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从层级到智能:Block 想用 AI 重写公司组织

这篇文章最有价值的判断是:AI 的真正机会不是给员工提效,而是替代公司里“靠人肉传递上下文”的协同机制;但 Block 目前给出的更多是雄心勃勃的组织宣言,而不是已经被充分验证的普适答案。
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2026-04-01 原文链接 ↗
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核心观点

  • 层级的本质被说清了 作者把罗马军队、普鲁士总参谋部、铁路公司、泰勒制、矩阵组织串起来,核心判断是:大型组织之所以需要层级,不是因为人类偏爱官僚,而是因为管理幅度有限、信息传递昂贵,所以必须用层层汇报来压缩复杂性;这个解释是全文最站得住的部分。
  • Block 真正想替换的不是员工,而是“中层的信息路由功能” 文章的野心不是“给每个人一个 Copilot”,而是用“公司世界模型 + 客户世界模型”承担状态同步、优先级协调、资源感知这些过去靠管理链条完成的工作;这个方向有前瞻性,但它只触及管理的一部分,绝不是管理全貌。
  • “能力—模型—智能—界面”四层架构有很强的方法论价值 Block 把公司拆成原子能力、世界模型、智能层和界面,判断上是对的:真正可复利的资产不是单个产品页面,而是底层能力和可持续更新的模型;这对平台型、金融型、复杂 SaaS 公司尤其有启发。
  • “钱是最诚实的信号”有力量,但被说得过满 交易数据确实比问卷和访谈更接近真实行为,尤其适合做风控、推荐、触发式服务,但它主要是结果信号,不是全部真相;把交易数据近似成“客户现实本身”,会系统性低估情绪、文化、品牌、线下约束和潜在需求。
  • 去中层化叙事很激进,但治理难题被刻意轻描淡写 DRI 跨团队调资源、AI 生成优先级、智能层主动组合金融产品,这些设想都很大胆;但谁来仲裁资源冲突、谁为模型错误负责、监管红线如何控制,文章没有给出硬机制,这不是细节问题,而是成败分水岭。

跟我们的关联

  • 对 ATou 意味着什么、下一步怎么用 这篇文章最适合 ATou 用来重构“组织 = 信息处理系统”的思考框架;下一步不是盲信去管理层,而是先盘点团队里哪些管理动作只是传话、同步、催进度,哪些可以被系统化仪表盘、知识库和 AI 状态层替代。
  • 对 Neta 意味着什么、下一步怎么用 Neta 应该把“能力—模型—智能—界面”当成分析产品和公司的新模板;下一步可以拿手头项目做拆解:哪些是能力层,哪些是真实信号,哪些只是 UI 包装,从而判断自己是在堆功能还是在积累复利资产。
  • 对 Uota 意味着什么、下一步怎么用 Uota 可以重点抓住“诚实信号”这个判断,但不能被“交易即真相”洗脑;下一步应区分高价值行为数据和虚假反馈,同时补上定性访谈、文化语境和非结构化线索,避免只优化可见指标而错过结构性变化。
  • 对投资判断意味着什么、下一步怎么用 这篇文章提示一个重要筛选标准:AI 公司真正的护城河不是接了模型 API,而是是否掌握高频、闭环、难伪造的真实信号,并能把信号转成能力与模型;下一步看标的时要追问数据闭环、责任链条和组织落地,而不是只听“AI 重构组织”的故事。

讨论引子

1. AI 最有可能先替代管理中的哪一部分:信息路由、资源协调、绩效评估,还是一个都替代不干净? 2. 如果 DRI 没有正式层级权威,却要跨团队调资源,现实中靠什么避免隐性政治比显性层级更严重? 3. 在金融、医疗、教育这类强监管或高信任行业里,“智能层自动组合解决方案”到底是未来,还是危险的技术乌托邦?

在红杉,我们看到,速度是预测创业公司成功的最佳指标。大多数公司把 AI 当作提升生产力的工具。很少有人关注 AI 改变我们协作方式的潜力。Block 正在展示一种从根上重新思考组织设计的样子,最终借助 AI 提升速度,并把速度变成可复利的竞争优势。

在第一张企业组织结构图出现之前两千年,罗马军队就解决了一个至今每个大型组织仍要面对的问题:在沟通有限的情况下,如何跨越辽阔距离协调成千上万的人?

他们的答案是嵌套式的层级结构,并在每一层保持一致的管理幅度。最小单位是帐篷组(contubernium):八名士兵共用一顶帐篷、装备和一头骡子,由十夫长(decanus)带领。十个帐篷组组成一个百人队(century),共八十人,由百夫长(centurion)指挥。六个百人队组成一个大队(cohort)。十个大队组成一个约 5,000 人的军团(legion)。在每一层,都有一位有名有姓的指挥官,拥有明确权力,从下级汇总信息,并把上级决策向下传达。这套结构(8 → 80 → 480 → 5,000)是一种信息路由协议,围绕着一个简单的人类限制而构建:一位领导者能有效管理的人数大致在三到八人之间。罗马人在几个世纪的战争中发现了这一点。即使在今天,美国陆军的层级链条仍遵循类似模式。如今我们把它称为管理幅度(span of control),它依然是地球上每一个大型组织的根本约束。

下一次重大变化来自普鲁士。1806 年,拿破仑的军队在耶拿战役中击溃普鲁士部队之后,以沙恩霍斯特(Scharnhorst)和格奈森瑙(Gneisenau)为首的一群改革者围绕一个令人不舒服的事实重建了军队:不能指望顶层某个天才。需要的是一套系统。他们设立了总参谋部(General Staff),建立一类受过训练的专职军官,他们的工作不是作战,而是筹划行动、处理信息,并在各单位之间协调。沙恩霍斯特希望这些参谋军官能够“支持无能的将军,为领导者和指挥官补上否则可能欠缺的才能”。这就是中层管理,在这个词出现之前就已经存在:一群职业人士,其目的在于路由信息、预先推演决策,并在复杂组织中维持一致性。军队也正式区分了 line 与 staff 两类职能。line 推进核心任务。staff 提供专业支持。直到今天,每一家企业仍在使用这套词汇。

军队层级通过 1840 年代和 1850 年代的美国铁路进入商业世界。美国陆军把西点军校培养的工程师借调给私营铁路公司,这些军官把军队的组织思维一并带了进去。参谋与直线的层级、事业部结构、官僚式的汇报与控制体系:这些在铁路采纳之前,早已在军队中发展出来。在 1850 年代中期,纽约与伊利铁路公司的 Daniel McCallum 绘制了世界上第一张组织结构图,用来管理一套延伸超过 500 英里、拥有数千名工人的系统。适用于小型铁路的非正式管理方式开始失灵。列车相撞正在夺走生命。McCallum 的图把罗马人曾经使用过的同一套层级逻辑正式化:权力分层、明确的汇报关系、结构化的信息流动。它成为现代公司的蓝图。

