开放模型当前的权力格局

我为国会准备的证词扩展版 The expanded form of a testimony I prepared for Congress.

我最近受邀向一群国会议员和工作人员做简报，从美中竞争的视角介绍开放权重模型的现状。我在此分享我的准备发言，作为一份面向更广泛受众的开放模型现状报告。

I was recently invited to brief a group of Congressional members and staff on the state of open-weight models in the lens of U.S.-China competition. I'm sharing my prepared remarks as a state of the union on open models that is accessible to a broader audience.

回顾：开源模型、开放权重模型与闭源模型之辨 Recap: What is an open source v. open-weight vs. closed model?

开放语言模型是指其权重可公开获取以供检查或下游使用的人工智能模型。这类模型通常与所谓的“闭源”人工智能模型形成对比。闭源模型仅通过应用程序编程接口（API）提供访问，开发者可用其直接查询模型，例如 GPT-4 或 Claude Opus 4.5，或通过产品提供访问，例如 ChatGPT 和 Claude Code。

Open language models are AI models where their weights are publicly available for inspection or downstream use. These are most often contrasted to so-called "closed" AI models. Closed models offer access only through Application Programming Interfaces (APIs) that developers can use to directly query a model, like GPT-4 or Claude Opus 4.5, or through products, like ChatGPT and Claude Code.

开放语言模型主要分为两类：开放权重模型和开源模型。开放权重模型是最常见的形式，例如 Meta 的 Llama、阿里巴巴的 Qwen、Google 的 Gemma 或 DeepSeek 的模型等流行模型。这些模型受许可证管辖，许可证是规定下游使用允许范围的管理文件，并且通常附带推理代码，位于 Transformers、VLLM、SGLANG 等库中。自 2025 年 4 月左右以来，中国人工智能公司在开放权重模型领域已明显领先。

Open language models primarily are bucketed into two categories, open-weight and open-source models. Open-weight models are the most common form, such as popular models like Meta's Llama, Alibaba's Qwen, Google's Gemma, or DeepSeek's models. These models are governed by licenses, governing documents dictating what is allowed with downstream use, and are often accompanied by inference code in libraries such as Transformers, VLLM, SGLANG, etc. Since about April 2025, Chinese AI companies have been the clear leader in open-weight models.

真正的“开源”模型与此类似，因为它们包含权重、许可证和推理代码，但它们还包含复现模型所需的完整信息——训练代码和训练数据。最突出的开源模型是在美国构建的，最近由艾伦人工智能研究所的 Olmo 模型引领，我在该研究所最近 2.5 年的工作中帮助构建了这些模型。其他突出的开源模型也由美国非营利组织构建，包括 OpenAthena 的 Marin 模型和 EleutherAI 的 Pythia 模型。

True "open-source" models are similar to these, as they include the weights, licenses, and inference code, but they also include the complete information needed to reproduce the model – the training code and training data. The most prominent open-source models have been built in the United States, led recently by the Allen Institute for AI's Olmo models that I helped build in my recent 2.5 years there. The other prominent open-source models are also built by American non-profit organizations, including OpenAthena's Marin models and EleutherAI's Pythia models.

开放权重、开源以及模型的任何其他标签——包括主要通过 API 提供的闭源模型——都存在于一个谱系上。例如，Nvidia 的 Nemotron 模型比大多数开放权重模型开放得多，在宽松许可证下发布了大量训练数据，但它们并非完全开源，因为它们没有发布所有数据。闭源模型也根据 API 揭示的信息和使用条款存在于一个谱系上。

Open-weight, open-source, and every other label for a model – including closed models primarily offered via an API – exist on a spectrum. For example, Nvidia's Nemotron models are far more open than most open-weight models, releasing large quantities of their training data under permissive licenses, but they're not fully open-source because they do not release all of the data. Closed models also exist on a spectrum based on what information the API reveals and the terms of use.

中美开放权重模型之间的竞争态势（单位经济性、技术能力等） The state of competition between American and Chinese open-weight models (unit economics, technical capabilities, etc.)

