The AI industry has entered a cycle of eliminating falsehood and preserving truth, deeply analyzing the boundary between real industry demand and conceptual speculation

Since the outbreak of self generated large-scale models, the AI industry has experienced multiple rounds of market fluctuations, and market awareness is undergoing an important iteration: the era of comprehensive price increases in the early stages of the industry has come to an end, and the industry has officially entered a stage of differentiation of "eliminating falsehood and retaining truth". The pricing standards in the capital market have shifted from "whether they are involved in AI concepts" to "whether they can generate sustained orders and achieve stable commercialization", clarifying the boundary between real demand and speculative themes, and becoming the core of grasping the subsequent market trends.

Looking back at the industry narrative of the past few years, the market has gone through multiple stages, including a wave of large-scale model development, speculation on computing hardware, and imaginative application scenarios. In the early stages of the market, funds value forward space, and as long as there is a connection between the business and artificial intelligence, valuation can receive a premium; But after multiple rounds of gains, investors began to calmly consider the core question: how much incremental revenue can AI create for enterprises? Can capital expenditures be converted into sustained profits?

Breaking down the industrial chain from top to bottom, upstream computing hardware is currently the link with the highest degree of fulfillment. Global cloud providers continue to increase their capital expenditures on computing power, with rigid support for AI servers, optical modules, high-speed connectors, and storage chips. A large number of enterprise orders continue to land, and financial revenue can continue to verify the industry's prosperity. But even in the upstream track, there is still differentiation: enterprises with stable overseas customers and prominent technological barriers continue to benefit; Manufacturers who simply engage in low-end contract manufacturing and lack core technology find it difficult to enjoy the benefits of the industry.

The competition landscape in the midstream large model industry is fiercely internal. The homogenization competition of a large number of universal large models requires huge R&D investment, but the difficulty of commercialization and monetization is relatively high. The general large-scale model track is gradually entering a reshuffle period, and the number of surviving enterprises in the future is limited. In contrast, vertical industry models have ushered in development opportunities, with customized models for sub sectors such as industry, finance, and healthcare, making it easier to find payment scenarios and a clearer commercialization path.

The downstream application end is the area with the greatest divergence. The market is flooded with a large number of conceptual applications, and products lack the willingness to pay, relying solely on short-term hype through traffic; Truly valuable AI applications can help businesses reduce costs and increase efficiency, create new revenue, and have sustainable business models. There are clear criteria for distinguishing the authenticity of applications: whether they have stable paying customers, whether they can form continuous repeat purchases, and whether AI related businesses can independently contribute profits.

The biggest risk in the market comes from poor expectations. Many investors still follow the early speculative ideas and pursue concept stocks without landing products or order support. Once the performance during the financial reporting window cannot be realized, the valuation will quickly decline. Institutional funds are continuously avoiding pure subject matter targets, gradually concentrating on leading companies with real cash flow, and the internal "strong always strong" pattern of the track is constantly strengthening.

In the medium to long term, the growth logic of the AI industry has not been disrupted, but the pace of the market will significantly slow down. The future market trend is no longer driven by grand narratives, but by continuous verification of quarter after quarter financial reports. The evolution path of the industry can be roughly divided into three steps: starting with the computing power base, implementing vertical models, and fully popularizing large-scale applications. The misalignment of business cycles in different stages means that investment needs to grasp the pace of rotation and cannot be generalized.

For market participants, a rational screening framework should be established. Prioritize tracking three major indicators: revenue growth rate of enterprise AI related businesses, order continuity, and changes in gross profit margin. Stay away from theme companies that only release cooperation announcements and are unable to implement projects for a long time. The technology wave will not disappear, but the foam will continue to clear. Only enterprises that truly create commercial value can cross the cycle.

Artificial intelligence is a new round of industrial transformation, but the capital market will not always pay for the long-term story. In the new stage of eliminating falsehood and preserving truth, it is necessary to deeply cultivate fundamentals and identify real needs in order to avoid the trap of fluctuations and grasp the long-term growth dividends of the AI industry.