【文章标题】：America is still beating China in the AI race
【标题翻译】：美国仍在人工智能竞赛中领先中国
【文章正文】：
Art by GPT-6
由GPT-6生成的艺术作品
Most of the debate around AI, at least in the U.S., is not about the international aspect. The local political debate is all about data center construction; the national economic debate is mostly about fear of job loss, with a side discussion about a potential bubble; and the technological discussion, at least in public, is mostly about AI safety and risk. U.S.-China competition gets mentioned in certain circles, but it’s probably safe to say that it’s not Americans’ chief topic of concern.
围绕AI的讨论（至少在美国）大多不涉及国际层面。地方政治争论聚焦数据中心建设；国家经济讨论主要担忧失业问题，附带提及潜在泡沫；而技术讨论（公开场合下）则多围绕AI安全与风险展开。中美竞争虽在某些圈子被提及，但可以说这并非美国民众最关心的话题。
But it still matters! For one thing, there’s the military aspect to think about. Cyberwarfare so far hasn’t been decisive in military conflicts, but AI’s incredible cybersecurity prowess could change that. If AI ends up strengthening defense more than offense — say, by finding all of the available exploits and patching them before an attacker can get to them — then cyberwarfare will become less important. But if those who possess the best AI models are able to successfully hack anyone using a less capable model to defend, it could lead to a decisive shift in the balance of power.
但这依然重要！首先需要考虑军事层面。网络战迄今未在军事冲突中起决定性作用，但AI惊人的网络安全能力可能改变这一局面。如果AI最终强化防御多于进攻——例如在攻击者利用漏洞前发现并修补所有可用漏洞——网络战的重要性将降低。但若拥有最先进AI模型的一方能成功入侵使用次等防御模型的对象，则可能导致力量平衡的决定性转变。
AI hacking doesn’t have mutually assured destruction, like nuclear warfare does. Imagine if China were to gain a big lead in AI models that gave it the power to easily hack into American banks and brokerage accounts and erase people’s wealth. It would cause absolute chaos in American society, but how could the U.S. retaliate? Launch nukes? Nor could the U.S. hack China in return, since China’s more capable AI would also be used to defend.
AI黑客攻击不像核战那样具有相互确保毁灭性。试想若中国在AI模型上取得巨大领先，能轻易入侵美国银行和证券账户抹除民众财富。这将在美国社会引发彻底混乱，但美国如何反击？发射核弹？美国也无法反向入侵中国，因为中国更强的AI同样会用于防御。
If either country opens up a large, sustained lead in AI capabilities, it might upend the balance of power between the two.
若任一方在AI能力上取得巨大且持续的领先，都可能颠覆两国间的力量平衡。
Not all AI issues are zero-sum, of course. If the U.S. and China both continue pushing forward with AI research at maximum speed, it may quickly cause safety issues. The recent AI agent swarm attack on Hugging Face shows that AI has reached the level where it can pose a significant hazard to human companies and organizations — and perhaps soon to human society itself.
当然并非所有AI问题都是零和博弈。若美中两国持续全速推进AI研究，可能迅速引发安全问题。近期Hugging Face遭遇的AI智能体集群攻击表明，AI已具备对人类公司和组织（甚至很快对人类社会自身）造成重大危害的能力。
Bioterror risk is certainly the most terrifying, but there are plenty of other ways that highly capable AI could cause chaos.
生物恐怖风险无疑最令人恐惧，但高性能AI还有许多其他制造混乱的方式。
The U.S. and China have a shared incentive to implement strict safeguards against these catastrophic risks, and perhaps even to regulate the pace of AI development. But given the Chinese Communist Party’s power-seeking nature, it seems much more likely that China would agree to cooperate on AI safety if U.S. capabilities were comfortably ahead. So even if the goal is cooperation, the U.S. should be thinking about how to keep its technological edge.
