Steering AI Beyond Automation
打开互动全文版（逐段中英对照 + 针对本篇提问）→本文认为，硅谷主流的人工智能议程狭隘地聚焦于自动化人类劳动，其驱动力是技术决定论和经济激励；而另一条方向——人机增强与协作——仍发展不足且阐述不清。作者主张，试图完全自动化所有人类劳动的极端化努力在技术和经济层面都难言乐观，且过多的人工智能研究投入到了自动化上，牺牲了协作方向。为超越单纯的批评，他们勾勒了一个协作式人工智能的初步研究纲领，包括揭示中间过程的白盒智能体、保持工作者心流的低延迟系统、支持持续引导而非逐轮委托的架构，以及评估人机团队的新方法。他们还概述了一种自下而上、市场驱动的引导人工智能开发与部署的路径。作者总结道，计算机科学缺乏一套系统性的协作式人工智能架构研究纲领，他们将这一草图作为种子，以期催生更广泛、持续、实验性和集体性的研究议程。
This essay argues that the dominant Silicon Valley agenda for AI is narrowly focused on automating human labor, driven by technological determinism and economic incentives, while an alternative direction—human-AI augmentation and collaboration—remains underdeveloped and poorly articulated. The authors contend that maximalist attempts to fully automate all human labor are unpromising on both technical and economic grounds, and that too much AI research is devoted to automation at the expense of collaboration. To move beyond criticism, they sketch a tentative research program for collaborative AI, including white box agents that reveal intermediate processes, low-latency systems that preserve workers' flow, architectures enabling continuous steering rather than turn-by-turn delegation, and new methods for evaluating human-AI teams. They also outline a bottom-up, market-driven approach to steering AI development and deployment. The authors conclude that computer science lacks a systematic program for collaborative AI architecture, and they offer their sketch as a seed for a broader, sustained, experimental, and collective research agenda.
_作者：Gabriel Unger 与 Arvind Narayanan。Unger 是斯坦福数字经济实验室的经济学家。_
_By Gabriel Unger and Arvind Narayanan. Unger is an economist at Stanford’s Digital Economy Lab._
当今 AI 领域最根本的问题之一是：AI 项目在多大程度上纯粹是为了自动化人类劳动？
One of the most fundamental questions for the field of AI today is: to what extent is the project of AI purely about automating human labor?
当前硅谷主流的 AI 计划（兼具产业界与学术界面貌）给出的答案是：“几乎完全如此。”¹ 其中隐含一种技术决定论：AI 具有一种偏向自动化的深层内在逻辑。继续朝这个方向推进，就是走向经济增长、繁荣与利润。AGI（通用人工智能）的愿景正是这一计划的成功完成：创造出能力足够强大、能够自主完成人类所能做的一切事情的 AI 系统。
The dominant Silicon Valley program for AI right now, with both industry and academic faces, answers: “almost entirely.”¹ There is an implicit technological determinism: that AI has a deep internal logic that favors automation. To keep pushing in this direction is to move towards economic growth, prosperity, and profit. The vision of AGI is the successful completion of this project: to create AI systems capable enough to autonomously do everything a human can do.
还有一种替代观点：AI 是一种技术（或一类技术），与任何技术一样，可以朝不同方向发展。完全自动化是其中一个方向。但还有其他方向，包括更侧重于人类增强，以及提升人类与 AI 模型协同工作的团队的协作与集体力量的方向。
There is an alternative view: that AI is a technology (or class of technologies) that, like any technology, can develop in different directions. Total automation is one direction. But there are others, including directions that focus more on human augmentation, and increasing the collaborative and collective power of teams of humans and AI models working together.
这种观点——也是我们的观点——并不是说自动化从来都不好。显然有时它是好的，也显然有时某些人会出于最狭隘的短期私利而试图反对它，以牺牲更广泛的社会与经济进步为代价。但我们认为，AI 也可以以许多有趣的方式增强人类工作者；某些试图完全自动化所有人类劳动的极端主义尝试，在技术和经济层面都毫无前景；而目前太多的 AI 研究投入到了后者，牺牲了前者。对自动化议程的偏向，部分源于经济激励，但部分也源于计算机科学作为一门学科更深层的智识与文化因素。
This view — our view — is not that automation is never good. Clearly sometimes it is, and clearly sometimes some subset of people try to oppose it out of the narrowest short-term self interest, at the expense of broader social and economic progress. But we think AI can also augment human workers in many interesting ways, that some of the maximalist attempts to completely automate all human labor are deeply unpromising on both technical and economic grounds, and that right now too much of AI research is devoted to the latter at the expense of the former. The bias toward the automation agenda is partly because of economic incentives, but partly because of some deeper intellectual and cultural aspects of computer science as a field.
这种替代观点在计算机科学史中有着深厚的思想根源，尤其是在人机交互这一子领域。此外，现在有令人信的技术和经济理由去追求它。在目睹了尝试实施第一个项目所带来的经济失望之后，少数但越来越多的企业开始转向这一观点。尽管如此，它今天仍然是一个少数派立场，鲜为人知，且缺乏组织。最重要的是，当今计算机科学领域缺乏对人工智能架构的系统性计划的清晰阐述，而这样的计划将推进这一议程。
The alternative view has deep intellectual roots in the history of computer science, especially the subfield of human-computer interaction. Moreover, there are compelling technical and economic reasons to pursue it now. A small but growing number of businesses are now coming around to it, after witnessing the economic disappointments of attempting to implement the first program. Despite this, it remains a minority position today, poorly understood, and disorganized. Above all, computer science as a field today lacks a clear articulation of a systematic program for the architecture of AI that would further this agenda.
在本文中，我们简要讨论这里的利害关系、这场辩论在计算机科学中的历史、这场辩论今天在经济学和其他领域的现状，以及大学计算机科学文化和课程中的自动化议程。最重要的是，我们在第 1 节中勾勒出几个推动人机协作方向的人工智能研究方向。设计真正协作的人工智能需要解决与当今模型和智能体非常不同的一组技术问题，这些问题常常被忽视，甚至不被理解为问题。我们勾勒的具体方向是尝试性和实验性的。但重点是明确构想一个不同于硅谷主流项目的人工智能项目，并超越批评，积极地阐述一个替代方向。我们希望这能成为一个更广泛、持续、实验性和集体性研究计划的种子。
In this essay, we briefly discuss what is at stake here, the history of this debate in CS, the status of this debate today in economics and elsewhere, and the automation agenda in the culture and curricula of CS in the university. Above all, we sketch in Section 1 a few research directions for AI that push in the direction of human-AI collaboration. Designing AI for true collaboration requires solving a very different set of technical problems than today’s models and agents, problems that are often ignored, or not even understood to be problems. The specific directions we sketch are intended to be tentative and experimental. But the point is to explicitly conceive of a project for AI that differs from the dominant Silicon Valley one, and moves beyond criticism to the positive articulation of an alternative direction. Our hope is that this can serve as a seed for a much broader, sustained, experimental, and collective research program.
