10种用机会AI放大思维的方法

当GPT-6 Astra发布时，人们很快注意到它和之前的模型发布有些不同。

When GPT-6 Astra was released, people quickly noticed that it was a little bit different than other previous model releases.

在某些方面，它比我们见过的任何东西都要先进得令人难以置信。

In some ways, it was unbelievably more advanced than anything we'd seen before.

但在其他方面，不仅在某些使用场景中改进不一定特别明显，有时甚至感觉像是一种退步。

And yet in others, not only were the improvements not necessarily super noticeable in certain use cases, but sometimes it actually felt like a regression.

我们到达的AI发展历史中有趣的一点是，广泛的模型能力已经达到了一个水平，新模型的价值往往不在于仅仅更好地做你一直在用AI做的事情，而在于真正解锁你从未考虑过的全新能力。

The interesting point in the history of the development of AI that we've come to is that broad model capability has reached a level where the value of new models is very frequently not going to be in just doing the same things that you've been doing with AI better, but actually about totally unlocking new capabilities that you've never even considered.

话虽如此，解锁你从未考虑过的新能力，顾名思义，意味着你还没有考虑过它们。

That said, unlocking new capabilities that you've never considered, by definition, means you haven't considered them.

那么，你怎么弄清楚甚至什么是有价值的AI用途呢？

And so how do you figure out even what is valuable to use that AI for?

这就是今天节目的目标，给你提供一些思考的起点，关于我所说的机会AI的用例。

That is the goal of today's episode, to give you a set of thought starters about what I call use cases for opportunity AI.

《AI每日简报》是一个每日播客和视频，关于AI领域最重要的新闻和讨论。

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

好了，朋友们，在开始之前先快速说几个通知。

All right, friends, quick announcements before we dive in.

首先，感谢今天的赞助商：Blitzy、Section、Robots and Pencils 和 Hyperagent。

First of all, thank you to today's sponsors, Blitzy, Section, Robots and Pencils, and Hyperagent.

想要收听无广告版本，请访问 patreon.com/aidailybrief，或者也可以在 Apple Podcasts 上订阅。

To get an ad-free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts.

如果想了解赞助节目的更多信息，请发送邮件至 sponsors@aidailybrief.ai。

And to learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai.

今天我们要聊的是如何更好地使用 AI，而这次探讨的具体背景是最新一代模型，Fable 5.1，尤其是 GPT-6 Astra。

Today, we are talking about ways to use AI way better, and the specific context for this exploration is around the latest generation of models, Fable 5.1 and especially GPT-6 Astra.

Astra 在某些方面来说，确实是个非常奇怪的模型。

Astra is, in some ways, a really strange model.

就像我在节目里说的，我觉得它既意义重大，又让人摸不着头脑。

Like I said in my show, I called it so significant but also so confounding.

自从它发布这一周以来，如果你关注那些高级 AI 用户，就能看到不少这样的例子。

And over the last week since it's been available, you can kind of see examples of this if you're paying attention to the advanced AI users.

比如，几天前，Francesco在X上写道：“我换回了GPT-5.6 Sol和Fable 5.1。”

For example, a couple of days ago, Francesco on X wrote, "Moved back to GPT-5.6 Sol and Fable 5.1.

GPT-6 Astra可能是我用过的最聪明也最笨的模型。

GPT-6 Astra might be the smartest and dumbest model I've ever worked with.

我受不了它的情绪波动，尤其是它为了达到技术上能用的结果而采取的荒谬捷径。”

I can't deal with its mood swings, especially the absurd shortcuts it takes to arrive at something technically working."

Theo转发了那条，并说：“还记得大家说我反应过度，说Astra有愚蠢的峰值吗？”

Theo reposted that and said, "Remember when everyone said I was overreacting to Astra's spikes of stupidity?"

与此同时，OpenCode的Dax写道：“我们团队有一部分人已经换回了Sol。”

Meanwhile, OpenCode's Dax wrote, "A portion of our team has gone back to Sol.

Astra不错，也能做一些新颖的事情，但它有一些缺点。

Astra is good and can do some novel things, but it has some downsides.

到目前为止，我们的实际支出看起来翻了一倍，所以很难证明其合理性。”

And so far, our effective spend looks doubled, so tough to justify."

现在，这些人主要都在处理编码用例，但这并不一定是Astra最初引起兴奋的主要领域。

Now, all of these folks are dealing primarily with the coding use case, but that's not necessarily where a lot of the initial excitement about Astra has been.

相反，大家对Astra的热情主要集中在其解锁的全新能力上，比如视频编辑、3D设计和建模，换句话说，这些并不是我们日常工作中会用到的。

Instead, the excitement about Astra has been around some totally new capabilities that get unlocked around things like video editing and 3D design and modeling, things, in other words, that are not currently part of our day-to-day work.

那么，从商业角度来看，这对OpenAI来说是不是一个好策略，那就是另一个问题了。

Now, whether that's a good strategy for OpenAI from a business perspective is a whole separate question.

但这背后涉及的一个更大的概念，是我经常讨论的一个观点，那就是效率型AI和机会型AI的区别。

But the larger concept that this gets into is one of my most frequently discussed ideas, the difference between efficiency AI and opportunity AI.

效率型AI，当然就是帮助你更好地完成现有工作的AI。

Efficiency AI is, of course, AI that helps you do your existing work better.

更好可以意味着更快、更高效、更便宜。

Better can mean faster, more efficiently, more cheaply.

而机会型AI，则是能够解锁全新机会的AI。

Opportunity AI, on the other hand, is AI that unlocks entirely new opportunities.

在我多次讨论中，效率型AI和机会型AI的区别，更多是关于使用和思考AI的一种心态，而不是模型本身有什么具体的不同。

Now, efficiency versus opportunity AI, in most times that I've discussed it, has been more about a mindset for how to use and think about AI than some actual specific difference in the models.

而Astra是我第一次看到真正在这个区别中游走的模型。

With Astra, it's one of the first times that I've seen a model that actually dances inside this difference.

