The Two-Speed AI Economy Nobody’s Talking About
Here’s a riddle for the AI era: If companies are ditching expensive frontier models for cheaper open source alternatives, why is Anthropic still raking in more than half of all AI spending on major platforms?
That contradiction sits at the heart of a provocative new argument from Decagon CEO Jesse Zhang. In a post titled “Everyone is wrong about open source AI in the enterprise,” Zhang proposes that frontier labs and open source models aren’t really competing. They’re playing different roles in a single lifecycle.
Expensive frontier models handle the messy, high-risk early stages of a new use case. Once the process is proven and predictable, companies hand it off to leaner, cheaper open source models. The result? Frontier spending barely dips, because new discovery projects keep popping up to replace the ones that mature.
“The frontier labs will keep owning discovery,” Zhang writes. “Open source will increasingly own production.”
What the Data Actually Shows
Zhang doesn’t offer hard numbers, but the data is easy to find — and it largely backs up his thesis.
Take Vercel‘s AI gateway dashboard. Over the past week, DeepSeek has surged to the lead in token volume, processing just over a third of all tokens flowing through Vercel’s infrastructure. Z.ai, the lab behind the popular GLM-5.2 model, jumped to fourth place in the same period.
But scroll down to spend, and the picture flips. Anthropic still accounts for more than half of all AI spending on the platform. That share has slipped slightly — partly because Anthropic raised prices — but hasn’t collapsed.
OpenRouter tells a similar story across a broader, slightly less enterprise-focused slice of the market. DeepSeek V4 Flash dominates by raw usage, processing 5.3 trillion tokens weekly. The most popular frontier model, Opus 4.8, handles just over 2 trillion. But the price gap is enormous: Opus costs roughly 23 times more per token ($1.37 per million tokens versus DeepSeek’s 6 cents). That means Opus likely still captures the majority of actual dollars spent.
And that’s before factoring in Nvidia’s Nemotron, which is poised to leapfrog competitors thanks to Nvidia’s deep enterprise relationships and the model’s extreme adaptability.
Why Frontier Labs Aren’t Panicking
The numbers don’t fully prove Zhang’s lifecycle theory, but they do explain why Anthropic isn’t sweating the open source surge — at least not yet.
One reason: the total pool of AI-addressable problems is expanding so rapidly that frontier labs can maintain their position simply by dominating new, unproven use cases. Every time a mature workflow migrates to open source, a fresh batch of harder problems appears to take its place.
Another explanation: some use cases are genuinely too difficult for lighter models. Even as clients experiment with cheaper alternatives, they keep a foot in the frontier door for the toughest tasks. That creates a sticky, high-margin revenue base that open source models can’t easily erode.
What This Means for Enterprise AI Buyers
For companies building on AI, the implication is clear: don’t treat frontier and open source models as an either/or choice. Use frontier models to explore and validate. Once the process is stable, switch to open source for production. It’s a hybrid strategy, not a migration.
This two-tiered economy could become a stable feature of the AI market. Frontier labs keep the premium pricing they need to fund R&D. Open source models get the volume that drives ecosystem growth. And enterprises get a cost-effective path from experimentation to deployment.
As recently as last September, many analysts — including this one — predicted that foundation labs would end up as commodity providers, selling “coffee beans to Starbucks” while the application layer captured the value. Some of that prediction came true: vertical AI startups did switch to lighter models, and the economics of “GPT wrapper” companies have remained stable.
But we’re also seeing that frontier providers have held onto the most desirable part of the marketplace: the premium token price. And that doesn’t look likely to change anytime soon.
For a deeper look at how companies are balancing cost and capability, check out our analysis of enterprise AI adoption strategies. And for more on the specific models driving this shift, see our breakdown of how DeepSeek is reshaping the open source AI landscape.