在第 2 节中，我们讨论我们项目的高层次动机，在第 3 节中，我们简要介绍这些思想的一些历史。在第 4 节中，我们将这一切置于当今经济学和政策中关于人工智能和自动化的辩论背景下，在第 5 节中，我们概述一种自下而上、市场驱动的方法来引导人工智能开发和部署的方向。
In Section 2, we discuss the high level motivation for our project, and in Section 3, we give a brief history of some of these ideas. In Section 4, we put this all into the context of the debates today about AI and automation in economics and in policy, and in Section 5, we outline a bottom-up, market-driven approach to steering the direction of AI development and deployment.
一个严肃的人机协作研究议程会提出一些新颖的技术问题，而 AI 的自动化议程更可能忽视这些问题，或将其视为相对不重要。我们给出四个这样的示例性方向。
A serious research program for human-AI collaboration elevates novel technical problems that the automation agenda for AI is more likely to ignore or treat as relatively unimportant. We present four such illustrative directions.
AI 模型与智能体的主流范式是“黑盒”：系统仅依据其最终输出接受评估。中间步骤通常不向用户披露；即便披露，也可能令人费解，因为这些步骤与人类处理任务的方式相去甚远，而且它们并未被视为任务表现的一个方面而加以对待和优化。黑盒范式的一个原因是模型训练中缺乏过程轨迹。如果我们用一千幅文艺复兴时期的画作训练一个新 AI 模型，却没有任何关于创作任何一幅画所涉及的众多中间步骤与修改的数据，那么我们只能期待得到一个黑盒模型。也许它能生成一幅风格相似的新画作图像，但它无法主动向我们讲解创作这样一幅画所涉及的步骤。
The dominant paradigm for AI models and agents is "black box": the system is evaluated based on the final output it produces. The intermediate steps are generally not revealed to the user, may be mysterious even if revealed because they depart greatly from how people approach the task, and are not treated and optimized as an aspect of task performance. One reason for the black box paradigm is the absence of process traces in model training. If we train a new AI model on a thousand paintings from the Renaissance, but we have no data on any of the many intermediate steps and revisions involved in the creation of any given painting, we can't expect anything other than a black box model. Maybe it can generate the image of a new painting in a similar style, but it will not be able to actively talk us through the steps involved in creating such a painting.
我们所说的“白盒智能体”则是指这样一类 AI 系统：它们在认知上对人类用户更为可读，并明确揭示其中间过程（来源、假设、方法、不确定性、过程产物）。白盒智能体可为各种更直接的人机协作形式打开大门：如果人类能看到中间过程，他们就能直接与之交互。
What we instead call "white box agents" are AI systems that are epistemically more legible to their human users, and explicitly reveal their intermediate processes (sources, assumptions, methods, uncertainties, process artifacts). White box agents could open the door to all kinds of more direct forms of human-AI collaborations: if humans can see the intermediate processes, they can directly interact with them.
白盒智能体的成功开发需要解决许多技术子问题。其中最主要的是：为更广泛的人类劳动捕获过程轨迹。过去几年 AI 模型的训练与部署不成比例地聚焦于软件编程。原因很多，但一个重要原因是我们受益于大量编程仓库的公开可得性，这些仓库包含软件开发过程每一步的轨迹。但对于当今经济中许多其他重要的专业职业——法律、医学、许多科学领域——并不存在这样神奇的、公开可得、免费、全面且易于编码的过程轨迹数据仓库。
The successful development of white box agents will require tackling many technical sub-problems. Chiefly among them: capturing process traces for a much broader range of human labor. The past few years of AI model training and deployment have had a disproportionate focus on software coding. There are many reasons for this, but a big one is that we benefit from the very public availability of a massive number of coding repositories, which include traces of every step of the process of software development. But for so many of the other important professional occupations in the economy today — law, medicine, many sciences — no such magical repositories of publicly available, free, comprehensive and easily codifiable process trace data exist.
这里有一个思想实验。想象一个反事实世界，其中开源软件不存在，而编程智能体（出于必要）被训练成以黑盒方式直接输出编译后的软件二进制文件，而非源代码。我们认为，软件工程师会将这种能力体验为反乌托邦式的，而非解放性的，其效果将是取代他们，而非增强他们的能力。我们所倡导的转变，相当于将代码仓库和编程智能体引入这个世界——并在每一个目前缺乏它们的领域都这样做。
Here's a thought experiment. Imagine a counterfactual world in which open-source software didn't exist, and coding agents (out of necessity) were trained to directly output compiled software binaries instead of source code, in a black box way. We think this capability would be experienced as dystopian instead of liberating by software engineers and it would have the effect of replacing them instead of amplifying their abilities. The shift we advocate is the equivalent of introducing code repositories and coding agents into this world — and doing so in every domain that currently lacks them.
一场悄然的淘金热正在展开：为更广泛的职业捕获过程轨迹，以输入 AI 模型，参与者既有完全以此为目的的公司（Mercor），也有无情地试图研究自家员工以便更好地取代他们的传统科技公司（Meta）。矛盾的是，这一切往往是为自动化范式服务的：想出训练数据，在更多领域造出更多黑盒智能体。但它也可能在更好地促成新一代白盒智能体的开发方面至关重要。
There is a quiet gold rush to capture process traces for a broader range of occupations to feed into AI models, by both companies entirely dedicated to the purpose (Mercor) and legacy tech companies ruthlessly trying to study their own employees, the better to replace them (Meta). Paradoxically, this is all often done in the service of the automation paradigm: come up with training data to make more black box agents in more domains. But it could also be vital in better enabling the development of a new generation of white box agents.
主动人类协作的一个基本动态是低延迟。想象两个人进行真正的对话。如果每次你问一个人问题，他都要离开房间思考 20 分钟，那么你们就不再是在对话；充其量可能是在向他委派任务。能够以更自然类似人类交互的速度工作的模型，可能更好地实现更深层次的人机协作形式。
A basic dynamic of active human collaboration is low latency. Imagine two people having a real conversation. If one person has to leave the room to think for 20 minutes every time you ask him a question, you’re no longer having a conversation; at best maybe you’re delegating to him. Models that can work at a speed that more naturally resembles human interaction might better enable forms of deeper human-AI collaboration.
如今，我们被迫在两种选择之间做出取舍：一种是能快速但肤浅地回答问题的聊天机器人，另一种是能彻底执行任务但会打断对话流程的智能体。我们不必固守这种权衡。
Today, we are forced to pick between chatbots that can answer questions quickly but superficially, and agents that can perform tasks thoroughly but break the flow of conversation. We don’t have to be stuck with this tradeoff.
