公司的未来

作者注：这是我们关于下一代经济的系列文章中的第二篇，也是一个全新平台的一部分。该平台旨在汇聚独立声音，共同探索通用人工智能（AGI）的未来。本文反映的是我们的观点，并不代表 OpenAI 或我们同事的观点。Authors’ note: This is the second essay in our series on the next economy, part of a new platform to host independent voices exploring an AGI future. It reflects our views, not those of OpenAI or our colleagues.

我们俩刚开始在 OpenAI 管理自己的团队时，都是二十四岁。等你见过我们的团队之后，就不会觉得这有多了不起了。
The two of us were twenty-four when we started managing our own teams here at OpenAI. That is less impressive once you meet our teams.

我们本该只是基层打杂的。二十四岁时，你处在公司晋升阶梯的最底层，负责做会议记录、写几份备忘录。苦干多年之后，你（但愿）终于会被赋予更广泛的职责。We’re supposed to be grunts. At twenty-four, you’re at the bottom of the corporate ladder. You take the meeting notes and write a few memos. After years of drudgerous work, you are (hopefully) granted a broader remit.

我们的职业道路——以及当今许多年轻人的职业道路——正在变得不同。我们刚进入职场，身边就已经有“员工”听候差遣。这些助手日夜工作，周末和节假日也不休息。他们很擅长处理繁重琐碎的工作。我们甚至有数学奥林匹克竞赛金牌得主在团队中为我们出谋划策。新来的下属仍然会犯不少错误。但他们正在进步，逐渐了解我们的需求，而我们也在学习如何管理他们。Our path—and the path of many young people today—is looking different. We enter the workforce already with “employees” at our behest. These deputies work day and night, weekends and holidays. They are great at the grunt work. We even have math olympiad gold medalists on staff to advise us. Our new reports still make their share of mistakes. But they are getting better, learning our needs, and we are learning how to manage them.

我们俩都没有在企业里步步高升。相反，管理层降到了我们这一层。这篇文章探讨的是我们的晋升，以及企业在迈入智能时代之际可能进行的其他重组方式。Neither of us climbed the corporate ladder. Management has instead come down to our rung. This essay is about our promotion, and the other ways firms might reorganize as we enter the intelligence age.

当 AI 进入企业后，它会应用于哪些领域？我们介绍三个示例场景：When AI enters the firm, where does it go? We present three illustrative scenarios:

这些情景都不会完全按照我们的描述上演，但我们认为，三者中的某些方面都会成为现实。事实上，它们都已经以不易察觉的方式发生了。None of these scenarios will play out exactly as we describe, but we think aspects of all three will come true. All three are already happening in subtle ways.

我们的关注点并非全面自动化，而是经济转型。我们想了解，随着 AI 不断进步并融入现有组织，嵌合型企业将会是什么样子。Our focus here isn’t total automation but a transitioning economy. We are interested in what chimeric firms will look like as AI improves and laces itself into existing organizations.

我们认为，混合型企业在概念上十分重要，因为 AI 的有效采用并非一蹴而就。当模型只能完成部分任务时，熟悉相关工作的基层员工可能最容易采用它们。一旦模型能够端到端地执行广泛的职能，高层管理者就可以直接使用它们。在我们看来，能力越强的 AI 越容易被采用：企业必须学习如何使用技术，而劳动力会了解企业，并能自行融入其中。We believe the hybrid firm is conceptually important because productive adoption of AI isn’t instantaneous. When the models only do a subset of tasks, adoption may be easiest by ground-level employees familiar with the work. Once the models perform broad functions end-to-end, higher-ups can use them directly. In our view, more capable AI is more adoptable: firms must learn technologies, but labor learns the firm and can onboard itself.

我们将剖析三种新型组织架构，并探讨它们对官僚主义、劳动者能动性、企业规模和薪酬差距的未来意味着什么。We’ll dissect the three new classes of org chart, then examine what they mean for the future of red tape, worker agency, firm size, and pay disparity.

我们的第一个场景似乎最不具颠覆性。在这个场景中，人们仍坐在原来的位置，汇报关系也保持不变，表面上看，做的工作也和以前一样。从今天到这个未来，人员组织架构图看起来没有变化。Our first scenario seems least disruptive. Here, people stay in the same seats with the same reporting lines, ostensibly doing the same jobs as before. The human org chart looks unchanged between today and this future.

变化发生在我们之下。每位个人贡献者的下方，都有一支新的 AI 劳动力队伍向我们每个人汇报。我们的组织架构多了一个地下室，而我们也都成了管理者。The difference is below us. Beneath each individual contributor, a new AI labor force reports up to each of us. Our org chart gets a basement, and we all become managers.

起初，这个地下室还没有装修。我们每人先配上一两个智能体，与其说把它们当作经验丰富的员工，不如说是热心的实习生——需要我们监督，也值得我们宽容对待。我们的期望很低：发封邮件、起草一份备忘录、到处回复 Slack 消息。和初级员工一样，它们一开始自主性不强，也得不到太多信任。At first, this basement is unfinished. We start with one or two agents apiece, less as seasoned employees, more as eager interns we must supervise and readily forgive. Our expectations are low: send an email here, draft a memo there, reply to Slack messages everywhere. Like junior human employees, they begin with little autonomy and trust.

但随着模型不断进步，我们的智能体承担的任务越来越多，所需监督却越来越少。它们彼此协作。可能会有经理型 AI 和执行型 AI、会计和律师，全都按照你的指示协同工作。最终，你的运作方式不再像是在事无巨细地带一个实习生，而更像是一家小型公司，有自己的组织架构，而你则是董事会。But as models improve, our agents take on more tasks with less supervision. They coordinate with each other. There might be manager AIs and grunt AIs, accountants and lawyers, all working together at your behest. Far from handholding an intern, your operation eventually looks like a small firm in its own right, with its own org chart and you as its board of directors.

