如果您希望可以时常见面，欢迎标星收藏哦~ 在日前于苏州举办的ICDIA 2025峰会同期的高峰论坛上，巨霖科技创始人兼董事长孙家鑫先生 发表了《Julin's Unified CPS Sign Off Simulation Solution》主题演讲。如他所说，当今，芯片 复杂度飙升、高速接口普及和系统集成挑战加剧，传统的点工具仿真已难以满足从芯片裸片、封 装互连到系统板级的协同设计与精准签核需求。 为此，孙家鑫认为，EDA产业需要创新以解决开发者碰到的问题。尤其是在当前中美的竞合关系 下，面向未来打造适合的EDA工具尤为重要。 客户不会为国产情怀买单 作为一家成立于2019年的EDA企业，巨霖科技专注于解决行业内痛点问题,提供从芯片、封装到 系统的EDA仿真签核方案。 据介绍，公司的核心产品——信号完整性仿真平台SIDesigner已经于2024年在华为实现规模化商 用。这个产品不仅完美解决了传统的高精度SI瞬态仿真问题，还针对日益迫切的高精度高速信号 统 计眼图仿 真 挑 战 ， 让 面 向 未来的超高速SerDes&DDR5仿真设 计 变 得 可 能 ， 真 正 解 决 了 5G- 5.5G-6G行业关 ...

Core Viewpoint - The article discusses the challenges faced by NVIDIA in the automotive sector, particularly in the context of its partnerships with major car manufacturers and the increasing competition from Chinese companies developing their own chips and software solutions [3][5][18]. Group 1: NVIDIA's Automotive Business Challenges - NVIDIA's automotive business, while significant, accounts for less than 2% of its total revenue of $130.5 billion, indicating that it is a relatively small segment for the company [11][58]. - The collaboration between NVIDIA and General Motors has faced internal criticism, with GM executives describing NVIDIA's autonomous driving solutions as "very scary" [5][6]. - Other automakers, such as Mercedes-Benz, have also expressed dissatisfaction with NVIDIA's performance, leading to a shift towards competitors like Momenta for autonomous driving solutions [9][11]. Group 2: Competition from Chinese Companies - Chinese automakers are increasingly developing their own AI chips, with companies like NIO and Xpeng already delivering their self-developed chips, posing a significant threat to NVIDIA's market share [19][30]. - The article highlights that the delay in NVIDIA's Thor chip delivery has prompted companies like Xpeng to pivot towards their self-developed chips, indicating a loss of confidence in NVIDIA's ability to meet delivery timelines [24][25]. - The competitive landscape is shifting, with Chinese companies rapidly advancing in autonomous driving software and hardware, making it difficult for NVIDIA to maintain its previous dominance [66][68]. Group 3: Implications of Chip Development - The development of self-research chips by automakers is seen as a strategic necessity, driven by the need for cost reduction and better integration with AI capabilities [45][49]. - The article notes that the challenges faced by NVIDIA in delivering the Thor chip have inadvertently accelerated the self-development of chips among leading Chinese automakers [31][30]. - The long development cycle for automotive chips, which can take up to four years, contrasts sharply with the faster-paced software development cycles seen in the industry [33][50]. Group 4: Cultural and Operational Differences - NVIDIA's corporate culture, which emphasizes long-term technological advancements, may not align with the immediate delivery needs of automotive clients, leading to operational friction [51][62]. - The article points out that NVIDIA's team in China lacks decision-making power compared to its larger U.S. team, which may hinder its responsiveness to local market demands [65]. - The disparity in urgency and operational focus between NVIDIA and its automotive partners has created a gap that competitors are eager to exploit [67][68].

