The curtain has fallen on the semi-annual reporting season for A-share listed companies, revealing a clearer picture of the artificial intelligence sector. Rather than an evenly distributed industry-wide boom, the AI wave is proving to be a structural transformation that ripples sequentially along the supply chain, with differentiation emerging as its most prominent characteristic.
The computing power track is displaying exceptionally strong growth momentum, with upstream hardware manufacturers being the first to reap the rewards. Substantial capital expenditure by tech giants on AI infrastructure is directly translating into tangible revenue and profits for hardware companies. Across niche segments such as high-speed optical modules, servers, storage chips, advanced packaging and testing, and liquid cooling thermal management, numerous listed companies have posted significant increases in revenue and non-GAAP net profit.
Moving to the midstream, most general-purpose large model companies continue to grapple with persistently high R&D and sales expenses, leaving profitability under pressure. At the downstream level, monetization paths for AI tools aimed at general consumers remain challenging. The primary application scenarios are concentrated in government and enterprise private deployments, with only a select few companies achieving meaningful profit contributions from their AI operations.
Three key insights emerge from the mid-year data that warrant careful market consideration. First, the realization of benefits across the AI supply chain follows a clear sequence. Industry gains flow first to hardware infrastructure before gradually extending further along the chain. Hardware benefits before applications—this is the inevitable rhythm of industrial evolution. Moreover, for AI applications to complete their commercial loop, mature technology alone is insufficient; they must identify suitable scenarios and convince customers to pay, which demands additional time.
Second, intra-sector divergence is intensifying, with the strong becoming stronger. Although companies share the same computing power track, their fortunes are already diverging. Leading enterprises, supported by R&D investment, economies of scale, and deeply integrated customer relationships, are forming a virtuous cycle of technological leadership, concentrated orders, higher profits, and reinvestment. Meanwhile, smaller players often find themselves trapped in a downward spiral of lost orders and insufficient investment. In this capital-intensive, technology-barrier-heavy arena, industry resources are accelerating their concentration toward market leaders.
Third, investors must rationally evaluate the drivers behind earnings performance, distinguishing between cyclical recovery and genuine AI-driven growth. Take the storage industry as an example: some companies have seen notable earnings rebounds, driven not only by AI computing power stimulating demand but also by multiple factors such as inventory cycle reversals and a low prior-year base. Investors should approach the primary causes of corporate growth with a level head, avoiding the blanket attribution of all gains to AI narratives or over-amplifying the role of technology storytelling.
Today, the valuation logic of the capital markets is undergoing a profound shift. Whether orders can materialize, revenue structures are improving, and earnings quality is solid have become the core yardsticks for assessing AI companies. In the near term, computing power hardware remains the earnings cornerstone of the industry chain; in the long run, the ultimate value of AI will be realized through deployment across countless industries and scenarios.