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Résumé : Since the introduction of AlphaFold2, AI-driven methods have greatly advanced protein structure and complex prediction. However, the performance varies strongly across interaction classes. The performance drops when dynamics are important and when interactions lack a strong co-evolutionary history. We propose to organize these limitations into distinct regions: interactions that are largely tractable by current methods; interactions in which structural compatibility is ambiguous due to competition, paralogy, or stoichiometry; interactions that lack sufficient co-evolutionary signal; and interactions whose biological relevance depends on cellular context, dynamics, or disorder. These limitations define distinct categories of interaction prediction challenges, each requiring different computational and experimental approaches. Future progress will require methods that rely less on multiple sequence alignments, better distinguish interaction strength and specificity, and move beyond static structure prediction toward modeling interaction landscapes in their cellular contexts.