The Knowledge Economy Is Over.
Welcome to the Allocation Economy
知识经济已结束，欢迎进入分配经济时代

Time isn’t as linear as you think. It has ripples and folds like smooth silk. It doubles back on itself, and if you know where to look, you can catch the future shimmering in the present.
时间并不像你想象的那样线性。它像光滑的丝绸一样，有着涟漪和褶皱。时间会回溯，如果你知道该往哪里看，就能在现在捕捉到未来的闪烁。

(This is what people don’t understand about visionaries: They don’t need to predict the future. They learn to snatch it out of the folds of time and wear it around their bodies like a flowing cloak.)
（这就是人们对远见者的误解：他们并不需要预测未来，而是学会从时间的缝隙中捕捉未来，并将其像披风一样披在身上。）

I think I caught a tiny piece of the future recently, and I want to tell you about it.
我觉得我最近捕捉到了一小部分未来的景象，想和你分享。

Last week I wrote about how ChatGPT changed my conception of intelligence and the way I see the world. I’ve started to see ChatGPT as a summarizer of human knowledge, and once I made that connection, I started to see summarizing everywhere: in the code I write (summaries of what’s on StackOverflow), and the emails I send (summaries of meetings I had), and the articles I write (summaries of books I read).
上周我写了关于 ChatGPT 如何改变我对智能的理解以及我看待世界的方式。我开始把 ChatGPT 视为人类知识的总结者，一旦我建立了这种联系，我就开始在各个地方看到总结：在我写的代码中（对 StackOverflow 上内容的总结），在我发送的电子邮件中（对我参加的会议的总结），以及我写的文章中（对我阅读的书籍的总结）。

Summarizing used to be a skill I needed to have, and a valuable one at that. But before it had been mostly invisible, bundled into an amorphous set of tasks that I’d called “intelligence”—things that only I and other humans could do. But now that I can use ChatGPT for summarizing, I’ve carved that task out of my skill set and handed it over to AI. Now, my intelligence has learned to be the thing that directs or edits summarizing, rather than doing the summarizing myself. 
总结曾经是我必须具备的一项重要技能，但在此之前，它大多是隐形的，融入了我所称的 “智力” 这一模糊的任务集合中 —— 只有我和其他人类才能完成的事情。然而，现在我可以使用 ChatGPT 进行总结，我已经将这项任务从我的技能中剥离出来，交给了人工智能。现在，我的智力学会了成为指导或编辑总结的角色，而不是亲自去做总结。

As Every’s Evan Armstrong argued several months ago, “AI is an abstraction layer over lower-level thinking.” That lower-level thinking is, largely, summarizing.
正如 Every 的 Evan Armstrong 几个月前所说，“人工智能是对低级思维的抽象层。” 这种低级思维主要是指总结。

If I’m using ChatGPT in this way today, there’s a good chance this behavior—handing off summarizing to AI—is going to become widespread in the future. That could have a significant impact on the economy.
如果我今天以这种方式使用 ChatGPT，那么将总结工作交给人工智能的做法在未来很可能会变得普遍。这可能会对经济产生重要影响。

This is what I mean by catching the future in the present and the non-linearity of time. If we extrapolate my experience with ChatGPT, we can glean what the next few years of our work lives might look like.
这就是我所说的在当下捕捉未来以及时间的非线性。如果我们根据我与 ChatGPT 的经验进行推测，我们可以预见未来几年我们工作生活的样子。

The end of the knowledge economy
知识经济的结束

We live in a knowledge economy. What you know—and your ability to bring it to bear in any given circumstance—is what creates economic value for you. This was primarily driven by the advent of personal computers and the internet, starting in the 1970s and accelerating through today.
我们生活在一个知识经济时代。你所掌握的知识，以及在特定情况下运用这些知识的能力，决定了你所创造的经济价值。这一变化主要源于个人电脑和互联网的出现，始于 1970 年代，并持续加速发展至今。

But what happens when that very skill—knowing and utilizing the right knowledge at the right time—becomes something that computers can do faster and sometimes just as well as we can? 
但是，当这种技能 —— 在正确的时间掌握并运用正确的知识 —— 变成计算机能够更快，有时甚至和我们一样出色地完成时，会发生什么呢？

