The mix of AI and CRISPR gene editing is changing how we upgrade grain crops, which feed much of the world. In this inaugural perspective, we propose a transformative framework to close the gap between computational prediction and field performance. Rather than presenting new data, we call for a paradigm shift toward explainable AI, digital twins, federated learning, and breeder-centric platforms. We argue that only through integrated, transparent, and collaborative systems can we realize the full promise of precision breeding for global food security. Still, translating computational predictions into successful crop performance in the field often fails or exhibits rapid performance decline.... More >