由于我们的业务处于人工智能、国家安全和网络安全的交汇点，我经常被问到一个问题：“我们在人工智能竞赛中是否正在战胜中国？”我的回答依然是肯定的。从最高层面来看，我们拥有最顶尖的芯片技术、最先进的模型，更重要的是，我们拥有最好的经济体系和持续获胜所需的人才。
Because we operate at the intersection of AI, national security, and cyber, I often get asked the question, "Are we winning the AI race with China?" My answer is still yes.
但在更深层次上，我们仍需就“获胜”的定义达成共识。从根本上讲，获胜是指打造出最尖端的技术，还是打造出能够普及全球的技术？
At the highest level, we have the best chip technology the best models and, more importantly, we have the best economic system and the talent to continue to win. But at a deeper level, we still have to agree on what winning looks like.
克鲁兹称，赢得对华人工智能竞赛是美国“最重要的经济问题”。科技界充斥着许多优越技术败给劣质技术，而后者最终占领市场并成为行业标准的案例。几十年前，在竞争影响较小时，VHS击败了Betamax。而在更近的时期，且与我们国家安全更为相关的是，华为击败了西半球的竞争对手，成为全球电信设备领域的领导者。这让中国获得了在全球范围内收集信息、情报和施加影响力的重大机遇。
Fundamentally, is it building the best technology, or is it building the technology that becomes ubiquitous? CRUZ SAYS WINNING AI RACE AGAINST CHINA IS 'SINGLE MOST IMPORTANT ECONOMIC QUESTION' FOR AMERICA The tech world is littered with examples of superior technologies that lost to inferior ones that took over the market and became the standard. Decades ago, when the stakes were much lower, VHS beat Betamax. More recently, and much more relevant to our national security, Huawei beat its Western Hemisphere competitors to become the global leader in telecommunications gear. This handed China a major opportunity to gather information, intelligence, and leverage around the world.
因此，关于对华竞赛更完整的答案是：这是一场关于全球采用率的竞赛，而不仅仅是技术优势的竞争。当尘埃落定、局势明朗时，最重要的是谁的技术成为了全球标准。世界各地的人们正在使用哪种人工智能架构来获取信息、实现自动化、提高生产力、进行分析并做出决策？
So, the more complete answer on the race with China is that it is a race for global adoption, not just for technology superiority. When the dust settles and the picture becomes clearer, what will matter most is whose technology has become the global standard. Which AI stacks are people around the world using to become informed, to automate, to increase productivity, to analyze, and decide?
超越杀手机器人：专家警告称，中国对美国真正的人工智能威胁已经到来。在全球人工智能采用率的竞赛中，竞争十分复杂，更像是一场三项全能运动，且这三个赛段正在同时进行。
BEYOND KILLER ROBOTS: CHINA'S REAL AI THREAT TO AMERICA IS ALREADY HERE, EXPERTS WARN Within the global AI adoption race, the contest is complex and better described as a triathlon, with all three legs being run simultaneously. The first leg is the innovation race, where America is well ahead.
第一阶段：创新竞赛在这一阶段，美国处于明显领先地位。专家估计，由于我们在光刻技术方面的根本优势，以及 Nvidia（其台湾合作伙伴 TSMC）等公司的巨大进步，美国在芯片研发方面至少领先两年。在人工智能模型方面，虽然差距正在缩小，但美国的前沿研究机构仍领先两到三代（即八到十二个月的时间）。虽然这个时间跨度看似较短，但在人工智能领域，这其实相当于一个“世代”的发展时间。
Experts estimate we have at least a two-year lead on chips thanks to our fundamental advantage in lithography and incredible advances by Nvidia, its Taiwanese partner TSMC, and many others. And on the models themselves, while the gap is narrowing, our frontier labs are leading by perhaps two to three generations — so eight to 12 months. That may feel short, but it is a lifetime in frontier AI. US ADVERSARY TURNS UP THE HEAT ON AMERICA’S AI LEAD WITH AN UNDERESTIMATED EDGE Take the example of AI in cybersecurity which has rightly been much in the news.
美国的竞争对手正在加大竞争压力以网络安全领域的人工智能为例——这一领域最近备受关注。Booz Allen 公司发布的“Cyber Weapon Index”指数评估了这些模型在发起网络攻击方面的能力。Anthropic 公司的 “Mythos” 模型和 OpenAI 公司的 “Astra” 模型在测试中表现优异，远远超过了其他模型。这是个好消息，因为这两家公司都公开表示会负责任地使用这些技术，并与美国政府合作将其推广到全球。
Booz Allen’s Cyber Weapon Index codifies how good these models are at conducting a cyberattack. Two models, Mythos from Anthropic and Astra from OpenAI, outscored all others by a wide margin. This is good news because both companies are speaking publicly about the need to behave responsibly and in partnership with the U.S. government in releasing these technologies to the world. But several Chinese models have shown initial capability and are growing in expertise — getting better in every generation — likely with fewer guardrails than their American counterparts.
不过，中国的多个模型也展现出了初步的能力，并且其技术水平正在不断提升（每一代模型的性能都在提升），而且这些模型的开发可能受到的限制较少（即没有美国模型那样的严格监管）。
The second leg of the AI adoption triathlon is based on cost.
第二阶段：人工智能应用的普及人工智能应用的普及主要取决于成本。虽然美国的前沿模型性能强大，但成本也相对较高。大致来说，美国最先进的模型与中国的最先进模型之间的成本差距约为 5 到 10 倍。尽管中国的模型无法完全复制美国前沿研究机构的全部功能，但它们已经足以满足许多实际应用的需求，因此被广泛使用——尤其是那些对成本敏感的客户，比如需要控制 IT 预算的大型跨国公司、资金紧张的硅谷初创企业，以及资源有限的发展中国家政府。
Frontier AI models are powerful but not cheap. Roughly speaking, the difference between the best American models and the best Chinese models is 5-10x the cost per token. And while the Chinese models cannot fully replicate the capabilities of our frontier labs, they are often good enough for many tasks. As a result, they are being used widely, especially by cost-sensitive customers.
