我的征尘是星辰大海。。。
The dirt and dust from my pilgrimage forms oceans of stars...
-------当记忆的篇章变得零碎，当追忆的图片变得模糊，我们只能求助于数字存储的永恒的回忆
作者:黄教授
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华为盘古模型走错了路吗
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原始脚本
并非方向出错，而是时机偏差。 过早垂直化拖累大模型自进化效率。 在大模型产业赛道中，深耕垂直行业、落地场景应用是所有通用大模型最终的必经之路。 华为盘古选择走向工业，行业落地的大方向本身并无错误。 但问题的关键不在于要不要做垂直应用，而在于何时启动垂直化。 过早扎进工业等实体行业场景，非但没能形成发展优势，反而大幅拖慢了模型的自迭代、自进化速度，陷入了笨鸟先飞却越跑越慢的困境。 对比以编程大模型为核心打磨底座能力的路线，二者迭代效率的差距本质是场景属性与进化逻辑的天然分野。 这一点也恰好印证了清华教授沈洋关于 AI 自进化新物种的核心研判。 清华沈洋团队提出，编程大模型是驱动 AI 自主进化的核心载体。 核心原因在于代码世界是人类构建出的极致严谨的逻辑闭环。 编程体系拥有两大特质，一是绝对的强逻辑，语法规则。 运行逻辑清晰明确，不存在模糊解读与主观判断。 二是百分百的强检验机制，代码能否运行，结果是否达标。 可由计算机自动判定，及时反馈。 这种环境让大模型形成了自主编写、自动校验、自我修正的完整进化闭环。 模型能够依托高质量、高信噪比的信号持续优化，迭代成本低、速度快，进化效率拉满。 依托编程能力筑牢底座，模型的通用推理、逻辑思考。 自我优化能力会持续跃迁，待到底座实力足够强悍后，再向下渗透各个垂直领域，便是典型的磨刀不误砍柴工，最终实现后发先至。 反观华为盘古选择的路径，在模型通用底座尚未打磨成熟时，就率先发力工业等垂直领域，试图依靠行业场景数据反哺模型成长。 这条路线在资金化层面存在明显短板。 工业及各类实体行业场景普遍属于弱逻辑、弱检验的环境，行业规则掺杂经验、人情、现场特殊工况等非标准化因素。 很多行为与结果没有唯一标准答案。 同时场景效果的校验高度依赖人工、设备与实际工况，反馈链条长，判断标准模糊。 放在模型训练与迭代中，就意味着行业场景产出的数据信号杂乱，有效信息占比低。 想要从这类低质量数据中提炼出真正的通用能力。 模型需要海量数据堆叠，训练成本居高不下，有效迭代的周期被无限拉长，模型学到的也并非可迁移的通用逻辑。 只是特定场景下的固化行为模式，泛化能力严重不足。 就像专精单一领域的专家系统，在固有场景内尚可使用，却无法完成能力跃迁，更谈不上自主进化。 不少从业者抱着笨鸟先飞的心态，认为抢先落地垂直应用就能抢占市场先机。 但在大模型的进化逻辑里，这套思路并不成立。 垂直化是所有大模型的最终归宿，这一点毋庸置疑。 但垂直化必须建立在强大通用底座之上。 建议编程这类强逻辑、强检验的场景锤炼模型，让 AI 拥有自主迭代、自我进化的底层能力。 再切入行业场景，进化速度和落地效果都会远超早早陷入垂直场景的模型。 华为盘古并非战略方向失误，而是落地节奏出现了偏差。 过早拥抱垂直应用，用低信噪比的行业数据驱动模型成长。 等于主动进入了一条迭代慢、成本高、上限低的赛道。 当对手凭借编程大模型完成底座质变，手握超强通用能力再入局时。 前期所谓的先发优势便会荡然无存，后发先至也就成了必然。 大模型的竞争终究是底层进化能力的竞争，找对迭代的土壤。 把控好发展的节奏远比一味求快更加重要。
修正脚本
并非方向出错，而是时机偏差。 过早垂直化拖累大模型自进化效率。 在大模型产业赛道中，深耕垂直行业、落地场景应用是所有通用大模型最终的必经之路。 华为盘古选择走向工业，行业落地的大方向本身并无错误。 但问题的关键不在于要不要做垂直应用，而在于何时启动垂直化。 过早扎进工业等实体行业场景，非但没能形成发展优势，反而大幅拖慢了模型的自迭代、自进化速度，陷入了笨鸟先飞却越跑越慢的困境。 对比以编程大模型为核心打磨底座能力的路线，二者迭代效率的差距本质是场景属性与进化逻辑的天然分野。 这一点也恰好印证了清华教授沈洋关于 AI 自进化新物种的核心研判。 清华沈洋团队提出，编程大模型是驱动 AI 自主进化的核心载体。 核心原因在于代码世界是人类构建出的极致严谨的逻辑闭环。 编程体系拥有两大特质，一是绝对的强逻辑语法规则，运行逻辑清晰明确，不存在模糊解读与主观判断。 二是百分百的强检验机制，代码能否运行，结果是否达标，可由计算机自动判定，及时反馈。 这种环境让大模型形成了自主编写、自动校验、自我修正的完整进化闭环。 模型能够依托高质量、高信噪比的信号持续优化，迭代成本低、速度快，进化效率拉满。 依托编程能力筑牢底座，模型的通用推理、逻辑思考、自我优化能力会持续跃迁，待到底座实力足够强悍后，再向下渗透各个垂直领域，便是典型的磨刀不误砍柴工，最终实现后发先至。 反观华为盘古选择的路径，在模型通用底座尚未打磨成熟时，就率先发力工业等垂直领域，试图依靠行业场景数据反哺模型成长。 这条路线在进化层面存在明显短板。 工业及各类实体行业场景普遍属于弱逻辑、弱检验的环境，行业规则掺杂经验、人情、现场特殊工况等非标准化因素。 很多行为与结果没有唯一标准答案。 同时场景效果的校验高度依赖人工、设备与实际工况，反馈链条长，判断标准模糊。 放在模型训练与迭代中，就意味着行业场景产出的数据信号杂乱，有效信息占比低。 想要从这类低质量数据中提炼出真正的通用能力，模型需要海量数据堆叠，训练成本居高不下，有效迭代的周期被无限拉长，模型学到的也并非可迁移的通用逻辑。 只是特定场景下的固化行为模式，泛化能力严重不足。 就像专精单一领域的专家系统，在固有场景内尚可使用，却无法完成能力跃迁，更谈不上自主进化。 不少从业者抱着笨鸟先飞的心态，认为抢先落地垂直应用就能抢占市场先机。 但在大模型的进化逻辑里，这套思路并不成立。 垂直化是所有大模型的最终归宿，这一点毋庸置疑。 但垂直化必须建立在强大通用底座之上。 建议以编程这类强逻辑、强检验的场景锤炼模型，让 AI 拥有自主迭代、自我进化的底层能力。 再切入行业场景，进化速度和落地效果都会远超早早陷入垂直场景的模型。 华为盘古并非战略方向失误，而是落地节奏出现了偏差。 过早拥抱垂直应用，用低信噪比的行业数据驱动模型成长。 等于主动进入了一条迭代慢、成本高、上限低的赛道。 当对手凭借编程大模型完成底座质变，手握超强通用能力再入局时，前期所谓的先发优势便会荡然无存，后发先至也就成了必然。 大模型的竞争终究是底层进化能力的竞争，找对迭代的土壤，把控好发展的节奏远比一味求快更加重要。
