This is a wild signal for those of us following the AI workflow trend. It proves that even a cash cow like Google is feeling the burn of training and serving frontier models. The shift from "experimental AI" to "integrated AI" requires a level of compute power and energy that is fundamentally changing how these tech giants manage their balance sheets.
For developers and prompt engineering enthusiasts, this explains why we're seeing a sudden push toward efficiency. Whether it's the move toward smaller, distilled models or the optimization of LLM agents to reduce token costs, the industry is realizing that raw power isn't sustainable if the cash flow goes negative. We are entering an era where "performance per dollar" will be the only metric that actually matters for long-term deployment. Kids treat LLMs like living beings far more than adults do 9h ago
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