The narrative of the global AI race is shifting. The era of purely computational power competitions and parameter stacking, once wildly pursued by capital, is fading. The market's focus is increasingly on a more pragmatic metric—who can truly sell AI, and who will be the winner.
This doesn't mean technology is no longer important, but rather that the evaluation system is quietly upgrading. The previous question was "whose model is stronger," but now it has shifted to "who can embed the model into actual business, continuously generate revenue and cash flow." Capital is voting with its feet; it prefers to invest in companies that possess replicable commercialization capabilities.
The underlying logic supporting this path is quite clear, consisting of three main pillars: sufficient data scale, stable electricity supply, and abundant engineering talent reserves. When external environments become more complex and competition more fierce, the resilience of the supply chain directly determines whether AI can be continuously pushed to market. Based on this framework, industry research institutions estimate the potential market size for cloud-based AI at around $50 billion (by 2027 standards), and also point out that several key players have already emerged within the top-tier global models. This has led to a new market consensus: upstream computing power is just infrastructure; the real drivers of investment returns are platform companies with strong ecosystem barriers and rapid deployment capabilities.
Under this new framework, the logic of winning becomes clearer. It’s no longer enough to achieve a single breakthrough in technology; instead, a complete closed loop of "model foundation + delivery system + business scenarios" must be formed. Those who can seamlessly connect model capabilities, cloud computing delivery, and real business scenarios will continuously draw from the market. The stronger the resilience of the supply chain, the broader the scene coverage, and the faster the data feedback, the deeper the moat becomes.
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The narrative of the global AI race is shifting. The era of purely computational power competitions and parameter stacking, once wildly pursued by capital, is fading. The market's focus is increasingly on a more pragmatic metric—who can truly sell AI, and who will be the winner.
This doesn't mean technology is no longer important, but rather that the evaluation system is quietly upgrading. The previous question was "whose model is stronger," but now it has shifted to "who can embed the model into actual business, continuously generate revenue and cash flow." Capital is voting with its feet; it prefers to invest in companies that possess replicable commercialization capabilities.
The underlying logic supporting this path is quite clear, consisting of three main pillars: sufficient data scale, stable electricity supply, and abundant engineering talent reserves. When external environments become more complex and competition more fierce, the resilience of the supply chain directly determines whether AI can be continuously pushed to market. Based on this framework, industry research institutions estimate the potential market size for cloud-based AI at around $50 billion (by 2027 standards), and also point out that several key players have already emerged within the top-tier global models. This has led to a new market consensus: upstream computing power is just infrastructure; the real drivers of investment returns are platform companies with strong ecosystem barriers and rapid deployment capabilities.
Under this new framework, the logic of winning becomes clearer. It’s no longer enough to achieve a single breakthrough in technology; instead, a complete closed loop of "model foundation + delivery system + business scenarios" must be formed. Those who can seamlessly connect model capabilities, cloud computing delivery, and real business scenarios will continuously draw from the market. The stronger the resilience of the supply chain, the broader the scene coverage, and the faster the data feedback, the deeper the moat becomes.