请注意，这种情形并不意味着裁员。在这里，我们采用 AI，却没有让自己被自动化取代。相反，我们自动化的是过去的自己，并在层级中不断晋升。AI 承担了越来越多我们过去做的工作。我们的岗位依然存在，因为需要管理的事情更多了，而我们也开始对更大规模的产出承担责任。Notice how this scenario doesn’t imply layoffs. Here we adopt AI without automating ourselves. We instead automate our past selves, and we move up in the hierarchy. AI takes on more and more of what we used to do. Our jobs remain because there is more to manage, and we gain responsibility for a greater sum of output.

工作并非一成不变（在新窗口中打开）。假设 AI 能完成你之前所做工作的百分之九十。你可能会得出结论，自己只剩下百分之十的工作要做。另一种可能是，你仍然在做一份完整的工作，只是工作内容不同了——如今专注于一个规模大十倍的目标。这正是技术的经典馈赠：生产率提高，雄心随之增长，而工资也能随着雄心上涨（在新窗口中打开）。Jobs aren’t static⁠(opens in a new window). Suppose that AI can do ninety percent of what you did before. You might conclude you’re left with only ten percent of the work. The other possibility is that you’re still doing a full job, just a different one—now focused on an objective ten times larger. That is the classic gift of technology: productivity rises, ambition rises to meet it, and wages can rise⁠(opens in a new window) with the ambition.

这一切都取决于我们是否仍能为生产创造价值⁠（在新窗口中打开）。本文聚焦于未来几年，在这段时期，自动化尚未普遍具备相应能力，也尚未被普遍采用。即使 AI 能够包办一切，我们或许仍会因为某样东西出自人手而更加珍视它，或者坚持由人来做最终决定。This all depends on whether we still add value⁠(opens in a new window) to production. This essay focuses on the coming years where automation isn’t universally capable or adopted. Even if AI could do it all, we might still value something more because a person made it, or insist that a person have the final say.

这也取决于我们的需求。如果作为消费者，我们并不在乎产出增加十倍，均衡就会转向更少的劳动者或更短的工时。从历史上看，产出增加并没有让我们满足。我们拥有的多了很多⁠(在新窗口中打开)，但工作时间只减少了一点⁠(在新窗口中打开)。如果随着产出倍增，我们的需求也持续增长——而我们仍能为生产增添价值——我们就会选择继续用劳动换取更多消费。反之，如果生产变得极其充裕，以至于我们感到满足，我们或许会自愿选择规模更小的劳动力队伍。It also depends on our appetite. If, as consumers, we don’t care for ten times more output, the equilibrium will shift toward fewer workers or fewer hours worked. Historically, increases in output have not satiated us. We have a lot more⁠(opens in a new window) but work only a little less⁠(opens in a new window). Should our appetite continue to grow as output multiplies—and we remain additive to production—we will choose to keep trading our labor for more consumption. If instead production becomes so abundant that we are satiated, we might volunteer a smaller labor force.

我们稍后还会讨论更多反对意见（例如，我们是否会看到数量更多、规模更小的公司）。我们仍然认为这一情景很重要，因为采用总得有个起点。公司需要一种方式，能够从当前的组织结构稳步演进。与彻底重组相比，增强现有员工的工作能力或许是更容易迈出的第一步。如今我们就能看到这种情况正在发生：一个又一个员工把工作委托给 AI，而迄今为止并未引发组织层面的变革。There are more objections we discuss later (e.g. whether we’ll have a larger number of smaller firms). We still think this scenario is important because adoption has to start somewhere. Firms need a way to stably evolve from their current org structures. Bolstering the work of existing employees may be an easier first step than wholesale restructuring. We see this in action today: one worker after another delegating to AI, without an organizational revolution so far.

这种情形的前提是一线员工先被聘用。推动 AI 应用的将是一线员工，而不是经理和高管。只要最擅长让 AI 完成这项工作的人，正是过去亲自做这项工作的人，这种情况似乎就很可能发生。 This scenario relies on the front line hiring first. Ground-level workers—not managers and executives—lead on AI adoption. That appears likely, so long as the person who best gets AI to do the work is the person who used to do it themselves.

模型已经能够自动完成许多数据科学任务，但不熟悉相关方法和细节的部门主管，很难充分利用这些工具。在人工智能足够主动、能够告诉你自己有哪些未知之处之前（见我们的下一个场景），它可能最适合那些已经熟悉工作流程的人使用。Models can already automate many data science tasks, but a department head unfamiliar with the methods and minutiae will struggle to leverage the tools. Until AI becomes proactive enough to tell you what you don’t know (see our next scenario), it will probably work best for those already familiar with how the work is done.

这一情景中最大的变化会发生在初级员工身上。最底层的岗位将不复存在，至少对人类而言是如此。我们雇用年轻人时，不会再把他们安排在公司最底层做苦差事。这些岗位将由不介意做这些事的东西来填补。 The biggest change in this scenario is for entry-level workers. There would no longer be a lowest rung, at least not for humans. When we hire young people, it won’t be to place them at the drudgerous bottom of the firm. Those positions will be filled by something that doesn’t mind.

