Synergizing genome editing and artificial intelligence for predictive crop design

The convergence of genome editing (GE) and artificial intelligence (AI) is shifting crop improvement from empirical optimization toward predictive design. In this Perspective, we propose that GE and AI are linked by a reciprocal innovation cycle. AI improves GE by enabling more accurate guide RNA design, editing outcomes, and off-target predictions, and data-driven development of next-generation editing systems. In turn, GE provides a powerful experimental platform for validating AI predictions, dissecting causal genotype-to-phenotype relationships, and generating high-resolution perturbation datasets for iterative model refinement. This bidirectional interplay is accelerating the engineering of complex agronomic traits, including trait stacking, metabolic rewiring, climate resilience, stress tolerance, nutritional enhancement, and de novo domestication. Here, we argue that this synergy is emerging most clearly in predictive editing design and iterative model validation, although robust breeding-scale evidence remains limited. We also discuss emerging opportunities for AI-guided closed-loop editing platforms and generative biological design, as well as key bottlenecks, including limited training data, biological complexity across scales, model interpretability, and heterogeneous regulatory landscapes. We argue that integrating AI with GE can establish a practical framework for predictive crop design and enable more efficient and sustainable crop engineering.