On August 27, 2026, Nvidia released a report card nearly filled with the words "exceeding expectations": Q2 revenue for the fiscal year 2027 was approximately $96.2 billion, a year-on-year increase of about 106%, and adjusted earnings per share were about $2.22, up nearly 120% year-on-year. They also provided guidance that revenue for fiscal year 2028 would grow approximately 70%—well above the market consensus of around 45%. Pre-market stock prices surged nearly 6%, and Jensen Huang emphasized during the conference call that this was the company's first-ever advance guidance given a year ahead, seemingly announcing a growth trajectory locked in early. However, this was not met with only cheers. Turning such a growth curve into actual computing power requires capital expenditures measured in hundreds of billions of dollars. It is no longer just a corporate-level issue on the balance sheets of a few giants; it quickly evolves into a macro variable that both the bond and equity markets must reprice accordingly. Thus, the same financial report was torn into two halves in different people's eyes: one side excitedly celebrated the performance and guidance, while the other side felt potential anxiety about future financing costs, leverage levels, and return cycles, with market sentiment appearing both excited and uneasy under the pull of these two forces.
Record-Breaking Financial Report: Huang Bets on 70% Growth
If we piece together the two halves torn by emotions back into numbers, the picture is very simple and straightforward: In Q2 of fiscal year 2027, Nvidia reported revenue of about $96.2 billion, soaring approximately 106% year-on-year; adjusted earnings per share of about $2.22 reflect a year-on-year increase of nearly 120%. Such growth cannot come solely from traditional graphics businesses, and the market almost unanimously attributes the credit to the demand for AI computing power—whether for large model training or inference deployment, global tech companies have pushed their orders onto Nvidia's production line while "buying the future," thus supporting every number in the report.
What truly complicates emotions is Huang's bet thrown out during the conference call— the company expects revenue for fiscal year 2028 to grow approximately 70%, while before this, the market consensus was only about 45%. More importantly, he emphasized that this is Nvidia's first time providing performance guidance a year in advance, effectively publicly declaring that the current wave of AI capital expenditures is not just a short-term sprint, but a mid-term cycle that can be quantified and promised. Pre-market stock prices surged nearly 6%, but relative to often-volatile emotional outbursts, this reaction seemed restrained, as investors incorporated the explosive performance and exceeding guidance into valuations while also inputting longer, heavier, and more uncertain AI capital expenditure curves into their models. The final price offered by the market reflects a cautious attitude towards whether this 70% growth bet can traverse a complete cycle.
Bond Market Stretched: AI Giants Increase Debt Spreads
As stock prices fluctuated on screens, another less flamboyant financing channel has already been pushed to its limits—the bond market. In order to match the Nvidia-like revenue explosion and the bet on 70% growth, AI giants have started to bundle capital expenditures into long-term debt in waves. Matthias Reschke, head of European investment-grade financing at JPMorgan, stated directly: this round of massive bond issuance is not about whether the bonds can be sold, but rather at what price and spread level the market is willing to take them on. Bond investors have begun to use higher risk premiums to hedge against the uncertainty of this "AI expenditure curve."
The data has already provided answers. Compared to other companies with the same rating, the corporate bond spreads for massive tech companies are about 55 basis points higher, which is not merely a premium but a market's collective pricing of years of high-intensity AI capital expenditures. Reschke noted that the key is not whether the issuance channels are smooth, but rather at what spread level will bondholders feel that this bet is worthwhile. Because when part of the AI capital expenditure is financed through the bond market, these 55 basis points shift from being just an annotation to an industry story to a macro variable materialized on credit spreads: with every additional basis point the spread extends outward, overall financial conditions tighten, and those borrowers not considered AI giants have to shoulder the costs of this wave of computing power competition amid higher funding costs. The transmission chain from AI capital expenditure to credit spreads to macro financial conditions is becoming a critical variable in assessing whether the next round of the AI cycle can land smoothly.
A New Battlefield of Hundreds of Billions: South Korean Funds Bet on Digital Assets
While the U.S. and European credit markets are still pricing the debt issuance of AI giants, South Korean capital has already provided answers on another front. Future Assets has released a new blueprint worth about 150 trillion Korean won (about $109 billion): using a digital asset business segment to absorb the overflow funds from this wave of capital expenditures. From cryptocurrencies to fiat currency-pegged digital tokens, to on-chain physical assets represented by RWA, and security tokens representing "on-chain securities," this entire planning seems more like a pipeline reconnecting traditional balance sheets and the new infrastructure of the AI era, rather than a simple expansion of new business lines.
Echoing this are industrial giants on the same land. SK Group chose a path not to continue to struggle with AI capital expenditures on its balance sheet, but rather plans to sell a 49% stake in its Ulsan AI data center to institutions like KKR, using equity transfer to energize assets and bring in external capital. One is designing a new stack for "on-chain assets"; the other is facilitating the sale of "offline data centers." Though their styles may seem vastly different, the underlying logic is consistent: in this AI infrastructure race ignited by Nvidia's financial report, large financial and industrial groups in Asia are accelerating the dismantling, bundling, and reconfiguration of the capital originally confined to their balance sheets, seeking to find a new balance among "computing power—data center—digital assets."
