저자: CathieWood외EdwardDowd등—제목·설명란에명시된출연자기준미상부분포함 · 외국인
요약 보기
Cathie Wood는 AI 성장으로 명목 GDP 6~8%, 금리 장기화 등 거시 낙관을 제시하고, Edward Dowd는 AI 과대홍보 비판과 장기 낙관을 말함. 후반부(영상 내 Nvidia 관련 해설)는 Nvidia 목표가 $515, 시총 $6조 접근, 메모리·네트워크·전력 등 AI 인프라 병목과 확장 기회를 강조. 단기 방향 예측은 명시되지 않음.
다듬은 글
revolution, you had an inverted yield curve that didn't cause a recession, and you expect something like that again. Can you explain why short-term rates are rising while long-term rates are expected to remain stable? Why is this happening in the economy that you predict is doing this? So, again, taking some lessons from the Industrial Revolution, short rates, that was for the 50 years ending in 1929 , before the Great Depression.
Short-term rates averaged the same level as nominal GDP growth, which was then 4.8%. Long-term rates averaged 3.8%. So, short-term rates were oriented towards nominal growth . Long-term rates then were more oriented towards deflation or the absence of inflation . So, the yield curve was inverted over 60% of the time by an average of about 100 basis points.
So, we think we could see nominal GDP growth in the range of 6 to 8%. This would be the level at which we expect short-term rates to reach , with long-term rates at 5–7%. So, in terms of government profitability, we are already at the lower end of this range. I don't know if you heard Joe Lonsdale say the other day that we were talking about $40 trillion in debt, that's such a daunting number that it seems like we have no way out.
He said, " Don't worry, you know , everyone wants to raise taxes. Don't raise taxes. We're going to enter a disinflationary decade starting in 2030, thanks to AI." The growth will be so high that you won't even need to raise taxes, and it will all take care of itself. Did you see him say that? Katie, do you agree?
I didn't see him say it, but I generally agree with the idea that we will get out of this through growth. I was at the beginning of my career during the Reaganomics era, and the same discussions were going on then . And, of course, growth solved the problem then, and I believe it will solve it again .
You know, it's interesting, I think a lot of people will start competing for this growth rate. We expect 7–8%, and I saw that Elon Musk seems to be predicting over 20% real GDP growth. So I agree that the surprise will be how fast this growth will be, and we are already seeing it in AI companies. This is a stunning, simply stunning growth rate.
Edward, you know, I've been reading your posts and Substack for a while now. You are quite active in opposing all this talk about the demise of humanity due to AI and their supporters. Let's start the conversation with this, because, in my opinion , this is not going away, and the question is why is this happening?
Well, the AI industry itself has done a poor job of marketing. They started these apocalyptic talks six months ago, claiming that office jobs would disappear within a year. Well, it's been half a year and I don't see any signs that jobs for office workers are disappearing anywhere. So the AI industry has failed its PR campaign.
The reality is that human bottlenecks exist. There will always be obstacles when new technology is created . It is necessary to attract capital. We usually reinvest. First a downturn, then a slow adoption curve. So I'm optimistic about AI in the long term. And the mathematical argument is quite interesting.
Mathematicians are already complaining. They say it's a huge amount without peer review. So, they have to check all of this. And they say: you just dumped a bunch of materials on us , and we don't know if it's right. Also, they say it's a closed model. We cannot verify the results you obtained because OpenAI used an internal model that is not yet public.
They also refer to citation guidelines and publication standards . Rightly. So this causes a lot of controversy. But I will say this: there is its own sociology in the academic environment. There is groupthink there. And I think AI is a very powerful tool that will give those who are considered heretics or apostates in these fields— those who are considering other theories—a very powerful PhD-level colleague to help them advance their ideas and theories.
Previously, they couldn't find people to help them. Therefore, I believe that AI will ultimately shorten the time to innovation and breakthroughs. So, I'm a big optimist, but I don't think mathematicians will disappear tomorrow. I think progress on all of this is slower than people say. And once again, the AI industry has done itself no favors by claiming that white- collar jobs will disappear in 12 months.
Yes, I think one of the fathers, or even progenitors of AI, made this prediction a while ago, and it worked against him. Hinton did something similar, and it failed miserably. Every industrial revolution has always given rise to a kind of Luddite mentality. But you know, when you're sitting in the stands, maybe it's easier to see: if AI can do all the math, will mathematicians just become fact checkers?
This, this, this is a valid observation. But always, you know, spheres become ossified , old, and sometimes stagnant. And I think there will be a huge opportunity for those who have new ideas to break through and take us in other directions. So, those who know how to use AI as a tool, who are creative and have new ways of thinking, will win.