Frederick Taylor(1856-1915)常被称为“科学管理之父”,他优化了这种层级结构内部的运作方式。泰勒把工作拆成专门化的任务,分配给受训的专家,并用度量而不是直觉来管理。这催生了职能金字塔式组织:在军队开创、铁路商业化的信息路由系统之上,把效率优化到极致的一种结构。

职能层级第一次真正经受压力测试,是在二战期间。曼哈顿计划要求物理学家、化学家、工程师、冶金学家以及军官在学科边界之外协作,在极端保密与时间压力下围绕一个共同目标推进。罗伯特·奥本海默把洛斯阿拉莫斯按职能划分,但坚持跨部门的开放协作,抵抗军方按领域封隔的本能。1944 年,内爆问题变得关键时,他围绕这一问题重组实验室,建立了当时美国企业界几乎见不到的跨职能团队。这种做法奏效了,但它是由一位非凡人物主导的战时例外。战后商业世界面对的问题是:这种跨职能协同能否变成日常。

二战后企业规模扩张并走向全球化,职能设计的规模上限变得尖锐。1959 年,麦肯锡的吉尔伯特·克利(Gilbert Clee)和阿尔弗雷德·迪·西皮奥(Alfred di Scipio)在《哈佛商业评论》发表 Creating a World Enterprise 一文,为把职能专长与事业部单元结合起来的矩阵式组织提供了理论框架。在马文·鲍尔(Marvin Bower)的领导下,麦肯锡帮助壳牌(Shell)、通用电气(GE)等公司落地这些原则,在统一的中央标准与本地灵活性之间取得平衡。这形成了推动战后全球经济的“职业化”或“现代化”公司。

随着时间推移,其他框架也出现,用来应对矩阵结构的复杂、僵硬与官僚化。麦肯锡 7-S 框架由汤姆·彼得斯(Tom Peters)和罗伯特·沃特曼(Robert Waterman)在 1970 年代末提出,把 hard Ss(Strategy 战略、Structure 结构、Systems 系统)与 soft Ss(Shared Values 共同价值观、Skills 技能、Staff 人员、Style 风格)区分开来。核心思想是:仅靠结构性要素并不够。组织要有效,必须在文化特质与那些决定战略能否真正成功的人性因素上实现对齐。

在更近的几十年里,科技公司对组织结构做了激进实验。Spotify 推广了跨职能的小队(squad)与短周期冲刺。Zappos 尝试了 Holacracy,彻底取消管理头衔。Valve 以扁平结构运作,没有正式层级。每一次实验都揭示了传统层级的局限,但没有一次解决根本问题。Spotify 在规模扩大后又回归更传统的管理方式。Zappos 出现了显著的人员流失。Valve 的模式很难扩展到几百人以上。当组织规模增长到数千人时,它们会回到层级式协同,因为至今没有任何替代性的信息路由机制足够强大到可以取而代之。

约束和罗马人当年面对、海军陆战队在二战中重新发现的一样:管理幅度变窄,就必须增加指挥层级;而层级越多,信息流动就越慢。两千年的组织创新,一直是在不打破这组权衡的前提下,想办法绕开它。

那么,现在有什么不同?

在 Block,正在质疑一个底层假设:组织必须按层级组织,并以人作为协同机制。相反,目标是替换掉层级结构所做的事。如今大多数公司使用 AI,是给每个人配一个副驾驶,让现有结构在不改变的前提下稍微运转得更好。追求的是另一件事:把公司建成一种智能(或 mini-AGI)。

试图超越传统层级的,不止我们。海尔的人单合一模式、平台型组织、“数据驱动”管理:这些都是对同一问题的真实尝试。它们缺少的,是一种真正能执行层级结构所提供的协同功能的技术。AI 就是那种技术。第一次,系统可以持续更新对整个业务的模型,并用它来协调工作,而过去这需要人把信息通过一层层管理传递下去。

要让这套思路成立,公司需要两样东西:一种关于自身运营的“世界模型”,以及足够丰富、能让这个模型发挥作用的客户信号。

Block 以远程优先为基础。我们做的每件事都会留下产物。决策、讨论、代码、设计、计划、问题与进展,都以可记录的行动存在。这就是公司世界模型的原材料。在传统公司里,经理的工作是知道团队在发生什么,并把上下文在链条上向上向下传递。在远程优先的公司里,工作本身已经可被机器读取,AI 可以持续构建并维护这幅全景:在做什么、卡在哪里、资源分配到哪里、哪些有效、哪些无效。这些原本由层级结构携带的信息,将由公司世界模型来携带。

但系统的能力,取决于喂给它的客户信号质量。而钱,是世界上最诚实的信号。

人们会在问卷上说谎。会无视广告。会弃购。但当他们花、存、转、借、还时,那才是真相。每一笔交易,都是某个人生活中的一条事实。Block 每天都能看到数以百万计的交易两端,买方通过 Cash App,卖方通过 Square,再加上商户经营产生的运营数据。这让客户世界模型拥有一种罕见的东西:基于诚实信号、按客户与商户粒度构建的金融现实认知,并且它会复利式积累。信号越丰富,模型越好。模型越好,交易越多。交易越多,信号越丰富。

公司世界模型与客户世界模型一起,构成了另一种公司的基础。与其由产品团队去构建预设的路线图,不如去构建四样东西。

第一,能力(capabilities)。也就是金融的原子级基元:支付、放贷、发卡、银行业务、先买后付、薪资发放,等等。这些不是产品,而是难以获取和维护的积木块(其中一些具有网络效应与监管许可)。它们本身没有 UI。它们只有可靠性、合规性和性能目标。

第二,世界模型。这有两面。公司世界模型是公司如何理解自身的运营、表现和优先级,用它替代过去通过层层管理传递的信息。客户世界模型是基于专有交易数据、按客户、按商户、按市场构建的表示。它今天从原始交易数据起步,随着时间推移演进为完整的因果与预测模型。

第三,智能层(intelligence layer)。它把能力组合成面向特定客户、特定时刻的解决方案,并主动交付。比如,模型发现某家餐厅的现金流会在季节性淡季前收紧,而这种模式它以前见过。智能层就用放贷能力组合出一笔短期贷款,用支付能力调整还款计划,并在商户还没想到去找融资之前就把方案推送给他。又比如,某个 Cash App 用户的消费模式发生了变化,而模型把这种变化与搬到新城市关联起来。智能层就组合出新的工资直存设置、一张在新社区提供加成类别的 Cash App Card,以及一个按更新后的收入校准的储蓄目标。没有任何产品经理决定要做这两种解决方案。能力早已存在。智能层识别到时机,并把它们组合起来。

第四,界面(硬件与软件)。Square、Cash App、Afterpay、TIDAL、bitkey、proto。它们是智能层交付组合方案的载体。它们很重要,但价值并不是在这里被创造出来的。价值在模型与智能之中。

当智能层尝试组合某个方案,却因为缺少相应能力而无法完成时,那条失败信号就是未来的路线图。传统的路线图靠产品经理猜测下一步该做什么,这是任何公司最终的限制因素。在这种模型里,客户现实直接生成需求队列。

如果公司构建的是这些东西,那问题就变成:人要做什么?

