中国为何免费开放其最强 AI 模型？

硅谷过去一周大部分时间都处于高度警戒状态，消化 Moonshot AI 的 Kimi K3 发布所带来的影响。这是一款中国 AI 模型，据称能以远低于美国公司所建系统的成本，击败其中一些最强模型。

仅凭其性能，Kimi K3 就足以加剧美中竞争。但 Moonshot 计划免费发布该模型的权重，以及其明确瞄准美国用户，进一步引发了更深层的担忧：随着能力日益增强的开放替代方案进入市场，封闭的美国模型能否继续占据主导地位？

开放权重模型赋予开发者远高于专有系统的控制权：他们可以检查 AI 的运作方式，在自己的基础设施上本地运行 AI，定制系统，并在不依赖单一供应商的情况下构建新产品。它们通常也便宜得多。这就带来一个显而易见的问题：一家 AI 公司为何要投入巨资训练 AI 模型，却又免费送出其中最有价值的部分？

与其他开放权重 AI 模型一样，Kimi K3 并非完全“开放”。在软件领域，“开源”已有明确的定义：源代码可被公开使用、修改和自由再分发，唯一要求是这些行为也应以开放方式进行。AI 系统更为复杂，真正符合传统软件意义上开源标准的系统极少。多数公司发布的是所谓模型权重——即 AI 在训练期间学习到的数值参数——同时将训练数据、代码、模型架构和配置方法等其他关键组成部分保密。大多数模型还附带限制性许可，对其使用或再分发方式加以约束。

综合来看，这意味着开放权重 AI 无法像真正的开源软件那样从零开始复现。但它确实提供了足够的能力和灵活性，使公司能够从中获利。

“免费提供一组权重，并不等于免费提供 AI 服务。”

“免费提供一组权重，并不等于免费提供 AI 服务。”福特汉姆大学法学院教授 Chinmayi Sharma 表示，“一家公司可以免费提供模型权重，同时在技术栈的其他环节赚钱。”可操作的空间很大。运行模型仍需要计算基础设施、工程能力、安全保障、维护和支持，公司可通过托管访问或其他安排对此收费。对一些公司而言，回报可能更广泛，例如带动云计算服务或高端计算机芯片的需求增长。

开放也可以是获取竞争优势的有力策略。发布模型权重可以鼓励更多公司和开发者使用该模型，进而促成围绕它构建完整的工具和基础设施生态。随着时间推移，这能帮助一个模型成为“事实标准”，Sharma 表示。乔治城大学安全与新兴技术中心高级研究分析师 Kyle Miller 也提出类似观点。他援引阿里巴巴庞大的Qwen 开放权重 AI 模型家族为例，说明一个开放系统能够如何在整个行业中深度嵌入。

这给美国 AI 巨头带来了明确问题。如果一代工具和开发者开始围绕 Kimi K3 这类能力强大的开放权重模型进行构建，行业重心可能开始从 Gemini、Claude 和 ChatGPT 等专有平台转移。前沿级开放权重模型在实际运行中是否真的更便宜，仍有待观察；但从历史上看，它们一直是专有系统成本更低的替代方案。在美国实验室正收紧访问权限，并对其最新模型施加更严格护栏之际，这些模型也为开发者提供了更大的自由度。已有迹象显示，一些美国公司正转向成本更低的中国模型。

中国支持开放权重 AI 并无单一原因，但这似乎是现实约束与政治战略的结合。开放生态使中国公司能够在获取先进芯片和算力受限的情况下，仍有一条接近前沿创新的路径；同时，这也与北京更广泛的产业战略高度契合，即推动中国模型、工具和基础设施得到更广泛采用。这一方式也有利于在海外扩大中国的技术影响力，以及政治影响力。例如，本月早些时候，国家主席习近平在上海的一场会议上公开挑战美国在世界舞台上的 AI 领导地位，称鉴于美国采取封闭路线，中国可以成为更平等的合作伙伴。