We’ll go from makers to managers, from doing the work to learning how to allocate resources—choosing which work to be done, deciding whether work is good enough, and editing it when it’s not. 
我们将从制造者转变为管理者，从实际工作转向学习如何分配资源 —— 选择要完成的任务，判断工作是否达标，以及在不达标时进行修改。

It means a transition from a knowledge economy to an allocation economy. You won’t be judged on how much you know, but instead on how well you can allocate and manage the resources to get work done. 
这意味着从知识经济转向资源配置经济。你将不再因知识的多少而被评判，而是因你如何有效地分配和管理资源以完成工作。

There’s already a class of people who are engaged in this kind of work every day: managers. But there are only about 1 million managers in the U.S., or about 12% of the workforce. They need to know things like how to evaluate talent, manage without micromanaging, and estimate how long a project will take. Individual contributors—the people in the rest of the economy, who do the actual work—don't need that skill today.
目前已经有一类人每天都在从事这种工作：经理。在美国，经理大约有 100 万，约占劳动力的 12%。他们需要掌握评估人才、有效管理而不进行微观管理，以及估算项目所需时间等技能。而个体贡献者 —— 经济中其他从事实际工作的人员 —— 目前并不需要这些技能。

But in this new economy, the allocation economy, they will. Even junior employees will be expected to use AI, which will force them into the role of manager—model manager. Instead of managing humans, they’ll be allocating work to AI models and making sure the work gets done well. They’ll need many of the same skills as human managers of today do (though in slightly modified form). 
但在这个新的经济体中，即分配经济，员工们将会这样做。即使是初级员工也会被期望使用人工智能，这将迫使他们扮演管理者的角色 —— 模型管理者。与其管理人类，他们将把工作分配给人工智能模型，并确保工作顺利完成。他们需要的技能与今天的人类管理者相似（尽管形式略有调整）。

From maker to manager
从创作者到管理者

Here are a few qualities that managers of today need that individual contributors of tomorrow—model managers—will need as part of the allocation economy.
这里有一些今天的管理者所需的品质，而明天的个体贡献者 —— 模范管理者 —— 将在资源分配经济中需要这些品质。

A coherent vision 一个清晰的愿景

Today's managers need to have a coherent vision of the work they want to accomplish. Managers of humans need to craft a vision that is articulate, specific, concise, and rooted in a clear purpose. Model managers will need that same ability.
当今的管理者需要对他们希望实现的工作有一个清晰的愿景。作为人类管理者，他们需要制定一个清晰、具体、简洁，并且根植于明确目标的愿景。优秀的管理者同样需要具备这种能力。

The better articulated your vision is, the more likely the model is going to be to carry it out appropriately. As prompts become more specific and concise, the work done will improve. Language models might not, themselves, need a clear purpose, but model managers will likely have to identify a clear purpose for their own sake and engagement with the work.
你的愿景越清晰，模型就越有可能恰当地实现它。随着提示变得更加具体和简洁，工作质量也会提高。虽然语言模型本身可能不需要明确的目标，但模型管理者很可能需要为自己的利益和参与工作设定一个清晰的目标。

Articulating a concise, specific, and coherent vision is difficult. It’s a skill that is acquired over years of work. Much of it comes down to developing a taste for ideas and language. Luckily, that’s a place that language models can help as well.
清晰、具体且连贯地表达愿景是非常困难的。这是一项需要多年努力才能掌握的技能。关键在于培养对思想和语言的敏感度。幸运的是，语言模型在这方面也能提供帮助。

A clear sense of taste
清晰的味觉体验

The best managers know what they want and how to talk about it. The worst managers are the ones who say, “It’s not right,” but when asked, “Why?” can’t express the problem. 
最优秀的经理清楚自己想要什么，并能有效地表达出来。而最差的经理则是那些说 “这不对” 的人，但当被问 “为什么” 时却无法说明问题所在。

Model managers will face the same issue. The better defined their taste, the better language models will be able to create something coherent for them. Luckily, language models are quite good at helping humans articulate and refine their taste. So it’s a skill that will probably become significantly more widely distributed in the future.
模型管理者将面临相同的问题。他们的品味越明确，语言模型就能为他们创造出更连贯的内容。幸运的是，语言模型在帮助人类表达和提升品味方面非常有效。因此，这项技能在未来可能会变得更加普及。

