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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国之大事不可不察

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原始脚本

美国国运级巨头 Palantir 暴涨15倍背后，国之大事，不可不察。
在美股市场里， Palantir 被华尔街定义为地缘国运型企业。
二零二三年初至二零二六年短短三年，股价暴涨15倍，涨幅大幅跑赢英伟达等 AI 算力巨头，市值突破3000亿美元。
甚至超越洛克希德马丁、雷神等传统军工龙头总和。
这家名字取自指环王、针织晶石、 Planta 的硅谷公司，表面是大数据情报软件服务商。
实则已是美国五角大楼、 CIA 北约情报体系离不开的数字中枢。
它的崛起从不是偶然风口，而是创始人理念、独创技术架构。
独家人才体系、深度军政绑定、实战战绩层层叠加的结果，壁垒极难复制。
一、名字寓意与创立初心。
瞄准美军情报最大痛点，数据烟囱 Palantir 在魔戒设定里是千里透视、跨地域互通影像的水晶球，完美对应公司核心定位。
穿透割裂信息，拼接全域态势全景图。
美军、美国政府各军种、情报机构、职能部门信息化建设跨度长达数十年。
陆军、海军、空军、 CIA  FBI 卫星侦察、人力档案系统完全独立搭建、数据库、编程语言、加密协议。
操作系统互不兼容，每一套系统就像一座封闭烟囱。
原始数据只存放在本地机房，横向无法互通调取。
早年传统数据挖掘有致命短板，想要做多元关联分析，必须把海量涉密原始数据导出到独立算力服务器。
但 PB 级情报数据传输成本极高，同步延迟数天。
更致命的是，涉密铭文外移存在巨大泄密风险。
如果直接在业务数据库跑复杂计算。
又会挤占服务器资源，干扰执勤查询等核心业务稳定。
彼得蒂尔联合创始人在创立之初就抓住本质矛盾。
美国军方从来不缺海量情报数据，缺的是安全、就地、不迁移原始数据的跨系统融合研判能力。
而快速决策才是战场与反恐行动决胜的第一要素。
这套认知构成了 Palantir 二十多年不变的底层逻辑。
二、核心技术方案。
就地下发算力任务，原始数据全程不出本地。
原理听起来各大软件厂商都能看懂。
但工程落地难度天差地别。
一、计算下发，数据不动，原始涉密资料永久所在各单位自有服务器，Palantir 平台只存储元数据。
权限标签、运算模板。
分析师在统一控制台编写挖掘、匹配、图谱关联脚本。
平台不拖拽数据，而是把轻量化、可执行算子加密下发到每一个烟囱节点本地运行。
二、只回传脱敏聚合结果。
各节点本地完成筛选、匹配、特征提取后，仅向上推送统计数值、匹配 id 关联线索、模糊坐标等摘要信息，完整报文、档案、录音、高清影像等原始铭文绝不流出本地。
顶层平台只负责拼接结果、渲染态势可视化、二次推演，全程触碰不到一手涉密素材。
三、算力优先级隔离保障稳定下发的分析任务，被限制 CPU。
内存低优先级调度，永远优先保障军方日常执勤业务，杜绝分析运算拖垮核心作战系统。
断网离线场景下，节点可独立运算。
链路恢复后增量同步结果，适配战场不稳定网络环境。
这套模式同时解决算力冲突、数据泄密、时效滞后三大难题。
也是区别于普通大数据 ETL 工具的核心差异。
三、旁人复刻不了的三重硬核壁垒，人才驻场模式、老旧系统适配一。
稀缺古董技术人才池，主力为退役军政技术骨干。
美军 it 体系奉行能用绝不升级原则，大量机房还跑着 Slarest  Cobol  Ada 等早已脱离民用市场的古早技术栈。
市面上年轻程序员几乎不会接触这类技能，学了离开军工体系很难就业，市场人才供给近乎枯竭。
Palantir 的核心解法是大批量吸纳退役五角大楼 IT 运维。
