How Elon Musk solves problems: First principles thinking explained | Lex Fridman Podcast Clips

How Elon Musk solves problems: First principles thinking explained | Lex Fridman Podcast Clips - https://www.youtube.com/watch?v=54OSbbtXrdI

Can you then zoom back in to specific problems with starship or any engineering problems you work on? Can you try to introspect your particular biological neural network, your thinking process, and describe how you think through problems, through different engineering and design problems? Is there like a systematic process? You've spoken about first principles thinking, but is there kind of process to it? Well, yeah, I like saying physics is the law and everything else is a recommendation. I've met a lot of people who can break the law, but I haven't met anyone who could break physics. So first, any kind of technology problem, you have to just make sure you're not violating physics. And, you know, first principles analysis, I think, is something that can be applied to really any walk of life, anything really. It's really just saying, let's boil something down to the most fundamental principles. The things that we are most confident are true at a foundational level. And that sets your axiomatic base. And then you reason up from there, and then you cross check your conclusion against the axiomatic truths. So some basics in physics would be like, are you violating conservation of energy or momentum or something like that? Then it's not going to work. So that's just to establish, is it possible? Another good physics tool is thinking about things in the limit. If you take a particular thing and you scale to a very large number or to a very small number, how do things change, both in number of things you manufacture or something like that, and then in time. Yeah, let's say take an example of manufacturing, which I think is just a very underrated problem. And like I said, it's much harder to take an advanced technology product and bring it into volume manufacturing than it is to design it in the first place. Orders magnitude. So let's say you're trying to figure out why is this part or product expensive? Is it because of something fundamentally foolish that we're doing, or is it because our volume is too low? And so then you say, okay, well, what if our volume was a million units a year? Is it still expensive? That's what I mean by thinking about things in the limit. If it's still expensive at a million units a year, then volume is not the reason why your thing is expensive. There's something fundamental about design. And then you then can focus on the reducing complexity or something like that in the design, change the design to change the part to be something that is not fundamentally expensive. But that's a common thing in rocketry because the unit volume is relatively low. And so a common excuse would be, well, it's expensive because our unit volume is low. And if we were in automotive or something like that, or consumer electronics, then our costs would be lower. I'm like, okay, so let's say now you're making a million units a year, is it still expensive? If the answer is yes, then economies of scale are not the issue. Do you throw into manufacturing, do you throw supply chain. You talked about resources and materials and stuff like that. Do you throw that into the calculation of. Trying to reason from first principles, like how we're going to make the supply chain work here. Yeah, yeah. And then the cost of materials, things like that. Or is that too much? Exactly. So, like another, like a good example I think of thinking about things in the limit is if you take any, you know, any product, any machine or whatever, like take a rocket or whatever and say if you've got. If you look at the raw materials in the rocket, so you're going to have like aluminum, steel, titanium, inconel, specialty alloys, copper, and you say, what's the weight of the constituent elements of each of these elements and what is their raw material value? And that sets the asymptotic limit for how low the cost of the vehicle can be unless you change the materials. And then when you do that, call it maybe the magic wand number or something like that. So that would be like if you had just a pile of these raw materials here and you could wave the magic wand and rearrange the atoms into the final shape, that would be the lowest possible cost that you could make this thing for unless you change the materials. And that is almost always a very low number. So then what's actually causing things to be expensive is how you put the atoms into the desired shape. Yeah. Actually, if you don't mind me taking a tiny tangent, I often talk to Jim Keller, who's somebody that worked with you as a philosopher. Jim was, yeah. Did great work at Tesla. So I suppose he carries the flame of the same kind of thinking that you're talking about now. And I guess I see that same thing at Tesla and SpaceX. Folks who work there, they kind of learn this way of thinking and it kind of becomes obvious almost. But anyway, I had argument, not argument. He educated me about how cheap it might be to manufacture a Tesla bot. We just, we had an argument, how can you reduce the cost of scale of producing a robot? Because I got a chance to interact quite a bit, obviously, in the academic circles with humanoid robots and then Boston Dynamics and stuff like that. And they're very expensive to build and Then Jim kind of schooled me on saying, like, okay, this kind of first principles thinking of how can we get the cost of manufacturing down. I suppose you do that. You have done that kind of thinking for Tesla Bot and for all kinds of. All kinds of complex systems that are traditionally seen as complex. And you say, okay, how can we simplify everything down? Yeah, I mean, I think if you are really good at manufacturing, you can basically make. At high volume, you can basically make anything for a cost that asymptotically approaches the raw material value of the constituents, plus any intellectual property that you need to license, anything. But it's hard. It's not like that's a very hard thing to do, but it is possible for anything. Anything in volume can be made, like I said, for a cost that asymptotically approaches its raw material constituents plus intellectual property license rights. What will often happen in trying to design a product is people will start with the tools and parts and methods that they are familiar with and try to create a product using their existing tools and methods. The other way to think about it is actually try to imagine the Platonic ideal of the perfect product or technology, whatever it might be, and say, what is the perfect arrangement of atoms? That would be the best possible product. And now let us try to figure out how to get the atoms in that shape. I mean, it sounds. It's almost like Rick and Morty absurd until you start to really think about it. And you really should think about it in this way, because everything else is kind of. If you think you might fall victim to the momentum of the way things were done in the past, unless you think in this way, well, just as a function of inertia, people will want to use the same tools and methods that they are familiar with. That's what they'll do by default. And then that will lead to an outcome of things that can be made with those tools and methods, but is unlikely to be the Platonic ideal of the perfect product. So that's why it's good to think of things in both directions, like, what can we build with the tools that we have? But also, what is the. What is the theoretical perfect product look like? That theoretical perfect product is going to be a moving target, because as you learn more, the definition for that perfect product will change because you don't actually know what the perfect product is, but you can successfully approximate a more perfect product. So thinking about it like that and then saying, okay, now what tools, methods, materials, whatever do we need to create in order to get the atoms in that shape, but people rarely think about it that way. But it's a powerful tool.