公司的未来
人工智能如何进入兽群图 - 腹部，侧面，以及我们之间的。
作者们的笔记：这是我们系列关于下一个经济的第二篇论文，它反映了我们的观点，而不是OpenAI或我们的同事的观点。
在OpenAI开始管理自己的球队时，我们两人才24岁。 一旦你见到我们的队伍，那就不那么令人印象深刻了。
我们本该是咕噜声。 24岁时，你处于企业阶梯的最底层。 你拿会议指出写一些备忘录。 经过多年的艰苦工作，你(希望)被授予更广泛的权限。
我们的道路——以及今天许多年轻人的道路——看起来不同。 我们已奉命与“雇员”一起进入劳动力队伍。 这些代表日夜工作，周末和节假日工作。 他们很在行我们甚至有数学奥林匹亚金牌获得者来给我们建议我们的新报告仍然有错误。 但他们正在好转，学习我们的需求，我们正在学习如何管理这些需求。
我们都没有爬上公司梯子相反，管理层已经到了我们的地步。 这篇文章是关于我们晋升的，而其他的方式公司可能重组当我们进入智能时代。
当AI进入事务所时，它会去哪里？我们提出三种说明性设想：
在我们下面。 每个人都保持自己的座位，一个新的地下室AI劳工成长在下面。 我们都成为管理者。 首先我们得到助理，然后是参谋长，并最终整个小型公司向每个人报告。 每个人都得到晋升。
除了我们。 AI 加入与作为同类同事的人一起，直接为现有的人类管理人员和行政人员工作。 它从承担这项任务起步。 随着时间的推移，它履行更广泛的职能，向旗杆上方报告。
我们之间最不直觉的病例：上面的人类和下面的人类，中间有AI. 它协调并监测工作，执行行政领导的设想，同时承认创业工人的独特贡献。
这些假想不会像我们所描述的那样发生，但我们认为这三种设想的方方面面都将实现。 三相已已入微妙相。
我们的焦点不是完全自动化，我们感兴趣的是，当人工智能改进并把自己带入现有组织时，黑社会公司会是什么样子。
我们认为混合企业在概念上很重要，当模型只执行一个子集的任务时，熟悉工作的地级员工可能最容易采用。 一旦模型履行广泛的功能端到端，上层就可以直接使用。 我们认为，更有能力的AI更容易被采纳：企业必须学习技术，而劳动力则学习企业，可以自己上船。
我们将解析三种新种类的兽类图，然后研究它们对未来繁文缛节、工人代理、企业规模和薪酬差距意味着什么。
我们的第一种情况似乎没有什么破坏性。 在这里，人们在同一个席位上保持同样的报告线，表面上做的工作与以前相同。 在今天和这个未来之间，人类圆形图看起来没有变化。
区别在我们下面。 在每一个贡献者之下，一个新的人工智能劳动力向我们所有人报告。 我们的兽群图有地下室，我们都成为管理者。
起初这个地下室还没有完工。 我们从一个或两个agents开始，少像老练的员工，更多的热情的实习生，我们必须监督并随时原谅。 我们的期望很低：在这里发送电子邮件，在那里起草一份备忘录，在各地回复Slack消息。 和初级人类雇员一样，他们一开始几乎没有自主权和信任。
但是，随着模型的改进，我们的agents承担更多的任务，同时减少监督。 他们互相协调可能还有经理AI和GruntAI，会计师和律师，都在你的命令下合作。 你的行动远非手持实习生，最终看起来像一个小公司本身，有自己的Org图，你作为它的董事会。
Jace仍然在他的公司做会计，但他不再点击电子表格。 他的agent手下也这样做。 Jace的工作更像是几年前的经理。 他的经理现在有更大的权限
杰斯的日常变化已经完全改变，现在专注于沟通，协调和创造力。 他工作少，感觉更受挑战。 他的工作生活有更大的差异，他用他之前无法展示的领导能力和远见来区分自己。
会计团队并未收缩，因为公司其他部分也提高了产量，agents还不能尽其团队所能。 执行官不会了解收集金融数据的第一件事
请注意，在这里，我们采用AI 而不自动化自己。 相反，我们实现了我们过去自我的自动化，并且我们在等级体系中上升。 大赦国际越来越多地承担我们过去的工作。 我们的工作仍然存在，因为还有更多的工作需要管理，我们有责任增加产出。
工作不是静态的(在新窗口打开)。 假设人工智能能完成你以前九成的工作你可能会认为你只有10%的工作。 另一种可能性是，你仍然在做一份完整的工作，只是一份不同的工作——现在专注于十倍大的目标。 这就是技术的经典天赋：生产力的提高，满足其抱负的提高，以及工资的提高(在新窗口打开)与抱负的提高。
这都取决于我们是否还在生产中增加价值(在新窗口中打开). 这篇文章关注未来几年，即使AI可以做这一切，我们可能仍然更重视一件事，因为一个人做到了，或者坚持一个人有最后发言权。
这也取决于我们的胃口。 如果作为消费者，我们不关心十倍的产量，平衡就会转向工人少或工作时间少。 历史上，产出的增加并没有使我们感到满足。 我们有更多的(在新窗口打开)，但工作少一点(在新窗口打开). 如果我们的胃口随着产出的倍增而继续增长——我们仍然是生产添加剂——我们将选择继续以劳动力换取更多的消费。 如果生产变得如此丰富以至于我们满足，我们可能会志愿一支较小的劳动力。
我们以后会讨论更多的反对意见(例如我们是否拥有更多的小公司)。 我们仍然认为，这种情况很重要，因为收养必须从某个地方开始。 企业需要一种方法，从目前的矿石结构中稳定地演变。 推动现有雇员的工作可能比批发重组更容易迈出第一步。 我们今天看到这一点在行动：一个工人又一个下放给大赦国际，但迄今没有发生组织革命。
这个方案首先依靠前线的雇用。 基层工人——而不是管理人员和行政主管——领导大赦国际的收养工作。 这看起来很可能会，只要最能让AI做工作的人是以前自己做的人。
模型已经可以实现许多数据科学任务的自动化，但是一个部门主管对方法和minutiae不熟悉，将难以利用这些工具。 在AI主动地告诉你你不知道的事(见我们的下一个情景)之前
这一情景的最大变化是初级工人。 不会再有最低的暴风雨了至少人类不会我们雇用年轻人时，这些职位将由一些无关紧要的东西填补。
由此，我们失去了一些教育年轻工人的传统途径：学徒(在新窗口打开)，边学边干，挣取stripes. 这些都是真正的代价，但我们大多认为这是一种特权。 最绿色的雇用一个好主意有资源进行实验。 大楼里最古老的借口，一个低年级的员工，在她头上有一个伟大的电子游戏并且没有路径到人头计算现在可以简单地建立它。
