能力悬垂

运用你的深度知识、广博知识、品味与能动性 Using your deep knowledge, wide knowledge, taste, and agency

我们仍处在 AI 发展的指数曲线上。我尽量每两周左右发一篇 Substack 文章，然而随着节奏加快，这有时显得太慢。自我上一篇文章以来的几周里，我们目睹了 AI 似乎破解了数学中最著名的问题之一（伴随争议），以及关于 AI 所带来风险及应对措施的广泛讨论（同样伴随争议）。我认为这些担忧，连同日益增多的其他忧虑，归结为与我发文章相同的难题：我们非常人类化的系统和流程，在跟上 AI 发展步伐方面是多么缓慢。

We are still on an exponential curve of AI development. I try to put out a Substack post every couple weeks or so, yet, as the pace speeds up, that sometimes feels too slow. In the weeks since my last post, we had the apparent cracking of one of the most famous problems in math by an AI (accompanied by controversy) and widespread discussions about the risks posed by AI and what to do about it (also accompanied by controversy). I think these concerns, along with a mounting set of other worries, come down to the same problem I have with my posts: how slowly our very human systems and processes work to keep up with the pace of AI development.

我不认为担心此事的人是错的，但我也认为，仅关注未来的 AI——尽管这很重要——忽视了这样一个事实：AI 现在就已经极其强大。事实上，新的 GPT-6 Astra 和 Fable 5.1 已足以在经济的大部分领域产生变革性影响，并且在得到适当引导和利用时，它们能可靠地完成数周的人类工作量。

I don't think the people worried about this are wrong, but I also think a sole focus on future AIs, as important as that is, ignores the fact that AI, right now, is already incredibly capable. In fact, the new GPT-6 Astra and Fable 5.1 are already enough for transformative impact in large sections of the economy and they can reliably do weeks worth of human work when properly guided and harnessed.

几个有趣的例子：我让 GPT-6 Astra 将 1977 年的文字冒险游戏 Zork 变成了一款可玩的全 3D 动作冒险游戏。Zork 没有图形，每个地点都是一段散文，因此 AI 必须决定白房子长什么样、grue 长什么样（原版只告诉你，在黑暗中你很可能被它吃掉），以及如何将“与巨魔战斗”转化为动作序列。我还让 Fable 5.1 尝试以 3D 重建意大利作家翁贝托·埃科的图书馆。埃科在米兰的公寓里藏有数万本书，AI 找不到平面图，于是决定依据十几个视频、基金会拍摄的每个书柜的照片以及两份图书馆目录来工作。它逐帧读取书脊，推断房间布局，并将它能识别出的约 5,000 本书放入 27,000 个书架槽位中。它将每本书标记为确定、猜测或未知，并将摄像机从未到达的书柜画在雾中。这项任务，如同 Zork 游戏和我最近让 AI 做的许多现实世界工作一样，若由人类完成，将需要研究人员、程序员和设计师数周的工作。但我们现在做到了。

A few fun examples of that: I had GPT-6 Astra turn a 1977 text adventure game called Zork into a full 3D action-adventure game you can play. Zork has no graphics and each location is a paragraph of prose, so the AI had to decide what the white house looks like, what a grue looks like (the original only tells you that you are likely to be eaten by one in the dark), and how to turn “fight the troll” into an action sequence. I also had Fable 5.1 try to reconstruct Italian author Umberto Eco’s library in 3D. Eco kept tens of thousands of books in his Milan apartment and the AI could not find a floor plan, so it instead decided to work from a dozen videos, the foundation's photographs of each bookcase, and two library catalogues. It read spines frame by frame, inferred the rooms, and placed the 5,000 or so books it could identify among 27,000 shelf slots. It marked every book certain, guess, or unknown, and drew the bookcases the cameras never reached in fog. This task, like the Zork game and a lot of real-world work I have had the AI do recently, would have taken weeks of human work involving researchers, coders, and designers. But here we are.