这样一来，我们失去了一些培养年轻员工的传统途径：学徒制（在新窗口中打开）、在繁重琐碎的工作中学习，以及靠资历一步步晋升。这些确实是代价，但我们大多把这视为一种特权。最资浅的新员工只要有个好点子，就有资源去试验。公司里最老套的借口——“我们就是人手不够”——正在逐渐消亡。一个脑海里有款绝妙电子游戏、却没有机会获得编制的初级员工，如今只需亲手把它做出来。With that, we lose some traditional routes to educate young workers: the apprenticeship⁠(opens in a new window), the learning-by-grunt-work, the earning of stripes. These are genuine costs, but we mostly see this as a privilege. The greenest hire with a good idea has resources to experiment. The oldest excuse in the building, we just don’t have the manpower, is dying. A junior employee with a great video game in her head and no path to the headcount can now simply build it.

在我们看来，这种“晋升”情境仍有很多东西值得初级员工学习——判断力、品味、管理能力和创意视野。我们会有动力培养这些技能，因为它们会体现在我们的工作成果中。我们仍会在实践中学习（在新窗口中打开），但承担的职责范围会更广。公司的资深人士仍会有动力以负责任的守护者身份帮助我们（他们的权益与我们的成功息息相关）。这一机会意味着，无论处于哪个层级，每个人都能担任更具启发性的角色。任何员工都能在更大的职责范围内展现才华，这对原本毫无职责可言的入门级员工尤其重要。In our view, this “promoting” scenario still has much for junior workers to learn—judgment, taste, the muscles of management and creative vision. We will be incentivized to cultivate these skills because they show up in our work product. We will still learn by doing⁠(opens in a new window), but at a higher level of scope. Firm elders remain incentivized to help us as responsible stewards (with equity stakes in our success). The opportunity here is a more stimulating role for everyone at every rung. Any worker can showcase their talents with a bigger remit, which matters most to entry workers who had no remit at all.

AI进入企业的第二条路径，是沿着现有组织架构展开的。在这里，智能体不是边角料，而是同事。传统经理会把团队的一部分工作交给模型，就像她可能把工作委派给任何一位员工一样。AI融入人类身边，而不只是处于人类之下。AI’s second way into the firm weaves through the existing org chart. Here the agents aren’t dregs but colleagues. A traditional manager hands a slice of the team’s work to a model the way she might delegate to any employee. AI inserts itself next to humans, not just under them.

与第一种情形的关键区别在于：在这里，现有的人类管理者也会采用生产力型 AI，而不只是当前的个人贡献者。为此，AI 必须足够胜任，能够成为一名真正的团队成员。The key difference from the first scenario: here, productive AI is adopted by existing human managers, not only by current individual contributors. To do so, it must be competent enough to be a fully fledged team member.

人类是多面手。锤子只能帮你完成一项任务，而人类能做的远不止这些。AI 从工具走向劳动力的历程，意味着自动化的范围不断扩大：先是自动化一项任务，然后是一个岗位，再然后是一个团队，最终扩展到一家企业。自动化范围每扩大一步，对能力广度的要求就更高。Humans are generalists. A hammer helps with one task; a human does a whole lot more. AI’s journey from tool to labor is a widening ambit: automating the task, then the job, then a team, and finally a firm. Every step up in scope requires broader competence.

一份工作就像一个装满各种任务的行李箱。对于整个团队来说，这个行李箱里塞满了五花八门的工作。职责范围内的任务越多，潜在的瓶颈就越多。如果团队中哪怕只有一项关键任务涉及体力劳动，就需要配备一整套新的外围设备。要想直接替代整个团队，AI 就必须胜任团队承担的各种职责。A job is a suitcase full of many tasks. For a whole team, that suitcase is crammed with all sorts of diverse work. More tasks in the remit mean more potential chokepoints. If even one essential task in the team involves physical labor, a whole new set of peripherals is required. To be a slot-in substitute for an entire team, the AI must be capable across all its varied responsibilities.

能够处理更大范围任务的 AI 更难构建。但对企业而言——这正是整个转型的核心不对称之处——更大范围的能力却更容易被采用。AI that can handle bigger scope is harder to build. But for firms—and this is the central asymmetry of the whole transition—bigger scope is easier to adopt.

高管可以直接采用端到端自动化团队或覆盖面广泛的职能。能力范围较窄的工具则无法如此轻易地自上而下推行。较简单的工具需要获得全体员工的广泛认同，并对员工进行再培训。An end-to-end automated team or broad function can be directly adopted by an executive. A tool with narrower capabilities can’t so simply be implemented from on high. The simpler tool relies on widespread buy-in and retraining across the workforce.

传统上，新技术会让一组有限任务的执行成本降低。随后，负担便转移到了买方身上。企业必须弄清楚如何利用这项新工具，并围绕它重新培训员工、调整组织结构、重建流程（在新窗口中打开）。电子表格软件并没有自行进入会计部门——是会计部门围绕这款软件重塑了自身。New technologies traditionally make it cheaper to do a confined set of tasks. Then the burden shifts to the buyer. The firm must figure out how to leverage the new tool, and must retrain, reorganize, and rebuild its processes⁠(opens in a new window) around it. Spreadsheet software didn’t install itself into the accounting department—the accounting department reshaped itself around the software.

劳动则恰恰相反。实习生、员工和优秀的团队会想办法为你提供帮助。他们会不断追问你，吸收你提供的背景信息，留意你的需求。他们会告诉你那些你甚至没意识到自己不知道的事。最优秀的劳动者会主动完成上手。Labor is the opposite. An intern, an employee, and a good team figure out how to be useful to you. They interrogate you, absorb your context, notice your needs. They tell you what you didn’t even realize you didn’t know. The best labor onboards itself.