CIA 数据库管理员、军种系统架构师、高密级安全工程师。
这批人掌握未公开私有接口、内部隐性规范、口头传承的调试坑点，外部文档完全没有对应资料。
很多人员相当于换用工主体，依旧在熟悉的系统体系内工作，无缝适配老旧烟囱。
普通企业既招募不到这类熟手，也无法获取高等级涉密背景审查资质。
连进入军方机房调试的资格都没有。
二、 fde 前线驻场员工制度，深度嵌入军政业务流程。
fde， forward deployed employee。
前置派驻员工是 Palantir 独有的绑定模式。
FDE 全部持有美国 TSSCI 最高涉密许可，大量本身就是前情报、军方职员。
直接进驻情报中心、军事基地和参谋分析师同工位办公。
不同于普通乙方远程对接需求， fde 全程参与研判行动。
同步掌握军方未来数年数字化改造规划，产品迭代贴合真实作战工作流，调试、排障、规则优化全部现场落地。
涉密生产环境容错空间极低，只有十几年行业资深人员才有操作资质，应届生完全无法上手。
三、几十年异构系统适配工程沉淀，美军烟囱数量庞大。
技术杂乱无章，不少老旧军用系统根本没有标准 API 接口。
Palantir 打磨了上千套定制轻量代理程序，无需改动原有业务代码，即可挂在算子执行。
整套适配、安全审计、红对攻防、密钥分级流程经过到的8570军工安全标准全链路核验。
每一次版本迭代都同步配合五角大楼安全部门审查，十几年磨合形成的工程经验无法短期复制。
四、实战战绩筑牢顶级信任。
从反恐到俄乌战场证明不可替代性，军工情报行业极度厌恶替换风险，稳定验证的实战案例是最高信任背书。
一。
击毙本拉登标志性一战， CIA 堆积十余年碎片化海量线索，多家老牌军工软件厂商整合全部失败。
Palantir 多元关联图谱精准锁定藏身院落。
一战奠定在反恐情报领域的龙头地位。
二、跨国制裁与人员追踪。
依靠资产、人脉、通讯多元数据融合，完成针对马杜罗等目标的资金网络。
人员链路摸排，支撑美国对外制裁行动。
三、俄乌战场实战淬炼，为乌方提供战场情报整合能力、无人机、雷达、通讯。
卫星数据30秒内完成融合输出打击参考，成为现代 AI 数字化作战的标杆应用。
常年零重大泄密事故，稳定运行20余年。
如今五角大楼、北约多国情报体系深度绑定 Palantir，替换成本极高。
整套研判流程、人员培训、涉密权限架构完全依附平台，军方已经达到难以脱离的程度。
五、创始人底层商业与地缘理念。
做从0到1的垄断型基础设施，两位核心创始人奠定公司长期战略底色。
一、彼得蒂尔，PayPal 联合创始人。
秉持从零到一核心理念，竞争是失败者的游戏，真正成功的企业要创造独家垄断赛道，拒绝同质化内卷。
他不跟风民用消费互联网热潮。
笃定国防情报数据基础设施是长期国运刚需，愿意承受前期十几年持续亏损，深耕赛道。
二、 ceo 亚历克斯。
卡普拥有哲学学术背景，明确公司宏大使命定位，将业务深度绑定西方地缘战略利益，坚定承接其他硅谷大厂回避的高敏感国防合同。
收获华府高层人脉与政策倾斜。
高管圈层大量吸纳退役军政高层，构建政企互通的稳固人脉网络。
对比市面上改开源代码套壳包装的低端信创厂商。
Palantir 从底层架构、安全体系、场景适配全栈自研，没有依赖外部开源内核，这也是它能切入最高密级国防项目的关键。
六、长远展望。
中美军事竞争的核心赛场落在 AI 情报数据体系。
卡普在达沃斯论坛公开表态，全球只有中美两国真正具备实战化 AI 军备竞赛的完整能力。
未来大国博弈的胜负手不再单纯比拼硬件装备，而是 AI 情报整合、快速决策、全域态势感知能力。
现代战争节奏急速压缩。
谁能更快打通各军种、多维度数据烟囱，用 AI 完成线索关联、推演预判，谁就能形成信息差战略优势。
AI 与大数据军事化应用已是国之大事。
情报数据融合体系的建设速度与成熟度将深刻影响未来数十年大国战略平衡。

