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I think as long as these tools are working, and there is that kind of de-risking and cost savings in drug development, it's more insulated than a general consumer AI or vertical AI application in a different segment." And although prospects of a popped AI bubble might make computing power a scarce commodity or slow down the proliferation of AI companies, drug development is a "different business model," Riccio said. Microsoft's presence in AI drug discovery is extensive, including a partnership with Novo Nordisk to build a platform that has resulted in "predictive AI models for advanced risk detection in cardiovascular diseases, including an algorithm that can predict patients' cardiovascular risk better than the best clinical standards." The tech juggernaut also developed TamGen, a chemical language model for developing target-specific drug compounds for tuberculosis drug research. The model found a novel cancer mechanism to potentially solve a major immunotherapy problem: making "cold" tumors, which are invisible to the body's immune system, "hot." Not only did the model predict which drugs might work, but it confirmed the prediction in the lab by testing it in human neuroendocrine cell models. "Not only can you use these models to make the drugs, but you can use them to take that next step of validation and find new drugs." The new model builds on C2S-Scale, a family of open-source large language models released this year that are trained to "read" and "write" biological data at the single-cell level, and is only a part of Google's expanding work in this area."