需要注意的是，效率型AI本身绝对没有问题。

Now, it's important to note that there is absolutely nothing wrong with efficiency AI.

它将成为我们使用AI的基础。

It is going to be the foundation for most of our use of AI.

AI的很多初始价值都会从这里产生。

It's where a lot of the initial value for AI is going to come.

我一直讨论机会型AI的原因是，我觉得尤其是在商业战略层面，如果公司只把AI看作一种提升效率的技术，它们会错过很多机会，而那些最终胜出的公司不会错过这些机会。

The reason that I've always discussed opportunity AI is that I think especially on a business strategic level, if companies only think about AI as an efficiency technology, they're going to miss a lot of the opportunities that the companies that ultimately win will not.

换句话说，所有那些提升效率的用例都会变成基本要求，并重新设定人们对工作方式的期望。

In other words, all of those efficiency use cases will become table stakes and will reset the expectations of how work gets done.

但那些积极寻找和发现新机会的公司，即使这些机会目前与他们的业务无关，也很可能会脱颖而出，真正为这个新时代转型。

But the companies that lean into finding and discovering new opportunities, even if those opportunities are currently orthogonal to what they do, are likely to be the ones that race out ahead and really transform themselves for this new era.

所以，几天前我关于Astra的讨论，重点全在机会型AI上。

So where I left the Astra conversation a few days ago was that it was all about opportunity AI.

我敢肯定，你们中有些人会想：“好吧，这听起来不错，但用这个模型来解锁全新的机会到底是什么意思？”

And I guarantee that some of you are sitting there thinking, "Well, that's well and good, but what the heck does it even mean to use this model to unlock entirely new opportunities?"

可能我们有些人有一些一直想做但以前做不到的事情，但我发现，至少对我自己来说，很多我归类为机会型AI的用例，我都是摸索着、跌跌撞撞才找到的。

Maybe some number of us have some things that we'd like to do that we've never been able to before, but I have found that certainly for myself, a lot of what I would categorize as my opportunity AI use cases, I kind of had to stumble and bumble into.

换句话说，我并不是坐在那儿等着技术突破，然后去建一个能自动把节目切成可分享片段、再推送到社交和视频渠道的新网站。

In other words, it wasn't like I was sitting around just waiting for the technology unlock to build a new type of website that could automatically turn these episodes into shareable chunks and then push them into a pipeline that could provide social and video.

那是经过很多摸索和实验，审视自己遇到的问题，尝试用新方法解决之后才出现的。

That came after a lot of stumbling around and experimenting and looking at problems that I had and trying to solve them in new ways.

我觉得如果你只是让大家去用AI找新机会，你可能会面临一个很大的空白页问题。

And I think that if you just ask people in general to go use AI to find new opportunities, you're going to have a pretty massive blank page problem.

简单说，人们通常不会随身带着一份完整的清单，列出所有他们可能做或可能创造的事情。

In short, people do not generally walk around with some complete inventory of all the things they might do or that they might make.

我们有的是一份更小、更熟悉的清单，由我们的工作、工具、经验和周围人的行为塑造而成。

We have a smaller, more familiar inventory shaped by our job, our tools, our experience, and what we see the people around us doing.

所以，考虑到这一点，我想在这期长读/深度思考的节目中，真正去探索和扩展这个充满可能性的新世界。

So with that in mind, what I wanted to do for this long read/big think episode was try to actually explore and expand that whole new world of possibilities.

我会把我现在正在讲解的网络体验添加到 aidailybrief.ai 网站上，这样你也可以去看看。

I'll be adding the web experience that I'm now talking over to the aidailybrief.ai website so that you can check it out as well.

基本上，这分为两部分。

And basically, this is two parts.

第一部分是展示12个想法，你可以用像GPT-6 Astra这样的模型来构建、做或使用，这些想法你现在可能没想到过。

The first is an exhibition of 12 ideas of things that you could build or do or use a model like GPT-6 Astra for right now that you might not have thought of.

换句话说，它们是启发思考的起点。

They are, in other words, thought starters.

每个想法都有高层概念和该概念的具体应用。

Each of them has both a high-level concept as well as a specific application of that concept.

而且每个想法都有一个个性化设置，你可以告诉AI一些关于你自己的信息，然后找到一些可能更适合你生活和工作的类似可能性。

And for each, there is a little personalizer where you can tell the AI a little bit about yourself and find some similar possibilities that maybe better fit your life and work.

现在，如果你不想跟我一起看完所有这些，有一个捷径：很多“机会AI”的“机会”并不是全新的、你从未做过的事情，而是那些你可能特别做不到、但其他人能做到的事情。

Now, one shortcut, if you don't want to go through all of these with me, is that a lot of the "opportunities" of opportunity AI are not totally novel new things you can do, but things that maybe you specifically haven't been able to do but other people have been able to do.

换句话说，一个很好的寻找不同AI使用方式、可能让你走得更远的方法是，看看其他职业的人在做什么你觉得很酷的事情。

In other words, a good shorthand for looking at different ways to use AI that may stretch you even farther is to look around at what people with other jobs are doing that you think is really cool.

你听下去就会明白我的意思了。

You'll see as we get into it what I mean.

所以第一个启发性的想法是，做一些人们可以玩的东西，特别是那种可以玩的营销。

So the first thought starter is to make something that people can play, specifically marketing that people can play.

从历史上看，营销内容一直是视觉和印刷品的结合，最近又加入了视频。

Historically, the content for marketing has been some combination of visual and print, and more recently, video.

但AI在互动和交互性方面创造了全新的机会。

But AI creates a whole new opportunity around interaction and interactivity.

人们用Astra做的最激动人心的首批实验之一，就是创造游戏或者重建已有的游戏。

Some of the most exciting first experiments that people did with Astra were to create games or to rebuild games that already existed.

所以，如果像Astra这样的工具具备这种能力，为什么不考虑在工作场景中构建游戏呢？

And so if that is a capability of something like Astra, why not think about where building games could be useful inside of work?

