When “Scarcity Dividend” Meets “Long-Term Contract Smoothing”: Reconsidering Crypto Asset Allocation in the AI Infrastructure Cycle

In July 2026, Korea Investment & Securities (KIS) cut its forward earnings forecast for SK hynix, alongside a market phenomenon of periodic outflows from spot Bitcoin ETF funds. Together, they point to a core proposition that has been overlooked: the AI infrastructure supercycle has not ended, but the way “scarcity premium” gets realized is undergoing a structural shift. Starting with HBM long-term contract pricing mechanisms, this article examines the evolution of profit release patterns across the AI industrial chain, and proposes an asset allocation framework with gold as a risk-control anchor and Bitcoin as the core growth engine—offering investors strategy references that combine defensiveness with offensiveness during a valuation re-pricing period.

I. The True Signal in the KIS Report: Not Demand Reaching a Peak, but Profit Release Undergoing Reconfiguration

On July 13, a report issued by Korea Investment & Securities (KIS) triggered a chain reaction in the AI industrial chain. The report cut its forecasts for SK hynix’s operating profit for 2026 and 2027 by 9% and 11%, respectively, but kept the target price unchanged at 3.8 million Korean won. On the surface, this looks like a bearish call; in reality, it reveals a deeper shift in pricing logic in the AI memory market.

KIS’s key judgment is not a denial of HBM demand—in fact, SK hynix’s estimated operating profit for 2026 Q2 is still as high as 60.4 trillion Korean won, with an operating margin of nearly 75%. In Q1, revenue was already 52.58 trillion Korean won and operating profit 37.61 trillion Korean won, for a margin of 72%. Put these figures into any manufacturing context, and they are astonishingly high profitability. The real issue is this: part of what the market previously priced in came from the imagination that “profits could continue to be revised upward beyond expectations,” and KIS believes that portion needs to be discounted.

The underlying logic for this judgment is the widespread adoption of long-term supply agreements (LTA). According to a DigiTimes report, the world’s three largest HBM producers—SK hynix, Samsung, and Micron—are signing long-term deals lasting three to five years with top-tier AI customers such as NVIDIA, Google, and Microsoft, locking in global memory supply. By 2027, about half of global DRAM total production capacity will be fully unavailable to smaller buyers. UBS Group predicts that HBM demand will rise 90% year over year to about 33.1 billion Gb in 2026, and then increase another 77% to about 58.7 billion Gb in 2027; the structural shortage scenario is expected to persist at least into the mid-2028 period.

Long-term contracts mean a fundamental business model shift for the memory industry. Traditional memory profits rely heavily on spot price volatility. When demand is strong, prices rise quickly and amplify profits; when supply becomes excessive, prices crash and compress profitability. If the industry shifts to 3-to-5-year long contracts, manufacturers gain more stable orders, capacity utilization, and cash flow. The trade-off is that even if spot prices continue to move higher, the additional upside premium that can be captured may be smoothed out by the contracts.

This does not necessarily mean long-term contracts compress all profits. Details such as price elasticity mechanisms embedded in contract terms, spot-linked clauses, and prepayment arrangements have not been fully disclosed yet. But as long as profit elasticity is partially locked in, analysts have reason to trim previously overly aggressive forward earnings assumptions. NH Investment & Securities still raised its target price to 4.1 million Korean won, forecasting operating profits of 289.4 trillion Korean won in 2026 and 470 trillion Korean won in 2027. KB Securities also lifted its view to 4.2 million Korean won. What the optimists discuss are the demand curve and the supply-demand gap in the industry. What KIS discusses is how demand enters an earnings-per-share model—where the former answers “can it sell well,” while the latter asks “if it sells well, can it keep generating profit upside revisions.”

II. Switching Valuation Language: From High-Elasticity Growth Stocks to High-Certainty Cycle Leaders

In an interview with Reuters on July 10, SK hynix CEO Kwak Noh-jung explicitly said that, from a supply perspective, 2027 could be the tightest year; customer demand may still exceed the company’s ability to supply even after 2030. This statement does not contradict the KIS report—it rather indicates that the market is undergoing a migration of its valuation anchor.