据模型交易市场OpenRouter的数据显示，过去一年中，约50%的模型调用量都来自中国模型。目前已有不少美国初创企业在使用中国模型——有些企业甚至并未意识到或公开说明这些模型的真实来源——将其用作代码辅助工具或开发应用的基础。这一点至关重要：布兹艾伦公司的研究指出，相较于为中国本土应用编写代码，中国模型在为美国应用编写代码时更容易引入安全漏洞。长此以往，这些细微漏洞的累积可能会破坏支撑美国经济未来的整个软件供应链。
Think large global companies trying to manage IT budgets, cash-strapped startups in Silicon Valley, and developing-country governments with limited resources. According to OpenRouter, a model marketplace where users can access a range of models, roughly 50% of tokens used in the last year were consumed on Chinese models. And we know of many U.S. startups that are using Chinese models — sometimes not realizing or disclosing their actual provenance — as code assistants or as the substrate for their applications. This matters because Booz Allen’s research demonstrates that Chinese models introduce more vulnerabilities when they code for an American application than when they do for a Chinese one.
人工智能技术普及的第三个关键要素便是信任。企业、政府和个人都不太可能使用那些自己无法掌控、或被认为会损害自身利益的技术。基于自身的价值观、生活方式、自由市场经济体制以及历史传统，美国完全有理由在这一领域取得成功。举例而言，当美国的创新成果催生了互联网后，全球各国纷纷欣然接纳——这在很大程度上得益于互联网那种去中心化且透明的规则体系，使得人们易于使用、理解并最终产生信任。
Over time, the accumulation of these tiny vulnerabilities could undermine the entire software supply chain on which the U.S. economic future rests. And the third leg of the AI adoption triathlon is trust. Companies, governments, and individuals are less likely to use a technology they can’t control or believe is operating against their interests. Here America — based on our values, way of life, free-market system, and history — has a right to win.
相比之下，在中国防火墙背后的互联网环境则受到更为严格的国家管控与无处不在的监控，这样的模式恐怕难以被世界上大多数民主国家所接受。
When American ingenuity created the internet, for example, the world eagerly adopted it, in no small part because the decentralized, transparent system of rules made it easy to use, to understand, and ultimately to trust.
中国正在人工智能数据中心竞赛中“火上浇油”，议员们如此指出。尽管美国在整体上占据优势，但双方在信任建设方面的竞争却远比预期要激烈。中美两国及其各自的人工智能生态体系都在无谓地破坏着必要的信任基础。例如，中国训练其人工智能模型，使其拒绝回答任何违背共产党教条的问题，甚至拒绝执行那些模型认为有悖于中共利益的任务。
By contrast, the internet as it exists behind China’s Great Firewall, with its more rigid state controls and pervasive surveillance, would not be a good fit for most democracies around the world. CHINA ‘THROWING GASOLINE ON THE FIRE’ IN AI DATA CENTERS RACE, SAY LAWMAKERS Despite the underlying American advantage, the trust race is much closer than it should be. Both countries and their respective AI ecosystems are eroding necessary trust unnecessarily. China, for example, trains its models to refuse to answer questions that go against Communist Party dogma, and to even refuse to perform certain tasks that the model perceives as being contrary to CCP interests.
而在美国，由于虚假信息泛滥以及缺乏明确的监管框架等诸多原因，民意调查显示民众对建设数据中心以及人工智能发展速度等议题的态度正逐渐转向负面。
In America, for many reasons including disinformation and the lack of a clear framework, polls suggest our fellow citizens are turning negative on broad issues from the construction of data centers to the speed of AI advances.
美国必须在三个关键领域同步发力，才能在这场人工智能应用竞赛中胜出。这场胜利关乎国家安全、经济安全以及美国的国际地位。我们必须继续保持在核心技术层面的领先地位，大力开发成本更低的人工智能替代方案，并努力重建公众信任。总统提出的《美国人工智能行动计划》为此指明了方向。为进一步完善该计划，我们可参考以下几点建议：数据中心问题很可能成为2026年中期选举中的关键议题。
America must win this adoption triathlon by simultaneously pushing on all three fronts. Winning is key to our national security, our economic security, and our global standing. We must continue our leadership on the actual technology stack, invest in lower-cost alternatives to the frontier models, and rebuild trust. The President’s America’s AI Action Plan provides a roadmap. A few ideas to strengthen it would include:
行动必须迅速。目前，人工智能模型的能力大约每四个月就能提升一倍。一个切实可行的目标就是：在2026年底之前确立相关关键基础设施认定标准、监管框架及沟通机制——务必赶在模型实现自我优化（即所谓的递归式自我改进）之前，也赶在行动迟缓导致中国的人工智能生态体系顺利走向全球应用之前。未来已然到来。让我们进一步拉大领先优势吧。
DATA CENTERS COULD BE THE SLEEPER ISSUE OF THE 2026 MIDTERM ELECTIONS And move fast. By one measure, AI models now double their capability every four months. A good goal would be to have a critical infrastructure designation, framework, and communication mechanism by the end of 2026 — before models are so advanced that they are designing themselves (aka recursive self-improvement, or RSI), or we have slowed ourselves down so much that the Chinese ecosystem is well on its way to global adoption. The future has arrived. Let’s widen our lead.