修正脚本

美国国运级巨头 Palantir 暴涨15倍背后，国之大事，不可不察。
在美股市场里， Palantir 被华尔街定义为地缘国运型企业。
二零二零年初至二零二三年短短三年，股价暴涨15倍，涨幅大幅跑赢英伟达等 AI 算力巨头，市值突破3000亿美元。
甚至超越洛克希德马丁、雷神等传统军工龙头总和。
这家名字取自指环王的真知晶石Palantir的硅谷公司，表面是大数据情报软件服务商。
实则已是美国五角大楼、CIA、北约情报体系离不开的数字中枢。
它的崛起从不是偶然风口，而是创始人理念、独创技术架构、独家人才体系、深度军政绑定、实战战绩层层叠加的结果，壁垒极难复制。
一、名字寓意与创立初心。
瞄准美军情报最大痛点：数据烟囱。Palantir 在魔戒设定里是千里透视、跨地域互通影像的水晶球，完美对应公司核心定位。
穿透割裂信息，拼接全域态势全景图。
美军、美国政府各军种、情报机构、职能部门信息化建设跨度长达数十年。
陆军、海军、空军、CIA、FBI、卫星侦察、人力档案系统完全独立搭建，数据库、编程语言、加密协议、操作系统互不兼容，每一套系统就像一座封闭烟囱。
原始数据只存放在本地机房，横向无法互通调取。
早年传统数据挖掘有致命短板，想要做多元关联分析，必须把海量涉密原始数据导出到独立算力服务器。
但 PB 级情报数据传输成本极高，同步延迟数天。
更致命的是，涉密明文外移存在巨大泄密风险。
如果直接在业务数据库跑复杂计算。
又会挤占服务器资源，干扰执勤查询等核心业务稳定。
联合创始人彼得蒂尔在创立之初就抓住本质矛盾。
美国军方从来不缺海量情报数据，缺的是安全、就地、不迁移原始数据的跨系统融合研判能力。
而快速决策才是战场与反恐行动决胜的第一要素。
这套认知构成了 Palantir 二十多年不变的底层逻辑。
二、核心技术方案。
就地下发算力任务，原始数据全程不出本地。
原理听起来各大软件厂商都能看懂。
但工程落地难度天差地别。
一、计算下发，数据不动，原始涉密资料永久存在各单位自有服务器，Palantir 平台只存储元数据、权限标签、运算模板。
分析师在统一控制台编写挖掘、匹配、图谱关联脚本。
平台不拖拽数据，而是把轻量化、可执行算子加密下发到每一个烟囱节点本地运行。
二、只回传脱敏聚合结果。
各节点本地完成筛选、匹配、特征提取后，仅向上推送统计数值、匹配 id 关联线索、模糊坐标等摘要信息，完整报文、档案、录音、高清影像等原始明文绝不流出本地。
顶层平台只负责拼接结果、渲染态势可视化、二次推演，全程触碰不到一手涉密素材。
三、算力优先级隔离保障稳定：下发的分析任务，被限制CPU、内存低优先级调度，永远优先保障军方日常执勤业务，杜绝分析运算拖垮核心作战系统。
断网离线场景下，节点可独立运算。
链路恢复后增量同步结果，适配战场不稳定网络环境。
这套模式同时解决算力冲突、数据泄密、时效滞后三大难题。
也是区别于普通大数据 ETL 工具的核心差异。
三、旁人复刻不了的三重硬核壁垒。一、稀缺古董技术人才池，主力为退役军政技术骨干，适配老旧系统的人才驻场模式。
美军 it 体系奉行能用绝不升级原则，大量机房还跑着 Slarest  Cobol  Ada 等早已脱离民用市场的古早技术栈。
市面上年轻程序员几乎不会接触这类技能，学了离开军工体系很难就业，市场人才供给近乎枯竭。
Palantir 的核心解法是大批量吸纳退役五角大楼 IT 运维。
CIA 数据库管理员、军种系统架构师、高密级安全工程师。
这批人掌握未公开私有接口、内部隐性规范、口头传承的调试坑点，外部文档完全没有对应资料。
