The AI revolution is here. Will the economy survive the transition? 기사를 읽으며

https://post.substack.com/p/the-ai-revolution-is-here-will-the

The AI revolution is here. Will the economy survive the transition?

 

 

내용 간단 요약

 

1. AI의 발전 역사와 현재

 

트랜스포머 프레임워크, 스케일링 법칙.

현재는 사전 훈련된 모델을 기반으로 에이전트를 다시 구축하는 추세

 

지금 보는 것이 최악이다(This is the worst it will ever be!) 

“what you see today is the floor, not the ceiling”

 

2. 마이클 버리의 관점 : 놀라운 것들

 

구글이 리드가 아니라는 것,

 

I thought one of the best moments of Dwarkesh’s interview with Satya Nadella was the acknowledgement that all the big software companies are hardware companies now, capital-intensive, and I am not sure the analysts following them even know what maintenance capital expenditure is.

드와르케시가 사티아 나델라와 진행한 인터뷰에서 가장 인상 깊었던 부분 중 하나는 모든 대형 소프트웨어 회사들이 이제는 하드웨어 회사처럼 자본 집약적이라는 점을 인정한 것이었습니다. 그리고 저는 이 회사들을 분석하는 애널리스트들이 유지보수 자본 지출이 무엇인지조차 제대로 알고 있는지 의문입니다.

 

// ㅋㅋ ㅠㅠ

// 소프트웨어 회사들이 자본집약적이게 되면서 그들의 주가가 올라갔던 강점을 잃게됨 ㄷㄷ 

 

3. Do AI tools actually improve productivity?

 

yes 관점 근거 의견 중 하나 : 

massive and unprecedented uptake of coding tools does suggest people are seeing some major subjective benefit from using them—it would be very unintuitive if an increasing percentage of developers were enthusiastically making themselves less productive.

 

비판적 관점 중 하나

self-reported productivity being way higher than—and potentially even in the opposite direction of—true productivity is predicted by the METR study.

 

4. Which company is winning?

 

Anthropic: Claude Opus

Google: Gemini 

OpenAI : ChatGPT

 

Dwarkesh: 

Curious on people’s take on this: how many failed training runs/duds of models could Anthropic or OpenAI or Google survive? Given the constant need to fundraise (side question: for what exactly?) on the back of revenue and vibes.

Anthropic, OpenAI, Google 같은 회사들은 얼마나 많은 실패한 학습 과정이나 불량 모델을 견뎌낼 수 있을까요? 특히 수익과 브랜드 이미지에 기반한 지속적인 자금 조달(덧붙여 질문하자면, 정확히 무엇을 위한 자금 조달일까요?)을 고려했을 때 말입니다.

 

마이클 버리: 

The secret to Google search was always how cheap it was, so that informational searches that were not monetizable (and make up 80% or more) did not pile up as losses for the company. I think this is the fundamental problem with generative AI and LLMs today—they are so expensive. It is hard to understand what the profit model is, or what any one model’s competitive advantage will be—will it be able to charge more, or run cheaper?

구글 검색의 성공 비결은 항상 저렴한 가격에 있었습니다. 덕분에 수익 창출이 불가능한 정보 검색(전체 검색의 80% 이상을 차지)으로 인한 손실이 누적되지 않았습니다. 저는 이것이 오늘날 생성형 AI와 LLM(Learning Leadership Model)의 근본적인 문제라고 생각합니다. 바로 비용이 너무 많이 든다는 것입니다. 수익 모델이 무엇인지, 또는 특정 모델의 경쟁 우위가 무엇일지 이해하기 어렵습니다. 더 높은 가격을 책정할 수 있을지, 아니면 더 저렴하게 운영할 수 있을지 인가요 

// 은근 마이클 버리가 웃기면서도 확실히 똑똑한 것 같다 ㅋㅋ 

Perhaps Google will be the one that can run cheapest in the end, and will win the commodity economy that this becomes.

 

Dwarkesh: 
Ultimately, the price of something is upper-bounded by the cost to replace it. So foundation model companies can only charge high margins (which they currently seem to be) if progress continues to be fast and, to Jack’s point, becomes eventually self-compounding.

//기반모델 회사들이 고마진책정이 가능한 것은 발전이 계속 빠르게 유지될 때 까지임.

