寻找最简单有效的方案

我们都见过SpaceX火箭猛禽发动机的照片，如果观察各个迭代版本，会发现它们从易于修改逐渐变得难以改动，因为最新版本几乎没有多少可供调整的部件。

We've all seen the pictures of the Raptor engine for the SpaceX rockets, and if you look at the various iterations, they go from easy to vary to hard to vary because the most recent version just doesn't have that many parts that you can fool around with.

早期版本有无数可调节部件——你可以改变厚度、宽度、材料等等。

The earlier versions have a million different parts where you could change the thickness of it, the width of it, the material, and so on.

当前版本几乎没剩下任何可供操作的部件了。

The current version barely has any parts left for you to do anything with.

有一种复杂性理论认为：当你在自然界中发现一个复杂系统在运作时，它通常是由一个极其简单的系统或事物经过反复迭代产生的。

There's a theory, an complexity theory, that whenever you find a complex system working in nature, it's usually the output of a very simple system or thing that was iterated over and over.

我们最近在人工智能研究中就看到了这种现象。

We're seeing this lately in AI research.

你只需要采用非常简单的算法，然后往里面灌入越来越多的数据。

You're just taking very simple algorithms and dumping more and more data into them.

它们就会变得越来越聪明。

They keep getting smarter.

反过来则效果不佳。

What doesn't work as well is the reverse.

当你设计一个非常复杂的系统，再试图将其扩展成大型功能系统时，它就会分崩离析。

When you design a very complex system, and then you try to make a functioning large system out of that, it just falls apart.

系统内部的复杂性太高了。

There's too much complexity in it.

所以很多产品设计就是不断迭代自己的方案，直到找到那个有效的简单核心。

So a lot of product design is iterating on your own designs until you find the simple thing that works.

通常你会围绕核心添加许多不必要的元素，之后又不得不回头从这些噪音中重新提炼出简洁的本质。

And often, you've added stuff around it that you don't need, and then you have to go back and extract the simplicity back out of the noise.

在个人计算领域就能看出这一点，macOS的使用难度仍明显高于iOS。

You can see this in personal computing, where macOS is still quite a bit harder to use than iOS.

iOS更接近操作系统的理想形态，而基于大语言模型的操作系统用自然语言交互，可能更接近这一理想。

IOS is closer to the platonic ideal of an operating system, although an LLM based operating system might be even closer speaking in natural language.

要实现规模化，最终必须精简部件，猛禽发动机就是典型案例。

Eventually, you have to remove things to get them to scale, and the Raptor engine is an example of that.

当你摸清哪些设计有效时，就会意识到哪些是冗余部件，进而将其移除。

As you figure out what works, then you realize what's unnecessary, and you can remove parts.

这正是马斯克的核心原则之一——他明确提出：系统优化应该是最后才考虑的事。

And this is one of Musk's great driving principles, where he he basically says, before you optimize a system, that's among the last things that you do.

在思考如何提升效率之前，首先要做的是质疑需求本身。

Before you start trying to figure out how to make something more efficient, The first thing you do is you question the requirements.

你要问：这个需求为什么存在？

You're like, why does the requirement even exist?

约根森新书提到的'埃隆方法'之一就是：追溯需求的源头，不是哪个部门提出的需求，而是——

One of the Elon methods in Jorgensen's new book is you first go and you've tracked down the requirement, and not which department came up with the requirement.

需求必须来自具体的个人。

The requirement has to come from an individual.

到底是哪个个体说过'这就是我想要的'？

Who's the individual who said, this is what I want?

你要回头去问：这个需求真的必要吗？

You go back and say, do you really need this?

然后把这个需求剔除。

You eliminate the requirement.

当你剔除了不必要的需求后，剩下的需求数量就减少了。

And then once you've eliminated the requirements that are unnecessary, then you have a smaller number of requirements.