这听起来像是一个简单的目标，但它意味着一系列全面的技术挑战，可能涵盖从模型推理到检索方法、shell、浏览器、工具等各个方面。我们如何构建能够实现足够低延迟以支持协作的 AI 系统？智能体技术栈的哪些部分可以改变，或需要改变，以实现这一目标？
This sounds like a simple goal, but it implies a comprehensive set of technical challenges, that potentially span everything from model inference to retrieval methods, shells, browsers, tools, and more. How can we build AI systems that achieve latency low enough for collaboration? What parts of the agent stack could be changed, or need to be changed, to achieve this?
低延迟尤其有前景，因为它可以让工作者保持心流状态。心流是心理学中的一个概念，描述人们在进行攀岩或下棋等因内在原因而被吸引的活动时的体验。它是一种改变了的体验状态，在这种状态下，执行者忘记时间和疲劳，沉浸在当下。大多数程序员都熟悉心流，这是他们被编程吸引的一个重要原因。
Low latency is particularly promising because it could enable workers to stay in a state of flow. Flow is a concept from psychology that describes people’s experience when performing activities such as rock climbing or chess that people are drawn to for intrinsic reasons. It is a state of altered experience in which the performer forgets time and fatigue, and is immersed in the moment. Most programmers are familiar with flow, and it is a big reason they are drawn to programming.
在心流状态下工作需要清晰和即时的反馈——也就是低延迟。到目前为止，智能体削弱了我们实现心流的能力。许多程序员报告说，从手动编程转向监督编码智能体导致了心流丧失、认知过载和倦怠。但我们的假设是，有可能重新设计智能体，不仅能恢复心流，甚至能增强人们实现心流的能力。
Working in a state of flow requires clear and immediate feedback — that is, low latency. So far, agents have weakened our ability to achieve flow. Many programmers report that the shift from manual programming to supervising coding agents has resulted in a loss of flow, cognitive overload and burnout. But our hypothesis is that it is possible to redesign agents to not only restore flow, but even increase people’s ability to achieve flow.
在自动化范式下，人机交互的基本模型是委托：我告诉智能体要做什么，然后离开，稍后回来验证。许多“人类在环”的版本仍然回归到这一点；从技术上讲，人类在环，但只是站在起跑线上，然后在终点线等待。这与协作模型截然不同，在协作模型中，人类积极参与过程，并在智能体工作时持续引导它。
The basic model of human-AI interaction, under the automation paradigm, is delegation: I tell my agent what to do, walk away, then come back later to verify. Many versions of “human in the loop” still come back to this; the human is technically in the loop, but just standing at the starting line and then waiting at the finish line. This is quite different from a model of collaboration, where the human is actively participating in the process, and continually steering the agent as it works.
如今，主流编程智能体中存在一种持续引导的原始版本，但仅限于在智能体工作时向其提供额外指令。这是对用户和智能体轮流交替范式的轻微调整，而不是背离。如果用户试图编辑与智能体相同的文件，它会感到困惑，一切都会乱套。
A primitive version of continual steering exists in mainstream coding agents today, but is limited to giving additional instructions to the agent as it works. It is a slight tweak to the paradigm of the user and the agent alternately taking turns, rather than a departure from it. If the user tried to edit the same file as the agent, it would get confused and all bets are off.
真正的协作提出了一系列新颖的技术挑战。系统需要显示连续变化而非逐轮变化的可见状态，拥有共享工作区，并适应中断、并行工作、同时编辑和其他协作模式。这在难以事先精确指定任务，或验证复杂且可能与实现紧密相关的领域尤为重要。应对这一挑战需要设计交互架构，允许持续控制而不会在中断下崩溃。
True collaboration poses a novel set of technical challenges. Systems will need to display visible states that change continuously instead of turn-by-turn, have shared workspaces, and accommodate interruption, parallel work, simultaneous editing, and other collaboration patterns. This is especially important in domains where it is difficult to precisely specify the task ex ante, or verification is complicated and perhaps bound up in implementation. Meeting this challenge would require designing architectures of interaction that allow continuous control without melting down under the interruption.
当前大多数 AI 基准测试评估的是模型独立完成任务的能力，而非模型与人类协作的能力。（这很可能是基准性能与实际有用性之间存在差距的部分原因）。
Most AI benchmarks today test how good a model is at completing a task on its own, not on how well a model collaborates with a human. (This is likely part of the reason for the gap between benchmark performance and real-world usefulness).
评估 AI 与人类团队的联合努力是困难的。这是一个相对容易阐述的挑战，但取得进展却并非易事。理解如何最好地评估人机协作更加困难，并且它提出了一系列复杂的问题，乍看之下可能显得令人不适地主观或令人困惑。AI 模型与人类之间多少分歧是合适的？模型应该多顺从，人类应该拥有多少控制权？富有成效的协作中，各方有多少空间向对方学习？我们如何考虑团队的短期与长期成功？评估协作成功的最客观方法是什么，从数据中提取的最佳信号和代理指标又是什么？
Evaluating the joint efforts of AI-human teams is hard. This is a relatively simple challenge to articulate, but non-trivial to make progress on. It is more difficult to understand how to best evaluate AI-human collaboration, and it raises a complex set of questions that at first blush might seem uncomfortably subjective or confusing. How much disagreement between the AI model and the human is appropriate? How deferential should the model be, and how much control should the human have? How much space does a productive collaboration have for each party to learn from the other? How do we think about short term vs long term success of a team? What are the most objective ways to evaluate collaborative success, and what are the best signals and proxies to pick out of the data?
提升协作技能可能并不适合采用基准测试和爬山范式，尽管这种范式曾导致自主任务性能的快速提升。基准任务作为现实世界任务的代理足够有用，因此在一个任务上的表现可以反映在另一个任务上的表现。由于协作涉及人类，静态基准测试行不通。模拟用户是一种有前景的方法，但超过一定程度后，模拟用户与真实用户行为之间的微小偏差也可能导致在模型训练中奖励错误的东西。
Improving collaboration skill might not be amenable to the benchmarking and hill-climbing paradigm that has led to rapid improvements in autonomous task performance. Benchmark tasks are a useful enough proxy for real-world tasks that performance on one is informative about performance on the other. Since collaboration involves a human, static benchmarks won’t work. Simulating the user is a promising approach, but beyond a point, even minor deviations between the behavior of simulated and real users might lead to rewarding the wrong thing in model training.
