DeepSeek's decision to cut pricing on its V4-Pro model by 75% has not been entirely beneficial for enterprise AI vendors and developers, as cheaper models do not automatically translate into healthier margins. The reason for this is that agent systems are consuming tokens faster than prices are declining, leading to higher costs for vendors. This is known as the 100x problem, where the same user-visible request can cost a lot more to serve as an agentic workflow than as a chatbot or retrieval-augmented generation response. The scale of the problem is clear in how model providers are pricing developer relationships, with OpenAI's proposed program to give every Y Combinator startup $2 million in API credits being an admission of what it now costs to run an AI-native company. Token amplification is a major issue, where a single user message can produce hundreds or thousands of model calls, leading to high costs for vendors. The dominant pricing story for enterprise AI has been seat-based SaaS, but token amplification breaks this assumption, leading to negative gross margins for vendors. Several vendors are now privately reporting negative gross margins on heavy users, and the visible symptoms are starting to leak into public coverage. The strategic implication is that the dominant business model assumed by most AI-native company plans does not survive contact with agentic workloads. To survive, companies need to make inference cost a first-class metric, budget like a media buyer, treat the router as core infrastructure, audit prompts quarterly, and negotiate volume commits early. The next 24 months will be crucial for companies to adapt to the new reality of AI infrastructure pricing, and those that survive will be the ones whose agents are smart and know what they cost to think.