现在你有了零部件，要尽可能去除多余部件，只保留绝对必要的需求。

Now you have parts, and you try to get rid of as many parts as you can to fulfill the requirements that are absolutely necessary.

之后，或许你才会开始考虑优化问题。

And then after that, maybe then you start thinking about optimization.

现在你要思考：如何最高效地制造这个部件并将其安装到正确位置？

And now you're trying to figure out how can I manufacture this part and fit it in the right place most efficiently?

最后，你可能会考虑成本效益和规模经济这类因素。

And then finally, you might get into cost efficiencies and economies of scale and those sorts of things.

将伟大产品从零到一打造出来最关键的人物，往往是能全局把握问题、做出权衡取舍，并理解每个组件存在意义的个人——通常是创始人。

The most critical person to take a great product from zero to one is the single person, usually the founder, who can hold the entire problem in their head and make the trade offs and understand why each component is where it is.

他们不一定要亲自设计每个组件、参与制造或了解所有细节，但必须能理解每个部件存在的理由。

And they don't necessarily need to be the person designing each component, manufacturing, or knowing all the ins and outs, but they do need to be able to understand why is this piece here.

如果移除部件A，那么部件B、C、D、E及其相关需求和考量会受到什么影响。

And if part a gets removed, then what happens to parts b, c, d, e, and their requirements and considerations.

这就是对产品的整体性把握。

It's that holistic view of the whole product.

你可以在猛禽发动机设计中看到这一点。

You'll see this in the Raptor engine design.

埃隆举的例子我觉得很典型：他当时想提高特斯拉电池顶部玻璃纤维垫的生产效率。

The example that Elon gives that I thought was a good one, he was trying to get these fiberglass mats on top of the Tesla batteries produced more efficiently.

于是他来到耗时过长的生产线，铺开睡袋直接驻扎在了现场。

So he went to the line where it was taking too long, put his sleeping bag down, and he just stayed at the line.

他们试图优化那个将玻璃纤维垫粘到电池上的机器人。

And they tried to optimize the robot that was gluing the fiberglass mats to the batteries.

他们试图更高效地安装这些垫子或加快生产线速度。

They were trying to attach them more efficiently or speed up that line.

他们确实做到了。

And they did.

他们设法改进了一些，但速度仍然慢得令人沮丧。

They managed to improve it a bit, but it was still frustratingly slow.

最后他问道，为什么会有这个要求？

And finally, he said, why is this requirement here?

为什么我们要在电池上铺玻璃纤维垫？

Why are we putting fiberglass mats on top of the batteries?

电池组的人说这其实是为了降噪。

The battery guy said it's actually because of noise reduction.

所以你得去和噪音与振动团队谈谈。

So you gotta go talk to the noise and vibration team.

于是他就去找了噪音与振动团队。

So he goes to the noise and vibration team.

他问道，我们为什么要用这些垫子？

He's like, why do we have these mats here?

噪音与振动问题到底是什么？

What is the noise and vibration issue?

而他们却说，不是这样的。

And they're like, no.

不。

No.

不存在噪音和振动问题。

There's no noise and vibration issue.

它们是因为热量才在那里的。

They're there because of heat.

电池着火了。

The battery catches fire.

然后他回到电池那里，像是在问：我们需要这个吗？

And then he goes back to the battery to be like, do we need this?

他们回答：不需要。

And they're like, no.

这里不存在火灾问题。

There's not a fire issue here.

这不是一个热保护问题。

It's not a heat protection issue.

那已经过时了。

That's obsolete.

这是一个噪音和振动问题。

It's a noise and vibration issue.

他们每个人都在按照自己受训的方式和以往的做法行事。

They had each been doing things the way they were trained to do and the way things had been done.

他们对安全性进行了测试，通过安装麦克风并追踪噪音来测试，最终认定不需要它。

They tested it for safety, and they tested it by putting microphones on there and tracking the noise, they decided they didn't need it.

于是他们就把这部分去掉了。

And so they eliminated the part.