关于自动化潜在过度问题的争论往往集中在纯粹的分配问题上。批评者担心，大规模自动化可能会造成一大类失业、贫困或丧失权力的工人。经济学家通常怀疑这种情况是否会发生（现代经济史表明，技术进步的节奏是正常的：一些自动化，然后工作重新分配）。在硅谷，更常见的做法是接受大规模失业的经验前提，但认为人们不必“必须”工作是一件好事，并由 Universal Basic Income 支付或一些更奇特的福利计划支持。
Debates about the potential excesses of automation tend to focus on purely distributive concerns. Critics worry that mass automation might create a broad class of workers who are unemployed, poor, or otherwise disempowered. Economists are conventionally skeptical that this would happen at all (modern economic history suggests a normal rhythm of technological progress, some automation, and then job reallocation). It is more common in Silicon Valley to accept the empirical premise of mass unemployment, but view it as a good thing that people won’t “_have_ to work”, supported by Universal Basic Income payments or some even more exotic welfare scheme.
这种“自动化并补偿”交易存在的诸多问题之一是，它完全超出了当今美国可识别的政治经济范围。自 1980 年代以来，收入不平等急剧增加，而美国精英并没有以慷慨的新福利扩张政治计划作为回应。美国政治也没有在其他方面产生一个由精英之外的选民组成的广泛联盟，要求并赢得这样的扩张。（许多基本的现金福利计划在同一时期被削减）。而且，即使某种类似 UBI 的计划在政治上是可行的，它对硅谷以外的普通人来说也毫无吸引力。大多数美国人想要有真实工作的真实生活，而不是福利支票。
One of the many problems with this “automate and compensate” bargain is that it is wildly outside the recognizable political economy of the US today. Since the 1980s, income inequality has increased dramatically, and the American elite did not respond with generous new political programs of welfare expansion. Nor did the politics of the US otherwise produce a broad coalition of voters outside the elite who demanded and won such an expansion. (Many basic cash welfare programs were cut back over this same period). And even if some UBI-like program was politically feasible, it would be profoundly unattractive to normal people outside Silicon Valley. Most Americans want real lives with real work, not welfare checks.
其次，即使我们接受自动化议程，它目前也在自身条件下挣扎。在自动化议程下，工业 AI 部署的努力越来越承诺提供高度能力的智能体，这些智能体同时部署成本低（不需要特定任务的开发或配置）、可靠且真正自主。这些目标彼此直接冲突，以至于形成了一个三难困境——（1）完全自主，（2）在高风险环境中可靠，（3）真正通用——只能选两个。
Second, even if we were to accept the automation agenda, it is currently struggling on its own terms. Efforts for industrial AI deployment, under the automation agenda, increasingly promise to deliver highly capable agents that are simultaneously cheap to deploy (not requiring task-specific development or configuration), reliable, and genuinely autonomous. These goals are in direct tension with each other, to the point of imposing a trilemma – (1) completely autonomous, (2) reliable in high-stakes settings, (3) truly general purpose – pick two.
当今 AI 行业最明显的问题之一是经济性是否合理：数十亿美元的资本支出与许多企业不明朗的投资回报率。放宽自动化议程的限制实际上可能为更具经济可持续性的 AI 版本打开大门。
One of the most obvious questions today about the AI industry is whether the economics can make sense: billions of dollars of capex versus unclear ROI for many businesses. Relaxing the constraints of the automation agenda may actually open the door to a more economically sustainable version of AI.
最后，AI 具有真正的经济增长潜力。在美国生产力增长基本停滞了 50 年之后，充分利用这一机会非常重要。AI 最激进和有趣的用途很可能是那些让我们做以前无法做的事情的用途。完全专注于简单地自动化我们已经知道如何做的事情，其变革性要小得多，而且似乎不太可能带来快速的经济增长。行业和政策制定者普遍认识到，AI 的积极愿景尚未被阐明。我们认为，阐明这样的愿景首先需要摆脱对自动化的狭隘关注。
Finally, AI has real potential for economic growth. After a 50-year period of mostly stagnant productivity growth in the US, it is important to make the most of this opportunity. The most radical and interesting uses of AI are likely to be those that let us do things we could not do before. Focusing entirely on simply automating the things we already know how to do is far less transformative and seems unlikely to lead to rapid economic growth. There is a widespread recognition in the industry and among policymakers that a positive vision for AI has yet to be articulated. We think that articulating such a vision will first require moving away from a narrow focus on automation.
在现代计算的早期（20 世纪 40 年代至 60 年代），关于计算机应该成为什么以及我们与计算机的关系应该是什么，存在三种不同的愿景。它们在不同点上有所重叠，但彼此不同。一种愿景——后来成为经典人工智能领域——渴望几乎拟人化的机器，能够推理、解决问题、具有能动性、通用且能说人类语言。“人工智能”一词是在 1956 年达特茅斯会议上创造的（甚至这个名字也在试图拟人化！）。
In the early days of modern computing (1940s-1960s), there were three different visions for what computers should become, and what our relationship to computers should be. They overlapped in different points but were distinct. One vision, for what would later become the field of classical AI, aspired to almost anthropomorphic machines that could reason, solve problems, have agency, be general purpose, and speak human language. The term “artificial intelligence” was coined at a 1956 Dartmouth conference (even the name is trying to anthropomorphize!).
第二种愿景——后来成为机器学习——强调在非常明确指定的一组任务上实现窄而深的性能；使用大量数据进行预测。通常有一种隐含的理解，即人类在选择任务和运用预测方面的作用。
A second vision, what would become machine learning, emphasized narrow and deep performance on a very well specified set of tasks; using lots of data to make predictions. There was often an implicit understanding of the human role in choosing the tasks and employing the predictions.
第三种愿景（后来成为人机交互（HCI）子领域）非常明确地渴望人类与机器的协作。JCR Licklider 1960 年的《人机共生》和 Engelbart 1962 年的《增强人类智力》等著作认为，人类和计算机具有不同的能力，我们应该考虑设计最佳可能的团队和组织来结合它们，而不仅仅是孤立地设计最好的计算机，并且最好的计算机将是那些能够与人类迭代和交互良好工作的计算机，而不是那些尽可能自主完成更多工作的计算机。
A third vision (that would later become the subfield of HCI) very explicitly aspired to a collaboration of humans and machines. Works like JCR Licklider’s 1960 “Man-Computer Symbiosis” and Engelbart’s 1962 “Augmenting Human Intellect” argued that humans and computers have different abilities, that we should think about designing the best possible teams and organizations for combining them, not just trying to design the best computers in isolation, and that the best computers would be the ones that can work well iteratively and interactively with humans, not the ones that autonomously do as much work as possible.
