저자: CathieWood · 외국인
요약 보기
비트코인은 약세장(50% 하락)을 거쳐 바닥권으로 판단하며, 장기 목표가 70만~150만 달러(2030년)를 제시. 스테이블코인 확산으로 신흥국 자금이 결국 비트코인으로 이동한다고 봄. 이더리움·솔라나·하이퍼리퀴드 등 주요 체인의 장기 역할을 강조. AI 붐은 초기 단계로 클라우드 인프라 수혜가 클 것이라 주장.
다듬은 글
So even Bitcoin veterans say, “ Okay, we understand stablecoins.” Because in emerging markets , people live paycheck to paycheck. They are very worried that their currencies are destroying their purchasing power and their wealth. They can't afford the volatility of Bitcoin. So this is an intermediate stage, but they say that over time these people will understand: Bitcoin is better than the dollar, which mostly provides stablecoins.
So they will build their wealth with the help of Bitcoin. Interestingly, we talked a lot about Bitcoin and stablecoins. What about the rest of the crypto ecosystem? Do you have a view on Ethereum and other coins? We believe the big four are Bitcoin, Ethereum, Solana, and Hyperledger. During a bear market, developers are really working, and we watch what they create and how ecosystems adapt to each other.
Katie Wood believes the most important part of the story is yet to come as Bitcoin approaches another turning point. Bitcoin has faced many challenges following significant market volatility, changing investor sentiment, and increased attention to artificial intelligence. However, Wood argues that these advances could solidify Bitcoin's position as digital money, a new asset class, and the foundation for a more decentralized economy in the long term, with a possible price of $1.5 million by 2030.
Her long-term view on Bitcoin remains very ambitious. To receive . Over the past almost a year, we have experienced another cryptowinter. We fell, at least in relation to Bitcoin, by 50%. During previous crypto winters, Bitcoin fell by 75-95%. Some progress. Yes. Yes. Yes. Yes. But the reason for this winter is primarily the so- called “flash crash” on October 10th of last year, when a software failure occurred on Binance and the so-called “ automatic reduction of leverage” occurred, when a hedge fund or DeFi company operated on two exchanges, having a long on one and a short on the other, in order to be perfectly hedged.
During automatic reduction of leverage, a software glitch on Binance caused deleveraging . As a result, they found themselves in a completely lose-lose situation, and I think it caused losses, according to various estimates, from 15 to 30 billion dollars. Many people simply collapsed. So, that was the first one.
The second was quantum computing. Quantum computing. Many believe that the most attractive target is Bitcoin, because it is cryptographic and contains Satoshi coins. We don't know if Satoshi is a group of people or one person. But this group or individual did not move these coins from the beginning.
So they essentially became easy prey. They haven't moved their wallets to more quantum-resistant computers, and of course there are a lot of lost bitcoins. So this is a big and delicious goal. However, the more we work on quantum technologies, the clearer it becomes that it will take years and years.
Currently, artificial intelligence research is accelerating progress, but not by that much . To give you an idea, they're currently working with 100 qubits at a time. This is the maximum. We need hundreds of thousands of qubits to crack the code, so to speak. This will not happen soon . And the third thing that happened is artificial intelligence .
AI was replacing everything else , and Bitcoin miners were at the forefront. They switched from mining bitcoins to powering data centers . Yes? So, these three things happened in the last year. I think we've hit rock bottom. We went from 60,000, just below that level, to around 80,000. We still think Bitcoin is three revolutions in one.
First: he introduced the technology inherent in the Internet and the currency inherent in the Internet, which we did not have. Why wasn't it there before? It didn't exist because in the early 90s, when Internet developers were writing code, no one knew that financial services and trade would take place on the Internet.
So there was no built-in level to service them. And now he is. So, this technological revolution is a global monetary system. It is a digital private asset, unsupervised , rules-based, and crucially, mathematically capped at 21 million units, guaranteeing long-term purchasing power and wealth creation in a way that fiat currencies do not.