我们正处在一个 GLM-5.2 和 Kimi K3 等最新领先中国模型已经使开放模型的商业可行性发生阶跃式变化的时代——它们在智能体式能力上跨越了 Anthropic 的 Claude Code 在 2025 年 12 月跨越的类似门槛。

We are living in a world where GLM-5.2 and Kimi K3, some of the latest, leading Chinese models, have enacted a step change in the commercial viability of open models — crossing a similar threshold in agentic capabilities that Anthropic's Claude Code crossed in December of 2025.

美国曾是开放语言模型的早期领导者，主要通过 Meta 的 Llama 系列模型，这些模型被广泛用于研究和商业任务。大约 18 个月前，中国开放权重模型在这两个关键领域超越了美国开放权重模型。展示这一点的简单指标是 Hugging Face 下载量，中国在 2025 年 7 月取得领先，主要得益于阿里巴巴 Qwen 系列模型的成功。我个人维护了追踪这些数据的工具，自 2025 年 8 月首次发布 American Truly Open Models (ATOM) 项目以来，中国的下载领先优势已增长到约 16 亿次——总下载量达 32 亿次，是美国总量的两倍。

America was the early leader in open language models, primarily through Meta's Llama models, which were used extensively across research and commercial tasks. Chinese open-weight models surpassed American open-weight models in these two key areas about 18 months ago. The simple metric showing this is Hugging Face Downloads, where China took the lead in July of 2025 primarily through the success of Alibaba's Qwen models. I personally maintain tools to track this data, and since I first published the American Truly Open Models (ATOM) Project in August of 2025, China's download lead has grown to about 1.6B – with a total of 3.2B downloads, twice that of America's total.

在流行能力基准上，如 Artificial Analysis Intelligence Index (AAII)，中国开放权重模型明显领先于美国同行。截至 2026 年 9 月 14 日撰写本文时，排名前三的中国模型是 Z.ai 的 GLM-5.3 和 GLM-5.3-Flash 以及 Moonshot AI 的 Kimi K3，得分分别为 45、42 和 44。相比之下，领先的美国模型是 Thinking Machines 的 Inkling 和 Inkling Small，得分均为 26，以及 Nvidia 的 Nemotron 3 Ultra，得分为 23。美国顶级模型于 2026 年 6 月和 7 月发布，更新频率低于中国同行。例如，中国实验室比美国公司早 2-6 个月发布得分超过这些美国模型的模型（如 GLM-5 或 DeepSeek V4 Pro）。有 _更多_ 美国公司发布模型的趋势，包括 Arcee AI、Poolside 和 IBM 等，但它们并未迅速缩小这一性能差距。其他基准也讲述了类似的故事。

On popular capabilities benchmarks, such as the Artificial Analysis Intelligence Index (AAII), the Chinese open-weight models have a clear lead over American counterparts. The top three Chinese models as of writing this on September 14, 2026 are Z.ai's GLM-5.3 and GLM-5.3-Flash and Moonshot AI's Kimi K3 with scores of 45, 42, and 44 respectively. By comparison, the leading American models are Thinking Machines' Inkling and Inkling Small, both with a score of 26, and Nvidia's Nemotron 3 Ultra, with a score of 23. The top American models were released in June and July of 2026, and are updated less frequently than their Chinese counterparts. For example, Chinese labs released models with scores above these American models 2-6 months before the American companies got there (e.g. GLM-5 or DeepSeek V4 Pro). There is a trend of _more_ American companies releasing models, including names like Arcee AI, Poolside and IBM, but they are not rapidly closing this performance gap. Other benchmarks tell a similar story.

在 Artificial Analysis Index 上，美国顶级开放模型落后于其他 15 个中国制造的模型。

The top American open models on the Artificial Analysis Index are behind 15 other Chinese made models.

总体而言，中国开放权重模型大约落后美国闭源前沿模型 2-5 个月，而美国开放权重模型大约落后 OpenAI 和 Anthropic 等模型 6-9 个月。中国实验室在具有明确用户需求的任务（如智能体式编程）上最为接近，在更开放式的科学任务（如物理或生物学）上则落后更多。

Together, Chinese open-weight models are approximately 2-5 months behind the closed American frontier, with the open-weight American models being approximately 6-9 months behind the likes of OpenAI and Anthropic. The Chinese labs are closest in tasks with clear user demand, such as agentic coding, and further behind on more open-ended scientific tasks, such as physics or biology.

