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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论中美AI竞争中的持久战
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论中美 AI 竞争中的持久战。 戒骄稳进，客观看待中美人工智能格局。 引言。 当下国内人工智能行业存在两种极端形态。 一部分人抱着速胜幻想，频繁宣扬短期内全面对标、超越海外顶尖模型。 另一部分人陷入悲观论调，认定硬件、底层技术代差难以逾越。 回望历史，论持久战精准驳斥了抗战时期的速胜论与亡国论，为漫长博弈指明方向。 放在中美 AI 长期竞争这场马拉松里。 这套辩证思路同样完全适配，既不存在一夜反超的速胜捷径，也没有无法追赶的绝境。 唯有戒骄去躁，认清强弱现实，战略从容。 战术审慎才是长期缩小差距、稳步突围的正道。 一、破除速胜论，盲目浮躁，急于赶超有害无益。 速胜论的核心误区。 是无视海外多年沉淀的硬核底盘，幻想靠短期营销、跑分数据抹平实打实的积累差距。 这一点在行业内表现得十分鲜明。 从硬件底座来看，美国拥有成熟的 N v l 七二高端算力机柜生态、先进制程工艺、完备的 c o s 封装与液冷配套体系。 过去十余年，英伟达依托完整软件栈 CUDA，构建了全球 AI 算力的底层标准。 OpenAI、Anthropic 等企业持续迭代六七年。 积攒了海量工业级严谨代码、学术推理标注数据集，打磨出成熟的模型置信校准、代码自检、长逻辑纠错工程体系。 反观国内，高端芯片供给受限，先进制程量产成本高昂，高质量工程严谨数据集，底层算子资深人才，全链路校验工具链都存在积累时差。 代差客观存在，绝非一两年可以消弭。 速胜心态催生了一系列短视行为，厂商为冲榜单跑分调整训练奖惩机制。 模型宁可胡乱填充答案，也不坦诚承认能力边界。 资源一味倾斜顶尖复杂工程赛道，瞧不上体量庞大、性价比优先的大众轻量脚本市场。 为追求回复速度，省略语法校验、分步验算这类基础质控步骤，频繁出现低级代码错误。 就像学生依靠蒙题拉高模拟分数。 看似纸面数据亮眼，实则没有形成稳定可复现的解题能力，真实落地工程场景时漏洞百出，持续消耗用户信任，浪费研发资源。 更关键的是，AI早已告别算力无上限狂飙的时代。 硅芯片制成触碰量子隧穿散热的物理天花板，N V L 七十二等高阶集群受同缆传输功耗。 单机架超高电力负荷制约，扩张边际成本暴涨。 美国本土电网并网周期漫长，能源供给承载力有限，头部 AI 企业扎堆上市，恰恰印证。 一级市场烧钱红利鉴定，野蛮高速增长期已然落幕。 对方增速放缓不代表实力崩塌，我们如果抱着速胜心态冒进，忽视基础打磨，盲目对标顶尖赛道。 只会陷入投入巨大，回报微薄的内耗困局。 二、摒弃悲观论，我们自有持久战的深厚底气，承认差距不等于全盘看衰。 论持久战点名敌小我大、敌退步我进步、敌寡助我多助的核心对比。 平移到 AI 竞争中，我们的长期竞争底盘十分扎实。 其一，市场与产业纵深远超对手。 美国 AI 高端服务仅能覆盖少数头部科技企业、精密研发场景，客户体量狭小。 而国内海量制造业、中小企业运维、政务数字化、民生数据分析、中小开发者脚本需求构成巨大的基础市场。 绝大多数用户不需要顶尖复杂工程模型，只渴求稳定评价零低级失误的轻量化 AI 工具。 这正是我们现阶段完全有能力稳稳拿捏的基本盘。 依托庞大内需场景，我们能持续收获稳定现金流，反哺国产芯片、数据集、人才长期培育，形成良性内循环。 其二，硬件追赶路线务实，容错空间足。 我们不在死磕极致两纳米、三纳米先进制程，转向 Chiplet。 小芯片堆叠，74纳米成熟产能扩产，存算一体，异构算力集群等差异化路线，避开对方的专利与制程封锁壁垒。 东数西算工程提前统筹风光水火储能配套。 电网调度统筹能力强，不会出现美国算力机房有机柜无电力的扩张枷锁，算力规模化落地的长期承载力更稳定。 其三。 全球市场包容性更强。 美国高端算力模型订阅定价昂贵，供给收紧。 我们评价可靠的 AI 方案适配东南亚、中东、拉美等广大新兴市场。 能输出适配本地化产业需求的轻量化 AI 服务。 在全球增量市场中持续积累落地经验与数据样本，形成敌寡助我多助的格局。 其次，迭代节奏可持续稳健。 我们不必复刻对方堆超大算力、烧百亿美金冲击极致性能的模式，分层递进发展。 先把轻量脚本、基础数据分析等能力圈内的场景做到百分百可靠，补齐语法自检、置信度评估等基础工程流程，夯实底盘后。 在阶梯式承接中等复杂度工程需求，一步一个脚印缩小代差。 对方受物理、电力、资本多重约束，增速放缓，我们匀速稳步追赶。 差距不会持续拉大，相持阶段已然到来。 三、博弈核心心法，战略藐视对手，战术万分重视差距。 一、战略层面从容自信，不必被差距吓倒，长远来看。 美国先发的算力数据红利存在明确上限，物理瓶颈、能源约束、资本退烧、高端人力成本高企都是难以根治的短板。 我们依托大国体量、完整供应链、广阔内需与全球增量市场，拥有十年尺度、持久战的耐力，不必渲染不可战胜的恐慌。 认清博弈是长期赛道，时间站在踏实沉淀、自主可控的一方。 二、战术层面正视差距，拒绝粉饰与自大差距是客观现实。 无需刻意掩盖，也不能张口就喊短期超越。 顶尖模型在超长工程代码、精密数理推导、底层工具链成熟度上的优势肉眼可见。 坦然承认技不如人并非示弱，反而是清醒进步的前提。 战术落地要守住两条底线。 一、能力之内，务求百分百稳定可靠。 像 Python 语法校验这类零技术门槛。 毫秒级轻量工具，必须设为代码输出强制前置流程，杜绝低级失误。 训练体系优化奖惩规则，对编造错误答案施加明显惩罚。 教会模型自我评估置信度。 能力范围里的任务，依靠标准化流程消除偶然性失误，靠稳定口碑积累信任与现金流。 二、能力之外，坦然示弱，拒绝硬撑输出。 面对超出当前稳定承载能力的复杂硬核工程需求，不盲目打包票交付半成品。 主动标注风险，拆分分布实现，坦诚告知局限。 正如学生遇到超纲压轴题，坦然放弃，不瞎蒙步骤浪费精力，把有限研发资源集中投入自己能做好、有市场、能盈利的赛道。 四，结语。 中美人工智能竞争不是一场百米冲刺，而是耗时十年乃至更久的持久战。 速胜论、浮躁、短视、虚无、乐观。 只会带来资源浪费与新人透支。 悲观论放大代差，丧失底气，白白浪费我们身后的产业与市场底盘。 真正的破局之路永远是戒骄务实、稳健。 战略上从容自信，看清我们持久战的底层优势。 战术上审慎谦卑，直面差距，打磨基本功，守住可靠底线。 不求一夜反超，但求每日精进。 不慕虚名跑分，但求落地扎实。 日复一日，夯实基础，稳步缩小差距。 时间终将回馈踏实前行者。