If you have clear taste and a coherent vision, the next thing you need to do is be able to evaluate who (or what) is capable of executing it.
如果你有清晰的品味和一致的愿景，接下来你需要做的就是评估谁（或什么）能够实现它。

The ability to evaluate talent
评估人才的能力与素质

Every manager knows that hiring is everything. If employees are doing the work, the quality of the output is going to be a direct reflection of their skills and abilities. Being able to adequately judge employees’ skills and delegate tasks to people who can carry them out is a significant part of what makes a good manager. 
每位经理都明白，招聘至关重要。如果员工在工作，输出的质量将直接反映他们的技能和能力。能够准确评估员工的技能，并将任务合理分配给能够完成的人，是优秀经理的重要特质。

Model managers of tomorrow will need to learn the same things. They’ll need to know which AI models to use for which tasks. They’ll need to be able to quickly evaluate new models that they’ve never used before to determine if they’re good enough. They’ll need to know how to break up complex tasks between different models suited to each piece of work in order to produce one work of the highest quality.
未来的模型经理需要掌握相同的技能。他们需要了解哪些 AI 模型适合特定任务，能够迅速评估从未使用过的新模型，以判断其是否足够优秀。他们还需懂得如何将复杂任务拆分给适合各个工作环节的不同模型，从而产出高质量的成果。

Evaluation of models will be a skill in its own right. But there’s reason to believe it will be easier to evaluate models than it is humans, if only because the former are easier to test. A model is accessible day or night, it’s usually cheap, it never gets bored or complains, and it returns results instantly. So model managers of tomorrow will have an advantage in learning these skills, because management skills of today are gate-kept by the relative expense of giving someone a team of people to work with.
对模型的评估将成为一项独立的技能。然而，有理由相信，评估模型比评估人类要容易，主要是因为模型更容易进行测试。模型随时可用，成本通常较低，永远不会感到无聊或抱怨，并且能够立即返回结果。因此，未来的模型管理者在学习这些技能时将占据优势，因为如今的管理技能受到为某人提供团队所需的相对高昂费用的限制。

Once they’ve assembled the resources they need to get work done, they’ll face the next challenge: making sure the work is good.
一旦他们准备好完成工作的资源，他们将面临下一个挑战：确保工作的质量。

Knowing when to get into the details
了解何时需要深入细节

The best managers know when and how to get into the details. Inexperienced managers make one of two mistakes. Some micromanage tasks to the point that they are doing the work for their employees, which doesn’t scale. Others delegate tasks to such a degree that they aren’t performed well, or are not done in a way that aligns with the organization’s goals.
优秀的经理知道何时以及如何深入细节。而缺乏经验的经理往往会犯两个错误：一些人对任务进行过度的微观管理，甚至为员工完成工作，这样是无法扩展的；另一些人则将任务委派得过于宽泛，以至于任务没有得到很好的执行，或者没有与组织的目标保持一致。

Good managers know when to get into the details, and when to let their reports take the ball and run. They know which questions to ask, when to check in, and when to let things be. They understand that just because something isn’t done how they would do it doesn’t mean it hasn’t been done well. 
优秀的经理知道何时需要深入细节，何时应该让下属自主发挥。他们清楚该问哪些问题，何时进行跟进，以及何时让事情顺其自然。他们明白，事情的完成方式与他们的不同，并不意味着它没有做好。

These are not problems that individual contributors in the knowledge economy have to deal with. But they are the exact kind of problems that model managers in the allocation economy will face. 
这些问题并不是知识经济中的个体贡献者需要处理的。然而，它们正是分配经济中模型管理者所面临的具体问题。

Knowing when and how to get into the details is a learnable skill—and luckily, language models will be built to intelligently check in during crucial periods where oversight is needed. So it won’t be completely on model managers to do this. 
了解何时以及如何深入细节是一项可以学习的技能 —— 幸运的是，语言模型将被设计成在需要监督的关键时刻智能地进行检查。因此，这并不完全依赖于模型管理者。