今天的人工智能代表了经典人工智能和机器学习的融合。而人机交互仍然是一个充满活力的子领域。像计算机鼠标、图形学以及我们视为理所当然的个人计算的许多设计特征等发明，都来自这个最关心制造能与人类良好协作的机器的群体。但像 Licklider 和 Engelbart 等人机交互先驱的基本愿景——人机协作——在今天似乎是一种少数派立场。经典人工智能的愿景成为新的正统。为什么它如此占主导地位？
AI today represents a convergence of classical AI and ML. And HCI remains a vibrant subfield. Inventions like the computer mouse, graphics, and many design features of personal computing that we take for granted came from this community of people who cared most about making machines that could collaborate well with people. But the basic vision of HCI forerunners like Licklider and Engelbart, of human-machine collaboration, seems like a minority position today. The vision of classical AI is the new orthodoxy. Why is it so dominant?
将 AI 视为纯粹自动化的吸引力有多种原因。首先，存在表面上的经济理由（即使最终并不成立）。对大多数行业中的大多数公司而言，最大的生产成本是劳动力。在美国 20 世纪的大部分时间里，劳动力获得了生产收入的三分之二。对高管来说，这通常是最明显可以削减的部分。解雇工人还有一个额外吸引力：它会立即体现在短期利润和损失中。此外，确切的成本节省很容易估算。相比之下，对提升生产力的雄心勃勃的投资可能在 10 年后才有回报，但难以提前估算，并且不会带来即时的财务满足感。
The appeal of AI as pure automation has many causes. First, there is the ostensible economic case (even if it doesn’t ultimately hold up). The biggest cost of production, for most firms in most industries, is labor. For much of the 20th century in the US, labor received two-thirds of income from production. For executives, it is usually the most obvious thing to try to reduce. Firing workers has the additional attraction of showing up immediately in short-run profit and loss. Besides, the exact cost savings are easy to estimate. In contrast, ambitious investments in augmenting productivity might pay off in 10 years, but are hard to estimate in advance and don’t give immediate financial gratification.
所有这一切都发生在这样一个政策环境中：在许多方面偏袒资本而非劳动力（例如考虑相对的税收待遇），以及数十年来实际利率持续下降。如果你是一家财富 500 强公司缺乏想象力的 CEO，你总是对支付人类工作的替代方案充满兴趣。如果你是一家 AI 初创公司的创始人，向一位希望产品能讨这位 CEO 喜欢的风投推销，你会敏锐地意识到这一点。
All of this is in the context of a policy environment that in many ways favors capital over labor (consider for instance the relative tax treatment) and many decades of declining real interest rates. If you are an unimaginative CEO of a Fortune 500 company, you are always intensely interested in alternatives to paying humans to work. If you are an AI start-up founder, pitching to a VC who wants products that this CEO will like, you are acutely conscious of this.
经济学是故事中显而易见的部分。但还有额外的、不太显眼的、更微妙的层面，存在于计算机科学内部。
The economics are the obvious part of the story. But there are additional, less visible, more subtle layers, internal to CS.
有一系列技术原因使得自动化对研究人员更具吸引力。自主模型/智能体非常适合非常清晰的基准测试。模型完成任务的效果如何？损失函数、性能度量、模型之间的比较，都可以非常优雅，远比一些混乱的评估——比如人类与 AI 团队在某种现实环境中协作表现如何——要优雅得多。
There are a set of technical reasons that make automation more attractive to researchers. Autonomous models/agents lend themselves to very clean benchmarks. How well did the model do the task? The loss function, the performance measure, the comparisons between models, can all be elegant, much more so than some messy evaluation of how a human-AI team collaboratively performed in some real-world setting.
最后——最晦涩，但相当真实——是当今 AI 以及更广泛的计算机科学的知识文化的深层背景。自主系统作为一种审美理想呈现给学生。“端到端”系统是一种胜利。需要人类监督是一种缺陷，甚至人类参与通常被讨论的方式——例如“human in the loop”（人在回路中）——也常常将人类呈现为一种临时的不便。Peter Thiel 在《从 0 到 1》中称此为“计算机科学的意识形态”：
Finally – most obscure, but quite real – there is the deep background of the intellectual culture of AI, and CS more broadly, today. Autonomous systems are presented to students as a kind of aesthetic ideal. An “end to end” system is a triumph. Requiring human supervision is a defect, and even the way human involvement is generally discussed – e.g. “human in the loop” – often presents the human as a temporary inconvenience. Peter Thiel refers to this in “Zero to One” as “the ideology of computer science”:
在 AI/ML 的教学中，标准方法是向学生教授模型流水线（问题定义、数据、模型选择、训练、评估、后训练、部署、监控），其中构建的核心对象是准自主的，而关于人机协作的问题往往被搁置一旁。人类可能偶尔出现（作为数据标注员，或帮助提供强化反馈），但本身并未被呈现为核心智能体。智能体就是模型。
In the pedagogy of AI/ML, the standard approach is to teach a student a model pipeline (problem formulation, data, model selection, training, evaluation, post-training, deployment, monitoring) in which the central object under construction is quasi-autonomous and questions about human-machine collaboration are often set to the side. Humans may appear episodically (as data labelers, or helping provide reinforcement feedback), but are not presented as central agents themselves. The agent is the model.
在财富 500 强 CEO 的经济学登场前十步，计算机科学作为领域的深层文化就已经塑造了我们构建 AI 的整个方法。
Ten steps before the economics of the Fortune 500 CEO shows up on the scene, the deep culture of CS as a field already shapes our whole approach to building AI.
人工智能对劳动力市场影响的实证研究是一个快速发展的课题。但在更深层次上，经济学家普遍认为技术就是技术本身——我们无法控制其方向或发展，试图控制是幼稚、不可能、尴尬且注定失败的混合体。如果人工智能只是一个自动化机器，用计算机任务表现取代人类任务表现，那么它就是如此。技术进步是一种单向的线性运动，经济政策的首要任务就是不要阻碍它。技术能够真正朝不同方向发展——无论是作为一般情况还是具体在人工智能背景下——这一想法仍然相当陌生。
The empirics of the labor market impacts of AI is a rapidly evolving topic. But at a deeper level, a common view amongst economists is that technology simply is what it is – we don’t control its direction or development and attempts to do so are some mix of naïve, impossible, embarrassing, and doomed to failure. If AI is a little automation machine that replaces human task performance with computer task performance, then that’s just what it is. Technological progress is a kind of linear movement, in a single direction, and the first job of economic policy is just to not get in the way of this. The idea that technology can genuinely develop in different directions, as a general matter or specifically in the context of AI, remains rather alien.
