This goes back to that famous Bill Gates line, where he said he liked programming computers as a kid because they always did what he told them to. They would never do anything different. A big difference between biology and software is that software does what it is told, and biology doesn’t.Unlike computer science, biology doesn't have the equivalent of the Church-Turing thesis that, essentially, guarantees an implementability of a valid algorithm. The success of Silicon Valley is built on top this important discovery of the 20th century. That is, once a "computation" entrepreneur, either in software or hardware, finds a way to express his useful idea in an algorithmic way, he or she can be sure that it will work, provided the computational power, storage, and networking capacity grow exponentially. Most famously, Larry Ellison created his Relational Database business in mid-1970s when people did not understand implications of the Moore's Law yet.
One of the challenges with biotechnology generally is that biology feels too complicated and too random. It feels like there are too many things that can go wrong. You do this one little experiment and you can get a good result. But then there are five other contingencies that have to work the right way as well. I think that creates a world where the researchers, the scientists, and the entrepreneurs that start companies don’t really feel that they have agency.
Biology is different. Vernon Vinge, a science fiction writer, aptly calls our future successes in medicine "A Minefield Made in Heaven" because it's hard to predict the specific locations of magical "mines" that we are going to discover and cure various diseases.
Peter Thiel uses word "random" to describe biology; but from a practical perspective it's actually worse than that. If it were random we could use known randomization techniques from computer science and make new biological discoveries by almost brute force. We can't. Therefore, I'd rather use a different term – arbitrary, and there's no algorithm for generating useful arbitrariness yet - only human ingenuity.
The good news is that some of the life sciences fields are compatible with computation. We are going to make a lot of progress in areas where we can hook up analog biological experiments to the exponentially growing computing platforms. Diagnostics and pattern matching for known problems seem to be the most promising field.
tags: biology, innovation, science, technology, silicon valley