Previously, investors were willing to pay a high premium for SK hynix because it had both demand certainty and profit elasticity. Now, long-term contracts increase certainty but may weaken elasticity. The market needs to decide again: how much premium should be paid for earnings visibility, and how much premium should be deducted from the imagination that profits can be revised upward infinitely.

This switch in valuation language has a strikingly similar mapping in the cryptocurrency market. In July 2026, after a period of consecutive outflows, Bitcoin spot ETFs saw recovery in daily net inflows of about $90.44 million on July 10. BlackRock’s IBIT contributed $86.80 million. But looking back over the prior three weeks, cumulative outflows from Bitcoin ETFs exceeded $604k, and the Bitcoin price fell back from the $70,000 level to trade around $64,000.

Similar to the logic of long-term contracts for SK hynix, the Bitcoin market is also experiencing a structural change of “increased certainty, reduced elasticity.” The launch of spot ETFs brings institutional-grade certainty-driven buying power for Bitcoin, but it also means that the price discovery mechanism includes more “allocation-driven capital” rather than “speculation-driven capital.” When ETF fund flows become a dominant force in price action, Bitcoin’s volatility characteristics are evolving from a “high-elasticity risk asset” toward an “institutional allocation asset.”

III. The HBM4 Price Surge and Valuation Resonance with Bitcoin

One signal worth paying attention to is the HBM4 pricing trend. A DigiTimes report shows that, driven by a surge in AI demand and structural supply constraints, HBM4 prices in the second half of 2026 may rise from $2 per thousand to $4–$5 and above. The production cycle lasts as long as four to six months, initial yields are significantly low, and the wafer capacity consumed by HBM production is about three times that of standard DDR5 DRAM.

SK hynix officially began trading on the Nasdaq on July 10 in the form of an ADR, setting the record for the largest IPO by a foreign company in history at $26.5 billion. In terms of global market share for HBM in 2026 Q1, the company ranked first; 2026 capacity has already been fully sold out, and 2027 capacity is also locked in by major mainstream customers through long-term agreements.

This combination of “capacity locked-in + price surging” resonates interestingly across markets with Bitcoin’s supply mechanism. Bitcoin’s hard cap of 21 million coins is a code-level constraint, while HBM’s capacity constraints are physical-level constraints—limited cleanroom space, extremely high barriers in 16-layer stacking technology, and the need to reduce wafer thickness from 50 microns to 30 microns. Both are going through a “scarcity narrative” validation phase, but scarcity is priced in fundamentally different ways.

Bitcoin’s scarcity is algorithm-fixed, globally transparent, and does not require contract lock-ins; HBM’s scarcity must be secured through multi-year long-term contracts, and its price elasticity is artificially smoothed. This means Bitcoin still has higher freedom in how “scarcity premium” gets realized, while the HBM makers’ scarcity dividend is being redistributed through contract mechanisms.

IV. Asset Allocation Framework: An AI Cycle Strategy Anchored by Gold for Risk Control

During a valuation re-pricing period in the AI infrastructure cycle, investors face a core question: when profit elasticity for industry leaders is smoothed by long-term contracts, and crypto volatility characteristics are reshaped by institutional capital, how do you build an asset portfolio that is both defensive and offensive?

Based on the current market environment, the following allocation framework is suggested:

Gold as a risk-control anchor (30%–40% allocation). In the AI infrastructure cycle, gold plays the role of a “physical-asset ballast.” When HBM long-term contracts smooth profit volatility for chipmakers, and when Bitcoin ETF fund flows become the dominant factor in price, gold’s independent pricing logic becomes more prominent. It does not rely on any single technical route or contract mechanism. Amid continued central bank accumulation and rising geopolitical uncertainty, it offers the purest downside protection.

