AI Computing Power Competition Reconfigures: Optimization Emerges as Key Factor
The competitive landscape for AI computing power is undergoing a significant shift, moving away from a sole focus on hardware accumulation. For the past two years, the industry was dominated by a "GPU arms race," where having more cards and larger clusters provided a competitive edge. This paradigm, based on "winning by scale," is now being dismantled. Under a token-based billing model, the focus has changed from selling hardware to selling tokens, diminishing the marginal returns of simply accumulating hardware. Consequently, efficiency has become the new battleground.
Recent developments highlight this transition. In early July, the JingSuan Token Factory was launched, symbolizing a move towards standardized computing power supply. Concurrently, Qunjing Technology secured over 1 billion yuan in financing within six months, reflecting strong investor interest in optimization technologies. Both trends point to computing power optimization moving from a background element to a central determinant of corporate profitability and a key factor in reshaping the domestic computing power ecosystem. However, challenges such as fragmented chip architectures, a shortage of full-stack talent, and insufficient cluster stability persist, indicating that a standardized system has yet to be established and the long-term competition in AI computing power's "soft power" is just beginning.
The shift from a hardware-centric to an efficiency-driven model in AI computing power reflects a maturing market where scalability alone yields diminishing returns. The introduction of token-based billing incentivizes optimization, aligning provider and user interests towards maximizing computational output per unit cost. This evolution suggests a future where specialized optimization firms and integrated hardware-software solutions will gain prominence, potentially democratizing access to advanced AI capabilities. However, the persistent challenges of fragmentation and talent scarcity highlight systemic issues in the AI infrastructure supply chain. Addressing these will be crucial for fostering sustainable growth and preventing a concentration of power among those who can navigate these complexities, ensuring broader participation in the AI era.
AI-generated to prompt reflection — not editorial opinion, not advice, not a statement of fact. How this works.