Figure 的机器人技术突破：人形机器人

我刚录完整个播客节目，结果发现麦克风根本没开。

I just finished recording this full podcast episode and realized that my microphone was not turned on.

所以我又得重录一遍，说不定这次会更熟练，或者还能加一点额外的见解。

So I get to record it again, and maybe I'll be more practiced, or I'll add a little bit of extra insights in this.

希望这次效果更好。

Hopefully, it's even better.

如果我说话特别快，那是因为这些话我已经说过一遍了，所以可能就是这个原因。

If I talk really fast, I've already said this all once, so that's probably why.

今天在播客里，我想聊聊人形机器人领域目前最大的突破之一，一个巨大的技术飞跃。

Today on the podcast, I want to talk about one of the biggest unlocks in humanoid robotics that we've seen to date, a massive technological breakthrough.

说实话，这让我非常兴奋，而且这一切都来自Figure，一家正在和特斯拉人形机器人竞争的大型人形机器人公司。

Honestly something that I'm very excited about, and this is all coming from Figure, one of the biggest humanoid robot companies that's competing against Tesla's humanoid robot.

还有其他几家，但Figure目前是领先者之一。

There's a handful of others, but Figure's one of the big leading ones right now.

他们基本上创造了一个巨大的突破，让他们的机器人能够从事通用型工作。

They've created basically a massive breakthrough that allows their robots to do generalist work.

基本上，你把它放到一个新环境里，它现在就能适应了。

Basically, you take it and put it in a new environment, and it's able to adapt now.

所以我想分析一下他们是怎么做到这一点的。

So I want to break down how they're able to accomplish this.

他们解决了一些别人没解决，或者说可能别人也遇到过的问题，但他们的方案真的很厉害。

They built some really incredible solutions to problems that no one else, or I guess maybe other people had these problems.

不过在我们深入之前，我想提一下，如果你还没看过的话，我希望你去看看aibox.ai，那是我自己创立的公司，你可以在那里访问超过90种不同的AI模型，并且能在同一个聊天线程里使用它们。

But before we get into all of that, I wanted to mention that if you haven't already, I would love for you to go check out aibox.ai, which is my very own startup, where you can get access to over 90 different AI models, and you can work with them all in the same chat thread.

如果你厌倦了每个月给十个不同的平台各付20美元订阅费，用来生成音频、图片和视频，那你可以在一个地方获得来自Google、OpenAI、Anthropic的所有最佳模型。

If you're sick of paying subscriptions, $20 a month to 10 different platforms for audio, image, video generation, you can get all of the best models from Google, OpenAI, Anthropic, all in one place.

而且如果你用像Claude这样的工具，比如Claude Cowork，你还可以通过AI Box连接器直接在Claude里生成图片、视频或音频。

And if you use something like Claude for working something like Claude Cowork, you can actually get it to generate images right inside of Claude, or video or audio with the AI Box connector.

所以它每月只要8.99美元起。

So it starts at $8.99 a month.

如果你有非常大的项目，我们也有更高的套餐，但我希望这能帮你节省大量时间。

We have higher tiers if you have really big projects, but I hope this is something that saves you a ton of time.

最近我有个项目，让它在Claude里直接生成了1300张图片。

I recently had a project where I got it to generate 1,300 images right inside of Claude.

它把整个项目都帮我搞定了。

It was able to do the whole project for me.

我都不用去折腾，因为它用的是AI Box连接器。

I didn't have to mess around with it because it was using the AI Box connector.

它做完之后，我发现我网站的流量因为这个产品和这个项目暴涨了一大截。

And when it did that, I saw a massive spike in traffic to my website from that product and from that project that it worked on.

所以如果你想试试的话，描述区里有aibox.ai的链接。

So if you want to give it a try, there's a link in the description to aibox.ai.

好了，我们来聊聊Figure最近的情况。

All right, let's talk about what's going on with Figure.

昨天，他们的CEO发了一条推文，说：“嘿，我们马上要公布一个重大的突破。”

So yesterday, their CEO put out a tweet where he said, "Hey, we have a massive basically unlock that we're going to be sharing soon."