修正脚本
论中美 AI 竞争中的持久战。 戒骄稳进，客观看待中美人工智能格局。 引言。 当下国内人工智能行业存在两种极端形态。 一部分人抱着速胜幻想，频繁宣扬短期内全面对标、超越海外顶尖模型。 另一部分人陷入悲观论调，认定硬件、底层技术代差难以逾越。 回望历史，论持久战精准驳斥了抗战时期的速胜论与亡国论，为漫长博弈指明方向。 放在中美 AI 长期竞争这场马拉松里。 这套辩证思路同样完全适配，既不存在一夜反超的速胜捷径，也没有无法追赶的绝境。 唯有戒骄去躁，认清强弱现实，战略从容、战术审慎才是长期缩小差距、稳步突围的正道。 一、破除速胜论，盲目浮躁，急于赶超有害无益。 速胜论的核心误区，是无视海外多年沉淀的硬核底盘，幻想靠短期营销、跑分数据抹平实打实的积累差距。 这一点在行业内表现得十分鲜明。 从硬件底座来看，美国拥有成熟的 N v l 七二高端算力机柜生态、先进制程工艺、完备的 COB 封装与液冷配套体系。 过去十余年，英伟达依托完整软件栈 CUDA，构建了全球 AI 算力的底层标准。 OpenAI、Anthropic 等企业持续迭代六七年，积攒了海量工业级严谨代码、学术推理标注数据集，打磨出成熟的模型置信校准、代码自检、长逻辑纠错工程体系。 反观国内，高端芯片供给受限，先进制程量产成本高昂，高质量工程严谨数据集、底层算子资深人才、全链路校验工具链都存在积累时差。 代差客观存在，绝非一两年可以消弭。 速胜心态催生了一系列短视行为，厂商为冲榜单跑分调整训练奖惩机制。 模型宁可胡乱填充答案，也不坦诚承认能力边界。 资源一味倾斜顶尖复杂工程赛道，瞧不上体量庞大、性价比优先的大众轻量脚本市场。 为追求回复速度，省略语法校验、分步验算这类基础质控步骤，频繁出现低级代码错误。 就像学生依靠蒙题拉高模拟分数。 看似纸面数据亮眼，实则没有形成稳定可复现的解题能力，真实落地工程场景时漏洞百出，持续消耗用户信任，浪费研发资源。 更关键的是，AI早已告别算力无上限狂飙的时代。 硅芯片制程触碰量子隧穿散热的物理天花板，N V L 七十二等高阶集群受铜缆传输功耗、单机架超高电力负荷制约，扩张边际成本暴涨。 美国本土电网并网周期漫长，能源供给承载力有限，头部 AI 企业扎堆上市，恰恰印证一级市场烧钱红利见顶，野蛮高速增长期已然落幕。 对方增速放缓不代表实力崩塌，我们如果抱着速胜心态冒进，忽视基础打磨，盲目对标顶尖赛道。 只会陷入投入巨大、回报微薄的内耗困局。 二、摒弃悲观论，我们自有持久战的深厚底气，承认差距不等于全盘看衰。 论持久战点明敌小我大、敌退步我进步、敌寡助我多助的核心对比。 平移到 AI 竞争中，我们的长期竞争底盘十分扎实。 其一，市场与产业纵深远超对手。 美国 AI 高端服务仅能覆盖少数头部科技企业、精密研发场景，客户体量狭小。 而国内海量制造业、中小企业运维、政务数字化、民生数据分析、中小开发者脚本需求构成巨大的基础市场。 绝大多数用户不需要顶尖复杂工程模型，只渴求稳定平价零低级失误的轻量化 AI 工具。 这正是我们现阶段完全有能力稳稳拿捏的基本盘。 依托庞大内需场景，我们能持续收获稳定现金流，反哺国产芯片、数据集、人才长期培育，形成良性内循环。 其二，硬件追赶路线务实，容错空间足。 我们不再死磕极致两纳米、三纳米先进制程，转向 Chiplet 小芯片堆叠、7纳米成熟产能扩产，存算一体，异构算力集群等差异化路线，避开对方的专利与制程封锁壁垒。 东数西算工程提前统筹风光水火储能配套。 电网调度统筹能力强，不会出现美国算力机房有机柜无电力的扩张枷锁，算力规模化落地的长期承载力更稳定。 其三，全球市场包容性更强。 美国高端算力模型订阅定价昂贵，供给收紧。 我们平价可靠的 AI 方案适配东南亚、中东、拉美等广大新兴市场。 能输出适配本地化产业需求的轻量化 AI 服务。 在全球增量市场中持续积累落地经验与数据样本，形成敌寡助我多助的格局。 其次，迭代节奏可持续稳健。 我们不必复刻对方堆超大算力、烧百亿美金冲击极致性能的模式，分层递进发展。 先把轻量脚本、基础数据分析等能力圈内的场景做到百分百可靠，补齐语法自检、置信度评估等基础工程流程，夯实底盘后，再阶梯式承接中等复杂度工程需求，一步一个脚印缩小代差。 对方受物理、电力、资本多重约束，增速放缓，我们匀速稳步追赶。 差距不会持续拉大，相持阶段已然到来。 三、博弈核心心法，战略藐视对手，战术万分重视差距。 一、战略层面从容自信，不必被差距吓倒，长远来看。 美国先发的算力数据红利存在明确上限，物理瓶颈、能源约束、资本退烧、高端人力成本高企都是难以根治的短板。 我们依托大国体量、完整供应链、广阔内需与全球增量市场，拥有十年尺度、持久战的耐力，不必渲染不可战胜的恐慌。 认清博弈是长期赛道，时间站在踏实沉淀、自主可控的一方。 二、战术层面正视差距，拒绝粉饰与自大，差距是客观现实。 无需刻意掩盖，也不能张口就喊短期超越。 顶尖模型在超长工程代码、精密数理推导、底层工具链成熟度上的优势肉眼可见。 坦然承认技不如人并非示弱，反而是清醒进步的前提。 战术落地要守住两条底线。 一、能力之内，务求百分百稳定可靠。 像 Python 语法校验这类零技术门槛、毫秒级轻量工具，必须设为代码输出强制前置流程，杜绝低级失误。 训练体系优化奖惩规则，对编造错误答案施加明显惩罚。 教会模型自我评估置信度。 能力范围里的任务，依靠标准化流程消除偶然性失误，靠稳定口碑积累信任与现金流。 二、能力之外，坦然示弱，拒绝硬撑输出。 面对超出当前稳定承载能力的复杂硬核工程需求，不盲目打包票交付半成品。 主动标注风险，拆分分步实现，坦诚告知局限。 正如学生遇到超纲压轴题，坦然放弃，不瞎蒙步骤浪费精力，把有限研发资源集中投入自己能做好、有市场、能盈利的赛道。 四、结语。 中美人工智能竞争不是一场百米冲刺，而是耗时十年乃至更久的持久战。 速胜论、浮躁、短视、盲目乐观，只会带来资源浪费与人力透支。 悲观论放大代差，丧失底气，白白浪费我们身后的产业与市场底盘。 真正的破局之路永远是戒骄务实、稳健。 战略上从容自信，看清我们持久战的底层优势。 战术上审慎谦卑，直面差距，打磨基本功，守住可靠底线。 不求一夜反超，但求每日精进。 不慕虚名跑分，但求落地扎实。 日复一日，夯实基础，稳步缩小差距。 时间终将回馈踏实前行者。
英文翻译