你可以邀请一个人去探索一个世界，扮演一个角色，创造一些东西，或者尝试一个挑战。

You could invite a person to explore a world, take on a role, make something, attempt a challenge.

他们与品牌或产品的关系，会通过他们在那种情境中的行为而发展。

Their relationship to the brand or the product develops through what they do in that situation.

这是一个广阔的创意领域，在很多方面比决定在页面上放什么信息要广阔得多。

This is a vast creative territory, in many ways larger than deciding which message to put on a page.

想想看，被告知某个地方奖励好奇心，和给你一个小谜团让你对这个地方产生好奇，这两者之间的差别。

Consider the difference between being told a place rewards curiosity and being given some small mystery that makes you curious about the place.

第一种情况下，好奇心是信息的内容，而第二种情况下，好奇心是体验要求你去运用的东西。

In the first case, curiosity is the subject of the message, but in the second, curiosity is something the experience asks you to exercise.

与那些把受众纯粹当作接收者的营销方式不同，制作游戏会调动受众的主动性，而且将游戏与品牌信息结合的方式可以有很多种。

As opposed to other approaches to marketing where the recipient is purely a receiver, creating a game implicates the audience's agency, and there is a huge variety of what it could mean to integrate a game with the branded messages.

举个例子，游戏的规则本身就可以承载产品或服务的核心主张。

In one example, the rules of the game could carry the argument for the product or service.

假设你为成长型企业提供咨询，并且认为随着团队扩大，协调会成为一个更大的问题。

Suppose you advise growing businesses and believe that coordination becomes a bigger problem as teams expand.

一个简短的游戏可以要求玩家在增加人手、管理交接的同时完成一个项目。

A short game could ask someone to deliver a project while adding people and managing handoffs.

如果增加人手也带来了更多的协调工作，玩家就会亲身体验到你的建议所基于的那种关系。

If adding capacity also creates more coordination work, the player encounters the relationship your advice is built around.

另一种类型的游戏，可以通过玩来帮助人们发现某个问题为什么对他们很重要。

In another type of game, play could help someone discover why a problem matters to them.

想象一下，有个顾问的服务在销售演示里听起来很抽象，说是要改善跨团队的决策。

Imagine a consultant whose services sound abstract in a sales deck, improving cross-team decision-making.

通过游戏，你可以给潜在客户一个五分钟的虚构发布体验。

With a game, you could give a prospect a five-minute fictional launch.

销售承诺了日期，产品发现了依赖问题，而支持部门需要的信息还没人整理好。

Sales promises a date, product discovers a dependency, and support needs information nobody has assembled.

潜在客户做出选择，然后遇到随之而来的混乱。

The prospect makes choices and encounters the resulting confusion.

这种体验能让他们找到描述工作中遇到问题的语言。

The experience can give them language for a problem they recognize from work.

再比如，游戏可以让人表达自己是谁。

In another instance, play can let people express who they are.

想想一个“让这个别扭的公寓能住人”的挑战。

Think about a make this awkward apartment work challenge.

玩家有一间房间、几个互相冲突的需求，还有有限的空间。

The player has a room, a few competing needs, and limited space.

他们移动棋子，选择什么值得突出，最终得出一个特定的解决方案。

They move pieces, choose what deserves to be highlighted, and arrive at a particular solution.

这种体验把产品放在一个客户能理解的问题里，让客户发挥自己的品味。

The experience places the products inside a problem the customer can understand and lets the customer exercise taste.

其实，这种做法以前也有人做过。

Now, this is not something that hasn't been done before.

品牌早就尝试过用游戏来做营销。

Brands have experimented with games as marketing.

只不过以前做这些事受限于开发资源，成本很高，不像现在，你作为个人创业者，这个周末就能动手试试。

It's just that in the past, these things were extremely constrained by the sort of development resources that it took to make them, as opposed to something that you literally could this weekend experiment with as a solopreneur.

关于这个概念，最后我想说的是，它也提醒我们，尤其是在处理机会型AI时，不是所有东西都能成功。

And the last thing I'll note on this concept is that it also highlights the fact that especially when you're dealing with opportunity AI, not everything is going to hit.

当然，游戏设计不仅仅是告诉一个编程代理要做什么。

Game design is, of course, about more than just being able to tell a coding agent what to build.

很多游戏发布后并不流行，这是有原因的。

There's a reason that a lot more games are released than ever become popular.

而且这些游戏，哪怕是失败的，也几乎都是专业游戏设计师做出来的。

And pretty much all of those, even the ones that fail, are created by professional game designers.

所以，虽然打造一个吸引人的体验确实不容易，但有趣的是，这些新模型和机会AI的思维方式让你真的可以去尝试。

So without at all minimizing the challenge of building a compelling experience, the fun thing is that these new models and opportunity AI mindsets allow you to actually try.

第二个思考起点，还是把别人在做的事情，引入到自己的工作领域里。

Second thought starter is once again taking something that some people do and bringing it into your own work world.

这些新模型越来越擅长的一件事，就是搭建视频制作流程。

One of the things that these new models are getting very good at is building video production pipelines.

如果你看过AI每日简报的任何片段，那些并不是用像Opus这样的专门视频剪辑工具做出来的。

If you have ever seen any clips from the AI Daily Brief, those are not created by a dedicated video clipping product like Opus.

那是一个定制的Claude Run流程，它接收脚本和原始视频素材，然后用一系列工具来生成视频。

That is a custom-built Claude Run pipeline that ingests scripts, the raw video material, and then uses a set of tools to produce the videos.

说实话，我觉得我对这个流程的潜力还只是触及了皮毛。

And to be clear, I think that I am barely scratching the surface on what that pipeline can do.

我看到有人用Astra做视频剪辑，效果非常惊艳，所以如果这些更先进的模型也能像编程代理那样，让视频制作变得大众化，我一点也不会惊讶。

I have seen people doing some amazing things with video editing with Astra, and I wouldn't be surprised if these more advanced models have a similar sort of democratization effect on video production as the coding agents have had with building software.