And the third is a new asset class. This is truly the first of its kind new asset class and we believe it will be the biggest because these are three very important ideas and we are projecting a base case of 700,000 and an optimistic case of 1.5 million, but when exactly? We were talking about 2030, and it could very well happen now, especially if institutions enter this space en masse, and tokenization as a concept attracts more and more institutions to decentralized finance.
One of the things that has somewhat tarnished Bitcoin's shine in terms of fundamentals is stablecoins. So even the Bitcoin “old-timers” say, “ Okay, we understand stablecoins.” After all, in emerging markets , people live from paycheck to paycheck. They are very worried that their currencies are destroying their purchasing power, destroying their wealth.
They can't afford the volatility of Bitcoin. So this is an intermediate stage. But they say that over time these people will understand: Bitcoin is better than the dollar, which is what mostly powers stablecoins. So, they will build their fortunes with Bitcoin. Interestingly, we talked a lot about Bitcoin and stablecoins.
What about the rest of the crypto ecosystem? Do you have an opinion on Ethereum and other coins? Yes, we have. We believe that the “big four” are Bitcoin, Ethereum, Solana, and Hyperliquid. During a bear market, developers are really building, and we watch what they create, as well as how ecosystems adapt to each other and become more specialized.
Hyperliquid is primarily perpetual futures. Solana also provides high throughput for financial transactions. Ethereum is all about smart contracts , and we think that's going to be really important for agent commerce, but as we've heard from companies like Cloudflare, they're saying that to enable agent commerce and agent payments, we're going to need an ecosystem, a blockchain, that allows for 20 to 100 million transactions per second.
These are microtransactions, where agents communicate with each other and make deals in micro-denominations, you know? That is, one hundredth, one millionth of a dollar, something like that . So, Matthew Prince, CEO of Cloudflare, says we need between 20 million and 100 million. Well, we see that if Visa, the traditional payment ecosystem, does 20,000 payments per second...
think about it, and he's talking about 20–100 million, while Ethereum and Solana have very low throughput. I'm an advisor to a blockchain called LayerZero, and they're aiming to reach 2 million and grow that number over time. So we're keeping an eye on the payments space, agent payments, and agent commerce to understand how quickly it will evolve, scale, and so on.
And I think that will be very important for Ethereum , in particular, to see if it can handle that kind of volume. He can't now . It is at such moments that founders become very inventive. Wood's cryptocurrency thesis goes beyond Bitcoin. She highlights four networks: Bitcoin, Ethereum, Solana, and Hyperliquid, which she believes are significant.
Instead of expecting all blockchains to perform the same function. She believes each network will play a unique role. The main goals of Bitcoin are to make money and act as a store of value. The foundation for decentralized applications and smart contracts is provided by Ethereum. Her main argument is that the technology can still benefit from the difficult times in the crypto market.
Developers continue to build and improve the underlying infrastructure even after the rumors and hype die down. This could eventually lead to the emergence of more specialized networks with clearer and more practical functions within the broader digital asset ecosystem. . We believe we are in the early stages of the artificial intelligence revolution.
I'm really glad that many people are talking about the hype around AI. Doesn't this sound like a bubble in the technology and telecommunications sectors? Won't this end badly? As is usually the case, such things often end badly because too much capital is chased after a single opportunity, and this reduces the return on invested capital.
We have n't even come close to that yet. If you look at Anthropic's annual revenue, we own a stake in our venture fund. Its annual revenue grew from $9 billion in December to $65 billion in half a year. So Anthropic added as much revenue in seven months as Salesforce—and I understand we're in a Salesforce building.
Salesforce has added over its entire 25-year history. And what we learn —it's just a rumor, I think the FT wrote it, I don't remember—but we find it to be reasonable: Anthropic's gross margin is 80%, and the company has been profitable for two consecutive quarters from an operating perspective. Of course, she's getting ready to go public, so I'm sure she wants to look her best, and that's part of what's going on.
But, you know, besides Anthropic, look at what happened with SpaceX's XAI. It built data centers. Then it was decided... first, SpaceX bought it and decided to reorganize it because it was lagging far behind the big frontier language models. And you know, the skeptics said, "Yeah, he only bought it because it was losing so much money, and he kind of wanted to hide it in this much bigger company." Now they are laughing at them.
