\n<\/p>\n
On Monday, Decagon CEO Jesse Zhang published a provocative new theory, posted under the title \u201cEveryone is wrong about open source AI in the enterprise.\u201d<\/a> The post grapples with one of the most interesting contradictions of today\u2019s 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.<\/p>\n It\u2019s a new way to think about the relationship between frontier and open source models. In Zhang\u2019s telling, they aren\u2019t competitors, and open source models\u2019 success isn\u2019t coming at the expense of frontier labs. Instead, they\u2019re 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.<\/p>\n As more mature use cases switch to lighter models<\/a>, new use cases keep arising \u2014 and the overall spend on frontier models barely goes down.<\/p>\n Zhang doesn\u2019t give much data to support the point, but the data isn\u2019t hard to find. Vercel\u2019s AI gateway dashboard<\/a> 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\u2019s infrastructure. Z.ai \u2014 the lab behind the popular GLM-5.2 model \u2014 jumped into a respectable fourth place over the same period.\u00a0<\/p>\n But if you scroll down to overall token spend, you\u2019ll 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\u2019s own rising prices, the share has dropped slightly over the past month, but not significantly.<\/p>\n OpenRouter<\/a> tells a similar story, 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\u2019t 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\u2019s share of spending.<\/p>\n Those figures don\u2019t even capture the newest arrival, Nvidia\u2019s Nemotron, which is poised to leap to the front of the pack<\/a> by virtue of Nvidia\u2019s strong connections and the model\u2019s own extreme adaptability.<\/p>\n Those figures don\u2019t fully prove Zhang\u2019s point about the AI life cycles, but they do show frontier labs like Anthropic aren\u2019t suffering too much from the rise of open source \u2014 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, \u201cThe frontier labs will keep owning discovery. Open source will increasingly own production.\u201d Another explanation might be that, even as clients move to open source, many use cases are so difficult that they can\u2019t be entirely replaced with cheaper alternatives.<\/p>\n Either way, this two-tiered economy of models may become a relatively stable feature of the AI economy.<\/p>\n As recently as last September, I was writing about the possibility that foundation labs would end up selling coffee beans to Starbucks<\/a> \u2014 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 \u201cGPT wrapper\u201d startups have remained mostly stable.\u00a0<\/p>\n But we\u2019re 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\u2019t seem likely to change any time soon.<\/p>\n<\/div>\n When you purchase through links in our articles, we may earn a small commission<\/a>. This doesn\u2019t affect our editorial independence.<\/em><\/p>\n