저자: CathieWood · 외국인
요약 보기
Wood는 비트코인 장기 강세 시나리오(2030년 150만달러)를 유지하며, 가격 하락을 매수 기회로 본다. 스테이블코인이 신흥국에서 비트코인 역할을 일부 대체하고, OG 매도와 금 선행 흐름이 최근 약세의 배경이라고 설명한다. 우주·방산과 AI·로보틱스 펀드 성과를 강조하며 AI 신약개발과 자율주행(테슬라)을 핵심 성장 축으로 제시한다. 거시 위험(세계 대공황)은 기술 도입을 늦출 수 있으나 장기적으로는 도약을 준비하는 구조라고 본다. 중국과의 AI 경쟁은 미국 발전을 가속한다는 입장이다.
다듬은 글
over the last year or two that has done exceedingly well. True, it's had a correction and uh from our point of view uh this is more a buy the dip moment in our long-term forecast which our bull case uh for uh 2030 has been uh $1.5 million. Uh that would have taken $2 to $300,000 off of that price target.
Our conviction has actually gone up uh as uh Bitcoin has gone down here. The biggest risk is more macro. Kathy Wood begins by reaffirming and strengthening her core Bitcoin thesis. In her view, Bitcoin represents the first truly global digital private monetary system in history. One that operates outside direct government control, rulemaking, or discretionary monetary policy.
This is not simply an alternative asset or a speculative technology. It is a fundamentally new form of money with properties no prior system has combined at scale. Her conviction has increased not decreased over the past year even as price performance has lagged other assets. The reason is structural rather than cyclical.
; We do believe uh and our conviction has increased that uh Bitcoin is the first global digital private meaning no government oversight rules-based most important part of it monetary system in history that is a very big idea. Now what has happened uh in in the last year or so stable coins have uh have been usurping one of the roles that many of us thought bitcoin would play uh in the emerging markets uh people who are living handto-mouth meaning they don't have a lot of savings uh they would much prefer uh the the dollar and stable coins are backed pretty much 100% sent uh by dollar or dollar-based assets.
Uh and so that is their preferred um uh insurance policy against their own currencies devaluation and the loss of purchasing power and wealth. Uh so from that point of view in our long-term forecast which our bull case uh for uh 2030 has been uh $1.5 million uh that would have taken $2 to $300,000 off of that price target.
But what else has happened in the last year? We've seen gold um really go parabolic and uh Bitcoin has not done the same. Well, how can that be? It's digital gold, we thought. Well, if you look at the correlation between gold and Bitcoin, you'll see it's very low, especially since 2019, uh, since more traditional investors have become have been getting involved with it.
The correlation is 0.14. So, almost no correlation. Um, but if you z if you zoom out and you look at the longer term price chart of Bitcoin, what you'll see is gold often leads and certainly in the last two cycles, Bitcoin's uh performance. Uh, so gold is a leading indicator uh in in in at least the last two cycles.
Um, we think the other thing going on is we're seeing a lot of OGs uh selling. Those who were very early on, very early adopters of Bitcoin are selling. Now, why are they selling? Well, uh, according to some of them, you know, Bitcoin has lost the plot. Too too much traditional finance is get or financial players are getting involved.
uh and from our point of view all along and this is in uh our collaboration with Dr. Arthur Lafer, known for the LER curve, uh he has said the more that Bitcoin becomes involved in the traditional financial ecosystem, meaning traditional financial institutions start adopting it, uh the the stronger his conviction will be that it is indeed uh the new global monetary system I described.
So our conviction has actually gone up uh as uh Bitcoin has gone down here. You can tell from price action recently uh well let's just say in the last year anything with space and defense tech has had very strong performance. So the market is beginning to understand how big that opportunity is. Uh and the same thing with uh AI.
So our a AI and robotics fund ARKI uh over the last year or two that has done exceedingly well. True. It's had a correction and uh from our point of view uh this is more a buy the dip moment. the the the fund that has been the most challenged uh and therefore where we're seeing the most skepticism is the genomic fund ARKG and as I mentioned before we believe that the most profound application of AI will be in healthcare from the point of view of collapsing the cost and time to discover new drugs.
Uh so going from 2.4 4 billion including failures along the way down to 600 million 600 or 700 million during the next 5 years and going from 13 years uh to 8 years or less. The biggest risk is more macro. Um and uh I as I think I've explained uh before on your show um our models are are focused on something called Wright's law.
And Wright's law helps us figure out the learning curve associated with each technology. robotics, energy storage, AI itself, the most the biggest catalyst out there, uh blockchain technology and multiomics technologies in the life science space. And those learning curves are expressed in cost declines.
