AI Hardware Leads Tech Rebound as Fund Managers Adopt More Rational Approach at Market Highs

Recently, the global AI investment landscape has presented a tale of two markets: U.S. tech stocks have staged a strong rebound driven by AI hardware, while A-share AI stocks have fallen into high-level volatility under earnings verification pressure. Behind this divergence lies a fundamental reassessment of AI investment logic, prompting an increasing number of fund managers to return to rationality when selecting stocks at elevated valuations.

A-Share AI Sector Faces 'Earnings Test' as Crowded Trade Logic Loosens

Since the start of the second quarter, some A-share "optical" theme concept stocks have continued to underperform, becoming a key drag on the broader artificial intelligence sector. Several AI concept companies previously favored by capital flows have exposed severe disconnects between fundamentals and valuations after releasing first-quarter earnings, and the reality of missing expectations has abruptly cooled market sentiment.

Previously, the A-share AI sector had experienced an extreme crowded trade, with massive capital flooding into a handful of popular names and driving share prices sharply higher in a short period. However, when earnings "report cards" were laid on the table, the gap between some companies' revenue growth and profit performance versus market expectations became the "last straw" that broke overextended valuations. Skepticism toward the extreme crowded trade strategy has grown louder, and investors have begun to reflect: beneath the halo of AI concepts, how many companies truly possess real profitability?

A public fund manager overseeing more than 10 billion yuan in assets candidly stated: "The market's earlier pricing of the AI sector was overly optimistic, with some individual stocks' valuations already discounting two to three years of future earnings expectations. When Q1 earnings data came out, the valuation pullback was actually a healthy form of market self-correction."

U.S. AI Hardware Stages Strong Rebound, Hardware Investment Logic Reaches 'Cyclical Peak'

In stark contrast to A-share volatility, the U.S. market is experiencing a major tech rebound led by AI hardware. AI hardware companies represented by chip giants such as NVIDIA and AMD have once again entered an upward trajectory, powered by consistently better-than-expected earnings and robust market demand.

The core driver behind this rebound is the accelerating buildout of global AI infrastructure. Whether it is major cloud computing vendors continuously expanding capital expenditure or governments worldwide strategically investing in AI computing infrastructure, highly certain growth momentum is being injected into the AI hardware supply chain. Demand from data centers for high-performance GPUs, AI accelerator chips, high-bandwidth memory, and other hardware is growing at an unprecedented pace.

From a supply chain perspective, AI hardware sits at the very top of the entire artificial intelligence value chain, with the clearest business model and the strongest earnings certainty. Compared to AI application-layer companies still exploring commercialization paths, hardware manufacturers have been the first to deliver on earnings promises — and this is the fundamental reason capital currently favors the hardware segment.

Analysts have pointed out that the strong performance of U.S. AI hardware stocks essentially reflects the market's bet on a continuously escalating "AI arms race." Against the backdrop of global tech giants racing to increase AI investments, the hardware segment's prosperity is expected to sustain over a relatively long cycle.

Fund Managers Grow More Divided, Shifting from 'Chasing Hot Spots' to 'Selecting Quality'

Facing the starkly different performance of the Chinese and U.S. markets, domestic fund managers have shown clear divergence in their AI investment strategies.

Some fund managers have chosen to "follow the trend," tilting their allocation focus from A-share AI concept stocks toward hardware supply chain companies with global competitiveness. They believe that at the current stage of AI industry development, the certainty of the hardware segment far exceeds that of the application layer, and portfolios should be built along the "computing infrastructure" theme, with particular attention to domestic supply chain companies deeply integrated with overseas AI hardware giants.

Another group of fund managers has opted for "contrarian thinking," arguing that the A-share AI sector pullback actually provides a window to reassess and carefully select individual stocks. In their view, after valuation digestion, AI companies with genuine core technology moats and clear business models are actually presenting better entry opportunities. The key is to abandon the previous "one-size-fits-all" crowded trade strategy in favor of more refined individual stock screening.

Still others have adopted a more cautious stance, choosing to reduce overall AI sector exposure and wait for clearer earnings inflection point signals. They believe that in an environment where both industry trends and valuation frameworks are changing rapidly, maintaining portfolio flexibility is more important than placing blind bets.

Notably, despite the divergence in allocation strategies, nearly all fund managers interviewed reached one consensus: AI investing is transitioning from a "concept-driven" phase to an "earnings verification" phase, and future excess returns will increasingly come from deep fundamental research on companies rather than simple thematic speculation.

Outlook: AI Investment Enters a New Phase of 'Separating the Wheat from the Chaff'

From a broader perspective, the global AI investment market is currently at a critical turning point. After more than two years of broad-based concept-driven rallies, the market is entering a differentiation phase of separating the wheat from the chaff.

In the short term, the AI hardware segment, with its earnings certainty and industry prosperity, will remain the core destination for market capital. However, over the medium to long term, as large language model technology continues to mature and application scenarios gradually materialize, investment opportunities in the AI application layer are equally worth anticipating. The key is that investors need to be more patient in waiting for the window of business model verification.

For the A-share market, high-level volatility in the AI sector is not necessarily a bad thing. It helps squeeze out valuation froth accumulated in the earlier period, allows market pricing to return to rationality, and helps guide capital toward quality companies with genuine long-term competitiveness. As multiple fund managers have noted, the long-term trend of the AI industry has not changed, but investment pacing and stock selection criteria need to evolve with the times.

In this new phase of AI investing, filled with both opportunities and challenges, rationality, professionalism, and deep research will become the core competencies for generating excess returns. Those AI enterprises that can weather short-term volatility and achieve sustained breakthroughs in technology and commercialization will ultimately earn long-term market recognition.