Artificial intelligence is demonstrating a remarkable capacity to devise novel and highly effective experimental designs in physics, leading to substantial improvements in areas ranging from gravitational wave detection to dark matter research. A recent report by Quanta Magazine highlights how AI-generated approaches, often defying conventional human intuition, are proving successful, pushing the boundaries of scientific discovery. The success of these AI-driven methods underscores their ability to explore complex problem spaces that are too vast for human researchers to navigate manually. According to the report, AI has delivered designs for gravitational-wave detectors that are 10% to 15% sharper, enabling more sensitive measurements of cosmic phenomena. This advancement is crucial for enhancing our understanding of the universe's most energetic events. One notable instance involves a Caltech team that tasked an AI with optimizing a gravitational wave interferometer. The AI, named Urania and developed by Dr. Mario Krenn and his team, assembled components in an unconventional, asymmetrical manner. It further augmented the design with an additional three-kilometer light-storage ring, a feature that effectively tames quantum shot noise. Subsequent analysis revealed this design matched an obscure Soviet theory that had never been experimentally pursued, demonstrating AI's capacity to rediscover and validate overlooked scientific principles. Beyond gravitational waves, AI has also made strides in theoretical physics. The technology has formulated a compact equation that predicts dark-matter clump density with greater accuracy than existing hand-tuned versions. Additionally, AI has successfully extracted Lorentz symmetry directly from raw data generated by Large Hadron Collider events, showcasing its power in identifying fundamental physical laws from complex datasets. The implications of AI's burgeoning role in experimental design are profound. As stated in the tweet, "The success shows AI explores physics problems too wide for grad students to map by hand." This paradigm shift suggests a future where AI acts as a "muse" for scientific inquiry, generating innovative hypotheses and experimental setups that accelerate the pace of discovery. The continued integration of AI into scientific research holds the promise of exponentially expanding our scientific knowledge.