这在非常复杂的系统和设计中经常发生。

This happens a lot with very complex systems and complex designs.

挺有意思的。

It's funny.

大家都说我是通才，这其实是他们逃避成为专家的借口。

Everybody says I'm a generalist, which is their way of copying out on being a specialist.

但真正应该追求的是成为博学者——一个能掌握每个专业至少二八法则水平的通才，这样才能做出明智的权衡。

But really what you wanna be is a polymath, which is a generalist who can pick up every specialty at least to the eighty twenty level so they can make smart trade offs.

我建议人们通过这种方式获得博学者能力：如果要学习或上学，就去研究那些适用范围最广的理论。

The way that I suggest people gain that polymath capability being a generalist that can pick up any specialty is if you are going to study something, if you are going to go to school, study the theories that have the most reach.

我可以进一步总结为：直接去学物理。

I would summarize that further and just say study physics.

一旦学了物理，你就是在研究现实世界的运行规律。

Once you study physics, you're studying how reality works.

如果有扎实的物理基础，你就能掌握电气工程、计算机科学、材料科学，以及统计概率学。

And if you have a great background in physics, you can pick up electrical engineering, you can pick up computer science, you can pick up material science, you can pick statistics and probability.

你还能掌握数学，因为它是物理的一部分。

You can pick up mathematics because it's part of it.

这是应用层面的。

It's applied.

我见过几乎所有领域最优秀的人都有物理背景。

The best people that I've met in almost any field have a physics background.

如果你没有物理学背景，别哭。

If you don't have a physics background, don't cry.

我的物理学背景也很糟糕。

I have a failed physics background.

你仍可通过其他途径达到目标，但物理教会你与现实互动，它如此严苛，能将所有美好的假象从你身上剔除。

You can still get there the other ways, but physics trains you to interact with the reality, and it is so unforgiving that it beats all the nice falsities out of you.

而如果你身处社会科学领域，你可以持有各种古怪信念。即便你掌握了社会科学中某些深奥数学，可能只有10%是真知，剩下90%可能是谬误。

Whereas if you're somewhere in social science, you can have all kinds of cuckoo beliefs Even if you pick up some of the abtruse mathematics they use in social sciences, you may have 10% real knowledge, but you may have 90% false knowledge.

物理学的好消息是，你可以学习相当基础的物理知识。

The good news about physics is you can learn pretty basic physics.

不必深究夸克和量子物理之类的领域。

You don't have to go all the way deep into quarks and quantum physics and so on.

只需从基础的小球滚斜坡开始，这实际上就是很好的知识铺垫。

You can just go with basic balls rolling down a slope, and it's actually a good backgrounder.

但我认为任何STEM学科都值得学习。

But I think any of the STEM disciplines are worth studying.

如果你已错过选择学习方向的机会，那就与他人合作吧。

Now if you don't have the choice of what to study and you're already past that, just team up with people.

事实上，最优秀的人未必只学物理。

Actually, the best people don't necessarily even just study physics.

他们是动手实践者。

They're tinkerers.

他们是创造者。

They're builders.

他们正在建造东西。

They're building things.

修补匠总是处于知识的前沿，因为他们总是使用最新的工具和零件来打造最酷的东西。

The tinkerers are always at the edge of knowledge because they're always using the latest tools and the latest parts to build the cool things.

所以是那个在无人机成为军事装备前就造出竞速无人机的人，或是在机器人成为军事装备前就造出格斗机器人的人，又或是那个因为想要家里有电脑而不满足于在学校使用电脑，于是组装个人电脑的人。

So it's the guy building the racing drone before drones are a military thing, or the guy building the fighting robots before robots are a military thing, or the person putting to the the personal computer because they want the computer in their home and they're not satisfied going to school and using the computer there.

这些人最了解事物，他们推动知识进步的速度也最快。

These are the people who understand things the best, and they're advancing knowledge the fastest.