我们仍然处于人工智能发展的指数曲线上。我尽量每隔几周发布一次Substack文章，但随着发展速度加快，这有时感觉太慢了。在我上一篇文章发布后的几周里，我们看到了一位人工智能攻克了数学中最著名问题之一的迹象（伴随争议）：https://openai.com/index/navier-stokes-solution/，以及关于人工智能带来的风险和应对措施的广泛讨论（同样伴随争议）。我认为这些担忧，以及其他越来越多的忧虑，都归结到我对自己文章的同感问题：我们非常人性化的系统和流程跟不上人工智能发展的速度。
我不认为这些担忧的人是错的，但我也认为，单纯关注未来的人工智能——虽然很重要——却忽视了一个事实，即人工智能现在已经非常强大。事实上，新的GPT-6 Astra和Fable 5.1在经济的很大部分领域已经能够产生变革性的影响，并且在正确引导和利用下，它们能够可靠地完成数周的人类工作量。
我的观点是，尽管关于未来模型会做什么有很多争论，但现有模型的现有能力几乎未被利用，而且通常甚至不为人所充分理解。例如，直到GPT-6 Astra 实际操作了一次，我还不知道它可以使用Blender（一款复杂的3D建模软件）。
我给了它一本我即将出版的书，Co-Existence：https://co-existence.ai/，并要求它从人工智能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，它使用Blender创建了完整的3D动画场景（这可不是一件容易的事），同时还写了一个包含一些笑话和小惊喜的脚本（我确实拒绝了它添加的第一个笑话，但第二个非常不错）。然后，它还想办法生成了声音、音乐和音效，在45分钟后给了我这部影片。最终作品的感觉比我希望的稍微严肃了一些，但那是人工智能的决定，不是我的。
为了看看它还能做得多远，我提示道：“不错，但我实际上想让你根据《共存》制作一个动作电影预告片。尽情发挥吧。不要超过30秒。” 它再次写了一个剧本，并在Blender中制作了一个3D原型。当我要求更具电影感的版本时，它使用Blender动画作为分镜，通过我的浏览器操作视频生成器，并将生成的镜头编辑成最终的预告片。我给出了一些小的创意反馈，但从未参与任何制作决策，甚至不完全清楚它是如何完成这些任务的。你可以在这里看到结果。
这些尝试中有很多缺陷是显而易见的。但它们也是AI行使某种判断力和创造力的例子，而这些东西在不久前还被认为是人类独有的特质。而且，这些都只是使用我付费的ChatGPT账户中一小部分的令牌预算完成的。我认为这些是有趣的展示，但也有些令人害怕，因为AI在那些曾经纯属人类的事情上变得越来越擅长。然而，这些项目没有一个是自动发生的。我选择了它们，我对《佐克》和《生态》以及我自己的书了解得足够多，能够看出AI出错的地方，并在第一次不合适时要求第二个版本。能力过剩，即这些模型可以做到的事情与几乎任何人实际使用它们的差距，是一个机会，因为大多数人没有将自己的优势带入AI，而那些利用优势的人会从中获得更多。
这就是为什么我认为我们需要专注于在AI能力提升时仍然有用的个人特质。你不应该试图与AI在产出上竞争，那是一个必输的游戏。相反，你要利用你的人类优势作为与AI合作的基础，去做那些你们单独无法完成的事情。在我的书中，我概述了四种特别重要的个人优势，如果你希望以独特和增强的方式使用AI，这些优势将非常重要：深度知识、广泛知识、品味和能动性。
前两个优势来自你的知识。深度知识是一种专业技能，它来自于对某个领域或学科的深入理解，以至于你能够围绕它形成直觉，从而快速而准确地做出决策。这就是为什么有经验的会计师只需瞥一眼电子表格就能发现问题，或者职业高尔夫教练只看一挥杆就能立刻理解球手的错误。同样，这也是为什么我能在几秒钟内判断出第一支预告片比书本本身更令人不安。深度知识是专家的领域，它是唯一真正理解“崎岖前沿”形态的方法，因为只有专家才能理解AI在其专业领域中成功或失败的模式。它还帮助你适应变化，因为深度知识使从执行工作到管理工作变得更容易。Anthropic 最近的研究也表明，专业知识同样影响着AI输出成果的质量：https://cdn.sanity.io/files/4zrzovbb/website/b93c7465925dc052b9102209b29b58f11df4fe55.pdf。专家不仅能从AI中获得更好的工作成果，还能从中获取更多的工作量。
但你不仅需要深度知识，还需要广泛的知识。大语言模型（LLM）的训练数据涵盖了人类大量的输出内容。AI学习了设计思维、贝叶斯推理、丰田生产系统、罗杰式疗法以及马克思主义文学批评等知识。但除非你知道去提问，否则AI通常不会主动展示这些模式。
这时候广泛知识就显得至关重要。举个例子：AI处理设计工作的方式。如果你让AI创建一个网页，它会有一些偏好，包括一种非常令人恼火的习惯——在你的标题上再加一个小标题。如果你对设计没有任何基础，你可能不会意识到需要告诉AI停止“在作品上加眉毛”。这也是为什么我知道将Blender动画用作视频生成器的故事板是一种合理的制作影片的方式，而不是AI随意发挥。如果你知道正确的术语，提出修改会很容易。为了获得广泛知识，你需要广泛阅读和学习，跨越不同领域、形式和传统。这本身就很有价值（人文教育的回归！），在AI时代更是价值翻倍。
现在让我们回到上面演示的视频和项目……你可能对其中某一个或某几个产生了直觉反应，或者全都不喜欢。你可能发现了一个主题或想法，希望看到更多。在这样做的过程中，你正在使用 AI 时代的第三个人类差异因素——品味。在 AI 出现之前，创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时间。一篇学术论文可能需要几年。限制因素总是创作足够多的东西。而现在创作既快速又便宜。稀缺的资源是你利用自己的品味在众多作品中进行选择的能力。同样，在预告片中，我拒绝了第一个笑话，保留了第二个。我要求制作一个更具电影感的版本。这是我在预告片中做的唯一决定，但它们都是基于我的品味。
有些人的品味是大众普遍喜爱的，有些人的品味是独一无二的，还有一些人的品味偏好新奇和创新。是的，生成式 AI 大多会产生垃圾作品：大量彼此非常相似的内容。但品味可以克服这些垃圾作品。使用 AI 创造优秀作品意味着知道哪些 AI 输出值得保留，哪些应被舍弃，以及哪些可以作为原材料，用于创造 AI 本身永远不会生成的东西。
最后的人类优势——主体能动性，也许是最重要且最难讨论的，因为它难以定义，也存在大量争议。但在 AI 的背景下，我认为它是指在每个人都对 AI 能做什么同样困惑时，愿意去测试可能性的态度。崎岖的前沿在你的领域尚未被绘制，所以主体能动性意味着成为一个探险者。这与你等待别人告诉你 AI 现在能做什么和通过尝试自己发现它之间存在差异。这也是我做许多奇怪 AI 实验的部分原因——比如试图让 AI 玩游戏：https://x.com/emollick/status/2097178401991712854?s=20——它教会我很多关于 AI 能力的内容。
我在《共存》中讨论了这四个优势，以及更多内容，该书将于10月20日出版。如果您预订并在 co-existence.ai 告诉我：https://co-existence.ai/（预订真的能大力支持作者），我们会发送给您一个免费的 AI 语音采访链接。它会询问您的知识、喜好和尝试过的事物，然后给您一份关于您自身深度知识、广泛知识、品味和自主性的报告，并提供围绕这些内容构建的使用案例和提示。
