# 高盛香港禁用Claude，AI访问权成地缘政治新战场

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-04-29 16:50
- AIHOT 分数：54
- AIHOT 链接：https://aihot.virxact.com/items/cmoju182n0785slzpz9p3t6lm
- 原文链接：https://x.com/rohanpaul_ai/status/2049410932594683925

## AI 摘要

高盛依据其与Anthropic的企业协议，切断了香港银行家对Claude模型的访问权限，原因是该模型未在香港正式支持。此事凸显了AI访问权限正从单纯的产品质量考量，转变为受中美政策风险与战略竞争影响的控制性资源。核心矛盾在于“模型蒸馏”风险——美方AI公司担忧，来自中国市场的开放访问可能导致模型行为、答案和推理模式被提取，用于训练本地竞争对手。这使香港等连接中国与全球金融体系的中间地带处境尴尬。对金融机构而言，前沿AI模型是压缩分析、编码与建模周期的关键生产力工具，访问受限虽不会导致生产力崩溃，但会削弱当地团队在速度、选择余地与工作质量上的竞争优势。

## 正文

Goldman Sachs has cut Hong Kong bankers off from Anthropic's Claude models.

AI access inside global banks can now depend on US-China policy risk， not just product quality.

The trigger seems to be Goldman's strict reading of its enterprise deal with Anthropic， because Anthropic's Claude products were reportedly not officially supported in Hong Kong.

The deeper issue is model distillation， which means using a strong foreign AI model （Claude） again and again to train or improve a local rival model （i.e. Chinese models） by copying its behavior， answers， and reasoning patterns.

US AI firms worry that open access from China-linked markets could leak useful model behavior， while banks worry that blocking tools creates uneven productivity between teams in New York， London， Singapore， and Hong Kong.

The story is larger than Goldman or Anthropic.

It shows a new equation in the US-China AI race. If American AI labs fear model distillation， IP leakage， or strategic transfer， access itself becomes a controlled resource， even for employees of a blue-chip U.S. bank.

For years， Hong Kong sat in an awkward middle position， connected to China's market but still plugged into global finance's Western infrastructure.

That middle position is becoming harder to maintain when AI models are treated not just as productivity tools， but as strategic assets.

The real asset in modern finance is not just capital or deal flow， but the ability to compress analysis， coding， drafting， and modeling into faster internal cycles.

When one frontier model disappears from that stack， productivity does not collapse， but the local team loses optionality， speed， and sometimes quality at the margin.

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