AI-related semiconductor stocks face correction amid market reassessment

Since July, the U.S. stock market's semiconductor sector has experienced significantly increased volatility. For many investors, this kind of price movement can easily disrupt their emotions.

Many people will have several questions at once:

Did I buy at the top?

After such a big drop, is it still worth averaging down?

Has this AI line already reached its end?

This anxiety is completely normal. Over the past year, the AI trend has been exceptionally strong—so strong that many previously cautious individuals have gradually come to accept the view that this may not be just another ordinary theme-driven rally, but rather an industry-level opportunity.

The issue is that the market often suddenly tests the quality of this consensus with price volatility when consensus is strongest.

Currently, the market’s approach to pricing AI assets is shifting. The trends in the AI industry clearly won’t disappear due to a single downturn. However, investors are now less willing to pay high prices solely for distant speculative potential. The era of “buying AI blindly” has largely ended.

In this article, the RockFlow research team will review the recent sharp decline in U.S. semiconductor stocks to analyze shifts in market expectations and address the key questions most concerning investors:

Is this downturn a short-term fluctuation or a fundamental shift? Does the demand for AI still hold? How long can cloud providers' capital expenditures continue to support it? And how should you reclassify your semiconductor holdings?

During periods of sharp volatility, the question investors most often ask is:

Should I sell now?

Can you buy the dip?

Can you still hold NVIDIA, AMD, Micron, TSMC, and Broadcom?

Has AI ended?

These questions may seem different, but they all point to the same thing: what type of asset your shares actually are.

During rallies, the market tends to group all AI-related companies together. Any company even remotely connected to AI, capable of discussing computing power, or having even a slight link to NVIDIA’s supply chain seems to benefit from a revaluation.

During downturns, the market reclassifies.

Some companies have seen their valuations decline.

Some companies are experiencing a decline in trading elasticity.

Some companies are declining due to flawed business models.

These types of assets are handled in completely different ways. In this market environment, the most urgent question isn't necessarily whether to sell. A more valuable question is: What exactly did I buy in the first place?

Is it a bottleneck in the industrial chain?

Are you a follower in a leading market trend?

Is it an elastic asset within the upward cycle?

Or is it still just speculative trading based on themes?

Clarify this issue first, so your subsequent actions won’t be chaotic. If you should endure volatility, don’t be shaken out by panic; if you should reduce your position, don’t stubbornly hold on; if you made a wrong purchase, don’t disguise a short-term loss as a long-term belief.

After the semiconductor sector plunged, many investors may feel confused.

The AI demand does not appear to have worsened.

NVIDIA GPUs remain in short supply.

HBM orders remain strong.

Cloud providers are still investing in AI infrastructure.

The construction of the data center has also not stopped.

That’s all correct. Industry demand doesn’t disappear just because stock prices have dropped for a few days. But there’s a harsh reality about growth stock trading: the market cares less about whether something is “good or not,” and more about whether it’s “better than expected.”

When valuations are low, strong performance is typically a positive sign. But at stages of high valuation, high congestion, and elevated expectations, good performance is merely a baseline requirement. What truly continues to drive the stock price upward are upgrades, acceleration, and results that exceed even already optimistic expectations.

This is why some companies' earnings may double, yet their stock prices don't rise—or even fall—because the market already anticipated the doubling, and that growth was already priced into the stock.

Investors are concerned about whether the next revision will be upward, whether revenue growth can continue to accelerate, whether gross margins can continue to exceed expectations, and whether orders can be fuller than anticipated.

When AI trading is at its most crowded, strong performance is just the entry ticket. To sustain high valuations, companies need to consistently provide even stronger evidence.

This is one of the key reasons the semiconductor sector is currently under pressure. It’s not that demand has suddenly disappeared, but rather that after expectations were overly optimistic, the market has begun to reassess the growth trajectory.

One of the biggest drivers of the AI hardware chain over the past year has been the continued increase in capital expenditures by major tech companies.

Microsoft, Google, Meta, and Amazon are continuously increasing their investments in AI infrastructure. The market naturally interprets this as a positive development for the semiconductor supply chain.

Cloud providers increasing their CapEx must purchase more GPUs.

Buying more GPUs requires more HBM.

The rising demand for HBM will also drive growth in advanced packaging, advanced manufacturing processes, semiconductor equipment, optical modules, power supply, cooling, and data center infrastructure.

This logic previously held strong and drove a reevaluation of the entire AI hardware chain.

Now, the market is entering a more discerning phase. Capex upgrades, which once felt like a stimulant, now resemble exam questions.

The market will naturally ask:

Can the subscription revenue generated by Copilot cover the depreciation and operational costs of Microsoft’s AI infrastructure?

Can Meta's investments in AI recommendations and generative AI translate into higher ad ROI?

Can Google's AI Search and Gemini protect search advertising while generating additional revenue?

Can AWS's AI services generate sufficient incremental cloud revenue?

Will free cash flow be squeezed if AI application revenues lag behind infrastructure expansion?

A very subtle situation has arisen here.

Cloud providers continue to significantly increase AI-related capital expenditures, providing short-term benefits to the semiconductor supply chain; however, investors worry that the return on investment cycle is too long, or that spending is being front-loaded. If cloud providers slow down capital expenditures, the supply chain may fear that order peaks are near and growth expectations will need to be revised downward.

Therefore, capex can no longer be simply viewed as a one-way positive. The market now wants to see whether these investments can generate high-quality revenue.

How far the story of selling shovels can go ultimately depends on whether the gold miners make a profit. If gold miners continue to increase their investments but fail to deliver clear enough returns, the valuations of shovel sellers will also be reassessed.