有趣的问题是，视频能在你的工作中帮上什么忙？

The interesting question becomes where could video help you in your work?

是为了解释某件事，无论是向内部员工，还是向外部客户和潜在客户？

Is it about explaining something, either internally to employees or externally to customers and potential customers?

视频在营销领域会不会更有价值？

Would video be more valuable in the marketing realm?

你能不能把教育的目的和先进模型的视频能力结合起来，制作出既能当培训材料又能当营销内容的视频？

Could you combine an educational impulse with the new video capabilities of advanced models to produce training materials that also serve as marketing?

在你纠结于视频拍摄本身之前，我们现在都有笔记本电脑和手机，至少可以用来拍我们对着它们说话。

And before you get hung up on the video capture part itself, we all have laptops and phones at this point that at the very least could capture us talking into them.

所以，如果你想试试这个，我给你布置的作业是这样的。

So here's the homework I'll give you if you want to try this out.

先从简单的事情开始，比如为你做的或卖的东西制作一个教育性营销视频。

Start by doing something simple, like an educational marketing video for whatever it is that you build or sell.

和AI合作写一个60秒的脚本，然后用最简单的方式录制，就用你的iPhone。

Work with AI to write a 60-second script and record it in the simplest way possible, just with your iPhone.

然后把那个视频交给Codex或者Claude Code，让它为你的视频设计一个视觉主题。

Then give that video either to Codex or Claude Code and ask it to design a visual motif for videos that you do.

让它设计转场元素或分层图形，告诉它你想要一个完整的制作流程，这样你只需要把原始视频放进去，它就能接手处理。

Ask it to design transition elements or layered graphics, and tell it you want a full production pipeline so that all you have to do is drop the source video in, and it can take it from there.

无论它给出什么结果，至少给它一轮反馈，看看能不能让它更进一步。

Whatever it comes back with, give it at least one round of feedback and see if you can push it farther.

然后你坐下来想想，这种视频创作是不是你现在可以认真考虑的事情，而以前你从未想过？

And then sit back and ask, is this sort of video creation something that you could now actively consider in a way that you never have before?

换句话说，如果你对发布视频的贡献仅限于启动脚本制作和录制自己朗读脚本，那这是否足以降低门槛，让视频成为你工作中的工具？

In other words, if your contribution to releasing video comes down to initiating script production and recording yourself reading that script Does that sufficiently lower the barrier to entry where video could become a tool in your work?

从那里开始，你可以把视频带到很多不同的方向，走得更远。

And from there, you can take video in a lot of different directions and go a lot farther.

我还建议，如果你对尝试这种能力感兴趣，但想不出工作中哪里特别有用，那就和AI合作写一个90秒的小电影，然后尝试从那里建立制作流程和策略。

I would also suggest that if you are interested in experimenting with this sort of capability but can't think of any place that it would be super useful in your work, go partner with an AI to write a little 90-second movie and try to build a production pipeline and strategy from there.

市面上每个AI编程工具首先都会做同样的事情。

Every AI coding tool on the market does the same thing first.

它一开始就写代码。

It starts writing code.

Blitzy 的做法正好相反。

Blitzy does the opposite.

在写一行代码之前，Blitzy 会花好几天时间逆向工程你的整个代码库。

Before writing a single line, Blitzy spends days reverse engineering your entire code base.

成千上万的代理处理数百万行代码，梳理每一个依赖关系、每一个未记录的约束、以及过去十年里做出的每一个架构决策。

Thousands of agents ingest millions of lines, mapping every dependency, every undocumented constraint, every architectural decision made over the last decade.

结果就是一个动态的知识图谱，它理解你的软件，就像一位资深工程师在项目里干了三十年那样。

The result is a dynamic knowledge graph that understands your software the way a principal engineer would after 30 years in the building.

其他工具靠 grep 搜索和 markdown 文件来猜测上下文。

Other tools guess at context with grep searches and markdown files.

Blitzy 从不猜测。

Blitzy never guesses.

它先建立真正的理解，然后自主交付整个软件史诗的 80% 以上。

It builds true understanding first, then delivers over 80% of entire software epics autonomously.

经过验证、端到端测试、达到生产标准的拉取请求。

Validated end-to-end tested production-grade pull requests.

这就是为什么财富500强的工程团队把最重要的代码库交给Blitzy。

That's why Fortune 500 engineering teams trust Blitzy with the code bases that matter most.

你自己去blitzy.com看看吧。

See for yourself at blitzy.com.

网址是B-L-I-T-Z-Y.com。

That's B-L-I-T-Z-Y.com.

这里有个残酷的事实。

Here's a harsh truth.

你的公司可能花了成千上万甚至上百万美元买AI工具，但这些工具被严重浪费了。

Your company is probably spending thousands or millions of dollars on AI tools that are being massively underutilized.

一半的公司有AI工具，但只有12%的公司用它们创造商业价值。

Half of companies have AI tools, but only 12% use them for business value.

大多数员工还在用AI来总结会议纪要。

Most employees are still using AI to summarize meeting notes.

如果你在公司负责AI的落地应用，那你需要了解一下Section。

If you're the one responsible for AI adoption at your company, you need Section.

Section是一个平台，帮你管理整个组织的AI转型。

Section is a platform that helps you manage AI transformation across your entire organization.

它指导员工在实际场景中使用AI，追踪谁在用AI创造业务价值，还能让你清楚看到AI在哪里创造了价值，哪里没有。

It coaches employees on real use cases, tracks who's using AI for business impact, and shows you exactly where AI is and isn't creating value.

这样一来，你就不再是简单推出工具，而是能推动AI带来实实在在的价值。

The result, you go from rolling out tools to driving measurable AI value.

你的员工不再只是做会议纪要，而是去解决真正的业务问题，而且你能证明投资回报率。

Your employees move from meeting summaries to solving actual business problems, and you can prove the ROI.

别再猜你的AI投资到底有没有效果了。

Stop guessing if your AI investment is working.

去sectionai.com看看Section吧。

Check out Section at sectionai.com.