尽管资源少于美国同行，中国实验室仍能产出这些强大模型的原因仍是一个开放争论，深受不同工作文化影响，但也受几个关键技术因素影响。中国实验室发布模型更快，并聚焦于稍窄的任务分布，这使它们在公开基准上略有虚高。更快发布有助于获得更高分数，因为所有实验室都在持续进步，所以一旦你“完成”一个模型准备发布，它就是当时性能的快照——完成时间更晚的实验室往往得分更高。尽管如此，中国实验室构建的模型确实强大，对美国产业构成真正竞争。随着闭源实验室修补其 API 中可被蒸馏的漏洞，这种竞争不会显著减弱。

The reasons why Chinese labs can produce these strong models, despite having fewer resources than American counterparts, is still an open debate and heavily influenced by different work cultures, but is also influenced by a few key technical factors. The Chinese labs release their models faster and focus on a slightly narrower distribution of tasks, flattering them slightly on public benchmarks. Releasing faster helps them score higher because all the labs are making consistent progress, so once you "finish" a model to be released, it is a snapshot of performance at that given time — labs where that time is later tend to score higher. Still, the models built by the Chinese labs are genuinely strong and represent real competition to the American industry. This competition will not decrease meaningfully as the closed labs patch vulnerabilities in their API offerings which enable distillation.

蒸馏在新领域影响最大，并不能轻易创建一个普遍强大的最终模型。我估计，如果完全防止蒸馏，例如在 Anthropic 和 OpenAI 使用了解你的客户（KYC）工具，美国最强模型与中国开放权重模型之间的差距只会增加 1-2 个月。

Distillation is most impactful in new domains and does not make it trivial to create a universally strong final model. I estimate that if distillation was fully prevented, e.g. with know-your-customer (KYC) tools at Anthropic and OpenAI, the gap from the strongest American models to Chinese open-weight models would only increase by 1-2 months.

例如，中国实验室在 2026 年正迅速改变对支付训练数据的姿态。今年早些时候，包括 Moonshot AI 和 Z.ai 在内的顶级中国实验室强烈倾向于内部构建数据工作流，但到夏天，它们已开始从美国老牌公司和中国新创公司购买前沿数据——用于智能体任务的挑战性强化学习环境。

For example, the Chinese labs are rapidly changing their posture towards paying for training data in 2026. Earlier in the year, the top Chinese labs including Moonshot AI and Z.ai had a strong preference towards building data workflows in-house, but by the summer they had begun to buy the cutting edge data – challenging RL environments for agentic tasks – from both established American companies and new Chinese startups.

随着中国开放权重模型向能力前沿推进，以及最近关于前沿模型在网络安全等领域风险增长的记录（例如 OpenAI-HuggingFace 事件），监管不确定性日益增加：_持续发布如何能促成更安全的生态系统？_

With the advance of open weight models in China towards the frontier of capabilities, and the recent documentation of growing risks around frontier models in areas such as cybersecurity (e.g. the OpenAI-HuggingFace incident), there's growing regulatory uncertainty on _how continued releases can enable a safer ecosystem?_

开放权重模型的一个结构性挑战是，很少有有效方法阻止开放软件片段落入恶意行为者手中。如果因为中国最强开放权重模型放大风险而试图限制其访问，受挫的将是美国企业。我们有一个例子——HuggingFace 使用中国开放权重模型来理解网络攻击，因为闭源模型不会回答他们的请求。因此，管理开放权重模型的风险往往归结为_生态系统准备_。

A structural challenge in open-weight models is that there are few effective methods for stopping pieces of open software from reaching bad actors. If an attempt was made to restrict access to the strongest open-weight models from China because they amplify risks, the parties who would be set back are American businesses. We have an example of this – HuggingFace used a Chinese open-weight model to understand the cyberattack because closed models would not answer their requests. Thus, managing the risks of open-weight models often comes down to_ecosystem_preparation.

开放权重模型正成为 AI 扩散的重要工具，而要规避美国公司依赖中国构建的模型所带来的风险与不平衡关系，最佳路径是继续支持美国对开放模型的投入。拥有开放模型有助于更好地协调和防范具有全球性质的风险，同时加速 AI 服务在国内经济中的扩散。

Open-weight models are becoming an essential tool for AI diffusion, and the best path to get ahead of these risks and unbalanced relationships where American companies rely on models built in China is to continue to enable investment in open models in the US. Ownership of open models allows better coordination and preparation of risks that are global in their nature while accelerating diffusion of AI services throughout the domestic economy.

