AI Training

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.

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

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

AI training tasks have greater temporal flexibility than real-time inference and can be designed as flexible loads that respond to grid conditions, smoothing the power curve through throttling, migration, or pausing.

AI训练与谷歌图书案的关键差异在于训练需摄入作品完整内容，且训练后模型可生成与原作品相似的输出，可能与原作品形成市场竞争

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

If AI training is required to pay high licensing fees for every piece of data, only well-funded tech giants will be able to afford compliance costs, potentially exacerbating market monopolization