Why the rise of open source AI isn't hurting Anthropic ... yet
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. 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."
Key Takeaways
- 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.
- 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.
- 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.
- As Zhang puts it, "The frontier labs will keep owning discovery.
- And that doesn't seem likely to change any time soon.
Stats & Key Facts
- #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.
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. 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.
For more details please read the original article at TechCrunch AI.
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