开放模型采用现状：开源如何被学术界、企业和其他国家使用？ The state of open model adoption: How is open-source being used by academia, businesses, and other countries?

2026 年，开放权重语言模型在普遍关注度和经济可行性上大幅提升，使得我们得以在 Hugging Face 指标之外，初步窥见更直接地比较美国、中国或其他地区模型采用情况的方式。OpenRouter 的使用情况就是一个例子。OpenRouter 是一个流行的 LLM 推理平台，提供单一接口，可在来自美国和中国的开放与闭源模型之间切换。该平台主要以尝试不同的开放权重模型而闻名。该平台自 2025 年 1 月 1 日起分享了顶级模型的使用数据，并显示使用量从 2025 年 9 月某一周开放模型处理的约 1T token 增长到如今的每周约 80T token。在此期间，中国模型的市场份额从约 70% 增长到使用量的 80% 以上。其他旨在将开放模型商业化的平台也显示出类似数据，例如开源编码智能体 OpenCode，其推理量中约 95% 或更高比例来自中国模型。

Open-weight language models have grown substantially in general interest and economic viability in 2026, allowing early glimpses of more direct ways to compare adoption of models from the US, China, or elsewhere on top of Hugging Face metrics. One example is OpenRouter usage. OpenRouter is a popular LLM inference platform that supplies a single interface to switch between models, open and closed, from the US and China. This platform is primarily known for trying different open-weight models. The platform has shared usage data for the top models since Jan. 1, 2025, and shown growth in usage from ~1T tokens processed from open models in a week of September 2025 to ~80T tokens per week today. In that time, Chinese models have grown from ~70% market share to over 80% of usage. Other platforms that are designed to commercialize open models show similar data, such as the open-source coding agent OpenCode, which shows an inference volume of ~95% or higher with Chinese models.

这些开放平台是我们对开放模型使用情况的最佳 _近似_——很大一部分开放模型使用发生在不披露按模型细分数据的平台上，例如 Together AI 或 Fireworks AI，以及企业应用的私有部署中。

These open platforms are the best _approximation_ of open model usage we have – a large proportion of open model usage is on platforms that do not disclose per-model breakdowns, such as Together AI or Fireworks AI, and in private deployments for enterprise applications.

许多知名科技公司和初创公司一直在基于中国的开放权重模型构建其 AI 功能，例如法律智能体 Harvey、编码智能体 Cursor，以及 DoorDash 使用 Kimi 模型、Airbnb 使用 Qwen、Perplexity 使用 DeepSeek。这些知名公司只是冰山一角，大量较年轻的硅谷初创公司正在基于中国模型构建，以获得低成本、灵活的选择。一个日益增长的趋势是，美国初创公司和公司与中国模型实验室签订企业协议，以获得在其产品中使用这些模型的许可——这是我在职业生涯中未曾见过的跨境技术合作新形式。

Many prominent technology companies and startups have been building on Chinese open-weight models for their AI features, such as Harvey, the legal agent, Cursor, the coding agent, and DoorDash's use of Kimi models, Airbnb's use of Qwen, or Perplexity's use of DeepSeek. These prominent companies are the tip of the iceberg, where a large swath of younger Silicon Valley startups are building on Chinese models in order to have low-cost, flexible options. There is a growing trend of American startups and companies entering enterprise agreements with Chinese model labs in order to get permission to use their models in their products – a new form of cross-border technology collaboration I have not witnessed in my career.

基于中国模型的创新基础进一步延伸到 AI 生态系统中。粗略估计，大多数学术研究是在阿里巴巴的 Qwen 系列模型上进行的。在我访问中国期间，我见到了 Qwen 领导团队的多位成员，他们对这种采用方式非常投入且有意为之，要将其拉回美国模型并非易事。

The foundation of innovation on Chinese models extends further into the AI ecosystem. To a first order approximation, most of academic research is conducted on Alibaba's Qwen family of models. Having met multiple members of the Qwen leadership team during my trip to China, they are very invested in and intentional about this type of adoption, which will not be easy to claw back to American models.

