Why the rise of open source AI isn’t hurting Anthropic … yet

为什么开源人工智能的兴起尚未对 Anthropic 造成冲击

On Monday, Decagon CEO Jesse Zhang published a provocative new theory, posted under the title “Everyone is wrong about open source AI in the enterprise.” The post grapples with one of the most interesting contradictions of today’s AI economy: More mature AI deployments are switching to lighter models, he says, even at his own company. But the overall spend on expensive state-of-the-art models has barely budged. 周一，Decagon 首席执行官 Jesse Zhang 发表了一个颇具挑衅性的新理论，文章标题为《关于企业开源人工智能，所有人都错了》。这篇文章探讨了当今人工智能经济中最有趣的矛盾之一：他指出，即使在他自己的公司，更成熟的人工智能部署也正在转向更轻量的模型。然而，在昂贵的尖端模型上的总支出却几乎没有变动。

It’s a new way to think about the relationship between frontier and open source models. In Zhang’s telling, they aren’t competitors, and open source models’ success isn’t coming at the expense of frontier labs. Instead, they’re two phases of the same life cycle, with expensive frontier models being used to prove out use cases that can be passed along to cheaper open source alternatives as they mature. As more mature use cases switch to lighter models, new use cases keep arising — and the overall spend on frontier models barely goes down. 这是一种思考前沿模型与开源模型之间关系的新方式。在 Zhang 看来，它们并非竞争对手，开源模型的成功并非以牺牲前沿实验室的利益为代价。相反，它们是同一生命周期的两个阶段：昂贵的前沿模型被用于验证用例，而当这些用例成熟后，便可以转移到更便宜的开源替代方案上。随着更多成熟的用例转向轻量模型，新的用例不断涌现，前沿模型的总支出几乎没有下降。

Zhang doesn’t give much data to support the point, but the data isn’t hard to find. Vercel’s AI gateway dashboard shows that, in just the past week, DeepSeek has surged into the lead for token volumes, now processing just over a third of the tokens passing through the company’s infrastructure. Z.ai — the lab behind the popular GLM-5.2 model — jumped into a respectable fourth place over the same period. But if you scroll down to overall token spend, you’ll see Anthropic still accounts for more than half of the overall AI spend on the platform. Given that much of the recent change comes from Anthropic’s own rising prices, the share has dropped slightly over the past month, but not significantly. Zhang 并没有提供太多数据来支持这一观点，但数据并不难找。Vercel 的 AI 网关仪表板显示，仅在过去一周，DeepSeek 在 Token 处理量上就跃居首位，目前处理了流经该公司基础设施中超过三分之一的 Token。GLM-5.2 模型背后的实验室 Z.ai 在同期也跃升至第四位。但如果你向下滚动查看总 Token 支出，你会发现 Anthropic 仍然占据了该平台人工智能总支出的一半以上。考虑到最近的大部分变化源于 Anthropic 自身价格的上涨，其份额在过去一个月里略有下降，但并不显著。

OpenRouter tells a similar story, capturing a much larger (but slightly less enterprise-y) segment of the market. DeepSeek V4 Flash is the main winner on overall usage, processing 5.3 trillion tokens weekly. The most popular frontier model, Opus 4.8, handles just over 2 trillion. OpenRouter doesn’t rank models by total spend, but it registers the average token cost for Opus 4.8 as roughly 23x higher than V4 Flash ($1.37 per million tokens, compared to just 6 cents), which would mean Opus was still probably capturing the lion’s share of spending. Those figures don’t even capture the newest arrival, Nvidia’s Nemotron, which is poised to leap to the front of the pack by virtue of Nvidia’s strong connections and the model’s own extreme adaptability. OpenRouter 的情况也类似，它占据了市场中更大（但企业属性稍弱）的份额。DeepSeek V4 Flash 是总使用量方面的最大赢家，每周处理 5.3 万亿个 Token。最受欢迎的前沿模型 Opus 4.8 处理量仅略高于 2 万亿。OpenRouter 没有按总支出对模型进行排名，但它记录的 Opus 4.8 的平均 Token 成本大约是 V4 Flash 的 23 倍（每百万 Token 1.37 美元，而后者仅为 6 美分），这意味着 Opus 可能仍然占据了支出的大头。这些数字甚至还没有计入最新推出的 Nvidia Nemotron，凭借 Nvidia 强大的关系网和模型本身极强的适应性，它有望跃居前列。

Those figures don’t fully prove Zhang’s point about the AI life cycles, but they do show frontier labs like Anthropic aren’t suffering too much from the rise of open source — at least not yet. One explanation is that the market of AI-addressable tasks is growing so fast that the top models are able to maintain their position just by dominating early-stage deployments. As Zhang puts it, “The frontier labs will keep owning discovery. Open source will increasingly own production.” Another explanation might be that, even as clients move to open source, many use cases are so difficult that they can’t be entirely replaced with cheaper alternatives. Either way, this two-tiered economy of models may become a relatively stable feature of the AI economy. 这些数字虽然不能完全证明 Zhang 关于人工智能生命周期的观点，但它们确实表明，像 Anthropic 这样的前沿实验室并没有因为开源的兴起而遭受太大损失——至少目前还没有。一种解释是，人工智能可处理的任务市场增长速度极快，顶级模型仅通过主导早期部署就能维持其地位。正如 Zhang 所言：“前沿实验室将继续主导探索，而开源将越来越多地主导生产。”另一种解释可能是，即使客户转向开源，许多用例的难度依然很高，无法完全被更便宜的替代方案取代。无论如何，这种双层模型经济可能会成为人工智能经济中一个相对稳定的特征。

As recently as last September, I was writing about the possibility that foundation labs would end up selling coffee beans to Starbucks — that is, serving as commodity inputs while the application layer reaped the benefits. Some parts of that prediction came true: Vertical AI plays switched to lighter models, for one, and the economics of “GPT wrapper” startups have remained mostly stable. But we’re also seeing that, token for token, frontier providers have been able to hold on to the most desirable part of the marketplace — the premium token price. And that doesn’t seem likely to change any time soon. 就在去年九月，我还曾写过关于基础模型实验室最终可能沦为“向星巴克卖咖啡豆”的预测——即作为大宗商品投入，而应用层则坐享其成。这一预测的部分内容已经实现：例如，垂直领域的人工智能应用转向了更轻量的模型，“GPT 套壳”初创公司的经济状况也基本保持稳定。但我们也看到，在 Token 层面，前沿模型提供商依然能够守住市场上最令人垂涎的部分——即高溢价的 Token 价格。而且这种情况在短期内似乎不太可能改变。