很多人员相当于换用工主体，依旧在熟悉的系统体系内工作，无缝适配老旧烟囱。
普通企业既招募不到这类熟手，也无法获取高等级涉密背景审查资质。
连进入军方机房调试的资格都没有。
二、 fde 前线驻场员工制度，深度嵌入军政业务流程。
fde， forward deployed employee。
前置派驻员工是 Palantir 独有的绑定模式。
FDE 全部持有美国 TSSCI 最高涉密许可，大量本身就是前情报、军方职员。
直接进驻情报中心、军事基地和参谋分析师同工位办公。
不同于普通乙方远程对接需求， fde 全程参与研判行动。
同步掌握军方未来数年数字化改造规划，产品迭代贴合真实作战工作流，调试、排障、规则优化全部现场落地。
涉密生产环境容错空间极低，只有十几年行业资深人员才有操作资质，应届生完全无法上手。
三、几十年异构系统适配工程沉淀，美军烟囱数量庞大。
技术杂乱无章，不少老旧军用系统根本没有标准 API 接口。
Palantir 打磨了上千套定制轻量代理程序，无需改动原有业务代码，即可挂在算子执行。
整套适配、安全审计、红蓝攻防、密钥分级流程经过DoD 8570军工安全标准全链路核验。
每一次版本迭代都同步配合五角大楼安全部门审查，十几年磨合形成的工程经验无法短期复制。
四、实战战绩筑牢顶级信任。
从反恐到俄乌战场证明不可替代性，军工情报行业极度厌恶替换风险，稳定验证的实战案例是最高信任背书。
一、
击毙本拉登标志性一战， CIA 堆积十余年碎片化海量线索，多家老牌军工软件厂商整合全部失败。
Palantir 多元关联图谱精准锁定藏身院落。
一战奠定在反恐情报领域的龙头地位。
二、跨国制裁与人员追踪。
依靠资产、人脉、通讯多元数据融合，完成针对马杜罗等目标的资金网络、人员链路摸排，支撑美国对外制裁行动。
三、俄乌战场实战淬炼，为乌方提供战场情报整合能力，无人机、雷达、通讯、卫星数据30秒内完成融合输出打击参考，成为现代 AI 数字化作战的标杆应用。
常年零重大泄密事故，稳定运行20余年。
如今五角大楼、北约多国情报体系深度绑定 Palantir，替换成本极高。
整套研判流程、人员培训、涉密权限架构完全依附平台，军方已经达到难以脱离的程度。
五、创始人底层商业与地缘理念。
做从0到1的垄断型基础设施，两位核心创始人奠定公司长期战略底色。
一、彼得蒂尔，PayPal 联合创始人。
秉持从零到一核心理念，竞争是失败者的游戏，真正成功的企业要创造独家垄断赛道，拒绝同质化内卷。
他不跟风民用消费互联网热潮。
笃定国防情报数据基础设施是长期国运刚需，愿意承受前期十几年持续亏损，深耕赛道。
二、 ceo 亚历克斯·卡普拥有哲学学术背景，明确公司宏大使命定位，将业务深度绑定西方地缘战略利益，坚定承接其他硅谷大厂回避的高敏感国防合同。
收获华府高层人脉与政策倾斜。
高管圈层大量吸纳退役军政高层，构建政企互通的稳固人脉网络。
对比市面上改开源代码套壳包装的低端信创厂商。
Palantir 从底层架构、安全体系、场景适配全栈自研，没有依赖外部开源内核，这也是它能切入最高密级国防项目的关键。
六、长远展望。
中美军事竞争的核心赛场落在 AI 情报数据体系。
卡普在达沃斯论坛公开表态，全球只有中美两国真正具备实战化 AI 军备竞赛的完整能力。
未来大国博弈的胜负手不再单纯比拼硬件装备，而是 AI 情报整合、快速决策、全域态势感知能力。
现代战争节奏急速压缩。
谁能更快打通各军种、多维度数据烟囱，用 AI 完成线索关联、推演预判，谁就能形成信息差战略优势。
AI 与大数据军事化应用已是国之大事。
情报数据融合体系的建设速度与成熟度将深刻影响未来数十年大国战略平衡。