为了量化开放模型在学术界的采用情况，我扫描了 arXiv（AI 研究中流行的预印本平台）5 个最受欢迎的机器学习类别（cs.AI、cs.CL、cs.CV、cs.LG、stat.ML）中的每一篇论文。结果清晰地印证了我对 AI 研究中领导地位演变的理解，显示 LLM 正成为机器学习研究的基础层——任何开放模型的提及率在 2023 年 1 月为 2%，到 2026 年 9 月为 50%——以及同期领导角色从美国向中国的转移。

To quantify the adoption of open models across academia, I scanned every paper in the 5 most popular ML categories of arXiv (cs.AI, cs.CL, cs.CV, cs.LG, stat.ML), the preprint platform popular in AI research. The results clearly track my understanding of the evolving leadership in AI research, showing LLMs becoming a foundational layer of ML research – mentions of any open model were 2% in January of 2023 and 50% in September of 2026 – and the leading role shift from the U.S. to China in the same time period.

例如，在 2023 年 4 月至 5 月，即 Meta 发布原始 Llama（一个反向首字母缩略词，全称 Large Language Model Meta AI，于 2023 年 2 月首次发布）几个月后，arXiv 上 12,000 篇新 AI/ML 论文中约有 2,600 篇提到了至少一个著名的开放模型系列。在所有扫描的论文中，约 5.5%提到了 Llama，约 1%提到了一个中国模型。2024 年秋季，在 Llama 的巅峰期，约 23%的论文提到了 Llama，约 7.5%提到了 Qwen——最直接的中国竞争对手。如今，Llama 在学术界已失去领先地位，仍有约 21%的论文提及它，这一持久性令人瞩目，但 Qwen 的份额已上升至 30%的论文。总体而言，任何中国开放权重模型在超过 40%的论文中被提及，超过美国的 30%，且中国的份额持续增长。

For example, in April to May of 2023, a few months after Meta's original Llama (a backronym, Large Language Model Meta AI, first released in Feb. of 2023), about 2,600 of 12,000 new AI/ML papers on arXiv mentioned at least one prominent open model family. Of all those scanned papers, ~5.5% mentioned Llama and ~1% mentioned a Chinese model. In the fall of 2024, during Llama's peak, about 23% of papers mentioned Llama with about 7.5% mentioning Qwen, the most direct Chinese competition. Today, Llama has lost its lead in academia, being mentioned in about 21% of papers still, which is remarkable longevity, but Qwen's share has risen to 30% of papers. Overall, any Chinese open weight model is mentioned in over 40% of papers, over the U.S.'s 30%, with China's share continuing to grow.

这表明，在开放权重语言模型时代，要重新确立美国作为 AI 研究中心的地位，我们显然还有大量工作要做。但也存在希望的迹象。

This shows that we clearly have a lot of work to do in order to re-establish the U.S. as the home of AI research in the era of open-weight language models. There are signs of hope.

在我们的研究中，我们发现，与中国模型相比，能力与规模区域相当的美国模型被采用的比率不成比例地高。在过去一年中，我们看到 OpenAI 自 ChatGPT 以来的首个开放权重模型 gpt-oss 成为有史以来采用最广泛的开放权重模型之一。此后，Google 的 Gemma 4 模型是少数几个采用率能与 Qwen 最受欢迎的小模型相媲美的模型之一，而 Nvidia 的 Nemotron 模型尽管在相同规模点上拥有众多更强大的模型，其采用率却较为有限。

In our research, we find that American models of comparable capabilities-to-size regions to their Chinese counterparts get adopted at disproportionate rates. In the last year we've seen OpenAI's first open-weight models since ChatGPT, gpt-oss, become one of the most adopted open-weight models of all time. Since then, Google's Gemma 4 models have been some of the only ones ever to show similar adoption numbers to Qwen's most popular small models, and Nvidia's Nemotron models have modest adoption despite numerous more capable models at the same size point.