能力不断提升的中国开放权重模型，也正在从 AI 行业内部加大对 OpenAI 和 Anthropic 等闭源模型提供商的压力。鉴于 Kimi K3 的出现，美国可能限制开放权重 AI 访问的前景，迅速在科技行业引发反弹，并获得一些最大参与者的支持。由 IBM、Microsoft、Meta、Nvidia、Perplexity 和 Palantir 等 25 家科技公司组成的联盟发布公开信，敦促政策制定者避免“过早限制”，认为开放权重 AI 模型对于确保美国 AI 领导地位、避免这项技术的力量和利益“集中在少数人手中”至关重要。值得注意的是，Google、OpenAI 和 Anthropic 等大公司均未出现在最初的名单中。

这种压力再次加剧。周一，Nvidia、Microsoft、SpaceX 以及更广泛的一批大型科技公司呼吁美国加大对开放权重模型的支持。这项倡议直接回应了人们对先进 AI 系统安全性的担忧：一款失控的 OpenAI 模型在测试期间逃脱控制并发动攻击，另一家公司不得不依靠一款中国开放权重模型进行防御，原因是美国前沿模型的安全护栏过于严格。

目前尚不清楚美国最大的 AI 实验室准备作出多大让步。Google和 OpenAI后来加入了反对仓促限制开放模型的行列，但两者均未签署周一这项聚焦网络安全的倡议。值得注意的是，Anthropic 两项行动都未支持。

Miller 表示，这一切长期将如何发展仍是一个“开放问题”。他说，美国公司可能会发布自己能力更强的开放权重模型，并指出中国公司的压力，部分促成了 OpenAI 在去年发布开放权重模型 GPT-OSS。“但我不认为 Anthropic 这样的公司会走向这个方向。”他说。Google 的开放权重 Gemma 模型，也被部分视为对中国竞争的回应。不过，这两者都远不如各自公司的专有旗舰模型。

“对美国公司而言，问题可能越来越会变成：我们需要公开释放多少能力，才能防止中国模型成为开放生态系统的默认平台？”Sharma 表示。她认为，更可能出现的结果将是一种“组合策略”：公司保留“最优秀的模型为专有模型，同时发布能力越来越强的开放权重模型，以维持开发者采用率和生态影响力”。

Kimi K3 能否赢得美国开发者的青睐，仍需要时间验证。但随着北京日益倡导开放权重 AI，它几乎肯定不会是最后一个试图打入美国市场的模型。摆在美国最大 AI 公司面前的问题，不再只是美国如何能够领先中国，而是封闭 AI 能否——或是否应该——继续保持封闭。

Silicon Valley has spent much of the past week on red alert, digesting the arrival of Moonshot AI’s Kimi K3, a Chinese AI model that can allegedly beat some of the best systems built by US companies at a fraction of the cost.

Its performance alone would have been enough to intensify the rivalry between the US and China. But Moonshot’s plan to release the model’s weights for free — and its clear targeting of US users — has fueled deeper unease about whether closed American models can continue to dominate as increasingly capable open alternatives enter the market.

Open-weight models give developers far greater control than proprietary systems, allowing them to inspect how the AI functions, run the AI locally on their own infrastructure, customize the systems, and build new products without depending on a single provider. They’re often a lot cheaper, too. That raises an obvious question: Why would an AI company spend vast sums of money training an AI model, only to give away some of the most valuable parts?

Kimi K3, like other open-weight AI models, isn’t fully “open.” In software, “open source” has a settled definition: Source code is publicly available to use, modify, and redistribute freely, only requiring that this is also done openly. AI systems are more complicated, and very few are truly open in the traditional software sense. Most companies instead release something called model weights — the numerical parameters learned during an AI’s training period — while keeping other crucial components, including training data, code, model architecture, and configuration methods, private. Most also come with restrictive licenses limiting how they can be used or redistributed.

Together, this means open-weight AI cannot be re-created from the ground up in the way true open-source software can. But it does provide enough power and flexibility that a company can make money off of it.

“A free set of weights is not a free AI service.”

“A free set of weights is not a free AI service,” said Fordham Law School professor Chinmayi Sharma. “A company can give away the model weights while making money elsewhere in the stack.” There are ample opportunities to do so. Running a model still requires computing infrastructure, engineering, security, maintenance, and support, all of which companies can charge through hosted access or other arrangements. For some companies, the payoff may be broader, such as an increased demand for cloud computing services or advanced computer chips.