组织结构由此推演,并且把传统图景翻转过来。在传统公司里,智能分散在人身上,由层级结构去路由。在这种模型里,智能在系统里。人处在边缘。边缘才是行动发生的地方。

边缘是智能与现实接触的地方。人可以深入到模型暂时还到不了的场景里,感知模型还无法感知的东西:直觉、带立场的方向、文化语境、信任关系的动态、房间里的气氛。他们也会做出模型不该独自做的判断,尤其是涉及伦理决策、全新情境,以及高风险时刻,在那里,判断错误的代价可能是生存性的。一个触不到世界的世界模型,只是一套数据库。但边缘并不需要层层管理来协同。世界模型把每个处在边缘的人所需的上下文直接给到他们,让行动不必等待信息在指挥链上来回奔波。

在实践中,这意味着我们把角色规范为三类。

个人贡献者(IC)负责构建并运行能力、模型、智能层与界面。他们是某一层系统上的深度专家。世界模型提供了过去经理提供的上下文,因此 IC 可以对自己这一层做决策,而不必等别人告诉他们该做什么。

直接责任人(DRI)负责某个跨团队的具体问题或机会,以及对应的客户结果。比如,一名 DRI 可能在 90 天内负责某一细分领域的商户流失问题,并拥有完整权限,按需从世界模型团队、放贷能力团队与界面团队调动资源。DRI 可以长期负责某些问题,也可以转到别处解决新的问题。

球员教练(player-coach)把做事与培养人结合起来。他们取代传统经理那种以信息路由为主要工作内容的角色。球员教练仍然写代码、建模型或做界面设计,同时也投入时间促进身边人的成长。他们不会把日子耗在状态会议、对齐会以及优先级拉扯上。对齐由世界模型负责。战略与优先级由 DRI 结构负责。球员教练负责手艺与人。

不再需要一个永久的中层管理层。旧层级做的其他事情,由系统来协同;每个人都被赋权,角色离工作与客户更近。

Block 还处在这次转型的早期阶段。它会很难,其中一些部分很可能在真正运转之前就先坏掉。之所以现在写下这些,是因为相信每家公司最终都必须面对同一个问题:你的公司理解什么东西,而这件东西确实很难理解?这种理解是否每天都在加深?

如果答案是什么也没有,AI 就只是一段成本优化的故事。裁掉人手,几个季度里利润率更好看,最终会被更聪明的东西吞并。如果答案很深,AI 并不是在增强公司。它是在揭示公司到底是什么。

Block 的答案是经济图谱:数百万商户与消费者,交易的两端,实时被观测到的金融行为。这种理解在系统运行的每一秒都在复利式累积。我们相信,这背后的模式——公司以智能而不是层级来组织——重要到足以在未来几年重塑各类公司的运作方式。Block 已经走得足够远,能够证明这个想法不只是理论(当然,我们也欢迎辩论与反馈,用来压力测试并改进我们的想法)。

公司快或慢,取决于信息流动。层级结构与中层管理会阻碍信息流动。两千年来,从罗马的帐篷组(contubernium)到今天的全球企业,我们都没有真正的替代方案。八名共帐的士兵需要一名十夫长(decanus)。八十人需要一名百夫长(centurion)。五千人需要一名军团长(legate)。问题从来不是是否需要层级。问题是,执行这些层级职能的,是否只能是人。现在不再是了。Block 正在构建下一阶段的形态。

https://block.xyz/inside/from-hierarchy-to-intelligence

At Sequoia, we see that speed is the best predictor of start-up success. Most companies are focused on AI as a productivity enhancer. Few are focused on the potential of AI to change how we work together. Block is showing what it looks like to fundamentally rethink organization design, ultimately harnessing AI to increase speed as a compounding competitive advantage.

在红杉,我们看到,速度是预测创业公司成功的最佳指标。大多数公司把 AI 当作提升生产力的工具。很少有人关注 AI 改变我们协作方式的潜力。Block 正在展示一种从根上重新思考组织设计的样子,最终借助 AI 提升速度,并把速度变成可复利的竞争优势。

Two thousand years before the first corporate org chart, the Roman Army solved a problem that every large organization still faces: how do you coordinate thousands of people across vast distances with limited communication?

在第一张企业组织结构图出现之前两千年,罗马军队就解决了一个至今每个大型组织仍要面对的问题:在沟通有限的情况下,如何跨越辽阔距离协调成千上万的人?

Their answer was a nested hierarchy with a consistent span of control at every level. The smallest unit was the contubernium, eight soldiers who shared a tent, equipment, and a mule, led by a decanus. Ten contubernia formed a century of eighty men under a centurion. Six centuries made a cohort. Ten cohorts made a legion of roughly 5,000. At each layer, a named commander held defined authority, aggregated information from below, and relayed decisions from above. The structure (8 → 80 → 480 → 5,000) was an information routing protocol built around a simple human limitation: a leader can effectively manage somewhere between three and eight people. The Romans discovered this through centuries of warfare. Even today, the US Army's hierarchical chain follows a similar pattern. We now call it "span of control," and it remains the governing constraint of every large organization on earth.

他们的答案是嵌套式的层级结构,并在每一层保持一致的管理幅度。最小单位是帐篷组(contubernium):八名士兵共用一顶帐篷、装备和一头骡子,由十夫长(decanus)带领。十个帐篷组组成一个百人队(century),共八十人,由百夫长(centurion)指挥。六个百人队组成一个大队(cohort)。十个大队组成一个约 5,000 人的军团(legion)。在每一层,都有一位有名有姓的指挥官,拥有明确权力,从下级汇总信息,并把上级决策向下传达。这套结构(8 → 80 → 480 → 5,000)是一种信息路由协议,围绕着一个简单的人类限制而构建:一位领导者能有效管理的人数大致在三到八人之间。罗马人在几个世纪的战争中发现了这一点。即使在今天,美国陆军的层级链条仍遵循类似模式。如今我们把它称为管理幅度(span of control),它依然是地球上每一个大型组织的根本约束。

The next big change came from Prussia. After Napoleon's army destroyed the Prussian forces at the Battle of Jena in 1806, a group of reformers led by Scharnhorst and Gneisenau rebuilt the military around an uncomfortable truth: you cannot depend on individual genius at the top. You need a system. They created the General Staff, a dedicated class of trained officers whose job was not to fight but to plan operations, process information, and coordinate across units. Scharnhorst intended these staff officers to "support incompetent Generals, providing the talents that might otherwise be wanting among leaders and commanders." This was middle management before the term existed. Professionals whose purpose was to route information, pre-compute decisions, and maintain alignment across a complex organization. The military also formalized the distinction between "line" and "staff" functions. Line advances the core mission. Staff provides specialized support. Every corporation still uses this vocabulary today.