我的观点是，尽管关于未来模型能做什么有很多争论，但现有模型的当前能力几乎未被充分利用，甚至常常未被充分理解。例如，直到 GPT-6 Astra 操作了 Blender（一款复杂的 3D 建模软件），我才知道它能做到。

My point is that, while there is a lot of debate over what future models will do, the current capabilities of existing models are barely being used, and are often not even well understood. For example, I did not know GPT-6 Astra could operate Blender (a sophisticated piece of 3D modelling software) until it did.

我给了它一本我即将出版的书《共存》（Co-Existence）的副本，并要求它从 AI 的视角为这本书制作一个预告片。在没有我明确指示的情况下，它开始使用 Blender 构建出整个动画 3D 场景（这并非易事），还编写了包含一些笑话和揭示的脚本（我确实拒绝了它添加的第一个笑话，但第二个相当不错）。接着，它弄清楚了如何生成语音、音乐和音效，并在 45 分钟后给了我这部影片。最终成品感觉比我想要的更不祥，但那是 AI 的决定，不是我的。

I gave it a copy of my upcoming book, Co-Existence, and asked it to create a trailer for the book from the perspective of an AI. Without clear instructions from me, it proceeded to use Blender and build out an entire animated 3D scene (not an easy task), along with a script with some jokes and reveals (I did reject the first joke it added, but the second was quite good). It then figured out how to generate voices and music and sound effects and gave me this film 45 minutes later. The final product feels a little more ominous than I would like, but that was the AI’s decision, not mine.

为了看看它还能走多远，我提示道：“这很好，但我其实想要你基于《共存》制作一个动作电影预告片。尽情发挥。不超过 30 秒。”它再次编写了脚本，并在 Blender 中制作了一个 3D 原型。在我要求一个更具电影感的版本后，它使用 Blender 动画作为故事板，通过我的浏览器操作视频生成器，并将生成的镜头剪辑成最终预告片。我给出了一些小的创意反馈，但从未干预任何制作决策，甚至不知道它究竟是如何完成任务的。你可以在这里看到结果。

To see how much further it could go, I prompted: “That’s good, but I actually want you to make an action movie trailer based on Co-Existence. Have fun with it. No more than 30 seconds.” Again, it wrote a script and made a 3D prototype in Blender. After I asked for a more cinematic version, it used the Blender animation as a storyboard, operated a video generator through my browser, and edited the generated shots into the final trailer. I gave some minor creative feedback, but never touched any production decision or even knew exactly how it was accomplishing its tasks. You can see the results here.

这些努力中有很多你可以发现的缺陷。但它们也是 AI 行使某种判断力和创造力的例子，这些不久前还被认为是人类独有的特质。而所有这些都只用了我所付费的 ChatGPT 账户 token 预算的一小部分。我认为这些是有趣的演示，但也有些令人害怕，因为 AI 在曾经纯粹属于人类的事情上变得越来越好。不过，这些项目都不是自行发生的。我选择了它们，我对 Zork、Eco 和我自己的书足够了解，能够看出 AI 哪里出错，并在第一版不对时要求第二版。能力过剩，即这些模型能做什么与几乎所有人正在用它们做什么之间的差距，是一个机会，因为大多数人没有把自己的优势带给 AI，而那些这样做的人则从中获益更多。

There are plenty of flaws in these efforts that you can spot. But they are also examples of the AI exercising a kind of judgement and creativity, things that not long ago were considered uniquely human traits. And they were all done with just a fraction of the token budget of the ChatGPT account I pay for. I think these are fun demonstrations, but they are also a bit scary because AI is getting better at things that were once purely human. Still, none of these projects happened on their own. I chose them, I knew enough about Zork and Eco and my own book to see where the AI went wrong, and to ask for a second version when the first wasn't right. The capability overhang, the gap between what these models can do and what almost anyone is doing with them, is an opportunity because most people don't bring their own advantages to AI, and those who do get much more out of it.