Core Insights - The emergence of Agentic AI, which possesses perception, reasoning, decision-making, and execution capabilities, is becoming a focal point for global tech giants [1][2] - Amazon Web Services (AWS) has launched several key products and services aimed at deploying Agentic AI, establishing a foundation for "Agent-as-a-Service" [2][3] - The competition among cloud providers is shifting from merely providing computational power to becoming intelligent service providers that enable the practical application of AI agents [3][4] Industry Trends - Key technological elements for the rise of Agentic AI include advanced model reasoning capabilities, standardized protocols, and improved operational efficiency [3][4] - Gartner predicts that by 2028, the proportion of daily work decisions made autonomously by agent-based AI will increase from 0% in 2024 to over 15% [2] - The cost of inference has significantly decreased, with a reported reduction of 280 times over the past two years, making AI more accessible [4][5] Technological Developments - The introduction of the Model Context Protocol (MCP) is facilitating the integration of AI agents with enterprise data and APIs, enhancing their functionality [6][7] - The development of multi-agent collaborative applications has become simpler, with significant reductions in the amount of code required for implementation [7][8] - Automated Reasoning Checks in Amazon Bedrock are designed to mitigate hallucination issues by verifying results against known facts [5][6] Application in Industries - The software development sector is rapidly adopting AI, with tools like Amazon Q Developer enabling programming through natural language, significantly increasing productivity [8][9] - Companies are increasingly recognizing the potential of Agentic AI, with some already integrating it into their operations to maximize value creation [9][10] Adoption Challenges - Companies are divided into two categories: those actively embracing Agentic AI and those hesitant to adopt it due to management's lack of understanding [9][10] - The successful implementation of AI requires top management to recognize its importance beyond just technical departments [10][11] Future Outlook - The technology adoption curve indicates that while some companies are early adopters of AI, others remain skeptical, which could impact their competitive edge [14] - AWS aims to support a growing number of clients in leveraging AI for innovation, emphasizing the importance of practical application and internal organizational change [14][15]

" 从BAT的地图竞合，到智驾「第一梯队」的追逐，程鹏在跑一场 创业「马拉松」。 " 作者丨李雨晨 编辑丨 林觉民 编者按： 从高精度传感器到车载芯片，从车联网数据平台到智能驾驶算法，供应链的敏捷、稳定和创新直接决 定了车企的智能化落地效率。 价格战白热化的今天，车企和供应链需打破零和博弈。雷峰网推出《车企供应链的老牌与新贵》高管对话，探讨 供应链的转型与价值重塑，主动进化才能成为新生态的构建者。 过去15年的移动互联网创业和AI浪潮，造就新贵也推翻旧王者。当诺基亚CEO约玛·奥利拉含泪说出"我们并没 有做错什么，但不知为什么我们输了"时，2010年的创业者们正在用智能手机重构整个商业文明。 2010年5月18日，四维图新上市，这家导航地图领域的龙头，却迎来"最舒适"和"最糟糕"的五年。 BAT的入局让四维图新的基本盘面临冲击：阿里出手收购"死对头"高德进而宣布商业免费；六年的合作伙伴百度 解约，掉转枪头自成一派；2014年腾讯入股，四维图新被卷入巨头的三国混战。 作为这家早期以地图为核心业务的公司CEO，程鹏没有觉得BAT们"不讲武德"。相反他很清楚，地图已经成为一 个基础设施。如果自己一直将其当成盈利单元 ...

一、"战略就是占领一个地方" 陈为：关于战略，很多人对它有不同定义、不同解读。 您的《经营方略》（全新修订版）开篇第一章就是讲战略的。说什么是战略呢？战略就是占领一个地 方。这个定义好像挺通俗，但又很本质，让人印象很深。为什么这么讲，这背后有什么思考？ 宋志平：战略就是占领一个地方，这是巴顿将军的一个核心思想，他当年打仗的时候，就是快速地占领 那些他认为最重要的地方。当然，这也意味着不可能同时占领很多地方，会有取舍，就是做什么和不做 什么。我们通俗地讲，战略就是研究做什么的学问。包括做什么和不做什么；多做什么，少做什么；先 做什么，后做什么。所以，我引用了巴顿将军的这句话，"战略就是占领一个地方"，让大家更加清晰地 去理解战略。到底你要去干什么，要占领什么地方？ 陈为：选择赛道也是战略考量的一部分，您提到一个观点，企业一定得深耕大行业。我们近几年的观察 发现，有的企业家管理能力未必更高明，但是他选的赛道特别好，就能脱颖而出。其实，大赛道往往竞 争更激烈，您为什么鼓励大家要深耕大赛道呢？ 宋志平：像中国建材、国药等这些比较大的央企，深耕大赛道是我对他们的战略建议。如果这样的大企 业选择小赛道，这个小赛道就像一个 ...