From T+2 to Real-Time: Blockchain Eliminates Settlement Waiting Periods
In this wave of AI capital competition sparked by Nvidia's report, the speed of transaction matching is no longer a bottleneck; what truly holds back the system is the "window" written into the rules. Simon Gerovich, CEO of Metaplanet, pointed out an issue that financial professionals are accustomed to yet have never truly resolved: from trade execution to the final settlement of funds and securities, these few days may seem merely backend processes but actually signify a blatant credit risk window—when you see "transaction complete" on the screen, whether the counterparty can hold on until settlement day depends solely on luck and regulatory frameworks. Once market volatility strikes, even leading institutions may turn from "reliable counterparties" into sources of default during this period.
Gerovich's solution is to eliminate this time period from an institutional perspective. He predicts that if Japan truly promotes the use of blockchain and tokenizes deposits, compressing stock and government bond settlements from "T+ several days" to "near real-time," the market will no longer need to speculate on counterparties' solvency in a few days' black box. On-chain bookkeeping and tokenized positions are viewed as tools for synchronously moving funds and securities: once order matching is complete, ownership of assets in the ledger changes almost instantly, significantly compressing the time exposed to credit risk. As the scale of financing related to AI and digital infrastructure continues to rise, frequent capital flow between bonds, stocks, and crypto-related assets, any delay in settlement could potentially transmit and amplify risks throughout the leverage chain, thus whether settlement infrastructure upgrades to "quasi-real time" is shifting from a backend technical issue to a front-end variable determining the entire systemic risk profile.
Intersecting Crypto and Tech Stocks: A New Liquidity Channel
The settlement layer's upgrade is still just a blueprint, while funds on the market have already crossed boundaries ahead of time. On a certain trading day, MSTR's daily trading volume temporarily surpassed several tech and finance giants like Dell. Under the same matching system, crypto-related stocks and traditional tech stocks were switched back and forth by the same batch of funds; buyers and sellers in the order book no longer self-identified by asset class, but naturally layered based on "whether betting on the AI expansion cycle." For these funds, moving from Nvidia to MSTR is not a leap from the stock market to "another world," but a shift of chips within the same liquidity pool.
This cross-functional type of trading has a mirror in the broader market. The A-share computing hardware and robotics sectors strengthened simultaneously, with "the first humanoid robot stock," Ubtech, surging approximately 3.97%, with a total market capitalization of about 248.8 billion yuan. AI chain sentiment has overflowed along the lines of "chips—computing power—robotic bodies," spilling over into different assets. On the application side, Grok Bot's advanced capabilities have been delegated to users of the Cursor Pro subscription at about $20 monthly, accompanied by a reset of usage quotas. The core product manager at OpenAI has also hinted at a reset of usage quotas, allowing more users to access greater AI invocation space. The declining costs of development and utilization, combined with the liquidity cross between the stock market and crypto assets, have allowed developers and retail investors to converge on the same path of capital and tools, with AI narratives constructing a new channel oscillating between the stock market, crypto assets, and development tools through this cost reduction and liquidity crossing.
After the Earnings Surge: AI Becomes a New Macro Variable
From Nvidia's record-breaking performance to massive tech companies exercising high-intensity capital expenditures leading to a new wave of bond issuance, this chain of events is no longer confined to a single company's profit and loss statement. On the funding end, AI giants are utilizing both equity and bond markets to finance computing power and data centers. JPMorgan has observed that such corporate bond spreads are approximately 55 basis points higher than those of other companies. Reschke's assessment is that "selling them is not a problem; the issue is pricing," as financing costs are being repriced, turning AI expenditure into a variable for the credit market rather than just a stock market narrative. On the other hand, South Korea's Future Assets is planning about $109 billion for its digital asset business, ranging from crypto assets to RWAs and security tokens, attempting to incorporate this new generation of assets and infrastructure into the same balance sheet. Meanwhile, Metaplanet is focusing on bridging the credit risk window between trade execution and settlement, betting on blockchain and tokenization to push stock and government bond settlements toward near real-time. The trajectory of financing costs, whether the settlement infrastructure can be implemented as designed, and different jurisdictions' regulatory attitudes toward such new structures are all becoming the most unpredictable thresholds for AI narratives. In the next few quarters, investors need to monitor not only how much revenue and profit growth Nvidia et al. can maintain, but also whether the spreads of AI-related corporate bonds will continue to widen or narrow, as well as to what stage infrastructure and digital asset projects like Future Assets and Metaplanet can progress, since these indicators will determine whether AI remains a mere valuation story or entirely matures into a macro variable impacting stock, bond, and digital asset ecosystems.
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