我真是等得心急如焚，然后今天大概一个小时前，消息终于放出来了。

I was on pins and needles, and today we got the drop about an hour ago.

顺便说一下，如果你正在看这个视频，或者你在Apple Podcasts上听，你也可以看我们的视频。

And by the way, if you're watching this on, or if you're listening on Apple Podcasts, you can watch us on video.

我们在Apple Podcasts上有视频，我会分享一些他们实际机器人的片段，看看它到底在做什么等等。

We have video on Apple Podcasts, and I'll be sharing some clips from their actual robot, what it's actually doing, et cetera.

但首先，我来解释一下。

But first, I'll explain.

他们发布了一个叫Helix 2.5的新东西。

So they released a new thing called Helix 2.5.

这是他们的新AI模型，也是他们机器人的一次新升级，让它能做通用清洁。

This is their new AI model, and a new update to their robots that let it do general purpose cleaning.

意思是，过去他们最大的问题是，他们能让机器人在熟悉的环境里打扫房子，也就是它训练过的环境，对吧？

Meaning in the past, the biggest problem that they had was that they could get their robot to clean a house in a familiar environment that it had been trained on, right?

它看过这个特定房子的训练数据，也见过它已经训练过的特定物体。

It had seen training data of this particular house, and with particular objects that it had already been trained on.

所以如果你把一堆东西扔在地上，而训练数据里有那些东西，它就知道怎么把那些东西从地上捡起来。

So if you threw a bunch of objects on the floor and the training data had those objects in it, it knew how to pick up those objects off the floor.

但如果遇到新物体，或者它不熟悉的新环境，它就做不到了。

But if you had new objects, if you had new environments that it was unfamiliar with, it was unable to do this.

所以这算是一个普遍性的问题：你可以让它针对特定环境进行训练，但它没法覆盖所有环境。

So there's kind of this generalized issue where you could get it trained on specific environments, but it didn't go to all environments.

他们想出了一个非常有创意的解决方案，我待会儿会解释，但首先我想告诉你们这个方案的结果。

They came up with a really creative solution, which I will explain in a second, but first I want to tell you the results of the solution.

这个方案的结果是，他们能够利用这一点，并且进行了一次大规模更新来测试它。

The results of the solution were that they were able to take this, and they did a big update to test this out.

他们在湾区租了30个不同的Airbnb房源。

They rented 30 different Airbnbs across the Bay Area.

这些房子都是Figure机器人从未见过、从未训练过的，而且他们还在房子里放了新物体。

So all homes that this Figure robot had never seen before, had never been trained on before, and they threw new objects in the home.

他们会把玩具扔得满地都是。

So they would throw toys all over the floor.

他们还会把床弄乱，那些床它从来没见过。

They would go mess up the beds, beds it had never seen.

他们会把一堆衣服扔在地上。

They would go put piles of laundry down.

它能够在完全陌生的环境里打扫房子、捡起所有玩具、整理床铺、叠好衣服，而这些物品它之前从未见过。

It was able to go clean the house, pick up all the toys, make all the beds, fold all the laundry in completely different environments where it had never seen these different objects before.

所以这就是泛化的清洁能力。

So this is the generalized cleaning.

他们在30个不同的家庭里测试，就是为了验证这个模型真的有效。

They were able to do this on 30 different homes to validate that this model actually works.

他们是怎么做到的呢？

How were they able to get this result?

这正是让我特别兴奋的地方。

This is what I was really excited about.

通常来说，这些模型获取训练数据的方式，传统上都是专门为这些机器人定制的。

So the way that these models get their training data typically and traditionally has been specifically for these robots.

有几种不同的方法。

There's a couple different ways.

其中一种方式是通过动作捕捉，基本上就是有人穿上一套特制的衣服。

One of them is through motion capture, so essentially that's a person wearing a special kind of suit.

这种技术在很多动画制作中也会用到，衣服上有小圆点，甚至还有特制的手套可以戴，然后你去做一个任务，摄像头会通过所有传感器观察你的一举一动，这样它就能准确理解你的动作。

You use the same technology for a lot of animation, but it's got little dots on the suit, and you even have special gloves you can wear, and you go and do a task with a camera watching how you do everything with all of the sensors, so it understands exactly how you move.

