Contrary to the political priors of VCs, I think the real answers are pretty mundane:
1. Funding. Drugs have a low probability of success and a long lag time. Investors think in discount rates. A high-risk venture like biology is less appealing than an advertising-based tech platform with zero marginal costs.
2. Costs. Biology uses a LOT of proprietary instruments, kits, and chemical reagents. A lot. It also needs a lot of manual labor that would be difficult to roboticize.
3. Time. Biological experiments operate on biological timescales. Code takes seconds to run. Cell cultures take a day to grow. Even fancy new multiplexed sequencing assays take a while. You have the library prep time, the sequencing, and the downstream analysis. Its a long process. Now imagine waiting years and years to see if a drug in clinical trial prevents Alzheimer's.
4. Complexity. How do you make an equation for a giant network of weakly-interacting parts? Biology is a very "data-driven" field for this reason. The introduction of new microscopy, chemical conjugation techniques, and high-throughput assays has only made things worse. I genuinely hope some black box AI will be able to help us make sense of this mess and cure cancer. But medicine is full of interventions and incomplete prior histories, which will make naive association models hard to use.
1. Funding. Drugs have a low probability of success and a long lag time. Investors think in discount rates. A high-risk venture like biology is less appealing than an advertising-based tech platform with zero marginal costs.
2. Costs. Biology uses a LOT of proprietary instruments, kits, and chemical reagents. A lot. It also needs a lot of manual labor that would be difficult to roboticize.
3. Time. Biological experiments operate on biological timescales. Code takes seconds to run. Cell cultures take a day to grow. Even fancy new multiplexed sequencing assays take a while. You have the library prep time, the sequencing, and the downstream analysis. Its a long process. Now imagine waiting years and years to see if a drug in clinical trial prevents Alzheimer's.
4. Complexity. How do you make an equation for a giant network of weakly-interacting parts? Biology is a very "data-driven" field for this reason. The introduction of new microscopy, chemical conjugation techniques, and high-throughput assays has only made things worse. I genuinely hope some black box AI will be able to help us make sense of this mess and cure cancer. But medicine is full of interventions and incomplete prior histories, which will make naive association models hard to use.