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A recent joint study by Google, DeepMind, and MIT reveals that artificial intelligence is dramatically speeding up research work. Scientists using AI report saving roughly 30 hours each month, translating into higher lab output for about 80% of respondents and an optimism that 90% expect further gains. Beyond sheer speed, two‑thirds of users say AI opens doors to insights from other fields, helping break the silos of hyper‑specialization. However, the surge in ideas is moving the biggest hurdles downstream—now the bottlenecks lie in experimental execution, clinical validation, and data collection. As a result, researchers are spending a substantial portion of their saved time (about a quarter) double‑checking AI‑generated results. These trends signal a new era where AI acts as a productivity catalyst while reshaping the workflow priorities of modern science. [[1]]