目前关于 AI 的大多数焦虑都集中在未来模型以及我们是否能控制它们上。政府和 AI 实验室讨论如何管理开发速度以减轻这些风险似乎是合理的。但放慢速度并不会消除已经存在的技术。如果明天每个实验室都停止训练新模型，这并不会改变 GPT-6 Astra 和 Fable 5.1 已经足够改变大部分经济运作方式的事实。这些模型今天的能力与大多数人实际使用它们的方式之间的差距是巨大的。
因此，不论前沿的速度如何变化，变化都是不可避免的。它不会一蹴而就，并且会呈现不均衡状态，但它是必然的。然而，必然的变化并不意味着变化的类型是不可避免的。作为一个社会，我们越来越需要发展并分享能够增强而不是仅仅替代人类劳动的 AI-人类工作模型。同样重要的是，作为个体，我们也应以增强而不是替代自身努力的方式使用 AI。我认为没有明确界限可以指出 AI 永远不会跨越（见上文）。但您的四大优势是今天可以开始的起点。
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我也相信我们现在拥有的 AI 拥有深厚且未开发的潜力和用途，我的观点是，在急于追求更大、更快、更强之前，我们应花时间学习如何使用，更重要的是如何管理 AI 现有的能力。
尤其是在这种情况带来了如此巨大的生态成本，而清洁水源的可用性正以惊人的速度急剧下降时。
我确实很好奇，这些数据中心的规划者是否曾经问过 AI 怎样让它环保且具成本效益，或者他们只是认为可重复使用的冷却胶太贵，甚至都没去看看其他替代方案 🤷
对当前可用的一些 AI 能力的总结非常有趣，很多人可能并不清楚——但你整体的论点是我们不该担心“未来”的能力，却忽略了这样一个事实：根据 AI 领域领先专家的说法，“未来”以及可能带来的灾难性伤害已不再是理论上的或未来才会担心的问题——它已经到来，它就在这里——所以立即尝试应对这一现实是至关重要的
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 ：https://openai.com/index/navier-stokes-solution/ 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.
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.
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.
I gave it a copy of my upcoming book, Co-Existence ：https://co-existence.ai/ , 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.
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.
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.
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.
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：https://cdn.sanity.io/files/4zrzovbb/website/b93c7465925dc052b9102209b29b58f11df4fe55.pdf . Experts not only get better work out of AI, they get more work out of it.
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.
This is where wide knowledge comes in. Lets 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
Now let’s go back 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.
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.
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：https://x.com/emollick/status/2097178401991712854?s=20 — it teaches me a lot about what AI can do.
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：https://co-existence.ai/ (pre-ordering really helps authors), we will send you a link to a free voice interview with an AI. 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.
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.
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.
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I also believe the AI we have now has deep unmined depths and uses , and my point of view is that we should take time tomlearn how to use and more importantly manage what AI can do now before rushing ahead and making it bigger, faster, stronger.
Especially when it comes at such a huge ecological cost at a time where clean water availability is shrinking in shocking leaps and bounds.
I do wonder if these datacenter planners have asked ai how to make it environmentally friendly and cost effective, or if they just decided that reusable cooling gel is too expensive and haven't even bothered looking at alternatives 🤷
Very interesting summary of some presently available AI capabilities people may be unaware of-but your overall thesis that we shouldnt 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 - its arrived, its here - so its essential to immediately try to deal with that reality
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