下一次重大变化来自普鲁士。1806 年,拿破仑的军队在耶拿战役中击溃普鲁士部队之后,以沙恩霍斯特(Scharnhorst)和格奈森瑙(Gneisenau)为首的一群改革者围绕一个令人不舒服的事实重建了军队:不能指望顶层某个天才。需要的是一套系统。他们设立了总参谋部(General Staff),建立一类受过训练的专职军官,他们的工作不是作战,而是筹划行动、处理信息,并在各单位之间协调。沙恩霍斯特希望这些参谋军官能够“支持无能的将军,为领导者和指挥官补上否则可能欠缺的才能”。这就是中层管理,在这个词出现之前就已经存在:一群职业人士,其目的在于路由信息、预先推演决策,并在复杂组织中维持一致性。军队也正式区分了 line 与 staff 两类职能。line 推进核心任务。staff 提供专业支持。直到今天,每一家企业仍在使用这套词汇。

Military hierarchy entered the business world through the American railroads in the 1840s and 1850s. The U.S. Army lent West Point-trained engineers to private railroad companies, and these officers brought military organizational thinking with them. Staff and line hierarchies, divisional structure, bureaucratic systems of reporting and control: all of it was developed in the military before the railroads adopted it. In the mid-1850s, Daniel McCallum of the New York and Erie Railroad created the world's first organizational chart to manage a system stretching over 500 miles with thousands of workers. The informal management styles that worked for smaller railroads were failing. Train collisions were killing people. McCallum's chart formalized the same hierarchical logic the Romans had used: layers of authority, defined reporting lines, structured information flow. It became the blueprint for the modern corporation.

军队层级通过 1840 年代和 1850 年代的美国铁路进入商业世界。美国陆军把西点军校培养的工程师借调给私营铁路公司,这些军官把军队的组织思维一并带了进去。参谋与直线的层级、事业部结构、官僚式的汇报与控制体系:这些在铁路采纳之前,早已在军队中发展出来。在 1850 年代中期,纽约与伊利铁路公司的 Daniel McCallum 绘制了世界上第一张组织结构图,用来管理一套延伸超过 500 英里、拥有数千名工人的系统。适用于小型铁路的非正式管理方式开始失灵。列车相撞正在夺走生命。McCallum 的图把罗马人曾经使用过的同一套层级逻辑正式化:权力分层、明确的汇报关系、结构化的信息流动。它成为现代公司的蓝图。

Frederick Taylor (1856-1915), often called the "Father of Scientific Management," optimized what happened within that hierarchy. Taylor broke work into specialized tasks, assigned them to trained experts, and managed through measurement rather than intuition. This produced the functional pyramid organization - a structure optimized for efficiency within the information routing system that the military had pioneered and the railroads had commercialized.

Frederick Taylor(1856-1915)常被称为“科学管理之父”,他优化了这种层级结构内部的运作方式。泰勒把工作拆成专门化的任务,分配给受训的专家,并用度量而不是直觉来管理。这催生了职能金字塔式组织:在军队开创、铁路商业化的信息路由系统之上,把效率优化到极致的一种结构。

The first real stress test of functional hierarchy came during World War II. The Manhattan Project required physicists, chemists, engineers, metallurgists, and military officers to work across disciplinary boundaries toward a single objective under extreme secrecy and time pressure. Robert Oppenheimer organized Los Alamos into functional divisions but insisted on open collaboration across them, resisting the military's instinct to compartmentalize. When the implosion problem became critical in 1944, he reorganized the lab around it, creating cross-functional teams unlike anything in corporate America at the time. It worked, but it was a wartime exception led by a singular figure. The question the postwar business world faced was whether that kind of cross-functional coordination could be made routine.

职能层级第一次真正经受压力测试,是在二战期间。曼哈顿计划要求物理学家、化学家、工程师、冶金学家以及军官在学科边界之外协作,在极端保密与时间压力下围绕一个共同目标推进。罗伯特·奥本海默把洛斯阿拉莫斯按职能划分,但坚持跨部门的开放协作,抵抗军方按领域封隔的本能。1944 年,内爆问题变得关键时,他围绕这一问题重组实验室,建立了当时美国企业界几乎见不到的跨职能团队。这种做法奏效了,但它是由一位非凡人物主导的战时例外。战后商业世界面对的问题是:这种跨职能协同能否变成日常。

With the growth and globalization of companies after World War II, the scale limitations of functional design became acute. In 1959, McKinsey's Gilbert Clee and Alfred di Scipio published "Creating a World Enterprise" in the Harvard Business Review, providing an intellectual framework for a matrix organization that combined functional specialties with divisional units. Under the leadership of Marvin Bower, McKinsey helped companies like Shell and GE implement these principles, balancing central standards with local agility. This became the "professional" or "modern" corporation that propelled the postwar global economy.

二战后企业规模扩张并走向全球化,职能设计的规模上限变得尖锐。1959 年,麦肯锡的吉尔伯特·克利(Gilbert Clee)和阿尔弗雷德·迪·西皮奥(Alfred di Scipio)在《哈佛商业评论》发表 Creating a World Enterprise 一文,为把职能专长与事业部单元结合起来的矩阵式组织提供了理论框架。在马文·鲍尔(Marvin Bower)的领导下,麦肯锡帮助壳牌(Shell)、通用电气(GE)等公司落地这些原则,在统一的中央标准与本地灵活性之间取得平衡。这形成了推动战后全球经济的“职业化”或“现代化”公司。

Over time, other frameworks emerged to address the complexity, rigidity, and bureaucracy of matrix structures. The McKinsey 7-S framework, developed in the late 1970s by Tom Peters and Robert Waterman, distinguished the "hard Ss" (Strategy, Structure, Systems) from the "soft Ss" (Shared Values, Skills, Staff, Style). The core idea was that structural elements alone were insufficient. Organizational effectiveness required alignment across cultural traits and the human factors that determine whether a strategy actually succeeds.

随着时间推移,其他框架也出现,用来应对矩阵结构的复杂、僵硬与官僚化。麦肯锡 7-S 框架由汤姆·彼得斯(Tom Peters)和罗伯特·沃特曼(Robert Waterman)在 1970 年代末提出,把 hard Ss(Strategy 战略、Structure 结构、Systems 系统)与 soft Ss(Shared Values 共同价值观、Skills 技能、Staff 人员、Style 风格)区分开来。核心思想是:仅靠结构性要素并不够。组织要有效,必须在文化特质与那些决定战略能否真正成功的人性因素上实现对齐。

In more recent decades, technology companies have experimented aggressively with organization structure. Spotify popularized cross-functional squads with short sprint cycles. Zappos attempted Holacracy, eliminating management titles entirely. Valve operated with a flat structure and no formal hierarchy. Each of these experiments revealed something about the limitations of traditional hierarchy, but none solved the underlying problem. Spotify moved back toward conventional management as it scaled. Zappos saw significant attrition. Valve's model proved difficult to scale beyond a few hundred people. As organizations grow into the thousands, they revert to hierarchical coordination because no alternative information routing mechanism has been powerful enough to replace it.