这种方式可以很好地转移到机器人身上。

That can transfer to a robot really well.

但问题就是我之前提到的，它只擅长特定的环境和特定的物体。

The problem is what I mentioned earlier, where it's just specific environments with specific objects that it's really good at.

如果你想让它做得更好，就需要更大的数据集。

And if you wanted to get better, you'd need a much bigger data set.

所以Figure公司实际上联系了很多不同的供应商，向他们索要数据集。

So Figure actually went to a bunch of different suppliers and requested data sets from them.

其中最大的供应商之一可能是Scale AI，你可能听说过这家公司。

One of the biggest is probably Scale AI that you might be familiar with.

Scale AI是一家被Meta收购的公司，我记得Meta花了20亿美元收购了这家公司大约50%的股份。

Scale AI is a company that was purchased by Meta, and I think about 50% of the company was purchased for $2 billion.

Scale AI的CEO后来转投Meta，现在担任Meta AI的首席执行官。

They had the CEO of Scale AI move over and is now the CEO of Meta AI.

这是一家大公司，为OpenAI、Anthropic、Google和Meta以及其他许多公司提供了大量的训练数据。

And so this was a huge company that did a lot of training data for OpenAI, Anthropic, Google, and Meta, and a lot of other players.

当然，中间还发生了一系列风波。当时他们拥有独家收购权，OpenAI表示不再从他们那里购买数据，因为Scale AI被Meta收购了，而Meta是他们的竞争对手，诸如此类。

Then, of course, there's all the drama where when they had sole acquisition and OpenAI said, "Hey, look, we're not buying any more data from them because they're purchased by Meta, and it's a competitor," yada yada.

所以事情还挺复杂的。

So there's a bunch of stuff that went on.

不过，Scale AI 做的可不仅仅是买数据。

Scale AI specifically, though, wasn't just perhaps buying data.

他们主要做的是数据的标注和分类，而且是批量处理的。

What they were doing was annotating and labeling the data, and they were doing it in bulk.

这也是他们公司估值那么高的原因。他们会去获取数据，比如法律方面的数据。

So the reason why their company was worth so much money, they would go and get data, and for example, they would go and get legal data.

然后他们会请律师来对这些数据进行标注和标记，说明这个代表什么意思、那个代表什么意思，最后打上标签。

Then they would have lawyers go and annotate the data and mark it up and say, "This thing means this, this thing means this," and kind of go and label it.

他们会在很多不同的行业、很多不同类型的数据上这么做。

And they would do this with a lot of different industries, a lot of different types of data.

Figure 公司去找像 Scale AI 这样的公司，说：“嘿，我们需要大量的第一视角数据。”

Figure went to companies like Scale AI and tried to say, "Hey, look, we need a whole bunch of egocentric data."

第一视角数据的意思就是，你戴一顶棒球帽，上面装一个朝下的摄像头。

Egocentric data just means you wear basically a baseball cap and a camera that's pointing down.

通常用你的智能手机就行。

Usually just your smartphone works.

它朝下拍。

It points down.

它必须能看到你的手。

It has to be able to see your hands.

如果你的手不在镜头范围内，那视频就没法用，但只要能看到你的手，你就可以去做各种任务。

If your hands are outside of the lens, then the footage won't work, but it's going to see your hands, and you go and do tasks.

比如你去洗碗、洗衣服什么的，只要摄像头能看到你在做什么，你就可以用这些第一视角的视频来训练机器人模型。

So you would go and clean your dishes, you would go and do the laundry, whatever, and if your camera can see your tasks, you can use this egocentric footage To train robot models.

像 Scale AI 这样的公司会从人们那里收集这些数据，构建小型数据集，然后进行销售。

Companies like Scale AI would go and gather this from people, build little datasets, and sell them.

我对这个行业非常熟悉，因为我正在为我自己的公司做一个项目。

This is an industry that I'm quite familiar with because I'm working on a project for my own company.

这个网站叫 fiund.com，拼写是 F-I-U-N-D。

It's called fiund.com, F-I-U-N-D.

顺便说一下，我们专门处理播客、音视频以及图像数据集方面的业务。

So by the way, we specifically work with podcasting, audio and video, and image datasets.

