Contrasting Trends in AI Economy
One of the most intriguing contradictions in today’s AI economy is the coexistence of mature AI deployments switching to lighter models and the overall spend on expensive state-of-the-art models barely budging. Decagon CEO Jesse Zhang recently shed light on this phenomenon, suggesting that frontier and open source models are not competitors, but rather two phases of the same life cycle.
Source: techcrunch.com
According to Zhang, expensive frontier models are used to prove out use cases that can be passed along to cheaper open source alternatives as they mature. This approach is supported by data from Vercel’s AI gateway dashboard, which shows that DeepSeek has surged into the lead for token volumes, processing just over a third of the tokens passing through the company’s infrastructure.
However, if you scroll down to overall token spend, you’ll see that 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.
A similar story is told by OpenRouter, 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.
While these figures don’t fully prove Zhang’s point about the AI life cycles, they do show that 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.
As recently as last September, some experts predicted 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 have come true: Vertical AI plays switched to lighter models, for one, and the economics of ‘GPT wrapper’ startups have remained mostly stable.
However, 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.