The big question is: Is all of this a good thing?
这个重要的问题是：这一切真的是好事吗？

Is the allocation economy good for humanity?
分配经济对人类有益吗？

A transition from a knowledge economy to an allocation economy is not likely to happen overnight. When we talk about doing “model management,” that’s going to look like replacing micro-skills—like summarizing meetings into emails—rather than entire tasks end to end, for a while, at least. Even if the capability is there to replace tasks, there are many parts of the economy that won’t catch up for a long time, if ever.
从知识经济转向配置经济不太可能在短时间内实现。当我们谈论 “模型管理” 时，这更像是替换一些微技能 —— 例如将会议内容总结成电子邮件 —— 而不是一次性完成整个任务，至少在一段时间内是这样。即使具备替换任务的能力，经济中许多部分也可能需要很长时间才能跟上，甚至可能永远无法赶上。

I recently got my pants tailored in Cobble Hill, Brooklyn. When I pulled out my credit card to pay for it, the lady behind the counter pointed at a paper sign taped to the wall: “No credit cards.” I think we’ll find a similar pace of adoption for language models: There will be many places where they could be used to augment or replace human labor where they are not. These will be for many different reasons: inertia, regulation, risk, or brand.
我最近在布鲁克林的 Cobble Hill 定制了我的裤子。当我拿出信用卡准备付款时，柜台后面的女士指着贴在墙上的纸条：“不接受信用卡。” 我认为语言模型的普及速度会类似于这种情况：在许多地方，它们本可以用来增强或替代人类劳动，但实际上却没有被使用。这背后有许多原因，比如惯性、监管、风险或品牌形象。

This, I think, is a good thing. When it comes to change, the dose makes the poison. The economy is big and complex, and I think we’ll have time to adapt to these changes. And the slow handoff of human thinking to machine thinking is not new. Generative AI models are part of a long-running process.
我认为这是一件好事。谈到变化时，剂量决定了毒性。经济庞大而复杂，我相信我们会有时间去适应这些变化。而人类思维向机器思维的逐步转变并不是新鲜事，生成性人工智能模型是这一长期过程的一部分。

In his 2013 book Average Is Over, economist Tyler Cowen wrote about a stratification in the economy driven by intelligent machines. He argued that there is a small, elite group of highly skilled workers who are able to work with computers that will reap large rewards—and that the rest of the economy may be left behind:
在他 2013 年的书《平均已过时》中，经济学家泰勒・科文讨论了由智能机器引发的经济分层现象。他指出，只有一小部分高技能的工人能够与计算机协作，从中获得丰厚的回报，而其他人可能会被经济发展所抛弃。

“If you and your skills are a complement to the computer, your wage and labor market prospects are likely to be cheery. If your skills do not complement the computer, you may want to address that mismatch. Ever more people are starting to fall on one side of the divide or the other. That’s why average is over.”
“如果你和你的技能能够与计算机相辅相成，那么你的工资和就业前景可能会非常乐观。如果你的技能无法与计算机相匹配，你可能需要解决这个问题。越来越多的人开始站在这条分界线的两侧。这就是为什么‘平均’这个概念已经不再适用。”

At the time, he wasn’t writing about generative AI models. He was writing about iPhones and the internet. But generative AI models extend the same trend. 
那时，他并没有写关于生成性人工智能模型的内容，而是在讨论 iPhone 和互联网。但生成性人工智能模型延续了这一趋势。

People who are better equipped to use language models in their day-to-day lives will be at a significant advantage in the economy. There will be tremendous rewards for knowing how to allocate intelligence.
能够更好地在日常生活中使用语言模型的人将在经济中获得显著优势。懂得如何合理分配智力将会带来丰厚的回报。

Today, management is a skill that only a select few know because it is expensive to train managers: You need to give them a team of humans to practice on. But AI is cheap enough that tomorrow, everyone will have the chance to be a manager—and that will significantly increase the creative potential of every human being.
如今，管理是一项只有少数人掌握的技能，因为培训管理者的成本很高：你需要给他们一个团队来进行实践。然而，人工智能的成本足够低，明天每个人都有机会成为管理者 —— 这将大大提升每个人的创造潜力。

It will be on our society as a whole to make sure that, with the incredible new tools at our disposal, we bring the rest of the economy along for the ride.
这将由我们整个社会共同努力，利用手中这些令人惊叹的新工具，确保其他经济部门也能跟上这趟旅程。