Show HN: I built a tool showing how AI providers (should) throttle their modelsOP here: this project was born out of the frustration/paranoia that AI providers are throttling their models when their server load is too high. So, I set out to model and study the problem mathematically to understand what was happening, what I found was quite surprising.The idea seems natural: as the data center demand increases momentarily through the day, throttling their models (either using a quantized version, reducing the context window or lowering the tier of the model to a smaller one) seems appealing as the model in principle uses less electricity. The problem is that this can cause in fact the opposite effect: as users are trying to solve a question, if the AI gives a bad answer, the user is likely to keep re-asking. On the AI provider side this looks paradoxical: throttling to a lower model creates in fact more demand for their data center.This problem is even worse for agentic workflows, as these are more likely to create a re-ask storm, and maybe explains the outages and