这就是为什么我认为我们需要关注我们拥有的、即使 AI 能力提高也仍然有用的个人特质。你不是试图在产出上与 AI 竞争，那是一场必输的游戏。相反，你想利用你的人类优势作为与 AI 合作的基础，去做你们单独都无法做的事情。在我的书中，我概述了四个特别重要的个人优势，如果你想以独特和增强的方式使用 AI：深度知识、广度知识、品味和能动性。

That is why I think we will need to focus on the individual traits we have that remain useful even as AI abilities improve. You are not trying to compete with AI in producing outputs, that is a losing game. Instead, you want to use your human advantages as basis of working with AI to do things that neither of you could do alone. In my book, I outline four particular personal advantages that matter a lot if you want to use AI in unique and enhancing ways: deep knowledge, wide knowledge, taste, and agency.

四大优势 The Four Advantages

前两个优势来自你所知道的东西。深度知识是一种专业知识，它源于对某个领域或主题理解得如此透彻，以至于你围绕它建立起直觉，从而快速而准确地做出决策。这就是为什么一位经验丰富的会计师能扫一眼电子表格就知道哪里出了问题，或者一位高尔夫职业选手能观看一次挥杆就立刻明白高尔夫球手犯的错误。这也是为什么我能在几秒钟内判断出第一个预告片比原著实际上更加不祥。深度知识是专家的领域，也是真正理解锯齿状前沿（Jagged Frontier）形状的唯一途径，因为只有专家才能理解 AI 在何处成功或失败的模式，至少在其专业领域内是如此。它还能帮助你适应变化，因为深度知识让你更容易从执行工作的人转变为管理工作的人。Anthropic 最近的研究表明，专业知识也会影响 AI 反馈的质量。专家不仅能从 AI 获得更好的工作成果，还能从中获得更多的工作成果。

The first two advantages come from what you know. Deep knowledge is the expertise that comes from understanding a field or subject so well that you build intuition around it to quickly and accurately make decisions. It is how an experienced accountant can glance at a spreadsheet and know something is wrong, or how a golf pro can watch a swing and instantly understand the mistake the golfer is making. It is also why I could tell within seconds that the first trailer was more ominous than the book actually is. Deep knowledge is the realm of the specialist, and it is the only way to truly understand the shape of the Jagged Frontier, because only experts can understand the patterns of where AI succeeds or fails, at least in their area of expertise. It also helps you adapt to change because deep knowledge makes it easier to switch from being someone who does the work to someone who manages it. And recent work from Anthropic suggests that expertise also shapes the quality of what AI gives back. Experts not only get better work out of AI, they get more work out of it.

但你不仅需要深度知识，还需要广度知识。LLM 的训练数据是人类庞大产出的一大部分。AI 已经学到了设计思维、贝叶斯推理、丰田生产系统、罗杰斯疗法和马克思主义文学批评的一些东西。但 AI 往往不会主动提供这些模式，除非你知道如何提问。

But you don’t just need deep knowledge, you also want wide knowledge. The training data for LLMs is a large swath of humanity’s vast output. The AI has learned something of design thinking and Bayesian reasoning and the Toyota Production System and Rogerian therapy and Marxist literary criticism. But AI tends not to volunteer any of these patterns unless you know to ask.

这就是广度知识发挥作用的地方。让我们举一个例子：AI 处理设计工作的方式。如果你让 AI 创建一个网页，它会有某些偏好，包括一个非常恼人的习惯，即在你的标题上方添加小标题。如果你没有设计基础，你可能不会意识到你需要让 AI 停止在工作中“添加眉毛”。这也是为什么我知道使用 Blender 动画作为视频生成器的故事板是一种明智的电影制作方式，而不是 AI 在乱来。如果你确实知道正确的术语，要求修改就很容易。要获得广度知识，你需要广泛阅读和学习，跨越领域、格式和传统。这本身就有价值（文科的回归！），在 AI 时代更是如此。

This is where wide knowledge comes in. Let's take one example: the way AI handles design work. If you ever ask AI to create a webpage, it will have certain preferences, including a very annoying habit of adding little headlines on top of your headlines. If you don’t have any grounding in design, you may not realize that you need to ask the AI to stop “adding eyebrows” to the work. It is also how I knew that using a Blender animation as a storyboard for a video generator was a sensible way to make a film, and not the AI wandering off. If you do know the right terms, asking for changes is easy. To gain wide knowledge you need to read and study widely, across fields and formats and traditions. This is valuable in and of itself (the return of the liberal arts!) but doubly so in the age of AI.