所以，如果你手头有这类数据集，或者你运营播客已经好几年了，欢迎联系我们。

So if you have any of those types of datasets, if you have a podcast you've run for years, let us know.

直接联系我吧。

Go reach out to me.

我会在简介里放一个链接，指向我的网站 Fiund。

I'll leave a link in the description to my website, Fiund.

我们可以授权这些内容，如果你拥有相关数据集，我们还能帮你实现商业化变现。

And we license that, and we help you monetize it if you have datasets.

但不管怎样，Figure 去找了这些公司，包括 Scale 在内的很多第三方供应商，他们说：“嘿，听着，我们需要买这些数据。”

But in any case, Figure went to these companies, lots of third-party vendors, including Scale, and they said, "Hey, look, we need to buy this data."

但对方说他们拿不到足够的量。

And they said they couldn't get enough quantity.

所以他们基本上是被卡住了，因为拿不到足够的数据供应。

So they were basically bottlenecked by not being able to get enough supply of this data.

那他们怎么办呢？

So what did they do?

Figure 决定干脆自己做一个产品，叫 Index。

Figure decided to go and just build their own product, which is called Index.

Figure 的 Index 项目基本上就是他们会寄给你一个特殊的摄像头和帽子，你戴上就行。

And Figure's Index program is essentially where they will send you a special camera and hat that you wear.

如果你在苹果设备上看的话，你可以看到它长什么样。

And if you're looking at this on Apple, you can see what this looks like.

它其实不太像 VR 头显。

It's not really like a VR headset.

它更像是一个绕在头上的带子，但带子上有个摄像头。

It's more like a band that goes around your head, but there's a camera on the band.

我之前提到的棒球帽加手机的方法其实也能用，但要是被撞歪了，就可能出问题。

The way I mentioned it earlier with the baseball cap and your phone is sort of a crude example that does work, but if you get bumped the wrong way, it could mess it up.

所以戴上这个头带，它就能拍你，然后他们会说：‘嘿，去洗衣服、去洗碗吧’，然后你做了这些事，他们就会付钱给你。

And so with this headband, it's able to just film you and they'll say, "Hey, go do your laundry, go do your dishes," and they'll pay you for doing that.

现在，这个项目有很多不同的赚钱方式，而且他们有很多不同类型的地方在帮他们收集这些数据。

Now, there's a lot of different monetization methods with this, and they have a lot of different types of places that are capturing this data for them.

但基本上，通过这个Index项目，他们现在每天有4000人往他们的数据集里提交内容。

But essentially what they were able to do with this Index program is they now have 4,000 people a day that are submitting content to their dataset.

他们之前搞不到足够大的数据集，所以不得不自己开发一个定制方案，你可以订购这种头带，去做你的日常事务，然后就能拿到报酬。

They couldn't get a big enough dataset, so they literally had to go build their own custom solution where you can order one of these headbands, go do your work, and you get paid for them.

我听说过好几家公司都有类似的项目。

I've heard about programs like this from a number of different companies.

最近有个叫Shift的项目火了，因为他们说：‘嘿，如果你让清洁工戴上这种带摄像头的帽子，我们就免费帮你打扫房子’，这样就能为清洁工作收集训练数据。

There's Shift that went viral recently because they said, "Hey, look, we'll clean your house for free if you let the cleaners wear one of these hats with a camera on it," so that it's training data for cleaning.

做这件事有很多不同的方式，但归根结底，没有人能达到Figure需要的规模。

There's a lot of different ways to do this, but at the end of the day, no one was able to get it at the volume that Figure needed.

所以他们自己建了解决方案，就是Figure Index，现在他们有4000名贡献者。

So they built their own solution, the Figure Index, and now they have 4,000 contributors.

他们收集了所有这些数据。

They took all of that data.

我记得他们提到有26.4万次应用下载。

I think they said they have 264,000 app downloads.

他们收到了来自108个不同国家的1600万个视频，而且他们说已经向创作者支付了1500万美元。

They have 16 million videos that have been uploaded from 108 different countries, and they said that they've paid out $15 million to creators.

