The AI Life Cycle Theory
On Monday, July 7, 2026, Decagon CEO Jesse Zhang published a provocative new theory that grapples with one of the most interesting contradictions of today’s AI economy. According to Zhang, more mature AI deployments are switching to lighter models, even at his own company, but the overall spend on expensive state-of-the-art models has barely budged.
Zhang’s Provocative Post
Zhang’s theory suggests that frontier and open source models are not competitors. Instead, they represent two phases of the same life cycle: expensive frontier models are used to prove out use cases, which can then be passed along to cheaper open source alternatives as they mature. As mature use cases decline, new use cases keep arising, keeping overall spend on frontier models nearly constant. Zhang does not provide much data to support his point, but the data is not hard to find.
Data from Vercel and OpenRouter
Token Volumes vs. Spend
Data from Vercel shows that in 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. However, when looking at overall token spend, Anthropic still accounts for more than half of the overall AI spend on the platform. Much of the recent change comes from Anthropic’s own rising prices, so the share has dropped slightly over the past month, but not significantly.
OpenRouter data tells a similar story, capturing a much larger 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 tokens. OpenRouter does not 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. That means Opus is still probably capturing the lion’s share of spending.
These figures do not even capture the newest arrival, Nvidia’s Nemotron, which is notable by virtue of Nvidia’s strong connections and the model’s own extreme adaptability.
Implications for Frontier Labs
The data does not fully prove Zhang’s point about AI life cycles, but it does show frontier labs like Anthropic are not 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 top models maintain their position by dominating early-stage deployments. As Zhang puts it, “The frontier labs will keep owning discovery. Open source will increasingly own production.” Another explanation is that many use cases are so difficult that they cannot be entirely replaced with cheaper alternatives.
A Two-Tiered Economy
Either way, this two-tiered economy of models may become a relatively stable feature of the AI economy. As recently as last September, Author Russell Brandom wrote about the possibility that foundation labs would end up as commodity inputs while the application layer reaped the benefits. Some parts 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 are 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 does not seem likely to change anytime soon.