Decagon CEO: Open source AI isn't killing frontier models
Curated by the Inblix editorial team
Decagon CEO Jesse Zhang dropped a spicy new theory: open source AI isn’t actually eating frontier models’ lunch. Instead, they’re two sides of the same coin. As enterprises find success with pricey frontier models, they often switch to cheaper open source versions for production, but the overall spending on top-tier models stays steady because new use cases keep popping up. Hard data backs this up: on Vercel, DeepSeek now handles over a third of tokens, but Anthropic still claims more than half the spend. Same story on OpenRouter, where DeepSeek V4 Flash processes 5.3 trillion tokens weekly versus Opus 4.8’s 2 trillion, but Opus costs 23x more per token. Frontier labs like Anthropic aren’t hurting because they own the discovery phase, while open source scales production. Think of it like a conveyor belt: frontier models prove what’s possible, then hand off to open source for the heavy lifting. Why it matters: This flips the narrative from a zero-sum battle to a symbiotic cycle, explaining why frontier AI spending won’t collapse even as open source adoption explodes.
💡 Key Takeaways
- Frontier and open source AI models serve different lifecycle phases: discovery vs. production, not direct competitors.
- Enterprise spending on premium frontier models remains high despite rising open source adoption because new use cases continuously emerge.
- Data from platforms like Vercel and OpenRouter shows open source dominates token volume, but frontier models capture the majority of dollar spend.
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