Those learning curves depend very importantly on unit growth. If we went into a worldwide depression, I think that would uh that would delay these technologies. The irony is during a depression or a serious recession is when many companies are looking for new ways of doing things faster, cheaper, uh more creative, more efficient, more productive.
uh and so the adoption even though the unit growth dynamics aren't there the mindset develops and so we usually come out of those periods flying these new technologies do so uh a a recession a bad recession would slow progress down but it would not stop it in another way it would help evolve a coiled spring so that the end of the recession would be a massive takeoff in these new technologies.
While autonomous systems may scale revenues fastest, Wood believes the most profound impact of artificial intelligence will occur in healthcare, particularly in genomics and drug discovery. Today, developing a new drug costs roughly $2.4 billion and takes around 13 years when accounting for failures.
AIdriven approaches could reduce those costs to $600700 million and compress timelines to 8 years or less within the next 5 years. This transformation is not incremental. It is exponential. By applying machine learning to multiomics data, researchers can identify viable compounds faster, predict failure earlier, and personalize treatments at scale.
Yet this is also the area where investor skepticism is highest making it in Woodsview one of the most asymmetric opportunities. I was in the bubble investing uh portfolio manager during the bubble and what we witnessed back then were investors throwing money at any company with dot or internet in its uh description.
Uh today couldn't be more different. We are climbing a wall of worry. investors are very hesitant to continue fueling the AI boom because they're worried that we are in a hype cycle. As a portfolio manager, I would much prefer to invest in that kind of market. Why? Uh because walls of worry, climbing walls of worry, those are the most durable bull markets.
And I remember uh as way back in the 80s and 90s those were the kinds of bull markets we were in in the late 90s uh that was an irrational out of control end to a massive 20-year cycle. Well, I do agree that China is the competitor. The deep seek moment uh was a wake-up call for all of us to understand that um that what China has done actually has been encouraged by our software companies.
Our software companies stopped selling into China because of fears of IP theft. So they moved into the open-source movement and they are running hard with it. uh we are big believers in the open-source movement. Now, in terms of uh competition, uh we think competition is very good for the United States.
That's when we're at our best. Uh and so I think the AI revolution is uh occurring faster perhaps than otherwise might have been the case because China is so advanced. Many uh experts would say China is about six months uh behind the US in terms of the frontier models. Uh and uh our research corroborates that uh we're seeing leapfrogs China uh uh kind of encroaching on the US but then the US moving further ahead.
Again, competition is good. I think it's uh it's encouraging uh faster momentum in the cycle. And just to uh answer one other um way, the previous question, if you think about the tech and telecom bubble in the late '9s, uh that was all about laying fiber for broadband connectivity. Uh well, that fiber was dark for many, many years.
There was just too much too soon, too few opportunities. It's completely different today. every GPU in the world is being utilized. Uh and I do think it's this dynamic China uh US competition uh that is speeding the revolution along. I would have to say that um the opportunities are are more in embodied AI meaning bringing the physical and the digital together.
Uh and so autonomous mobility we think will be the biggest revenue generator. It'll scale the most quickly uh to the 10 trillion uh mark uh which is quite meaningful in a world where global GDP is about 130 trillion. We think that will happen within the next 10 to uh uh 5 to 10 years. And um you know this movement has happened slowly slowly and now it's happening all at once.
Elon Musk was talking about autonomous mobility in his master plan uh 10 uh 12 years ago and uh here we are uh finally he launched this year. So slowly slowly and now he's saying by the end of this year depending on the regulatory environment uh Tesla will be autonomous in 25 to 50% of uh all of the US major cities.
So that's one and then the other one is in health care. While uh we may see the scaling of revenues uh the most quickly in uh autonomous transportation of all kinds, uh we think the most profound applications of AI are going to be in healthcare. And uh from a regulatory point of view here in the United States at least, we're just strangling uh the health care sector and uh with with too much focus on safety uh and not enough on efficacy.
She notes that this transition has followed a familiar pattern. Slow incremental progress for years followed by sudden acceleration. Elon Musk discussed autonomous mobility more than a decade ago and now Tesla is beginning real world deployment. regulatory conditions permitting, autonomous functionality could reach 25 to 50% of major US cities within a year.
Finally, Wood offers a pointed critique of healthcare regulation, particularly in the United States. She argues that the system overemphasizes safety at the expense of efficacy, effectively strangling innovation. While regulation is necessary, the balance has tilted too far toward risk avoidance, delaying life-saving technologies and inflating costs.
In contrast, areas like autonomous mobility and AI infrastructure are benefiting from competitive pressure and pragmatic experimentation, allowing innovation to move faster.
Comments 0