网址是S-E-C-T-I-O-N-A-I.com。

That's S-E-C-T-I-O-N-A-I.com.

最优秀的团队并不是靠一个明星队员扛着所有人。

The best teams don't have a single star carrying everyone else.

他们了解自己的长处，也清楚彼此的短板，并且能善用这两点。

They know their own strengths and each other's weaknesses and play to both.

这正是Robots & Pencils团队刻意打造出来的。

That's the team Robots & Pencils has built on purpose.

在那里，没有人为了凑人头数而埋头做那些琐碎的工作。

Nobody there is grinding through busy work to pad a headcount number.

人们为了挑战难题而来，留下则是因为周围的每个人都在同步成长。

People come for the hard problems, and they stay because everyone around them is leveling up at the same time.

在一个满是只想快速招人的公司市场里，这确实值得一看。

In a market full of companies that are just trying to hire fast, that's worth a look.

去robotsandpencils.com/careers看看吧。

Check out robotsandpencils.com/careers.

本期《AI每日简报》由Hyperagent赞助播出，你可以通过它管理团队协作的智能体集群。

This episode of the AI Daily Brief is brought to you by Hyperagent, where you run fleets of agents your team can manage together.

别再依赖本地代理和聊天工作流，等着你在笔记本电脑上操作了。

Forget local agents and chat workflows waiting on your laptop to be prompted.

Hyperagent 在云端部署全天候运行的代理，在你团队已经使用的工具中完成实际工作。

Hyperagent deploys always-on agents in the cloud, doing real work across the tools your team already uses.

营销代理能把竞争对手的动作变成落地页。

Marketing agents turn competitor moves into landing pages.

销售代理能丰富潜在客户信息、起草邮件，并更新客户关系管理系统。

Sales agents enrich leads, draft emails, and updates the CRM.

运营代理负责跟进文书工作，追踪预算。

Ops agent chases the paperwork and tracks the budget.

每个代理都能访问共享上下文，并遵循你关于范围和审批的规则。

Every agent has access to shared context and follows your rules about scope and approvals.

是时候拥有像队友一样的代理了。

It's time you had agents that feel like teammates.

去 Hyperagent 雇佣你的代理吧。

Hire yours at Hyperagent.

在hyperagent.com/aidailybrief上领取100美元积分。

Get $100 in credits at hyperagent.com/aidailybrief.

第三个思路：让客户可以自由探索的产品演示。

Third thought starter idea, product demos that people can explore.

一个核心的新可能性是，让客户自己决定如何理解产品。

A central new possibility is giving a customer control over how they come to understand a product.

一个产品包含很多种可能的解释，不同的买家带着不同的问题来。

A product contains many potential explanations, and different buyers arrive with different questions.

交互式的展示可以让问题决定路径。

An interactive representation can let the question determine the path.

比如现在，销售演示必须选择一个顺序。

Right now, for example, a sales presentation has to choose an order.

关心维护的买家可能得先听完他们已经懂的功能，才能看到他们关心的部分。

A buyer concerned about maintenance may sit through features they already understand before reaching the part that matters to them.

关心配置的买家可能需要看到标准流程里从未展示的关系。

A buyer concerned about configuration may need to see a relationship that the standard sequence never shows.

探索式的演示可以围绕操作来组织。

An exploratory demo can organize itself around actions.

打开这个，隔离那个组件，检查布局，查看结果。

Open this, isolate that component, check the arrangement, inspect the result.

访客的好奇心变成了导航说明的一种方式。

The visitor's curiosity becomes a way to navigate the explanation.

要问的问题是：你的客户需要能够自己检查什么，你的产品或服务才能真正让他们理解？

The question to ask is: what do your customers need to be able to inspect for themselves before your product or service really makes sense?

而这是否是交互式演示能够解决的问题？

And is that something that an interactive demo could solve?

第四个想法来自类似的思路，就是让客户能够修改的提案。

Thought starter four comes from a similar place and is proposals that clients can shape.

现在的提案都是单向的。

Right now, proposals point in a single direction.

提案人把方案交给对方，希望对方喜欢。

The proposer hands the proposed to a plan and hopes they like it.

不过，现在提案完全可以变成双方共同做决策的工具了。

However, there's no reason anymore that a proposal can't become a shared instrument for making a decision.

它可以把范围、资源和时间之间的关系都包含进去，这样客户就能自己探索不同的方案，而不是只能接受一个固定的计划。

It can contain enough of the relationship between scope, resources, and time that clients can explore alternatives instead of receiving only one selected plan.

你可以这样想。

Think about it this way.

提案本身其实就包含了一个模型，哪怕这个模型是隐形的。

A proposal already contains a model, even when that model is invisible.

作者已经对工作量、哪些事情可以并行、有哪些资源可用，以及修改需求会如何影响交付时间做了假设。

The author has made assumptions about how much work is involved, what can happen in parallel, which resources are available, and how changing the brief affects delivery.

当客户问：“我们能不能把另一个部门也加进来？”

When a client asks, "Could we include another department?"

作者就会重新运行这个模型的一部分。

the author runs part of that model again.

答案可能涉及增加几批人、多一个协调员，或者交付日期要往后推。

The answer may involve more cohorts, another facilitator, or a later completion date.

所以，能不能把推理中的某些部分拿出来，让客户也能用呢？

And so instead, can't you make selected parts of that reasoning usable by the client?

机会就在于，把你模型里的部分推理拿出来，让客户也能用。

The opportunity is to make selected parts of the reasoning in your model usable by the client.

一个交互式的提案可以展示，如果改变计划的一部分，会带来什么结果。

An interactive proposal could show what would follow from changing a part of the plan.

比如，“我们想更快完成”就变成“如果团队能更频繁地参与，我们就能更快完成”，或者“我们需要更多人手来并行开展这些活动”。

So for example, "We want it sooner," becomes, "We can finish sooner if the team can attend more often," or, "We need more capacity to run these activities in parallel."