所以你现在就可以去加入Index项目赚钱。

So you could go today and join the Index program and make money.

关于支付方面，我建议你去了解一下。

As far as the payments, and I would say go look into this.

这挺有意思的。

This is interesting.

人们做的任务五花八门，比如搬 groceries 回家、倒垃圾、洗车、洗炉子、耙石头。

There's all sorts of tasks people doing, bringing in their groceries, taking out the trash, washing their car, washing their stove, raking rocks.

还有各种各样的任务，比如在他们的网站上摘草莓、在机加工车间里干活、叠披萨盒、洗衣服、做墨西哥卷饼。

There's all sorts of tasks that-- picking strawberries on their website, inside of machine shops, folding pizza boxes, doing laundry, making quesadillas.

他们的网站上展示了各种各样的任务。

All sorts of tasks are demoed on their website.

所以在这里你可以做很多不同的事情。

So there's a lot of different things that you can do here.

不过，根据我看到的一些估算，他们为五分钟的视频支付一美元。

That being said, from a lot of the estimates that I was seeing, they're paying a dollar for a five-minute video.

所以如果你去倒垃圾，就是一美元。

So if you go take out your trash, it's a dollar.

如果你去做这个，也是一美元。

If you go do this, it's a dollar.

在某些情况下，这可能值很多钱。

In some cases, it could be worth a lot of money.

我还看到其他地方每小时只付20美元，顺便说一句，他们会去找仓库工人说：‘嘿，你是仓库工人，每小时赚25美元。

I saw other places that were just paying $20 an hour, which, by the way, they would go to find warehouse workers and say, "Hey, you're a warehouse worker making 25 bucks an hour.

我们每小时再付你20美元，只要你戴着这个头戴式摄像头工作。’

We'll pay you another $20 an hour just to wear this headband camera around with you while you do it."

我觉得很多人都会接受这个提议。

And I think a lot of people would take that up.

现在他们每小时赚45美元，所以这很划算。

Now they're making 45 bucks an hour, and so it makes a lot of sense.

所以Figure公司能够利用他们现在拥有的所有这些数据，而且这个模型并没有在训练中。

So Figure was able to take all of this data that they now have, and the model is not being trained.

他们的做法并没有什么重大突破。

There's not some big breakthrough in how they're doing it.

他们只是能够获得更好的数据。

They were just able to get better data.

他们把所有这些数据都放进去，这基本上就是他们最大的解锁点。

They put all of this data in, and this is basically what the big unlock was for them.

所以现在Figure的机器人能够在全新的、从未见过的环境中四处走动并打扫房屋，基本上实现了这种通用的清洁能力。

So now the Figure robots are able to go around and clean houses in completely new environments that they've never seen before, and they've basically unlocked this generalized cleaning.

那么，或者说通用工作，这将来会怎么发展呢？

So where does-- or generalized work.

未来会走向何方？

Where does this go in the future?

我觉得他们现在拥有的很多数据都是人们在家里的，就是一些普通人。

I think a lot of the dataset that they have right now is people around their house, just random people.

我觉得他们会更多地去获取专业化的数据集。

I think they're going to get more into specialized datasets.

这就是他们需要重点关注的地方。

This is where they're going to have to focus.

所以他们可能会说，理论上有可能他们会说：“嘿，听着，我们在打扫房屋方面已经饱和了。”

So they might say, it's theoretically possible that they could say, "Hey, look, we're saturated on cleaning the house.

我们的机器人是世界上最棒的房屋清洁工。

Our robot is the greatest house cleaner in the world.

我们不想再看叠衣服的视频了。

We don't want any more laundry folding videos.

求求你们，别发了。

We beg you, please stop."

我有可能这么说。

There's a chance I would say that.

我觉得他们会继续收集数据，打造一个极其庞大的数据集。

I think they'll just keep collecting data and just making an absolutely astronomical dataset.

我觉得对他们来说，成本相比这能带来的收益来说微不足道。

I think the cost for them is nominal compared to what this could unlock for them.

但我觉得未来他们会不可避免地更加注重把机械师引入他们的Index项目。

But I think where this will go in the future is they're going to inevitably put a bigger focus on trying to onboard people into their Index program that are mechanics.