我们认为，这种 " 促进 " 的情景仍然对初级工人学习很多 -- -- 判断、品味、管理肌肉和创造性远见。 我们将受到激励来培养这些技能，因为它们出现在我们的工作产品中。 我们仍然会边干边学(在新窗口打开)，但范围更高。 坚定的长者们继续激励我们作为负责任的管理者帮助我们(在我们取得成功时有公平的利益)。 这里的机会对每个人来说都是一种更刺激的作用。 任何工人都可以以更大的权限展示其才能，这对根本没有权限的入职工人最为重要。
AI的第二道途径是通过现存的兽形图来织入公司。 agents不是渣滓，传统经理将团队的一分工作交给一个模特儿，她可以把工作交给任何雇员。 AI将自身插入到人类的旁边，而不仅仅是在人类的下面。
与第一种情况的主要区别是：在这里，有成果的AI被现有的人的管理者采用，而不仅是目前的个人贡献者。 为此，它必须有能力成为正式的小组成员。
Sheila是一家家具公司的副总裁。 她从没做过广告宣传她给一个AI营销团队一个预算，和一个新的沙发来出售。 它将发布任务分为自己的agents. 有人采访了推销员其他人与工厂协调他们实际能够交付的东西。 一个副代理发布进行在线调查，而另外几个原型不同的视觉风格。
草案和修订稿在不经过她的办公桌的情况下通过。 她的人体销售和设计团队仍然向她报告，与销售agents的团队并列。 并询问照片是否让沙发看起来太蓝了。 没有广告专家的主动宣传，她不可能意识到这种细节。 她授权agents与人类摄影师协调进行重拍。
人类是通才。 锤子可以帮助完成一项任务；人类的功用更大。 AI从工具到劳动力的旅程是一个日益扩大的范围：将任务自动化，然后是工作，然后是团队，最后是企业。 扩大范围的每一步都需要更广泛的能力。
一份工作是一个充满许多任务的手提箱。 对于整个团队来说，这个手提箱被挤满了各种各样的工作。 职权范围中更多的任务意味着更多的潜在阻塞点。 如果团队中哪怕一项重要任务涉及体力劳动，就需要一套全新的外围工程。 为了替代整个团队，大赦国际必须有能力履行其各种职责。
能处理更大范围的AI更难建立。 但是，对于公司来说，这是整个过渡的中心不对称现象，而越轨范围更容易采用。
端到端自动团队或广义功能可以由执行官直接采用。 一个能力较弱的工具不能从高处简单实施。 较简单的工具依靠广大劳动力的接受和再培训。
新技术传统上使完成一套有限的任务更便宜。 然后负担转移到买方身上。 公司必须想出如何利用新工具，必须重新训练、重组和重建其周围的程序(在新窗口打开)。 电子表格软件没有安装在会计部门
劳动是相反的。 一个实习生，一个员工，一个很好的团队找出如何对你有用。 他们审讯你，吸收你的背景，注意
本文由 AI宝库自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
The future of the firm
How AI might enter the org chart—beneath, beside, and between us.
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.
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.
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.
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.
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.
When AI enters the firm, where does it go? We present three illustrative scenarios:
Beneath us. Everyone keeps their seat, and a new basement of AI labor grows underneath. We all become managers. First we get assistants, then chiefs of staff, and eventually entire miniature firms reporting up to each of us. Every person gets a promotion.
Beside us. AI joins the org chart alongside people as a colleague of sorts, working directly for existing human managers and executives. It starts out by taking up this task and that role. In time, it performs broader functions, reporting higher up the flagpole.
Between us. The most unintuitive case: humans above and humans below, with AI in between. It coordinates and monitors work, enforcing an executive’s vision while recognizing unique contributions from enterprising workers.
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.
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.
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.