在更近的几十年里,科技公司对组织结构做了激进实验。Spotify 推广了跨职能的小队(squad)与短周期冲刺。Zappos 尝试了 Holacracy,彻底取消管理头衔。Valve 以扁平结构运作,没有正式层级。每一次实验都揭示了传统层级的局限,但没有一次解决根本问题。Spotify 在规模扩大后又回归更传统的管理方式。Zappos 出现了显著的人员流失。Valve 的模式很难扩展到几百人以上。当组织规模增长到数千人时,它们会回到层级式协同,因为至今没有任何替代性的信息路由机制足够强大到可以取而代之。

The constraint is the same one the Romans faced and the Marine Corps rediscovered in World War II: narrowing span of control means adding layers of command, but more layers mean slower information flow. Two thousand years of organizational innovation has been an attempt to work around this tradeoff without breaking it.

约束和罗马人当年面对、海军陆战队在二战中重新发现的一样:管理幅度变窄,就必须增加指挥层级;而层级越多,信息流动就越慢。两千年的组织创新,一直是在不打破这组权衡的前提下,想办法绕开它。

So what's different now?

那么,现在有什么不同?

At Block, we're questioning the underlying assumption: that organizations have to be hierarchically organized with humans as the coordination mechanism. Instead, we intend to replace what the hierarchy does. Most companies using AI today are giving everyone a copilot, which makes the existing structure work slightly better without changing it. We're after something different: a company built as an intelligence (or mini-AGI).

在 Block,正在质疑一个底层假设:组织必须按层级组织,并以人作为协同机制。相反,目标是替换掉层级结构所做的事。如今大多数公司使用 AI,是给每个人配一个副驾驶,让现有结构在不改变的前提下稍微运转得更好。追求的是另一件事:把公司建成一种智能(或 mini-AGI)。

We are not the first to try to move beyond traditional hierarchy. Haier's rendanheyi model, platform organizations, "data-driven" management: these are real attempts at the same problem. What they lacked was a technology capable of actually performing the coordination functions that hierarchy exists to provide. AI is that technology. For the first time, a system can maintain a continuously updated model of an entire business and use it to coordinate work in ways that previously required humans relaying information through layers of management.

试图超越传统层级的,不止我们。海尔的人单合一模式、平台型组织、“数据驱动”管理:这些都是对同一问题的真实尝试。它们缺少的,是一种真正能执行层级结构所提供的协同功能的技术。AI 就是那种技术。第一次,系统可以持续更新对整个业务的模型,并用它来协调工作,而过去这需要人把信息通过一层层管理传递下去。

For this to work, a company needs two things: a kind of "world model" of its own operations, and a customer signal rich enough to make that model useful.

要让这套思路成立,公司需要两样东西:一种关于自身运营的“世界模型”,以及足够丰富、能让这个模型发挥作用的客户信号。

Block is remote-first. Everything we do creates artifacts. Decisions, discussions, code, designs, plans, problems, and progress all exist as recorded actions. It's the raw material for a company world model. In a traditional company, a manager's job is to know what's happening across their team and relay that context up and down the chain. In a remote-first company where work is already machine-readable, AI can build and maintain that picture continuously. What's being built, what's blocked, where resources are allocated, what's working and what isn't. That's the information the hierarchy used to carry. The company world model carries it instead.

Block 以远程优先为基础。我们做的每件事都会留下产物。决策、讨论、代码、设计、计划、问题与进展,都以可记录的行动存在。这就是公司世界模型的原材料。在传统公司里,经理的工作是知道团队在发生什么,并把上下文在链条上向上向下传递。在远程优先的公司里,工作本身已经可被机器读取,AI 可以持续构建并维护这幅全景:在做什么、卡在哪里、资源分配到哪里、哪些有效、哪些无效。这些原本由层级结构携带的信息,将由公司世界模型来携带。

But the capability of the system is only as good as the quality of the customer signal feeding it. And money is the most honest signal in the world.

但系统的能力,取决于喂给它的客户信号质量。而钱,是世界上最诚实的信号。

People lie on surveys. They ignore ads. They abandon carts. But when they spend, save, send, borrow, or repay, that's the truth. Every transaction is a fact about someone's life. Block sees both sides of millions of these transactions every day, the buyer through Cash App and the seller through Square, plus the operational data from running the merchant's business. That gives the customer world model something rare: a per-customer, per-merchant understanding of financial reality built from honest signal that compounds. The richer the signal, the better the model. The better the model, the more transactions. The more transactions, the richer the signal.

人们会在问卷上说谎。会无视广告。会弃购。但当他们花、存、转、借、还时,那才是真相。每一笔交易,都是某个人生活中的一条事实。Block 每天都能看到数以百万计的交易两端,买方通过 Cash App,卖方通过 Square,再加上商户经营产生的运营数据。这让客户世界模型拥有一种罕见的东西:基于诚实信号、按客户与商户粒度构建的金融现实认知,并且它会复利式积累。信号越丰富,模型越好。模型越好,交易越多。交易越多,信号越丰富。

Together, the company world model and the customer world model form the foundation for a different kind of company. Instead of product teams building predetermined roadmaps, you build four things.

公司世界模型与客户世界模型一起,构成了另一种公司的基础。与其由产品团队去构建预设的路线图,不如去构建四样东西。

First, capabilities. The atomic financial primitives: payments, lending, card issuance, banking, buy-now-pay-later, payroll, and so on. These are not products. They are building blocks that are hard to acquire and maintain (some have network effects and regulatory permission). They have no UIs of their own. They have reliability, compliance, and performance targets.

第一,能力(capabilities)。也就是金融的原子级基元:支付、放贷、发卡、银行业务、先买后付、薪资发放,等等。这些不是产品,而是难以获取和维护的积木块(其中一些具有网络效应与监管许可)。它们本身没有 UI。它们只有可靠性、合规性和性能目标。

Second, a world model. This has two sides. The company world model is how the company understands itself and its own operations, performance, and priorities, replacing the information that used to flow through layers of management. The customer world model is the per-customer, per-merchant, per-market representation built from proprietary transaction data. It starts with raw transaction data today and evolves toward full causal and predictive models over time.

第二,世界模型。这有两面。公司世界模型是公司如何理解自身的运营、表现和优先级,用它替代过去通过层层管理传递的信息。客户世界模型是基于专有交易数据、按客户、按商户、按市场构建的表示。它今天从原始交易数据起步,随着时间推移演进为完整的因果与预测模型。

Third, an intelligence layer. This is what composes capabilities into solutions for specific customers at specific moments and delivers them proactively. A restaurant's cash flow is tightening ahead of a seasonal dip the model has seen before. The intelligence layer composes a short-term loan from the lending capability, adjusts the repayment schedule using the payments capability, and surfaces it to the merchant before they even think to look for financing. A Cash App user's spending pattern shifts in a way the model associates with a move to a new city. The intelligence layer composes a new direct deposit setup, a Cash App Card with boosted categories for their new neighborhood, and a savings goal calibrated to their updated income. No product manager decided to build either solution. The capabilities existed. The intelligence layer recognized the moment and composed them.

