Researchers at the University of Gothenburg and Chalmers University of Technology have engineered SmartTrap, an artificial intelligence platform that fully automates the operation of optical tweezers, significantly advancing high-throughput biophysical analysis. Published in Nature Methods, the system addresses longstanding limitations in microscopic manipulation by eliminating the need for continuous human oversight, a bottleneck that previously restricted experimental throughput and introduced operator-dependent variability. Optical tweezers, which utilize precisely focused laser beams to capture and manipulate objects ranging from individual DNA strands to living cells, earned the 2018 Nobel Prize in Physics. Despite their scientific utility in studying molecular motors, cellular mechanics, and disease pathology, traditional setups require skilled researchers to manually position samples, adjust parameters, and monitor tests. SmartTrap overcomes these constraints by integrating real-time deep learning, advanced image analysis, custom electronics, and closed-loop fluid control. The AI system autonomously captures particles, positions them with nanometer-scale precision in three dimensions, executes measurements, and sequentially loads fresh samples without interruption. During rigorous testing, the platform demonstrated remarkable efficiency and accuracy. It successfully sorted and characterized hundreds of particles per hour and executed ten to fifteen complex single-molecule DNA stretching experiments in the same timeframe, operations that typically demand manual labor and extended hours from human operators. Additional trials validated the system capability to assess red blood cell mechanical stiffness and map nanoscale electrostatic forces across varying salt concentrations. Performance metrics confirmed that the AI matched or surpassed the precision of experienced human technicians across all tested biophysical assays. Built on open-source software, SmartTrap is designed as a scalable, shared infrastructure for academic and industrial laboratories. Lead researcher Giovanni Volpe notes that as AI-driven microscopy matures, autonomous platforms will fundamentally restructure scientific workflows, mirroring the automation revolution that transformed manufacturing sectors. By enabling continuous, standardized, and high-volume experimentation, the technology positions to accelerate discoveries in nanotechnology, molecular biology, and pharmaceutical development.