现在让我们回到我上面展示的视频和项目……你可能对其中一个或另一个产生了本能反应，或者讨厌它们全部。你可能发现了一个你想看到更多的主题或想法。在这样做时，你正在使用 AI 时代人类的第三个差异化因素：品味。在 AI 之前，制作东西既困难又缓慢。写一份草稿需要几个小时。生成二十个产品概念需要一个团队一周的时间。一篇学术论文可能需要数年。限制始终是 _制作足够多的东西_。现在制作既快速又便宜。稀缺资源是你用自己的品味在众多东西中进行选择的能力。再次，在预告片中，我拒绝了第一个笑话，保留了第二个。我要求了一个更具电影感的版本。这些是我在预告片上做的唯一决定，但它们基于我的品味。

Now let’s go back to the videos and projects I demonstrated above... You may have reacted viscerally to one or another, or hated them all. You may have found a theme or idea you would like to see more of. In doing this, you are using the third human differentiator in the age of AI: taste. Before AI, making things was hard and slow. Writing a draft took hours. Generating twenty product concepts took a team a week. An academic paper could take years. The constraint was always _making enough stuff_. Now making is fast and cheap. The scarce resource is your ability to select among stuff using your own taste. Again, in the trailers, I rejected the first joke and kept the second. I asked for a more cinematic version. Those were the only decisions I made on the trailer, but they were based on my taste.

有些人的品味倾向于许多人会觉得流行的东西，有些人的品味是独特的，还有些人的品味是新颖和新奇的东西。是的，生成式 AI 主要导致垃圾内容：大量彼此非常相似的作品。但垃圾内容可以被品味击败。用 AI 制作伟大的东西意味着知道哪些 AI 输出要保留，哪些要丢弃，哪些要作为原材料，用于 AI 自己永远不会生成的东西。

Some people have a taste for things that many people will find popular, others have a taste that is unique to them, and still others have a taste for what is novel and new. Yes, generative AI leads mostly to slop: a flood of work that is very similar to each other. But slop can be defeated by taste. Making great things with AI means knowing which AI outputs to keep, which to discard, and which to use as raw material for something the AI would never have generated on its own.

最后一项人类优势——能动性——或许最为重要，也最难言说，因为它难以定义，且争议颇多。但在 AI 的语境下，我认为它指的是一种意愿：当所有人对 AI 能做什么都同样困惑时，仍愿意去试探可能性的边界。在你的领域里，锯齿状的前沿尚未被绘制成图，因此能动性意味着成为一名探索者。它关乎的是：是等待别人告诉你 AI 现在能做什么，还是通过亲自尝试去自己发现。这也是我做那么多古怪 AI 实验的部分原因——比如试着让 AI 玩游戏——它让我对 AI 能做什么有了很多了解。

The final human advantage, agency, might be the most important and the hardest to talk about, because it is difficult to define and the subject of a lot of debate. But in the context of AI, I think it is a willingness to test the boundaries of what's possible when everybody is equally confused about what AI can do. The jagged frontier is unmapped in your field, so agency is about becoming an explorer. It's the difference between waiting for someone to tell you that AI can now do something, and discovering it yourself by trying. That is part of why I do so many weird AI experiments — like trying to get the AI to play games — it teaches me a lot about what AI can do.

