일론머스크가 생각하는 방법 1원칙 사고법

🤔 문제 해결과 설계적 사고: 일론 머스크의 접근 방식

1원칙 사고법

1. ⚙️ 문제 해결의 기본 원칙

• 물리학 법칙 준수: "물리학은 법칙이고 나머지는 권고 사항" - 물리학 법칙 위반 여부 확인.
• ==제1원리==: 현상을 가장 기본적인 원리로 분해하고, 그 토대 위에서 추론.
  • 공리적 기반 설정 후 추론.
  • 결론을 공리적 진실과 교차 검증.

2. 🔬 물리적 사고 도구

2.1. 에너지/운동량 보존 법칙

• 에너지 보존 법칙, 운동량 보존 법칙 위배 여부 점검.

2.2. 극한 조건 고려

• 특정 변수를 극단적인 값으로 설정하여 문제 분석.
  • 예: 생산량 100만 개 가정 시 비용 변화.

3. 🏭 제조 문제 분석

3.1. 제조의 중요성

• 첨단 기술 제품 양산이 설계보다 훨씬 어려움.

3.2. 비용 분석

• 높은 비용의 원인이 근본적인 설계 문제인지, 낮은 생산량 때문인지 분석.
• 백만 개 생산 시에도 비싸다면 설계 문제에 집중.

3.3. 원자재 가치 접근

• 제품 내 모든 원소의 무게와 원자재 가치를 계산
• 이상적인 최소 비용을 설정.
• 실제 비용은 원자를 원하는 형태로 만드는 과정에서 발생.

4. 🤖 테슬라 봇과 비용 절감

4.1. 짐 켈러의 영향

• 짐 켈러와의 협업을 통한 사고방식 공유.

4.2. 로봇 제조 비용 절감

• 로봇 생산 비용 절감을 위한 제1원리 적용.

4.3. 대량 생산의 가능성

• 높은 생산량에서 제품 비용은 원자재 가치 + 지적 재산권 라이선스 비용에 근접 가능.

5. 🎯 이상적인 제품 설계

5.1. 기존 방식의 문제점

• 익숙한 도구와 부품으로 제품을 만들려는 경향.

5.2. 플라톤적 이상 (Platonic ideal)

• 완벽한 제품의 이상적인 형태 상상.
• 원자를 어떻게 배열해야 최적인지 고민.

5.3. 양방향 사고

• 기존 도구로 만들 수 있는 것 + 완벽한 제품의 이론적 형태 고려.

6. 🛠️ 도구와 방법론 혁신

• 완벽한 제품을 만들기 위해 필요한 새로운 도구, 방법, 재료 개발.
• 기존 방식의 관성에서 벗어나 새로운 접근 시도.

7. 🎯 완벽한 제품의 진화

• 학습을 통해 완벽한 제품의 정의는 계속 변화.
• 완벽에 가까워지기 위한 지속적인 노력.

