A new framework called the "AI Deputization Audit" is helping knowledge workers answer a critical question in the age of AI: which tasks to hand off to AI assistants, which to tackle collaboratively, and which to keep for themselves. Introduced on The AI Daily Brief, the framework arrives as AI capabilities accelerate—from OpenAI's Computer History features to Grok's task-learning abilities—enabling systems to better understand and replicate individual work patterns. The three-tier model guides workers through deliberate delegation decisions rather than blanket automation. The framework's emergence reflects a rapidly evolving market. Recent releases including Google's Gemini 3.7 Flash and OpenAI's GPT-5.6 Sol demonstrate substantial capability leaps, while the episode highlights an often-overlooked concern: the true cost of cheaper AI models. As models become more capable at pattern recognition and task execution, the podcast suggests the real competitive advantage lies not in automation itself, but in thoughtful deployment—knowing where humans add irreplaceable value. The timing underscores growing workplace pressure to optimize AI integration. With OpenAI experiencing executive turnover and the vendor landscape crowded with new agents and automation tools, the Deputization Audit provides practical guidance for individuals and teams navigating deployment decisions. The framework suggests that indiscriminate delegation carries hidden costs, while strategic human-AI collaboration may drive better outcomes.