
GDPx addresses the critical need for rigorous AI evaluation by replacing flawed, generic benchmarks with domain-specific tasks that mirror real-world professional environments. By partnering with experts in fields like chip design, finance, and cybersecurity, the benchmark ensures tasks are both difficult and fair, avoiding the "hill-climbing" issues common in public datasets. The methodology emphasizes high-fidelity simulations, such as replaying private equity deal rooms, to test whether models can handle complex, long-horizon workflows. While many day-to-day tasks are already saturated by current models, frontier intelligence remains essential for high-ROI domains where accuracy is paramount. By utilizing a mix of binary and continuous grading, GDPx provides a clearer signal of model performance at the absolute edge of capability, helping to track the rapid evolution of frontier intelligence as new, more powerful models emerge.
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