他们会吸引一大批修车店加入。

They get a bunch of mechanic shops on.

也许他们会和O'Reilly Auto Parts达成协议，让O'Reilly要求所有机械师都佩戴这个设备，或者和沃尔玛的货架补货员合作，或者你可以想象他们会建立一些企业合作伙伴关系。

Maybe they'll make a deal with O'Reilly Auto Parts and have O'Reilly mandate that all of the mechanics need to wear this, or they'll go in with Walmart shelf stockers, or you could imagine that they would have some corporate partnerships that they would create.

然后我觉得他们会去找那些专业行业，比如做空调、暖通空调的人，这些人做的事情非常专业。

And then I think they'll just go and find specialized industries where people are doing very specialized things like an AC, an HVAC person.

他们还会让电工参与进来。

They'll have electricians go in.

最终，通过这种方法，他们就能训练人形机器人去做很多这类工作。

Eventually, with this method, they'll be able to train humanoid robots to be able to do a lot of this work.

这未来会走向哪里呢？

Where does this go in the future?

这个话题我已经聊了大概两年了。

This is something I've been talking about for about two years.

如果你了解我，就知道我一直说这迟早会来，AI和聊天机器人并不是终点。

If you know me, I've been saying that this is coming, that AI and a chatbot is not the end of it.

终点是人形机器人，因为到目前为止，机器人一直有个问题，就是脚上装轮子，或者在仓库里跑来跑去搬货架和箱子，而且训练机器人基本上只能针对非常具体的任务和非常特定的环境，很难让它做更多的事情。

The end of it is humanoid robots, because for up until now, robots have always had this problem where they got wheels on their feet, or they zip around a warehouse picking up pallets and crates, and you train the robot based off of basically for a very specific task and a very specific environment, and it's hard to get it to do much more than that.

这种通用化，有点像通用人工智能。

This generalized, it's kind of like artificial general intelligence.

现在机器人也有了这种能力，而他们能解锁它的方式就是更多的训练数据，因为这些仿人机器人——我也听人说过，比如“你肯定能想出更好的外形吧。”

We've got that for robots now, and how they've been able to unlock it is just way more training data because these humanoid robots-- I've also heard people say, like, "Surely you can come up with a better form factor.

为什么要做仿人机器人呢？

Why are we doing humanoid robots?

为什么不做成特殊形状的机器人呢？"

Why don't we just make robots that are special shapes?"

就是因为训练数据。

It's because of the training data.

我们可以直接给它们看人类做事的视频，而且人类已经把整个世界都建造成适合人类的样子了，对吧？

We can just give them videos of how humans do things, and humans have built the entire world for humans, right?

比如门把手的高度，你开车门的方式，一切都是为人类设计的，所以一个最有用的机器人，看起来和行动起来完全像人类，这完全合理。

Like The way that the height of a doorknob, the way that you open your car, everything is built for a human, so it would make perfect sense that a robot that's the most useful, it just looks and acts exactly like a human.

它可以用完全相同的数据集来训练。

It can be trained off of the exact same data set.

所以我觉得他们会投入更多——他们会在这方面加大投资。

So I think they're going to be purchasing more-- They're going to be putting more investments into this.

他们特别提到，在这些方面，他们每年会投入超过20亿美元用于计算，来训练这些模型和机器人。

They said specifically on all of this that they're going to budget over $2 billion a year on compute for training these models and training these robots.

所以从那个角度来看，会有大量的投资涌入，我觉得我们可以期待这一切加速发展，包括这些机器人能做什么，以及它们会进入哪些行业。

So there's going to be some massive investments that are coming into it from that end, and I think we can just expect all of this to accelerate at what these robots are going to be capable of doing and the industries that they will go into.

那这意味着什么呢？

Now, what does this mean?

我知道很多人，可能有点危言耸听地说，这是工作的终结，机器人接管一切，替我们做所有事情，那我们该怎么办？

I know a lot of people have said, I don't know, in alarmist ways, it's the end of jobs and what are we going to do when the robots take over and they're doing everything for us?

