2025年11月，一封"全员无需到岗"的通知在自动驾驶行业内引发连锁反应。毫末智行，这家曾凭借长城汽车的资源支 持与百度系技术团队，累计融资超20亿元、估值突破10亿美元的明星创业公司，以近乎"静默"的方式退出舞台。从资 本宠儿到黯然退场，毫末的轨迹不仅折射出企业自身的发展困境，更成为自动驾驶行业融资环境剧变的缩影。 近年来，自动驾驶领域曾经历资本的狂热追捧。2021年，全球自动驾驶相关融资总额高达932亿元，催生了无数创业 神话。然而，2023年成为行业分水岭，资本热度骤降，融资总额断崖式下滑至200亿元，降幅达78%。资金从"广撒 网"转向"精准投"，不同企业间开始加速分化。毫末的退场，恰是这场行业洗牌的标志性事件。 资本逻辑切换下的行业降温 在毫末智行发展的初期，自动驾驶行业正处于资本追捧的"黄金时代"，新技术的崛起让市场对其未来充满想象，资本 纷纷布局，行业融资规模持续攀升，大量资金涌入为众多创业公司提供了快速成长的土壤，毫末智行正是这一浪潮中 的受益者。 彼时，投资者对技术和场景的想象充满热情，愿意为尚未量产的算法模型或尚未验证的商业模式买单。只要企业拥有 核心技术团队、明确的发展方向，即便尚未实现 ...

Core Viewpoint - The article discusses the challenges and pressures faced by a leading automotive company's self-driving research team as they approach critical deadlines for developing advanced autonomous driving technologies, highlighting the competitive landscape and the importance of effective management in achieving technological advancements [6][8][14]. Group 1: Development Goals and Challenges - The self-driving research team of a leading automotive company has set ambitious internal goals to develop a no-map urban Navigation on Autopilot (NOA) by September 30 and an end-to-end system by December 30 [6]. - The company is currently lagging behind new entrants and leading autonomous driving firms by at least a year in terms of research and development progress [8]. - The pressure is high for the smart driving leaders, as failure to meet these deadlines could lead to accountability issues and organizational turmoil [7][8]. Group 2: Investment and Talent Acquisition - The company has significantly increased its investment in autonomous driving technology, surpassing that of some new entrants, and is willing to offer competitive salaries to attract top talent [9]. - Unlike some new entrants that offer compensation packages tied to stock performance, this leading company provides more cash to avoid fluctuations in employee compensation due to stock price volatility [9]. Group 3: Technical and Management Issues - Despite substantial investments, the company faces challenges in the end-to-end development process, particularly in data management, which is crucial for training models effectively [10]. - Traditional automotive companies often struggle with a lack of algorithmic expertise among their leadership, which affects their ability to manage and innovate in autonomous driving technology [13]. - The management approach in traditional firms tends to focus on coding output rather than the underlying algorithmic thought processes, which contributes to lower technical output compared to new entrants [14]. Group 4: Future Outlook and User Experience - The company plans to widely implement high-level urban NOA in numerous models next year, contingent on the success of its self-developed end-to-end system [15]. - The upcoming year is expected to be pivotal for end-to-end systems, as both new entrants and leading firms are achieving performance levels that meet consumer expectations [15]. - The emphasis will shift towards ensuring that the technology not only functions but also provides a satisfactory user experience, as performance differences among various end-to-end systems can significantly impact consumer perception [16].