DeepMind published AlphaGenome in Nature, an AI model that predicts regulatory activity from long stretches of noncoding DNA up to one million base pairs and scores variant effects across thousands of molecular modalities. The model promises faster interpretation of noncoding variants and new avenues for target identification and synthetic DNA design. AlphaGenome’s creators touted its scale and speed; peer scientists have already requested head‑to‑head comparisons and independent benchmarking. Community scrutiny centers on reproducibility, validation against experimental datasets, and the model’s performance on rare or tissue‑specific regulatory events. Early adopters and reviewers urged transparent validation and release of evaluation datasets to assess the tool’s utility for rare‑variant interpretation and therapeutic target discovery. DeepMind made components available to researchers, but reviewers noted that broad adoption will hinge on independent replication and integration with wet‑lab follow‑ups.