大摩认为，市场已对人形机器人的技术预期充分定价，投资者现在最关心的问题是：谁能率先实现订单落地并验证商业价 值。大多数集成商设定2025年交付数百至数千台的目标，落地情况将成为衡量行业进展的关键指标。 投资者现在最关心的问题是：谁能率先实现订单落地并验证商业价值。大摩预计，随着政府持续支持，预 计2025年下半年中国人形机器人订单将加速落地，同时核心技术也将有突破性进展。 01 市场动态转变： 从技术热潮到商业价值验证 硬·AI 作者 | 卜淑情 编辑 | 硬 AI 狂热的技术炒作结束后，人形机器人行业已进入商业落地关键期。 据追风交易台消息，摩根士丹利最新研究显示，2025年下半年人形机器人行业将从技术狂热阶段转向关注 实际商业落地，订单获取和实际应用将成为驱动市场情绪的决定性因素。 市场已经对技术预期充分定价。报告指出，在经历了2025年第一季度37%的强劲上涨后，由于部分集成商 下调交付目标且缺乏突破性技术进展，行业在3-7月期间出现了6%的回调。 2025年第一季度，人形机器人价值链迎来一波强劲上涨，中国相关股票从1月至3月上涨37%，明显跑赢 MSCI中国指数。这主要由以下几个因素推动： 科技巨头纷 ...

行业深度研究 重视 3D 数据资产+物理仿真引擎双主线，看好中国物理 AI 稀缺资产。 通用机器人 Day1 L4 路线缺乏商业化基础的风险；仿真合成数据质量不及预期的风险；模型及软件解决方案三方公司 长期产业链话语权较低的风险。 敬请参阅最后一页特别声明 1 从生物智能五阶段映射具身智能，模拟、规划能力是当前缺失环节。具身智能发展至今，从物理形态到大脑机理，机 器人无一不在以"仿生"的脉络发展演绎。我们认为，虽然目前人形机器人的产业发展阶段尚处早期，但市场往往会 高估原子层面的变化，而低估比特层面的变化——具身智能模型侧的发展日新月异，因而我们试图在本篇报告中详细 梳理生物智能五阶段的变化，并逐阶段地映射产业界的产品形态与模型算法。生物体亿万斯年的演化历程，蕴含着解 读目前具身智能发展阶段的钥匙，我们认为，当前具身智能真正缺乏的是第三阶段的生物智能——模拟学习的能力， 而物理 AI 正是构建模拟学习的核心。 复盘智能驾驶模型算法演绎历史，世界模型≈空间智能+物理 AI。正如"线虫学会转向"是生物智能的起点，"行动导 航"也是"具身智能"的起点，因而理解智能驾驶算法模型的演绎，对于理解机器人具身智能模型的发展 ...

7月22日，国家数据局举行2025中国国际大数据产业博览会新闻发布会。 国家数据局副局长余英在发布会上介绍，中国国际大数据产业博览会（以下简称数博会）自2015年举办 以来，始终秉持"全球视野、国际战略、产业视角、企业立场"的办会理念，不断深化内涵、丰富内容、 创新形式，经过十年的探索和积淀，已经成为我国数据领域引领创新趋势、展示行业成果、促进开放合 作的重要平台和载体。 2025数博会由国家数据局主办，贵州省人民政府承办，将于8月28日至30日在贵州省贵阳市举行。本届 数博会以"数聚产业动能智启发展新篇"为主题，旨在全面展现数据要素与人工智能技术融合创新的最新 成果，推动数据资源的高效汇聚和开发利用，为产业转型升级和经济高质量发展注入强劲动力。 贵州发展以行业大模型为重点的人工智能产业 当前，贵州正在加速推进人工智能、AI大模型与各行业场景深度融合。贵州省人民政府副省长罗强在 发布会上介绍，贵州大数据产业的发展从无到有，已经打下了不错的基础，现在正在推动算力、数据、 应用和产业协同联动，做强做优数字经济。 "其中很重要的一个方面，就是要发展以行业大模型为重点的人工智能产业，为千行百业装上'智能钥 匙'、插上 ...

