Trillion-parameter, science-tuned foundation models can speed discovery, but only inside an AI-native Scientific Discovery Platform (SDP) that connects models to tools, data, HPC, and robotics. I argue for community co-development of the SDP, via open interfaces, shared schedulers, knowledge substrates, provenance, and evaluation, alongside shared models. Early results suggest that such a co-designed stack can boost throughput and reliability in materials and bio workflows, enabling human–AI teams to turn knowledge into experiments and experiments into insight.