英文翻译
It is not a wrong direction, but a mistiming. Premature verticalization hinders the self-evolution efficiency of large models. In the large model industry track, deep vertical industry immersion and scenario-based application deployment are the inevitable paths for all general-purpose large models. Huawei’s Pangu model chose to move toward industrial scenarios, and the general direction of industry landing itself is not wrong. But the key issue is not whether to pursue vertical applications, but when to initiate verticalization. Plunging too early into physical industry scenarios like manufacturing not only fails to create a competitive advantage but also significantly slows down the model’s self-iteration and self-evolution speed, falling into the trap of “the early bird that flies first but runs slower and slower.” Comparing this with the approach of building foundational capabilities centered on code large models, the difference in iteration efficiency between the two essentially stems from the natural divergence in scenario attributes and evolutionary logic. This point also confirms Professor Shen Yang from Tsinghua University on his core insight regarding AI self-evolving new species. Shen Yang’s team at Tsinghua University proposes that code large models are the core driver of AI autonomous evolution. The fundamental reason is that the code world is an extremely rigorous logical closed loop constructed by humans. The coding system has two major characteristics: first, absolute strong-logic syntax rules with clear and unambiguous operational logic, leaving no room for fuzzy interpretation or subjective judgment. Second, a 100% strong verification mechanism—whether code can run and whether results meet standards can be automatically judged by computers with timely feedback. This environment enables large models to form a complete evolutionary loop of autonomous writing, automatic verification, and self-correction. Models can continuously optimize using high-quality, high-signal-to-noise-ratio signals, with low iteration costs and fast speed, thereby maximizing evolutionary efficiency. By strengthening the foundation with coding capabilities, the model’s general reasoning, logical thinking, and self-optimization abilities will continuously leap forward. Once the foundation is strong enough, then penetrating various vertical domains is a classic case of “sharpening the axe will not delay the cutting of wood,” ultimately achieving the latecomer’s advantage. In contrast, the path chosen by Huawei’s Pangu—launching into industrial and other vertical fields before the general-purpose foundation is mature, trying to feed model growth with industry scenario data—has obvious