Because biologists aren't looking at the code, but the product.

For example, I really liked NN 4.7. It was responsive and consistent. But the codebase was so poor, that they rewrote it - and nuked themselves. Joel on Software wrote an essay about how as a codebase evolves to cope with more and more special cases, it gets more complex and ugly. But that's because it needs to be (according to Joel).

In other words, no one cares about your code. They care about what it does. If it does something really cool and amazing, people will want to know how you did it. What it "does" can go beyond performance and features - what code does can include realizing a new user-interaction approach, or a new algorithm, or a new way to model something. Imagine the jury-rigged prototype of the first telephone, light-bulb, aeroplane - the slickness of the implementation just doesn't matter. The invention matters.

If, one day, we work out how DNA codes for a brain, we may be able to code it better. But what we lack is any idea of how to do it at all. Poor code that works is infinitely better and more cool than no idea.

This is why biologists might be interested in the poor code: How the hell did he do that?

In fact, it may be that it is impossible to determine how the brain thinks from our physical reality.

The situation is made more complex by the fact that this hardware is interacting with different types of other hardware such as viruses and bacteria.

If you're looking for some insight into what makes it so beautiful, The Selfish Gene is an excellent place to start.

In genetic programming, the products of evolution on a computer are completely incomprehensible.

What makes natural evolution any different?

What exactly would "evidence" of something as subjective as "elegance" look like?

I would rather use genetic algorithms for my own purposes rather than trying to understand the messy artifacts of nature.

But yes, the fear of death will give such research a lot of funding.

I think that's an interesting problem, regardless of the quality of the code.

Bert Hubert - DNA seen through the eyes of a coder http://ds9a.nl/amazing-dna/index.html

Insofar as the code metaphor goes, I don't think you can comment on the quality unless you completely understand what it is doing and how robust it is. For example, the code that architected your neocortex is significantly smaller than the source for Open Office. I find that impressive.

Until you are capable of producing something better, you can't make those statements.

'Code' created by a long series of chaotic events manages to survive or not survive.

The research is rewarding because it is challenging. Both the discover of the raw data and the interpretation of said data is dumbfoundingly complex, to the point where any tiny amount of comprehension is the result of more work than you can possibly imagine.

In short, it's hard, and success is uncommon and amazing.

Also, evolution isn't always elegant, but it often is. Think of the symmetry and self-similarity found so often in nature.