在这个意义上，AI 越像劳动力，越具备主动性和适应能力，公司就越不必学习如何驾驭它，而它则越能摸索出如何为公司效力。高管大概不会坐下来听一堂设计软件入门课，学习使用新工具。但如果有人提出：“您只要开口，我就能帮您搭建整个营销职能部门。”他一定会乐于听听。The more AI becomes like labor in this sense, with initiative and adaptability, the less the firm must learn how to harness it and the more it figures out how to be useful to the firm. An executive probably won’t sit through a design software 101 lecture to learn a new tool. But he will happily entertain an offer that sounds like: say the word, and I’ll set up your whole marketing function.

我们常常把通用人工智能（AGI）的终极目标说成是自动化一份工作。诚然，我们甚至还没做到这一点。工作之所以极其复杂，是因为我们的能力令人惊叹。即便如此，我们经济产出中也只有很少一部分来自单打独斗的劳动者。高效的工作为团队作出贡献，团队为企业作出贡献，而企业生产了我们消费的大部分商品和服务。 Too often, we name the holy grail of AGI as automating a job. Granted, we haven’t even reached that yet. Jobs are so extraordinarily complicated because we are so impressively capable. Even so, very little⁠(opens in a new window) of our economy’s output comes from lone-wolf workers. Productive jobs contribute to teams, teams to firms, and firms produce most everything we consume.

AGI 的“工作”标准并没有充分认可我们人类的价值。劳动不只是完成任务，甚至也不只是处理非例行的工作内容：劳动还包括卓越的协调能力，以及我们灵活适应、完成更大成果中一小部分工作的能力。把我们说成只是装着任务的袋子，简直是严重低估了我们的价值。人类的能力更多体现在我们的组织（在新窗口中打开）中，而非个体身上。真正具有经济意义的 AGI，必须达到以人群而非单个人为标准的水平。The “job” standard for AGI doesn’t give us humans enough credit. Labor isn’t just about the tasks, or even handling the non-routine components: our labor is also about prodigious coordination, our plasticity to do small parts of bigger outputs. Selling us as mere sacks of tasks is pennies on the dollar. Human capacity is in our organizations⁠(opens in a new window) more than the individual. An economic AGI must reach the standard of people, not person.

因此，如果 AI 仍像工具或员工，而不像团队或公司，那么“交错”场景的效率可能会很低。每位员工身边都塞进一个模型，意味着要处理上千处衔接和交接（在新窗口中打开）。这种零敲碎打的部署方式可能会拖慢普及进程。As a result, the “interleaving” scenario may be quite inefficient if the AI is still tool-like or even worker-like, instead of team-like or firm-like. A model wedged beside every worker means a thousand seams and handoffs⁠(opens in a new window). This piecemeal setup may slow adoption.

我们看到的是早期能力较弱的实现与后期能力更强的模型之间的一场竞赛。胜负取决于未来模型的采用速度是否足够快，能否弥补它们达到这些能力水平所需的额外时间。We see a race between earlier, less capable implementations and later, more capable models. The victor hinges on whether adoption of future models is so much faster that it overcomes the extra time it takes to reach those competencies.

这种“交织”情景可以与第一个“促进”情景并存。随着 AI 将某些职能自动化，我们可能会看到企业缩减人力规模，而留下来的员工则在智能体员工队伍的辅助下承担范围广得多的职责。重要的是，单凭这一情景并不意味着整体人类就业人数会减少。我们可能会发现，企业数量增加了，但每家企业的员工人数都更少。 This “interleaving” scenario can coexist with the first “promoting” scenario. We may see firms shrink their human footprint as AI automates certain functions, while the remaining humans get much broader mandates aided by their agent workforces. Importantly, by itself this scenario doesn’t imply lower overall human employment. We could find ourselves with more firms, each with a lower headcount.

我们的第三种情境最令人困惑。在这里，AI 进入公司的层级，位于人之上。层级不再是人高于机器，而是人、AI、人：一层自动化夹心。有些人“向”AI 汇报。Our third scenario is the most confounding. Here, AI enters the firm above people. The stack is no longer humans over machines but human, AI, human: an automation sandwich. Some people “report” to an AI.

这听起来有些牵强，尤其是作为企业开始采用的起点。但我们认为，以没那么激进的方式，这一趋势已经开始了。It sounds far-fetched, especially as a starting point for firm adoption. But we think, in less radical ways, this has already begun.

即使我们把 AI 视为认知劳动的机械化工具，也往往只关注较低层级。我们通常会说，AI 自动化的是电子表格和演示文稿，而非管理工作。我们的直觉是，自动化会沿着层级向上推进：先是认知方面的苦差事，最后才是判断，而管理者则是最后的堡垒。Even when we think of AI as a mechanizer of cognitive labor, we tend to focus on the lower rungs. We typically say AI automates spreadsheets and slide decks, not management. Our instinct is that automation climbs the hierarchy: cognitive gruntwork first and judgment last, with the manager as the final redoubt.

但管理者的工作大多发生在电脑前和脑海中：阅读、开会、权衡、掌握背景信息、做出决策。亲临现场、巡视办公楼层、与员工待在一起，确实有其价值。然而，基层工作即使主要是脑力劳动，也比管理工作更多地与实体事物和工具打交道。被派去复印机旁的是实习生，不是他的老板。But a manager’s work largely lives on a computer and in the mind: reading, meeting, weighing, holding context, making decisions. There is value to physical presence, to touring the office floors and being around the troops. Yet it is ground-level work, even when it is mostly cognitive work, that tangles with atoms and tools more than management. The intern is sent to the photocopier, not his boss.