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.
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.
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.
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.
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.
Jace still does the accounting at his firm, but he’s no longer clicking around the spreadsheets. His agent underlings do that. Jace’s job is more like his manager’s from a few years ago. His manager, in turn, now has an even bigger remit.
Jace’s day-to-day has completely changed, now focused on communication, coordination, and creativity. He works less and feels more challenged. His work life has more variance, and he distinguishes himself with leadership skills and a vision he couldn’t showcase before.
The accounting team hasn’t shrunk because the rest of the company has grown more productive too and demands more accounting than ever before. The agents can’t yet do everything his team does. The execs wouldn’t know the first thing about assembling financial data and need his domain expertise to use the agents effectively.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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’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.
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.
Sheila is a VP at a furniture company. She has never run an ad campaign. She gives an AI marketing team a budget and a new sofa to sell. It divides the launch tasks among its own agents. Some interview the salespeople. Others coordinate with the factory on what they can actually deliver. One subagent launches an online survey while a few more prototype different visual styles.
The drafts and revisions pass between them without crossing her desk. Her human sales and design teams still report to her, alongside the team of marketing agents. On Monday, she is handed a campaign to approve and a question about whether the photos make the sofa look too blue. She would never have realized that sort of detail without an ad expert proactively flagging it. She authorizes the agents to coordinate with the human photographer on a reshoot.
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.
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.
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.
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.
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.
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
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