On the Protracted War in Sino-US AI Competition. Avoiding Arrogance and Advancing Steadily: An Objective View of the Sino-US AI Landscape. Introduction. Currently, there are two extreme attitudes within the domestic AI industry. Some people hold fantasies of a quick victory, frequently promoting the idea of fully matching or surpassing top overseas models in the short term. Others fall into a pessimistic mindset, believing that the gap in hardware and underlying technology is insurmountable. Looking back at history, the theory of protracted war accurately refuted the notions of quick victory and national subjugation during the War of Resistance, providing direction for a long-term struggle. In the context of the long-term Sino-US AI competition, which is a marathon, this dialectical approach is entirely applicable. There is neither a shortcut to overnight overtaking nor an insurmountable dead end. Only by avoiding arrogance and impatience, recognizing the realities of strengths and weaknesses, and adopting a strategic calmness with tactical prudence can we steadily narrow the gap and break through over the long term. I. Breaking the Myth of Quick Victory: Blind Impatience and Hasty Catch-Up Are Harmful. The core misconception of the quick victory theory is ignoring the solid foundation accumulated overseas over many years and fantasizing about leveling the real accumulated gap through short-term marketing and benchmarking scores. This is vividly reflected in the industry. From the hardware perspective, the United States has a mature ecosystem of high-end computing cabinets such as NVIDIA's H100, advanced process technology, complete COB packaging, and liquid cooling support systems. Over the past decade or more, NVIDIA, with its complete software stack CUDA, has established the global standard for AI computing power. Companies like OpenAI and Anthropic have iterated for six or seven years, accumulating massive amounts of industrial-grade rigorous code, academic reasoning annotation datasets, and refined engineering systems for model confidence calibration, code self-checking, and long-logic error correction. In contrast, domestic supply of high-end chips is limited, the cost of advanced process mass production is high, and there is a time lag in accumulating high-quality rigorous engineering datasets, deep talent in underlying operators, and full-chain verification toolchains. The gap is objective and cannot be bridged in just one or two years. The quick victory mindset has led to a series of short-sighted behaviors, such as manufacturers adjusting training reward and punishment mechanisms to boost benchmark scores. Models would rather fabricate answers than honestly admit their capability limits. Resources are unilaterally tilted toward top-tier