Author: Zhou, ChainCatcher
On June 22, SK Hynix's stock price surge drove its market capitalization to $1.35 trillion, surpassing Bitcoin's total market cap of approximately $1.29 trillion. During the session, it briefly overtook Samsung Electronics to become the highest-valued company in South Korea.
According to Coinglass data, in global asset rankings, SK Hynix rose to 16th place, while Bitcoin slipped to 18th.
HBM, and a 13-Year Bet
The core driver of SK Hynix's current rally is HBM (High Bandwidth Memory). AI training and inference demand extremely high memory bandwidth, and SK Hynix is Nvidia's primary HBM supplier, holding over 60% market share.
Financial report data shows SK Hynix's Q1 revenue was 52.58 trillion won, with an operating profit of 37.61 trillion won, achieving a 72% profit margin. Analysts' current consensus for SK Hynix's Q2 operating profit is around 62~65 trillion won, with some brokerages' optimistic forecasts raised to over 68 trillion won.
In early April, most market expectations for Q2 were still in the 50 trillion won range. Since then, as memory prices remained strong, brokerages broadly revised estimates significantly upward. Management stated during the earnings call that the structural memory shortage driven by AI will last for at least several years, and plans to significantly increase capital expenditure to expand advanced production capacity.
It is reported that SK Hynix began betting on HBM technology as early as 2009, when the market paid almost no attention to this complex technology with initially limited demand. From the first generation of HBM to HBM3E, this solitary bet lasted nearly 13 years, only reaching its crowning moment with the emergence of ChatGPT.
Image Source: AI Generated
SK Hynix's journey to today would not have been possible without a crucial instance of external assistance. After the dot-com bubble burst in 2001, Hynix fell deep into a debt crisis, its stock price once dropping to junk levels, and it even negotiated a sale to Micron Technology, which ultimately failed. For the next decade, the company remained under creditor control.
In 2012, SK Group Chairman Chey Tae-won overrode board opposition and acquired it through the group's investment holding subsidiary SK Square for approximately $3 billion, renaming it SK Hynix and injecting massive R&D funds. It was this investment that allowed the company to continue advancing HBM technology, which was still a niche field at the time. Currently, SK Square holds about 20% of SK Hynix's shares, making it the largest single shareholder.
Notably, SK Square itself also attempted to enter the crypto market. In 2021, it acquired a 35% stake in the Korean crypto exchange Korbit for about 90 billion won and planned to issue its own token, SK Coin. According to public reports, after the Terra/LUNA crash in 2022, the market cooled sharply, and the SK Coin issuance plan was subsequently shelved, with no substantial progress since.
According to Reuters, citing sources familiar with the matter, SK Hynix plans to list on Nasdaq as early as August this year, which will lower the trading threshold for U.S. institutional and passive funds, potentially attracting further capital inflows. Nvidia CEO Jensen Huang also recently stated that the collaboration between Nvidia and SK Hynix is expected to bring hundreds of billions of dollars in business opportunities to South Korea in the future.
Why is Capital Buying In? Crypto AI in the Mirror
In this wave of AI, the market is more willing to pay a premium for segments that have already generated actual orders and possess visible supply bottlenecks. Assets directly participating in the AI supply side—such as computing power, memory, and electricity—have received priority allocation because their revenue is quantifiable and their moats are verifiable.
HBM production capacity is highly concentrated in the hands of three players: SK Hynix, Samsung, and Micron, with expansion cycles lasting 2 to 3 years. This physical scarcity is not constructed by narratives but locked in by production cycles and technological barriers. The valuation logic for the memory industry is also shifting from "cyclical stock" to "growth stock."
SK Hynix's market cap surpassing Bitcoin is a public statement by the capital market on two types of scarcity. The physical layer has already formed such high barriers, and the situation of Crypto AI also warrants re-examination.
The Crypto AI sector has been telling a story for the past two years: decentralized computing power will reshape AI infrastructure, and open networks will surpass closed corporate data centers. The potential in this direction is real, but standing before SK Hynix's market cap figure today, several realities must be faced squarely.
The IC3 report, jointly released by Cornell University and 12 other universities, points out that the convergence of Crypto and AI is still in its early stages, and the hype surrounding this intersection has overshadowed actual progress. Decentralized computing, data markets, and governance mostly remain at the conceptual stage.
At the project level, taking Bittensor, the most representative project in the Crypto AI sector, as an example, its token TAO has fallen 20% over the past 3 months. Bittensor co-founder const posted on X that the project's economic incentive layer is still dominated by the core team, who chose to trade centralization for rapid iteration, and it is expected to take another year and a half to complete the core mechanism construction. In other words, their underlying mechanisms are still being patched.
Crypto mining companies, which are closer to the hardware layer, are also facing difficulties. According to Galaxy Research data, Bitcoin miners are entering a "capitulation phase," with the current network mining difficulty having dropped over 20% from its historical peak, marking the largest pullback since China's crackdown on Bitcoin mining in 2021, as some miners continue to exit the network or shut down equipment.
Seeking transformation, mining companies like Core Scientific, TeraWulf, and Hut 8 have announced moves into the AI and high-performance computing sectors. However, according to a VanEck report, this transition faces a short-term funding gap of about $50 billion, with long-term capital needs around $221 billion, and the industry has only delivered about 25% of the leased AI capacity—companies missing construction milestones are already facing investor downgrades.
The IC3 report, jointly released by Cornell University and 12 other universities, mentions that the convergence of Crypto and AI is still in its early stages, and the hype surrounding this intersection has overshadowed actual progress. Decentralized computing, data markets, and governance mostly remain at the conceptual stage.
On the funding front, Arthur Hayes pointed out in his recently published article "Reality Test" that since the release of ChatGPT in 2022, the AI industry has cumulatively issued about $1.5 trillion in debt, roughly equivalent to the increase in USD M2 over the same period—AI has absorbed almost all new liquidity, and Bitcoin never had a chance. Hayes argues this is not a logic of "if AI falls, funds will flow back to crypto." The upcoming large-scale IPOs of Anthropic and OpenAI will further siphon market funds. Once the AI bubble bursts, the contraction in bank credit will simultaneously tighten liquidity, and Bitcoin will be sold off along with AI.
Since the second half of last year, many traders originally active in the crypto market have begun shifting their attention to U.S. and Korean stocks, chasing AI hardware trends. The logic of capital flowing to AI infrastructure is also straightforward: real orders, physical barriers, and quantifiable profit margins.
This certainty is the fundamental reason capital is currently willing to offer high premiums, and what the crypto market's AI narrative lacks is precisely this certainty.
In other words, the dividends of AI infrastructure currently tend to be captured by entities possessing technological barriers and real supply capabilities. In this process, crypto networks need to more clearly define their position in the value chain.
