Woofun AI reports that the AI industrial chain has undergone a fundamental profit redistribution, with upstream hardware suppliers absorbing the vast majority of capital while downstream model developers face severe margin compression. The financial disparity is stark: Micron posted revenue of $41.456 billion and operating profit of $33.318 billion in its most recent quarter, achieving an operating margin of 80.4%. The company's forward guidance projects revenue reaching approximately $50 billion with a gross margin near 86% for the next quarter. In parallel, SK Hynix recorded an operating profit of 37.61 trillion Korean won in the first quarter of 2026, while Samsung's Semiconductor DS division secured 53.7 trillion Korean won during the same period. Although these figures encompass broader storage activities, corporate disclosures explicitly attribute these results to surging AI application demand, record Memory sales, and rising industry prices. When aggregating the operating profits of SK Hynix, Samsung's DS division, and Micron for a single quarter, the total approaches $100 billion. For context, NVIDIA's operating profit for the identical period stood at approximately $53.5 billion, meaning the combined earnings of these three storage entities more than double that of the chip giant. Even tech titans like Tencent and Apple have been forced to yield to the pricing power of these major storage manufacturers.

The scale of this divergence becomes even more apparent when comparing global hardware earnings against domestic market performance. The 608 companies listed on the STAR Market reported a total annual net profit of 58.624 billion yuan in 2025.

However, the operating profit generated by SK Hynix, Samsung's DS division, and Micron in just one quarter exceeds this entire annual figure by more than ten times when converted into yuan. This data illustrates a structural shift where the model development phase is under duress to reduce costs. On the OpenAI side, API pricing has become hyper-competitive, with distinct pricing tiers for different models, context lengths, caching options, and batch processing discounts. The strategic objective is to continuously lower the cost per token while maximizing utilization rates. Google Gemini employs similar logic, utilizing tiered pricing based on capabilities and offering special enterprise discounts to drive volume. Microsoft adopts an even more direct approach by developing proprietary models while integrating third-party options, including DeepSeek, into the Azure AI Foundry, enabling customers to easily compare and switch between providers. This 'model supermarket' strategy systematically erodes the bargaining power of individual model developers.

Woofun AI data shows that this trend of margin erosion is equally pronounced in the Chinese market, where cost structures are becoming increasingly rigid. ByteDance, which previously offered low-cost or free services, has transitioned to a fee-based model with tiered pricing due to unsustainable operational expenses. As usage volume increases, costs explode, with the majority of capital expenditure directed toward GPUs and storage infrastructure. Consequently, model development has evolved into a business model defined by low margins and high transaction volumes. While model companies compete aggressively on price and functionality at the front end, their back-end cost structures remain inflexible. Conversely, the storage sector is capturing substantial value because technologies such as HBM, server DRAM, and eSSD cannot be scaled rapidly. Supply growth is constrained by slow expansion cycles and time-consuming verification processes, yet demand from AI data centers, training systems, inference tools, and Agents continues to push prices upward. This supply-demand imbalance drives significant increases in profit margins for storage providers, creating a scenario where model companies fight on price while storage companies generate massive cash flows.

The economic reality of the current AI landscape suggests that selling tokens has become analogous to the food delivery sector, where transaction volumes may rise but prices remain transparent and competition intensifies through constant discounting. Both platforms and providers are forced to offer incentives to attract customers, squeezing profitability. In contrast, the business model for providing storage solutions resembles a toll booth system: any entity wishing to build a data center, perform inference tasks, or deploy Agent technologies must pay upfront fees that are difficult to reduce. At least for the foreseeable future, the largest profits within the AI industry chain are generated in memory and storage segments rather than in intelligent model development. In the face of storage and semiconductor hardware constraints, virtually every company in the ecosystem becomes a dependent player. This marks a definitive shift where hardware bottlenecks, not software innovation, dictate the flow of capital in the artificial intelligence revolution.