英文翻译

Behind the 15-fold surge of Palantir, a U.S. national-level giant, matters of national importance cannot be overlooked.  
In the U.S. stock market, Palantir is defined by Wall Street as a geopolitical national-enterprise.  
From early 2020 to 2023, in just three years, its stock price surged 15 times, far outperforming AI computing giants like Nvidia, with a market value exceeding $300 billion.  
It even surpasses the combined market cap of traditional military-industrial leaders like Lockheed Martin and Raytheon.  
This Silicon Valley company, named after the Palantir from *The Lord of the Rings*, is outwardly a big data intelligence software service provider.  
But in reality, it has become the digital hub that the Pentagon, CIA, and NATO intelligence systems cannot do without.  
Its rise is no accident of timing but the cumulative result of its founder’s vision, unique technical architecture, exclusive talent system, deep military-government ties, and proven combat performance—creating barriers that are extremely difficult to replicate.

**I. Name Meaning and Founding Vision**  
Targeting the U.S. military’s biggest pain point: data silos. In *The Lord of the Rings*, the Palantir is a crystal ball that allows remote viewing and cross-regional communication, perfectly aligning with the company’s core positioning:  
Penetrating fragmented information and stitching together a panoramic view of the overall battlefield.  
The信息化 construction of the U.S. military, various government branches, intelligence agencies, and functional departments spans decades.  
The Army, Navy, Air Force, CIA, FBI, satellite reconnaissance, and personnel archive systems are all independently built, with incompatible databases, programming languages, encryption protocols, and operating systems—each system is like a closed chimney.  
Raw data is stored only in local server rooms, with no horizontal interoperability.  
Early traditional data mining had a fatal flaw: to perform multi-dimensional correlation analysis, massive amounts of classified raw data had to be exported to independent computing servers.  
But the cost of transmitting petabytes of intelligence data was extremely high, with synchronization delays of several days.  
More critically, moving classified plaintext outside posed a huge risk of leaks.  
Running complex calculations directly on operational databases would consume server resources, disrupting core business like duty queries.  
Co-founder Peter Thiel identified the core contradiction from the start:  
The U.S. military never lacks massive intelligence data; it lacks the ability to securely, locally, and without migrating raw data, perform cross-system fusion analysis.  
And rapid decision-making is the primary factor in winning battles and counterterrorism operations.  
This insight formed Palantir’s unchanging underlying logic for over two decades.

**II. Core Technical Solution**  
Distribute computing tasks locally, with raw data never leaving its local environment.  
In principle, this sounds understandable to any major software vendor.  
But the engineering difficulty varies vastly.  
1. **Compute is sent, data stays put:** Raw classified materials permanently reside on each unit’s own servers. The Palantir platform only stores metadata, permission tags, and computation templates. Analysts write scripts for mining, matching, and graph association on a unified console. The platform does not drag data; instead, it encrypts and sends lightweight, executable operators to each silo node for local execution.  
2. **Only anonymized aggregated results are returned:** After local screening, matching, and feature extraction, each node pushes only summary information like statistical values, matching ID correlation clues, and fuzzy coordinates upward. Original plaintext such as full reports, files, recordings, or high-definition images never leave the local site. The top platform is only responsible for piecing together results, rendering situational visualizations, and conducting secondary simulations—never touching primary classified materials.  
3. **Priority isolation for computing ensures stability:** Assigned analysis tasks are limited to low-priority CPU and memory scheduling, always prioritizing the military’s daily operational tasks, preventing analysis computations from bogging down core combat systems. In offline scenarios, nodes can operate independently. Once the link is restored, incremental results are synchronized, adapting to unstable battlefield network environments.  
This model simultaneously solves three major problems: computing conflicts, data leaks, and time delays.  
It is also the core difference from ordinary big data ETL tools.

**III. Three Hard-to-Replicate Barriers**  
1. **Scarce pool of vintage technical talent:** The main workforce consists of retired military and government technical experts, with a personnel model suited for adapting to legacy systems. The U.S. military IT system follows the principle of “if it works, don’t upgrade.” Many server rooms still run archaic tech stacks like Slarest, Cobol, and Ada, which have long left the civilian market.  
   Young programmers today rarely encounter such skills; learning them makes it hard to find jobs outside the military-industrial complex, and the talent supply has nearly dried up.  
   Palantir’s core solution is to absorb large numbers of retired Pentagon IT operators, CIA database administrators, military system architects, and high-security engineers.  
   This group possesses unpublished private interfaces, internal implicit norms, and orally transmitted debugging pitfalls—none of which are documented externally.  
   Many personnel essentially operate within familiar systems under a different employer, seamlessly adapting to old silos.  
   Ordinary companies can neither recruit such experienced hands nor obtain the high-level security clearance required to even enter military server rooms for debugging.