我的看法是，是的，我们会有很多机器人做很多事情，但归根结底，就像ChatGPT本身不会替你做工作，不会替你经营生意一样。

This is my opinion is yes, we'll have a lot of robots doing a lot of things, but at the end of the day, just like ChatGPT in and of itself isn't doing your job for you, it's not running your business for you.

你还是得坐在那里管理它。

You're still sitting there managing it.

即使有智能体帮你做事，你也得监督它们。

Even if you have agents doing things, you have to monitor your agents.

你得设置好你的工作流程。

You have to set up your workflows.

当有东西出故障时，你得去优化它们。

You have to optimize them when something breaks.

我觉得机器人也是一样的道理，归根结底，你总是需要一个人和一个组织来监督机器人，管理它们在做什么。

It's going to be the same thing with robots, I think in, at the end of the day, that you're always going to need a person and an organization to oversee the robots, to manage what they're doing.

我觉得有很多有趣的概念我已经聊了很久了，感觉它们越来越接近现实了。

I think there's a lot of interesting concepts that I've been talking about for a long time, and it feels like they're getting a lot closer.

其中一个就是，你可以想象一家园林绿化公司，有一辆特斯拉Cybercab，再说一遍，非常——或者就是一辆具备自动驾驶功能的特斯拉。

One of them being, you can imagine something like a landscaping company where you have a Tesla Cybercab, again, getting very-- or just a Tesla has self-driving.

你让一个机器人进去——想象一下，你给其中一个机器人一台电动割草机。

You have a robot go into there with-- Imagine you just give an electric lawnmower to one of these robots.

你让它进入Cybercab，这在经济上可行吗？

You have it go into a Cybercab, and is this economically viable?

更好的问题是，但同样，很多公司花在Anthropic积分上用来重建软件的那些代币，在经济上可行吗？

Better question, but also is all of the tokens that a lot of these companies spend on Anthropic credits to rebuild software economically viable?

那里也有一个争论，可能人们花钱太多了。

There's also a debate there that maybe people are overspending.

但不管怎样，你可以给其中一个机器人配一台电动割草机，把它放进赛博出租车里，让它到处跑。假设你经营一家草坪护理公司，它就能替你去完成你的草坪护理路线。

But regardless, you could give an electric lawnmower to one of these robots, stick it in a Cybercab, have it drive around, and let's say you had a lawn care business, it could go do your lawn care route for you.

所以，这挺有意思的。

So, this is pretty interesting.

那么，如果出了什么问题，比如它不小心把你的玫瑰花坛给割了，谁来负责呢？

Now, who's liable if something goes wrong, if it accidentally mows over your rosebed?

有一堆理论上的问题，你可能还是需要一个真人在场，也许你只是让机器人快速去干活，而你在一旁管理和监督。

There's a bunch of theoretical problems where you might want to have a real human there, and maybe you just have your robot to go and do things quickly while you're going and managing and watching it.

所以我不清楚这些事情的监管是怎么样的。

So I don't know what the oversight of these things are.

我不知道远程控制是怎么运作的。

I don't know how remote control works.

我不知道这会发展到什么程度，但你可以想象，会有完全由这些机器人运营的企业，而且我觉得很多婴儿潮一代正在退休，出售他们的企业。

I don't know where this gets to, but you can imagine there will be full businesses that are running with these robots to the point where I think there's a lot of the baby boomer generation that is retiring and selling businesses.

在私募股权领域有个著名的笑话或说法，就是你会去收购某个人的暖通空调公司，他手下有……或者你会把某个城市里100家暖通空调公司的老板的企业整合成一个大集团，然后更高效地运营它们，等等等等。

It's a famous joke or a famous thing in private equity where you're going to go and buy some guy's HVAC company where he's got-- Or you're going to roll up 100 HVAC guys' companies into a big HVAC conglomerate in a specific city, and you're going to run them all more efficiently, yada yada.

有很多这样的人，他们经营公司很久了，现在要退休了。

There's a lot of these people that have run companies for a long time, and they're retiring.