也有明显的例外。Acemoglu 和 Restrepo 有一系列论文认为存在“过度自动化”。Acemoglu 和 Johnson 在《权力与进步》一书的结尾主张支持增强型人工智能。Brynjolfsson 将人工智能过度专注于纯粹人类模仿的危险称为“图灵陷阱”。Autor 写道，人工智能的真正前景可能实际上是将隐性知识民主化给更广泛的工人，而不是自动化他们。Mullainathan 及其合著者正在围绕我们应该促进人机协作的观点启动一系列研究项目，但“……不幸的是，今天驱动人工智能的是另一种愿景：思维的替代品。对我们来说，这是一个相当不鼓舞人心的未来愿景。自动化降低了成本，但几乎无助于提高上限。仅仅复制人类能力也意味着我们受限于人类能力的极限……我们致力于为思维建造自行车——增强人类能力的算法和人工智能，而不是取代它。”
There are notable exceptions. Acemoglu and Restrepo have a series of papers arguing that there is “excess automation”. Acemoglu and Johnson conclude their book “Power and Progress” with an argument for pro-augmentation AI. Brynjolfsson calls the danger of AI excessively focused on pure human imitation the “Turing Trap”. Autor writes about how the real promise of AI may actually be the democratization of tacit knowledge to a broader set of workers, not their automation. Mullainathan and coauthors are launching a series of research projects around the argument that we should promote the human-AI collaboration view, but “….unfortunately, a different vision animates AI today: replacements for the mind. To us, that is a pretty uninspiring vision of the future. Automation cuts costs but does little to raise the ceiling. Merely replicating human capability also means we are limited by the limits of human capacity... we are committed to building bicycles for the mind — algorithms and AI that enhance human capacity, not replace it.”
显然，在整个经济学界，就像在计算机科学界一样，有同行者。但这仍然是少数立场。许多经济学家对 Acemoglu 和 Johnson 这本书的回应，特别是在这一点上，往往是轻蔑和居高临下的。
Clearly throughout economics, as in CS, there are fellow travelers. But it remains a minority position. The response of many economists to the Acemoglu and Johnson book, particularly on this point, has often been dismissive and condescending.
部分分歧可以解释为对如何真正引导人工智能朝着有利于工人的方向发展的困惑。怀疑者似乎想到的方法是笨拙的、苏联式的自上而下的命令，规定经济应该是什么样子，甚至直接禁止许多形式的自动化，如自动驾驶汽车或仓库机器人，以保住工作岗位。
Part of the disagreement could be explained by confusion about how to go about actually steering AI in a pro-worker direction. The approach skeptics seem to have in mind is a clumsy, Soviet-style top-down set of commands about what the economy should look like, even down to banning many forms of automation outright, like self-driving cars or warehouse robots, in order to save jobs.
这不是我们所倡导的。自动化并非普遍坏事。许多工作极其常规，自然适合自动化。拯救它们的事后行动没有荣耀；自动化它们是重要的社会和经济进步。1900 年代芝加哥肉类加工厂的男人们每天工作 12 小时，每周 6 天，换取低工资、截肢和死于坠落机器的特权。弗吉尼亚煤矿的工人可能更糟。自动化在这两个行业都很棒。美国每年有 4 万人死于车祸，因为人类不擅长驾驶；有朝一日，从自动驾驶汽车的世界回顾这一统计数据，我们可能会带着类似的恐惧。
This is not what we are advocating for. Automation is not a universally bad thing. Many jobs are extremely routine and naturally amenable to automation. There is no glory in a rearguard action to save them; automating them is important social and economic progress. The men in the meat processing factories in Chicago in the 1900s worked 12 hours a day, 6 days a week, for the privilege of low pay, amputated limbs, and death from falling machines. The ones in the coal mines of Virginia perhaps had it worse. Automation has been great in both industries. 40,000 people die in car crashes in the US every year because humans are not very good drivers; we will presumably look back on this stat one day, from our world of self-driving cars, with similar horror.
知识工作中的自主 AI 智能体也有其适用场景。在许多任务中，更具人类特质的属性（隐性知识、判断力、对情境和变化目标的感知）相对不重要，活动本身是常规性的，输出易于验证，少量错误相对无成本，或者对人类工作者构成的风险或痛苦很高。上述某些组合可能证明在特定领域完全自动化是合理的。
Autonomous AI agents in knowledge work also have their time and place. There are many tasks where the more distinctively human attributes (tacit knowledge, judgment, sense of context and shifting goals) are relatively unimportant, the activities themselves are routine, output is easy to verify, a little bit of error is relatively costless, or the risk or suffering posed to human workers is high. Some combination of the above might justify complete automation in a given domain.
但支持增强的 AI 计划的关键在于：（1）存在第二类更广泛的活动，在当今 AI 条件下并不适合自动化；（2）令人遗憾的是，如果在一系列技术、经济和知识混淆下，AI 领域致力于完成（1）的误导性且不成功的努力，而本可以更成功地开展推进人机协作、增强我们在世界中实际生产力的计划。
But the point of a pro-augmentation program for AI is that (1) there is a second much broader set of activities that do not lend themselves well to automation under AI as we have it today, and (2) it would be regrettable if, under a set of technical, economic, and intellectual confusions, the field of AI devoted itself to a misguided and unsuccessful effort to accomplish (1), when it could be more successfully embarking on a program to advance human-AI collaboration, and increase our actual productive powers in the world.
我们认为政策可以在边际上推动我们走向一个自下而上的世界，让人们尝试能更好赋能人类的 AI 形式。我们在下一节末尾提供一些具体想法。
We think policy can, on the margin, nudge us toward a bottom-up world of people experimenting with forms of AI that better empower human beings. We provide some concrete ideas at the end of the next section.
假设有人相信这样一种论点：当今的 AI 行业过度偏向于将 AI 视为自动化的狭隘愿景，这种偏向可能在广泛层面上并非最优，不仅对许多劳动者如此，对许多企业和更广泛的经济增长也是如此。我们该如何应对？稻草人式的答案是自上而下的政策。更严肃的答案是自下而上且多方面的。我们设想一种去中心化的技术实验机制、计算机科学内部的一些智识与文化转变、一些精心针对性的政策杠杆，以及企业逐渐发现自动化议程可能在其自身逻辑下难以为继。
Suppose one believes some version of the thesis that the AI industry today is excessively biased towards a narrow vision of AI as automation, in a way that might be broadly suboptimal, not only for many workers, but also for many firms and economic growth more broadly. What should we do about it? The strawman answer is top-down policy. The more serious answer is bottom-up and multi-faceted. We imagine a regime of decentralized technical experimentation, some intellectual and cultural shifts within CS, some carefully targeted policy levers, and firms gradually discovering that the automation agenda may struggle on its own terms.