The market is currently in its earnings verification period. Many investors are counting on earnings reports: if a company performs well, semiconductor stocks will rebound.

Reality may not be this simple. In today’s highly crowded and high-expectation AI trading environment, strong earnings reports may not be enough. The stock price cares more about the expectation gap.

Cloud provider earnings reports are especially critical, as they are the largest buyers of AI computing power.

Their capital expenditure guidance will impact supply chain order expectations.

Their interpretation of AI revenue conversion will impact market confidence in AI return on investment.

Their free cash flow performance will influence investors' assessment of the sustainability of this AI infrastructure expansion.

If cloud providers continue to heavily invest in AI but cannot clearly articulate the business returns, the market will worry that this round of AI capital spending has prematurely exhausted too much future demand.

If cloud providers begin to slow their investments, the semiconductor supply chain will again face concerns about order volumes reaching a ceiling.

This is precisely the most challenging aspect of current AI trading.

In this market, the most irresponsible advice is simply to say: "Run now," or "Dive in boldly."

Different types of semiconductor assets are handled in significantly different ways. The RockFlow research and investment team believes that AI-related semiconductor holdings can be categorized into four types.

Taxable assets

The company acts like a toll booth along the supply chain, such as TSMC. Whether it’s NVIDIA GPUs, AMD GPUs, Broadcom ASICs, Google TPUs, Amazon Trainium, or even more future custom AI chips, any product requiring advanced process technologies and advanced packaging cannot easily bypass TSMC’s core manufacturing capabilities.

The characteristics of such companies are clear: strong position in the industrial chain; broad customer coverage; high technological barriers; relatively solid cash flow; and essential capabilities required across multiple AI technology pathways.

Of course, these assets also carry risks. Capital expenditure cycles, gross margin fluctuations, geopolitical factors, customer concentration, and the pace of advanced packaging capacity ramp-up can all impact market pricing.

However, for companies in this sector, a short-term crash typically does not signify a direct collapse of the long-term thesis. More often, it is the result of combined factors such as macroeconomic conditions, sector sentiment, and valuation rebalancing.

If the industry position remains unchanged, a decline in stock price may warrant inclusion in the key monitoring range.

Sell the shovel-shaped leader

A typical example is NVIDIA. NVIDIA’s biggest issue right now is not demand, but expectations. From an industry standpoint, it remains one of the most important companies in the AI value chain. Its GPU capabilities, CUDA ecosystem, customer lock-in, hardware-software synergy, data center revenue, and cash flow strength all constitute strong competitive advantages.

But once the stock price reaches a certain stage, the market has already priced in a significant amount of future growth.

Next, the questions NVIDIA must answer become more demanding: Can it continue to exceed expectations?

Companies of this type can serve as core observation targets for the AI theme and may also form important foundational positions in a long-term portfolio. However, entering with high leverage when market sentiment is at its peak and valuations are fully stretched often results in an unfavorable risk-reward profile.

A good company and a good price are always two different things.

Cyclical flexible assets

For example, some companies in storage, equipment, and optical modules. These assets have one characteristic: they combine AI growth potential with cyclical volatility.

When the market rises, they are highly elastic. When the market falls, valuation, cycle, order, and supply expectations all decline together.

When evaluating companies in this sector, don't just focus on long-term AI demand—also monitor supply and demand, inventory levels, pricing, order sustainability, expansion pace, customer concentration, and gross margin trends.

These assets can be flexible, but long-term thinking should not obscure cyclical risks. The point at which many cyclical stocks are most likely to encounter problems is precisely when profit expectations are highest and the market most believes the boom will continue indefinitely.

Pure narrative assets

This is the category that requires the greatest caution. Some companies have no clear orders, no profit realization, no stable cash flow, and no real pricing power. Yet, during periods of high market risk appetite, simply being able to tell an AI story can cause their stock prices to rise rapidly.

When the market begins to scrutinize assets, these are often the first to come under pressure.

Retail investors often make this mistake with such stocks: they buy with the intention of short-term trading, but after suffering losses, they claim they were investing for the long term. If the original reason for buying was simply “it’s also in AI,” and the market no longer values that label, then it’s inappropriate to comfort yourself with long-term investing after the price drops.

Long-term investing is not psychological comfort after a loss. It must be based on solid fundamentals.

Since July, the decline in semiconductors should not be simplistically viewed as the bursting of an AI bubble. Previously, the market was willing to pay for AI-related imagination; now, it demands more concrete evidence of commercial returns from the supply chain.

What investors most need to do right now is not follow the crowd and sell in panic, nor rush to buy more after a sharp drop. A more practical step is to conduct a thorough review of their current holdings.

Are you holding a tax-affected asset, a shovel supplier leader, a cyclical growth stock, or a pure narrative company?

Did this company decline primarily due to valuation contraction or a problem with its industry logic?

Is the original reason for buying it still valid?

If the answer is clear, don't amplify short-term volatility into long-term fear. If the answer is unclear, don't use "long-term optimism about AI" to mask insufficient research.

The RockFlow research team believes: We should not be pessimistic about the AI industry's trends. True bottleneck assets will continue to be studied by the market even after volatility; true leading companies will undergo valuation rebalancing; and truly performance-driven growth stocks will await their next validation.

At the same time, those pseudo-leading projects that have risen solely on narrative without fundamental support may gradually be eliminated during this wave of volatility.

The AI demand hasn't disappeared. The market is simply losing patience with linear extrapolation. AI trading isn't over—just the days of blindly buying AI are behind us.

This article is from the WeChat public account "RockFlow Universe," authored by RockFlow.