According to a recent LinkedIn post from Centific, the company’s AI research team is examining why overall AI deployment costs may be rising even as per-token prices decline. The post indicates that more complex reasoning models, longer outputs, and multi-step or chained workflows are driving higher total consumption of tokens.
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The company’s LinkedIn post highlights that evaluation and judgment phases within AI workflows may consume more tokens, and therefore more cost, than the initial generation of content itself. This focus suggests Centific is positioning its research and service offerings around cost-optimization and efficiency in AI operations, which could appeal to enterprises looking to manage growing AI expenditures.
For investors, the post implies Centific is targeting a pain point in AI adoption: the gap between nominal model pricing and real-world usage costs. If the firm can translate this research into differentiated tooling or advisory services that lower total cost of ownership for clients, it could strengthen its value proposition and support pricing power in a competitive AI services market.