People who are just " calculators", well, " calculator people", they will have problems. Speaking of new thoughts and new ways of thinking: you used to be a Wall Street person, and now you consider yourself part of the “Team Humanity.” And your book, “Unknown Cause: The Sudden Death Epidemic of 2021-2022, ” has made you, in your own words, an alternative voice during the pandemic regarding the economic consequences.
And this way of thinking, you know, we have a conflict brewing in Russia right now, but this approach that led you to your epiphany seems to be giving the same epiphany to a lot of other people. Yes, listen, we are monitoring the data, and we are trying to get to the truth. So my Substack “Data Beyond Narrative” is an attempt to get to the truth in both the economic and, earlier, healthcare fields.
And what we found during the pandemic is that the response to it was catastrophic. All measures taken have led to excess mortality after 2020 and disability. This is very controversial. We believe the vaccine was a significant component of this. But we know for sure that the establishment and organizations do not want to talk about what is happening with health in the country and the world.
Hey, I only have a minute and a half left. Regarding this industrial revolution, the Fourth Industrial Revolution, you've already hinted that there will be winners and losers, but you're still pretty optimistic about all of this, right? Well, I'm optimistic in the long term, but I also study history, and we always recapitalize on any boom in new technology infrastructure.
I am convinced that AI will become a commodity, and that many of them who are now recapitalized will lose money and go bankrupt, but then they will be recapitalized, and the real value will come in 2, 3, 4, 5 years. Value will be added from above. People forget that most of the companies with the largest market capitalizations today went public after the dot-com crash.
Before we get to today's story, if you want deeper market analysis, exclusive information, and member-only content, consider becoming a channel sponsor. This will help support the channel and give you access to additional market-oriented content. Nvidia is approaching a milestone that once seemed nearly impossible.
The company is approaching a market cap of $6 trillion, but shares are just below their all-time high after five straight strong sessions. And, according to Raymond James analyst Simon Leopold, Nvidia still has significant growth potential. His price target is $515, one of the highest on Wall Street.
But the main story is not just Nvidia's stock price . This is what is happening across the AI infrastructure market as demand for Nvidia chips continues to grow across many sectors of the economy. Hyperscalers spend huge amounts. New cloud companies are raising capital. Companies like SpaceX are preparing large-scale investments in AI infrastructure.
And even though costs remain high, demand for computing power has not slowed significantly. And this is the key point. This is not the only company betting on AI. This is a whole industrial development, and Nvidia is right at the center of it. There is also a serious misconception about AI trading. Investors sometimes perceive the market as if there will be one winner and everyone else will lose, but the reality can be much more complicated.
There may be open AI models and advanced models, hyperscalers and specialized cloud providers, different types of processors, different network architectures, and different applications. The market is expanding in many directions simultaneously. Nvidia is positioned as one of the critical infrastructure providers that connects many of these pieces, but there's a problem.
The bottleneck is no longer just a question of whether companies want more GPUs. The physical infrastructure required to build these systems is becoming increasingly difficult to provide, with memory being one of the biggest constraints. , and the demand for it is growing faster than the supply. This imbalance has already led to higher memory prices.
Memory is not the only problem. Energy supply is becoming another critical constraint . . They need network infrastructure. They need cooling . They need data storage systems. They need advanced components, and they all have to work together harmoniously . So while investors continue to focus on GPU demand, the next stage of AI development may depend on everything surrounding GPUs.
This is where the concept of connectivity becomes extremely important . The first phase of the AI boom was driven by computing power. Then memory became an important part of this story. The industry is now increasingly focused on transferring information between all of these components quickly enough to keep expensive equipment from going down.
Think about what this means. You can create enormous computing power. You can install a huge amount of memory, but if those systems can't interact effectively, you're not getting the full economic return from the infrastructure. And as AI moves toward more complex thinking methods and generative workloads, the architecture becomes even more complex.
Different types of computers must constantly communicate with each other, and this creates another huge opportunity for networks, optical connections, switching, and related infrastructure. One analyst in the discussion pointed out how significantly the market for specialized and alternative infrastructure for AI has expanded.
The market, once valued at around $14 billion , is now projected to reach around $235 billion . This shows how quickly expectations for AI infrastructure have changed. This also explains why the possibilities are not limited to Nvidia. The semiconductor market is expanding. Compute, networking, memory, storage , connectivity, security, infrastructure software, applications—all of these layers can benefit as AI is implemented into everyday business operations.
And pay attention to. The economics of AI are starting to move beyond the idea of simply buying more chips. The real question is what economic return companies can get from all this computing power.
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