Deepseek 2.0 moment hasn't arrived: Chip stocks stabilize, U.S. stock market awaits earnings season

On July 21, after the U.S. stock market closed, the chip sector, previously thrown into turmoil by Kimi K3, finally managed to catch its breath.

The "DeepSeek 2.0 moment" that the market once feared did not escalate further. Although SK Hynix still closed down 1.86%, previously star performers like SanDisk and Micron have already turned upward, showing signs of stabilization and rebound. After more than a week of heavy selling, the entire semiconductor sector seems to have found a temporary equilibrium point.

Why, this time, did Kimi K3 not replicate the panic script of DeepSeek 1.0?

1. K3's Narrative Has Changed: Not "Enough Computing Power," but "Computing Power Shortage"

The reason DeepSeek 1.0 caused a massive stir in March-April last year was its core narrative of "low cost, high efficiency, no shortage of computing power" – it trained a near-top-tier model using very few GPU resources, directly challenging the investment logic that "AI performance must rely on massively piling up chips."

But Kimi K3's story is completely different.

While K3 shocked the industry with its 2.8 trillion parameters and extremely low single-inference cost, a notable side effect emerged after its release: a shortage of computing power. K3's popularity exceeded expectations, putting immediate pressure on Moonshot AI's inference infrastructure, making capacity expansion a top priority.

What does this mean? It means K3 did not prove "we don't need that many chips," but rather "even if model efficiency increases, the speed of demand growth will always outpace supply." This narrative of a "computing power shortage" is precisely the strongest support for the chip sector – it reassures investors that demand for AI chips will not disappear due to improved model efficiency, but may instead expand continuously due to the explosion of application scenarios.

DeepSeek 1.0's logic was "efficiency replaces scale, while K3's logic is "efficiency unleashes demand." These two narratives have diametrically opposite effects on chip stocks. This is the fundamental reason why the "DeepSeek 2.0" episode did not unfold.

2. Google's Game-Changer: Baking Gemini "Into the Chip"

At a critical moment when the chip sector was searching for direction, Google threw down a potential game-changing card.

Alphabet is developing a brand-new AI server chip, codenamed Frozen v2. This chip's design philosophy is extremely radical: it directly writes part of the Gemini model's architecture onto the silicon itself.

This is not the traditional "optimizing software to fit hardware, but rather "baking the model blueprint into the chip" – by reducing data movement between compute units and memory, it drastically lowers the power consumption and latency of each inference.

Google engineers estimate that Frozen v2's energy efficiency will reach staggering levels: the number of tokens processed per unit of power could be 6 to 10 times that of the current state-of-the-art Ironwood TPU. For comparison, Ironwood is already Google's seventh-generation TPU, and its improvement over the previous generation was merely a doubling of performance per watt. The generational leap of Frozen v2 far exceeds any of Google's previous chip upgrades.

Following the news, Alphabet's stock price briefly rose from around $350 to near $359 during trading. The market is clearly reassessing Google's long-term competitiveness in AI infrastructure – if Frozen v2 can be commercially deployed by 2028, Google will possess one of the world's most efficient large-model inference infrastructures, making it difficult for competitors to replicate its cost advantages in AI services.

However, for the entire chip sector, the significance of Frozen v2 extends far beyond "Google's own affairs."

It sends a key signal: AI giants are not slowing down hardware investment due to improved model efficiency. On the contrary, they are pushing competition to a lower level, a more customized dimension – dedicated ASICs. From Nvidia's general-purpose GPUs to Google's specialized TPUs, and now to Frozen v2 which bakes the model architecture directly into silicon, the competition in AI hardware is shifting from "who has more cards" to "whose card is smarter, more efficient, and more specialized."

This shift implies that demand for AI chips will not shrink just because "models are getting smaller and cheaper." Quite the opposite, the trend towards specialization and customization will create more diverse and segmented chip demand – a long-term positive for the entire semiconductor supply chain.

3. The Earnings Season Test: The Coming Week Will Determine the Direction

Although the chip sector has temporarily stabilized, the test is far from over.

In the coming week or so, core players in the AI supply chain will successively report their quarterly results. This earnings season is exceptionally significant – the market is not just looking at the numbers; it's looking for answers to several key questions that will determine whether the AI narrative can continue:

For Google, Meta, and Microsoft: Will capital expenditure continue to burn? Can the revenue generated from cloud services and AI products cover the increasingly heavy costs of depreciation, leasing, and electricity? If not, cracks will appear in the "AI monetization" narrative, and the inflection point for capital expenditure growth may arrive sooner than expected.

For SK Hynix: Can the money spent by cloud giants ultimately translate into pricing power for memory chips, market share expansion, and profit growth? Hynix needs to prove in its earnings report that it is a truly profitable link in the AI supply chain, not just an "intermediary taking a cut."

What this earnings season truly needs to assess is not just whether capital expenditure is "high," but whether it can "continue to exceed expectations." After multiple quarters of "surprises," the market's threshold for positive surprises is already extremely high. Any marginal slowdown – whether it's a downward guidance revision, softening language, or key data falling short – could become the trigger for a new wave of selling.

4. Final Thoughts: In the Eye of the Earnings Season Storm, Options Are a Key Anchor

The chip sector is navigating a highly uncertain window. Bulls argue that K3's computing power shortage and Google's Frozen v2 prove AI hardware demand is far from peaking; bears contend that capital expenditure growth will soon peak, valuations are already stretched, and earnings will be unable to sustain their high beat rate.

In this environment of information divergence, betting on a single direction carries far more risk than potential reward.

BIT's options functionality provides a well-suited tool for such a "directionless, highly volatile" market environment:

• Holding underlying chip stocks + Buying put options: Insures your position before earnings, locking in downside risk.
• Directional buying of call/put options: Using significantly less capital than buying the stock to bet on the post-earnings direction, with maximum loss limited to the premium paid.
• Buying both directions simultaneously (Straddle/Strangle): Unsure if earnings will be a surprise or a disappointment? Bet on both sides; profit as long as the volatility is significant enough.

The strong winds of earnings season are approaching. In an uncertain market, only those with options can afford to be calm.

Risk Disclosure: The market trends, valuation estimates, and product descriptions mentioned in this article are for reference only and do not constitute investment advice. Trading in U.S. stocks and their derivatives involves market fluctuations, leverage, and liquidity risks; short selling via margin loans may lead to unlimited losses; options trading carries the risk of total loss of premium; past performance does not guarantee future results. Investors should make prudent decisions based on their own risk tolerance and consult a professional investment advisor when necessary.