第三,智能层(intelligence layer)。它把能力组合成面向特定客户、特定时刻的解决方案,并主动交付。比如,模型发现某家餐厅的现金流会在季节性淡季前收紧,而这种模式它以前见过。智能层就用放贷能力组合出一笔短期贷款,用支付能力调整还款计划,并在商户还没想到去找融资之前就把方案推送给他。又比如,某个 Cash App 用户的消费模式发生了变化,而模型把这种变化与搬到新城市关联起来。智能层就组合出新的工资直存设置、一张在新社区提供加成类别的 Cash App Card,以及一个按更新后的收入校准的储蓄目标。没有任何产品经理决定要做这两种解决方案。能力早已存在。智能层识别到时机,并把它们组合起来。

Fourth, interfaces (hardware and software). Square, Cash App, Afterpay, TIDAL, bitkey, proto. These are delivery surfaces through which the intelligence layer delivers composed solutions. They are important, but they are not where the value is created. The value is in the model and the intelligence.

第四,界面(硬件与软件)。Square、Cash App、Afterpay、TIDAL、bitkey、proto。它们是智能层交付组合方案的载体。它们很重要,但价值并不是在这里被创造出来的。价值在模型与智能之中。

When the intelligence layer tries to compose a solution and can't because the capability doesn't exist, that failure signal is the future roadmap. The traditional roadmap, where product managers hypothesize about what to build next, is any company's ultimate limiting factor. In this model, customer reality generates the backlog directly.

当智能层尝试组合某个方案,却因为缺少相应能力而无法完成时,那条失败信号就是未来的路线图。传统的路线图靠产品经理猜测下一步该做什么,这是任何公司最终的限制因素。在这种模型里,客户现实直接生成需求队列。

If this is what the company builds, then the question becomes: what do the people do?

如果公司构建的是这些东西,那问题就变成:人要做什么?

The org structure follows from this, and it inverts the traditional picture. In a conventional company, the intelligence is spread throughout the people and the hierarchy routes it. In this model, the intelligence lives in the system. The people are on the edge. The edge is where the action is.

组织结构由此推演,并且把传统图景翻转过来。在传统公司里,智能分散在人身上,由层级结构去路由。在这种模型里,智能在系统里。人处在边缘。边缘才是行动发生的地方。

The edge is where the intelligence makes contact with reality. People reach into places the model can't go yet. They sense things the model can't perceive: intuition, opinionated direction, cultural context, trust dynamics, the feeling in a room. They make the calls the model shouldn't make on its own, especially ethical decisions, novel situations, and high-stakes moments where the cost of being wrong is existential. A world model that can't touch the world is just a database. But the edge doesn't need layers of management to coordinate it. The world model gives every person at the edge the context they need to act without waiting for information to travel up and down a chain of command.

边缘是智能与现实接触的地方。人可以深入到模型暂时还到不了的场景里,感知模型还无法感知的东西:直觉、带立场的方向、文化语境、信任关系的动态、房间里的气氛。他们也会做出模型不该独自做的判断,尤其是涉及伦理决策、全新情境,以及高风险时刻,在那里,判断错误的代价可能是生存性的。一个触不到世界的世界模型,只是一套数据库。但边缘并不需要层层管理来协同。世界模型把每个处在边缘的人所需的上下文直接给到他们,让行动不必等待信息在指挥链上来回奔波。

In practice, this means we normalize down to three roles.

在实践中,这意味着我们把角色规范为三类。

Individual contributors (ICs) who build and operate capabilities, the model, the intelligence layer, and the interfaces. They are deep specialists and experts in a specific layer of the system. The world model provides the context that a manager used to provide, so ICs can make decisions about their layer without waiting to be told what to do.

个人贡献者(IC)负责构建并运行能力、模型、智能层与界面。他们是某一层系统上的深度专家。世界模型提供了过去经理提供的上下文,因此 IC 可以对自己这一层做决策,而不必等别人告诉他们该做什么。

Directly Responsible Individuals (DRI) who own specific cross-cutting problems or opportunities and customer outcomes. A DRI might own the problem of merchant churn in a specific segment for 90 days, with full authority to pull resources from the world model team, the lending capability team, and the interface team as needed. DRIs may persist on certain problems or move elsewhere to solve new ones.

直接责任人(DRI)负责某个跨团队的具体问题或机会,以及对应的客户结果。比如,一名 DRI 可能在 90 天内负责某一细分领域的商户流失问题,并拥有完整权限,按需从世界模型团队、放贷能力团队与界面团队调动资源。DRI 可以长期负责某些问题,也可以转到别处解决新的问题。

Player-coaches who combine building with developing people. They replace the traditional manager whose primary job was information routing. A player-coach still writes code or builds models or designs interfaces. They also invest in the growth of the people around them. They don't spend their days in status meetings, alignment sessions, and priority negotiations. The world model handles alignment. The DRI structure handles strategy and priority. The player-coach handles craft and people.

球员教练(player-coach)把做事与培养人结合起来。他们取代传统经理那种以信息路由为主要工作内容的角色。球员教练仍然写代码、建模型或做界面设计,同时也投入时间促进身边人的成长。他们不会把日子耗在状态会议、对齐会以及优先级拉扯上。对齐由世界模型负责。战略与优先级由 DRI 结构负责。球员教练负责手艺与人。

There is no need for a permanent middle management layer. Everything else the old hierarchy did, the system coordinates, and everyone is empowered, with a role that's much closer to the work and the customer.

不再需要一个永久的中层管理层。旧层级做的其他事情,由系统来协同;每个人都被赋权,角色离工作与客户更近。

Block is in the early stages of this transition. It will be a difficult one, and parts of it will likely break before they work. We're writing about it now because we believe every company will eventually need to confront the same question we did: what does your company understand that is genuinely hard to understand, and is that understanding getting deeper every day?

Block 还处在这次转型的早期阶段。它会很难,其中一些部分很可能在真正运转之前就先坏掉。之所以现在写下这些,是因为相信每家公司最终都必须面对同一个问题:你的公司理解什么东西,而这件东西确实很难理解?这种理解是否每天都在加深?

If the answer is nothing, AI is just a cost optimization story. You cut headcount, improve margins for a few quarters, and eventually get absorbed by something smarter. If the answer is deep, AI doesn't augment your company. It reveals what your company actually is.