 

5. Why hasn’t AI stolen all our jobs?

 

// 신기하다 그냥 영어 더큐먼트 학습만 시켰는데 일본어도 할줄 알게 된다 함

 

jagged intelligence에 대한 토론

인간과 인공지능은 서로 다른 방식으로 jagged된 것일 뿐 

아마 인공지능도 인간에 대해 

어떻게 이런 실수를할까 이해못하는 부분이 있을 것

 

6. Why many workers aren’t using AI (yet)

 

코딩쪽은 난리임. 커서의 성장, 클로드 코드, 코덱스, 바이브 코딩... 다음 변화할 섹터는 어디? 

웹 검색, MCP의 등장으로 곧 코딩 이외에 지식 분야도 노동자들에게도 변화가 나타날 것이다. 

 

마이클 버리: Economies don’t have magically expanding pies. They have arithmetically constrained pies. Nothing fancy. The entire software pie—SaaS software running all kinds of corporate and creative functions—is less than $1 trillion. This is why I keep coming back to the infrastructure-to-application ratio—Nvidia selling $400 billion of chips for less than $100 billion in end-user AI product revenue.

..중략..

 

Dwarkesh: Michael, isn’t this the “lump of labor” fallacy? That there’s a fixed amount of software to be written, and that we can upper bound the impact of AI on software by that?

마이클: New markets do emerge, but they develop slower than acutely incentivized futurists believe.

 

7.Are engineers going to be out of work?

 

마이클 버리: 

I would track shareholder-based compensation’s (SBC) all-in cost before saying productivity is making a record run. At Nvidia, I calculated that roughly half of its profit is eliminated by compensation linked to stock that transferred value to those employees. Well, if half the employees are now worth $25 million, then what is the productivity gain on those employees? Not to mention, margins with accurate SBC costs would be much lower.

 

모든 지표를 능가하는 최고의 지표는 투자자본수익률(ROIC)이며, 이들 소프트웨어 회사의 ROIC는 매우 높았습니다. 하지만 이제 이들이 자본 집약적인 하드웨어 회사로 변모함에 따라 ROIC는 하락할 것이 확실하며, 이는 장기적으로 주가에 압력을 가할 것입니다. 장기적인 시장 추세를 예측하는 데 있어 ROIC만큼 중요한 지표는 없습니다. 상승할지 하락할지, 그리고 하락 속도는 얼마나 빠를지 예측하는 데 핵심적입니다. 현재 이들 회사의 ROIC는 매우 빠른 속도로 하락하고 있으며, 이러한 추세는 2035년까지 지속될 것입니다.

 

Dwarkesh: Naive question, but why is ROIC more important than absolute returns? I’d rather own a big business that can keep growing and growing (albeit as a smaller fraction of investment) than a small business that basically prints cash but is upper-bounded in size.

 

버리: At some point, this spending on the AI buildout has to have a return on investment higher than the cost of that investment, or there is just no economic value added. If a company is bigger because it borrowed a lot more or spent all its cash flow on something low-return, that is not an attractive quality to an investor, and the multiple will fall. There are many non-tech companies printing cash with no real prospects for growth beyond buying it, and they trade at about 8x earnings.

 

// ROIC가 주가에 중요함

 

8. where is the money going? 

 

Patrick: From a capital-cycle perspective, where do you think we are in the AI build-out—early over-investment, mid-cycle shakeout, or something structurally different from past tech booms? What would change your mind?

Michael: I do see it as different from prior booms, except in that the capital spending is remarkably short-lived. Chips cycle every year now; data centers of today won’t handle the chips of a few years from now. One could almost argue that a lot of this should be expensed, not capitalized. Or depreciated over two, three, four years.

 

// 버리 말 중에 "오늘날의 데이터 센터는 몇 년 후의 칩을 처리할 수 없을 것입니다." 이게 잘 이해가 안된다. 그러면 센터를 새로 지어야 몇 년 후의 칩을 처리할 수 있다는건가?? 기술적으로?? 

 

Another big difference is that private credit is financing this boom as much as or more than public capital markets. This private credit is a murky area, but the duration mismatch stands out—much of this is being securitized as if the assets last two decades, while giving the hyperscaler outs every four to five years. This is just asking for trouble. Stranded assets.

// 듀레이션 미스매치 -- 원래 일어날 수 있는 정상적인 것은 아닐지 궁금!

 

Also, construction in progress (CIP) is now an accounting trick that I believe is already being used. Capital equipment not yet “placed into service” does not start depreciating or counting against income. And it can be there forever. I imagine a lot of stranded assets will be hidden in CIP to protect income, and I think we are already seeing that potential.