市场上尚未出现真正的协作智能体。由 OpenAI 前 CTO Mira Murati 创立的实验室 Thinking Machines 已发布了一种此类智能体的架构。在他们的设想中，有一个面向用户的超低延迟多模态模型，将更复杂的工作交给能力更强的后台模型处理。然而，他们尚未推出可用的产品。尽管如此，有一家资金雄厚的公司对人类与 AI 协作下了大赌注，这仍令人欣慰。
There aren't yet true collaboration agents on the market. Thinking Machines, a lab founded by OpenAI's former CTO Mira Murati, has published an architecture for such agents. In their vision, there is a user-facing model that is ultra-low-latency and multimodal, farming off more complex work to a more capable background model. However, they have not yet shipped a working product. Still, it is reassuring that there is a well-capitalized company that has made a big bet on human-AI collaboration.
与此同时，三大前沿实验室（OpenAI、Anthropic 和 Google）都在大力推动低延迟，尤其是 OpenAI，这得益于其与 Cerebras 的合作，以及 Cursor 和 Cognition。我们预测，随着更多工作者体验到这种与 AI 协作的工作方式，对其需求将会增加，从而产生飞轮效应。
Simultaneously, there has been a big push for low latency across all three frontier labs (OpenAI, Anthropic, and Google), especially OpenAI due to its partnership with Cerebras, as well as Cursor and Cognition. We predict that as more workers experience this way of working with AI, there will be more demand for it, creating a flywheel effect.
另一类可能构建协作智能体的公司（尽管尚未构建）是那些专注于特定领域或垂直行业的公司，例如法律领域的 Harvey AI 或医学领域的 OpenEvidence。有效的协作系统通常需要领域特定的痕迹、隐性专家知识和领域专业知识。与能够将 AI 能力与深度领域知识相结合的行业特定公司相比，大型前沿 AI 实验室可能自身难以胜任这一点。这种组织模式最能发挥作用的许多行业，如法律和医学，都受到严格监管。受监管行业是协作式人类-AI 模型的天然目标，因为在许多决策点上法律本来就要求有人类参与。
Another set of companies that might build collaboration agents (though they haven't yet) are those that specialize in a particular domain or vertical, such as Harvey AI in law or OpenEvidence in medicine. Effective systems of collaboration often need domain specific traces, tacit expert knowledge, domain expertise. The big frontier AI labs may be poorly equipped to do this themselves, compared to industry-specific firms that can figure out how to combine AI capabilities with deep domain specific knowledge. Many of the industries in which this model of organization will work best, such as law and medicine, are heavily regulated. Regulated industries are a natural target for collaborative human-AI models because humans are legally required at many decision points anyway.
协作只是人机互补配置这一更广阔设计空间中的一个例子。下面列举其他几种配置，旨在说明而非穷尽。对于每一种替代配置，可以想象重复第 1 节的练习，将其分解为离散的技术挑战。
Collaboration is only one example of a much broader design space of configurations for human-AI complementarity. Here are a few others, intended to be illustrative rather than exhaustive. For each of these alternative configurations, one can imagine repeating the exercise of Section 1, of breaking it down into discrete technical challenges.
* **集体智能**：AI 可以转变和调解人类工作者团队_彼此之间_的协作方式。这可以采取多种不同形式：保存组织记忆、调解冲突、识别团队内的专业知识、综合分布式知识，以及以其他方式帮助协调。因此，这里的直接目标不是执行特定任务，而是普遍提高人类团队的生产力。
* **Collective Intelligence**: AI can transform and mediate how teams of human workers collaborate _with each other_. This can take a number of different forms: preserving organizational memory, mediating conflict, identifying expertise within a team, synthesizing distributed knowledge, and otherwise helping coordination. So the proximate goal here is not the execution of a particular task; it is raising the productivity of a team of humans in general.
* **新任务与新能力**：使用 AI 自动化现有的人类劳动与使用 AI 完成人类最初无法完成的新任务截然不同。历史上有许多技术创新明显属于这一类，例如飞机或显微镜。
* **New Tasks and Capabilities**: The use of AI to automate extant human labor is quite different from the use of AI to accomplish new tasks that no humans could do in the first place. There are any number of technological innovations from history that were clearly of this flavor, such as the airplane or the microscope.
* **技能提升**：从任务完成的短期工作退后一步看，AI 还可以帮助提升人类工作者的长期能力。AI 在内化隐性专家知识方面特别有效；通过有意识和深思熟虑的设计，它可能变得能够有效地使工作者在长期内成为其领域的更多专家。
* **Upskilling**: Stepping back from the short term work of task completion, AI can also help upskill human workers in their long term capabilities. AI is particularly effective at internalizing tacit expert knowledge; with intentional and thoughtful design, it then may become effective at making workers more expert in their fields in the long term.
* **环境智能体**：AI 模型/智能体的常规范式是，模型是我们可能称之为调用模型的东西：当你明确调用它并要求它做事时，它就会出现。相比之下，我们可以定义一个“环境智能体”：一种在整个组织中运行的 AI 模型，观察上下文并在未被明确调用的情况下提供帮助。虽然这带来了一系列独特的挑战（隐私、谨慎），但它也是人机协作的一种不同范式，具有独特的机会（更多上下文、更多组织知识、有趣的新协调形式）。
* **Ambient Agent**: The normal paradigm of AI models/agents, the model is what we might call an invoked model: it shows up when you explicitly call on it and ask it to do things. We might define, by contrast, an “ambient agent”: an AI model that operates across an organization, observing context and offering help even without being explicitly invoked. While this raises a distinct set of challenges (privacy, discretion), it is also a different paradigm of AI-human collaboration with distinct opportunities (more context, more organization knowledge, interesting novel forms of coordination).
人机交互（HCI）历史上曾是计算机科学（CS）内部认真对待人机协作的先锋。如今，它在 CS 中处于较为边缘化的位置。如果它要在帮助 CS 从自动化议程转向更有成效的方向上发挥超乎寻常的重要作用，那么两件事需要同时发生。
HCI was historically the vanguard, within CS, of taking human-machine collaboration seriously. Today it occupies a more marginalized position within CS. To the extent that it can play an outsized and important role in helping reorient CS away from the automation agenda towards something more productive, two things would need to happen simultaneously.
首先，计算机科学系、资助机构和其他议程设定者可以有意识地努力提升 HCI 的地位。其次，HCI 可以进化以应对当下的需求。它在智识和方法论上历来自我设限，这在一定程度上解释了其边缘化。简而言之，HCI 将人类控制视为公理而非工具性目标，其分析单元是用户，这使得它对关于企业、组织、激励、权力和政治经济的更广泛问题鲜有发言权。
First, computer science departments, funding organizations, and other agenda setters could try to make a conscious effort to elevate the status of HCI. Second, HCI could evolve to meet the moment. It has historically constrained itself intellectually and methodologically, and this partly explains its marginalization. To simplify a bit, HCI treats human control as axiomatic rather than an instrumental goal, and its unit of analysis is the user, leaving it with little to say on broader questions about firms, organizations, incentives, power, and political economy.