管理决策很难教给模型，因为成功的定义模糊，而且要经过很长时间才能衡量。正是出于类似的原因（在新窗口中打开），培养优秀的人类管理者也很难。我们还不知道模型的管理能力究竟有多强。我们对侧重执行的认知劳动有大量评估，但很少有评估能涵盖管理者和高管所负责的大规模统筹协调工作。Managerial decision-making is hard to teach to a model, because success is ambiguous and measured over long time horizons. It is for similar reasons⁠(opens in a new window) that training good human managers is hard. We don’t yet know how good the models are at management. Our evaluations abound for execution-focused cognitive labor, but few capture large-scale orchestration at the scope of managers and execs.

仍然值得考虑的是，当今的模型在能力上或许实际上更接近人类管理者，而非一线员工。有些管理职能可能会先于其所监督的工作实现自动化。It is still worth considering that today’s models may actually be closer in capability to human managers than ground-level workers. Some managerial functions may automate before the work they oversee.

也许，这种“分层”情景的完整形态是：由 AI 负责思考，再通过雇用向它汇报的人类承包商来弥补自身能力的不足。这样的世界对我们来说相当陌生。很多事情都需要改变。 Perhaps the full version of this “layering” scenario is an AI that does the thinking, then fills its capability gaps with human contractors who report to it. That world seems quite foreign to us. A lot would need to change.

但还有一种更自然的形态，而它的开场戏已经登台：提供建议。如今，高管可以让 ChatGPT 阅读公司各处数百个 Slack 频道、文档、信件和代码文件，然后回答问题。这个团队真正负责什么？那个漏洞出现在哪里？对她而言，组织的每个部分都越来越能够被解析（在新窗口中打开）。But there is a more organic incarnation, and its opening act is already onstage: advising. Today, an exec can ask ChatGPT to read hundreds of Slack channels, documents, missives, and code files from across her company, and then answer questions. What does this team really do? Where is that vulnerability showing up? Every part of the org is increasingly parsable⁠(opens in a new window) to her.

高管们长期以来都在组织架构中安插唯利是图的中间人——他们称之为咨询。管理咨询公司会短暂而昂贵地在你身边再加一层。今后，AI 顾问可能会便宜得几乎不值一提，而且无处不在。Executives have long laced the org chart with mercenary middlemen—they called it consulting. A management consultancy layers you, briefly and expensively. Going forward, the AI consultants could be vanishingly cheap and ever-present.

模型才刚刚开始能够可靠地解析企业文件和沟通内容。它们的判断力和主动性还需要进一步成熟。目前，ChatGPT 能帮助一位有进取心的高管更深入地了解自己的组织。我们认为，它最终会发展成一家提供全方位服务的咨询机构。它不会成为基层员工真正的“经理”，而会成为高管的内部联络人和顾问。它将运用商业专业知识扫描所有内容，主动发现痛点并协助分诊处理。Models are just beginning to reliably parse firm documents and communications. Their taste and proactivity must mature. For now, ChatGPT helps a motivated exec understand her org in greater detail. We see it eventually becoming a full-service consultancy. It won’t be a true “manager” of lower-level employees, but rather an in-house correspondent and advisor to executives. It will leverage business expertise to scan everything, proactively surfacing pain points and helping triage.

这究竟是利是弊，取决于高管本人。其结构性影响是权力集中。我们认为，这会缩短高管层与公司整体之间的距离。它将更多权力集中在高层，而过去的信息摩擦曾有利于更加分散的决策。Whether this is beneficial or detrimental will depend on the executive. The structural effect is centralization. We see this shortening the distance between C-suite and the company proper. It vests more power at the top, where information frictions used to favor more decentralized decision-making.

我们认为，这一情景意义深远，不仅因为它改变了权力归属，也因为以咨询服务形式提供的 AI 很容易被采用。和传统咨询一样，AI 咨询服务不要求高管具备技术专长。这不是那种由下而上、由会打字和使用终端的人率先采用的计算机。你只需和顾问交谈，技术知识就由它来处理。这一波采用潮可以从高管办公室开始；反过来，咨询服务也能加速 AI 在组织其他部门的普及。We find this scenario profound not just for how it shifts authority, but for how easily adoptable AI-as-a-consultancy can be. Just like with traditional consulting, the AI consultancy doesn’t require technical acumen from the executive. This is not the computer, adopted from below by whoever could type and terminal. You just talk to your consultant, and it handles the technical know-how. This adoption wave can start from the corner office. The consultancy, in turn, could accelerate AI adoption throughout the rest of the organization.

企业不信任远程办公和合同用工，有一个重要原因：可监督、可落实性。企业一直没有一种成本低廉的办法，能够以工作应有的丰富细节观察工作的实际开展。因此，它们衡量的是工作的哑剧式表演（在新窗口中打开）：点击次数、鼠标移动、绿色状态点——这些信号与工作是否真正做好相去甚远。 One reason looms large for why firms distrust remote work and contracting: enforceability. There has never been a cheap way for firms to observe the doing of work in its deserving richness. So they measure pantomimes⁠(opens in a new window) of it, clicks counted, mouse wiggles, green status dots, signals so divorced from whether good work is happening.

AI提供了一种能够理解的监控工具。它可以观察一个人完成的工作，并识别他们在实现更大目标方面取得的实质性进展。正如我们将在下一节探讨的，任何新的监控措施都不应草率实施（在新窗口中打开）。但在适当情况下，它可以增进信任，并鼓励企业给予员工更多自由。AI offers a monitor that understands. It can look at what a person produces and recognize real progress towards bigger objectives. As we explore in the next section, any new surveillance mustn’t be implemented lightly⁠(opens in a new window). But, under the right circumstances, it can increase trust and encourage firms to give their employees more freedom.