대본

can you try to introspect your particular biological neural network your thinking process and describe how you think through problems the 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 um you like saying like like physics is a law and everything else is a recommendation um like I've met a lot of people who can break the law but I haven't met anyone who could break physics so uh so the first for you know any kind of Technology problem you have to sort of just make sure you're not violating physics um and you know uh first principles analysis I think is something that can be applied to really any Walk of Life uh any anything really it's just it's it's really just saying um you know let's let's spil something down to the most fundamental uh principles the things that we are most confident are true at a foundational level and that sets your your sets your axiomatic base and then you reason up from there and then you cross check your conclusion against the the axiomatic truths um so um you know some basics in physics would be like are you violating conservation of energy or momentum or something like that you know then you it's not going to work um so uh uh that's you know so that's just to establish is is it is it possible and then another good physics tool is thinking about things in the limit U say take an example of like um like manufacturing which I think is just a very underrated problem um and and uh like I said it's much harder to take a an advanced technology product and bring it into volume manufacturing than it is to design it in the first place my ERS magnitude so um so let's say you're trying to figure out is um like why is this this uh part or product expensive is it um 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 like 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 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 change the part to be something that is uh uh not fundamentally expensive but but like that's a common thing in rock tree because the the unit volume is is relatively low and so a common excuse would be well it's expensive because our unit volume is low um and if we were in like Automotive or something like that or consumer electronics then our cost would be lower I'm like I'm like okay so let's say we SC now you're making a million units a year is it still expensive if the answer is yes then uh economies of scale are not the issue like another like a good example I think of thinking about things uh in the limit is um if you take any uh you know any any product any machine or whatever um like take a rocket or whatever and say um if you've got if you look at the RO if raw materials in the rocket um so you're going to have like uh I know aluminum steel titanium Inc canel uh special specialty Alloys um copper and and you say what are the how what what what's the weight of the constituent elements of of each of these elements and what is their raw material value and that sets the ASM totic limit for how uh low the cost of the vehicle can be unless you change the the material so then the the what's actually causing things to be expensive is how you put the atoms into the desired shape yeah I actually if you don't mind me taking a tiny tangent had uh I often talk to Jim Keller who's somebody that worked with you as as a Jim was yeah did great work at Tesla so um I suppose he carries the flame of the same kind of thinking that you're you're talking about now um and I guess I see that same thing at Tesla and and uh SpaceX folks who work there they kind of learn this way of thinking and it kind of becomes obvious almost but anyway I had um argument not argument uh he educated me about how cheap it might be to manufacture a Tesla bot we just we had an argument what is how can you reduce the cost of scale of producing a robot because so I gotten a chance to interact quite a bit um obviously in in the academic circles with humanoid robots and then mobos Dynamics and stuff like that and they're very expensive to to build and then uh Jim kind of schooled me on saying like Okay like this kind of first principle thinking of how can we get the cost of manufactur down um I suppose you do that you have done uh 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 I mean I think if you if you are really good at manufacturing you can basically make at high volume you can basically make anything for a cost that ASM totically approaches the raw raw material value of the constituents plus any intellectual property that you need to license anything right but it's hard it's not like that's a very hard thing to do but but it is possible for anything anything in volume can be made uh like I said for a cost that asymptotically approaches as raw material uh constituents plus intellectual property license rights so what will often happen in trying to design a product is is people will start with the tools and and parts and methods that they are familiar with um and then and try to create a product using their existing tools and methods um the other way to think about it is uh actually imagine the try to imagine the platon ideal of the perfect product or technology whatever it might be um and so what is this what what is the perfect arrangement of atoms that would be the the best possible product and now let us try to figure out how to get the atoms in that shape I mean it's it sounds um uh it's almost like Rick Morty absurd until you start to really think about it and it you really should think about it in this way cuz everything else is kind of uh uh if if you think uh you 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 uh want to use the same tools and methods that they are familiar with um they just that's what they'll do by default yeah um and then that that will lead to an outcome of things that can be made with those tools and methods but is unlikely to be the um platonic ideal of the perfect product um so then so that's why it's good to think of things in both directions so like what can we build with the tools that we have but then but but also what is the what ises the perfect the theoretical perfect product look like and and that that theoretical perfect product is going to be a moving Target because as you learn more the definition of or of that perfect product will will change because you don't actually know what the perfect product is but you can successfully approximate uh a a a more perfect product um so the thing 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 very rarely think about it that way but it's a powerful tool

🧠 엘론 머스크의 문제 해결 사고법

1. ⚛️ 물리학 기반 사고

• "물리학은 법칙이고, 나머지는 권고 사항이다."
• 물리학 법칙 위반 여부 확인: 에너지 보존, 운동량 보존 등
• 기술 문제 해결 시, ==물리학 법칙==을 어기는지 확인하는 것이 중요

2. 📉 First Principles 분석

2.1. ✅ 기본 원리 분해

• 어떤 문제든 가장 근본적인 원리, 즉 ==확실한 진실==로 분해
• 이를 ==기본 공리==로 설정

2.2. 🪜 공리 기반 추론

• 공리로부터 논리적으로 추론을 전개
• 결론을 공리와 교차 검증

2.3. 🎯 모든 분야 적용

• 기술 문제뿐 아니라 삶의 모든 영역에 적용 가능

3. 🧪 극한 상황 가정

3.1. ⚙️ 제조 문제 분석 예시

• 특정 부품/제품이 비싼 이유 분석: 근본적인 문제 vs. 낮은 생산량
• ==극단적인 생산량 가정: “만약 연간 백만 개 생산 시에도 비싼가?”==
• 극한 상황에서도 비싸다면, 생산량 문제가 아님 → 디자인 문제 집중

3.2. 🚀 로켓 재료 분석 예시

• 로켓 원자재 무게 및 가치 분석 (알루미늄, 강철, 티타늄 등)
• ==원자재 가치 총합==은 차량 비용의 점근적 한계 설정
• 비용 증가는 원자를 원하는 형태로 만드는 과정에서 발생

4. 🤖 테슬라봇 사례 (feat. 짐 켈러)

• 휴머노이드 로봇 생산 비용 절감 방안
• 기존 로봇의 높은 비용 문제 지적
• 짐 켈러: First Principles 기반 비용 절감 방안 제시

5. 🏭 대량 생산의 잠재력

• 고도의 제조 기술을 활용하면, 모든 제품은 원자재 가치 + IP 비용에 _점근적_으로 접근 가능
• 대량 생산 시, 비용 최적화 가능성 제시

6. 📐 이상적인 제품 설계

6.1. 🛠️ 기존 방식의 함정

• 기존 도구, 부품, 방법에 익숙해져 혁신적인 제품 창조에 실패할 수 있음

6.2. ✨ 플라톤적 이상

• 완벽한 제품의 이상적인 형태 상상: "원자를 어떻게 배열해야 최고의 제품이 될까?"
• 추상적으로 들릴 수 있지만, 근본적인 사고방식 전환 필요

