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.” 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 lifecycle, 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.
But if you scroll down to overall token spend, you’ll see 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.
OpenRouter tells a similar story, capturing a much larger (but slightly less enterprise-y) segment of the market. Deepseek V4Flash 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 V4Flash ($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.
Those figures don’t fully prove Zhang’s point about the AI lifecycles, but they do show 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, I was writing about the possibility 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 came true: vertical AI plays switched to lighter models, for one, and the economics of “GPT wrapper” startups have remained mostly stable.
But 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.
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Key takeaways
- The rise of open-source AI models could democratize access to technology in Brazil, benefiting startups.
- Frontier and open-source models can coexist, each serving different market needs.
- Investment in AI in Brazil may shift, with companies seeking more accessible and scalable solutions.
Editorial analysis
The discussion about the rise of open-source AI models and their impact on frontier labs like Anthropic is particularly relevant for the Brazilian tech sector. Brazil has seen a significant increase in the development of AI solutions, and the adoption of open models could democratize access to advanced technologies, allowing startups and smaller companies to experiment and implement AI without the high costs associated with proprietary models. This could stimulate local innovation and create a more competitive environment.
Moreover, the idea that open-source models and frontier models coexist at different phases of the AI lifecycle suggests that the market may be diversifying. While frontier models continue to be used to validate complex use cases, open models may become the preferred choice for simpler, scalable applications. This dynamic could lead to increased collaboration among tech companies, where best practices and learnings are shared, benefiting the ecosystem as a whole.
What to watch for next is how Brazilian companies will respond to this trend. With the growing popularity of open models, there may be a shift in how companies invest in AI. Startups may feel more inclined to adopt open solutions, while larger companies may need to reassess their investment strategies in proprietary models. This could result in a more agile and adaptable innovation environment, where companies constantly seek to optimize their resources and explore new opportunities.
Finally, it is important to consider the role of major tech companies like Nvidia in the Brazilian landscape. The presence of industry giants can influence the adoption of cutting-edge technologies and shape the future of AI in Brazil. Their ability to quickly adapt to local market needs will be crucial in determining how the sector develops in the coming years.
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