[smparkes]

Actually, my comments were towards what Steve said, not what you said. Sorry that that wasn't clear.

Steve says biotech is data mining, and it's not. It's a physics problem. A lot of very, very hard physics problems. On systems that are hard to observe, since observing them tends to kill them and/or otherwise change the way they function.

Andy Grove made an even more egregious example of this a few years ago. In a talk he gave, he spoke at length about how biotech needed to learn from tech. Anybody that knows anything about the complexity of biology would find his comments ... well, calling it naive would be very kind.

Most people, including tech people, don't understand how vastly more complex biology is than tech.

It's not actually clear how tech can help biotech. There was a rush of work in the 90s related to sequence reconstruction that has been very useful in reducing a lot drudgery. But not necessarily much that has been able to move higher up the stack, stuff like systems biology. These systems are so complex and non-linear that analytical tools often get overwhelmed or don't produce meaningful results because the models and observable data are so radically simplified.

It's not that it shouldn't be worked on. It's just that indications of the likelihood of a singularity/inflection point aren't so high as seem to be often spoken of.