客户可能会发现，他们最初的想法和他们更在意的事情有冲突。

The client could potentially discover that their initial preference conflicts with something they care about more.

他们可以自己探索这种冲突，而不必把每个替代方案都当成新的请求来让你解释。

They can explore that conflict without treating every alternative as a new request for you to interpret.

这个想法有意思的地方在于，如果做得好，它其实同时是效率型AI和机会型AI。

Now, what's fascinating about this one is that if it's done well, this is actually efficiency AI and opportunity AI all in one.

这不仅代表了一种以前几乎不可能实现的与客户互动的新方式，而且这样做，大大减少了每次新客户谈判中必不可少的来回沟通的延迟，而且在我看来，这将来会完全成为标准做法，我们会很难记起以前不是这样互动交流的日子。

Not only does this represent a new way of interacting with clients that wasn't really possible before, but in so doing, it radically reduces the latency of the back and forth that is a necessary part of every new client negotiation and has the feel to me of the type of thing that in the future is going to be completely de rigueur, and we're going to find it hard to remember a period where we didn't do things in this sort of interactive way.

第五个想法，我们把目光转向内部。

For our fifth thought starter, we're turning our focus to the internal.

对我来说，AI价值的一个主要方面是让我们能从不同角度思考战略决策。

To me, a primary category of AI value is allowing us to think about strategic decisions from different angles.

AI强大的信息吸收能力和输出大量可能性的能力，为战略决策增添了真正的维度。

AI's sheer ability to absorb information and output lots of possibilities adds a real dimension to strategic decision-making.

所以第五个想法是，为商业决策构建模拟器。

And so the fifth idea is to build simulators for business decisions.

顺便说一句，这也涉及我们Multiplayer AI Sprint中的一些概念，那个项目是关于存在于团队中心、而非仅由个人拥有的智能体。

For what it's worth, this also gets to some of the concepts in our Multiplayer AI Sprint, which is all about the agents that will exist at the center of teams rather than just owned by individuals.

商业决策模拟器的想法是，很多分歧实际上内部隐藏着多个分歧。

So the idea of the business decision simulator is that many disagreements actually contain several disagreements hidden inside.

“我们需要再招一个人”可能意味着当前流程太慢、工作分配不均，或者有人预期未来需求会激增。

"We need another hire" could mean that the current process is too slow, the work is unevenly distributed, or that someone expects some future surge in demand.

这意味着，人们最终是在争论结论，而他们想象的起始条件却各不相同。

What that means is that people end up arguing about the conclusion while imagining different starting conditions.

一个人想的是平均每周的情况，另一个人记得最糟糕的那天，还有一个人假设下个月流程会改进。

One person thinks about the average week, another remembers the worst day, a third assumes the process will improve next month.

构建模拟器会迫使你明确这些条件。

Constructing a simulator forces you to specify those conditions.

你必须决定每个阶段会发生什么，每个阶段的限制是什么，未完成的工作流向哪里，诸如此类。

You have to decide what happens at each stage, what limits each stage, where unfinished work goes, things like that.

而模拟器可以让一个假设的后果变得可以检验。

And a simulator can make the consequence of an assumption something that can be inspected.

你可以问，如果需求增长更慢，或者培训新员工需要时间，或者某个阶段处理更复杂的案例，同样的决定是否仍然合理。

You can ask whether the same decision still makes sense if demand rises more slowly, or if training a new employee takes time, or if one stage handles more complicated cases.

在这里，你会注意到另一个模式，它开始贯穿很多这些想法，其中许多在某种程度上都回到了构建“如果”机器上。

And in this, you'll notice another pattern that started to weave itself throughout a lot of these ideas that many of them come back to, in some way, building what-if machines.

这有点像互动提案的想法，对吧？

That was sort of the idea of the interactive proposal, right?

如果我们这样做会怎么样？

What if we did this?

那会有什么影响呢？

What would the implications be?

会涉及哪些权衡取舍呢？

What trade-offs would it implicate?

这就是一个假设机器，但用在某个具体决策上。

This is a what-if machine, but in the context of some specific decision.

第六个思考点，我其实想简单带过。

Our sixth thought starter I'm actually going to skim over.

这个会在网站上供大家查看，但具体例子可能稍微窄一点。

It will be on the website for you to check out, but the specific example is going to be perhaps a little bit more narrow.

所以我想提的是，Astra特别明显的一个能力就是能在三维空间里操作。

And so the thing that I wanted to touch on is just that one of the clear capabilities that particularly Astra exhibits is the ability to operate in 3D.

很多人最初对Astra感到兴奋的例子，就是用它配合Blender这样的3D软件，比如对Zillow上想买的房子做3D漫游，或者那些三维空间能带来很大不同的学习体验。

A lot of the first examples of people really being excited about Astra were them using it with 3D software like Blender to do things like 3D walkthroughs of Zillow houses that they might want to buy, or learning experiences where three dimensions can make a big difference.

我觉得，思考Astra在AI机会方面，最好的方式之一就是问：三维空间在哪里能带来变革性的影响？我建议关注的一个领域是学习体验，因为能互动并旋转3D数字对象，对学习效果会有很大不同。

I do think that one of the best ways to think about opportunity AI when it comes to Astra is to ask where three dimensions could be transformative in some way, and one area that I would suggest looking to are learning experiences where being able to interact with and rotate some 3D digital object can make a big difference in how you learn.

当然，3D的其他应用场景还包括营销，比如各种涉及3D生成的视频。

Some other ways to think about 3D are, of course, going to be marketing use cases, different types of videos that involve 3D generation.

说实话，我觉得总有一天，这个话题本身就能撑起一整期节目。

Honestly, I think at some point, this might be an entire show all on its own.

目前最重要的收获是，3D是Astra独特性的重要组成部分，也是值得投入机会时间的地方。

For now, the big takeaway is that 3D is a big part of what makes Astra unique and where it might be worth spending some of that opportunity time.

第七个思考起点是视频编辑流水线能力的另一个实例，具体建议是把客户故事变成影片。

A seventh thought starter is another instance of the video editing pipeline capabilities, and the proposal is to turn customer stories into films.

