Artificial intelligence is moving beyond analyzing biological data. It is beginning to help scientists generate new biological hypotheses, design molecules, and explore possibilities that would have taken years to investigate manually. But there is a fundamental problem we should not overlook: **Generating a biological hypothesis is not the same as proving it.** Recent developments illustrate this transition. Anthropic has reported that its AI-assisted research team identified a previously uncharacterized enzyme system with features reminiscent of CRISPR. Meanwhile, new industry collaborations are connecting AI-designed drug candidates with human-relevant 3D tissue models to evaluate their biological effects. These developments point toward a new research cycle: **AI Design → Laboratory Experiment → Biological Evidence → Model Improvement** Yet the hardest part may not be designing the next molecule. It may be producing reliable evidence that the molecule actually works. Biological experiments are highly sensitive to many variables: - Experimental protocols and conditions - Reagent quality and batch-to-batch variation - Reference materials and calibration standards - Cell models and biological systems - Data quality and reproducibility A model trained on incomplete or inconsistent experimental data can generate convincing predictions that fail in the laboratory. This raises a bigger question about the future of biotechnology. Perhaps the next major breakthrough will not come from a more powerful AI model alone. It will come from connecting fragmented scientific knowledge, experimental data, standardized methods, researchers, and laboratory resources into one continuous ecosystem. Science needs more than intelligent algorithms. It needs reliable connections between ideas and evidence. **What do you think is currently the biggest bottleneck in AI-driven biology: data quality, experimental validation, reproducibility, or access to laboratory infrastructure?** I'd be interested in hearing perspectives from researchers, scientists, and biotech professionals.