如果监控系统能够理解通往优质工作成果的多种不同路径，员工就能更自由地按照自己喜欢的方式工作。相比于一种便于老板追踪的规定方法，取得良好成果的进展会更受重视。智能监控系统能带来灵活性，并惩罚我们都厌恶的表演式忙碌。If the monitor comprehends the many disparate paths that can lead to good work product, employees can work more the way they want. Progress toward good results will matter more, rather than one prescribed method easy for bosses to track. An intelligent monitor affords flexibility and punishes the performative productivity we all loathe.

这里的价值主张类似于保险公司：如果你在车里安装追踪器，他们就会降低你的保费。如果你想钻系统的空子，这对你不利；如果你只是在认真工作，监控设备就能证明你的清白，让管理层更加信任你。The value proposition here resembles that of the insurers who reduce your rates if you install a tracker in your car. If you were trying to game the system, this hurts you; if you were simply doing your job, the monitor can vindicate you, and management trusts you more.

这种信任能够催生新事物。我们或许会看到一些公司展现出新的意愿（在新窗口中打开）：它们此前不愿接受混合办公、不愿接纳思维方式非传统的人或承包商，也不愿在遥远的国家聘用才华横溢的人。That trust is generative. We might see new willingness⁠(opens in a new window) from firms that were previously reluctant to hybrid work, to unorthodox minds, to contractors, or to hiring brilliant people in countries far away.

如今，基层员工对组织架构有很多不满。我们亲眼看到问题，却无从向上反映，这让人无比沮丧。被困在一位无能的经理手下，或者做出了出色的工作却得不到认可，都实在太不公平了。There are many problems that ground-floor employees have with the org chart today. It is immensely dispiriting to have no way to surface a problem up the ladder that we see firsthand. It’s a raw deal to be stuck under an ineffectual manager, or to do great work that goes unrecognized.

我们认为，上一波商业过度优化建立在有限的数据、粗略的准则和愚笨的算法之上。如果你能处理的只有齿轮传动比，那么在你眼里，每个人都只是一个齿轮。长期以来，企业一直难以衡量员工队伍的表现。智能监测系统可以识别出有价值的贡献和独特的方法，而这些正是当今粗糙的指标所遗漏的。We see the previous wave of business hyperoptimization as being built on limited data, rough guidelines, and dumb algorithms. If all you can process is gear ratios, then everyone looks like a cog. Firms have long had a measurement problem of their workforce. An intelligent monitor could recognize valuable contributions and unique approaches that today’s crude metrics miss.

综合这三个风格化情景，我们现在来探讨一些决定公司未来走向的关键问题。Drawing from across the three stylized scenarios, we now explore some of the determinative questions on the future of the firm.

构建通常相当昂贵。一个应用、一部电影、一份演示文稿、一个功能、一项分析：这些东西重做起来都代价高昂、耗时漫长且令人煎熬。当你实际上只有一次机会时，就会投入精力瞄准目标。各种规划周期、路线图评审和签批委员会，都是为了确保公司最终决定开工时，做的是值得做的事。我们的层级体系中设置了许多检查环节，正是因为执行成本高昂、弥足珍贵。 Building is generally quite expensive. An app, a film, a deck, a feature, an analysis: these are all costly, slow, and agonizing to redo. When you effectively get one shot, you invest in aim. There are planning cycles and roadmap ceremonies and sign-off committees all to ensure that when the firm finally says go, it goes at something worthwhile. Many checks in our hierarchies exist because execution was precious.

在许多领域，AI 正在降低构建的成本——至少将其降至原型阶段，甚至可能降至最终完成阶段。我们认为，公司的做法也应随之转变：少瞄准，多开火。公司可以构建原型、观察、快速学习、调整方向，然后重新构建。轻松进行原型开发（在新窗口中打开）应会让公司逐渐远离支撑规划密集型生产的制度。Across many domains, AI is collapsing the cost of building—at least to the level of prototyping, if not to final completion. We believe the firm’s approach should shift with it: aim less, fire more. A firm can build a prototype, observe, learn fast, reorient itself, and build it again. Easy prototyping⁠(opens in a new window) should drift firms away from the institutions which underpin planning-intensive production.

最简单的版本，是让一个团队更快地迭代。更激进的解读则是，团队本身也受到质疑。如果协作规划没那么重要，而彼此独立、各不相同的想法更为重要，那么由一个团队快速迭代更好，还是让许多人各自拿出可运行的原型更好？如果一个真实原型比任何范围界定文档都更能传达愿景，我们的沟通规范会因此改变吗？比如分享得更多、更早，并分享实际成果，而不是备忘录？The simplest version of this is a team that iterates more quickly. In a more radical interpretation, the team itself is called into question. If collaborative planning matters less and uncorrelated diverse ideas matter more, is it better to have a single team that iterates quickly, or many individuals each putting forward their own working prototypes? If a real prototype transmits a vision better than any scoping document ever could, will our norms for communication change: sharing more, earlier, and sharing artifacts rather than memos?

低成本构建可能会让公司更倾向于依靠个人。这意味着中层管理层级会减少，向上请示也会减少，因为过去请示比亲自做事成本更低，而现在情况不再如此。Cheap building may tilt the firm toward individuals. This would mean fewer layers of middle management and less asking for permission, because asking used to be cheaper than doing and now it is not.