总结 Summary

2026 年开放模型的故事，是一个确立经济相关性的故事。这是 AI 生态系统中许多故事的汇聚，可总结如下：

The story of open models in 2026 is one of establishing economic relevance. This is the convergence of many stories across the AI ecosystem, summarized as:

1. 过去 3 年里，用户可用的开放模型与闭源模型之间的能力差距一直在缩小。这一差距因任务而异，但可以估计为 2-5 个月的能力差距。由于整体能力进步如此之快，这使得开放权重 AI 模型在 2026 年解锁了可观的市场，并预示着不久的将来会出现更多拐点。

1. The capabilities gap from open to closed models available to users has been decreasing over the last 3 years. This varies by task, but can be estimated as a 2-5 month gap in capabilities. With capabilities overall progressing so fast, this has seen open-weight AI models unlock substantial markets in 2026 and points to more inflection points in the near future.

2. 开放模型的使用在高价值行业（如软件工程、法律服务、金融服务）中呈爆炸式增长，表明一个替代最佳闭源模型的生态系统正在兴起。主要提供开放模型推理的平台，如 Together、OpenRouter、Fireworks、Baseten 等，作为开放模型后训练经济的第一批赢家，正经历惊人的增长（其他层面包括 Thinking Machines 的 Tinker 等微调 API）。与此同时，AI 行业的技术人员中有许多轶事表明，他们使用 GLM-5.3 等开放权重模型作为 Claude 或 GPT 的替代品，原因是速度、更低的价格、可定制的产品以及隐私的结合。

2. Open model usage is exploding in high-value industries (e.g. software engineering, legal services, financial services), indicating an emergence of an alternative ecosystem to the best closed models. Platforms offering inference primarily on open models, from Together, OpenRouter, Fireworks, Baseten, etc., are seeing incredible growth as the first winners of an open model post-training economy (other layers include finetuning APIs such as Thinking Machines' Tinker). This is combined with numerous anecdotes from technical staff in the AI industry that uses open-weight models such as GLM-5.3 as an alternative to Claude or GPT due to a combination of speed, lower prices, customizable offerings, and privacy.

3. 中国 AI 公司在开放权重模型方面是明显的领导者。相对于 2025 年 DeepSeek R1 等中国模型以意外之举震撼 AI 世界，美国 AI 实验室在开放权重模型方面的地位一直在恢复，但尽管美国有更大量的投资，中国实验室仍经常产出明显更强、受到各类用户喜爱的模型。

3. Chinese AI companies are the clear leaders in open weight models. Relative to 2025, where Chinese models like DeepSeek R1 shook the AI world with surprise, the American AI labs have been recovering in their positions with open-weight models, but despite more substantial investment in the US, the Chinese labs regularly are producing notably stronger models adored by many types of users.

4. 中国实验室对美国 AI 模型的蒸馏并不能解释其成功的全部故事。蒸馏是一种行业标准技术，即利用通常更强的模型的输出来训练另一个 AI 模型。该技术在中国 AI 行业中最为普遍，该行业利用基本漏洞从美国公司未完全保护的产品中提取推理轨迹和额外数据。最佳估计是，蒸馏帮助中国公司相对于美国前沿模型将性能差距缩小了 1-2 个月。

4. Distillation of American AI models by Chinese labs does not explain the entire story of their success. Distillation is an industry standard technique of training another AI model on the outputs from a usually stronger model. The technique is most prevalent in the Chinese AI industry, which has used basic exploits to extract reasoning traces and additional data from American companies' products that are not fully secured. The best estimates are that distillation helps reduce the performance gap of Chinese companies relative to the American frontier by 1-2 months.

5. 中国模型，尤其是阿里巴巴的 Qwen 系列，已成为学术界和本地模型用户研究与开发的基础层。近几个月来，中国开放权重模型在 38% 的 AI 论文中被提及，高于美国的 28%——且中国的份额增长速度远快于美国。这一点，加上其他政治因素以及美国领先 AI 公司的封闭性，正加速美国作为全球首要 AI 研究中心的领先地位下滑。

5. Chinese models, particularly Alibaba's Qwen family, are established as a foundational layer of research and development across academia and local model users. In recent months, Chinese open weight models were mentioned in 38% of AI papers, above the U.S.'s 28% – and the Chinese share is growing much faster than its American counterparts. This, along with other political factors and the closed nature of leading American AI companies, is contributing to an accelerated decline in America's lead as the preeminent AI research hub in the world.

