AI Training

The core argument of AI companies is that training models on vast amounts of text constitutes 'transformative use,' as the training process extracts statistical patterns from text rather than copying content for human reading.

AI training typically requires thousands of high-end GPUs working in concert for weeks or even months, with extremely high demands on bandwidth, latency, and computational consistency, making it currently almost exclusively feasible in centralized data centers.

Meta recently updated its commercial terms, removing wording that could have allowed it to scrape news sites and other content to train AI.

If courts rule that AI training constitutes copyright infringement, legitimate activities such as search engine web indexing and caching, text mining, and large-scale data analysis in academic research could all be implicated

Data centers can defer non-urgent AI training tasks to periods of low electricity prices or surplus grid capacity, and rapidly shed load during peak times, effectively acting as a virtual power plant.

The training phase requires repeated forward and backward propagation, places extremely high demands on floating-point compute (FP16/BF16 or even FP8), memory bandwidth, and inter-card interconnect (such as NVLink), and often requires thousands of cards working together.

Training pursues extreme performance and interconnect bandwidth, with strong ecosystem lock-in; inference places more emphasis on unit cost, energy efficiency, and deployment flexibility.

Data centers are the physical carriers that support AI training and inference, typically characterized by extremely high power density and cooling demands.

AI training workloads are often continuous and rigid, making it difficult for them to respond flexibly to grid signals like general industrial loads.

Intermediate data such as checkpoints and logs generated by training can usually be cleaned up after training ends, whereas data generated by inference is directly tied to business operating state, and deleting it often means losing traceability and user experience

Training is a phased capital expenditure, with GPU clusters invested intensively over weeks to months, making the bill relatively concentrated and visible; inference is a continuous operating expenditure that fluctuates linearly or even exponentially with user request volume

The deployment of large-scale AI training and inference clusters has pushed the power consumption of a single data center to several times that of a traditional data center.

Training workloads emphasize large-scale parallel compute and data throughput, while inference workloads focus more on unit cost, latency, and elastic scaling.

The removal of the clause does not mean Meta will stop using public content to train AI, but it avoids an authorization dispute in black and white.

Meta's new terms redefine the purpose of use as 'AI training', falling into a gray area of the existing legal framework — most jurisdictions have not yet made a final ruling on whether AI training constitutes 'use' in the sense of copyright law, giving platforms an opportunity to argue that their conduct is not covered by payment obligations.

Some AI training tasks do not have the same extreme real-time requirements as financial trading systems, and can be paused for minutes or even hours during grid stress events.

In hyperscale training, a single node or link failure can cause the entire training job to be interrupted or rolled back to the previous checkpoint.

In large-scale AI training and inference scenarios, the efficiency of data exchange between chips directly determines the actual performance of the entire cluster.

Reasons for creators' resistance to AI include concern about their work being used to train models, the belief that AI dilutes the value of original creation, and insistence on human creativity.

Hyperscale data centers provide computing power for AI training and inference, and the operation of their chip clusters generates enormous heat that requires continuous cooling