Why the rise of open source AI isn’t hurting Anthropic … yet
Anthropic — open-source models’ success isn’t coming at the expense of frontier labs. Instead, they each seem to capture two phases of the same life-cycle.
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. 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. 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. 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.