Bitcoin as the core growth engine (35%–45% allocation). Although near-term price pressure has resulted from ETF fund outflows, Bitcoin’s long-term investment logic has not fundamentally changed. Unlike HBM makers, Bitcoin has no “long-term contract smoothing profits” problem—its supply curve is determined by code, while its demand curve is driven by global institutional and individual investors’ consensus. On July 10, when ETFs resumed net inflows and BlackRock became the largest buyer again, it suggested that around the $64,000 level there is already relatively good institutional allocation value. Michael Saylor’s recent discussion with Adam Back about BIP 110 also reflects a high degree of consensus within Bitcoin’s core community about the stability of consensus rules, which is the foundation of long-term value.

Quality mainstream altcoins as a factor for elasticity allocation (15%–25% allocation). Solana (Solana) recently obtained a spot ETF filing application submitted to the U.S. regulator by Morgan Stanley. Ripple (XRP) ETF recorded net inflows of $22.99 million. These signals indicate that compliant investment vehicles are expanding the group of investors in crypto assets. In periods when Bitcoin is under pressure, projects that demonstrate real “capital-leading” ability deserve closer attention.

Cash/stablecoins as a liquidity buffer (5%–10% allocation). Keeping a moderate level of liquidity during a valuation re-pricing period allows investors to capture add-on opportunities during pullbacks, while also managing uncertainty in key variables such as HBM contract details and Bitcoin ETF fund flow direction.

V. Core Variables and Risk Boundaries

The key variables to track going forward include three layers:

Contract elasticity layer. If SK hynix’s official results for Q2 come close to KIS’s estimates, and the company confirms in its guidance that long-term contracts will smooth future price increases, the market may re-price the stock—from a high-elasticity growth stock to a high-certainty cycle leader. Valuation may not collapse, but the premium structure will change. Conversely, if contract terms have more elasticity than the market expects, or if HBM4 price increases are sufficient to offset the smoothing effect of long-term contracts, then this earnings cut may only be a conservative calibration.

Competitive landscape layer. The ramp-up progress in HBM supply from Samsung and Micron is a critical variable. If a shortage in 2027 turns out to be less severe than expected, the downside protection provided by long-term contracts may not be enough to support the previously high valuation. SK hynix’s technical leadership in 16-layer HBM stacking (wafer thickness reduced to 30 microns) is its moat, but the speed of technological catch-up is difficult to predict precisely.

Crypto market layer. The sustainability of Bitcoin ETF fund flows, the stability of institutional allocation behavior, and changes in the global regulatory environment will determine whether Bitcoin can maintain the “digital gold” narrative position in the AI infrastructure cycle. Currently, exchange BTC balances remain low, there has been no abnormal whale sell-off, and the perpetual contract funding rates remain neutral—supply-side tightness still appears to be in place, which is a favorable signal.

KIS’s earnings downgrade for SK hynix and the segmental outflow of funds from Bitcoin ETFs may look like two independent events on the surface, but they share the same underlying logic: after the AI infrastructure supercycle enters deeper waters, the “scarcity narrative” needs to shift from “imagination premium” to “cash-flow validation.”

For SK hynix, long-term contracts represent an upgrade to the business model and also a form of valuation constraint. They reduce the industry’s historically violent cyclical profit swings, making future cash flows more predictable—but the market also can no longer simply equate spot shortages with infinite profit upside revisions. For Bitcoin, ETFs deliver institutional-grade certainty, but they also introduce a new source of volatility: when Wall Street allocation-driven capital becomes the main force behind pricing, the influence of short-term fund flows on price rises.

This valuation re-pricing test is not about whether investors still believe in AI infrastructure, but about what kind of asset they are willing to pay for: paying a high multiple for spot elasticity, or paying a more stable premium for sustainably high profits locked in by multi-year contracts? Or else, finding a value anchor that is not constrained by contracts within algorithm-fixed scarcity?

The answer may lie in a combination rather than a single choice. Gold provides a cross-cycle risk-control anchor, Bitcoin provides a long-term exposure to algorithmic scarcity, and the HBM industrial chain converts AI demand into predictable cash flows through the long-term contract mechanism—these three together form a triangular support for asset allocation in the AI era. When the market shifts from “trading imagination” to “trading certainty,” the logic of multi-dimensional allocation becomes even clearer. #PreIPOs第二期OpenAI认购 $BTC