找到最简单的有效方案
找到最简单的有效方案
Nivi: 我们都见过SpaceX火箭猛禽发动机的图片,如果你观察各个迭代版本,它们从易于变化发展到难以变化。因为最新版本几乎没有多少你可以随意摆弄的零件。
早期版本有数百万个不同的零件,你可以改变它们的厚度、宽度、材料等等。当前版本几乎没有任何零件让你可以动手改动。
Naval: 复杂性理论中有个理论认为,每当你在自然界中发现一个复杂的系统在运作时,它通常是一个非常简单系统或事物经过反复迭代的产物。
我们最近在人工智能研究中看到了这一点——你只是采用非常简单的算法,然后向它们输入越来越多的数据。它们变得越来越智能。
相反的做法效果不佳。当你设计一个非常复杂的系统,然后试图从中构建一个功能完善的大型系统时,它就会崩溃。里面有太多的复杂性。因此,很多产品设计都是对自己的设计进行迭代,直到找到简单的有效方案。通常你会在它周围添加一些不需要的东西,然后你必须回头从噪音中提取出简单性。
你可以在个人计算中看到这一点,macOS仍然比iOS难用得多。iOS更接近操作系统的柏拉图式理想。尽管基于LLM的操作系统可能更接近——用自然语言说话。
最终,你必须移除一些东西才能实现规模化,猛禽发动机就是一个例子。当你弄清楚什么有效时,你就会意识到什么是多余的,然后可以移除零件。
这是马斯克的重要驱动原则之一,他基本上说:在你优化系统之前,那是你最后要做的事情之一。在你开始尝试如何提高效率之前,你首先要做的是质疑需求。
你会问:“为什么会有这个需求?”
Jorgenson新书中的Elon方法之一是,你首先去追踪需求的来源。不是哪个部门提出了需求;需求必须来自个人。
是谁说:“这就是我想要的。”
你回去问:“你真的需要这个吗?”
你消除这个需求。一旦你消除了不必要的需求,你就有了更少的需求。现在你有了零件,你试图尽可能多地摆脱零件,以满足绝对必要的需求。
在那之后,也许你才开始考虑优化,现在你试图弄清楚如何制造这个零件并将其最有效地安装到正确的位置。最后,你可能会考虑成本效率和规模经济之类的事情。
将一个伟大产品从零到一打造出来的最关键人物是那个能够将整个问题装在脑子里、做出权衡取舍、并理解每个组件为何在那里的单个人——通常是创始人。
他们不一定需要是设计每个组件的人,或者制造或了解所有细节的人,但他们确实需要能够理解:为什么这个部件在这里?如果移除零件A,那么零件B、C、D、E及其需求和考虑因素会发生什么?
这就是对产品的整体视角。
你会在猛禽发动机设计中看到这一点。Elon给出的一个我认为很好的例子——他试图更高效地生产特斯拉电池顶部的玻璃纤维垫。
所以他去了那条花费时间太长的生产线,放下睡袋,就待在生产线上。他们试图优化将玻璃纤维垫粘到电池上的机器人。他们试图更有效地粘贴它们或加快那条生产线的速度。他们确实做到了——他们设法改进了一点,但仍然慢得令人沮丧。
最后他说:“为什么会有这个需求?为什么我们要在电池顶部放玻璃纤维垫?”
电池团队的人说:“实际上是为了降噪,所以你得去和噪音与振动团队谈谈。”
所以他去了噪音与振动团队。
他问:“我们为什么在这里放这些垫子?噪音和振动问题是什么?”
他们说:“不,不——没有噪音和振动问题。它们在那里是因为热量,如果电池着火的话。”
然后他回到电池团队问:“我们需要这个吗?”
他们说:“不需要。这里没有火灾问题。不是热保护问题。那是过时的。是噪音和振动问题。”
他们每个人都在按照他们被训练的方式做事——按照一直以来的方式做事。他们测试了安全性,通过在那里放置麦克风并跟踪噪音来测试,他们决定不需要它,于是消除了这个零件。
这在非常复杂的系统和复杂的设计中经常发生。
有趣的是——每个人都说”我是个通才”,这是他们逃避成为专家的方式。但真正你想成为的是博学家,这是一个能够掌握每个专业领域、至少达到80/20水平的通才,这样他们就能做出明智的权衡。
Nivi: 我建议人们获得那种博学能力——成为能够掌握任何专业领域的通才——的方法是,如果你要学习某样东西,如果你要去上学,就学习那些影响范围最广的理论。
Naval: 我会进一步总结,就说学习物理学。
一旦你学习物理学,你就是在研究现实如何运作。如果你有很好的物理学背景,你可以掌握电气工程。你可以掌握计算机科学。你可以掌握材料科学。你可以掌握统计学和概率论。你可以掌握数学,因为它是其中的一部分——它是应用性的。
我在几乎所有领域遇到的最优秀的人都有物理学背景。如果你没有物理学背景,不要哭泣。我有一个失败的物理学背景。你仍然可以通过其他方式达到目标,但物理学训练你与现实互动,它是如此无情,以至于它会把你所有美好的虚假想法都打掉。
而如果你在社会科学领域,你可以有各种疯狂的想法。即使你掌握了社会科学中使用的一些深奥数学,你可能只有10%的真实知识,但90%是虚假知识。
关于物理学的好消息是,你可以学习相当基础的物理学。你不必深入研究夸克和量子物理学等等。你只需要学习球从斜坡滚下的基础物理,这实际上是一个很好的基础。
但我认为任何STEM学科都值得学习。现在如果你没有选择学习什么,而且已经过了那个阶段,那就与人合作。实际上,最优秀的人不一定只学习物理学。他们是修补匠,是建造者,他们在建造东西。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最新的零件来建造酷炫的东西。
所以是在无人机成为军事事物之前建造竞速无人机的人,或者在机器人成为军事事物之前建造战斗机器人的人,或者因为想要家里的电脑而不满足于去学校使用电脑而组装个人电脑的人。这些人是理解事物最好的人,他们以最快的速度推进知识。
Find the Simplest Thing That Works
Nivi: 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.
