AI labs are shifting from traditional scaling laws to test-time compute, optimizing models during inference rather than pretraining, which could reduce computational demands, democratize AI access, and drive innovation in AI hardware and software. This transition presents both opportunities and challenges, including sustainability benefits, economic impacts on the AI chip industry, and the need for robust regulatory frameworks to ensure transparency and accountability.