Compute Power as a Network: Why the Real Scarcity Isn't Hardware

Compute Power No Longer Just a "Buy and Place" Resource

Compute infrastructure has appeared frequently in policy and industry discussions. China's "Action Plan for High-Quality Development of Compute Infrastructure" set 2025 targets across computing power, carrying capacity, storage capacity, and application enablement. National data infrastructure documents place compute foundation, network support, security protection, and data circulation in a single framework. Since 2026, the national integrated compute network has been positioned among the "six networks" of new infrastructure. This signals compute is becoming a digital public utility, not just a departmental or corporate asset.

Like highways or power grids, compute infrastructure cannot be measured only by capacity. When AI applications move from pilots to daily operations, the questions shift from "do we have machines?" to finer-grained issues:

Large model training: Surface issue — need more high-end compute; deep issue — can tasks be queued, split, migrated, and reused?

Inference services: Surface issue — sudden call spikes; deep issue — how to balance latency, cost, concurrency, and stability?

Government/industry systems: Surface issue — multiple businesses want AI; deep issue — can data flow compliantly and can results be audited?

Edge intelligence: Surface issue — on-site low latency needed; deep issue — which tasks must stay local, which can be scheduled across domains?

SMEs using AI: Surface issue — self-building too costly; deep issue — can they obtain trusted compute on-demand like cloud services?

The Real Change: "Compute Islands" Being Reorganized

Many industries have systems and devices but lack cross-system, cross-region, cross-vendor organizational capability. Different regions have different data centers, different units have different resource pools, different vendors have different platforms, different chips have different adaptation environments. They all call it "compute" but actual use faces resources that don't easily recognize, migrate, or measure uniformly.

For a single-point AI pilot, this is manageable. But in real business, multiple states coexist: daytime online inference, nighttime batch training and data processing; some tasks need low latency, others can be delayed; some data cannot leave its domain, others can move models or compute; some are cost-sensitive, others prioritize reliability. The question becomes not just "where are machines?" but "how to place tasks in the right location?"

The national integrated compute network aims to connect dispersed compute, network, data, and application demands in a unified way. However, integration doesn't mean all tasks should be remotely scheduled or all data can flow freely. The more integrated, the clearer boundaries must be defined.

Compute Scheduling's Difficulty Lies in Judgment, Not Just Dispatch

Many imagine a compute network as a larger cloud platform: enough resources, automatic allocation, problem solved. Reality is more complex. Scheduling must simultaneously handle four relationships:

Timeliness vs. cost: Real-time dialogue, video recognition, industrial control, emergency command are latency-sensitive and cannot just chase cheap resources. Offline training, batch analysis, historical data processing tolerate higher latency and can prioritize cost, energy efficiency, and utilization.

Data vs. compute: Not all scenarios suit "data moves to compute." Under strong data security, personal information protection, or industry regulation, "compute or model capabilities move near data" or trusted execution, desensitization, federated computing, and access control may be required to reduce flow risk.

General vs. heterogeneous: AI compute isn't uniform. Different chips, frameworks, models, and inference services have adaptation costs. A resource pool presenting uniformly doesn't guarantee seamless task migration. Real scheduling must understand what the task actually needs, not just resource labels.

Efficiency vs. accountability: On-demand compute introduces metering, auditing, billing, service quality, and security responsibility. Who initiated the task, which data was used, which model ran, how results are deployed, how to trace issues — these cannot stay only inside the technical platform.

The real difficulty in building a compute network is not connecting more nodes, but establishing an explainable scheduling judgment framework.

A Practical Judgment Framework: Where Should a Task Run?

Future AI systems will likely rely on multiple compute pools: local, city, regional, industry cloud, national hubs, and commercial compute services. Deciding where a task belongs can be assessed across five dimensions:

Latency requirement: Near-end for second/millisecond response with strong on-site linkage; cross-domain for queueable, deferrable, batch processing.

Data sensitivity: Near-end for sensitive, important, personal, or heavily regulated data; cross-domain for desensitized, authorized, controllably circulated data.

Task type: Near-end for small-model inference, on-site recognition, instant decision support; cross-domain for large-scale training, batch inference, simulation, offline analysis.

Cost objective: Near-end when stable guarantee is priority and cost isn't the only goal; cross-domain when cost elasticity, resource reuse, and peak-valley scheduling matter more.

Accountability requirement: Near-end when local logging, local audit, local explanation are needed; cross-domain when unified platform can handle metering, audit, and traceability.

This framework doesn't replace technical selection but reminds AI builders: don't start by asking "how much compute?" — first ask "what compute organization suits this task type?" Some tasks suit stable near-end operation, others suit external elastic resources, some need data-stationary/model-mobile, some need traceable results over speed. Without these judgments upfront, familiar problems emerge: resources bought but underutilized, models run but costs uncontrollable, systems integrated but audit trails unclear, business peaks hit and emergency scaling needed.

For Industry Software, Compute Networks Change Delivery Logic

Task orchestration becomes central: Beyond managing data, processes, permissions, systems must manage compute tasks: which run instantly, which queue, which go cross-domain, which must stay local.

Cost shifts from one-time build to continuous ops: AI inference isn't traditional software licensing; more calls, larger models, longer contexts increase cost visibly. Unified metering lets business units see which intelligent capabilities truly add value versus which are just noise.

Security boundaries move earlier: When data, models, tools, and compute interact, simple account permissions aren't enough. Task origin, data scope, model capability, output destination, and audit evidence must be viewed on a single chain.

SME AI adoption barriers may lower: Not every unit should build an intelligent computing center; not every app justifies heavy assets. More inclusive, measurable, standardized compute services let more industry apps move from "demo works" to "daily usable."

This doesn't mean the compute network instantly solves everything. It exposes problems previously hidden in machine rooms, procurement, cloud resources, and application interfaces: who schedules, who bills, who guarantees, who audits, who owns results.

What Truly Matters: Compute "Governability"

As AI penetrates industry sites, compute becomes less a standalone resource and more a foundation combining data circulation, model services, network connectivity, and security responsibility.

In the coming period, many organizations discussing AI construction will still focus on model parameters, knowledge base scale, agent capabilities, and application scenarios. But they should simultaneously ask: Are compute resources visible? Are tasks orchestratable? Are costs measurable? Are invocations auditable? Are security responsibilities traceable?

As compute becomes more like a network, the true scarcity isn't machines themselves, but the ability to organize machines, networks, data, models, and business goals together. This is a foundational test AI must pass to move from pilot to normalized application.