Since July, the sharp volatility in computing power stocks has drawn attention, yet the broader AI industry narrative continues to unfold. Capital has been rotating across the upstream, midstream, and downstream segments of the supply chain, with the AI application sector recently experiencing a wave of recovery. From our perspective, AI applications are now at a pivotal turning point, and their long-term investment value is becoming increasingly clear.
In the short term, three key factors have jointly driven this rally. On the earnings front, second-quarter results from North American cloud giants exceeded expectations, largely alleviating earlier market concerns about AI's "high spending without clear returns." More importantly, the customer base is shifting from leading large-model companies to general enterprise clients. Demand for AI in office scenarios is rising, and the number of paying users is expanding rapidly, signaling that AI's transmission and penetration into application layers is accelerating.
On the technology front, domestic models have recently undergone intensive upgrades. DeepSeek V4 Flash, ByteDance's Seedance 2.5, and MiniMax H3 have all been released, with notable improvements in coding agents and multimodal content generation capabilities. Some models are now approaching international frontier levels in specific tasks. On the capital front, trading and positioning in computing power hardware had become highly crowded, while the computer sector remained underweight for several consecutive quarters. As style rebalancing takes hold, capital naturally flows toward application-side opportunities. Combined with intensifying global discussions on the sustainability of AI capital expenditure, investors are beginning to reassess the cost-effectiveness of application-layer investments.
Looking at a longer time horizon, the AI story is still in the middle phase of its first half. The commercialization of large models is transitioning from the technical validation stage into a phase of revenue realization. A positive feedback loop is already emerging: stronger model capabilities lead to more token calls, which in turn raise enterprise willingness to pay. If the early stage of AI's first half was defined by competition in computing power hardware, with communication and electronics sectors taking the lead, then in the middle phase, hardware still offers the highest certainty, while cloud services and application software are beginning to show revenue and profit improvements driven by AI capabilities.
What will accelerate application-side penetration? The variables come from two directions. On one side, barriers are lowering. Domestic and international large models have been cutting prices recently. Coding scenarios remain the area with the highest AI penetration and the first to establish viable business models, with top companies' ARR growth trajectories continuing to slope upward. Meanwhile, application scenarios are expanding into multimodal use cases, office productivity, government and enterprise software, intelligent healthcare, and fintech. On the other side, capabilities are rising. Domestic large models are rapidly catching up, with some niche tasks approaching the global first tier. Once model capabilities align, downstream applications can truly thrive. Combining these two forces, the conclusion is clear: domestic AI applications and agents are poised for a flourishing period, and the inflection point for commercialization is worth anticipating.
Additionally, valuation logic is evolving. Market pricing is shifting from capital expenditure expansion toward ROI and earnings realization validation. In other words, while the market previously focused on "who is spending big on computing power," it now prioritizes "whether AI can convert into real profits." Optimistically, the AI application sector could experience dual opportunities: accelerated earnings release and valuation restructuring.
Drilling down into specific segments, I am particularly focused on three types of opportunities. First, domestic large-model companies with terminal scenario deployments. Second, vertical vendors in pioneering tracks such as programming, video, advertising, and healthcare that have closed the loop and possess strong scenario positioning and data moats. Third, computing power hardware with high earnings certainty.
However, the timing for a systemic reversal in AI applications may still require patience. While software sector valuations have corrected, whether the move transitions from valuation repair to a trend reversal ultimately depends on whether AI products can drive a recovery in overall revenue growth for software companies. The next one to two quarters will likely be a critical observation window, during which individual stock performance will probably diverge significantly.
Across the broader technology sector, the essence of this adjustment is a global rebalancing of AI asset valuations. Going forward, the market is likely to transition toward structural opportunities that emphasize earnings realization. Note: These views may change without notice as market conditions evolve. Funds carry risks, and investment requires caution.