Svmuu News: As companies begin to reevaluate the return on their AI investments, the industry is shifting from a high-consumption “tokenmaxxing” model to one that prioritizes efficiency, and AI large-model providers are facing new growth constraints. Several companies have already begun cutting or optimizing model call costs. For example, the CEO of AI startup Lindy stated that the company has switched 100% of its traffic from Anthropic’s Claude model to the lower-cost DeepSeek, expecting to save millions of dollars within a few months.
This shift reflects tightening enterprise AI budgets, as the past “tokenmaxxing” model—which allowed for “unlimited use of model resources”—is gradually being replaced by cost control and ROI-driven approaches. Some companies have even established tiered budgets for AI tool usage; for example, Uber has set a monthly cap on internal AI spending.
Analysts point out that as companies shift from “expanding usage” to “fine-tuning deployment,” the rapid growth model that OpenAI and Anthropic previously relied on is facing challenges. Industry data still shows strong growth: Anthropic’s annualized revenue is approximately $47 billion, while OpenAI’s run rate is close to $25 billion, but the market is beginning to focus on the sustainability of this growth.
At the same time, the way models are invoked is changing, with technologies such as “model routing” emerging—using low-cost models to replace high-end ones for simple tasks in order to optimize overall computing costs. Industry competition is also intensifying, with Microsoft, Amazon, and Google all accelerating the rollout of low-cost AI models and enterprise-grade tools, further squeezing pricing margins. Against the backdrop of more rational AI spending by enterprises, large-model companies may face a situation where “expectations of slowing growth” coexist with “pressure to go public.” (CNBC)