尽管如此，正如我们接下来将讨论的，同一种技术既能让个人以更低的风险进行原型开发，也可能加重繁文缛节。All that said, as we discuss next, the same technology which empowers individuals to prototype with less risk can also enforce larger spools of red tape.

企业实行自上而下管理的一个主要原因，是他们认为高管比初级员工更擅长做出与业务相关的决策（在新窗口中打开）。企业愿意为高层经验丰富、能力出众的资深人士支付更高薪酬；管理者通常接受过商业战略方面的培训，而个体贡献者则专注于各自的专业领域。如果 AI 能为所有员工提供出色的建议，那么决策质量上的差距就会缩小。管理者插手的理由也会减少。这将提升员工的自主性，并可能减少管理层级。 A major reason firms enforce top-down management is the belief that executives make better business-relevant decisions⁠(opens in a new window) than junior workers. Firms pay more for highly competent veterans at the top, and managers are often trained in business strategy while individual contributors are specialized in their expertise. If AI offers excellent advice to all workers, the gap in decision-making quality shrinks. There would be less reason for managers to meddle. This would increase worker autonomy, and potentially reduce layers of management.

同样，“AI 顾问”会确保任何影响重大的事情发生前，管理层都会收到提醒。如果某位员工即将发布一篇可能给公司带来严重后果的博客文章，顾问就会主动向高管发出警示。这有助于建立信任：管理层不必担心重大纰漏会在自己毫不知情的情况下发生，因此也就不必对员工如何开展工作管得那么严。Similarly, the “AI consultancy” ensures that nothing too consequential happens without an executive being alerted. If a worker is about to publish a blog post with serious repercussions for the company, the consultancy will proactively flag it to the exec. This begets trust: management need not worry big mishaps will happen without their awareness, so they needn’t keep such a tight leash⁠(opens in a new window) on how a worker practices their job.

但人工智能咨询顾问是一把双刃剑（在新窗口中打开）。它可以作为一名智能监控者，让员工更加信任它；也可以在整个组织中更强力地推行 CEO 偏好的做事方式。企业看重一致性（在新窗口中打开）：不仅要做出好决策，还要确保决策符合公司的文化、品牌和规模经济效益。连锁餐厅可能更倾向于让旗下各家加盟店采用统一菜单，这样就能批量订购食材，也能让顾客对产品保持一致的预期。But the AI consultancy is a double-edged sword⁠(opens in a new window). It can instill more trust in workers as an intelligent monitor; it can also more vigorously enforce a CEO’s preferred way of doing things throughout the organization. Firms value coherence⁠(opens in a new window): not just a good decision, but a decision compatible with the company’s culture, brand, and economies of scale. A restaurant chain may prefer a common menu across its franchises, so that ingredients can be bulk-ordered and customers can expect a consistent product.

同样，企业老板的激励机制并不总是与员工一致。员工追求所在领域的成功（在新窗口中打开），而高管关心的是整个企业。股权所有者与领薪员工的偏好不同，因为他们能从更努力的工作中获得回报（在新窗口中打开）。咨询公司可以强制严格遵守自上而下制定的具体方案。Relatedly, the incentives for firm bosses don’t always align with workers. Workers optimize for their domain’s success⁠(opens in a new window) while execs care about the firm overall. Equity owners have different preferences than wage workers, because they reap the rewards⁠(opens in a new window) of harder work. The consultancy could enforce stricter adherence to specific regimens prescribed from the top.

总的来说，我们认为劳动者将获得更多自主权。我们的乐观态度建立在这样的预期之上：企业会利用 AI 标记重大例外情况，同时把更多决策权留给劳动者。Our view is, on net, workers will gain agency. Our optimism rests on firms using AI to flag consequential exceptions while leaving more decisions with workers.

我们相信，AI 将发挥脚手架的作用——即使高管希望维护统一连贯的品牌形象，这也不过是一个需要在其中运作的约束。员工会更清楚地了解业务所面临的限制，也能更有保障地在这些限制之外发挥创造力。有了这些保障，高管就可以让员工尝试可能带来丰厚收益的新方法。在 AI 助手的辅助下，员工也将负责产出更重要的成果。我们认为，承担更复杂的工作时，创造力的发挥空间也会更大。We believe that AI will act as a scaffold—even if executives wish to maintain a coherent brand, this will merely be a constraint to operate within. Workers are made better aware of the business constraints at play and are better insured for creativity outside those restrictions. With their bases covered, execs can let employees try potentially lucrative new approaches. Aided by AI helpers beneath us, workers will also be responsible for more substantial outputs. We see more opportunities for creativity when commanding more complex work.

关于劳动者自主权的同一套逻辑，也适用于企业规模。罗纳德·科斯（在新窗口中打开）曾提出，为什么我们要在企业内部组织生产，而不是通过合同网络来进行？即便是飞机，也可以完全通过点对点签约的方式建造，但效率会非常低。企业能够实现规模经济、协调激励、组织大型项目，并免去持续讨价还价的必要。 The same logic on worker autonomy also applies to firm size. Ronald Coase⁠(opens in a new window) asked why we organize production inside firms rather than through a web of contracts. Even an airplane could be constructed purely through peer-to-peer contracting, but it would be very inefficient. Firms muster economies of scale, align incentives, organize large projects, and absolve the need for constant bartering.