6.3. 🔄 양방향 사고

• 현재 도구로 만들 수 있는 것 + 이론적 완벽 제품의 모습 함께 고려
• 완벽한 제품은 계속 변화 (학습을 통해 정의 업데이트)

6.4. 🛠️ 새로운 도구 개발

• 완벽한 제품을 만들기 위한 새로운 도구, 방법, 재료 개발에 집중
• 기존의 관성을 극복하고 혁신적인 결과 도출

대본

can you try to introspect your particular biological neural network your thinking process and describe how you think through problems the 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 um you like saying like like physics is a law and everything else is a recommendation um like I've met a lot of people who can break the law but I haven't met anyone who could break physics so uh so the first for you know any kind of Technology problem you have to sort of just make sure you're not violating physics um and you know uh first principles analysis I think is something that can be applied to really any Walk of Life uh any anything really it's just it's it's really just saying um you know let's let's spil something down to the most fundamental uh principles the things that we are most confident are true at a foundational level and that sets your your sets your axiomatic base and then you reason up from there and then you cross check your conclusion against the the axiomatic truths um so um you know some basics in physics would be like are you violating conservation of energy or momentum or something like that you know then you it's not going to work um so uh uh that's you know so that's just to establish is is it is it possible and then another good physics tool is thinking about things in the limit U say take an example of like um like manufacturing which I think is just a very underrated problem um and and uh like I said it's much harder to take a an advanced technology product and bring it into volume manufacturing than it is to design it in the first place my ERS magnitude so um so let's say you're trying to figure out is um like why is this this uh part or product expensive is it um 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 like 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 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 change the part to be something that is uh uh not fundamentally expensive but but like that's a common thing in rock tree because the the unit volume is is relatively low and so a common excuse would be well it's expensive because our unit volume is low um and if we were in like Automotive or something like that or consumer electronics then our cost would be lower I'm like I'm like okay so let's say we SC now you're making a million units a year is it still expensive if the answer is yes then uh economies of scale are not the issue like another like a good example I think of thinking about things uh in the limit is um if you take any uh you know any any product any machine or whatever um like take a rocket or whatever and say um if you've got if you look at the RO if raw materials in the rocket um so you're going to have like uh I know aluminum steel titanium Inc canel uh special specialty Alloys um copper and and you say what are the how what what what's the weight of the constituent elements of of each of these elements and what is their raw material value and that sets the ASM totic limit for how uh low the cost of the vehicle can be unless you change the the material so then the the what's actually causing things to be expensive is how you put the atoms into the desired shape yeah I actually if you don't mind me taking a tiny tangent had uh I often talk to Jim Keller who's somebody that worked with you as as a Jim was yeah did great work at Tesla so um I suppose he carries the flame of the same kind of thinking that you're you're talking about now um and I guess I see that same thing at Tesla and and uh SpaceX folks who work there they kind of learn this way of thinking and it kind of becomes obvious almost but anyway I had um argument not argument uh he educated me about how cheap it might be to manufacture a Tesla bot we just we had an argument what is how can you reduce the cost of scale of producing a robot because so I gotten a chance to interact quite a bit um obviously in in the academic circles with humanoid robots and then mobos Dynamics and stuff like that and they're very expensive to to build and then uh Jim kind of schooled me on saying like Okay like this kind of first principle thinking of how can we get the cost of manufactur down um I suppose you do that you have done uh 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 I mean I think if you if you are really good at manufacturing you can basically make at high volume you can basically make anything for a cost that ASM totically approaches the raw raw material value of the constituents plus any intellectual property that you need to license anything right but it's hard it's not like that's a very hard thing to do but but it is possible for anything anything in volume can be made uh like I said for a cost that asymptotically approaches as raw material uh constituents plus intellectual property license rights so what will often happen in trying to design a product is is people will start with the tools and and parts and methods that they are familiar with um and then and try to create a product using their existing tools and methods um the other way to think about it is uh actually imagine the try to imagine the platon ideal of the perfect product or technology whatever it might be um and so what is this what what is the perfect arrangement of atoms that would be the the best possible product and now let us try to figure out how to get the atoms in that shape I mean it's it sounds um uh it's almost like Rick Morty absurd until you start to really think about it and it you really should think about it in this way cuz everything else is kind of uh uh if if you think uh you 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 uh want to use the same tools and methods that they are familiar with um they just that's what they'll do by default yeah um and then that that will lead to an outcome of things that can be made with those tools and methods but is unlikely to be the um platonic ideal of the perfect product um so then so that's why it's good to think of things in both directions so like what can we build with the tools that we have but then but but also what is the what ises the perfect the theoretical perfect product look like and and that that theoretical perfect product is going to be a moving Target because as you learn more the definition of or of that perfect product will will change because you don't actually know what the perfect product is but you can successfully approximate uh a a a more perfect product um so the thing 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 very rarely think about it that way but it's a powerful tool