关于我新书预售福利的插叙 An interlude about the pre-order bonus for my book

我在《共存》（_Co-Existence_）一书中讨论了这四大优势以及更多内容，该书将于 10 月 20 日出版。如果你预购此书，并通过 co-existence.ai 告知我（预购对作者帮助很大），我们将在一两天内发送给你一个链接，让你与 AI 进行免费的语音访谈。它会询问你所知、所好以及所尝试过的事物，然后根据你的深度知识、广度知识、品味和能动性生成一份报告，并附上围绕这些内容构建的使用案例和提示词。

I discuss these four advantages, and a lot more, in _Co-Existence_, which comes out October 20. If you pre-order it and let me know at co-existence.ai (pre-ordering really helps authors), we will send you a link to a free voice interview with an AI within a day or two. It asks you about what you know, what you like, and what you have tried, and then gives you a report on your own deep knowledge, wide knowledge, taste, and agency, along with use cases and prompts built around them.

访谈生成报告的一个示例。这里的受访者是一个对水獭着迷的 LLM；你的报告显然会关于你自己。

An example of the report the interview produces. The interviewee here was an LLM with an otter obsession; yours will be about you, obviously.

这一切将我们置于何地 Where this all leaves us

目前关于 AI 的大部分焦虑都集中在未来的模型以及我们能否控制它们上。政府与 AI 实验室就如何管理发展速度以缓解这些风险展开争论，似乎是合理的。但放缓并不能抹去已经存在的东西。如果所有实验室明天都停止训练新模型，这也不会改变 GPT-6 Astra 和 Fable 5.1 已经足以改变大部分经济运作方式的事实。这些模型今天能做的事情与大多数人正在用它们做的事情之间，存在着巨大的能力悬置。

Most of the anxiety about AI right now is about future models and whether we will be able to control them. It seems reasonable for governments and AI labs to be arguing about how to manage the speed of development to mitigate these risks. But a slowdown does not undo what already exists. If every lab stopped training new models tomorrow, that wouldn't change the fact that GPT-6 Astra and Fable 5.1 are already enough to change how large parts of the economy work. The capability overhang between what those models can do today and what most folks are using them for is massive.

因此，无论前沿进展如何被调节，变化都会到来。它不会一次性发生，也不会均匀，但它是不可避免的。然而，不可避免的变化并不意味着变化的类型也是不可避免的。作为社会，我们越来越需要开发和分享那些能够增强而非仅仅替代人类劳动的 AI-人类工作模式。同样重要的是，作为个人，我们使用 AI 的方式应当增强而非仅仅替代我们自己的努力。我不认为存在我们可以指着说 AI 永远不会跨越的明确界限（见上文）。但你的四个优势是今天就可以开始的地方。

So change is coming no matter how the frontier is paced. It will not happen all at once and it will be uneven, but it is inevitable. Yet inevitable change does not mean the type of change is inevitable. It is increasingly important that we, as a society, develop and share models of AI-human work that enhance, rather than only replace, human labor. And it is equally important that we, as individuals, use AI in ways that enhance, rather than only replace, our own efforts. I don't think there are bright lines we can point to and say AI will never cross them (see above). But your four advantages are a place to start today.

_Zork 项目和 Library 项目都是开源的，如果你想修改它们，请随意（Zork 本身就是开源的）。_

_The Zork project and Library project are both open source, feel free to modify them if you want (Zork is itself open source)._

关于本文的讨论 Discussion about this post

当我玩 Zork 3D 时，我经历了一个真正的“WTF”时刻。

I had a genuine WTF moment when I played Zork 3D.

这是对人们可能尚未意识到的一些当前可用 AI 能力的非常有趣的总结——但你关于我们不应担心“未来”能力的总体论点忽略了一个事实：根据 AI 领域的顶尖专家所言，那个“未来”以及灾难性伤害的可能性已不再是假设性的，也不再是未来才需担忧的事——它已经到来，就在眼前——因此，立即着手应对这一现实至关重要。

Very interesting summary of some presently available AI capabilities people may be unaware of—but your overall thesis that we shouldn't worry about "future" capabilities ignores the fact that, according to the leading experts in AI, that "future" and the possibility of catastrophic harm is no longer hypothetical or a worry for down the road—it's arrived, it's here—so it's essential to immediately try to deal with that reality.