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. The current version barely has any parts left for you to do anything with.
Naval: There’s a theory in 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.
We’re seeing this lately in AI research—you’re just taking very simple algorithms and dumping more and more data into them. They keep getting smarter.
What doesn’t work as well is the reverse. When you design a very complex system and then you try to make a functioning large system out of that, it just falls apart. There’s too much complexity in it. So a lot of product design is iterating on your own designs until you find the simple thing that works. 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.
You can see this in personal computing where macOS is still quite a bit harder to use than iOS. 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.
Eventually, you have to remove things to get them to scale, and the Raptor engine is an example of that. As you figure out what works, then you realize what’s unnecessary and you can remove parts.
And this is one of Musk’s great driving principles where he basically says: Before you optimize a system, that’s among the last things that you do. Before you start trying to figure out how to make something more efficient, the first thing you do is you question the requirements.
You’re like, “Why does the requirement even exist?”
One of the Elon methods in Jorgenson’s new book is you first go and you track down the requirement. And not which department came up with the requirement; the requirement has to come from an individual.
Who’s the individual who said, “This is what I want.”
You go back and say, “Do you really need this?”
You eliminate the requirement. And then once you’ve eliminated the requirements that are unnecessary, then you have a smaller number of requirements. 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.
And then after that, maybe then you start thinking about optimization, and now you’re trying to figure out how can I manufacture this part and fit it into the right place most efficiently. And then finally, you might get into cost efficiencies and economies of scale and those sorts of things.
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.
And they don’t necessarily need to be the person designing each component, or manufacturing or knowing all the ins and outs, but they do need to be able to understand: Why is this piece here? And if Part A gets removed, then what happens to Parts B, C, D, E and their requirements and considerations?
It’s that holistic view of the whole product.
You’ll see this in the Raptor engine design. 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.
So he went to the line where it was taking too long, put his sleeping bag down, and just stayed at the line. And they tried to optimize the robot that was gluing the fiberglass mats to the batteries. They were trying to attach them more efficiently or speed up that line. And they did—they managed to improve it a bit, but it was still frustratingly slow.
And finally he said, “Why is this requirement here? Why are we putting fiberglass mats on top of the batteries?”
The battery guy said, “It’s actually because of noise reduction, so you’ve got to go talk to the noise and vibration team.”
So he goes to the noise and vibration team.
He’s like, “Why do we have these mats here? What is the noise and vibration issue?”
And they’re like, “No, no—there’s no noise and vibration issue. They’re there because of heat, if the battery catches fire.”
And then he goes back to the battery team like, “Do we need this?”
And they’re like, “No. There’s not a fire issue here. It’s not a heat protection issue. That’s obsolete. It’s a noise and vibration issue.”
They had each been doing things the way they were trained to do—in the way things had been done. They tested it for safety, and they tested it by putting microphones on there and tracking the noise, and they decided they didn’t need it, and so they eliminated the part.
This happens a lot with very complex systems and complex designs.
It’s funny—everybody says “I’m a generalist,” which is their way of copping out on being a specialist. But really what you want to be is a polymath, which is a generalist who can pick up every specialty, at least to the 80/20 level, so they can make smart trade-offs.
Nivi: 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.
Naval: I would summarize that further and just say study physics.
Once you study physics, you’re studying how reality works. 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 up statistics and probability. You can pick up mathematics because it’s part of it—it’s applied.
The best people that I’ve met in almost any field have a physics background. If you don’t have a physics background, don’t cry. I have a failed physics background. You can still get there the other ways, but physics trains you to interact with reality, and it is so unforgiving that it beats all the nice falsities out of you.
Whereas if you’re somewhere in social science, you can have all kinds of cuckoo beliefs. Even if you pick up some of the abstruse mathematics they use in social sciences, you may have 10% real knowledge, but 90% false knowledge.
The good news about physics is you can learn pretty basic physics. You don’t have to go all the way deep into quarks and quantum physics and so on. You can just go with basic balls rolling down a slope, and it’s actually a good backgrounder.
But I think any of the STEM disciplines are worth studying. Now if you don’t have the choice of what to study and you’re already past that, just team up with people. Actually, the best people don’t necessarily even just study physics. They’re tinkerers, they’re builders, they’re building things. 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.
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 together the personal computer because they want the computer in their home and they’re not satisfied going to school and using the computer there. These are the people who understand things the best, and they’re advancing knowledge the fastest.