很多组织其实已经拥有了制作这类影片的原始素材。

Organizations often already possess the raw material for this.

比如对客户的采访、项目回顾、一些录音、截图和照片。

Think about an interview with a customer, a project review, some recordings, screenshots, photographs.

这些素材，借助像Fable 51或Astra这样的模型，就能变成一部迷你纪录片。

Those are things that, with a model like Fable 51 or Astra, could be turned into a mini documentary.

这不仅仅是单纯为了用视频而用视频。

And this is more than just using video for video's sake.

叙事能让证据活起来。

Narratives help bring evidence to life.

一张完成工作流程的截图可能展示出存在什么，但它本身并不能解释为什么有人需要它。

A screenshot of a finished workflow may show what exists, but it doesn't, on its own, explain why anyone needed it.

一段采访可能解释为什么这个情况很重要，但观众仍然需要看到发生了什么变化。

An interview may explain why the situation mattered, but the viewer still needs to see what changed.

你可能明白我要说什么了。

You probably get where I'm going with this.

你把那些东西放在一起组合起来，就能回答这两个问题。

You take those things and put them together in combination, and it answers both questions.

想象一下，一个客户描述一次失败的交接。

Imagine a customer describing a failed handoff.

讲述者解释缺少了什么信息，以及是什么阻止了他们去做。

The speaker explains what information was missing and what prevented them from doing.

然后，新流程的录音展示了这些信息现在去了哪里。

A recording of the new process then shows where that information now goes.

结果已经具体到可以理解了。

The outcome has become specific enough to understand.

力量在于这些时刻之间的关系。

The strength is in the relationship between those moments.

影片让观众能够理解最初的问题，审视发明，并评估结果是否解决了所描述的问题。

The film recognizes the audience to recognize the original problem, inspect the invention, and evaluate whether the outcome addresses the problem described.

影片还提供了一个更广阔的舞台来展示判断力。

The film also creates an expanded canvas to show judgment.

考虑了哪些替代方案？

What alternatives were considered?

为什么某个决定很难做？

Why was one decision difficult?

团队在第一次尝试后学到了什么？

What did the team learn after the first attempt?

和之前的视频例子很相似，我的建议是，试着把客户旅程的原始素材拿过来，扔进Claude Code加Fable 51，或者Codex加Astra，至少第一次的时候，让它来架构并整合所有内容，看看它一次性会做出什么。

Very similar to the previous video example, my suggestion for this one would be to try to take the raw assets of a customer journey, dump them into either Claude Code with Fable 51 or Codex with Astra, and at least for the sake of the first time, ask it to architect and put everything together to see what it does in one shot.

不过，我觉得一次性搞定这些事并不是你长期想采用的方式，但一旦你真正看到并上手操作，就会更容易理解它的价值。

Now, I don't think one-shotting these things is the actual way that you're going to want to do this in the long run, but I think it's going to be easier to understand the value of this once you're actually seeing and interacting with it.

所以，在你对整体能力有更全面的了解之前，不用花太多时间提前把它做到完美。

And so don't take too much time in advance to get it perfect before you have a sense of the capability more generally.

第八个思考起点我觉得特别有意思，也让人兴奋，因为它基本上是用一种强大的新方式来扩展教育、学习和职业发展。

Thought starter eight is one that I think is really interesting and exciting because it's basically a way to extend education and learning and professional development in a powerful new way.

核心想法是在真实情况发生之前，先创造一个体验或模拟体验。

The core idea is to create an experience or a simulation of the experience before the real situation occurs.

比如，可以是一个让人们练习如何处理棘手客户情况或与管理层进行困难沟通的地方。

It could be a place, for example, for people to practice how they handle difficult customer situations or difficult communication practices with management.

学习体验中最难的一点是，很难为学习者创造大量空间，让他们有机会表达自己的判断并得到反馈。

One of the things that's hardest about learning experiences is that it's hard to create a lot of space for learners to have a chance to express and get feedback on judgment.

练习环境在这方面确实很有用，通过创建模拟环境，你可以即时获得关于判断的反馈，从而能够中途调整和迭代。

Practice environments can be really good for exactly that, and by creating a simulation-style environment, you have the possibility of getting immediate feedback on that judgment in a way that allows you to iterate and navigate it midstream.

这又是一个我认为会形成一整类职业发展体验的例子，这些体验都植根于内部学习的练习环境和模拟环境，我非常期待看到大家在这方面创造出什么。

This is another one where I actually think this is going to be a whole category of professional development experiences that share this broad root in practice environments and simulation environments for internal learning, and I'm super excited to see what people create on this front.

第九个思考起点对两类人都有用：一类是真正设计实体产品的产品设计师，另一类是那些想体验像Astra这样的3D建模功能，但自己不一定有具体应用场景的人。

Thought starter number nine is good either, A, for product designers who actually design physical products in the real world, or B, for people who want a way to experience the power of something like Astra with 3D modeling, but who don't necessarily personally have a use case for exactly that sort of capability.

这个思考起点是关于可以制作原型的实体产品，核心想法是把物理条件当作可以设计的东西。

The thought starter is about physical products that you can prototype, and the idea is to treat physical conditions as something you can design.

这可以是实际的产品，但也可以更抽象一些。

Now, this could be something like an actual product, but it could also be a little bit more abstract.

比如，老师可以制作一个带有可拆卸部件的物体，让一些难以解释的复杂关系变得更直观。

For example, a teacher might create an object that has removable pieces to make some difficult relationship they're trying to explain more tangible.

这里我想说的是，因为这对很多人来说可能感觉很抽象，所以我特意加入了“让它成为我的”这个功能。

And what I'll say here, because this probably feels pretty abstract to a lot of you, is that this is exactly why I built in this Make It Mine feature.

