AI has been applied for many years, why can't biotechnology experiments be independently completed on a large scale by AI?

AI has made impressive advances in data analysis, forecasting and scientific discovery, yet large scale independent biotechnology experimentation is still a tough challenge because laboratory research needs much more than computational decision making. AI can design candidate experiments, analyze genomic or proteomic data, predict molecular interactions and optimize experimental parameters. But biology is highly variable and many experiments require fine physical manipulation, constant monitoring and adaptation to unforeseen events. Human researchers are frequently asked to identify anomalies, diagnose equipment failures, assess sample quality, and adapt protocols based on observations that may not have a digital record. Reproducibility and validation are other important considerations. AI-generated hypotheses and experimental plans need to be tested with carefully controlled lab experiments. Ethical oversight, biosafety regulation and responsible research practices, such as experiments involving animals, human samples or genetically modified organisms, also require human accountability. In the future, advances in robotics, laboratory automation, digital twins and multimodal AI systems could enable autonomous labs to execute increasingly complex workflows. In the near future, the best approach will be a partnership between AI and human scientists, where AI enables discovery and decision support and trained researchers provide scientific judgment, ethical oversight, and final validation.