内容来源：https://nav.al/iterate

内容总结：

（本报讯）近日有观点指出，复杂系统往往源于对简单设计的持续迭代优化。这一规律在航天科技与人工智能领域得到充分印证——SpaceX的猛禽发动机经过多次升级，零部件数量显著精简；当前人工智能研究也表明，向简单算法持续输入海量数据能使系统日趋智能。

专家强调，优秀产品设计的关键在于化繁为简。特斯拉首席执行官马斯克倡导的"需求溯源法"颇具启发性：在优化系统前，首先质疑每个设计要求的必要性，通过消除冗余要求实现系统精简。据悉，特斯拉团队曾通过追溯电池隔音垫的设计初衷，发现该部件实际已无存在必要，最终成功简化了电池组装工艺。

研究显示，顶尖产品开发者往往具备跨学科思维。物理学基础训练被认为能培养把握系统本质的能力，而实践中的"动手派"——无论是自制竞速无人机的极客，还是组装个人电脑的爱好者——往往能最快掌握技术核心。专家建议，培养跨领域通识能力应优先学习具有广泛解释力的基础理论。

当前业界共识认为，保持系统架构的简洁性与可迭代性，是应对技术复杂化的有效路径。这一理念正推动着从操作系统设计到硬件制造的多个技术领域向更高效方向发展。

中文翻译：

复杂系统源自简单设计的迭代演变
尼维：我们都见过SpaceX火箭猛禽发动机的图片，如果观察其历代版本，会发现它们从"易于修改"逐步走向"难以改动"。因为最新版本的零部件几乎没有任何可随意调整的余地。

早期版本有无数零件可供调整——厚度、宽度、材料等等。而当前版本几乎没留下什么可改动的部件。

纳瓦尔：复杂性理论中有个观点：自然界中任何能正常运转的复杂系统，通常都是某个简单体系经过反复迭代的产物。

最近在人工智能领域就能看到这种现象——只需将海量数据输入简单算法，它们就会变得越来越智能。

反向操作往往行不通。当你设计出极其复杂的系统，并试图在此基础上构建大型可运行体系时，系统就会崩溃，因为复杂过度。因此产品设计的精髓在于持续迭代，直到找到有效的简约方案。过程中常会添加多余元素，这时就需要拨开迷雾重新提炼本质。

个人计算领域就是典型例证：macOS至今仍比iOS难用得多。iOS更接近操作系统的理想形态。不过基于大语言模型的操作系统或许会更进一步——毕竟能用自然语言交互。

要实现规模化，就必须做减法，猛禽发动机就是典范。当摸清运行规律后，就能识别冗余部分并予以剔除。

这正是马斯克的核心原则之一：优化系统应该是最后步骤。在提升效率之前，首先要质疑需求本身。

你会追问："这个需求为何存在？"

约根森新书提到的"埃隆方法"中，第一步就是追溯需求来源——不是哪个部门提出的需求，而是必须找到具体提出需求的个人。

找到那个说"这是我想要的"的人，直接询问："您真的需要这个吗？"

通过消除非必要需求，使需求总量缩减。接着在满足核心需求的前提下，尽可能减少零部件数量。这之后才考虑优化，比如如何高效制造部件并精准安装。最后阶段再研究成本效益和规模经济等问题。

将伟大产品从零到一打造出来，最关键的是需要有一个能全局掌控问题、协调权衡、洞悉每个组件存在意义的人——通常是创始人。

这类人未必需要亲自设计每个部件或精通制造细节，但必须能理解：这个部件为何在此？若移除A部件，B、C、D、E部件会受何影响？需要何种调整？这种对产品的整体认知至关重要。

猛禽发动机的设计就体现这种思维。马斯克举过个典型案例：他曾想提高特斯拉电池上玻璃纤维垫的安装效率。

当时生产线耗时过长，他直接带着睡袋驻守现场。团队试图优化粘贴垫片的机械臂，虽然略有改善，但进度依然缓慢。

最后他质疑："为什么需要这个步骤？为何要安装玻璃纤维垫？"

电池团队解释："为了降噪，您得咨询噪声与振动团队。"

噪声团队却表示："这与噪声无关，是为了电池起火时的隔热防护。"

再回头询问电池团队："我们真的需要这个吗？"
对方答："不需要。既没有火灾隐患，也不存在隔热问题，这个设计已经过时了。"

原来各部门只是沿袭传统做法。经过安装麦克风监测噪声等测试后，最终确认这个零件确实多余并予以移除。

这种情形在复杂系统和设计中屡见不鲜。

有趣的是，现在人人都自称"通才"，这往往是为逃避成为专才的托词。真正应该追求的是成为"博学家"——那种能掌握各领域至少80%核心知识的通才，从而做出明智的权衡。

尼维：我建议人们通过研究具有最广泛解释力的理论来培养这种博学能力。

纳瓦尔：更简洁地说就是学习物理学。

物理教会你理解现实世界的运行规律。打好物理基础后，你能轻松掌握电气工程、计算机科学、材料科学、统计概率等学科。数学本质也是物理的应用工具。

我接触过的顶尖人才大多有物理背景。若没有也不必气馁（我的物理就学得不好），可以通过其他途径补足。物理训练能培养人与现实交互的能力，其严苛性会涤除所有不切实际的幻想。

而社会科学领域常充斥各种荒诞观念。即便掌握社科领域使用的深奥数学工具，可能其中也只有10%是真知灼见，90%仍是谬误。

好在学习基础物理就足够受益，不必深究夸克或量子物理。掌握小球沿斜坡滚动这类基础原理，就能奠定良好认知基础。

其实所有STEM学科都值得研习。若已错过系统学习时机，只需与相关人才组队合作。真正优秀的人未必只学物理，他们往往是动手实践者：在无人机尚未军用化时组装竞速无人机，在机器人未成军事装备时制造格斗机器人，因不满足于学校计算机而自行组装个人电脑——正是这些始终运用最新工具和零件创造酷炫事物的人，最深刻理解技术本质，也最快推动认知边界拓展。

英文来源：

Complex Systems Emerge From Iterations On Simple Designs
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.