The Agent Era: Token Production Capacity Emerges as the New Battleground

At a recent forum on AI infrastructure innovation, Academician Zheng Weimin, a professor at Tsinghua University and member of the Chinese Academy of Engineering, presented a compelling shift in perspective regarding the current trajectory of artificial intelligence development.

Redefining Scarce Resources

Academician Zheng clearly stated that as we enter an era dominated by AI Agents, the industry's definition of scarce resources has evolved. While the focus has often been on computational power, measured in chips and FLOPs, the reality is more nuanced.

“The rapid expansion of computing scale does not directly translate into efficient, usable token production capacity,” Zheng emphasized. Here, “tokens” refer to the fundamental units AI models use to process and generate information. He argued that the true scarcity lies in the holistic system capability to generate tokens in a stable, low-cost, and high-quality manner. This encompasses a complex system of engineering involving hardware, software, and global orchestration beyond single-point optimization.

Token: From Metric to Production Factor

This assessment stems from a profound transformation in the role of tokens. They are no longer merely a technical metric for measuring model throughput but are evolving into a core production factor powering intelligent applications. Much like electricity for the industrial revolution or data for the internet age, the ability to provide a stable and reliable supply of tokens will form the foundational infrastructure of the future AI industry.

This shift is directly driving innovation in underlying technical architectures. Zheng pointed out that to meet this new production demand, AI inference systems are undergoing significant changes:

  • Distributed Architecture: Moving from reliance on single powerful servers to leveraging multi-node collaboration, enhancing overall reliability and scalability.
  • Caching Optimization: Intelligently caching frequently used intermediate results to avoid redundant computation, significantly reducing latency and cost.
  • Heterogeneous Computing: Flexibly utilizing different computing units (CPUs, GPUs, NPUs) to achieve an optimal balance of efficiency and cost.
  • Service-Oriented Deployment: Packaging AI capabilities into standardized, on-demand services, making token production capacity as accessible as utilities.

Implications for the Industry's Future

Zheng's viewpoint charts a course for AI infrastructure development. It implies that companies and research institutions must look beyond peak flops and focus on building a complete “token production system” encompassing hardware, software, scheduling, and operations. The essence of this competition is the race for industrialized AI production capability. Those who can deliver high-quality tokens in a more stable and economical manner are likely to seize the initiative in the Agent era.