorphic Chips - Intel's Loihi 2 chip mimics human brain synaptic plasticity, achieving energy efficiency in image recognition at 1/200th of GPU consumption [8] - Tesla's Dojo 2.0 supercomputer enhances autonomous driving training speed by five times [8] - Neuralink's technology allows paralyzed patients to control digital devices through thought, with a data transmission bandwidth of 1 Gbps [8] Group 4: Edge Intelligence and 5G-Advanced - 5G-Advanced reduces latency to 1 ms, enabling industrial robots to respond at human nerve signal levels [10] - Siemens' deployment of a "digital twin + edge AI" system in Germany achieves a 98% accuracy rate in equipment fault prediction [10] - Security issues remain, with 76% of edge nodes reported to have unpatched vulnerabilities [10] Group 5: Privacy Computing - Ant Group's "Yin Yu" framework enables data usage without visibility in multi-party collaborative modeling [12] - Federated learning in healthcare enhances cross-hospital tumor research efficiency by three times while complying with GDPR [12] - NVIDIA's H100 encryption acceleration engine reduces training time by 60%, although encrypted computing still incurs a 10-100x performance overhead [12] Group 6: Extended Reality (XR) - Meta's XR OS 2.0 supports multimodal interactions, with Quest 3 headset achieving 8K resolution and 120Hz refresh rate [13] - BMW utilizes XR systems to design virtual factories, reducing design cycles by 40% [13] - Apple’s Vision Pro addresses motion sickness issues with dynamic gaze rendering technology, maintaining latency under 3 ms [13] Group 7: Green Computing - AMD's EPYC 9005 processor utilizes 3D V-Cache stacking technology, improving energy efficiency by four times [14] - Microsoft's underwater data center project lowers PUE to 1.06 through seawater cooling [14] - Global data centers still account for 3% of electricity consumption, with liquid cooling technology adoption at only 15% [14] Group 8: Biofusion Technology - Neuralink's N1 chip enables wireless transmission of brain signals at 4 Kbps, with future potential for direct AI access [15] - Swiss teams have developed "electronic skin" that surpasses human fingertip sensitivity, though biological compatibility requires 5-10 years of validation [15] Group 9: Blockchain 3.0 - Ethereum 2.0's PoS mechanism reduces energy consumption by 99.9% and supports 100,000 transactions per second [16] - Walmart employs blockchain to track food supply chains, reducing loss rates by 30% [16] - Interoperability issues persist, with Polkadot's cross-chain protocol connecting over 50 blockchains but capturing only 1% of the market [16] Group 10: Autonomous Systems - Tesla's FSD V12 uses an end-to-end neural network, but its accident rate remains three times higher than human drivers [17] - Boston Dynamics' Atlas robot achieves fully autonomous navigation with a positioning error of less than 2 cm [17] - Legal frameworks are lacking, with the EU planning to introduce a "Robot Liability Bill" to clarify accident responsibility [17] Future Outlook - The ten technologies are not developing in isolation but are showing deep integration trends, such as quantum computing accelerating AI training and neuromorphic chips empowering edge intelligence [18] - Companies need to build a "technology matrix" capability rather than focusing on single technology deployments [18] - Gartner suggests that the technology leaders of 2025 will be those who can weave quantum, AI, and privacy computing into new value networks [18]