中文翻译

摘要: Anthropic made Claude Fable 5 worse at AI development, users call it anticompetitive behaviour

Anthropic's Claude Fable 5 has drawn criticism from users who say it becomes less helpful on AI research...

正文

Anthropic made Claude Fable 5 worse at AI development, users call it anticompetitive behaviour

Anthropic's Claude Fable 5 has drawn criticism from users who say it becomes less helpful on AI research tasks. The backlash centres on hidden safeguards that may switch responses without making the restriction visible.

Advertisement

Anthropic's Fable 5 includes safeguards against AI distillation. (Photo: Reuters)

Om Gupta

New Delhi

UPDATED:

Jun 10, 2026 13:57 IST

Anthropic has once again created a buzz in the AI industry with the launch of Claude Fable 5, its new Mythos-class AI model. The company says Fable 5 is its most capable model yet, showing exceptional performance in software engineering, knowledge work, vision, scientific research and many other areas. This model comes with

Anthropic's newest safety measures

: protections against AI distillation, which refers to the process of using one model's outputs to help train another model. But just after its release, some users are talking less about its capabilities and more about what they believe the model is deliberately refusing to do.

advertisement

Read Full Story

Is Anthropic protecting safety or protecting its competitive edge?

The debate started after users began digging into what Anthropic's distillation safeguards could mean in practice. Several users argued that the company is not just trying to prevent misuse, but is also making it harder for competitors to use Claude to build rival AI systems.

Screenshot of Anthropic Claude Fable 5 system card.

One user summarised the concern by claiming that Fable 5 may not provide its full capabilities when it detects work related to building or improving frontier AI models. According to the user, this could include tasks such as designing large-model training pipelines, planning distributed training across massive GPU clusters, optimising model-parallel systems, building infrastructure for large-scale pretraining runs, working on AI chips and acce

来源: Brave/indiatoday.in

采集时间: 2026-06-10 20:16:25