Arcee CTO：中国开源模型并不比其它开源软件更危险

随着中国开源权重 AI 模型能力和受欢迎程度的提升：https://techcrunch.com/2026/07/07/why-the-rise-of-open-source-ai-isnt-hurting-anthropic-yet/，关于如何应对它们的争论：https://techcrunch.com/2026/07/20/openai-is-scared-of-open-weight-models-should-the-us-be/ 再次达到白热化。

有人讨论：https://www.axios.com/2026/07/20/ai-us-china-open-source-kimi 特朗普政府可能会尝试禁止它们（尽管迄今尚未采取行动：https://x.com/SophiaCai99/status/2079254188349948069）。与此同时，专有模型制造商，尤其是 OpenAI 和 Anthropic，似乎：https://x.com/deanwball/status/2078133895766114412 对它们越来越担忧。

开源权重模型，如 Moonshot AI 的 Kimi K3 或阿里巴巴的 Qwen，其推理成本仅为这些大型美国实验室闭源模型的一小部分。人们担心它们也可能带来某种威胁。当然，它们威胁到大型专有 AI 实验室的利润率。

但是，运行这些模型的企业是否应屈服于担心它们可能成为中国黑客攻击的途径呢？

不必如此，Arcee 的 CTO Lucas Atkins 说：https://www.arcee.ai/，Arcee 正在构建开源模型，为美国公司提供本土化的中国模型替代方案。: https://techcrunch.com/2026/04/07/i-cant-help-rooting-for-tiny-open-source-ai-model-maker-arcee/

如果有任何初创公司会因禁止中国模型而受益，那就是 Arcee。但 Atkins 表示，中国的开源模型并不比公司可能使用的其他任何开源软件更危险。实际上，他说，它们甚至为他自己的公司提供了好处。

“很多人把这看作类似于中国的软件程序。比如，它是按这些 x、y、z 意图编码的，”他表示，这些意图可能被不良行为者轻易指挥。

“这从根本上不是这些模型的训练方式。实际上 Arcee 或阿里巴巴不可能制造一个模型，让别人运行在自己的环境中，而我们对其有任何访问权限，”他解释道。

虽然大多数这些模型被称为“开放权重”(open weight)，并不是真正的完全开源软件，但是如果从像 Hugging Face 这样的开源网站下载其源代码（实际在服务器上运行的部分），同样是可以大致看到并进行审查的。（不可获得的是用于训练模型的方法和数据。）

大型组织应将任何模型核心通过其安全测试和检查流程，并且他们通常还会对模型进行特定用途的后训练，并可以检查诸如偏见、有害内容、幻觉以及对某些话题的敏感性等方面。因此，他们在用户开始发送提示之前，会先使用、优化并理解这些模型。

用于编程的模型是否可能在其生成的代码中插入恶意后门？同样，虽然理论上可能，但这需要极高的操作技巧才能实现。

“没有理由认为一个足够复杂的行为者不能训练一个模型，使其在任何情况下都是一个完全出色的编程模型，但当遇到某种类型的代码库时……某些隐藏的训练会启动，”一直致力于训练模型的 Atkins 推测道。但他补充说：“我不知道你该如何做到这点。”

由于大型语言模型本质上具有创造性，要想让当代模型在面对事先计划好的完美情境和提示时生成恶意软件，几率很低。而企业随后使用这些代码的可能性就更低了。

这种情况未来可能发生吗？那就无从猜测了。但企业也在构建其 AI 应用，使其能够与模型无关并使用多个模型。因此，即使今天中国模型在性价比上最好，企业也不会永远被锁定在使用这些模型。

“Atkins 说：‘我认为，不应该讨论如何禁止中国模型，而应该讨论如何在美国培育一个良好、开放的生态系统。’”

Arcee 也从中国模型中获益。因为它们是开放的，这家初创公司“受益于这些模型的优秀，因为我们可以学习他们的做法。我们可以在其基础上构建，然后他们也可以学习我们的成果，”他说，“我们对构建这些模型的人、那些独立研究人员怀有极大的尊重。”

阿特金斯表示，最终与中国模型竞争的方法“是发布一个更好的模型。” “我们需要给他们一些值得讨论的东西。”

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There’s talk：https://www.axios.com/2026/07/20/ai-us-china-open-source-kimi that the Trump administration might try to ban them (though it hasn’t yet acted：https://x.com/SophiaCai99/status/2079254188349948069 on the idea). Meanwhile, proprietary model makers, particularly OpenAI and Anthropic, appear：https://x.com/deanwball/status/2078133895766114412 increasingly concerned about them.

Open-weight models such as Moonshot AI’s Kimi K3 or Alibaba’s Qwen offer inference at a fraction of the token cost of closed source models from these large U.S. labs. The fear is that they also pose some sort of threat. Certainly they threaten the profit margins of the large proprietary AI labs.

But should enterprises running these models in their own data centers succumb to the fear that they could be a vector for Chinese hackers?

No, says Lucas Atkins, the CTO of Arcee：https://www.arcee.ai/, which is building open models to give U.S. companies a homegrown alternative to Chinese models.：https://techcrunch.com/2026/04/07/i-cant-help-rooting-for-tiny-open-source-ai-model-maker-arcee/

If any startup would benefit from a ban on Chinese models, Arcee would. But Atkins says China’s open models are no more dangerous than any other open source software a company may use. In fact, he says, they even offer benefits even to his own company.

“A lot of people view this as similar to a Chinese software program. Like, it was coded with these x, y, z intentions” that a bad actor could simply command, he said.

“That is fundamentally not how these models are trained. There is really not any way for an Arcee, or an Alibaba, to make a model, have someone run it in their own environment and for us have any access to it whatsoever,” he explained.

While most of these models are what’s known as “open weight” and are not really fully open source software, the source code (the part that will actually run on servers), if it is downloaded from open source sites like Hugging Face, is similarly largely visible and reviewable. (What isn’t available is the methods and data used to train the models.)

Large organizations should put any model core through their security testing and inspection processes, and they will also often post-train the models for their specific uses and can examine areas like bias, toxicity, hallucinations, and sensitivity to certain topics. So they work with, optimize, and understand the models before people start sending them prompts.

Could a model that is used for coding somehow throw malicious backdoors into the code it writes? Again, while that’s theoretically possible, it would require acrobatic feats to accomplish.

“There’s no reason that a sophisticated enough actor couldn’t train a model to be a completely amazing coding model in every circumstance, but when presented with a certain type of code base … some hidden training would kick in,” Atkins, who spends his days training models, postulated. But he adds: “I don’t know how you would do this.”

Because large language models are by nature creative, the odds are slim of getting a contemporary model to spit out malware in response to a preplanned perfect storm of context and prompt. Even slimmer are the chances that any enterprise would then use that code.

Could it happen in the future? That’s anyone’s guess. But enterprises are also building their AI apps to be model-agnostic and to use multiple models. So even if Chinese models are the best for the price today, enterprises won’t be locked into using them forever.

“I think instead of the conversation being about how to ban Chinese models, it should be about how do we foster a good, open ecosystem here in the U.S.,” Atkins says.

Arcee also gains advantages from Chinese models. Because they are open, the startup “benefits from those models being good because we can learn what they did. We can build on top of them. Then they can learn what we do,” he says. “We have tremendous respect for the people building those models, the individual researchers.”

Ultimately, the way to compete with Chinese models “is to release a model that is better,” says Atkins. “We need to give them something to talk about.”

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