以人为中心的人工智能（HCAI）的新运动部分是对复兴和综合 HCI 与 AI 的尝试，尽管在体量和影响力上远不及更主流的人工智能领域，后者通常倾向于接受行业的优先事项。然而，在一种可能的未来中，一个更好的、转型后的 HCI（以及 HCAI）在大学内的地位上升，并拥有有意义的议程设定权力。
The new movement of Human-Centered AI is partly an attempt at revival and synthesizing HCI with AI, though it is dwarfed in volume and influence by the more mainstream AI field that generally tends to accept the industry’s priorities. In one possible future, however, a better, transformed version of HCI (and HCAI) ascends in status within the university and has meaningful agenda-setting power.
同样，AI 教学法有着以 AI 系统为中心而遗忘人类的历史。但不必如此。可以想象对教学大纲、基准设计、研讨会和资助优先事项进行改变。
Similarly, AI pedagogy has a history of centering the AI system and forgetting the human. It doesn’t have to be this way. One can imagine changes to syllabi, benchmark design, workshops, and funding priorities.
除了粗暴的自动化禁令或自上而下的技术强制令之外，更智能、更有针对性的政府政策也可以成为重新调整自动化议程这一更广泛成功叙事的一部分。
Beyond crude automation bans or top-down technological mandates, more intelligent and tailored government policies can also be one part of a broader success story in reorienting the automation agenda.
在供给侧，这些政策可以包括研究资助的优先方向，甚至 DARPA 式的奖项。在需求侧，美国税收政策目前偏向自动化，这种不对称性可以加以缓解，但这需要谨慎行事，避免以粗暴的方式试图在监管上把 AI 系统归类为自动化或增强。政府还可以利用其作为 AI 主要采购方的权力。最后，要求对 AI 进行人工监督的监管必须坚持**有效**监督，而不是走过场式盖章。
On the supply side, these could include research funding priorities and even DARPA-style prizes. On the demand side, US tax policy currently favors automation, and the asymmetry could be mitigated, though this requires care to avoid crude attempts at regulatory classification of AI systems as automation or augmentation. The government could also use its power as a major purchaser of AI. Finally, regulation that requires human oversight of AI must insist on _effective_ oversight instead of rubber stamping.
竞争政策也可以发挥作用。自动化范式拥有巨大的先发优势，政策制定者应确保客户不会被锁定。让劳动者在 AI 部署的协商中拥有更多发言权，是另一种在不强制要求的前提下引导走向增强的途径。
Competition policy could also help. The automation paradigm has a big head start, and policymakers should ensure clients don't get locked in. Giving workers more of a voice in bargaining over AI deployment is another way to steer toward augmentation without mandating it.
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当前，在关于 AI 发展方向的内部争论中，自动化议程正占据上风。此事利害攸关。历史上规模最大的和平时期私人资本部署之一，如今正主要投向一种自动化的 AI 愿景；这种愿景本身可能在经济上难以为继，同时也浪费了 AI 帮助我们去做我们原本做不到的事情的全部潜力。
The automation agenda for AI is currently winning the internal battle over the direction of AI. The stakes are very high. One of the biggest peace-time deployments of private capital in history is now being largely devoted to an automation vision of AI that may be both economically untenable on its own terms, and also a waste of all the potential of AI to help us do things we couldn’t already do.
幸运的是，有一大批人——经济学家、技术专家以及其他人士——正在试图理解并发展 AI 的另一个方向，这个方向更加强调人机协作，其目标是能够做新的事情，而不仅仅是自动化旧的事情。
Fortunately, there is a broad set of people — economists, technologists, and others — who are trying to understand and develop another direction for AI, one that places a greater emphasis on human-computer collaboration, in the service of being able to do new things, not just automate the old ones.
这第二个方向的核心任务是为服务于这种人机协作的 AI 定义并追求一个系统性的研究计划。我们希望，通过增强集体意识和意图，以及更好地汇聚和协调已有的众多努力，一种自下而上的、去中心化的方法能够成功地引导 AI 开发和部署的方向，而不需要自上而下的命令或指令。
A central task for this second direction is to define and pursue a systematic research program for AI in the service of this kind of collaboration. Our hope is that through greater collective awareness and intention, and better pooling and coordination of the many efforts that already exist, a bottom-up, decentralized approach can be successful at steering the direction of AI development and deployment, without requiring top-down commands or directives.
* 关于经济学家对此主题的写作样本，参见《The Turing Trap》和《Building Pro-Worker AI》。
* For a sample of economists’ writing on this topic, see The Turing Trap and Building Pro-Worker AI.
* 关于这些思想在 AI 和人机交互（HCI）领域的简要历史，参见《How AI Fails Us》的第 V.B 节。
* For a brief history of these ideas in AI and HCI, see Section V.B of How AI Fails Us.
* 论文《Human Amplification, Intelligent Agents, and the Aims of AI Research》从哲学角度提出了与本文相似的论点。我们的范围更窄，聚焦于面向工作者的人工智能工具。
* The paper *Human Amplification, Intelligent Agents, and the Aims of AI Research* makes arguments similar to ours from a philosophical perspective. Our scope is narrower and centers on AI tools for workers.
* 论文《AI Agents Push Humans Out of the Loop》批判了当前 AI 智能体的设计。
* The paper *AI Agents Push Humans Out of the Loop* critiques the design of current AI agents.
* 个人无需等待 AI 系统被重新设计，就可以做很多事情来增强自身的能动性：Arvind 在其 ICML 演讲中提出了一些想法。
* There is a lot that individuals can do to amplify their own agency without waiting for AI systems to be redesigned: Arvind presented some ideas in his ICML talk.
业界充斥着许多陈词滥调，声称其工具旨在帮助工作者而非取代他们。但实际显露出的优先事项——AI 智能体如何设计、如何评估，甚至 AI 课程教授什么内容——却讲述了一个截然不同的故事。自动化假设也是业界领袖频繁预测大规模失业的底层逻辑。OpenAI 的章程明确指出，其使命是 AGI（通用人工智能），定义为“在大多数具有经济价值的工作上超越人类的高度自主系统”。像 Mechanize 这样的公司也异常直白地宣称全面自动化是所谓不可避免的趋势。
The industry offers many platitudes about its tools being meant to help workers rather than automate them. But the revealed priorities — how AI agents are designed, how they are evaluated, and even what is taught in AI courses — tell a very different story. The automation assumption also underlies the frequent predictions of mass unemployment from industry leaders. OpenAI's charter is explicit that the mission is AGI, defined as "highly autonomous systems that outperform humans at most economically valuable work". Companies like Mechanize are also unusually blunt about the supposed inevitability of full automation.