6. 开放权重模型正进入新的能力水平，在此水平上，众多开放权重模型的可用性可能引发新的风险，例如网络安全风险，这需要在生态系统层面做好准备应对。这一新的风险时代也带来了政治不确定性时期：监管机构关注最强大的 AI 模型，但对于政策将如何在法律上落地存在巨大不确定性。与此同时，许多研究人员和工程师依赖开放模型，因为其保障措施更为宽松，而 Claude 和 GPT 等封闭模型常常拒绝关键的网络安全防御工作或生物学研究。

6. Open weight models are entering the capability levels where new risks, e.g. cybersecurity, can be enabled by numerous open-weight models being available, necessitating an ecosystem level response in preparation. This new era of risks is also enabling a period of political uncertainty, where there is regulatory attention on the strongest AI models, but massive uncertainty on how policy would be legally enacted. At the same time, many researchers and engineers rely on open models due to more permissive safeguards, where the closed models such as Claude and GPT often refuse critical cybersecurity defensive work or biology research.

_更多数据，请查看 Interconnects Dashboard。_

_For more data, view the Interconnects Dashboard._

结论 Conclusions

2026 年，中国实验室显然仍保持着开放权重 AI 生态系统的领导者地位。这一局面出现在开放权重模型在经济可行性上已越过拐点之际，也正值美国实验室作为模型竞争者活动日益频繁之时。领先的中国实验室在向全球扩展其企业级与研究采用时，似乎并未受到实质性的挑战。

In 2026 the Chinese labs are clearly maintaining their status as the leaders of the open-weight AI ecosystem. This comes as open-weight models have passed an inflection point in economic viability and in the face of increased activity from American labs as model competition. The leading Chinese labs do not appear to be meaningfully challenged, as they expand their enterprise and research adoption globally.

这一开放模型格局出现在更广泛的 AI 生态系统的关键时刻。我们看到 OpenAI 和 Anthropic 凭借其最新公开模型迈出巨大步伐，同时呼吁以协调一致的方式审慎管理 AI 进展的下一阶段。如今在少数 AI 实验室内部发生的事情——尤其是极高的人才与算力密度——是即将在开放模型生态系统中涌现之趋势的先兆。开放模型将成为世界上除少数真正前沿 AI 实验室之外所有其他方的基座，用以把握软件工程及其他计算实践的加速。对于使这种变革性智能得以广泛获取的组织而言，这代表着软实力、影响力与潜力的重要来源。

This landscape of open models comes at a crucial time in the broader AI ecosystem. We're seeing OpenAI and Anthropic take massive steps forward with their latest public models, and at the same time call for coordinated care on how we manage the next stage of AI progress. What is happening in the confines of a few AI labs today, especially with extreme talent and compute density, is a precursor to what will soon emerge in the open model ecosystem. Open models are going to be the substrate for everyone else in the world outside of the few true frontier AI labs, to harness an acceleration in software engineering and other computational practices. This represents a substantial source of soft power, influence, and potential for the organizations that enable this broad access to transformative intelligence.

随着这一未来临近，我们需要共同对开放模型将走的确切路径保持谦逊。开放模型存在许多未知——例如，我们缺乏关于它们在美国和中国以外国家如何被使用的良好数据。由于 ML 训练专业知识的分布广泛，即数十个组织和数千人处于能力前沿的一年之内，开放模型跨越能够催生新工作流的性能阈值只是时间问题，而非是否会发生的问题。集体应对之道应是理解如何将这种广泛可获取的开放智能用于善途，同时主动缓解潜在危害。

With this future coming soon, we need to collectively stay humble about the exact path open models will take. There are a lot of unknowns with open models – e.g. we don't have good data on how they're used in countries other than the U.S. and China. With the distribution of ML training expertise being broad, i.e. tens of organizations and thousands of people that are within a year of the frontier of capabilities, it is a matter of when, not if, open models cross the performance thresholds that enable new workflows. The collective approach should be to understand how to use this broadly accessible, open intelligence for good while proactively mitigating the potential harms.

_感谢 Florian Brand 和 Kevin Xu 对本文的反馈和/或建议。欲了解更多为本文提供依据的研究，请参阅开源 AI 阅读清单。_

_Thank you to Florian Brand and Kevin Xu for feedback and/or suggestions for this work. For more research informing this post, see the open-source AI reading list._