我绝对不保证它会完美，但如果你给它一些关于你的背景信息，比如“我正在建造一种新型播客工作室，想思考这种可原型化的实体产品如何融入我的工作方式或我需要做的事情”，

I am absolutely not promising it will be perfect, but if you give it a little bit more context about you, for example, I'm building a new type of podcast studio and interested to think about where this sort of prototypeable physical products might fit into how I work or what I'm going to need to do.

你给它这些信息，然后让它寻找可能性。

You give it that info, and then you ask it to find possibilities.

在这种情况下，它给出的三个想法之一是把你的节目流程变成客人可以触摸的滑动积木。

So in this case, the three ideas it came back to are turning your episode rundown into sliding blocks guests can touch.

对我来说不太合适，但确实挺有创意的。

Not really a fit for me, but certainly creative.

第二个想法，让房间的尺寸来决定你的声学面板的形状。

Number two, let the room's own measurements shape your acoustic panels.

这个更有潜力，可能真的会很有用。

That one has more promise as something that might actually be valuable.

还有把你的工作室做成一个手掌大小的模型，让客户可以自己摆弄。

And building your studio as a hand-sized kit clients rearrange.

我家那帮坐不住的孩子可能会喜欢这个，虽然我不确定这对我有多大用处，但你明白我的意思。

My fidgety kids might like that, although I'm not sure that that's particularly relevant for me, but you get the idea.

“Make It Mine”这个功能希望能给你一些启发，让你能把这些想法变成你自己的。

The Make This Mine panel is hopefully going to give you some nuggets of how you can, well, make these ideas yours.

现在时间不多了，所以我要跳过第十个想法“构建浏览器功能”和第十一个想法“为你的网站组建测试团队”。

Now we're running out of time, so I'm actually going to skip thought starter 10, building browser features, and thought starter 11, a test crew for your website.

但就像我提到的，所有这些内容都会发布在 aidailybrief.ai 上，你可以去看看。

But as I mentioned, all of this will be available on aidailybrief.ai for you to check out.

最后我们用一个来收尾，因为我觉得这个可能对很多知识工作者来说更相关，尤其是考虑到我知道你们中有很多人都在直接帮助客户解决特定类型的智力和知识工作挑战。

And we'll close with the last one because I think it's something that might be a lot more relevant for a lot of you knowledge workers, especially given how many of you I know are in some form of the business of helping clients directly with specific types of intellectual and knowledge work challenges.

所以这最后一个AI机会思维启动器的想法是，把你的专业知识当作产品，基本上就是把你判断力的某个特定部分，做成一个别人可以操作的形式。

So the idea behind this last opportunity AI thought starter is your expertise as a product, basically giving a specific part of your judgment a form someone can work through.

当然，专家帮助通常以对话的形式出现，但其中其实包含了好几种工作。

Now, of course, expert help tends to manifest itself as a conversation, but several kinds of work are happening inside that.

你在收集背景信息、识别模式、注意例外情况、排除那些有吸引力但不合适的选项，并且判断这个人接下来准备好做什么。

You are gathering context, recognizing patterns, noticing exceptions, ruling out attractive but inappropriate options, and deciding what the person is ready to do next.

你给出的可见建议只是这个过程的最后一步，但有一个有趣的问题是，这个过程中的任何一部分，是否真的能变成一个人们可以自己操作和互动的产品。

The visible advice is the end of that process, but there's an interesting question about whether any part of that could actually turn into a product that people could manipulate and interact with themselves.

稍微有点自我指涉地说，我在这期节目旁边发布的这个互动网页应用，某种程度上就是这样的一个例子，对吧？

To get just a little bit meta, this entire interactive web app that I'm publishing alongside this episode is sort of an example of this, right?

与其我和你们每个人坐下来，给你们我最好的AI机会想法，告诉你们可能想去追求什么，我已经把很多这样的内容嵌入到了这个互动体验中。虽然它当然比不上我全神贯注地坐在你身边，但它确实给了很多人相当一部分可能从那种对话中得到的价值。

Instead of me sitting with each of you, giving you my best ideas for what opportunity AI ideas you might want to go pursue, I've embedded a lot of that in this interactive experience that, while sure isn't the same as sitting and having my undivided attention, gives a lot of people a pretty good chunk of the value that might have come from that sort of conversation.

通过把我们的专业知识变成产品，我们可以让自己规模化。

By turning our expertise into products, we can scale ourselves.

顺便说一句，这不只适用于那些有客户的人。

And by the way, this doesn't just have to apply to people who have clients.

你也可以把自己的专业知识变成产品，供团队成员或整个公司、组织里的人使用。

You also might be able to turn your expertise into a product that's available to other members of your team or your broader company and organization as well.

有意思的是，我们最后又回到一个启发性的想法：虽然构建这样的产品明显属于机会型AI的范畴，因为大多数人从来没想过能不能做一个自己的数字顾问版本，但在很多情况下，这也算是效率型AI，尤其是在公司内部，很多时候我们都在对不同的人重复同样的事情。

And fascinatingly, once again, we close on a thought starter that even though the building of this is firmly in the opportunity AI camp, because most of us have never sat around asking whether we could build a digital advisor version of ourselves, in a lot of situations, this will amount to efficiency AI as well, especially inside those company contexts where a lot of our time is spent on repeating the same things to different people.

所以最后再说一遍，效率型AI和机会型AI并不是非此即彼、水火不容的关系。

So to close out again, efficiency AI and opportunity AI are not somehow locked in moral conflict.

机会型AI的目标很简单，就是让我们突破自己，去思考那些以前从没想过能做的事情。

The goal of opportunity AI is simply to stretch ourselves and ask what we might do now that we never would have before.

希望这期节目能给你一些启发，让你不必事先就知道机会具体长什么样，我很期待看到这些想法能带来什么。

This episode hopefully has given you some ideas that won't have required you in advance to know exactly what opportunities could look like for you, and I'm excited to see if anything comes of it.

好了，这期周末版的《AI每日简报》就到这里。

For now, that is going to do it for this weekend episode of the AI Daily Brief.

一如既往，感谢你的收听或收看，我们下次再见，祝一切安好。

Appreciate you listening or watching, as always, and until next time, peace.