美中有共同动机实施严格防护措施应对这些灾难性风险，甚至可能调控AI发展速度。但鉴于中国共产党追求权力的本质，若美国能力明显领先，中国更可能同意AI安全合作。因此即便以合作为目标，美国也应思考如何保持技术优势。
Fortunately, the U.S. is still beating China in the AI race. Our companies have better models, more compute, and far more revenue. But there are ways that the Trump administration, despite claiming to be the AI industry’s best friend, could squander America’s lead — especially by pushing Chinese AI talent out of the country.
所幸美国仍在AI竞赛中领先中国。我们的企业拥有更优模型、更强算力和更高收入。但特朗普政府虽自称AI产业最佳盟友，却可能以某些方式浪费美国优势——尤其是将中国AI人才拒之门外。
U.S. models are still better than Chinese models
美国模型仍优于中国模型
There have been several moments when it seemed as if China’s frontier models were catching up to America’s in capabilities. The most dramatic was the “DeepSeek Moment” in early 2025, which put Chinese AI on the map. More recently, the release of Moonshot’s Kimi K3 this July and Z.ai’s GLM-5.3 a few weeks ago seemed to indicate that Chinese models were nipping at the Americans’ heels.
中国前沿模型曾有数次看似即将追平美国能力。最戏剧性的是2025年初的”深度求索时刻”，让中国AI崭露头角。最近7月Moonshot发布Kimi K3，以及数周前Z.ai推出GLM-5.3，似乎显示中国模型正紧咬美国脚跟。
Z.ai especially made waves when it beat Anthropic’s famous Mythos model on one measure of cyber-hacking capabilities:
Z.ai尤其引发轰动，因其在衡量网络黑客能力的某项指标上击败Anthropic著名模型Mythos：
Chinese AI startup Z.ai said on Friday its open-source GLM-5.3 model had neared Anthropic’s restricted Mythos 5 in identifying software vulnerabilities…Z.ai said GLM-5.3 scored 84.5% on CyberGym, a test of whether a model can review code, identify security flaws and confirm that they are real. That was slightly higher than the 83.8% it reported for Mythos 5. The results have not been independently verified.
中国AI初创企业Z.ai周五表示，其开源模型GLM-5.3在识别软件漏洞方面已接近Anthropic受限版Mythos 5…Z.ai称GLM-5.3在CyberGym测试（评估模型审查代码、识别安全漏洞并确认其真实性的能力）中获得84.5%分数，略高于Mythos 5的83.8%。该结果尚未经独立验证。
Note that this is just one measure of cybersecurity prowess, and that Mythos was still comfortably ahead on other measures:
需注意这只是网络安全能力的一项指标，Mythos在其他指标上仍明显领先：
GLM-5.3 lagged behind Mythos 5 in converting discovered flaws into working attacks — a standard part of defensive security research. Z.ai said its model scored 54.4% on the ExploitBench test of this capability, versus 78.0% for Mythos 5…In a separate timed test, Z.ai said GLM-5.3 completed 105 attack-development tasks in two hours and 130 in six hours. Mythos 5 completed 181 and 247 tasks, respectively.
GLM-5.3在将发现漏洞转化为有效攻击方面落后Mythos 5——这是防御性安全研究的标准环节。Z.ai称其模型在此能力的ExploitBench测试中得分54.4%，而Mythos 5为78.0%…在另一项计时测试中，GLM-5.3两小时完成105个攻击开发任务，六小时完成130个；Mythos 5则分别完成181和247个。
But still, if Chinese AI could get within striking distance of America’s best, it was a big deal.
但即便如此，若中国AI能逼近美国顶尖水平，已是重大进展。
What this discourse rarely mentioned, though, is that Mythos is not America’s best. It was simply the best that’s been released. Mythos Preview came out in April, four months before GLM-5.3. And the original Mythos actually finished training three months earlier, in January, and was released internally in February.
然而这类讨论鲜少提及：Mythos并非美国最强模型，只是已发布的最佳模型。Mythos预览版发布于4月，比GLM-5.3早四个月。而原始Mythos实际在1月（早三个月）就完成训练，2月内部发布。
Anthropic delayed its release
Anthropic延迟了其发布