shortcomings in evolution. Industrial and various physical industry scenarios generally belong to weak-logic, weak-verification environments, where industry rules are mixed with non-standardized factors such as experience, human relationships, and special on-site conditions. Many behaviors and results lack a single correct answer. At the same time, the verification of scenario effects heavily relies on manual labor, equipment, and actual operating conditions, resulting in long feedback loops and vague judgment criteria. In model training and iteration, this means that the data signals produced by industry scenarios are noisy, with a low proportion of effective information. To extract genuine general capabilities from such low-quality data, models require massive amounts of data, leading to high training costs, infinitely extended effective iteration cycles, and the learned patterns are not transferable general logic but fixed behavioral patterns specific to particular scenarios, severely lacking generalization ability. It is like an expert system specialized in a single domain—usable within its fixed scenario but incapable of capability leaps, let alone autonomous evolution. Many practitioners hold the mindset of “the slow bird needs an early start,” believing that being the first to land vertical applications will seize market opportunities. But in the evolutionary logic of large models, this reasoning does not hold. Verticalization is the ultimate destination for all large models—there is no doubt about that. However, verticalization must be built on a strong general-purpose foundation. It is recommended to train models in strong-logic, strong-verification scenarios like coding, allowing AI to acquire the underlying capability for autonomous iteration and self-evolution. Then, when entering industry scenarios, the speed of evolution and the effectiveness of deployment will far surpass models that have been mired in vertical scenarios too early. Huawei’s Pangu is not a strategic direction mistake, but a timing deviation in landing. Embracing vertical applications too early and using low-signal-to-noise-ratio industry data to drive model growth is equivalent to actively entering a track with slow iteration, high costs, and low upper limits. When competitors, by leveraging code large models, complete a foundational qualitative leap and re-enter with powerful general capabilities, the so-called first-mover advantage will vanish, and the latecomer’s advantage becomes inevitable. The competition among large models is ultimately a competition of underlying evolutionary capability. Finding the right soil for iteration and controlling the pace of development is far more important than simply pursuing speed.
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