Thanks! I think advantages we had over previous generations of companies is that demand and value for software has become much clearer for biopharma. The models are beginning to actually work for practical problems, most companies have AI, data science or bioinformatics teams that apply these workflows, and AI has management buy-in.
Some of the same problems exist, large enterprises don't want to process their un-patented, future billion-dollar drug via a startup, because leaking data could destroy 10,000 times the value of the product being bought.
Pharma companies are especially not used to buying products vs research services, there's also historical issues with the industry not being served with high quality software, so it is kind of a habit to build custom things internally.
But I think the biggest unlock was just that the tools are actually working as of a few years ago.
Some of the same problems exist, large enterprises don't want to process their un-patented, future billion-dollar drug via a startup, because leaking data could destroy 10,000 times the value of the product being bought.
Pharma companies are especially not used to buying products vs research services, there's also historical issues with the industry not being served with high quality software, so it is kind of a habit to build custom things internally.
But I think the biggest unlock was just that the tools are actually working as of a few years ago.