What new requirements will AI have for network transmission in the future?

What new requirements will AI have for network transmission in the future?
The development of future AI is driving the transformation of networks from the traditional "general connection pipeline" to a "compute-enabled network". As AI applications evolve from single models to agent systems (AI Agents), network transmission will face unprecedented new requirements, mainly in the following five core dimensions:

1. Ultra-high uplink bandwidth and "uplink-downlink balance"
Traditional networks are dominated by "downloading", but the data flow in the AI era has fundamentally changed. Physical intelligent robots, AI glasses, and intelligent connected vehicles, among others, require real-time uploading of massive multimodal data (such as high-definition images, environmental perception data) to the cloud for inference. This requires the network to reconfigure the allocation of uplink and downlink resources, providing high-reliability, low-jitter uplink bandwidth, and achieving the transition from "dominant downlink" to "uplink-downlink balance" 68.
2. Deterministic low latency and low jitter
AI-driven human-computer interaction is extremely sensitive to latency. For example, if the control instructions for industrial robots have latency fluctuations, it may trigger safety accidents; if the latency jitter of AR glasses exceeds a certain threshold, it will cause users to feel dizzy 18. Therefore, the future network cannot merely provide "best-effort" low latency, but must provide microsecond-level response, stable, and "deterministic" low latency guarantees to ensure that instructions are "executed immediately" 15.
3. Massive connections and distributed computing offloading
In the future, billions of AI agents will be connected to the network, which requires the network to have an ultra-large-scale connection density 18. At the same time, many terminals are limited by size and power consumption and cannot complete complex computations locally. The network needs to transform from a simple data transmission channel to a distributed computing platform, through "cloud-edge-end" collaboration, to undertake the computing offloading requirements of terminals, achieving flexible scheduling of computing power in the network 38.
4. Zero packet loss and lossless transmission capability
In distributed training and real-time inference of petabyte-level AI clusters, the network is extremely sensitive to packet loss (even 2% packet loss may cause a sharp drop in throughput) 5. To ensure efficient collaboration of computing power and avoid GPU idling, the network must have cross-wide area lossless transmission capabilities, through explicit congestion control and micro-sudden buffer mechanisms, to completely eliminate random packet loss and jitter 59.
5. Intent-driven and network self-sensing (AI for Network)
Facing the exponential growth of network complexity and dynamic traffic, traditional manual operation is no longer sufficient. The future network needs to be inherently integrated with AI capabilities to achieve the transformation from "passive response" to "active autonomy" 68. Through the intent-driven mode, the network can automatically perceive business characteristics, predict failures, dynamically optimize routes and resource scheduling, achieving "network intelligence symbiosis" 47.
In conclusion, the future network will no longer merely be a physical medium for connecting devices, but will evolve into a "digital foundation" supporting the implementation of AI applications. Network and AI will form a mutually enabling symbiotic relationship: the network provides high throughput and low latency transmission guarantees for AI, while AI endows the network with the ability of self-evolution and intelligent scheduling 810.