你可以想象，现在的新商业模式就是接手别人的暖通空调公司，如果你不想自己去做空调的预防性维护，不想爬上红罗宾的屋顶拿着水管冲洗所有的空气过滤器，你可以让机器人去做这些。

You can imagine the new business move here would be go take over someone's HVAC company, and if you don't want to go do preventative maintenance on AC units yourself, you don't want to go and get on the roof of Red Robin with a hose and spray out all their air filters, you could just get a robot to go do that.

所以你可以收购这家公司，然后让机器人去做大部分工作。

So you could acquire this company and have the robot go and do a lot of the work.

你可以在那台机器上装个摄像头，每天结束时检查一下工作，确保一切正常，然后发账单，这就能成为一种新型的自动化业务。

You probably have a camera on that thing where you can review the work at the end of the day, make sure everything looks good, send out the bill, and it could be a new kind of automated business.

这并不适用于所有行业，还有很多非常棘手、复杂的问题需要人类来解决。

And this isn't for every industry, and there's a lot of things that probably are very tricky, complex problems that you want humans solving.

但是有很多枯燥乏味的工作，这些机器人其实都能做。

But there's a lot of the mundane stuff that these robots could actually do.

所以我觉得活在这个时代真是令人兴奋。

So I think this is a fascinating time to be alive.

毫无疑问，未来还会有更多的东西涌现出来。

Undoubtedly, there is so much more that's going to be coming out.

我们正处在最前沿。

We're at the cutting edge.

现在还没有人拥有这些机器人。

No one has these robots yet.

这些只是正在构建的概念和工具。

These are just the concepts and the tools that are being built.

但你可以想象，这绝对是巨大的。

But you can imagine this is absolutely colossal.

当我们谈到生产力的释放，人形机器人将会是关键。

When we talk about productivity being unlocked, humanoid robots are going to be it.

你把AI，比如来自Astra、OpenAI或Claude的智能，装进这些机器人里。

You get the AI, the intelligence from something like Astra or OpenAI or Claude, and you stick it into these robots.

这释放了巨大的潜力，也解决了我们面临的一个问题：需要雇佣大量人员从事低技能或低薪劳动。

That unlocks so much, and you get past this issue that I think we have where you need to hire mass amounts of people to do low-skill labor or low-pay labor.

想想仓库和工厂。

You think of warehouses and factories.

我觉得很多这样的工作都会由机器人来完成。

I think a lot of that will be done by robots.

那工作会变成什么样呢？

So what happens to jobs?

那些被取代的人会怎么样？

What happens to the people that get displaced by this?

这里面有很多问题需要考虑，但归根结底，当洗衣机、洗碗机被发明出来的时候，那些手洗衣服、手洗碗的人肯定也有抱怨，但最后，他们现在操作大型工业洗碗机，餐厅也能赚更多钱，利润更高，工作机会也重新分配了。

There's a lot of questions that go into it, but at the end of the day, when the washing machine, when the dishwasher was invented, I'm sure people that were washing it by hand complained, but at the end of the day, now they run the big industrial dishwasher, and the restaurant can make more money and have more profits and jobs shift around.

所以看看这个转变过程中会发生什么会很有趣，但最终，作为社会，我们无疑会因为这一切而变得更加高效。

So it'll be interesting to see what happens in the shift, but at the end of the day, we will, as a society, undoubtedly become more productive with all of this.

各位，这一集真的非常精彩。

Guys, this was a fascinating episode.

非常感谢你们的收听。

Thank you so much for tuning in.

如果你喜欢这一集，第一，如果你有任何数据集，如果你有播客，或者你认识有播客的人，请把他们介绍给我，Fiund，F-I-U-N-D，点com。

If you enjoyed this episode, number one, if you have any sort of data set, if you have a podcast, if you know someone with a podcast, send them my way to Fiund, F-I-U-N-D, .com.

我很乐意帮忙评估他们的数据集，如果他们感兴趣的话，也可以帮他们把这些数据变现。

I would love to help evaluate it and help them monetize their data set if that's something that they're interested in.

第二点，去了解一下我的创业项目 aibox.ai 吧，每月只需 8.99 美元，就能在一个地方使用所有 AI 模型。

And number two, go check out my startup, aibox.ai, if you want to get access to every AI model in one place for $8.99 a month.

好的。

All right.

各位，我们下期节目再见。

I'll catch you guys all in the next episode.