Openness can also be a powerful strategy for gaining a competitive edge. Releasing a model’s weights can encourage more companies and developers to use it, which in turn can lead to an entire ecosystem of tools and infrastructure being built around it. Over time, that can help a model become a “de facto standard,” Sharma said. Kyle Miller, a senior research analyst at Georgetown’s Center for Security and Emerging Technology, made a similar point, citing Alibaba’s large family of Qwen open-weight AI models in China as an example of how deeply embedded an open system can become across an industry.

That creates a clear problem for the US AI giants. If a generation of tools and developers start building around capable open-weight models like Kimi K3, the industry’s center of gravity could start to shift away from proprietary platforms like Gemini, Claude, and ChatGPT. While it remains to be seen whether frontier-level open-weight models are actually cheaper to run in practice, they have historically offered a lower-cost alternative to proprietary systems. They also offer more freedom for developers at a time when US labs are tightening access and imposing stricter guardrails for their latest models. There are already signs that some US companies are shifting toward cheaper Chinese models.

There is no single reason behind China’s support for open-weight AI, but it appears to be a mix of practical constraints and political strategy. An open ecosystem gives Chinese companies a way to innovate near the frontier despite tighter access to advanced chips and computing power, while fitting neatly into Beijing’s broader industrial strategy of encouraging wider adoption of Chinese models, tools, and infrastructure. The approach is also convenient for expanding China’s technological influence abroad, as well as its political influence. For example, earlier this month, President Xi Jinping openly challenged the US for leadership of AI on the world stage by pitching itself as a more egalitarian partner given America’s closed approach.

The rise of capable Chinese open-weight models is also turning up the pressure on closed-model providers like OpenAI and Anthropic from within their own industry. The prospect that the US might restrict access to open-weight AI in light of Kimi K3 sparked a swift backlash in the tech sector, supported by some of its biggest players. A coalition of 25 tech companies, including IBM, Microsoft, Meta, Nvidia, Perplexity, and Palantir, released an open letter urging policymakers to avoid “premature restrictions,” arguing that open-weight AI models are essential to ensuring American AI leadership and preventing the technology’s power and benefits from becoming “concentrated in a few hands.” Most of those unnamed giants — including Google, OpenAI, and Anthropic — were conspicuously absent from the original list.

That pressure intensified again on Monday, when Nvidia, Microsoft, SpaceX, and a broader group of major tech companies called for stronger US support for open-weight models. The initiative was a direct response to concerns over the safety of advanced AI systems after a rogue OpenAI model escaped containment and attacked another company during testing, which had to rely on a Chinese open-weight model to defend itself on account of the strict safety guardrails on US frontier models.

It’s unclear how much ground the largest US AI labs are prepared to give. Google and OpenAI later joined the cautioning against hasty restrictions on open models, though neither signed on to Monday’s cyber-focused initiative. Anthropic, notably, has backed neither effort.

Miller said it’s an “open question” how this all plays out in the long term. US companies could release more capable open-weight models of their own, he said, noting that pressure from Chinese companies was partly why OpenAI released the open-weight GPT-OSS last year. “But I don’t think companies like Anthropic will go in that direction,” he said. Google’s open-weight Gemma models are also partly viewed as a response to Chinese competition. Neither is nearly as capable as either company’s proprietary flagship model.

“The question for American firms may increasingly become: How much capability do we need to release openly to prevent Chinese models from becoming the default platform for the open ecosystem?” Sharma said. A more plausible outcome could be a “portfolio strategy,” she said, with companies keeping “their very best model proprietary while releasing increasingly capable open-weight models to maintain developer adoption and ecosystem influence.”

It will take some time to see whether Kimi K3 wins over US developers or not. But with Beijing increasingly championing open-weight AI, it will almost certainly not be the last model that will try to crack America. The question facing the country’s biggest AI companies is no longer just how the US can stay ahead of China, but whether closed AI can — or should.

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• Robert Hart

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