// CIP가 일부 있는건 정상일거 같은데 (추측임) 비율이 매우 높을지 궁금!

 

9. what the market gets wrong

 

Where does value accrue in the AI supply chain? How is this different from recent or historical technological advances? Who do you think the market is most wrong about right now?

// 질문들이 다 참 좋은 것 같다. 

 

마이클 버리: 엔비디아와 팔란티어에 부정적인 시각. 

I think the market is most wrong about the two poster children for AI: Nvidia and Palantir. These are two of the luckiest companies. They adapted well, but they are lucky because when this all started, neither had designed a product for AI. But they are getting used as such.

Nvidia’s advantage is not durable. SLMs and ASICs are the future for most use cases in AI. They will be backward-compatible with CUDA [Nvidia’s parallel computing platform and programming model] if at all necessary. Nvidia is the power-hungry, dirty solution holding the fort until the competition comes in with a completely different approach.

Palantir’s CEO compared me to [bad actors] because of an imagined billion-dollar bet against his company. That is not a confident CEO. He’s marketing as hard as he can to keep this going, but it will slip. There are virtually no earnings after stock-based compensation.

Dwarkesh: 이러한 내부적인 "자체 개발" 방식이 경쟁 우위를 유지할 수 없거나, AI로 인한 생산성 향상이 겉으로 보이는 것보다 작을 가능성이 있습니다.

 - dogfooding: 자체 개발 

 

// 고객에게 value가 accure 될거다는 결론

 

10. what would change their minds

 

private credit : 사모 펀드 

 

they fumbled it and gave an opening to competitors with far less going for them :  그들은 실수를 저질렀고, 훨씬 불리한 조건을 가진 경쟁자들에게 기회를 내주었다.

 

infatuation : 열정, 집착

 

11. how they actually use LLMs

 

shibboleth 특별한 관습 * perhaps the shibboleth value of pivot tables and VLOOKUP() will endure longer than they do,  //

 

dwarkesh: 웨이모를 우버보다 선호하는 사람들을 보며  It inclines me to think that the human premium for many jobs will not only not be high, but in fact be negative.

 

12. risk, power, and how to shape the future

 

gamut (개-멋): 전체, 전반, 영역, 범위, 종류

 

innate: 타고난

 

마이클 버리: dot the whole country with small nuclear reactors, while also building a brand-new, state-of-the-art grid for everyone. ~~  and secure it all from attack with the latest physical and cybersecurity; maybe even create a special Nuclear Defense Force that protects each facility, funded federally.

 

analogous 유사한

 

// 에너지 안보 중요하지 

영어 공부/ 단어 공부 

-  tabula rasa : 백지 상태 

 

Now, in a very funny way, things are coming full circle: people are starting to build agents again, but this time, they’re imbued with all the insights that come from pre-trained models. 

이제 아주 재밌는 방식으로 상황이 원점으로 돌아오고 있습니다. 사람들이 다시 에이전트를 만들기 시작했는데, 이번에는 사전 학습된 모델에서 얻은 모든 통찰력을 에이전트에 접목시키고 있습니다. 

-  imbued : 스며든

- piggybacks : 업다 

 

- cognitive substrate : 인지 기질

 

Without disclosing too much: 자세한 내용을 밝히지 않고

 

- self-compounding :자기복리화

 

- truism : 자명한 사실

 

- polyglots : 다국어 사용자

 

 

참여자들

• Michael Burry is a former hedge fund manager and writer who publishes investment analysis and market commentary on his Substack, Cassandra Unchained. He is best known for predicting the subprime mortgage crisis, as depicted in The Big Short, and has more recently voiced skepticism about AI-driven market exuberance.
• Jack Clark is the co-founder and head of policy at Anthropic, where he works on AI safety, governance, and the societal implications of frontier models. He also writes Import AI, a long-running newsletter analyzing advances in artificial intelligence, state power, and technological risk.
• Dwarkesh Patel is the founder and host of the Dwarkesh Podcast, where he interviews leading thinkers on AI, economics, and scientific progress. He also publishes essays and interviews on his Substack, focusing on long-term technological trajectories, AI alignment, and civilizational risk.

• Patrick McKenzie is a writer and software entrepreneur best known for his newsletter Bits About Money, where he explains finance, markets, and institutions. He also hosts the Complex Systems podcast and has previously worked in tech and payments, including at Stripe.

읽고 나서 

 

수준 높은 대화였던거같다... 마이클버리는 욕먹는거에 비해 똑똑하고 대단한 사람같음