2. **FDE (Forward Deployed Employee) system:** Deeply embedded in military and government business processes.  
   FDEs are Palantir’s unique binding model.  
   All FDEs hold the highest U.S. TS/SCI security clearances, and many are former intelligence or military personnel.  
   They are directly stationed in intelligence centers, military bases, and work side-by-side with staff analysts.  
   Unlike ordinary vendors who handle requirements remotely, FDEs participate fully in analysis and operations.  
   They stay updated on the military’s digital transformation plans for years ahead, iterating products to fit real combat workflows. Debugging, troubleshooting, and rule optimization are all done on-site.  
   The tolerance for error in classified production environments is extremely low, requiring personnel with decades of industry experience—new graduates cannot even start.

3. **Decades of heterogeneous system adaptation engineering:** The U.S. military has an enormous number of silos with chaotic technology, and many old military systems lack standard API interfaces.  
   Palantir has refined thousands of custom lightweight agent programs that can host operator execution without modifying original business code.  
   The entire process of adaptation, security auditing, red-blue teaming, and key classification has passed full-chain verification under the DoD 8570 military security standard.  
   Every version iteration is synchronized with Pentagon security department reviews. The engineering experience accumulated over more than a decade cannot be replicated quickly.

**IV. Proven Combat Performance Builds Top-Tier Trust**  
From counterterrorism to the Russia-Ukraine war, its irreplaceability is demonstrated. The military-intelligence industry abhors replacement risk, and stable, verified combat cases are the highest form of trust.  
1. **The killing of Bin Laden:** The CIA had piled up fragmented clues for over a decade, and many established military software vendors had failed to integrate them. Palantir’s multi-dimensional correlation graph precisely pinpointed the hideout compound. This single case cemented its leading position in counterterrorism intelligence.  
2. **Transnational sanctions and personnel tracking:** By fusing asset, network, and communication data, it mapped financial and personnel links for targets like Maduro, supporting U.S. sanctions operations.  
3. **The Russia-Ukraine battlefield:** It provides battlefield intelligence integration for Ukraine, fusing drone, radar, communication, and satellite data within 30 seconds to output targeting references, becoming a benchmark application for modern AI digital warfare.  
   With zero major security incidents for over 20 years, it has operated stably.  
   Today, the Pentagon and multiple NATO intelligence systems are deeply tied to Palantir, making replacement costs extremely high. The entire analysis workflow, personnel training, and classified permission architecture are dependent on the platform, to the point where the military cannot easily extricate itself.

**V. Founders’ Core Business and Geopolitical Philosophy**  
Building monopoly infrastructure from 0 to 1. The two core founders set the company’s long-term strategic tone.  
1. **Peter Thiel,** co-founder of PayPal, upholds the “Zero to One” philosophy: Competition is for losers; truly successful companies create unique monopolistic tracks and reject homogeneous competition. He did not follow the civilian consumer internet trend.  
   He firmly believed that defense intelligence data infrastructure is a long-term national imperative and was willing to endure over a decade of continuous losses to deeply cultivate this track.  
2. **CEO Alex Karp** has a philosophy academic background, defining the company’s grand mission: Deeply binding its business to Western geopolitical strategic interests, and resolutely taking on high-sensitivity defense contracts that other Silicon Valley giants avoid.  
   He has cultivated high-level relationships with Washington elites and policy preferences.  
   The executive circle heavily recruits retired military and government leaders, building a stable network bridging government and enterprise.  
   Compared to low-end domestic “Xinchuang” vendors that wrap open-source code, Palantir has developed its entire stack—from underlying architecture to security systems and scenario adaptation—without relying on external open-source kernels. This is key to its access to the highest-level classified defense projects.

**VI. Long-Term Outlook**  
The core arena of Sino-U.S. military competition lies in AI intelligence data systems.  
Karp publicly stated at the Davos Forum that only the U.S. and China truly possess the full capability for practical AI arms racing.  
The future outcome of great-power competition will no longer be solely about hardware equipment, but about AI intelligence integration, rapid decision-making, and full-domain situational awareness.  
The pace of modern warfare is rapidly compressing.  
Whoever can more quickly break through multi-service, multi-dimensional data silos, using AI for clue correlation, simulation, and prediction, will gain an information asymmetric strategic advantage.  
The militarized application of AI and big data is already a matter of national importance.  
The speed and maturity of building intelligence data fusion systems will profoundly affect the strategic balance of great powers for decades to come.