complex engineering tracks, while the vast, cost-effective lightweight script market is dismissed. In pursuit of fast response times, basic quality control steps like grammar verification and step-by-step validation are omitted, leading to frequent low-level code errors. This is like a student raising simulated test scores by guessing answers. While the paper data looks impressive, a stable and reproducible problem-solving ability has not been developed. When faced with real-world engineering scenarios, there are numerous flaws, continuously eroding user trust and wasting R&D resources. More critically, AI has long left the era of unlimited computing power. Silicon chip manufacturing is hitting the physical ceiling of quantum tunneling and heat dissipation. High-end clusters like NVIDIA's H100 are constrained by copper cable transmission power consumption and ultra-high power loads per rack, leading to skyrocketing marginal costs for expansion. In the United States, the grid interconnection cycle is lengthy, energy supply capacity is limited, and the IPO rush of leading AI companies precisely indicates that the primary market's burn-rate dividend has peaked. The era of barbaric high-speed growth has ended. A slowdown on the other side does not mean a collapse in strength. If we rush in with a quick victory mindset, neglect basic refinement, and blindly benchmark against top tracks, we will only fall into an internal struggle with huge investments and meager returns. II. Abandoning Pessimism: We Have Profound Confidence for a Protracted War. Acknowledging the gap does not mean total pessimism. The theory of protracted war highlights the core contrasts: the enemy is small and we are large, the enemy is regressive and we are progressive, the enemy has little support and we have much support. Translated into the AI competition, our long-term competitive foundation is very solid. First, our market and industrial depth far exceed those of our opponents. High-end AI services in the US can only cover a small number of top tech companies and precision R&D scenarios, with a narrow customer base. In contrast, China's vast manufacturing, SME operations, government digitalization, people's livelihood data analysis, and small developer script needs constitute a huge basic market. The vast majority of users do not need top-tier complex engineering models; they only need stable, affordable, zero-low-error lightweight AI tools. This is precisely the basic market we are fully capable of capturing at this stage. Relying on the huge domestic demand scenario, we can continuously obtain stable cash flow, which in turn supports the long-term cultivation of domestic chips, datasets, and talent, forming a virtuous internal