通过降低沟通成本，AI 可能让企业规模变大，也可能变小。传统企业是信息向上汇报、指令向下传达的基础设施，其中充满摩擦和“传话游戏”。如果 AI 充当协调层，那么员工人数更多的大型企业也不会那么笨重。有了“AI 顾问”，高管就能像创始人了解一家二十人初创公司那样了解自己的企业。她可以关注一项举措的落实情况，而不必等到季度汇报时才听人转述；一旦发生任何情况，她也会收到提醒。她的愿景可以得到更广泛的贯彻。每个人都能更清楚地了解其他人在做什么，也更清楚组织的整体优先事项。By easing communication, AI could justify larger or smaller firms. The classical firm is infrastructure for passing information up and instructions down, full of frictions and telephone-game. A larger firm (with more human employees) is less unwieldy if AI acts as a coordination layer. With the “AI consultancy,” an executive can know her firm the way a founder knows a twenty-person startup. She can watch an initiative’s implementation rather than await its quarterly retelling, and be pinged if anything happens. Her vision can infuse more broadly. Everyone is more aware of what everyone else is doing, and of the organization’s overall priorities.

情况也可能恰恰相反。如果沟通畅通无阻，合规情况也更容易监控，企业就不必将供应链的那么多环节纳入内部。企业也能更放心地信任外部供应商和承包商。It could instead work the other way. If communication is frictionless and compliance more monitorable, a firm needn’t internalize so much of its supply chain. The firm can better trust outside suppliers and contractors.

垂直一体化的一个重要动机是使各方的激励保持一致。这背后有一个不完全契约的概念（在新窗口中打开）——航空航天公司可以与喷气发动机公司签订合同，但对于合同中未明确要求的事项，喷气发动机公司就会受到激励，尽可能省成本，优先考虑自身的利润底线。为避免这种情况，航空航天公司会收购喷气发动机公司。这和员工与老板之间的关系很像：如果合同更容易监督，而且争议能够得到妥善裁决，而不是只靠逐项打勾来检查，那么企业可能会更愿意依赖其他企业。这样一来，事事都由自己包办的必要性就会降低。A major motivation for vertical integration is to align incentives. There’s the idea of incomplete contracts⁠(opens in a new window)—an aerospace company could contract with a jet engine company, but for everything not explicitly required in the contract, the jet engine company is incentivized to cheap out and look after its own bottom line. To avoid this, the aerospace company buys the jet engine company. Just like with workers and bosses: if contracts are more monitorable and arbitrated intelligently rather than by checking off boxes, firms may be more comfortable relying on other firms. There would be less cause to do everything in-house.

我们预计会看到许多规模较小的公司，但也会出现庞大的 AI“控股公司”，在同一家企业内开展大量不同项目。如果所有不同项目都能共享并利用某种共同资产，那么组建企业集团就更具经济性。这种共同资产可能是算力，尽管算力很容易按需以小规模配置。共用工厂或机器人群则是更有说服力的理由。We expect to see many smaller firms, but also sprawling AI “holding companies” where a huge number of different projects are conducted within one enterprise. The conglomerate is economical where there is some common asset leveraged by all the different projects. This common asset could be compute, though compute can easily be provisioned in small ad hoc quantities. A common factory or robot fleet is a better motivation.

大型“控股公司”也可以通过激励一致来证明其合理性。如果一个由人工智能和资本构成的大型组织完全致力于同一项使命——比如治愈癌症——那么就没那么需要单独的小型项目。人工智能公司可以避免权力斗争，共享所有信息，并在必要时让其智能体形成创造性多样性，从而探索不同的路径。The large “holding company” can also be justified by aligned incentives. If a large organization of AIs and capital is entirely devoted to a common mission—say, curing cancer—there is less need for separate smaller initiatives. An AI firm could avoid power struggles, share all information, and, where needed, instantiate creative diversity among its agents so that different paths are explored.

如今的制度本应奖励表现出众的员工。他们会步步高升，获得更高的报酬。但在顶尖模型顾问遍地的世界里，员工将如何脱颖而出？ Today’s system is supposed to reward standout workers. They rise through the ranks and are better compensated. But in a world of superb model advisors, how will workers distinguish themselves?

如果人工智能给每个人都提供同样出色的创意，我们就更难脱颖而出。薪酬差距应该缩小。If AI gives the same great idea to everyone, we have fewer opportunities to differentiate. Pay scales should compress.

能够抵御这种压缩的，是判断力、品味和工作范围。如果我们每个人手下都领导着 AI 公司，那么人类剩余的价值就在于决策能力。如果每个人负责的工作规模都更大，那么良好判断与糟糕判断之间的差异就会更加显著（在新窗口中打开）。What stands against this compression is judgment, taste, and scope. If we each lead AI firms beneath us, the residual human value is our decision-making. If each person is responsible for a larger effort under them, the difference between good judgment and bad rings louder⁠(opens in a new window).

我们早已将这种逻辑应用于组织架构的顶层。我们以“高层略微做出更好的决策，就能对利润产生巨大影响”为由，为 CEO 薪酬辩护（在新窗口中打开）。如果某些员工能更好地管理其下属的 AI 公司，部署稀缺资源，薪酬不平等可能会加剧。We have long applied this logic to the top of the org chart. We justify CEO pay⁠(opens in a new window) with the idea that marginally better decisions up top can have huge effects on the bottom line. Pay inequality could increase if some workers better manage the AI firms beneath them to deploy scarce resources.

stakes 越高，你相对于他人的微小优势就越会体现在薪水上。另一方面，AI 会给每个人提供好的建议。差异化减少，薪酬分布也会趋于集中。The more at stake, the more your small edge over someone else shows up in your paycheck. On the other hand, AI will give good advice to everyone. Less differentiation squishes the pay distribution.

在这里以及其他地方，起主导作用的力量或许取决于我们主要是在提供判断，还是在遵循判断。Here and elsewhere, the prevailing force will perhaps hinge on whether we are mostly providing judgment or following it.

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