如果答案是什么也没有,AI 就只是一段成本优化的故事。裁掉人手,几个季度里利润率更好看,最终会被更聪明的东西吞并。如果答案很深,AI 并不是在增强公司。它是在揭示公司到底是什么。

Block's answer is the economic graph: millions of merchants and consumers, both sides of every transaction, financial behavior observed in real time. That understanding compounds every second the system operates. We believe the pattern behind this, a company organized as an intelligence rather than a hierarchy, is significant enough that it will reshape how companies of all kinds operate over the coming years. Block is far enough along to show the idea is more than theory (though, we welcome debate and feedback to pressure test and improve our ideas).

Block 的答案是经济图谱:数百万商户与消费者,交易的两端,实时被观测到的金融行为。这种理解在系统运行的每一秒都在复利式累积。我们相信,这背后的模式——公司以智能而不是层级来组织——重要到足以在未来几年重塑各类公司的运作方式。Block 已经走得足够远,能够证明这个想法不只是理论(当然,我们也欢迎辩论与反馈,用来压力测试并改进我们的想法)。

Companies move fast or slow based on information flow. Hierarchy and middle management impede information flow. For two thousand years, from the Roman contubernium to today's global enterprises, we have had no real alternative. Eight soldiers sharing a tent needed a decanus. Eighty men needed a centurion. Five thousand needed a legate. The question was never whether you needed layers. The question was whether humans were the only option for what those layers do. They aren't anymore. Block is building what comes next.

公司快或慢,取决于信息流动。层级结构与中层管理会阻碍信息流动。两千年来,从罗马的帐篷组(contubernium)到今天的全球企业,我们都没有真正的替代方案。八名共帐的士兵需要一名十夫长(decanus)。八十人需要一名百夫长(centurion)。五千人需要一名军团长(legate)。问题从来不是是否需要层级。问题是,执行这些层级职能的,是否只能是人。现在不再是了。Block 正在构建下一阶段的形态。

https://block.xyz/inside/from-hierarchy-to-intelligence

https://block.xyz/inside/from-hierarchy-to-intelligence

At Sequoia, we see that speed is the best predictor of start-up success. Most companies are focused on AI as a productivity enhancer. Few are focused on the potential of AI to change how we work together. Block is showing what it looks like to fundamentally rethink organization design, ultimately harnessing AI to increase speed as a compounding competitive advantage.

Two thousand years before the first corporate org chart, the Roman Army solved a problem that every large organization still faces: how do you coordinate thousands of people across vast distances with limited communication?

Their answer was a nested hierarchy with a consistent span of control at every level. The smallest unit was the contubernium, eight soldiers who shared a tent, equipment, and a mule, led by a decanus. Ten contubernia formed a century of eighty men under a centurion. Six centuries made a cohort. Ten cohorts made a legion of roughly 5,000. At each layer, a named commander held defined authority, aggregated information from below, and relayed decisions from above. The structure (8 → 80 → 480 → 5,000) was an information routing protocol built around a simple human limitation: a leader can effectively manage somewhere between three and eight people. The Romans discovered this through centuries of warfare. Even today, the US Army's hierarchical chain follows a similar pattern. We now call it "span of control," and it remains the governing constraint of every large organization on earth.

The next big change came from Prussia. After Napoleon's army destroyed the Prussian forces at the Battle of Jena in 1806, a group of reformers led by Scharnhorst and Gneisenau rebuilt the military around an uncomfortable truth: you cannot depend on individual genius at the top. You need a system. They created the General Staff, a dedicated class of trained officers whose job was not to fight but to plan operations, process information, and coordinate across units. Scharnhorst intended these staff officers to "support incompetent Generals, providing the talents that might otherwise be wanting among leaders and commanders." This was middle management before the term existed. Professionals whose purpose was to route information, pre-compute decisions, and maintain alignment across a complex organization. The military also formalized the distinction between "line" and "staff" functions. Line advances the core mission. Staff provides specialized support. Every corporation still uses this vocabulary today.

Military hierarchy entered the business world through the American railroads in the 1840s and 1850s. The U.S. Army lent West Point-trained engineers to private railroad companies, and these officers brought military organizational thinking with them. Staff and line hierarchies, divisional structure, bureaucratic systems of reporting and control: all of it was developed in the military before the railroads adopted it. In the mid-1850s, Daniel McCallum of the New York and Erie Railroad created the world's first organizational chart to manage a system stretching over 500 miles with thousands of workers. The informal management styles that worked for smaller railroads were failing. Train collisions were killing people. McCallum's chart formalized the same hierarchical logic the Romans had used: layers of authority, defined reporting lines, structured information flow. It became the blueprint for the modern corporation.

Frederick Taylor (1856-1915), often called the "Father of Scientific Management," optimized what happened within that hierarchy. Taylor broke work into specialized tasks, assigned them to trained experts, and managed through measurement rather than intuition. This produced the functional pyramid organization - a structure optimized for efficiency within the information routing system that the military had pioneered and the railroads had commercialized.

The first real stress test of functional hierarchy came during World War II. The Manhattan Project required physicists, chemists, engineers, metallurgists, and military officers to work across disciplinary boundaries toward a single objective under extreme secrecy and time pressure. Robert Oppenheimer organized Los Alamos into functional divisions but insisted on open collaboration across them, resisting the military's instinct to compartmentalize. When the implosion problem became critical in 1944, he reorganized the lab around it, creating cross-functional teams unlike anything in corporate America at the time. It worked, but it was a wartime exception led by a singular figure. The question the postwar business world faced was whether that kind of cross-functional coordination could be made routine.

With the growth and globalization of companies after World War II, the scale limitations of functional design became acute. In 1959, McKinsey's Gilbert Clee and Alfred di Scipio published "Creating a World Enterprise" in the Harvard Business Review, providing an intellectual framework for a matrix organization that combined functional specialties with divisional units. Under the leadership of Marvin Bower, McKinsey helped companies like Shell and GE implement these principles, balancing central standards with local agility. This became the "professional" or "modern" corporation that propelled the postwar global economy.

Over time, other frameworks emerged to address the complexity, rigidity, and bureaucracy of matrix structures. The McKinsey 7-S framework, developed in the late 1970s by Tom Peters and Robert Waterman, distinguished the "hard Ss" (Strategy, Structure, Systems) from the "soft Ss" (Shared Values, Skills, Staff, Style). The core idea was that structural elements alone were insufficient. Organizational effectiveness required alignment across cultural traits and the human factors that determine whether a strategy actually succeeds.

In more recent decades, technology companies have experimented aggressively with organization structure. Spotify popularized cross-functional squads with short sprint cycles. Zappos attempted Holacracy, eliminating management titles entirely. Valve operated with a flat structure and no formal hierarchy. Each of these experiments revealed something about the limitations of traditional hierarchy, but none solved the underlying problem. Spotify moved back toward conventional management as it scaled. Zappos saw significant attrition. Valve's model proved difficult to scale beyond a few hundred people. As organizations grow into the thousands, they revert to hierarchical coordination because no alternative information routing mechanism has been powerful enough to replace it.

The constraint is the same one the Romans faced and the Marine Corps rediscovered in World War II: narrowing span of control means adding layers of command, but more layers mean slower information flow. Two thousand years of organizational innovation has been an attempt to work around this tradeoff without breaking it.