Core Insights - The report by Changcheng Strategic Consulting analyzes the characteristics of unicorn companies in China, focusing on aspects such as quantity, valuation, financing dynamics, and regional distribution [1] - Shenzhen is highlighted as a leading city in nurturing unicorns, with 13 new unicorns expected in 2024, driven by a vibrant market ecosystem and strong international presence [1][4] - Other cities like Suzhou have also shown significant growth in unicorns, particularly in sectors like biomedicine and new materials, benefiting from targeted government policies and collaboration with professional institutions [2][6] Unicorn Development Trends - The overall financing amount for unicorns has not increased, but the quality of investments is improving, with a focus on leading regions and sectors [3] - Unicorn financing is concentrated in key areas such as artificial intelligence, biomedicine, and integrated circuits, aligning with national strategies to support frontier technologies [3] - The structure of investors has shifted, with domestic RMB funds becoming predominant, particularly those backed by state-owned capital [3] Regional Characteristics - Shenzhen's success in cultivating unicorns is attributed to its active market ecosystem, strong international connections, and the presence of technology giants like Huawei and Tencent [4] - Suzhou's approach includes a focus on biomedicine and a systematic service model that has led to rapid growth in unicorn numbers, serving as a model for other cities [2][6] - The experience of Suzhou is being replicated in other cities like Wuxi and Changzhou, which are focusing on specific industries to foster unicorn development [6] Comparison with International Markets - There is a notable difference in unicorn distribution between China and the U.S., with China leaning towards hard technology sectors while the U.S. has a significant presence in fintech [7] - Regulatory constraints in China have limited the growth of unicorns in the fintech sector, leading to a decrease in domestic unicorns in this area [7]

Core Insights - Dingjie Smart held the 2025 Smart Future Summit, unveiling new AI solutions aimed at enhancing enterprise digital transformation [1] - The year 2023 is recognized as the year of AI Agents, marking a shift from weak to strong AI capabilities in enterprise software [1][2] - Dingjie has developed a native PaaS platform, Athena, focusing on data intelligence and knowledge encapsulation, which has been applied across various industries [2] AI Solutions and Applications - The newly launched AI solutions include data intelligence and enterprise intelligence suites, four industrial software AI suites (ERP/PLM/MES/WMS+AI), AIOT command center, and industrial mechanism AI suite [1][2] - These solutions aim to break down information silos and enhance decision-making speed and accuracy for enterprises [2] - The integration of AI with industrial software is designed to improve efficiency across the entire supply chain, from R&D to logistics [2] Human-Centric AI Development - The development of AI is seen as a means to empower human creativity rather than replace human labor, allowing individuals to focus on higher-value tasks [3] - The vision for the future includes adaptive intelligent ecosystems where humans and their digital counterparts work together for improved productivity and quality of life [3] Research and Reports - The summit featured the release of the "2025 Generative AI Enterprise Application Practical Report," co-authored by Dingjie and Zhejiang University, analyzing ten key areas of generative AI applications in enterprises [5] - The report includes over 30 practical application cases and provides a blueprint for enterprises navigating the new operational landscape [5] Summit Highlights - The summit included three parallel forums focusing on AI technology applications in various business scenarios, with discussions on strategic choices and risk management [6] - An exhibition area showcased cutting-edge AI applications and innovations from Dingjie and partners like Huawei Cloud, illustrating the future of smart enterprises [6] - According to Dingjie’s estimates, smart factories could achieve a 25% increase in space utilization, a 150% increase in capacity, and a 15% reduction in overall costs [6]