What happened: he turned around, building a data center for the equivalent of— estimates vary, but 25-30 billion per gigawatt. He turned around as he no longer had any use for it and sold it to, it seems, Anthropic. Yes, it was Anthropic. They turned around and sold it for $34 billion per year per gigawatt at that cost.
So you get, you know, over 100% return on your money, uh, in the first year, and then Google comes along and pays $50 billion per gigawatt. So we are in the very early stages. The demand is so huge. The profits are so huge. This is quite a provocative, you know, technology, and I gave you a bunch of examples from an embodied AI perspective that it's not just about productivity in companies and organizations.
This in itself is a very big opportunity. This is autonomous mobility of all kinds. Not only robo-taxis, but also drones, trucks, air taxis, etc. So you know, we live in a world of transformation, and I think a lot of people today are elders of the industry. They've been through the boom and bust of tech and telecom companies early in their careers, and they're essentially saying, "we've been through this before, we're not going to do it again." I am the leader of this firm or whatever .
I went through this. I understand what they are saying. You know, it was a crazy period, and it feels just as crazy as what's going on now, but it's the real deal. The seed was sown then. It was not ready to bloom. It sprouted for 25 years. We did n't have cloud technology until 2006 . We didn't have the first big breakthrough in AI until 2012, deep learning.
The second big breakthrough, transformer architecture, 2017 , big language models —all of this, it took us almost 20 years to make it happen, and now we're ready to go. And think about this: what I just described is 25 years of stretching a rubber band. You know, it's interesting, uh, if you listen to Alex Karp from Palantir, he's been saying for quite some time that these big language models are just commodities that are going to cost a pretty penny.
They don't have data, blah blah blah blah. Now he even says : well, maybe not goods, they create AI garbage, you know. So even he changes his mind, we actually think that if you look at what's happening in China compared to the US, you'll see that it's a little bit like iOS and Android. Android is open source.
iOS is a closed model, right ? Android has 80% of the device market share. Apple has 80% of the profits, right? We think it's a bit like that, as the cost structure for these large language models is pretty much the same no matter where you are. Is this subsidized by the Chinese government? I don't know.
Maybe, maybe. But the real winners from all of this are cloud providers. Because even in the US we use Chinese models. Not us at ARC. We work in the financial services industry. Therefore, we are regulated and we cannot use them. But many researchers use them, and who gets the revenue? Not to Chinese models, but to cloud providers.
This is where all the events are concentrated. So we start to understand, you know, use this analogy with Android and iOS, and it becomes a little clearer what's going on. You know, they don't have the most modern chips. So they have to make up for it by simply channeling the enormous power they have more of than we do.
So, both sides have their own difficulties with the cost structure. But what we're seeing in terms of revenue growth suggests that the market is saying: this is very valuable. This is extremely valuable. Let me tell you about our chief futurologist, Brett Wrangton. I think he spends $4,000 a month, not $20, but $4,000 a month.
And you know what? He doesn't want to train interns or junior analysts, he can do it all himself. He likes to do it himself. He is an MIT engineer by education , and for him this is a real pleasure . He can do 10 projects at once instead of one. Katie Wood argues that the current state of technology is significantly different from the tech bubble of the late 90s and that we are about to enter a period of significant technological change.
In her opinion, many of the technologies driving the current AI boom have been developing for decades. She believes that long-term advances such as cloud computing, deep learning, and transformer technology are finally coming together and reaching a stage where they can have a much greater impact on the economy.
These technologies took years to develop. Another factor that could accelerate the development of AI, according to Wood, is the intensifying competition between the US and China. She believes that the biggest beneficiaries will be not so much the companies that create the models themselves, but those that provide the cloud infrastructure and computing power needed to run these AI models.
It also highlights the speed with which artificial intelligence is penetrating many business sectors. Businesses are under pressure to adopt AI as companies strive to stay ahead of the competition and employees learn to use AI to improve productivity and maintain their position in the ever- changing workplace.
According to Wood, this incentive from both employers and employees could significantly accelerate the adoption of AI, much faster than many expect. so you don't miss , I wish you successful investments.
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