So what's different now?

At Block, we're questioning the underlying assumption: that organizations have to be hierarchically organized with humans as the coordination mechanism. Instead, we intend to replace what the hierarchy does. Most companies using AI today are giving everyone a copilot, which makes the existing structure work slightly better without changing it. We're after something different: a company built as an intelligence (or mini-AGI).

We are not the first to try to move beyond traditional hierarchy. Haier's rendanheyi model, platform organizations, "data-driven" management: these are real attempts at the same problem. What they lacked was a technology capable of actually performing the coordination functions that hierarchy exists to provide. AI is that technology. For the first time, a system can maintain a continuously updated model of an entire business and use it to coordinate work in ways that previously required humans relaying information through layers of management.

For this to work, a company needs two things: a kind of "world model" of its own operations, and a customer signal rich enough to make that model useful.

Block is remote-first. Everything we do creates artifacts. Decisions, discussions, code, designs, plans, problems, and progress all exist as recorded actions. It's the raw material for a company world model. In a traditional company, a manager's job is to know what's happening across their team and relay that context up and down the chain. In a remote-first company where work is already machine-readable, AI can build and maintain that picture continuously. What's being built, what's blocked, where resources are allocated, what's working and what isn't. That's the information the hierarchy used to carry. The company world model carries it instead.

But the capability of the system is only as good as the quality of the customer signal feeding it. And money is the most honest signal in the world.

People lie on surveys. They ignore ads. They abandon carts. But when they spend, save, send, borrow, or repay, that's the truth. Every transaction is a fact about someone's life. Block sees both sides of millions of these transactions every day, the buyer through Cash App and the seller through Square, plus the operational data from running the merchant's business. That gives the customer world model something rare: a per-customer, per-merchant understanding of financial reality built from honest signal that compounds. The richer the signal, the better the model. The better the model, the more transactions. The more transactions, the richer the signal.

Together, the company world model and the customer world model form the foundation for a different kind of company. Instead of product teams building predetermined roadmaps, you build four things.

First, capabilities. The atomic financial primitives: payments, lending, card issuance, banking, buy-now-pay-later, payroll, and so on. These are not products. They are building blocks that are hard to acquire and maintain (some have network effects and regulatory permission). They have no UIs of their own. They have reliability, compliance, and performance targets.

Second, a world model. This has two sides. The company world model is how the company understands itself and its own operations, performance, and priorities, replacing the information that used to flow through layers of management. The customer world model is the per-customer, per-merchant, per-market representation built from proprietary transaction data. It starts with raw transaction data today and evolves toward full causal and predictive models over time.

Third, an intelligence layer. This is what composes capabilities into solutions for specific customers at specific moments and delivers them proactively. A restaurant's cash flow is tightening ahead of a seasonal dip the model has seen before. The intelligence layer composes a short-term loan from the lending capability, adjusts the repayment schedule using the payments capability, and surfaces it to the merchant before they even think to look for financing. A Cash App user's spending pattern shifts in a way the model associates with a move to a new city. The intelligence layer composes a new direct deposit setup, a Cash App Card with boosted categories for their new neighborhood, and a savings goal calibrated to their updated income. No product manager decided to build either solution. The capabilities existed. The intelligence layer recognized the moment and composed them.

Fourth, interfaces (hardware and software). Square, Cash App, Afterpay, TIDAL, bitkey, proto. These are delivery surfaces through which the intelligence layer delivers composed solutions. They are important, but they are not where the value is created. The value is in the model and the intelligence.

When the intelligence layer tries to compose a solution and can't because the capability doesn't exist, that failure signal is the future roadmap. The traditional roadmap, where product managers hypothesize about what to build next, is any company's ultimate limiting factor. In this model, customer reality generates the backlog directly.

If this is what the company builds, then the question becomes: what do the people do?

The org structure follows from this, and it inverts the traditional picture. In a conventional company, the intelligence is spread throughout the people and the hierarchy routes it. In this model, the intelligence lives in the system. The people are on the edge. The edge is where the action is.

The edge is where the intelligence makes contact with reality. People reach into places the model can't go yet. They sense things the model can't perceive: intuition, opinionated direction, cultural context, trust dynamics, the feeling in a room. They make the calls the model shouldn't make on its own, especially ethical decisions, novel situations, and high-stakes moments where the cost of being wrong is existential. A world model that can't touch the world is just a database. But the edge doesn't need layers of management to coordinate it. The world model gives every person at the edge the context they need to act without waiting for information to travel up and down a chain of command.

In practice, this means we normalize down to three roles.

Individual contributors (ICs) who build and operate capabilities, the model, the intelligence layer, and the interfaces. They are deep specialists and experts in a specific layer of the system. The world model provides the context that a manager used to provide, so ICs can make decisions about their layer without waiting to be told what to do.

Directly Responsible Individuals (DRI) who own specific cross-cutting problems or opportunities and customer outcomes. A DRI might own the problem of merchant churn in a specific segment for 90 days, with full authority to pull resources from the world model team, the lending capability team, and the interface team as needed. DRIs may persist on certain problems or move elsewhere to solve new ones.

Player-coaches who combine building with developing people. They replace the traditional manager whose primary job was information routing. A player-coach still writes code or builds models or designs interfaces. They also invest in the growth of the people around them. They don't spend their days in status meetings, alignment sessions, and priority negotiations. The world model handles alignment. The DRI structure handles strategy and priority. The player-coach handles craft and people.

There is no need for a permanent middle management layer. Everything else the old hierarchy did, the system coordinates, and everyone is empowered, with a role that's much closer to the work and the customer.

Block is in the early stages of this transition. It will be a difficult one, and parts of it will likely break before they work. We're writing about it now because we believe every company will eventually need to confront the same question we did: what does your company understand that is genuinely hard to understand, and is that understanding getting deeper every day?

If the answer is nothing, AI is just a cost optimization story. You cut headcount, improve margins for a few quarters, and eventually get absorbed by something smarter. If the answer is deep, AI doesn't augment your company. It reveals what your company actually is.

Block's answer is the economic graph: millions of merchants and consumers, both sides of every transaction, financial behavior observed in real time. That understanding compounds every second the system operates. We believe the pattern behind this, a company organized as an intelligence rather than a hierarchy, is significant enough that it will reshape how companies of all kinds operate over the coming years. Block is far enough along to show the idea is more than theory (though, we welcome debate and feedback to pressure test and improve our ideas).

Companies move fast or slow based on information flow. Hierarchy and middle management impede information flow. For two thousand years, from the Roman contubernium to today's global enterprises, we have had no real alternative. Eight soldiers sharing a tent needed a decanus. Eighty men needed a centurion. Five thousand needed a legate. The question was never whether you needed layers. The question was whether humans were the only option for what those layers do. They aren't anymore. Block is building what comes next.

https://block.xyz/inside/from-hierarchy-to-intelligence

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