cycle. Second, our hardware catch-up path is pragmatic and has sufficient room for error tolerance. We no longer stubbornly pursue extreme 2nm or 3nm advanced processes, but instead turn to differentiated routes such as Chiplet stacking, expansion of 7nm mature capacity, memory-compute integration, and heterogeneous computing clusters, bypassing the opponent's patent and process blockade barriers. The "East Data West Computing" project has preemptively coordinated the supporting infrastructure of wind, solar, hydro, thermal, and energy storage. The grid dispatch coordination capability is strong, avoiding the expansion bottleneck of US computing centers having racks but no power. The long-term capacity for large-scale computing deployment is more stable. Third, the global market is more inclusive. High-end AI model subscriptions in the US are expensive and supply is tightening. Our affordable and reliable AI solutions are suitable for vast emerging markets such as Southeast Asia, the Middle East, and Latin America, capable of exporting lightweight AI services tailored to localized industrial needs. We continuously accumulate real-world deployment experience and data samples in the global incremental market, forming a pattern where the enemy has little support and we have much support. Fourth, the iteration pace is sustainable and steady. We do not need to replicate the opponent's model of piling on ultra-large computing power and burning billions of dollars to pursue extreme performance. Instead, we adopt a layered and progressive development approach. First, make the scenarios within our capability circle, such as lightweight scripts and basic data analysis, 100% reliable. Complete basic engineering processes like grammar self-check and confidence assessment. After solidifying the foundation, then stepwise take on medium-complexity engineering needs, narrowing the gap step by step. The opponent is constrained by physics, electricity, and capital, with slowing growth. We advance steadily and catch up at a constant pace. The gap will not continue to widen; the stage of stalemate has already arrived. III. Core Mindset for the Game: Strategic Contempt for the Opponent, Tactical Emphasis on the Gap. 1. Strategic level: Be calm and confident, not intimidated by the gap. In the long run, the opponent's first-mover advantage in computing power and data has clear limits. Physical bottlenecks, energy constraints, cooling capital, and high-end labor costs are all structural weaknesses that are hard to cure. Relying on the scale of a large country, a complete supply chain, vast domestic demand, and global incremental markets, we have the endurance for a ten-year-scale protracted war. There is no need to exaggerate an unbeatable panic. Recognize that the game is a long-term track. Time is on the side of those who are solid and self-controlled. 