Why Open Source AI Isn’t Hurting Anthropic – Yet

On Monday, Decagon CEO Jesse Zhang stirred up the AI conversation with a provocative theory, posted under the title “Everyone is wrong about open source AI in the enterprise.” He tackles one of the most compelling contradictions in today’s AI economy: more mature AI deployments are shifting to lighter models, even at his own company. Yet, the overall spending on expensive, state-of-the-art models has barely changed.

This offers a fresh perspective on the relationship between frontier models and open source alternatives. In Zhang’s view, they aren’t rivals. Open source success doesn’t come at the expense of frontier labs. Instead, they represent two phases of the same lifecycle. Expensive frontier models prove out new use cases, which are then handed off to cheaper open source options as they mature.

As established use cases migrate to lighter models, fresh use cases keep emerging. The result? Overall spending on frontier models remains largely stable.

Zhang doesn’t provide much data to back his claim, but it isn’t hard to find. Vercel’s AI gateway dashboard shows that, over the past week alone, DeepSeek has surged into the lead for token volumes, now processing just over a third of all tokens flowing through the company’s infrastructure. Z.ai, the lab behind the popular GLM-5.2 model, jumped to a respectable fourth place over the same period.

But scroll down to total token spend, and a different story emerges. Anthropic still accounts for more than half of all AI spending on the platform. While that share has dipped slightly over the past month due to Anthropic’s own rising prices, the decline is not significant.

OpenRouter tells a similar tale, capturing a larger though slightly less enterprise-focused segment of the market. DeepSeek V4 Flash leads in 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 reports the average token cost for Opus 4.8 is roughly 23 times higher than V4 Flash ($1.37 per million tokens versus just 6 cents). That suggests Opus still captures the lion’s share of spending.

These figures don’t even account for the newest arrival, Nvidia’s Nemotron, which is poised to leap to the front of the pack thanks to Nvidia’s strong connections and the model’s extreme adaptability.

While the data doesn’t fully prove Zhang’s point about AI lifecycles, it does show that frontier labs like Anthropic aren’t hurting much from the rise of open source,at least not yet. One explanation is that the market for AI-addressable tasks is growing so quickly that top models maintain their position simply by dominating early-stage deployments. As Zhang puts it, “The frontier labs will keep owning discovery. Open source will increasingly own production.” Another possibility is that many use cases are so difficult that they can’t be entirely replaced by cheaper alternatives.

Either way, this two-tiered economy of models may become a stable feature of the AI landscape.

As recently as last September, I wrote about the possibility that foundation labs would end up selling coffee beans to Starbucks,serving as commodity inputs while the application layer reaped the rewards. Some of that prediction came true. Vertical AI plays switched to lighter models, and the economics of “GPT wrapper” startups have remained mostly stable.

But we’re also seeing that, token for token, frontier providers have held on to the most desirable part of the marketplace: the premium token price. And that doesn’t seem likely to change anytime soon.