The field of artificial intelligence is moving towards more realistic and open-ended evaluations of model performance, particularly in high-stakes professional domains such as law and finance. Researchers are developing large-scale benchmarks and frameworks that assess AI models' ability to perform economically consequential tasks, providing a more nuanced understanding of their strengths and weaknesses. These efforts aim to bridge the gap between academic benchmarks and real-world professional contexts, where practical returns are paramount. Noteworthy papers in this area include PRBench, which introduces a large-scale expert rubric for evaluating high-stakes professional reasoning, and UpBench, which provides a dynamically evolving benchmark framework for human-centric AI. Additionally, research is exploring the use of large language models for career mobility analysis and labor market prediction, highlighting the potential for AI to democratize access to timely and trustworthy career intelligence.