2. Tactical level: Face the gap squarely, reject whitewashing and arrogance. The gap is an objective reality. There is no need to deliberately conceal it, nor should we shout about short-term surpassing. The advantages of top models in ultra-long engineering code, precise mathematical derivation, and maturity of underlying toolchains are obvious. Honestly admitting technical inferiority is not showing weakness; rather, it is a prerequisite for clear-headed progress. Tactical implementation must hold two bottom lines: First, within our capabilities, strive for 100% stability and reliability. For example, zero-technical-threshold, millisecond-level lightweight tools like Python syntax check must be mandatory pre-processing steps in code output to eliminate low-level errors. Optimize the training system's reward and punishment rules, impose obvious penalties for fabricating wrong answers, and teach the model to self-assess confidence. For tasks within our capability range, use standardized processes to eliminate accidental errors, and accumulate trust and cash flow through stable reputation. Second, beyond our capabilities, honestly show weakness and refuse to force output. When facing complex hard-core engineering demands beyond our current stable carrying capacity, do not blindly promise to deliver half-baked products. Proactively mark risks, split tasks into step-by-step implementation, and honestly inform limitations. Just as a student encountering an overly difficult final problem would honestly give up rather than guess steps and waste energy, concentrate limited R&D resources on tracks we can do well, have market, and can profit from. IV. Conclusion. The Sino-US AI competition is not a 100-meter sprint but a protracted war lasting ten years or more. The mentality of quick victory, impatience, short-sightedness, and blind optimism will only bring resource waste and human exhaustion. Pessimism magnifies the gap, erodes confidence, and wastes the industrial and market foundation behind us. The true path to breakthrough is always to avoid arrogance, be pragmatic, and remain steady. Strategically, be calm and confident, seeing the underlying advantages of our protracted war. Tactically, be prudent and humble, facing the gap, honing basic skills, and holding the bottom line of reliability. Do not seek overnight overtaking, but seek daily progress. Do not covet false benchmark scores, but seek solid deployment. Day after day, solidify the foundation and steadily narrow the gap. Time will eventually reward those who move forward steadily.
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